National Library of Energy BETA

Sample records for back-propagation neural network

  1. Computationally Efficient Neural Network Intrusion Security Awareness

    SciTech Connect (OSTI)

    Todd Vollmer; Milos Manic

    2009-08-01

    An enhanced version of an algorithm to provide anomaly based intrusion detection alerts for cyber security state awareness is detailed. A unique aspect is the training of an error back-propagation neural network with intrusion detection rule features to provide a recognition basis. Network packet details are subsequently provided to the trained network to produce a classification. This leverages rule knowledge sets to produce classifications for anomaly based systems. Several test cases executed on ICMP protocol revealed a 60% identification rate of true positives. This rate matched the previous work, but 70% less memory was used and the run time was reduced to less than 1 second from 37 seconds.

  2. Study of a transient identification system using a neural network for a PWR plant

    SciTech Connect (OSTI)

    Ishihara, Yoshinao; Kasai, Masao; Kambara, Masayuki [Mitsubishi Heavy Industries, Ltd., Yokohama (Japan); Mitsuda, Hiromichi; Kurata, Toshikazu; Shirosaki, Hidekazu [Inst. of Nuclear Safety System, Inc., Kyoto (Japan)

    1996-08-01

    This paper presents the procedure and results of a system for identifying PWR plant abnormal events, which uses neural network techniques. The neural network recognizes the abnormal event from the patterns of the transient changes of analog data from plant parameters when they deport from their normal state. For the identification of abnormal events in this study, events that cause a reactor to scram during power operation were selected as the design base events. The test data were prepared by simulating the transients on a compact PWR simulator. The simulation data were analyzed to determine how the plant parameters respond after the occurrence of a transient. A method of converting the pattern of the transient changes into characteristic parameters by fitting the data to pre-determined functions was developed. These characteristic parameters were used as the input data to the neural network. The neural network learning procedure used a generalized delta rule, namely a back-propagation algorithm. The neural network can identify the type of an abnormal event from a limited set of events by using these characteristic parameters obtained from the pattern of the changes in the analog data. From the results of this application of a neural network, it was concluded that it would be possible to use the method to identify abnormal events in a nuclear power plant.

  3. Artificial neural network implementation of chemistry with pdf simulation of H{sub 2}/CO{sub 2} flames

    SciTech Connect (OSTI)

    Christo, F.C.; Masri, A.R.; Nebot, E.M.

    1996-09-01

    A novel approach using artificial neural networks for representing chemical reactions is developed and successfully implemented with a modeled velocity-scalar joint pdf transport equation for H{sub 2}CO{sub 2} turbulent jet diffusion flames. The chemical kinetics are represented using a three-step reduced mechanism, and the transport equation is solved by a Monte Carlo method. A detailed analysis of computational performance and a comparison between the neural network approach and other methods used to represent the chemistry, namely the look-up table, or the direct integration procedures, are presented. A multilayer perceptron architecture is chosen for the neural network. The training algorithm is based on a back-propagation supervised learning procedure with individual momentum terms and adaptive learning rate adjustment for the weights matrix. A new procedure for the selection of training samples using dynamic randomization is developed and is aimed at reducing the possibility of the network being trapped in a local minimum. This algorithm achieved an impressive acceleration in convergence compared with the use of a fixed set of selected training samples. The optimization process of the neural network is discussed in detail. The feasibility of using neural network models to represent highly nonlinear chemical reactions is successfully illustrated. The prediction of the flow field and flame characteristics using the neural network approach is in good agreement with those obtained using other methods, and is also in reasonable agreement with the experimental data. The computational benefits of the neural network approach over the look-up table and the direct integration methods, both in CPU time and RAM storage requirements are not great for a chemical mechanisms of less than three reactions. The neural network approach becomes superior, however, for more complex reaction schemes.

  4. Identification and control of plasma vertical position using neural network in Damavand tokamak

    SciTech Connect (OSTI)

    Rasouli, H.; Rasouli, C.; Koohi, A.

    2013-02-15

    In this work, a nonlinear model is introduced to determine the vertical position of the plasma column in Damavand tokamak. Using this model as a simulator, a nonlinear neural network controller has been designed. In the first stage, the electronic drive and sensory circuits of Damavand tokamak are modified. These circuits can control the vertical position of the plasma column inside the vacuum vessel. Since the vertical position of plasma is an unstable parameter, a direct closed loop system identification algorithm is performed. In the second stage, a nonlinear model is identified for plasma vertical position, based on the multilayer perceptron (MLP) neural network (NN) structure. Estimation of simulator parameters has been performed by back-propagation error algorithm using Levenberg-Marquardt gradient descent optimization technique. The model is verified through simulation of the whole closed loop system using both simulator and actual plant in similar conditions. As the final stage, a MLP neural network controller is designed for simulator model. In the last step, online training is performed to tune the controller parameters. Simulation results justify using of the NN controller for the actual plant.

  5. Neural network based system for equipment surveillance

    DOE Patents [OSTI]

    Vilim, R.B.; Gross, K.C.; Wegerich, S.W.

    1998-04-28

    A method and system are disclosed for performing surveillance of transient signals of an industrial device to ascertain the operating state. The method and system involves the steps of reading into a memory training data, determining neural network weighting values until achieving target outputs close to the neural network output. If the target outputs are inadequate, wavelet parameters are determined to yield neural network outputs close to the desired set of target outputs and then providing signals characteristic of an industrial process and comparing the neural network output to the industrial process signals to evaluate the operating state of the industrial process. 33 figs.

  6. Neural network based system for equipment surveillance

    DOE Patents [OSTI]

    Vilim, Richard B.; Gross, Kenneth C.; Wegerich, Stephan W.

    1998-01-01

    A method and system for performing surveillance of transient signals of an industrial device to ascertain the operating state. The method and system involves the steps of reading into a memory training data, determining neural network weighting values until achieving target outputs close to the neural network output. If the target outputs are inadequate, wavelet parameters are determined to yield neural network outputs close to the desired set of target outputs and then providing signals characteristic of an industrial process and comparing the neural network output to the industrial process signals to evaluate the operating state of the industrial process.

  7. Prediction of Weld Penetration in FCAW of HSLA steel using Artificial Neural Networks

    SciTech Connect (OSTI)

    Asl, Y. Dadgar; Mostafa, N. B.; Panahizadeh, V. R. [Department of Mechanical Engineering, Shahid Rajaee Teacher Training University, Tehran (Iran, Islamic Republic of); Seyedkashi, S. M. H. [Department of Mechanical Engineering, Tarbiat Modares University, Tehran (Iran, Islamic Republic of)

    2011-01-17

    Flux-cored arc welding (FCAW) is a semiautomatic or automatic arc welding process that requires a continuously-fed consumable tubular electrode containing a flux. The main FCAW process parameters affecting the depth of penetration are welding current, arc voltage, nozzle-to-work distance, torch angle and welding speed. Shallow depth of penetration may contribute to failure of a welded structure since penetration determines the stress-carrying capacity of a welded joint. To avoid such occurrences; the welding process parameters influencing the weld penetration must be properly selected to obtain an acceptable weld penetration and hence a high quality joint. Artificial neural networks (ANN), also called neural networks (NN), are computational models used to express complex non-linear relationships between input and output data. In this paper, artificial neural network (ANN) method is used to predict the effects of welding current, arc voltage, nozzle-to-work distance, torch angle and welding speed on weld penetration depth in gas shielded FCAW of a grade of high strength low alloy steel. 32 experimental runs were carried out using the bead-on-plate welding technique. Weld penetrations were measured and on the basis of these 32 sets of experimental data, a feed-forward back-propagation neural network was created. 28 sets of the experiments were used as the training data and the remaining 4 sets were used for the testing phase of the network. The ANN has one hidden layer with eight neurons and is trained after 840 iterations. The comparison between the experimental results and ANN results showed that the trained network could predict the effects of the FCAW process parameters on weld penetration adequately.

  8. Imbibition well stimulation via neural network design

    DOE Patents [OSTI]

    Weiss, William

    2007-08-14

    A method for stimulation of hydrocarbon production via imbibition by utilization of surfactants. The method includes use of fuzzy logic and neural network architecture constructs to determine surfactant use.

  9. Analysis of Stochastic Response of Neural Networks with Stochastic Input

    Energy Science and Technology Software Center (OSTI)

    1996-10-10

    Software permits the user to extend capability of his/her neural network to include probablistic characteristics of input parameter. User inputs topology and weights associated with neural network along with distributional characteristics of input parameters. Network response is provided via a cumulative density function of network response variable.

  10. Artificial neural network cardiopulmonary modeling and diagnosis

    DOE Patents [OSTI]

    Kangas, L.J.; Keller, P.E.

    1997-10-28

    The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis. 12 figs.

  11. Artificial neural network cardiopulmonary modeling and diagnosis

    DOE Patents [OSTI]

    Kangas, Lars J.; Keller, Paul E.

    1997-01-01

    The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis.

  12. The next generation of neural network chips

    SciTech Connect (OSTI)

    Beiu, V.

    1997-08-01

    There have been many national and international neural networks research initiatives: USA (DARPA, NIBS), Canada (IRIS), Japan (HFSP) and Europe (BRAIN, GALA TEA, NERVES, ELENE NERVES 2) -- just to mention a few. Recent developments in the field of neural networks, cognitive science, bioengineering and electrical engineering have made it possible to understand more about the functioning of large ensembles of identical processing elements. There are more research papers than ever proposing solutions and hardware implementations are by no means an exception. Two fields (computing and neuroscience) are interacting in ways nobody could imagine just several years ago, and -- with the advent of new technologies -- researchers are focusing on trying to copy the Brain. Such an exciting confluence may quite shortly lead to revolutionary new computers and it is the aim of this invited session to bring to light some of the challenging research aspects dealing with the hardware realizability of future intelligent chips. Present-day (conventional) technology is (still) mostly digital and, thus, occupies wider areas and consumes much more power than the solutions envisaged. The innovative algorithmic and architectural ideals should represent important breakthroughs, paving the way towards making neural network chips available to the industry at competitive prices, in relatively small packages and consuming a fraction of the power required by equivalent digital solutions.

  13. Neural node network and model, and method of teaching same

    DOE Patents [OSTI]

    Parlos, A.G.; Atiya, A.F.; Fernandez, B.; Tsai, W.K.; Chong, K.T.

    1995-12-26

    The present invention is a fully connected feed forward network that includes at least one hidden layer. The hidden layer includes nodes in which the output of the node is fed back to that node as an input with a unit delay produced by a delay device occurring in the feedback path (local feedback). Each node within each layer also receives a delayed output (crosstalk) produced by a delay unit from all the other nodes within the same layer. The node performs a transfer function operation based on the inputs from the previous layer and the delayed outputs. The network can be implemented as analog or digital or within a general purpose processor. Two teaching methods can be used: (1) back propagation of weight calculation that includes the local feedback and the crosstalk or (2) more preferably a feed forward gradient decent which immediately follows the output computations and which also includes the local feedback and the crosstalk. Subsequent to the gradient propagation, the weights can be normalized, thereby preventing convergence to a local optimum. Education of the network can be incremental both on and off-line. An educated network is suitable for modeling and controlling dynamic nonlinear systems and time series systems and predicting the outputs as well as hidden states and parameters. The educated network can also be further educated during on-line processing. 21 figs.

  14. Neural node network and model, and method of teaching same

    DOE Patents [OSTI]

    Parlos, Alexander G.; Atiya, Amir F.; Fernandez, Benito; Tsai, Wei K.; Chong, Kil T.

    1995-01-01

    The present invention is a fully connected feed forward network that includes at least one hidden layer 16. The hidden layer 16 includes nodes 20 in which the output of the node is fed back to that node as an input with a unit delay produced by a delay device 24 occurring in the feedback path 22 (local feedback). Each node within each layer also receives a delayed output (crosstalk) produced by a delay unit 36 from all the other nodes within the same layer 16. The node performs a transfer function operation based on the inputs from the previous layer and the delayed outputs. The network can be implemented as analog or digital or within a general purpose processor. Two teaching methods can be used: (1) back propagation of weight calculation that includes the local feedback and the crosstalk or (2) more preferably a feed forward gradient decent which immediately follows the output computations and which also includes the local feedback and the crosstalk. Subsequent to the gradient propagation, the weights can be normalized, thereby preventing convergence to a local optimum. Education of the network can be incremental both on and off-line. An educated network is suitable for modeling and controlling dynamic nonlinear systems and time series systems and predicting the outputs as well as hidden states and parameters. The educated network can also be further educated during on-line processing.

  15. Comparison of a Recurrent Neural Network PV System Model with...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Neural Network PV System Model with a Traditional Component-Based PV System Model Daniel Riley, Sandia National Laboratories, Albuquerque, New Mexico, USA | Ganesh K....

  16. Genetic Algorithm Based Neural Networks for Nonlinear Optimization

    Energy Science and Technology Software Center (OSTI)

    1994-09-28

    This software develops a novel approach to nonlinear optimization using genetic algorithm based neural networks. To our best knowledge, this approach represents the first attempt at applying both neural network and genetic algorithm techniques to solve a nonlinear optimization problem. The approach constructs a neural network structure and an appropriately shaped energy surface whose minima correspond to optimal solutions of the problem. A genetic algorithm is employed to perform a parallel and powerful search ofmore » the energy surface.« less

  17. Beneficial role of noise in artificial neural networks

    SciTech Connect (OSTI)

    Monterola, Christopher; Saloma, Caesar; Zapotocky, Martin

    2008-06-18

    We demonstrate enhancement of neural networks efficacy to recognize frequency encoded signals and/or to categorize spatial patterns of neural activity as a result of noise addition. For temporal information recovery, noise directly added to the receiving neurons allow instantaneous improvement of signal-to-noise ratio [Monterola and Saloma, Phys. Rev. Lett. 2002]. For spatial patterns however, recurrence is necessary to extend and homogenize the operating range of a feed-forward neural network [Monterola and Zapotocky, Phys. Rev. E 2005]. Finally, using the size of the basin of attraction of the networks learned patterns (dynamical fixed points), a procedure for estimating the optimal noise is demonstrated.

  18. Using a neural network for abnormal event identification in BWRs

    SciTech Connect (OSTI)

    Ohga, Yukiharu; Seki, Hiroshi (Hitachi Ltd., Ibaraki (Japan))

    1991-01-01

    Information on anomalies such as abnormal events is considered to be important for operation support when choosing information to be offered to operators. The authors have applied neural network techniques to identify an abnormal event that causes a reactor scram in boiling water reactors. A primary feature of the method is that the result of the neural network is confirmed using the knowledge base on plant status when each event occurs. This improves the result's reliability. A second feature is that the neural network uses analog data such as reactor pressure, the acquisition of which is triggered by the scram signal. The event identification method is shown. The event identification method is tested using a workstation.

  19. Real-time neural network earthquake profile predictor

    DOE Patents [OSTI]

    Leach, Richard R. (Castro Valley, CA); Dowla, Farid U. (Castro Valley, CA)

    1996-01-01

    A neural network has been developed that uses first-arrival energy to predict the characteristics of impending earthquake seismograph signals. The propagation of ground motion energy through the earth is a highly nonlinear function. This is due to different forms of ground motion as well as to changes in the elastic properties of the media throughout the propagation path. The neural network is trained using seismogram data from earthquakes. Presented with a previously unseen earthquake, the neural network produces a profile of the complete earthquake signal using data from the first seconds of the signal. This offers a significant advance in the real-time monitoring, warning, and subsequent hazard minimization of catastrophic ground motion.

  20. Real-time neural network earthquake profile predictor

    DOE Patents [OSTI]

    Leach, R.R.; Dowla, F.U.

    1996-02-06

    A neural network has been developed that uses first-arrival energy to predict the characteristics of impending earthquake seismograph signals. The propagation of ground motion energy through the earth is a highly nonlinear function. This is due to different forms of ground motion as well as to changes in the elastic properties of the media throughout the propagation path. The neural network is trained using seismogram data from earthquakes. Presented with a previously unseen earthquake, the neural network produces a profile of the complete earthquake signal using data from the first seconds of the signal. This offers a significant advance in the real-time monitoring, warning, and subsequent hazard minimization of catastrophic ground motion. 17 figs.

  1. Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks

    SciTech Connect (OSTI)

    Ziaul Huque

    2007-08-31

    This is the final technical report for the project titled 'Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks'. The aim of the project was to develop an efficient chemistry model for combustion simulations. The reduced chemistry model was developed mathematically without the need of having extensive knowledge of the chemistry involved. To aid in the development of the model, Neural Networks (NN) was used via a new network topology known as Non-linear Principal Components Analysis (NPCA). A commonly used Multilayer Perceptron Neural Network (MLP-NN) was modified to implement NPCA-NN. The training rate of NPCA-NN was improved with the GEneralized Regression Neural Network (GRNN) based on kernel smoothing techniques. Kernel smoothing provides a simple way of finding structure in data set without the imposition of a parametric model. The trajectory data of the reaction mechanism was generated based on the optimization techniques of genetic algorithm (GA). The NPCA-NN algorithm was then used for the reduction of Dimethyl Ether (DME) mechanism. DME is a recently discovered fuel made from natural gas, (and other feedstock such as coal, biomass, and urban wastes) which can be used in compression ignition engines as a substitute for diesel. An in-house two-dimensional Computational Fluid Dynamics (CFD) code was developed based on Meshfree technique and time marching solution algorithm. The project also provided valuable research experience to two graduate students.

  2. Wind Power Plant Prediction by Using Neural Networks: Preprint

    SciTech Connect (OSTI)

    Liu, Z.; Gao, W.; Wan, Y. H.; Muljadi, E.

    2012-08-01

    This paper introduces a method of short-term wind power prediction for a wind power plant by training neural networks based on historical data of wind speed and wind direction. The model proposed is shown to achieve a high accuracy with respect to the measured data.

  3. Neural Network Based System for Equipment Startup Surveillance

    Energy Science and Technology Software Center (OSTI)

    1996-12-18

    NEBSESS is a system for equipment surveillance and fault detection which relies on a neural-network based means for diagnosing disturbances during startup and for automatically actuating the Sequential Probability Ratio Test (SPRT) as a signal validation means during steady-state operation.

  4. Fault diagnosis via neural networks: The Boltzmann machine

    SciTech Connect (OSTI)

    Marseguerra, M.; Zio, E. . Dept. of Nuclear Engineering)

    1994-07-01

    The Boltzmann machine is a general-purpose artificial neural network that can be used as an associative memory as well as a mapping tool. The usual information entropy is introduced, and a network energy function is suitably defined. The network's training procedure is based on the simulated annealing during which a combination of energy minimization and entropy maximization is achieved. An application in the nuclear reactor field is presented in which the Boltzmann input-output machine is used to detect and diagnose a pipe break in a simulated auxiliary feedwater system feeding two coupled steam generators. The break may occur on either the hot or the cold leg of any of the two steam generators. The binary input data to the network encode only the trends of the thermohydraulic signals so that the network is actually a polarity device. The results indicate that the trained neural network is actually capable of performing its task. The method appears to be robust enough so that it may also be applied with success in the presence of substantial amounts of noise that cause the network to be fed with wrong signals.

  5. Neural Networks for Analysis of Top Quark Production

    SciTech Connect (OSTI)

    B. Abbott et al.

    1999-08-04

    Neural networks (NNs) provide a powerful and flexible tool for selecting a signal from a larger background. The D0 collaboration has used them extensively in studying t{anti t} decays. NNs were essential to the measurement of the t{anti t} production cross section in the all-jets channel (t{anti t} {yields} b {anti b}qqqq), and were also used in the measurement of the mass of the top quark in the lepton+jets channel (t{anti t} {yields} b{anti b}l{nu}q{anti q}). This paper will describe two new applications of neural networks to top quark analysis: the search for single top quark production, and an effort to increase the sensitivity in the dilepton channel t{anti t} {yields} b{anti b}e{anti {mu}}{nu}{anti {nu}} beyond that achieved in the published analysis.

  6. Adaptive model predictive process control using neural networks

    DOE Patents [OSTI]

    Buescher, Kevin L.; Baum, Christopher C.; Jones, Roger D.

    1997-01-01

    A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data.

  7. Adaptive model predictive process control using neural networks

    DOE Patents [OSTI]

    Buescher, K.L.; Baum, C.C.; Jones, R.D.

    1997-08-19

    A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data. 46 figs.

  8. Laser programmable integrated circuit for forming synapses in neural networks

    DOE Patents [OSTI]

    Fu, C.Y.

    1997-02-11

    Customizable neural network in which one or more resistors form each synapse is disclosed. All the resistors in the synaptic array are identical, thus simplifying the processing issues. Highly doped, amorphous silicon is used as the resistor material, to create extremely high resistances occupying very small spaces. Connected in series with each resistor in the array is at least one severable conductor whose uppermost layer has a lower reflectivity of laser energy than typical metal conductors at a desired laser wavelength. 5 figs.

  9. Process for forming synapses in neural networks and resistor therefor

    DOE Patents [OSTI]

    Fu, Chi Y.

    1996-01-01

    Customizable neural network in which one or more resistors form each synapse. All the resistors in the synaptic array are identical, thus simplifying the processing issues. Highly doped, amorphous silicon is used as the resistor material, to create extremely high resistances occupying very small spaces. Connected in series with each resistor in the array is at least one severable conductor whose uppermost layer has a lower reflectivity of laser energy than typical metal conductors at a desired laser wavelength.

  10. Laser programmable integrated curcuit for forming synapses in neural networks

    DOE Patents [OSTI]

    Fu, Chi Y.

    1997-01-01

    Customizable neural network in which one or more resistors form each synapse. All the resistors in the synaptic array are identical, thus simplifying the processing issues. Highly doped, amorphous silicon is used as the resistor material, to create extremely high resistances occupying very small spaces. Connected in series with each resistor in the array is at least one severable conductor whose uppermost layer has a lower reflectivity of laser energy than typical metal conductors at a desired laser wavelength.

  11. Process for forming synapses in neural networks and resistor therefor

    DOE Patents [OSTI]

    Fu, C.Y.

    1996-07-23

    Customizable neural network in which one or more resistors form each synapse is disclosed. All the resistors in the synaptic array are identical, thus simplifying the processing issues. Highly doped, amorphous silicon is used as the resistor material, to create extremely high resistances occupying very small spaces. Connected in series with each resistor in the array is at least one severable conductor whose uppermost layer has a lower reflectivity of laser energy than typical metal conductors at a desired laser wavelength. 5 figs.

  12. Bump formation in a binary attractor neural network

    SciTech Connect (OSTI)

    Koroutchev, Kostadin; Korutcheva, Elka

    2006-02-15

    The conditions for the formation of local bumps in the activity of binary attractor neural networks with spatially dependent connectivity are investigated. We show that these formations are observed when asymmetry between the activity during the retrieval and learning is imposed. An analytical approximation for the order parameters is derived. The corresponding phase diagram shows a relatively large and stable region where this effect is observed, although critical storage and information capacities drastically decrease inside that region. We demonstrate that the stability of the network, when starting from the bump formation, is larger than the stability when starting even from the whole pattern. Finally, we show a very good agreement between the analytical results and the simulations performed for different topologies of the network.

  13. Pattern classification and associative recall by neural networks

    SciTech Connect (OSTI)

    Chiueh, Tzi-Dar.

    1989-01-01

    The first part of this dissertation discusses a new classifier based on a multilayer feed-forward network architecture. The main idea is to map irregularly-distributed prototypes in a classification problem to codewords that are organized in some way. Then the pattern classification problem is transformed into a threshold decoding problem, which is easily solved using simple hard-limiter neurons. At first the author proposes the new model and introduce two families of good internal representation codes. Then some analyses and software simulation concerning the storage capacity of this new model are done. The results show that the new classifier is much better than the classifier based on the Hopfield model in terms of both the storage capacity and the ability to classify correlated prototypes. A general model for neural network associative memories with a feedback structure is proposed. Many existing neural network associative memories can be expressed as special cases of this general model. Among these models, there is a class of associative memories, called correlation associative memories, that are capable of storing a large number of memory patterns. If the function used in the evolution equation is monotonically nondecreasing, then a correlation associative memory can be proved to be asymptotically stable in both the synchronous and asynchronous updating modes. Of these correlation associative memories, one stands out because of its VLSI implementation feasibility and large storage capacity. This memory uses the exponentiation function in its evolution equation; hence it is called exponential correlation associative memory (ECAM).

  14. Application Of An Artificial Neural Network Model To A Na-K Geothermom...

    Open Energy Info (EERE)

    for the artificial neural network. Reservoir temperatures of some geothermal fields in Turkey determined by this method are in accord with those determined from other methods....

  15. Communication: Separable potential energy surfaces from multiplicative artificial neural networks

    SciTech Connect (OSTI)

    Koch, Werner, E-mail: wkoch@thethirdrock.net; Zhang, Dong H. [State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian (China)

    2014-07-14

    We present a potential energy surface fitting scheme based on multiplicative artificial neural networks. It has the sum of products form required for efficient computation of the dynamics of multidimensional quantum systems with the multi configuration time dependent Hartree method. Moreover, it results in analytic potential energy matrix elements when combined with quantum dynamics methods using Gaussian basis functions, eliminating the need for a local harmonic approximation. Scaling behavior with respect to the complexity of the potential as well as the requested accuracy is discussed.

  16. Application of a fuzzy neural network model in predicting polycyclic aromatic hydrocarbon-mediated perturbations of the Cyp1b1 transcriptional regulatory network in mouse skin

    SciTech Connect (OSTI)

    Larkin, Andrew; Siddens, Lisbeth K.; Krueger, Sharon K.; Tilton, Susan C.; Waters, Katrina M.; Williams, David E.; Baird, William M.

    2013-03-01

    Polycyclic aromatic hydrocarbons (PAHs) are present in the environment as complex mixtures with components that have diverse carcinogenic potencies and mostly unknown interactive effects. Non-additive PAH interactions have been observed in regulation of cytochrome P450 (CYP) gene expression in the CYP1 family. To better understand and predict biological effects of complex mixtures, such as environmental PAHs, an 11 gene input-1 gene output fuzzy neural network (FNN) was developed for predicting PAH-mediated perturbations of dermal Cyp1b1 transcription in mice. Input values were generalized using fuzzy logic into low, medium, and high fuzzy subsets, and sorted using k-means clustering to create Mamdani logic functions for predicting Cyp1b1 mRNA expression. Model testing was performed with data from microarray analysis of skin samples from FVB/N mice treated with toluene (vehicle control), dibenzo[def,p]chrysene (DBC), benzo[a]pyrene (BaP), or 1 of 3 combinations of diesel particulate extract (DPE), coal tar extract (CTE) and cigarette smoke condensate (CSC) using leave-one-out cross-validation. Predictions were within 1 log{sub 2} fold change unit of microarray data, with the exception of the DBC treatment group, where the unexpected down-regulation of Cyp1b1 expression was predicted but did not reach statistical significance on the microarrays. Adding CTE to DPE was predicted to increase Cyp1b1 expression, whereas adding CSC to CTE and DPE was predicted to have no effect, in agreement with microarray results. The aryl hydrocarbon receptor repressor (Ahrr) was determined to be the most significant input variable for model predictions using back-propagation and normalization of FNN weights. - Highlights: ? Tested a model to predict PAH mixture-mediated changes in Cyp1b1 expression ? Quantitative predictions in agreement with microarrays for Cyp1b1 induction ? Unexpected difference in expression between DBC and other treatments predicted ? Model predictions for combining PAH mixtures in agreement with microarrays ? Predictions highly dependent on aryl hydrocarbon receptor repressor expression.

  17. Surface daytime net radiation estimation using artificial neural networks

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Jiang, Bo; Zhang, Yi; Liang, Shunlin; Zhang, Xiaotong; Xiao, Zhiqiang

    2014-11-11

    Net all-wave surface radiation (Rn) is one of the most important fundamental parameters in various applications. However, conventional Rn measurements are difficult to collect because of the high cost and ongoing maintenance of recording instruments. Therefore, various empirical Rn estimation models have been developed. This study presents the results of two artificial neural network (ANN) models (general regression neural networks (GRNN) and Neuroet) to estimate Rn globally from multi-source data, including remotely sensed products, surface measurements, and meteorological reanalysis products. Rn estimates provided by the two ANNs were tested against in-situ radiation measurements obtained from 251 global sites between 1991–2010more » both in global mode (all data were used to fit the models) and in conditional mode (the data were divided into four subsets and the models were fitted separately). Based on the results obtained from extensive experiments, it has been proved that the two ANNs were superior to linear-based empirical models in both global and conditional modes and that the GRNN performed better and was more stable than Neuroet. The GRNN estimates had a determination coefficient (R2) of 0.92, a root mean square error (RMSE) of 34.27 W·m–2 , and a bias of –0.61 W·m–2 in global mode based on the validation dataset. In conclusion, ANN methods are a potentially powerful tool for global Rn estimation.« less

  18. Neural networks for the monitoring of rotating machinery

    SciTech Connect (OSTI)

    Alguindigue, I.E.; Loskiewicz-Buczak; Uhrig, R.E. |

    1991-12-31

    Vibration monitoring of components in engineering systems and plants involves the collection of vibration data and detailed analysis to detect features which reflect the operational state of the machinery. The analysis leads to the identification of potential failures and their causes, and makes it possible to perform efficient preventive maintenance. This paper describes a methodology for the automation of some of the activities related to motion and vibration monitoring in these systems. The technique involves training a neural network to model the inter- relationship between signals from two related sensors mounted on an engineering system or component at a time when it is known to be operating properly. Then one signal (or its characteristics) is put into the neural network model to predict the second signal (or its characteristics). This predicted signal is continuously compared with the actual signal A deviation between the predicted and actual signal indicates a changing relationship, usually failure of the component or system. This deviation may be quantified and provides meaningful information about the degree of degradation and deterioration of the component.

  19. Neural networks for the monitoring of rotating machinery

    SciTech Connect (OSTI)

    Alguindigue, I.E.; Loskiewicz-Buczak . Dept. of Nuclear Engineering); Uhrig, R.E. . Dept. of Nuclear Engineering Oak Ridge National Lab., TN )

    1991-01-01

    Vibration monitoring of components in engineering systems and plants involves the collection of vibration data and detailed analysis to detect features which reflect the operational state of the machinery. The analysis leads to the identification of potential failures and their causes, and makes it possible to perform efficient preventive maintenance. This paper describes a methodology for the automation of some of the activities related to motion and vibration monitoring in these systems. The technique involves training a neural network to model the inter- relationship between signals from two related sensors mounted on an engineering system or component at a time when it is known to be operating properly. Then one signal (or its characteristics) is put into the neural network model to predict the second signal (or its characteristics). This predicted signal is continuously compared with the actual signal A deviation between the predicted and actual signal indicates a changing relationship, usually failure of the component or system. This deviation may be quantified and provides meaningful information about the degree of degradation and deterioration of the component.

  20. APPLICATION OF NEURAL NETWORK ALGORITHMS FOR BPM LINEARIZATION

    SciTech Connect (OSTI)

    Musson, John C.; Seaton, Chad; Spata, Mike F.; Yan, Jianxun

    2012-11-01

    Stripline BPM sensors contain inherent non-linearities, as a result of field distortions from the pickup elements. Many methods have been devised to facilitate corrections, often employing polynomial fitting. The cost of computation makes real-time correction difficult, particulalry when integer math is utilized. The application of neural-network technology, particularly the multi-layer perceptron algorithm, is proposed as an efficient alternative for electrode linearization. A process of supervised learning is initially used to determine the weighting coefficients, which are subsequently applied to the incoming electrode data. A non-linear layer, known as an ?activation layer,? is responsible for the removal of saturation effects. Implementation of a perceptron in an FPGA-based software-defined radio (SDR) is presented, along with performance comparisons. In addition, efficient calculation of the sigmoidal activation function via the CORDIC algorithm is presented.

  1. Surface daytime net radiation estimation using artificial neural networks

    SciTech Connect (OSTI)

    Jiang, Bo; Zhang, Yi; Liang, Shunlin; Zhang, Xiaotong; Xiao, Zhiqiang

    2014-11-11

    Net all-wave surface radiation (Rn) is one of the most important fundamental parameters in various applications. However, conventional Rn measurements are difficult to collect because of the high cost and ongoing maintenance of recording instruments. Therefore, various empirical Rn estimation models have been developed. This study presents the results of two artificial neural network (ANN) models (general regression neural networks (GRNN) and Neuroet) to estimate Rn globally from multi-source data, including remotely sensed products, surface measurements, and meteorological reanalysis products. Rn estimates provided by the two ANNs were tested against in-situ radiation measurements obtained from 251 global sites between 1991–2010 both in global mode (all data were used to fit the models) and in conditional mode (the data were divided into four subsets and the models were fitted separately). Based on the results obtained from extensive experiments, it has been proved that the two ANNs were superior to linear-based empirical models in both global and conditional modes and that the GRNN performed better and was more stable than Neuroet. The GRNN estimates had a determination coefficient (R2) of 0.92, a root mean square error (RMSE) of 34.27 W·m–2 , and a bias of –0.61 W·m–2 in global mode based on the validation dataset. In conclusion, ANN methods are a potentially powerful tool for global Rn estimation.

  2. Using a Neural Network to Determine the Hatch Status of the AERI...

    Office of Scientific and Technical Information (OSTI)

    Title: Using a Neural Network to Determine the Hatch Status of the AERI at the ARM North Slope of Alaska Site The fore-optics of the Atmospheric Emitted Radiance Interferometer ...

  3. Modeling of transport phenomena in tokamak plasmas with neural networks

    SciTech Connect (OSTI)

    Meneghini, O.; Luna, C. J.; Smith, S. P.; Lao, L. L.

    2014-06-15

    A new transport model that uses neural networks (NNs) to yield electron and ion heat flux profiles has been developed. Given a set of local dimensionless plasma parameters similar to the ones that the highest fidelity models use, the NN model is able to efficiently and accurately predict the ion and electron heat transport profiles. As a benchmark, a NN was built, trained, and tested on data from the 2012 and 2013 DIII-D experimental campaigns. It is found that NN can capture the experimental behavior over the majority of the plasma radius and across a broad range of plasma regimes. Although each radial location is calculated independently from the others, the heat flux profiles are smooth, suggesting that the solution found by the NN is a smooth function of the local input parameters. This result supports the evidence of a well-defined, non-stochastic relationship between the input parameters and the experimentally measured transport fluxes. The numerical efficiency of this method, requiring only a few CPU-?s per data point, makes it ideal for scenario development simulations and real-time plasma control.

  4. Neural networks for control of NO{sub x} emissions in fossil plants

    SciTech Connect (OSTI)

    Reifman, J.; Feldman, E.E.

    1997-04-01

    We discuss the use of two classes of artificial neural networks, multilayer feedforward networks and fully-recurrent networks, in the development of a closed-loop controller for discrete-time dynamical systems. We apply the neural system to the control of oxides of nitrogen (NO{sub x}) emissions for a simplified representation of a furnace of a coal-fired fossil plant. Plant data from one of Commonwealth Edison`s fossil power plants were used to build a recurrent neural model of NO{sub x} formation which is then used in the training of the feedforward neural controller. Preliminary simulation results demonstrate the feasibility of the approach and additional tests with increasingly realistic models should be pursued.

  5. Monitoring of vibrating machinery using artificial neural networks

    SciTech Connect (OSTI)

    Alguindigue, I.E.; Loskiewicz-Buczak, A. . Dept. of Nuclear Engineering); Uhrig, R.E. . Dept. of Nuclear Engineering Oak Ridge National Lab., TN )

    1991-01-01

    The primary source of vibration in complex engineering systems is rotating machinery. Vibration signatures collected from these components render valuable information about the operational state of the system and may be used to perform diagnostics. For example, the low frequency domain contains information about unbalance, misalignment, instability in journal bearing and mechanical looseness; analysis of the medium frequency range can render information about faults in meshing gear teeth; while the high frequency domain will contain information about incipient faults in rolling-element bearings. Trend analysis may be performed by comparing the vibration spectrum for each machine with a reference spectrum and evaluating the vibration magnitude changes at different frequencies. This form of analysis for diagnostics is often performed by maintenance personnel monitoring and recording transducer signals and analyzing the signals to identify the operating condition of the machine. With the advent of portable fast Fourier transform (FFT) analyzers and laptop'' computers, it is possible to collect and analyze vibration data an site and detect incipient failures several weeks or months before repair is necessary. It is often possible to estimate the remaining life of certain systems once a fault has been detected. RMS velocity, acceleration, displacements, peak value, and crest factor readings can be collected from vibration sensors. To exploit all the information embedded in these signals, a robust and advanced analysis technique is required. Our goal is to design a diagnostic system using neural network technology, a system such as this would automate the interpretation of vibration data coming from plant-wide machinery and permit efficient on-line monitoring of these components.

  6. Monitoring of vibrating machinery using artificial neural networks

    SciTech Connect (OSTI)

    Alguindigue, I.E.; Loskiewicz-Buczak, A.; Uhrig, R.E. |

    1991-12-31

    The primary source of vibration in complex engineering systems is rotating machinery. Vibration signatures collected from these components render valuable information about the operational state of the system and may be used to perform diagnostics. For example, the low frequency domain contains information about unbalance, misalignment, instability in journal bearing and mechanical looseness; analysis of the medium frequency range can render information about faults in meshing gear teeth; while the high frequency domain will contain information about incipient faults in rolling-element bearings. Trend analysis may be performed by comparing the vibration spectrum for each machine with a reference spectrum and evaluating the vibration magnitude changes at different frequencies. This form of analysis for diagnostics is often performed by maintenance personnel monitoring and recording transducer signals and analyzing the signals to identify the operating condition of the machine. With the advent of portable fast Fourier transform (FFT) analyzers and ``laptop`` computers, it is possible to collect and analyze vibration data an site and detect incipient failures several weeks or months before repair is necessary. It is often possible to estimate the remaining life of certain systems once a fault has been detected. RMS velocity, acceleration, displacements, peak value, and crest factor readings can be collected from vibration sensors. To exploit all the information embedded in these signals, a robust and advanced analysis technique is required. Our goal is to design a diagnostic system using neural network technology, a system such as this would automate the interpretation of vibration data coming from plant-wide machinery and permit efficient on-line monitoring of these components.

  7. Recurrent neural networks for NO{sub x} prediction in fossil plants

    SciTech Connect (OSTI)

    Reifman, J.; Vitela, J.E.; Feldman, E.E.; Wei, T.Y.C.

    1996-04-01

    The authors discuss the application of recurrent (dynamic) neural networks for time-dependent modeling of NO{sub x} emissions in coal-fired fossil plants. They use plant data from one of ComEd`s plants to train and test the network model. Additional tests, parametric studies, and sensitivity analyses are performed to determine if the dynamic behavior of the model matches the expected behavior of the physical system. The results are also compared with feedforward (static) neural network models trained to represent temporal information.

  8. Abnormal event identification in nuclear power plants using a neural network and knowledge processing

    SciTech Connect (OSTI)

    Ohga, Yukiharu; Seki, Hiroshi (Hitachi, Ltd. Energy Research Lab., Ibarakiken (Japan))

    1993-02-01

    The combination of a neural network and knowledge processing have been used to identify abnormal events that cause a reactor to scram in a nuclear power plant. The neural network recognizes the abnormal event from the change pattern of analog data for state variables, and this result is confirmed from digital data using a knowledge base of plant status when each event occurs. The event identification method is tested using test data based on simulated results of a transient analysis program for boiling water reactors. It is confirmed that a neural network can identify an event in which it has been trained even when the plant conditions, such as fuel burnup, differ from those used in the training and when the analog data contain white noise. The network does not mistakenly identify the nontrained event as a trained one. The method is feasible for event identification, and knowledge processing improves the reliability of the identification.

  9. A neural network model for predicting the silicon content of the hot metal at No. 2 blast furnace of SSAB Luleaa

    SciTech Connect (OSTI)

    Zuo Guangqing; Ma Jitang; Bo, B.

    1996-12-31

    To predict the silicon content of hot metal at No. 2 blast furnace, SSAB, Luleaa Works, a three-layer Back-Propagation network model has been established. The network consists of twenty-eight inputs, six middle nodes and one output and uses a generalized delta rule for training. Different network structures and different training strategies have been tested. A well-functioning network with dynamic updating has been designed. The off-line test and the on-line application results showed that more than 80% of the predictions can match the actual silicon content in hot metal in a normal operation, if the allowable prediction error was set to {+-}0.05% Si, while the actual fluctuation of the silicon content was larger than {+-}0.10% Si.

  10. Using a Neural Network to Determine the Hatch Status of the AERI at the ARM

    Office of Scientific and Technical Information (OSTI)

    North Slope of Alaska Site (Technical Report) | SciTech Connect Using a Neural Network to Determine the Hatch Status of the AERI at the ARM North Slope of Alaska Site Citation Details In-Document Search Title: Using a Neural Network to Determine the Hatch Status of the AERI at the ARM North Slope of Alaska Site The fore-optics of the Atmospheric Emitted Radiance Interferometer (AERI) are protected by an automated hatch to prevent precipitation from fouling the instrument's scene mirror

  11. Using a Neural Network to Determine the Hatch Status of the AERI at the ARM

    Office of Scientific and Technical Information (OSTI)

    North Slope of Alaska Site (Technical Report) | SciTech Connect Using a Neural Network to Determine the Hatch Status of the AERI at the ARM North Slope of Alaska Site Citation Details In-Document Search Title: Using a Neural Network to Determine the Hatch Status of the AERI at the ARM North Slope of Alaska Site × You are accessing a document from the Department of Energy's (DOE) SciTech Connect. This site is a product of DOE's Office of Scientific and Technical Information (OSTI) and is

  12. High quality garbage: A neural network plastic sorter in hardware and software

    SciTech Connect (OSTI)

    Stanton, S.L.; Alam, M.K.; Hebner, G.A.

    1993-09-01

    In order to produce pure polymer streams from post-consumer waste plastics, a quick, accurate and relatively inexpensive method of sorting needs to be implemented. This technology has been demonstrated by using near-infrared spectroscopy reflectance data and neural network classification techniques. Backpropagation neural network routines have been developed to run real-time sortings in the lab, using a laboratory-grade spectrometer. In addition, a new reflectance spectrometer has been developed which is fast enough for commercial use. Initial training and test sets taken with the laboratory instrument show that a network is capable of learning 100% when classifying 5 groups of plastic (HDPE and LDPE combined), and up to 100% when classifying 6 groups. Initial data sets from the new instrument have classified plastics into all seven groups with varying degrees of success. One of the initial networks has been implemented in hardware, for high speed computations, and thus rapid classification. Two neural accelerator systems have been evaluated, one based on the Intel 8017ONX chip, and another on the AT&T ANNA chip.

  13. Gene identification and analysis: an application of neural network-based information fusion

    SciTech Connect (OSTI)

    Matis, S.; Xu, Y.; Shah, M.B.; Mural, R.J.; Einstein, J.R.; Uberbacher, E.C.

    1996-10-01

    Identifying genes within large regions of uncharacterized DNA is a difficult undertaking and is currently the focus of many research efforts. We describe a gene localization and modeling system called GRAIL. GRAIL is a multiple sensor-neural network based system. It localizes genes in anonymous DNA sequence by recognizing gene features related to protein-coding slice sites, and then combines the recognized features using a neural network system. Localized coding regions are then optimally parsed into a gene mode. RNA polymerase II promoters can also be predicted. Through years of extensive testing, GRAIL consistently localizes about 90 percent of coding portions of test genes with a false positive rate of about 10 percent. A number of genes for major genetic diseases have been located through the use of GRAIL, and over 1000 research laboratories worldwide use GRAIL on regular bases for localization of genes on their newly sequenced DNA.

  14. A neural network system for prediction of RNA polymerase II promoters

    SciTech Connect (OSTI)

    Matis, S.; Shah, M.; Mural, R.; Uberbacher, E.

    1994-12-31

    One of the most difficult problems in the analysis of eucaryotic genes is the detection of RNA polymerase II promoter regions. Although promoter regions vary in the primary DNA sequence, a basic group of core promoter elements has been suggested in the literature. Many human promoter sequences contain a TATAA sequence element at approximately 30 bases upstream of the cap site (transcription start site). Other elements are the GC box which binds SPA and upregulates transcription, the CAAT box, and the ATG initiator codon. To characterize promoters, we constructed frequency matrices for each element using experimentally mapped human promoter regions. Additionally, we constructed histograms for the distances separating the various elements. We then used a neural network to combine these informational elements. The output of the neural network is then processed using a set of expert rules which depend on GRAIL`s ability to find exons in anonymous DNA. This improves the selectivity of promoter detection and reduces the false positive rate.

  15. GRAIL: A multi-agent neural network system for gene identification

    SciTech Connect (OSTI)

    Xu, Y.; Mural, R.J.; Einstein, J.R.; Shah, M.B.; Uberbacher, E.C.

    1996-10-01

    Identifying genes within large regions of uncharacterized DNA is a difficult undertaking and is currently the focus of many research efforts. The authors describe a gene localization and modeling system, called GRAIL. GRAIL is a multiple sensor-neural network-based system. It localizes genes in anonymous DNA sequence by recognizing features related to protein-coding regions and the boundaries of coding regions, and then combines the recognized features using a neural network system. Localized coding regions are then optimally parsed into a gene model. Through years of extensive testing, GRAIL consistently localizes about 90% of coding portions of test genes with a false positive rate of about 10%. A number of genes for major genetic diseases have been located through the use of GRAIL, and over 1,000 research laboratories worldwide use GRAIL on regular bases for localization of genes on their newly sequenced DNA.

  16. Closed loop adaptive control of spectrum-producing step using neural networks

    DOE Patents [OSTI]

    Fu, C.Y.

    1998-11-24

    Characteristics of the plasma in a plasma-based manufacturing process step are monitored directly and in real time by observing the spectrum which it produces. An artificial neural network analyzes the plasma spectrum and generates control signals to control one or more of the process input parameters in response to any deviation of the spectrum beyond a narrow range. In an embodiment, a plasma reaction chamber forms a plasma in response to input parameters such as gas flow, pressure and power. The chamber includes a window through which the electromagnetic spectrum produced by a plasma in the chamber, just above the subject surface, may be viewed. The spectrum is conducted to an optical spectrometer which measures the intensity of the incoming optical spectrum at different wavelengths. The output of optical spectrometer is provided to an analyzer which produces a plurality of error signals, each indicating whether a respective one of the input parameters to the chamber is to be increased or decreased. The microcontroller provides signals to control respective controls, but these lines are intercepted and first added to the error signals, before being provided to the controls for the chamber. The analyzer can include a neural network and an optional spectrum preprocessor to reduce background noise, as well as a comparator which compares the parameter values predicted by the neural network with a set of desired values provided by the microcontroller. 7 figs.

  17. Closed loop adaptive control of spectrum-producing step using neural networks

    DOE Patents [OSTI]

    Fu, Chi Yung

    1998-01-01

    Characteristics of the plasma in a plasma-based manufacturing process step are monitored directly and in real time by observing the spectrum which it produces. An artificial neural network analyzes the plasma spectrum and generates control signals to control one or more of the process input parameters in response to any deviation of the spectrum beyond a narrow range. In an embodiment, a plasma reaction chamber forms a plasma in response to input parameters such as gas flow, pressure and power. The chamber includes a window through which the electromagnetic spectrum produced by a plasma in the chamber, just above the subject surface, may be viewed. The spectrum is conducted to an optical spectrometer which measures the intensity of the incoming optical spectrum at different wavelengths. The output of optical spectrometer is provided to an analyzer which produces a plurality of error signals, each indicating whether a respective one of the input parameters to the chamber is to be increased or decreased. The microcontroller provides signals to control respective controls, but these lines are intercepted and first added to the error signals, before being provided to the controls for the chamber. The analyzer can include a neural network and an optional spectrum preprocessor to reduce background noise, as well as a comparator which compares the parameter values predicted by the neural network with a set of desired values provided by the microcontroller.

  18. Application of holographic neural networks for flue gas emissions prediction in the Burnaby incinerator

    SciTech Connect (OSTI)

    Zheng, L.; Dockrill, P.; Clements, B.

    1997-12-31

    This article describes the development of a parametric prediction system (PPS) for various emission species at the Burnaby incinerator. The continuous emissions monitoring system at the Burnaby incinerator is shared between three boilers and therefore actual results are only available 5 minutes out of every 15 minutes. The PPS was developed to fill in data for the 10 minutes when the Continuous Emission Monitor (CEM) is measuring the other boilers. It bases its prediction on the last few actual readings taken and parametrically predicts CO, SO2 and NOx. The Burnaby Incinerator is located in the commercial/industrial area of South Burnaby, British Columbia. It consists of three separate lines, each burning ten tonnes of garbage per hour and producing about three tonnes of steam for every tonne of garbage burned. The air pollution control system first cools the combustion products with water injection and then scrubs them with very fine hydrated lime. Carbon is added to the lime to enhance the scrubbing of the combustion products. The CEM monitors the levels of oxygen, carbon monoxide, nitrogen oxides, sulphur dioxide and opacity. In 1996, an expert system was installed on one of boilers at the Burnaby Incinerator plant to determine if it could improve the plant=s operations and reduce overall emission. As part of the expert system, the PPS was developed. Holographic Neural Technology (HNeT), developed by AND Corporation of Toronto, Ontario, is a novel neural network technology using complex numbers in its architecture. Compared to the traditional neural networks, HNeT has some significant advantage. It is more resilient against converging on local minima; is faster training and executing; less prone to over fitting; and, in most cases, has significantly lower error. Selection of independent variabs, training set preparation, testing neural nets and other related issue will be discussed.

  19. On using an adaptive neural network to predict lung tumor motion during respiration for radiotherapy applications

    SciTech Connect (OSTI)

    Isaksson, Marcus; Jalden, Joakim; Murphy, Martin J.

    2005-12-15

    In this study we address the problem of predicting the position of a moving lung tumor during respiration on the basis of external breathing signals--a technique used for beam gating, tracking, and other dynamic motion management techniques in radiation therapy. We demonstrate the use of neural network filters to correlate tumor position with external surrogate markers while simultaneously predicting the motion ahead in time, for situations in which neither the breathing pattern nor the correlation between moving anatomical elements is constant in time. One pancreatic cancer patient and two lung cancer patients with mid/upper lobe tumors were fluoroscopically imaged to observe tumor motion synchronously with the movement of external chest markers during free breathing. The external marker position was provided as input to a feed-forward neural network that correlated the marker and tumor movement to predict the tumor position up to 800 ms in advance. The predicted tumor position was compared to its observed position to establish the accuracy with which the filter could dynamically track tumor motion under nonstationary conditions. These results were compared to simplified linear versions of the filter. The two lung cancer patients exhibited complex respiratory behavior in which the correlation between surrogate marker and tumor position changed with each cycle of breathing. By automatically and continuously adjusting its parameters to the observations, the neural network achieved better tracking accuracy than the fixed and adaptive linear filters. Variability and instability in human respiration complicate the task of predicting tumor position from surrogate breathing signals. Our results show that adaptive signal-processing filters can provide more accurate tumor position estimates than simpler stationary filters when presented with nonstationary breathing motion.

  20. Prediction of U-Mo dispersion nuclear fuels with Al-Si alloy using artificial neural network

    SciTech Connect (OSTI)

    Susmikanti, Mike; Sulistyo, Jos

    2014-09-30

    Dispersion nuclear fuels, consisting of U-Mo particles dispersed in an Al-Si matrix, are being developed as fuel for research reactors. The equilibrium relationship for a mixture component can be expressed in the phase diagram. It is important to analyze whether a mixture component is in equilibrium phase or another phase. The purpose of this research it is needed to built the model of the phase diagram, so the mixture component is in the stable or melting condition. Artificial neural network (ANN) is a modeling tool for processes involving multivariable non-linear relationships. The objective of the present work is to develop code based on artificial neural network models of system equilibrium relationship of U-Mo in Al-Si matrix. This model can be used for prediction of type of resulting mixture, and whether the point is on the equilibrium phase or in another phase region. The equilibrium model data for prediction and modeling generated from experimentally data. The artificial neural network with resilient backpropagation method was chosen to predict the dispersion of nuclear fuels U-Mo in Al-Si matrix. This developed code was built with some function in MATLAB. For simulations using ANN, the Levenberg-Marquardt method was also used for optimization. The artificial neural network is able to predict the equilibrium phase or in the phase region. The develop code based on artificial neural network models was built, for analyze equilibrium relationship of U-Mo in Al-Si matrix.

  1. Imaging regenerating bone tissue based on neural networks applied to micro-diffraction measurements

    SciTech Connect (OSTI)

    Campi, G.; Pezzotti, G.; Fratini, M.; Ricci, A.; Burghammer, M.; Cancedda, R.; Mastrogiacomo, M.; Bukreeva, I.; Cedola, A.

    2013-12-16

    We monitored bone regeneration in a tissue engineering approach. To visualize and understand the structural evolution, the samples have been measured by X-ray micro-diffraction. We find that bone tissue regeneration proceeds through a multi-step mechanism, each step providing a specific diffraction signal. The large amount of data have been classified according to their structure and associated to the process they came from combining Neural Networks algorithms with least square pattern analysis. In this way, we obtain spatial maps of the different components of the tissues visualizing the complex kinetic at the base of the bone regeneration.

  2. Neural network system and methods for analysis of organic materials and structures using spectral data

    DOE Patents [OSTI]

    Meyer, Bernd J.; Sellers, Jeffrey P.; Thomsen, Jan U.

    1993-01-01

    Apparatus and processes for recognizing and identifying materials. Characteristic spectra are obtained for the materials via spectroscopy techniques including nuclear magnetic resonance spectroscopy, infrared absorption analysis, x-ray analysis, mass spectroscopy and gas chromatography. Desired portions of the spectra may be selected and then placed in proper form and format for presentation to a number of input layer neurons in an offline neural network. The network is first trained according to a predetermined training process; it may then be employed to identify particular materials. Such apparatus and processes are particularly useful for recognizing and identifying organic compounds such as complex carbohydrates, whose spectra conventionally require a high level of training and many hours of hard work to identify, and are frequently indistinguishable from one another by human interpretation.

  3. Neural network system and methods for analysis of organic materials and structures using spectral data

    DOE Patents [OSTI]

    Meyer, B.J.; Sellers, J.P.; Thomsen, J.U.

    1993-06-08

    Apparatus and processes are described for recognizing and identifying materials. Characteristic spectra are obtained for the materials via spectroscopy techniques including nuclear magnetic resonance spectroscopy, infrared absorption analysis, x-ray analysis, mass spectroscopy and gas chromatography. Desired portions of the spectra may be selected and then placed in proper form and format for presentation to a number of input layer neurons in an offline neural network. The network is first trained according to a predetermined training process; it may then be employed to identify particular materials. Such apparatus and processes are particularly useful for recognizing and identifying organic compounds such as complex carbohydrates, whose spectra conventionally require a high level of training and many hours of hard work to identify, and are frequently indistinguishable from one another by human interpretation.

  4. Application of artificial neural networks in power system security and vulnerability assessment

    SciTech Connect (OSTI)

    Qin Zhou; Davidson, J.; Fouad, A.A.

    1994-02-01

    In a companion paper the concept of system vulnerability is introduced as a new framework for power system dynamic security assessment. Using the TEF method of transient stability analysis, the energy margin [Delta]V is used as an indicator of the level of security, and its sensitivity to a changing system parameter p ([partial derivative][Delta]V/[partial derivative]p) as indicator of its trend with changing system conditions. These two indicators are combined to determine the degree of system vulnerability to contingent disturbances in a stability-limited power system. Thresholds for acceptable levels of the security indicator and its trend are related to the stability limits of a critical system parameter (plant generation limits). Operating practices and policies are used to determine these thresholds. In this paper the artificial neural networks (ANNs) technique is applied to the concept of system vulnerability within the recently developed framework, for fast pattern recognition and classification of system dynamic security status. A suitable topology for the neural network is developed, and the appropriate training method and input and output signals are selected. The procedure developed is successfully applied to the IEEE 50-generator test system. Data previously obtained by heuristic techniques are used for training the ANN.

  5. Neural network modelling of thermal stratification in a solar DHW storage

    SciTech Connect (OSTI)

    Geczy-Vig, P.; Farkas, I.

    2010-05-15

    In this study an artificial neural network (ANN) model is introduced for modelling the layer temperatures in a storage tank of a solar thermal system. The model is based on the measured data of a domestic hot water system. The temperatures distribution in the storage tank divided in 8 equal parts in vertical direction were calculated every 5 min using the average 5 min data of solar radiation, ambient temperature, mass flow rate of collector loop, load and the temperature of the layers in previous time steps. The introduced ANN model consists of two parts describing the load periods and the periods between the loads. The identified model gives acceptable results inside the training interval as the average deviation was 0.22 C during the training and 0.24 C during the validation. (author)

  6. Discrimination Analysis of Earthquakes and Man-Made Events Using ARMA Coefficients Determination by Artificial Neural Networks

    SciTech Connect (OSTI)

    AllamehZadeh, Mostafa

    2011-12-15

    A Quadratic Neural Networks (QNNs) model has been developed for identifying seismic source classification problem at regional distances using ARMA coefficients determination by Artificial Neural Networks (ANNs). We have devised a supervised neural system to discriminate between earthquakes and chemical explosions with filter coefficients obtained by windowed P-wave phase spectra (15 s). First, we preprocess the recording's signals to cancel out instrumental and attenuation site effects and obtain a compact representation of seismic records. Second, we use a QNNs system to obtain ARMA coefficients for feature extraction in the discrimination problem. The derived coefficients are then applied to the neural system to train and classification. In this study, we explore the possibility of using single station three-component (3C) covariance matrix traces from a priori-known explosion sites (learning) for automatically recognizing subsequent explosions from the same site. The results have shown that this feature extraction gives the best classifier for seismic signals and performs significantly better than other classification methods. The events have been tested, which include 36 chemical explosions at the Semipalatinsk test site in Kazakhstan and 61 earthquakes (mb = 5.0-6.5) recorded by the Iranian National Seismic Network (INSN). The 100% correct decisions were obtained between site explosions and some of non-site events. The above approach to event discrimination is very flexible as we can combine several 3C stations.

  7. Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches

    SciTech Connect (OSTI)

    Singh, Kunwar P.; Gupta, Shikha; Rai, Premanjali

    2013-10-15

    Robust global models capable of discriminating positive and non-positive carcinogens; and predicting carcinogenic potency of chemicals in rodents were developed. The dataset of 834 structurally diverse chemicals extracted from Carcinogenic Potency Database (CPDB) was used which contained 466 positive and 368 non-positive carcinogens. Twelve non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals and nonlinearity in the data were evaluated using Tanimoto similarity index and BrockDechertScheinkman statistics. Probabilistic neural network (PNN) and generalized regression neural network (GRNN) models were constructed for classification and function optimization problems using the carcinogenicity end point in rat. Validation of the models was performed using the internal and external procedures employing a wide series of statistical checks. PNN constructed using five descriptors rendered classification accuracy of 92.09% in complete rat data. The PNN model rendered classification accuracies of 91.77%, 80.70% and 92.08% in mouse, hamster and pesticide data, respectively. The GRNN constructed with nine descriptors yielded correlation coefficient of 0.896 between the measured and predicted carcinogenic potency with mean squared error (MSE) of 0.44 in complete rat data. The rat carcinogenicity model (GRNN) applied to the mouse and hamster data yielded correlation coefficient and MSE of 0.758, 0.71 and 0.760, 0.46, respectively. The results suggest for wide applicability of the inter-species models in predicting carcinogenic potency of chemicals. Both the PNN and GRNN (inter-species) models constructed here can be useful tools in predicting the carcinogenicity of new chemicals for regulatory purposes. - Graphical abstract: Figure (a) shows classification accuracies (positive and non-positive carcinogens) in rat, mouse, hamster, and pesticide data yielded by optimal PNN model. Figure (b) shows generalization and predictive abilities of the interspecies GRNN model to predict the carcinogenic potency of diverse chemicals. - Highlights: Global robust models constructed for carcinogenicity prediction of diverse chemicals. Tanimoto/BDS test revealed structural diversity of chemicals and nonlinearity in data. PNN/GRNN successfully predicted carcinogenicity/carcinogenic potency of chemicals. Developed interspecies PNN/GRNN models for carcinogenicity prediction. Proposed models can be used as tool to predict carcinogenicity of new chemicals.

  8. Combined expert system/neural networks method for process fault diagnosis

    DOE Patents [OSTI]

    Reifman, Jaques; Wei, Thomas Y. C.

    1995-01-01

    A two-level hierarchical approach for process fault diagnosis is an operating system employs a function-oriented approach at a first level and a component characteristic-oriented approach at a second level, where the decision-making procedure is structured in order of decreasing intelligence with increasing precision. At the first level, the diagnostic method is general and has knowledge of the overall process including a wide variety of plant transients and the functional behavior of the process components. An expert system classifies malfunctions by function to narrow the diagnostic focus to a particular set of possible faulty components that could be responsible for the detected functional misbehavior of the operating system. At the second level, the diagnostic method limits its scope to component malfunctions, using more detailed knowledge of component characteristics. Trained artificial neural networks are used to further narrow the diagnosis and to uniquely identify the faulty component by classifying the abnormal condition data as a failure of one of the hypothesized components through component characteristics. Once an anomaly is detected, the hierarchical structure is used to successively narrow the diagnostic focus from a function misbehavior, i.e., a function oriented approach, until the fault can be determined, i.e., a component characteristic-oriented approach.

  9. Combined expert system/neural networks method for process fault diagnosis

    DOE Patents [OSTI]

    Reifman, J.; Wei, T.Y.C.

    1995-08-15

    A two-level hierarchical approach for process fault diagnosis of an operating system employs a function-oriented approach at a first level and a component characteristic-oriented approach at a second level, where the decision-making procedure is structured in order of decreasing intelligence with increasing precision. At the first level, the diagnostic method is general and has knowledge of the overall process including a wide variety of plant transients and the functional behavior of the process components. An expert system classifies malfunctions by function to narrow the diagnostic focus to a particular set of possible faulty components that could be responsible for the detected functional misbehavior of the operating system. At the second level, the diagnostic method limits its scope to component malfunctions, using more detailed knowledge of component characteristics. Trained artificial neural networks are used to further narrow the diagnosis and to uniquely identify the faulty component by classifying the abnormal condition data as a failure of one of the hypothesized components through component characteristics. Once an anomaly is detected, the hierarchical structure is used to successively narrow the diagnostic focus from a function misbehavior, i.e., a function oriented approach, until the fault can be determined, i.e., a component characteristic-oriented approach. 9 figs.

  10. Using Artificial Neural Networks to Forecast Trichloroethylene Concentrations at the Paducah Gaseous Diffusion Plant

    SciTech Connect (OSTI)

    Kopp, Joshua D

    2007-05-01

    To determine the future extent of the TCE contamination plume at PGDP, a groundwater and solute transport model has been developed by the Department of Energy (DOE). The model used to perform these calculations is MODFLOWT which is an enhanced groundwater transport model developed by the United States Geological Survey (USGS). MODFLOWT models groundwater movement as well as the transport of species that are subject to adsorption and decay by using a finite difference method (Duffield et al 2001). A significant limitation of MODFLOWT is that it requires large amounts of data. This data can be difficult and expensive to obtain. MODFLOWT also requires excessive computational time to perform one simulation. It is desirable to have a model that can predict the spatial extent of the contaminant plume without as much required data and that does not require excessive computational times. The purpose of this study is to develop and alternative model to MODFLOWT that can produce similar results for possible use in a companion management model. The alternative model used in this study is an artificial neural network (ANN).

  11. SPECTRAL CLASSIFICATION OF GALAXIES AT 0.5 {<=} z {<=} 1 IN THE CDFS: THE ARTIFICIAL NEURAL NETWORK APPROACH

    SciTech Connect (OSTI)

    Teimoorinia, H.

    2012-12-01

    The aim of this work is to combine spectral energy distribution (SED) fitting with artificial neural network techniques to assign spectral characteristics to a sample of galaxies at 0.5 < z < 1. The sample is selected from the spectroscopic campaign of the ESO/GOODS-South field, with 142 sources having photometric data from the GOODS-MUSIC catalog covering bands between {approx}0.4 and 24 {mu}m in 10-13 filters. We use the CIGALE code to fit photometric data to Maraston's synthesis spectra to derive mass, specific star formation rate, and age, as well as the best SED of the galaxies. We use the spectral models presented by Kinney et al. as targets in the wavelength interval {approx}1200-7500 A. Then a series of neural networks are trained, with average performance {approx}90%, to classify the best SED in a supervised manner. We consider the effects of the prominent features of the best SED on the performance of the trained networks and also test networks on the galaxy spectra of Coleman et al., which have a lower resolution than the target models. In this way, we conclude that the trained networks take into account all the features of the spectra simultaneously. Using the method, 105 out of 142 galaxies of the sample are classified with high significance. The locus of the classified galaxies in the three graphs of the physical parameters of mass, age, and specific star formation rate appears consistent with the morphological characteristics of the galaxies.

  12. An Adaptive Landscape Classification Procedure using Geoinformatics and Artificial Neural Networks

    SciTech Connect (OSTI)

    Coleman, Andre M.

    2008-08-01

    The Adaptive Landscape Classification Procedure (ALCP), which links the advanced geospatial analysis capabilities of Geographic Information Systems (GISs) and Artificial Neural Networks (ANNs) and particularly Self-Organizing Maps (SOMs), is proposed as a method for establishing and reducing complex data relationships. Its adaptive and evolutionary capability is evaluated for situations where varying types of data can be combined to address different prediction and/or management needs such as hydrologic response, water quality, aquatic habitat, groundwater recharge, land use, instrumentation placement, and forecast scenarios. The research presented here documents and presents favorable results of a procedure that aims to be a powerful and flexible spatial data classifier that fuses the strengths of geoinformatics and the intelligence of SOMs to provide data patterns and spatial information for environmental managers and researchers. This research shows how evaluation and analysis of spatial and/or temporal patterns in the landscape can provide insight into complex ecological, hydrological, climatic, and other natural and anthropogenic-influenced processes. Certainly, environmental management and research within heterogeneous watersheds provide challenges for consistent evaluation and understanding of system functions. For instance, watersheds over a range of scales are likely to exhibit varying levels of diversity in their characteristics of climate, hydrology, physiography, ecology, and anthropogenic influence. Furthermore, it has become evident that understanding and analyzing these diverse systems can be difficult not only because of varying natural characteristics, but also because of the availability, quality, and variability of spatial and temporal data. Developments in geospatial technologies, however, are providing a wide range of relevant data, and in many cases, at a high temporal and spatial resolution. Such data resources can take the form of high-dimensional data arrays, which can difficult to fully use. Establishing relationships among high-dimensional datasets through neurocomputing based patterning methods can help 1) resolve large volumes of data into a meaningful form; 2) provide an approach for inferring landscape processes in areas that have limited data available but that exhibit similar landscape characteristics; and 3) discover the value of individual variables or groups of variables that contribute to specific processes in the landscape.

  13. Quantum and quasi-classical dynamics of the OH + CO → H + CO{sub 2} reaction on a new permutationally invariant neural network potential energy surface

    SciTech Connect (OSTI)

    Li, Jun; Guo, Hua E-mail: hguo@unm.edu; Chen, Jun; Zhang, Dong H. E-mail: hguo@unm.edu

    2014-01-28

    A permutationally invariant global potential energy surface for the HOCO system is reported by fitting a larger number of high-level ab initio points using the newly proposed permutation invariant polynomial-neural network method. The small fitting error (∼5 meV) indicates a faithful representation of the potential energy surface over a large configuration space. Full-dimensional quantum and quasi-classical trajectory studies of the title reaction were performed on this potential energy surface. While the results suggest that the differences between this and an earlier neural network fits are small, discrepancies with state-to-state experimental data remain significant.

  14. Real Time Selective Harmonic Minimization for Multilevel Inverters Connected to Solar Panels Using Artificial Neural Network Angle Generation

    SciTech Connect (OSTI)

    Tolbert, Leon M; Ozpineci, Burak; Filho, Faete; Cao, Yue

    2011-01-01

    This work approximates the selective harmonic elimination problem using artificial neural networks (ANNs) to generate the switching angles in an 11-level full-bridge cascade inverter powered by five varying dc input sources. Each of the five full bridges of the cascade inverter was connected to a separate 195-W solar panel. The angles were chosen such that the fundamental was kept constant and the low-order harmonics were minimized or eliminated. A nondeterministic method is used to solve the system for the angles and to obtain the data set for the ANN training. The method also provides a set of acceptable solutions in the space where solutions do not exist by analytical methods. The trained ANN is a suitable tool that brings a small generalization effect on the angles' precision and is able to perform in real time (50-/60-Hz time window).

  15. SU-E-T-206: Improving Radiotherapy Toxicity Based On Artificial Neural Network (ANN) for Head and Neck Cancer Patients

    SciTech Connect (OSTI)

    Cho, Daniel D; Wernicke, A Gabriella; Nori, Dattatreyudu; Chao, KSC; Parashar, Bhupesh; Chang, Jenghwa

    2014-06-01

    Purpose/Objective(s): The aim of this study is to build the estimator of toxicity using artificial neural network (ANN) for head and neck cancer patients Materials/Methods: An ANN can combine variables into a predictive model during training and considered all possible correlations of variables. We constructed an ANN based on the data from 73 patients with advanced H and N cancer treated with external beam radiotherapy and/or chemotherapy at our institution. For the toxicity estimator we defined input data including age, sex, site, stage, pathology, status of chemo, technique of external beam radiation therapy (EBRT), length of treatment, dose of EBRT, status of post operation, length of follow-up, the status of local recurrences and distant metastasis. These data were digitized based on the significance and fed to the ANN as input nodes. We used 20 hidden nodes (for the 13 input nodes) to take care of the correlations of input nodes. For training ANN, we divided data into three subsets such as training set, validation set and test set. Finally, we built the estimator for the toxicity from ANN output. Results: We used 13 input variables including the status of local recurrences and distant metastasis and 20 hidden nodes for correlations. 59 patients for training set, 7 patients for validation set and 7 patients for test set and fed the inputs to Matlab neural network fitting tool. We trained the data within 15% of errors of outcome. In the end we have the toxicity estimation with 74% of accuracy. Conclusion: We proved in principle that ANN can be a very useful tool for predicting the RT outcomes for high risk H and N patients. Currently we are improving the results using cross validation.

  16. Using artificial neural networks to predict the performance of a liquid sodium reflux pool boiler solar receiver

    SciTech Connect (OSTI)

    Fowler, M.M.; Klett, D.E.; Moreno, J.B.; Heermann, P.D.

    1997-03-01

    Liquid metal reflux receivers (LMRRs) have been designed to serve as the interface between the solar concentrator dish and the Stirling engine of a dish Stirling power system. Such a receiver has undergone performance testing at Sandia National Laboratory to determine cold- and hot-start characteristics, component temperatures, throughput power, and thermal efficiency, for various times of day and year. Performance modeling will play an important role in the future commercialization of these systems since it will be necessary to predict overall energy production for potential installation sites based on available meteorological data. As a supplement to numerical thermal modeling, artificial neural networks (ANNs) have been investigated for their effectiveness in predicting long-term energy production of a LMRR. Two types of data were used to train ANNs, actual on-sun test data, and ersatz data. ANNs were trained on both the raw on-sun test data and on pre-formatted versions of the data to determine if pre-formatting of the input data would improve network training efficiency and predictive abilities. Usable on-sun test data were available for only a few days of performance testing. Therefore, a set of year-long ersatz data was generated using a transient numerical model driven by one-minute meteorological data from the Solar Energy Meteorological Research and Training Sites (SEMRTS) data base for Davis, CA. The ersatz data were used to train ANNs based on warm-month data, cool-month data, and year-long data to investigate the impact of using seasonal test data on long-term predictive capabilities. The findings indicated that a network trained on data from a limited time span could successfully predict annual energy output of a liquid metal receiver.

  17. The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation

    SciTech Connect (OSTI)

    Jahandideh, Sepideh Jahandideh, Samad; Asadabadi, Ebrahim Barzegari; Askarian, Mehrdad; Movahedi, Mohammad Mehdi; Hosseini, Somayyeh; Jahandideh, Mina

    2009-11-15

    Prediction of the amount of hospital waste production will be helpful in the storage, transportation and disposal of hospital waste management. Based on this fact, two predictor models including artificial neural networks (ANNs) and multiple linear regression (MLR) were applied to predict the rate of medical waste generation totally and in different types of sharp, infectious and general. In this study, a 5-fold cross-validation procedure on a database containing total of 50 hospitals of Fars province (Iran) were used to verify the performance of the models. Three performance measures including MAR, RMSE and R{sup 2} were used to evaluate performance of models. The MLR as a conventional model obtained poor prediction performance measure values. However, MLR distinguished hospital capacity and bed occupancy as more significant parameters. On the other hand, ANNs as a more powerful model, which has not been introduced in predicting rate of medical waste generation, showed high performance measure values, especially 0.99 value of R{sup 2} confirming the good fit of the data. Such satisfactory results could be attributed to the non-linear nature of ANNs in problem solving which provides the opportunity for relating independent variables to dependent ones non-linearly. In conclusion, the obtained results showed that our ANN-based model approach is very promising and may play a useful role in developing a better cost-effective strategy for waste management in future.

  18. 3-D Inversion Of Borehole-To-Surface Electrical Data Using A...

    Open Energy Info (EERE)

    Inversion Of Borehole-To-Surface Electrical Data Using A Back-Propagation Neural Network Jump to: navigation, search OpenEI Reference LibraryAdd to library Journal Article: 3-D...

  19. Artificial neural network modeling of the spontaneous combustion occurring in the industrial-scale coal stockpiles with 10-18 mm coal grain sizes

    SciTech Connect (OSTI)

    Ozdeniz, A.H.; Yilmaz, N.

    2009-07-01

    Companies consuming large amounts of coal should work with coal stocks in order to not face problems due to production delays. The industrial-scale stockpiles formed for the aforementioned reasons cause environmental problems and economic losses for the companies. This study was performed in a coal stock area of a large company in Konya, which uses large amounts of coal in its manufacturing units. The coal stockpile with 5 m width, 10 m length, 3 m height, and having 120 tons of weight was formed in the coal stock area of the company. The inner temperature data of the stockpile was recorded by 17 temperature sensors placed inside the stockpile at certain points. In order to achieve this goal, the electrical signal conversion of temperatures sensed by 17 temperature sensors placed in certain points inside the coal stockpile, the transfer of these electrical signals into computer media by using analog-digital conversion unit after applying necessary filtration and upgrading processes, and the record of these information into a database in particular time intervals are provided. Additionally, the data relating to the air temperature, air humidity, atmospheric pressure, wind velocity, and wind direction that are the parameters affecting the coal stockpile were also recorded. Afterwards, these measurement values were used for training and testing of an artificial neural network model. Comparison of the experimental and artificial neural network results, accuracy rates of training and testing were found to be 99.5% and 99.17%, respectively. It is shown that possible coal stockpile behavior with this artificial neural network model is powerfully estimated.

  20. bib-neural | netl.doe.gov

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Big Bend Power Station Neural Network-Intelligent Sootblower (NN-ISB) Optimization - Project Brief [PDF-154KB] Tampa Electric Company, Apollo Beach, Hillsborough County, FL PROJECT FACT SHEET Big Bend Power Station Neural Network-Intelligent Sootblower (NN-ISB) Optimization [PDF-154KB] (Oct 2008) PROGRAM PUBLICATIONS Final Report Tampa Electric Company Big Bend Unit #2, Neural Network Based Intelligent Sootblowing System Project Performance and Review [PDF-2.2MB] (April 2005) PPII Reports:

  1. bib-neural | netl.doe.gov

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Big Bend Power Station Neural Network-Intelligent Sootblower (NN-ISB) Optimization - Project Brief PDF-154KB Tampa Electric Company, Apollo Beach, Hillsborough County, FL PROJECT...

  2. Application of computational neural networks in predicting atmospheric pollutant concentrations due to fossil-fired electric power generation

    SciTech Connect (OSTI)

    El-Hawary, F.

    1995-12-31

    The ability to accurately predict the behavior of a dynamic system is of essential importance in monitoring and control of complex processes. In this regard recent advances in neural-net based system identification represent a significant step toward development and design of a new generation of control tools for increased system performance and reliability. The enabling functionality is the one of accurate representation of a model of a nonlinear and nonstationary dynamic system. This functionality provides valuable new opportunities including: (1) The ability to predict future system behavior on the basis of actual system observations, (2) On-line evaluation and display of system performance and design of early warning systems, and (3) Controller optimization for improved system performance. In this presentation, we discuss the issues involved in definition and design of learning control systems and their impact on power system control. Several numerical examples are provided for illustrative purpose.

  3. A neural network for real-time retrievals of PWV and LWP from Arctic millimeter-wave ground-based observations.

    SciTech Connect (OSTI)

    Cadeddu, M. P.; Turner, D. D.; Liljegren, J. C.; Decision and Information Sciences; Univ. of Wisconsin at Madison

    2009-07-01

    This paper presents a new neural network (NN) algorithm for real-time retrievals of low amounts of precipitable water vapor (PWV) and integrated liquid water from millimeter-wave ground-based observations. Measurements are collected by the 183.3-GHz G-band vapor radiometer (GVR) operating at the Atmospheric Radiation Measurement (ARM) Program Climate Research Facility, Barrow, AK. The NN provides the means to explore the nonlinear regime of the measurements and investigate the physical boundaries of the operability of the instrument. A methodology to compute individual error bars associated with the NN output is developed, and a detailed error analysis of the network output is provided. Through the error analysis, it is possible to isolate several components contributing to the overall retrieval errors and to analyze the dependence of the errors on the inputs. The network outputs and associated errors are then compared with results from a physical retrieval and with the ARM two-channel microwave radiometer (MWR) statistical retrieval. When the NN is trained with a seasonal training data set, the retrievals of water vapor yield results that are comparable to those obtained from a traditional physical retrieval, with a retrieval error percentage of {approx}5% when the PWV is between 2 and 10 mm, but with the advantages that the NN algorithm does not require vertical profiles of temperature and humidity as input and is significantly faster computationally. Liquid water path (LWP) retrievals from the NN have a significantly improved clear-sky bias (mean of {approx}2.4 g/m{sup 2}) and a retrieval error varying from 1 to about 10 g/m{sup 2} when the PWV amount is between 1 and 10 mm. As an independent validation of the LWP retrieval, the longwave downwelling surface flux was computed and compared with observations. The comparison shows a significant improvement with respect to the MWR statistical retrievals, particularly for LWP amounts of less than 60 g/m{sup 2}.

  4. Automated detection of cloud and cloud-shadow in single-date Landsat imagery using neural networks and spatial post-processing

    SciTech Connect (OSTI)

    Hughes, Michael J. [University of Tennessee, Knoxville (UTK)] [University of Tennessee, Knoxville (UTK); Hayes, Daniel J [ORNL] [ORNL

    2014-01-01

    Use of Landsat data to answer ecological questions is contingent on the effective removal of cloud and cloud shadow from satellite images. We develop a novel algorithm to identify and classify clouds and cloud shadow, \\textsc{sparcs}: Spacial Procedures for Automated Removal of Cloud and Shadow. The method uses neural networks to determine cloud, cloud-shadow, water, snow/ice, and clear-sky membership of each pixel in a Landsat scene, and then applies a set of procedures to enforce spatial rules. In a comparison to FMask, a high-quality cloud and cloud-shadow classification algorithm currently available, \\textsc{sparcs} performs favorably, with similar omission errors for clouds (0.8% and 0.9%, respectively), substantially lower omission error for cloud-shadow (8.3% and 1.1%), and fewer errors of commission (7.8% and 5.0%). Additionally, textsc{sparcs} provides a measure of uncertainty in its classification that can be exploited by other processes that use the cloud and cloud-shadow detection. To illustrate this, we present an application that constructs obstruction-free composites of images acquired on different dates in support of algorithms detecting vegetation change.

  5. A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy

    SciTech Connect (OSTI)

    Mellit, Adel; Pavan, Alessandro Massi

    2010-05-15

    Forecasting of solar irradiance is in general significant for planning the operations of power plants which convert renewable energies into electricity. In particular, the possibility to predict the solar irradiance (up to 24 h or even more) can became - with reference to the Grid Connected Photovoltaic Plants (GCPV) - fundamental in making power dispatching plans and - with reference to stand alone and hybrid systems - also a useful reference for improving the control algorithms of charge controllers. In this paper, a practical method for solar irradiance forecast using artificial neural network (ANN) is presented. The proposed Multilayer Perceptron MLP-model makes it possible to forecast the solar irradiance on a base of 24 h using the present values of the mean daily solar irradiance and air temperature. An experimental database of solar irradiance and air temperature data (from July 1st 2008 to May 23rd 2009 and from November 23rd 2009 to January 24th 2010) has been used. The database has been collected in Trieste (latitude 45 40'N, longitude 13 46'E), Italy. In order to check the generalization capability of the MLP-forecaster, a K-fold cross-validation was carried out. The results indicate that the proposed model performs well, while the correlation coefficient is in the range 98-99% for sunny days and 94-96% for cloudy days. As an application, the comparison between the forecasted one and the energy produced by the GCPV plant installed on the rooftop of the municipality of Trieste shows the goodness of the proposed model. (author)

  6. Graybox and adaptative dynamic neural network identification models to infer the steady state efficiency of solar thermal collectors starting from the transient condition

    SciTech Connect (OSTI)

    Roberto, Baccoli; Ubaldo, Carlini; Stefano, Mariotti; Roberto, Innamorati; Elisa, Solinas; Paolo, Mura

    2010-06-15

    This paper deals with the development of methods for non steady state test of solar thermal collectors. Our goal is to infer performances in steady-state conditions in terms of the efficiency curve when measures in transient conditions are the only ones available. We take into consideration the method of identification of a system in dynamic conditions by applying a Graybox Identification Model and a Dynamic Adaptative Linear Neural Network (ALNN) model. The study targets the solar collector with evacuated pipes, such as Dewar pipes. The mathematical description that supervises the functioning of the solar collector in transient conditions is developed using the equation of the energy balance, with the aim of determining the order and architecture of the two models. The input and output vectors of the two models are constructed, considering the measures of 4 days of solar radiation, flow mass, environment and heat-transfer fluid temperature in the inlet and outlet from the thermal solar collector. The efficiency curves derived from the two models are detected in correspondence to the test and validation points. The two synthetic simulated efficiency curves are compared with the actual efficiency curve certified by the Swiss Institute Solartechnik Puffung Forschung which tested the solar collector performance in steady-state conditions according to the UNI-EN 12975 standard. An acquisition set of measurements of only 4 days in the transient condition was enough to trace through a Graybox State Space Model the efficiency curve of the tested solar thermal collector, with a relative error of synthetic values with respect to efficiency certified by SPF, lower than 0.5%, while with the ALNN model the error is lower than 2.2% with respect to certified one. (author)

  7. Full-dimensional and reduced-dimensional calculations of initial state-selected reaction probabilities studying the H + CH{sub 4} ? H{sub 2} + CH{sub 3} reaction on a neural network PES

    SciTech Connect (OSTI)

    Welsch, Ralph Manthe, Uwe

    2015-02-14

    Initial state-selected reaction probabilities of the H + CH{sub 4} ? H{sub 2} + CH{sub 3} reaction are calculated in full and reduced dimensionality on a recent neural network potential [X. Xu, J. Chen, and D. H. Zhang, Chin. J. Chem. Phys. 27, 373 (2014)]. The quantum dynamics calculation employs the quantum transition state concept and the multi-layer multi-configurational time-dependent Hartree approach and rigorously studies the reaction for vanishing total angular momentum (J = 0). The calculations investigate the accuracy of the neutral network potential and study the effect resulting from a reduced-dimensional treatment. Very good agreement is found between the present results obtained on the neural network potential and previous results obtained on a Shepard interpolated potential energy surface. The reduced-dimensional calculations only consider motion in eight degrees of freedom and retain the C{sub 3v} symmetry of the methyl fragment. Considering reaction starting from the vibrational ground state of methane, the reaction probabilities calculated in reduced dimensionality are moderately shifted in energy compared to the full-dimensional ones but otherwise agree rather well. Similar agreement is also found if reaction probabilities averaged over similar types of vibrational excitation of the methane reactant are considered. In contrast, significant differences between reduced and full-dimensional results are found for reaction probabilities starting specifically from symmetric stretching, asymmetric (f{sub 2}-symmetric) stretching, or e-symmetric bending excited states of methane.

  8. TEDANN: Turbine engine diagnostic artificial neural network

    SciTech Connect (OSTI)

    Kangas, L.J.; Greitzer, F.L.; Illi, O.J. Jr.

    1994-03-17

    The initial focus of TEDANN is on AGT-1500 fuel flow dynamics: that is, fuel flow faults detectable in the signals from the Electronic Control Unit`s (ECU) diagnostic connector. These voltage signals represent the status of the Electro-Mechanical Fuel System (EMFS) in response to ECU commands. The EMFS is a fuel metering device that delivers fuel to the turbine engine under the management of the ECU. The ECU is an analog computer whose fuel flow algorithm is dependent upon throttle position, ambient air and turbine inlet temperatures, and compressor and turbine speeds. Each of these variables has a representative voltage signal available at the ECU`s J1 diagnostic connector, which is accessed via the Automatic Breakout Box (ABOB). The ABOB is a firmware program capable of converting 128 separate analog data signals into digital format. The ECU`s J1 diagnostic connector provides 32 analog signals to the ABOB. The ABOB contains a 128 to 1 multiplexer and an analog-to-digital converter, CP both operated by an 8-bit embedded controller. The Army Research Laboratory (ARL) developed and published the hardware specifications as well as the micro-code for the ABOB Intel EPROM processor and the internal code for the multiplexer driver subroutine. Once the ECU analog readings are converted into a digital format, the data stream will be input directly into TEDANN via the serial RS-232 port of the Contact Test Set (CTS) computer. The CTS computer is an IBM compatible personal computer designed and constructed for tactical use on the battlefield. The CTS has a 50MHz 32-bit Intel 80486DX processor. It has a 200MB hard drive and 8MB RAM. The CTS also has serial, parallel and SCSI interface ports. The CTS will also host a frame-based expert system for diagnosing turbine engine faults (referred to as TED; not shown in Figure 1).

  9. Permutation parity machines for neural cryptography

    SciTech Connect (OSTI)

    Reyes, Oscar Mauricio; Zimmermann, Karl-Heinz

    2010-06-15

    Recently, synchronization was proved for permutation parity machines, multilayer feed-forward neural networks proposed as a binary variant of the tree parity machines. This ability was already used in the case of tree parity machines to introduce a key-exchange protocol. In this paper, a protocol based on permutation parity machines is proposed and its performance against common attacks (simple, geometric, majority and genetic) is studied.

  10. Network Maps

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Network Maps Engineering Services The Network Network Maps Network Traffic Volume Historical Network Maps Network Facts & Stats Connected Sites Peering Connections ESnet...

  11. A neural approach for the numerical modeling of two-dimensional magnetic

    Office of Scientific and Technical Information (OSTI)

    hysteresis (Journal Article) | SciTech Connect A neural approach for the numerical modeling of two-dimensional magnetic hysteresis Citation Details In-Document Search Title: A neural approach for the numerical modeling of two-dimensional magnetic hysteresis This paper deals with a neural network approach to model magnetic hysteresis at macro-magnetic scale. Such approach to the problem seems promising in order to couple the numerical treatment of magnetic hysteresis to FEM numerical solvers

  12. Associative memory in phasing neuron networks

    SciTech Connect (OSTI)

    Nair, Niketh S; Bochove, Erik J.; Braiman, Yehuda

    2014-01-01

    We studied pattern formation in a network of coupled Hindmarsh-Rose model neurons and introduced a new model for associative memory retrieval using networks of Kuramoto oscillators. Hindmarsh-Rose Neural Networks can exhibit a rich set of collective dynamics that can be controlled by their connectivity. Specifically, we showed an instance of Hebb's rule where spiking was correlated with network topology. Based on this, we presented a simple model of associative memory in coupled phase oscillators.

  13. Network Activity

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Statistics Network Activity Network Activity PDSF Network Uplinks to NERSC (dual 10 Gbps) NERSC Uplink to ESnet Last edited: 2011-03-31 22:20:59...

  14. Demultiplexer circuit for neural stimulation

    DOE Patents [OSTI]

    Wessendorf, Kurt O; Okandan, Murat; Pearson, Sean

    2012-10-09

    A demultiplexer circuit is disclosed which can be used with a conventional neural stimulator to extend the number of electrodes which can be activated. The demultiplexer circuit, which is formed on a semiconductor substrate containing a power supply that provides all the dc electrical power for operation of the circuit, includes digital latches that receive and store addressing information from the neural stimulator one bit at a time. This addressing information is used to program one or more 1:2.sup.N demultiplexers in the demultiplexer circuit which then route neural stimulation signals from the neural stimulator to an electrode array which is connected to the outputs of the 1:2.sup.N demultiplexer. The demultiplexer circuit allows the number of individual electrodes in the electrode array to be increased by a factor of 2.sup.N with N generally being in a range of 2-4.

  15. The Network

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Network Engineering Services The Network Network Maps Network Facts & Stats Connected Sites Peering Connections ESnet Site Availabiliy OSCARS Fasterdata IPv6 Network Network Performance Tools The ESnet Engineering Team Contact Us Technical Assistance: 1 800-33-ESnet (Inside US) 1 800-333-7638 (Inside US) 1 510-486-7600 (Globally) 1 510-486-7607 (Globally) Report Network Problems: trouble@es.net Provide Web Site Feedback: info@es.net The Network A Nationwide Platform for Science Discovery The

  16. Ambient temperature modelling with soft computing techniques

    SciTech Connect (OSTI)

    Bertini, Ilaria; Ceravolo, Francesco; Citterio, Marco; Di Pietra, Biagio; Margiotta, Francesca; Pizzuti, Stefano; Puglisi, Giovanni; De Felice, Matteo

    2010-07-15

    This paper proposes a hybrid approach based on soft computing techniques in order to estimate monthly and daily ambient temperature. Indeed, we combine the back-propagation (BP) algorithm and the simple Genetic Algorithm (GA) in order to effectively train artificial neural networks (ANN) in such a way that the BP algorithm initialises a few individuals of the GA's population. Experiments concerned monthly temperature estimation of unknown places and daily temperature estimation for thermal load computation. Results have shown remarkable improvements in accuracy compared to traditional methods. (author)

  17. Demultiplexer circuit for neural stimulation (Patent) | DOEPatents

    Office of Scientific and Technical Information (OSTI)

    all the dc electrical power for operation of the circuit, includes digital latches that receive and store addressing information from the neural stimulator one bit at a time. ...

  18. Neural Interface for Deep Brain Stimulation (Conference) | SciTech...

    Office of Scientific and Technical Information (OSTI)

    Neural Interface for Deep Brain Stimulation Citation Details In-Document Search Title: Neural Interface for Deep Brain Stimulation Authors: Tooker, A C ; Madsen, T E ; Crowell, A ; ...

  19. Achieving supercomputer performance for neural net simulation with an array of digital signal processors

    SciTech Connect (OSTI)

    Muller, U.A.; Baumle, B.; Kohler, P.; Gunzinger, A.; Guggenbuhl, W.

    1992-10-01

    Music, a DSP-based system with a parallel distributed-memory architecture, provides enormous computing power yet retains the flexibility of a general-purpose computer. Reaching a peak performance of 2.7 Gflops at a significantly lower cost, power consumption, and space requirement than conventional supercomputers, Music is well suited to computationally intensive applications such as neural network simulation. 12 refs., 9 figs., 2 tabs.

  20. Experimental Network Testbeds

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Galleries ESnet Awards and Honors Blog ESnet Live Home Network R&D Experimental Network Testbeds Network R&D Overview Experimental Network Testbeds 100G SDN...

  1. Electrode array for neural stimulation

    DOE Patents [OSTI]

    Wessendorf, Kurt O.; Okandan, Murat; Stein, David J.; Yang, Pin; Cesarano, III, Joseph; Dellinger, Jennifer

    2011-08-16

    An electrode array for neural stimulation is disclosed which has particular applications for use in a retinal prosthesis. The electrode array can be formed as a hermetically-sealed two-part ceramic package which includes an electronic circuit such as a demultiplexer circuit encapsulated therein. A relatively large number (up to 1000 or more) of individually-addressable electrodes are provided on a curved surface of a ceramic base portion the electrode array, while a much smaller number of electrical connections are provided on a ceramic lid of the electrode array. The base and lid can be attached using a metal-to-metal seal formed by laser brazing. Electrical connections to the electrode array can be provided by a flexible ribbon cable which can also be used to secure the electrode array in place.

  2. Sentient networks

    SciTech Connect (OSTI)

    Chapline, G.

    1998-03-01

    The engineering problems of constructing autonomous networks of sensors and data processors that can provide alerts for dangerous situations provide a new context for debating the question whether man-made systems can emulate the cognitive capabilities of the mammalian brain. In this paper we consider the question whether a distributed network of sensors and data processors can form ``perceptions`` based on sensory data. Because sensory data can have exponentially many explanations, the use of a central data processor to analyze the outputs from a large ensemble of sensors will in general introduce unacceptable latencies for responding to dangerous situations. A better idea is to use a distributed ``Helmholtz machine`` architecture in which the sensors are connected to a network of simple processors, and the collective state of the network as a whole provides an explanation for the sensory data. In general communication within such a network will require time division multiplexing, which opens the door to the possibility that with certain refinements to the Helmholtz machine architecture it may be possible to build sensor networks that exhibit a form of artificial consciousness.

  3. Network Policies

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Acceptable Use Policy About ESnet Our Mission The Network ESnet History Governance & Policies ESnet Policy Board ESCC Acceptable Use Policy Data Privacy Policy Facility Data Policy Career Opportunities ESnet Staff & Org Chart Contact Us Contact Us Technical Assistance: 1 800-33-ESnet (Inside US) 1 800-333-7638 (Inside US) 1 510-486-7600 (Globally) 1 510-486-7607 (Globally) Report Network Problems: trouble@es.net Provide Web Site Feedback: info@es.net ESnet Acceptable Use Policy The

  4. Historical Network Maps

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Network Maps Network Traffic Volume Historical Network Maps Network Facts & Stats Connected Sites Peering Connections ESnet Site Availabiliy OSCARS Fasterdata IPv6 Network Network Performance Tools The ESnet Engineering Team Network R&D Software-Defined Networking (SDN) Experimental Network Testbeds Performance (perfSONAR) Software & Tools Development Data for Researchers Partnerships Publications Workshops Science Engagement Move your data Programs & Workshops Science

  5. Nothing But Networking for Residential Network Members | Department...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Nothing But Networking for Residential Network Members Nothing But Networking for Residential Network Members Better Buildings Residential Network Peer Exchange Call: Nothing But...

  6. HPSS Yearly Network Traffic

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    HPSS Yearly Network Traffic HPSS Yearly Network Traffic Yearly Summary of IO Traffic Between Storage and Network Destinations These bar charts show the total transfer traffic for...

  7. NetworkX

    Energy Science and Technology Software Center (OSTI)

    2004-05-17

    NetworkX (abbreviated NX in the software and documentation) is a package for studying network structure using graph theory.

  8. Groundwater Monitoring Network

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Groundwater Monitoring Network Groundwater Monitoring Network The network includes 92 natural sources, 102 regional aquifer wells, 41 intermediate-depth wells and springs, and 67 wells in alluvium in canyons. August 1, 2013 Map of LANL's groundwater monitoring network Map of LANL's groundwater monitoring network

  9. Interconnection networks

    DOE Patents [OSTI]

    Faber, V.; Moore, J.W.

    1988-06-20

    A network of interconnected processors is formed from a vertex symmetric graph selected from graphs GAMMA/sub d/(k) with degree d, diameter k, and (d + 1)exclamation/ (d /minus/ k + 1)exclamation processors for each d greater than or equal to k and GAMMA/sub d/(k, /minus/1) with degree d /minus/ 1, diameter k + 1, and (d + 1)exclamation/(d /minus/ k + 1)exclamation processors for each d greater than or equal to k greater than or equal to 4. Each processor has an address formed by one of the permutations from a predetermined sequence of letters chosen a selected number of letters at a time, and an extended address formed by appending to the address the remaining ones of the predetermined sequence of letters. A plurality of transmission channels is provided from each of the processors, where each processor has one less channel than the selected number of letters forming the sequence. Where a network GAMMA/sub d/(k, /minus/1) is provided, no processor has a channel connected to form an edge in a direction delta/sub 1/. Each of the channels has an identification number selected from the sequence of letters and connected from a first processor having a first extended address to a second processor having a second address formed from a second extended address defined by moving to the front of the first extended address the letter found in the position within the first extended address defined by the channel identification number. The second address is then formed by selecting the first elements of the second extended address corresponding to the selected number used to form the address permutations. 9 figs.

  10. Anomaly Detection for Resilient Control Systems Using Fuzzy-Neural Data Fusion Engine

    SciTech Connect (OSTI)

    Ondrej Linda; Milos Manic; Timothy R. McJunkin

    2011-08-01

    Resilient control systems in critical infrastructures require increased cyber-security and state-awareness. One of the necessary conditions for achieving the desired high level of resiliency is timely reporting and understanding of the status and behavioral trends of the control system. This paper describes the design and development of a neural-network based data-fusion system for increased state-awareness of resilient control systems. The proposed system consists of a dedicated data-fusion engine for each component of the control system. Each data-fusion engine implements three-layered alarm system consisting of: (1) conventional threshold-based alarms, (2) anomalous behavior detector using self-organizing maps, and (3) prediction error based alarms using neural network based signal forecasting. The proposed system was integrated with a model of the Idaho National Laboratory Hytest facility, which is a testing facility for hybrid energy systems. Experimental results demonstrate that the implemented data fusion system provides timely plant performance monitoring and cyber-state reporting.

  11. Valley Entrepreneurs' Network (VEN) Monthly Network Meeting

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    VEN Monthly Network Meeting Valley Entrepreneurs' Network (VEN) Monthly Network Meeting WHEN: Mar 05, 2015 5:30 PM - 7:00 PM WHERE: Anthony's At the Delta North Paseo De Onate, Española, NM CATEGORY: Community INTERNAL: Calendar Login Event Description An evening of exciting enterprise networking with like-minded entrepreneurs. For more information, contact Alejandro, VEN Coordinator, at (505) 410-0959

  12. Chronic, Multi-Contact, Neural Interface for Deep Brain Stimulation...

    Office of Scientific and Technical Information (OSTI)

    Report Number(s): LLNL-CONF-644462 DOE Contract Number: W-7405-ENG-48 Resource Type: Conference Resource Relation: Conference: Presented at: IEEE Conference on Neural Engineering, ...

  13. Chronic, Multi-Contact, Neural Interface for Deep Brain Stimulation...

    Office of Scientific and Technical Information (OSTI)

    DOE Contract Number: W-7405-ENG-48 Resource Type: Conference Resource Relation: Conference: Presented at: IEEE Conference on Neural Engineering, San Diego, CA, United States, Nov ...

  14. Electrode-Immune System Interface Monitor through Neural Stimulation...

    Office of Scientific and Technical Information (OSTI)

    Title: Electrode-Immune System Interface Monitor through Neural Stimulation in American Cockroach (Periplaneta Americana) Authors: Chiu, C.-W. ; Gonzalez, J.M. ; Harlow, M. ; ...

  15. Damselfly Network Simulator

    Energy Science and Technology Software Center (OSTI)

    2014-04-01

    Damselfly is a model-based parallel network simulator. It can simulate communication patterns of High Performance Computing applications on different network topologies. It outputs steady-state network traffic for a communication pattern, which can help in studying network congestion and its impact on performance.

  16. HPSS Yearly Network Traffic

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    HPSS Yearly Network Traffic HPSS Yearly Network Traffic Yearly Summary of I/O Traffic Between Storage and Network Destinations These bar charts show the total transfer traffic for each year between storage and network destinations (systems within and outside of NERSC). Traffic for the current year is an estimate derived by scaling the known months traffic up to 12 months. The years shown are calendar years. The first graph shows the overall growth in network traffic to storage over the years.

  17. Network II Database

    Energy Science and Technology Software Center (OSTI)

    1994-11-07

    The Oak Ridge National Laboratory (ORNL) Rail and Barge Network II Database is a representation of the rail and barge system of the United States. The network is derived from the Federal Rail Administration (FRA) rail database.

  18. Class network routing

    DOE Patents [OSTI]

    Bhanot, Gyan; Blumrich, Matthias A.; Chen, Dong; Coteus, Paul W.; Gara, Alan G.; Giampapa, Mark E.; Heidelberger, Philip; Steinmacher-Burow, Burkhard D.; Takken, Todd E.; Vranas, Pavlos M.

    2009-09-08

    Class network routing is implemented in a network such as a computer network comprising a plurality of parallel compute processors at nodes thereof. Class network routing allows a compute processor to broadcast a message to a range (one or more) of other compute processors in the computer network, such as processors in a column or a row. Normally this type of operation requires a separate message to be sent to each processor. With class network routing pursuant to the invention, a single message is sufficient, which generally reduces the total number of messages in the network as well as the latency to do a broadcast. Class network routing is also applied to dense matrix inversion algorithms on distributed memory parallel supercomputers with hardware class function (multicast) capability. This is achieved by exploiting the fact that the communication patterns of dense matrix inversion can be served by hardware class functions, which results in faster execution times.

  19. Self-organization of network dynamics into local quantized states

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Nicolaides, Christos; Juanes, Ruben; Cueto-Felgueroso, Luis

    2016-02-17

    Self-organization and pattern formation in network-organized systems emerges from the collective activation and interaction of many interconnected units. A striking feature of these non-equilibrium structures is that they are often localized and robust: only a small subset of the nodes, or cell assembly, is activated. Understanding the role of cell assemblies as basic functional units in neural networks and socio-technical systems emerges as a fundamental challenge in network theory. A key open question is how these elementary building blocks emerge, and how they operate, linking structure and function in complex networks. Here we show that a network analogue of themore » Swift-Hohenberg continuum model—a minimal-ingredients model of nodal activation and interaction within a complex network—is able to produce a complex suite of localized patterns. Thus, the spontaneous formation of robust operational cell assemblies in complex networks can be explained as the result of self-organization, even in the absence of synaptic reinforcements.« less

  20. BER Science Network Requirements

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    BER Science Network Requirements Report of the Biological and Environmental Research Network Requirements Workshop Conducted July 26 and 27, 2007 BER Science Network Requirements Workshop Biological and Environmental Research Program Office, DOE Office of Science Energy Sciences Network Bethesda, MD - July 26 and 27, 2007 ESnet is funded by the US Dept. of Energy, Office of Science, Advanced Scientific Computing Research (ASCR) program. Dan Hitchcock is the ESnet Program Manager. ESnet is

  1. BES Science Network Requirements

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Network Requirements Report of the Basic Energy Sciences Network Requirements Workshop Conducted June 4-5, 2007 BES Science Network Requirements Workshop Basic Energy Sciences Program Office, DOE Office of Science Energy Sciences Network Washington, DC - June 4 and 5, 2007 ESnet is funded by the US Dept. of Energy, Office of Science, Advanced Scientific Computing Research (ASCR) program. Dan Hitchcock is the ESnet Program Manager. ESnet is operated by Lawrence Berkeley National Laboratory, which

  2. Science-Driven Network

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Science-Driven Network Requirements for ESnet Update to the 2002 Office of Science Networking Requirements Workshop Report February 21, 2006 1-1 Science-Driven Network Requirements for ESnet Update to the 2002 Office of Science Networking Requirements Workshop Report February 21, 2006 Contributors Paul Adams, LBNL (Advanced Light Source) Shane Canon, ORNL (NLCF) Steven Carter, ORNL (NLCF) Brent Draney, LBNL (NERSC) Martin Greenwald, MIT (Magnetic Fusion Energy) Jason Hodges, ORNL (Spallation

  3. Neural probe design & MEMS technology | GE Global Research

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Neural probe design accelerated with use of MEMS Technology Click to email this to a friend (Opens in new window) Share on Facebook (Opens in new window) Click to share (Opens in new window) Click to share on LinkedIn (Opens in new window) Click to share on Tumblr (Opens in new window) Neural probe design accelerated with use of MEMS Technology Craig Galligan 2015.04.01 In 2014, the bioelectrics team was tasked with exploring the neural probe technical space and identifying paths of interest. A

  4. Nothing But Networking for Residential Network Members | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy Nothing But Networking for Residential Network Members Nothing But Networking for Residential Network Members Better Buildings Residential Network Peer Exchange Call: Nothing But Networking for Residential Network Members, Call Slides and Discussion Summary, March 12, 2015. PDF icon Call Slides and Discussion Summary More Documents & Publications Better Buildings Residential Network Orientation Webinar Community Organizing and Outreach Outreach to Multifamily Landlords and Tenants

  5. Calorimetry Network Program

    Energy Science and Technology Software Center (OSTI)

    1998-01-30

    This is a Windows NT based program to run the SRTC designed calorimeters. The network version can communicate near real time data and final data values over the network. This version, due to network specifics, can function in a stand-alone operation also.

  6. Enerlogics Networks | Open Energy Information

    Open Energy Info (EERE)

    Networks Name: Enerlogics Networks Place: Pittsburgh, Pennsylvania Product: buidling automation control systems to utility software solutions to telecommunication systems...

  7. Neural Interface for Deep Brain Stimulation (Conference) | SciTech...

    Office of Scientific and Technical Information (OSTI)

    Report Number(s): LLNL-CONF-638803 DOE Contract Number: W-7405-ENG-48 Resource Type: Conference Resource Relation: Conference: Presented at: IEEE Neural Engineering, San Diego, CA, ...

  8. Internet protocol network mapper

    DOE Patents [OSTI]

    Youd, David W.; Colon III, Domingo R.; Seidl, Edward T.

    2016-02-23

    A network mapper for performing tasks on targets is provided. The mapper generates a map of a network that specifies the overall configuration of the network. The mapper inputs a procedure that defines how the network is to be mapped. The procedure specifies what, when, and in what order the tasks are to be performed. Each task specifies processing that is to be performed for a target to produce results. The procedure may also specify input parameters for a task. The mapper inputs initial targets that specify a range of network addresses to be mapped. The mapper maps the network by, for each target, executing the procedure to perform the tasks on the target. The results of the tasks represent the mapping of the network defined by the initial targets.

  9. Electrode-Immune System Interface Monitor through Neural Stimulation in

    Office of Scientific and Technical Information (OSTI)

    American Cockroach (Periplaneta Americana) (Journal Article) | SciTech Connect Electrode-Immune System Interface Monitor through Neural Stimulation in American Cockroach (Periplaneta Americana) Citation Details In-Document Search Title: Electrode-Immune System Interface Monitor through Neural Stimulation in American Cockroach (Periplaneta Americana) Authors: Chiu, C.-W. ; Gonzalez, J.M. ; Harlow, M. ; Vinson, S.B. ; Liang, H. ; , Publication Date: 2013-09-16 OSTI Identifier: 1093657 Report

  10. Inhibition of Sirt1 promotes neural progenitors toward motoneuron

    Office of Scientific and Technical Information (OSTI)

    differentiation from human embryonic stem cells (Journal Article) | SciTech Connect Inhibition of Sirt1 promotes neural progenitors toward motoneuron differentiation from human embryonic stem cells Citation Details In-Document Search Title: Inhibition of Sirt1 promotes neural progenitors toward motoneuron differentiation from human embryonic stem cells Research highlights: {yields} Nicotinamide inhibit Sirt1. {yields} MASH1 and Ngn2 activation. {yields} Increase the expression of HB9.

  11. 120-Channel, Chronically Implantable, Wireless, Polymer Neural Interface

    Office of Scientific and Technical Information (OSTI)

    (Conference) | SciTech Connect 120-Channel, Chronically Implantable, Wireless, Polymer Neural Interface Citation Details In-Document Search Title: 120-Channel, Chronically Implantable, Wireless, Polymer Neural Interface Authors: Tooker, A ; Shah, K ; Tolosa, V ; Sheth, H ; Felix, S ; Delima, T ; Pannu, S Publication Date: 2012-05-09 OSTI Identifier: 1083257 Report Number(s): LLNL-PROC-557232 DOE Contract Number: W-7405-ENG-48 Resource Type: Conference Resource Relation: Conference: Presented

  12. RESIDENTIAL NETWORK MEMBERS UNITE TO FORM GREEN BANK NETWORK | Department

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    of Energy RESIDENTIAL NETWORK MEMBERS UNITE TO FORM GREEN BANK NETWORK RESIDENTIAL NETWORK MEMBERS UNITE TO FORM GREEN BANK NETWORK The NY Green Bank logo. Residential Network members Connecticut Green Bank and NY Green Bank, a division of Residential Network member New York State Energy Research and Development Authority, have helped launch the Green Bank Network, a new international organization focused on collaborating to scale up private financing to meet the challenge of climate change.

  13. Radiant energy required for infrared neural stimulation

    SciTech Connect (OSTI)

    Tan, Xiaodong; Rajguru, Suhrud; Young, Hunter; Xia, Nan; Stock, Stuart R.; Xiao, Xianghui; Richter, Claus-Peter

    2015-08-25

    Infrared neural stimulation (INS) has been proposed as an alternative method to electrical stimulation because of its spatial selective stimulation. Independent of the mechanism for INS, to translate the method into a device it is important to determine the energy for stimulation required at the target structure. Custom-designed, flat and angle polished fibers, were used to deliver the photons. By rotating the angle polished fibers, the orientation of the radiation beam in the cochlea could be changed. INS-evoked compound action potentials and single unit responses in the central nucleus of the inferior colliculus (ICC) were recorded. X-ray computed tomography was used to determine the orientation of the optical fiber. Maximum responses were observed when the radiation beam was directed towards the spiral ganglion neurons (SGNs), whereas little responses were seen when the beam was directed towards the basilar membrane. The radiant exposure required at the SGNs to evoke compound action potentials (CAPs) or ICC responses was on average 18.9 ± 12.2 or 10.3 ± 4.9 mJ/cm2, respectively. For cochlear INS it has been debated whether the radiation directly stimulates the SGNs or evokes a photoacoustic effect. The results support the view that a direct interaction between neurons and radiation dominates the response to INS.

  14. Radiant energy required for infrared neural stimulation

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Tan, Xiaodong; Rajguru, Suhrud; Young, Hunter; Xia, Nan; Stock, Stuart R.; Xiao, Xianghui; Richter, Claus-Peter

    2015-08-25

    Infrared neural stimulation (INS) has been proposed as an alternative method to electrical stimulation because of its spatial selective stimulation. Independent of the mechanism for INS, to translate the method into a device it is important to determine the energy for stimulation required at the target structure. Custom-designed, flat and angle polished fibers, were used to deliver the photons. By rotating the angle polished fibers, the orientation of the radiation beam in the cochlea could be changed. INS-evoked compound action potentials and single unit responses in the central nucleus of the inferior colliculus (ICC) were recorded. X-ray computed tomography wasmore » used to determine the orientation of the optical fiber. Maximum responses were observed when the radiation beam was directed towards the spiral ganglion neurons (SGNs), whereas little responses were seen when the beam was directed towards the basilar membrane. The radiant exposure required at the SGNs to evoke compound action potentials (CAPs) or ICC responses was on average 18.9 ± 12.2 or 10.3 ± 4.9 mJ/cm2, respectively. For cochlear INS it has been debated whether the radiation directly stimulates the SGNs or evokes a photoacoustic effect. The results support the view that a direct interaction between neurons and radiation dominates the response to INS.« less

  15. A New Improved Na-K Geothermometer By Artificial Neural Networks...

    Open Energy Info (EERE)

    567-577), Truesdell (1975; Proc. 2nd UN Symposium), Tonani (1980; Proc. Adv. Eur. Geoth. Research, 2nd Symposium), Fournier (1979a; J. Volcanol. Geotherm. Res. 5, 1-16), Nieva and...

  16. National Highway Planning Network

    Energy Science and Technology Software Center (OSTI)

    1992-02-02

    NHPN, the National Highway Planning Network, is a database of major highways in the continental United States that is used for national-level analyses of highway transportation issues that require use of a network, such as studies of highway performance, network design, social and environmental impacts of transportation, vehicle routing and scheduling, and mapping. The network is based on a set of roadways digitized by the U. S. Geological Survey (USGS) from the 1980 National Atlasmore » and has been enhanced with additional roads, attribute detail, and topological error corrections to produce a true analytic network. All data have been derived from or checked against information obtained from state and Federal governmental agencies. Two files comprise this network: one describing links and the other nodes. This release, NHPN1.0, contains 44,960 links and 28,512 nodes representing approximately 380,000 miles of roadway.« less

  17. Reconfigureable network node

    DOE Patents [OSTI]

    Vanderveen, Keith B. (Tracy, CA); Talbot, Edward B. (Livermore, CA); Mayer, Laurence E. (Davis, CA)

    2008-04-08

    Nodes in a network having a plurality of nodes establish communication links with other nodes using available transmission media, as the ability to establish such links becomes available and desirable. The nodes predict when existing communications links will fail, become overloaded or otherwise degrade network effectiveness and act to establish substitute or additional links before the node's ability to communicate with the other nodes on the network is adversely affected. A node stores network topology information and programmed link establishment rules and criteria. The node evaluates characteristics that predict existing links with other nodes becoming unavailable or degraded. The node then determines whether it can form a communication link with a substitute node, in order to maintain connectivity with the network. When changing its communication links, a node broadcasts that information to the network. Other nodes update their stored topology information and consider the updated topology when establishing new communications links for themselves.

  18. Networking and Application Strategies

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Networking and Application Strategies Networking and Application Strategies Los Alamos Lab recruits the best minds on the planet and offers job search information and assistance to our dual career spouses or partners. Contact Us dualcareers@lanl.gov You know more people than you think Having strong existing connections along with building new ones is essential to finding a job-especially for a dual career family that is new to the area. Networking is a proven and effective way to increase your

  19. Energy Efficient Digital Networks

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    ... of diverse CE devices (audio and video) - Determine best ways for people to ... research * Further exploration of digital network energy issues - Special attention ...

  20. Energy Sciences Network (ESnet)

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    making it the standard for research institutions today. Read More ESnet Releases Open Source Software from MyESnet Portal for Building Online Interactive Network Portals ESnet...

  1. Form:Networking Organization | Open Energy Information

    Open Energy Info (EERE)

    Networking Organization Jump to: navigation, search Add a Networking Organization Input your networking organization name below to add to the registry. If your networking...

  2. Collective network routing

    DOE Patents [OSTI]

    Hoenicke, Dirk

    2014-12-02

    Disclosed are a unified method and apparatus to classify, route, and process injected data packets into a network so as to belong to a plurality of logical networks, each implementing a specific flow of data on top of a common physical network. The method allows to locally identify collectives of packets for local processing, such as the computation of the sum, difference, maximum, minimum, or other logical operations among the identified packet collective. Packets are injected together with a class-attribute and an opcode attribute. Network routers, employing the described method, use the packet attributes to look-up the class-specific route information from a local route table, which contains the local incoming and outgoing directions as part of the specifically implemented global data flow of the particular virtual network.

  3. RESIDENTIAL NETWORK MEMBERS UNITE TO FORM GREEN BANK NETWORK...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Residential Network members Connecticut Green Bank and NY Green Bank, a division of Residential Network member New York State Energy Research and Development Authority, have helped ...

  4. Chronic, Multi-Contact, Neural Interface for Deep Brain Stimulation

    Office of Scientific and Technical Information (OSTI)

    (Conference) | SciTech Connect Chronic, Multi-Contact, Neural Interface for Deep Brain Stimulation Citation Details In-Document Search Title: Chronic, Multi-Contact, Neural Interface for Deep Brain Stimulation Authors: Tooker, A C ; Madsen, T E ; Crowell, A ; Shah, K G ; Felix, S H ; Mayberg, H S ; Pannu, S S ; Rainnie, D G ; Tolosa, V M Publication Date: 2013-09-30 OSTI Identifier: 1108838 Report Number(s): LLNL-CONF-644462 DOE Contract Number: W-7405-ENG-48 Resource Type: Conference

  5. Thermal network reduction

    SciTech Connect (OSTI)

    Balcomb, J.D.

    1983-01-01

    A method is presented for reducing the number of elements required in a thermal network representation of a building. The method is based on matching the actual building response at two frequencies, the diurnal response and 3-day response. The procedure provides a straightforward methodology for combining all the various materials inside a discrete building zone into a few nodes while retaining a high degree of accuracy in the dynamic response. An example is given showing a comparison between a large network and the reduced network.

  6. Thermal network reduction

    SciTech Connect (OSTI)

    Balcomb, J.D.

    1983-06-01

    A method is presented for reducing the number of elements required in a thermal network representation of a building. The method is based on matching the actual building response at two frequencies, the diurnal response and 3-day response. The procedure provides a straightforward methodology for combining all the various materials inside a discrete building zone into a few nodes while retaining a high degree of accuracy in the dynamic response. An example is given showing a comparison between a large network and the reduced network.

  7. BES Science Network Requirements

    SciTech Connect (OSTI)

    Biocca, Alan; Carlson, Rich; Chen, Jackie; Cotter, Steve; Tierney, Brian; Dattoria, Vince; Davenport, Jim; Gaenko, Alexander; Kent, Paul; Lamm, Monica; Miller, Stephen; Mundy, Chris; Ndousse, Thomas; Pederson, Mark; Perazzo, Amedeo; Popescu, Razvan; Rouson, Damian; Sekine, Yukiko; Sumpter, Bobby; Dart, Eli; Wang, Cai-Zhuang -Z; Whitelam, Steve; Zurawski, Jason

    2011-02-01

    The Energy Sciences Network (ESnet) is the primary provider of network connectivityfor the US Department of Energy Office of Science (SC), the single largest supporter of basic research in the physical sciences in the United States. In support of the Office ofScience programs, ESnet regularly updates and refreshes its understanding of the networking requirements of the instruments, facilities, scientists, and science programs that it serves. This focus has helped ESnet to be a highly successful enabler of scientific discovery for over 20 years.

  8. Unlocking the brain's mysteries: Meet the bioengineers behind next-generation neural devices

    ScienceCinema (OSTI)

    Pannu, Sat; Shah, Kedar; Tolosa, Vanessa; Tooker, Angela

    2015-02-20

    Bioengineers in the Neural Technologies Group at Lawrence Livermore are creating the next generation of clinical- and research-quality neural interfaces. The goal is to gain a fundamental understanding of neuroscience, treat a variety of debilitating neurological disorders (such as Parkinson's, depression, and epilepsy), and restore lost neural functions such as sight, hearing, and mobility.

  9. Unlocking the brain's mysteries: Meet the bioengineers behind next-generation neural devices

    SciTech Connect (OSTI)

    Pannu, Sat; Shah, Kedar; Tolosa, Vanessa; Tooker, Angela

    2014-10-02

    Bioengineers in the Neural Technologies Group at Lawrence Livermore are creating the next generation of clinical- and research-quality neural interfaces. The goal is to gain a fundamental understanding of neuroscience, treat a variety of debilitating neurological disorders (such as Parkinson's, depression, and epilepsy), and restore lost neural functions such as sight, hearing, and mobility.

  10. NP Science Network Requirements

    SciTech Connect (OSTI)

    Dart, Eli; Rotman, Lauren; Tierney, Brian

    2011-08-26

    The Energy Sciences Network (ESnet) is the primary provider of network connectivity for the U.S. Department of Energy (DOE) Office of Science (SC), the single largest supporter of basic research in the physical sciences in the United States. To support SC programs, ESnet regularly updates and refreshes its understanding of the networking requirements of the instruments, facilities, scientists, and science programs it serves. This focus has helped ESnet to be a highly successful enabler of scientific discovery for over 20 years. In August 2011, ESnet and the Office of Nuclear Physics (NP), of the DOE SC, organized a workshop to characterize the networking requirements of the programs funded by NP. The requirements identified at the workshop are summarized in the Findings section, and are described in more detail in the body of the report.

  11. Network Requirements Reviews

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Reviews Science Engagement Move your data Programs & Workshops Science Requirements Reviews Network Requirements Reviews Documents and Background Materials FAQ for Case Study Authors BER Requirements Review 2015 ASCR Requirements Review 2015 Previous Reviews Requirements Review Reports Case Studies Contact Us Technical Assistance: 1 800-33-ESnet (Inside US) 1 800-333-7638 (Inside US) 1 510-486-7600 (Globally) 1 510-486-7607 (Globally) Report Network Problems: trouble@es.net Provide Web Site

  12. Software-Defined Networking (SDN)

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    ENOS Experimental Network Testbeds Performance (perfSONAR) Software & Tools Development Data for Researchers Partnerships Publications Workshops Science Engagement Move your data Programs & Workshops Science Requirements Reviews Case Studies News & Publications ESnet News Publications and Presentations Galleries ESnet Awards and Honors Blog ESnet Live Home » Network R&D » Software-Defined Networking (SDN) Network R&D Software-Defined Networking (SDN) ENOS Experimental

  13. ESnet Network Operating System (ENOS)

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    ENOS Experimental Network Testbeds Performance (perfSONAR) Software & Tools Development Data for Researchers Partnerships Publications Workshops Science Engagement Move your data Programs & Workshops Science Requirements Reviews Case Studies News & Publications ESnet News Publications and Presentations Galleries ESnet Awards and Honors Blog ESnet Live Home » Network R&D » Software-Defined Networking (SDN) » ENOS Network R&D Software-Defined Networking (SDN) ENOS

  14. High Density Sensor Network Development | The Ames Laboratory

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    High Density Sensor Network Development

  15. Network topology mapper

    DOE Patents [OSTI]

    Quist, Daniel A. (Los Alamos, NM); Gavrilov, Eugene M. (Los Alamos, NM); Fisk, Michael E. (Jemez, NM)

    2008-01-15

    A method enables the topology of an acyclic fully propagated network to be discovered. A list of switches that comprise the network is formed and the MAC address cache for each one of the switches is determined. For each pair of switches, from the MAC address caches the remaining switches that see the pair of switches are located. For each pair of switches the remaining switches are determined that see one of the pair of switches on a first port and the second one of the pair of switches on a second port. A list of insiders is formed for every pair of switches. It is determined whether the insider for each pair of switches is a graph edge and adjacent ones of the graph edges are determined. A symmetric adjacency matrix is formed from the graph edges to represent the topology of the data link network.

  16. Self-Configuring Network Monitor

    Energy Science and Technology Software Center (OSTI)

    2004-05-01

    Self-Configuring Network Monitor (SCNM) is a passive monitoring that can collect packet headers from any point in a network path. SCNM uses special activation packets to automatically activate monitors deployed at the layer three ingress and egress routers of the wide-area network, and at critical points within the site networks. Monitoring output data is sent back to the application data source or destination host. No modifications are required to the application or network routing infrastructuremore » in order to activate monitoring of traffic for an application. This ensures that the monitoring operation does not add a burden to the networks administrator.« less

  17. ASCR Science Network Requirements

    SciTech Connect (OSTI)

    Dart, Eli; Tierney, Brian

    2009-08-24

    The Energy Sciences Network (ESnet) is the primary provider of network connectivity for the US Department of Energy Office of Science, the single largest supporter of basic research in the physical sciences in the United States. In support of the Office of Science programs, ESnet regularly updates and refreshes its understanding of the networking requirements of the instruments, facilities, scientists, and science programs that it serves. This focus has helped ESnet to be a highly successful enabler of scientific discovery for over 20 years. In April 2009 ESnet and the Office of Advanced Scientific Computing Research (ASCR), of the DOE Office of Science, organized a workshop to characterize the networking requirements of the programs funded by ASCR. The ASCR facilities anticipate significant increases in wide area bandwidth utilization, driven largely by the increased capabilities of computational resources and the wide scope of collaboration that is a hallmark of modern science. Many scientists move data sets between facilities for analysis, and in some cases (for example the Earth System Grid and the Open Science Grid), data distribution is an essential component of the use of ASCR facilities by scientists. Due to the projected growth in wide area data transfer needs, the ASCR supercomputer centers all expect to deploy and use 100 Gigabit per second networking technology for wide area connectivity as soon as that deployment is financially feasible. In addition to the network connectivity that ESnet provides, the ESnet Collaboration Services (ECS) are critical to several science communities. ESnet identity and trust services, such as the DOEGrids certificate authority, are widely used both by the supercomputer centers and by collaborations such as Open Science Grid (OSG) and the Earth System Grid (ESG). Ease of use is a key determinant of the scientific utility of network-based services. Therefore, a key enabling aspect for scientists beneficial use of high performance networks is a consistent, widely deployed, well-maintained toolset that is optimized for wide area, high-speed data transfer (e.g. GridFTP) that allows scientists to easily utilize the services and capabilities that the network provides. Network test and measurement is an important part of ensuring that these tools and network services are functioning correctly. One example of a tool in this area is the recently developed perfSONAR, which has already shown its usefulness in fault diagnosis during the recent deployment of high-performance data movers at NERSC and ORNL. On the other hand, it is clear that there is significant work to be done in the area of authentication and access control - there are currently compatibility problems and differing requirements between the authentication systems in use at different facilities, and the policies and mechanisms in use at different facilities are sometimes in conflict. Finally, long-term software maintenance was of concern for many attendees. Scientists rely heavily on a large deployed base of software that does not have secure programmatic funding. Software packages for which this is true include data transfer tools such as GridFTP as well as identity management and other software infrastructure that forms a critical part of the Open Science Grid and the Earth System Grid.

  18. Network resilience; A measure of network fault tolerance

    SciTech Connect (OSTI)

    Najjar, W. . Dept. of Computer Science); Gaudoit, J.L. . Dept. of Electrical Engineering)

    1990-02-01

    The failure of a node in a multicomputer system will not only reduce the computational power but also alter the network's topology. Network fault tolerance is a measure of the number of failures the network can sustain before a disconnection occurs. It is expressed traditionally as the network's node degree. In this paper, the authors propose a probabilistic measure of network fault tolerance expressed as the probability f a disconnection. Qualitative evaluation of this measure is presented. As expected, the single-node disconnection probability is the dominant factor irrespective of the topology under consideration. They derive an analytical approximation of the disconnection probability and verify it with Monte Carlo simulation. Based on this model, the measures of network resilience and relative network resilience are proposed as probabilistic measures of network fault tolerance. These are then used to evaluate the effects of the disconnection probability on the reliability of the system.

  19. Better Buildings Residential Network Case Study: Partnerships...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Network Case Study: Partnerships Better Buildings Residential Network Case Study: Partnerships Better Buildings Residential Network Case Study: Partnerships, from the U.S. ...

  20. Vehicle Technologies Office: National Idling Reduction Network...

    Office of Environmental Management (EM)

    Idling Reduction Network News Archives Vehicle Technologies Office: National Idling Reduction Network News Archives The National Idling Reduction Network brings together trucking...

  1. Northwest Biodiesel Network | Open Energy Information

    Open Energy Info (EERE)

    Biodiesel Network Jump to: navigation, search Logo: Northwest Biodiesel Network Name: Northwest Biodiesel Network Address: 6532 Phinney Ave N Place: Seattle, Washington Zip: 98103...

  2. Sustainable Agriculture Network | Open Energy Information

    Open Energy Info (EERE)

    Agriculture Network Jump to: navigation, search Logo: Sustainable Agriculture Network Name: Sustainable Agriculture Network Website: clima.sanstandards.org References: Sustainable...

  3. National Network for Manufacturing Innovation: A Preliminary...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Network for Manufacturing Innovation: A Preliminary Design National Network for Manufacturing Innovation: A Preliminary Design The Federal investment in the National Network for ...

  4. Clean Economy Network Foundation | Open Energy Information

    Open Energy Info (EERE)

    Clean Economy Network Foundation Jump to: navigation, search Logo: Clean Economy Network Foundation Name: Clean Economy Network Foundation Address: 1301 Pennsylvania Ave NW, Suite...

  5. Fact Sheet: Better Buildings Residential Network | Department...

    Energy Savers [EERE]

    Fact Sheet: Better Buildings Residential Network Fact Sheet: Better Buildings Residential Network Fact Sheet: Better Buildings Residential Network, increasing the number of...

  6. Benefits of Better Buildings Residential Network Reporting |...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Benefits of Better Buildings Residential Network Reporting Benefits of Better Buildings Residential Network Reporting Better Buildings Residential Network All-Member Peer Exchange ...

  7. Energy Materials Network Workshop

    Broader source: Energy.gov [DOE]

    The Energy Materials Network (EMN) is a national lab-led initiative that aims to dramatically decrease the time-to-market for advanced materials innovations critical to many clean energy technologies. Through targeted consortia offering accessible suites of advanced research and development capabilities, EMN is accelerating materials development to address U.S. manufacturers' most pressing materials challenges.

  8. Microsystem process networks

    DOE Patents [OSTI]

    Wegeng, Robert S. (Richland, WA); TeGrotenhuis, Ward E. (Kennewick, WA); Whyatt, Greg A. (West Richland, WA)

    2007-09-18

    Various aspects and applications of microsystem process networks are described. The design of many types of Microsystems can be improved by ortho-cascading mass, heat, or other unit process operations. Microsystems having energetically efficient microchannel heat exchangers are also described. Detailed descriptions of numerous design features in microcomponent systems are also provided.

  9. Microsystem process networks

    DOE Patents [OSTI]

    Wegeng, Robert S [Richland, WA; TeGrotenhuis, Ward E [Kennewick, WA; Whyatt, Greg A [West Richland, WA

    2010-01-26

    Various aspects and applications or microsystem process networks are described. The design of many types of microsystems can be improved by ortho-cascading mass, heat, or other unit process operations. Microsystems having energetically efficient microchannel heat exchangers are also described. Detailed descriptions of numerous design features in microcomponent systems are also provided.

  10. Microsystem process networks

    DOE Patents [OSTI]

    Wegeng, Robert S. (Richland, WA); TeGrotenhuis, Ward E. (Kennewick, WA); Whyatt, Greg A. (West Richland, WA)

    2006-10-24

    Various aspects and applications of microsystem process networks are described. The design of many types of microsystems can be improved by ortho-cascading mass, heat, or other unit process operations. Microsystems having exergetically efficient microchannel heat exchangers are also described. Detailed descriptions of numerous design features in microcomponent systems are also provided.

  11. Transactional Network Platform: Applications

    SciTech Connect (OSTI)

    Katipamula, Srinivas; Lutes, Robert G.; Ngo, Hung; Underhill, Ronald M.

    2013-10-31

    In FY13, Pacific Northwest National Laboratory (PNNL) with funding from the Department of Energy’s (DOE’s) Building Technologies Office (BTO) designed, prototyped and tested a transactional network platform to support energy, operational and financial transactions between any networked entities (equipment, organizations, buildings, grid, etc.). Initially, in FY13, the concept demonstrated transactions between packaged rooftop air conditioners and heat pump units (RTUs) and the electric grid using applications or "agents" that reside on the platform, on the equipment, on a local building controller or in the Cloud. The transactional network project is a multi-lab effort with Oakridge National Laboratory (ORNL) and Lawrence Berkeley National Laboratory (LBNL) also contributing to the effort. PNNL coordinated the project and also was responsible for the development of the transactional network (TN) platform and three different applications associated with RTUs. This document describes two applications or "agents" in details, and also summarizes the platform. The TN platform details are described in another companion document.

  12. Software Defined Networking (SDN) Project

    Energy Savers [EERE]

    Software Defined Networking (SDN) Project Energy sector-focused SDN flow controller to manage control system networks centrally and securely Background Traditional information technology (IT) approaches to network administration and packet delivery are not always appropriate for electric industry applications. The nondeterministic latency and configuration complexity make network design difficult for the deterministic, static control systems of the energy sector. In the electric industry, it is

  13. Team develops 3-D sensor array for detection of neural responses

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    3-D sensor array for detection of neural responses Team develops 3-D sensor array for detection of neural responses Los Alamos researchers and collaborators have demonstrated a prototype neural interface device of a novel 3-D device architecture. December 2, 2014 Scanning electron micrograph (SEM) images of the 60-electrode device with three-dimensional pillar electrodes (Inset A & B) connected by platinum metal traces terminating at bond pads on the outside edge of the device (Inset C).

  14. Multiple network interface core apparatus and method

    DOE Patents [OSTI]

    Underwood, Keith D.; Hemmert, Karl Scott

    2011-04-26

    A network interface controller and network interface control method comprising providing a single integrated circuit as a network interface controller and employing a plurality of network interface cores on the single integrated circuit.

  15. Better Buildings Network View | March 2014

    Broader source: Energy.gov [DOE]

    The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network.

  16. Better Buildings Network View | January 2015

    Broader source: Energy.gov [DOE]

    The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network.

  17. Better Buildings Network View | June 2015

    Broader source: Energy.gov [DOE]

    The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network.

  18. Better Buildings Residential Network Orientation Webinar | Department...

    Broader source: Energy.gov (indexed) [DOE]

    11, 2014. Call Slides and Discussion Summary More Documents & Publications Better Buildings Residential Network Orientation Better Buildings Residential Network Orientation...

  19. Instructions for Using Virtual Private Network (VPN)

    Broader source: Energy.gov [DOE]

    Virtual Private Network (VPN) provides access to network drives and is recommended for use only from a EITS provided laptop.

  20. Renewable Energy Nongovernmental Organization Network (RENOVE...

    Open Energy Info (EERE)

    Nongovernmental Organization Network (RENOVE) Jump to: navigation, search Name: Renewable Energy Nongovernmental Organization Network (RENOVE) Place: Brasilia, Brazil Phone Number:...

  1. Better Buildings Network View | May 2014

    Broader source: Energy.gov [DOE]

    The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network.

  2. Modular sensor network node

    DOE Patents [OSTI]

    Davis, Jesse Harper Zehring; Stark, Jr., Douglas Paul; Kershaw, Christopher Patrick; Kyker, Ronald Dean

    2008-06-10

    A distributed wireless sensor network node is disclosed. The wireless sensor network node includes a plurality of sensor modules coupled to a system bus and configured to sense a parameter. The parameter may be an object, an event or any other parameter. The node collects data representative of the parameter. The node also includes a communication module coupled to the system bus and configured to allow the node to communicate with other nodes. The node also includes a processing module coupled to the system bus and adapted to receive the data from the sensor module and operable to analyze the data. The node also includes a power module connected to the system bus and operable to generate a regulated voltage.

  3. battery electrode percolating network

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    battery electrode percolating network - Sandia Energy Energy Search Icon Sandia Home Locations Contact Us Employee Locator Energy & Climate Secure & Sustainable Energy Future Stationary Power Energy Conversion Efficiency Solar Energy Wind Energy Water Power Supercritical CO2 Geothermal Natural Gas Safety, Security & Resilience of the Energy Infrastructure Energy Storage Nuclear Power & Engineering Grid Modernization Battery Testing Nuclear Fuel Cycle Defense Waste Management

  4. Insecurity of Wireless Networks

    SciTech Connect (OSTI)

    Sheldon, Frederick T; Weber, John Mark; Yoo, Seong-Moo; Pan, W. David

    2012-01-01

    Wireless is a powerful core technology enabling our global digital infrastructure. Wi-Fi networks are susceptible to attacks on Wired Equivalency Privacy, Wi-Fi Protected Access (WPA), and WPA2. These attack signatures can be profiled into a system that defends against such attacks on the basis of their inherent characteristics. Wi-Fi is the standard protocol for wireless networks used extensively in US critical infrastructures. Since the Wired Equivalency Privacy (WEP) security protocol was broken, the Wi-Fi Protected Access (WPA) protocol has been considered the secure alternative compatible with hardware developed for WEP. However, in November 2008, researchers developed an attack on WPA, allowing forgery of Address Resolution Protocol (ARP) packets. Subsequent enhancements have enabled ARP poisoning, cryptosystem denial of service, and man-in-the-middle attacks. Open source systems and methods (OSSM) have long been used to secure networks against such attacks. This article reviews OSSMs and the results of experimental attacks on WPA. These experiments re-created current attacks in a laboratory setting, recording both wired and wireless traffic. The article discusses methods of intrusion detection and prevention in the context of cyber physical protection of critical Internet infrastructure. The basis for this research is a specialized (and undoubtedly incomplete) taxonomy of Wi-Fi attacks and their adaptations to existing countermeasures and protocol revisions. Ultimately, this article aims to provide a clearer picture of how and why wireless protection protocols and encryption must achieve a more scientific basis for detecting and preventing such attacks.

  5. Bicriteria network design problems

    SciTech Connect (OSTI)

    Marathe, M.V.; Ravi, R.; Sundaram, R.; Ravi, S.S.; Rosenkrantz, D.J.; Hunt, H.B. III

    1997-11-20

    The authors study a general class of bicriteria network design problems. A generic problem in this class is as follows: Given an undirected graph and two minimization objectives (under different cost functions), with a budget specified on the first, find a subgraph from a given subgraph class that minimizes the second objective subject to the budget on the first. They consider three different criteria -- the total edge cost, the diameter and the maximum degree of the network. Here, they present the first polynomial-time approximation algorithms for a large class of bicriteria network design problems for the above mentioned criteria. The following general types of results are presented. First, they develop a framework for bicriteria problems and their approximations. Second, when the two criteria are the same they present a black box parametric search technique. This black box takes in as input an (approximation) algorithm for the criterion situation and generates an approximation algorithm for the bicriteria case with only a constant factor loss in the performance guarantee. Third, when the two criteria are the diameter and the total edge costs they use a cluster based approach to devise approximation algorithms. The solutions violate both the criteria by a logarithmic factor. Finally, for the class of treewidth-bounded graphs, they provide pseudopolynomial-time algorithms for a number of bicriteria problems using dynamic programming. The authors show how these pseudopolynomial-time algorithms can be converted to fully polynomial-time approximation schemes using a scaling technique.

  6. Computer Networking Group | Stanford Synchrotron Radiation Lightsource

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Computer Networking Group Do you need help? For assistance please submit a CNG Help Request ticket. CNG Logo Chris Ramirez SSRL Computer and Networking Group (650) 926-2901 | email Jerry Camuso SSRL Computer and Networking Group (650) 926-2994 | email Networking Support The Networking group provides connectivity and communications services for SSRL. The services provided by the Networking Support Group include: Local Area Network support for cable and wireless connectivity. Installation and

  7. Operating Innovative Networks Workshop Series

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Operating Innovative Networks Workshop Series Science Engagement Move your data Programs & Workshops CrossConnects Workshop Series Operating Innovative Networks Workshop Series Enlighten Your Research Global Program Science Requirements Reviews Case Studies Contact Us Technical Assistance: 1 800-33-ESnet (Inside US) 1 800-333-7638 (Inside US) 1 510-486-7600 (Globally) 1 510-486-7607 (Globally) Report Network Problems: trouble@es.net Provide Web Site Feedback: info@es.net Operating Innovative

  8. Network interdiction with budget constraints

    SciTech Connect (OSTI)

    Santhi, Nankakishore; Pan, Feng

    2009-01-01

    Several scenarios exist in the modern interconnected world which call for efficient network interdiction algorithms. Applications are varied, including computer network security, prevention of spreading of Internet worms, policing international smuggling networks, controlling spread of diseases and optimizing the operation of large public energy grids. In this paper we consider some natural network optimization questions related to the budget constrained interdiction problem over general graphs. Many of these questions turn out to be computationally hard to tackle. We present a particularly interesting practical form of the interdiction question which we show to be computationally tractable. A polynomial time algorithm is then presented for this problem.

  9. United States National Seismographic Network

    SciTech Connect (OSTI)

    Buland, R.

    1993-09-01

    The concept of a United States National Seismograph Network (USNSN) dates back nearly 30 years. The idea was revived several times over the decades. but never funded. For, example, a national network was proposed and discussed at great length in the so called Bolt Report (U. S. Earthquake Observatories: Recommendations for a New National Network, National Academy Press, Washington, D.C., 1980, 122 pp). From the beginning, a national network was viewed as augmenting and complementing the relatively dense, predominantly short-period vertical coverage of selected areas provided by the Regional Seismograph Networks (RSN`s) with a sparse, well-distributed network of three-component, observatory quality, permanent stations. The opportunity finally to begin developing a national network arose in 1986 with discussions between the US Geological Survey (USGS) and the Nuclear Regulatory Commission (NRC). Under the agreement signed in 1987, the NRC has provided $5 M in new funding for capital equipment (over the period 1987-1992) and the USGS has provided personnel and facilities to develop. deploy, and operate the network. Because the NRC funding was earmarked for the eastern United States, new USNSN station deployments are mostly east of 105{degree}W longitude while the network in the western United States is mostly made up of cooperating stations (stations meeting USNSN design goals, but deployed and operated by other institutions which provide a logical extension to the USNSN).

  10. Regional Networks for Energy Efficiency

    Broader source: Energy.gov [DOE]

    Better Buildings Neighborhood Program Sustainability Peer Exchange Call: Regional Networks for Energy Efficiency, call slides and discussion summary, December 6, 2012.

  11. Vector Network Analysis

    Energy Science and Technology Software Center (OSTI)

    1997-10-20

    Vector network analyzers are a convenient way to measure scattering parameters of a variety of microwave devices. However, these instruments, unlike oscilloscopes for example, require a relatively high degree of user knowledge and expertise. Due to the complexity of the instrument and of the calibration process, there are many ways in which an incorrect measurement may be produced. The Microwave Project, which is part of Sandia National Laboratories Primary Standards Laboratory, routinely uses check standardsmore » to verify that the network analyzer is operating properly. In the past, these measurements were recorded manually and, sometimes, interpretation of the results was problematic. To aid our measurement assurance process, a software program was developed to automatically measure a check standard and compare the new measurements with an historical database of measurements of the same device. The program acquires new measurement data from selected check standards, plots the new data against the mean and standard deviation of prior data for the same check standard, and updates the database files for the check standard. The program is entirely menu-driven requiring little additional work by the user.« less

  12. High Performance Network Monitoring

    SciTech Connect (OSTI)

    Martinez, Jesse E

    2012-08-10

    Network Monitoring requires a substantial use of data and error analysis to overcome issues with clusters. Zenoss and Splunk help to monitor system log messages that are reporting issues about the clusters to monitoring services. Infiniband infrastructure on a number of clusters upgraded to ibmon2. ibmon2 requires different filters to report errors to system administrators. Focus for this summer is to: (1) Implement ibmon2 filters on monitoring boxes to report system errors to system administrators using Zenoss and Splunk; (2) Modify and improve scripts for monitoring and administrative usage; (3) Learn more about networks including services and maintenance for high performance computing systems; and (4) Gain a life experience working with professionals under real world situations. Filters were created to account for clusters running ibmon2 v1.0.0-1 10 Filters currently implemented for ibmon2 using Python. Filters look for threshold of port counters. Over certain counts, filters report errors to on-call system administrators and modifies grid to show local host with issue.

  13. Network Bandwidth Utilization Forecast Model on High Bandwidth Network

    SciTech Connect (OSTI)

    Yoo, Wucherl; Sim, Alex

    2014-07-07

    With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2percent. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.

  14. Distributed downhole drilling network

    DOE Patents [OSTI]

    Hall, David R.; Hall, Jr., H. Tracy; Fox, Joe; Pixton, David S.

    2006-11-21

    A high-speed downhole network providing real-time data from downhole components of a drilling strings includes a bottom-hole node interfacing to a bottom-hole assembly located proximate the bottom end of a drill string. A top-hole node is connected proximate the top end of the drill string. One or several intermediate nodes are located along the drill string between the bottom-hole node and the top-hole node. The intermediate nodes are configured to receive and transmit data packets transmitted between the bottom-hole node and the top-hole node. A communications link, integrated into the drill string, is used to operably connect the bottom-hole node, the intermediate nodes, and the top-hole node. In selected embodiments, a personal or other computer may be connected to the top-hole node, to analyze data received from the intermediate and bottom-hole nodes.

  15. Wellbore Integrity Network

    SciTech Connect (OSTI)

    Carey, James W.; Bachu, Stefan

    2012-06-21

    In this presentation, we review the current state of knowledge on wellbore integrity as developed in the IEA Greenhouse Gas Programme's Wellbore Integrity Network. Wells are one of the primary risks to the successful implementation of CO{sub 2} storage programs. Experimental studies show that wellbore materials react with CO{sub 2} (carbonation of cement and corrosion of steel) but the impact on zonal isolation is unclear. Field studies of wells in CO{sub 2}-bearing fields show that CO{sub 2} does migrate external to casing. However, rates and amounts of CO{sub 2} have not been quantified. At the decade time scale, wellbore integrity is driven by construction quality and geomechanical processes. Over longer time-scales (> 100 years), chemical processes (cement degradation and corrosion) become more important, but competing geomechanical processes may preserve wellbore integrity.

  16. RNEDE: Resilient Network Design Environment

    SciTech Connect (OSTI)

    Venkat Venkatasubramanian, Tanu Malik, Arun Giridh; Craig Rieger; Keith Daum; Miles McQueen

    2010-08-01

    Modern living is more and more dependent on the intricate web of critical infrastructure systems. The failure or damage of such systems can cause huge disruptions. Traditional design of this web of critical infrastructure systems was based on the principles of functionality and reliability. However, it is increasingly being realized that such design objectives are not sufficient. Threats, disruptions and faults often compromise the network, taking away the benefits of an efficient and reliable design. Thus, traditional network design parameters must be combined with self-healing mechanisms to obtain a resilient design of the network. In this paper, we present RNEDEa resilient network design environment that that not only optimizes the network for performance but tolerates fluctuations in its structure that result from external threats and disruptions. The environment evaluates a set of remedial actions to bring a compromised network to an optimal level of functionality. The environment includes a visualizer that enables the network administrator to be aware of the current state of the network and the suggested remedial actions at all times.

  17. Global interrupt and barrier networks

    DOE Patents [OSTI]

    Blumrich, Matthias A. (Ridgefield, CT); Chen, Dong (Croton-On-Hudson, NY); Coteus, Paul W. (Yorktown Heights, NY); Gara, Alan G. (Mount Kisco, NY); Giampapa, Mark E (Irvington, NY); Heidelberger, Philip (Cortlandt Manor, NY); Kopcsay, Gerard V. (Yorktown Heights, NY); Steinmacher-Burow, Burkhard D. (Mount Kisco, NY); Takken, Todd E. (Mount Kisco, NY)

    2008-10-28

    A system and method for generating global asynchronous signals in a computing structure. Particularly, a global interrupt and barrier network is implemented that implements logic for generating global interrupt and barrier signals for controlling global asynchronous operations performed by processing elements at selected processing nodes of a computing structure in accordance with a processing algorithm; and includes the physical interconnecting of the processing nodes for communicating the global interrupt and barrier signals to the elements via low-latency paths. The global asynchronous signals respectively initiate interrupt and barrier operations at the processing nodes at times selected for optimizing performance of the processing algorithms. In one embodiment, the global interrupt and barrier network is implemented in a scalable, massively parallel supercomputing device structure comprising a plurality of processing nodes interconnected by multiple independent networks, with each node including one or more processing elements for performing computation or communication activity as required when performing parallel algorithm operations. One multiple independent network includes a global tree network for enabling high-speed global tree communications among global tree network nodes or sub-trees thereof. The global interrupt and barrier network may operate in parallel with the global tree network for providing global asynchronous sideband signals.

  18. Network Information System

    Energy Science and Technology Software Center (OSTI)

    1996-05-01

    The Network Information System (NWIS) was initially implemented in May 1996 as a system in which computing devices could be recorded so that unique names could be generated for each device. Since then the system has grown to be an enterprise wide information system which is integrated with other systems to provide the seamless flow of data through the enterprise. The system Iracks data for two main entities: people and computing devices. The following aremore » the type of functions performed by NWIS for these two entities: People Provides source information to the enterprise person data repository for select contractors and visitors Generates and tracks unique usernames and Unix user IDs for every individual granted cyber access Tracks accounts for centrally managed computing resources, and monitors and controls the reauthorization of the accounts in accordance with the DOE mandated interval Computing Devices Generates unique names for all computing devices registered in the system Tracks the following information for each computing device: manufacturer, make, model, Sandia property number, vendor serial number, operating system and operating system version, owner, device location, amount of memory, amount of disk space, and level of support provided for the machine Tracks the hardware address for network cards Tracks the P address registered to computing devices along with the canonical and alias names for each address Updates the Dynamic Domain Name Service (DDNS) for canonical and alias names Creates the configuration files for DHCP to control the DHCP ranges and allow access to only properly registered computers Tracks and monitors classified security plans for stand-alone computers Tracks the configuration requirements used to setup the machine Tracks the roles people have on machines (system administrator, administrative access, user, etc...) Allows systems administrators to track changes made on the machine (both hardware and software) Generates an adjustment history of changes on selected fields« less

  19. Spatio-spectral image analysis using classical and neural algorithms

    SciTech Connect (OSTI)

    Roberts, S.; Gisler, G.R.; Theiler, J.

    1996-12-31

    Remote imaging at high spatial resolution has a number of environmental, industrial, and military applications. Analysis of high-resolution multi-spectral images usually involves either spectral analysis of single pixels in a multi- or hyper-spectral image or spatial analysis of multi-pixels in a panchromatic or monochromatic image. Although insufficient for some pattern recognition applications individually, the combination of spatial and spectral analytical techniques may allow the identification of more complex signatures that might not otherwise be manifested in the individual spatial or spectral domains. We report on some preliminary investigation of unsupervised classification methodologies (using both ``classical`` and ``neural`` algorithms) to identify potentially revealing features in these images. We apply dimension-reduction preprocessing to the images, duster, and compare the clusterings obtained by different algorithms. Our classification results are analyzed both visually and with a suite of objective, quantitative measures.

  20. Collective network for computer structures

    DOE Patents [OSTI]

    Blumrich, Matthias A.; Coteus, Paul W.; Chen, Dong; Gara, Alan; Giampapa, Mark E.; Heidelberger, Philip; Hoenicke, Dirk; Takken, Todd E.; Steinmacher-Burow, Burkhard D.; Vranas, Pavlos M.

    2011-08-16

    A system and method for enabling high-speed, low-latency global collective communications among interconnected processing nodes. The global collective network optimally enables collective reduction operations to be performed during parallel algorithm operations executing in a computer structure having a plurality of the interconnected processing nodes. Router devices ate included that interconnect the nodes of the network via links to facilitate performance of low-latency global processing operations at nodes of the virtual network and class structures. The global collective network may be configured to provide global barrier and interrupt functionality in asynchronous or synchronized manner. When implemented in a massively-parallel supercomputing structure, the global collective network is physically and logically partitionable according to needs of a processing algorithm.

  1. Collective network for computer structures

    DOE Patents [OSTI]

    Blumrich, Matthias A; Coteus, Paul W; Chen, Dong; Gara, Alan; Giampapa, Mark E; Heidelberger, Philip; Hoenicke, Dirk; Takken, Todd E; Steinmacher-Burow, Burkhard D; Vranas, Pavlos M

    2014-01-07

    A system and method for enabling high-speed, low-latency global collective communications among interconnected processing nodes. The global collective network optimally enables collective reduction operations to be performed during parallel algorithm operations executing in a computer structure having a plurality of the interconnected processing nodes. Router devices are included that interconnect the nodes of the network via links to facilitate performance of low-latency global processing operations at nodes of the virtual network. The global collective network may be configured to provide global barrier and interrupt functionality in asynchronous or synchronized manner. When implemented in a massively-parallel supercomputing structure, the global collective network is physically and logically partitionable according to the needs of a processing algorithm.

  2. YAP/TAZ enhance mammalian embryonic neural stem cell characteristics in a Tead-dependent manner

    SciTech Connect (OSTI)

    Han, Dasol; Byun, Sung-Hyun; Park, Soojeong; Kim, Juwan; Kim, Inhee; Ha, Soobong; Kwon, Mookwang; Yoon, Keejung

    2015-02-27

    Mammalian brain development is regulated by multiple signaling pathways controlling cell proliferation, migration and differentiation. Here we show that YAP/TAZ enhance embryonic neural stem cell characteristics in a cell autonomous fashion using diverse experimental approaches. Introduction of retroviral vectors expressing YAP or TAZ into the mouse embryonic brain induced cell localization in the ventricular zone (VZ), which is the embryonic neural stem cell niche. This change in cell distribution in the cortical layer is due to the increased stemness of infected cells; YAP-expressing cells were colabeled with Sox2, a neural stem cell marker, and YAP/TAZ increased the frequency and size of neurospheres, indicating enhanced self-renewal- and proliferative ability of neural stem cells. These effects appear to be TEA domain family transcription factor (Tead)–dependent; a Tead binding-defective YAP mutant lost the ability to promote neural stem cell characteristics. Consistently, in utero gene transfer of a constitutively active form of Tead2 (Tead2-VP16) recapitulated all the features of YAP/TAZ overexpression, and dominant negative Tead2-EnR resulted in marked cell exit from the VZ toward outer cortical layers. Taken together, these results indicate that the Tead-dependent YAP/TAZ signaling pathway plays important roles in neural stem cell maintenance by enhancing stemness of neural stem cells during mammalian brain development. - Highlights: • Roles of YAP and Tead in vivo during mammalian brain development are clarified. • Expression of YAP promotes embryonic neural stem cell characteristics in vivo in a cell autonomous fashion. • Enhancement of neural stem cell characteristics by YAP depends on Tead. • Transcriptionally active form of Tead alone can recapitulate the effects of YAP. • Transcriptionally repressive form of Tead severely reduces stem cell characteristics.

  3. Phoebus: Network Middleware for Next-Generation Network Computing

    SciTech Connect (OSTI)

    Martin Swany

    2012-06-16

    The Phoebus project investigated algorithms, protocols, and middleware infrastructure to improve end-to-end performance in high speed, dynamic networks. The Phoebus system essentially serves as an adaptation point for networks with disparate capabilities or provisioning. This adaptation can take a variety of forms including acting as a provisioning agent across multiple signaling domains, providing transport protocol adaptation points, and mapping between distributed resource reservation paradigms and the optical network control plane. We have successfully developed the system and demonstrated benefits. The Phoebus system was deployed in Internet2 and in ESnet, as well as in GEANT2, RNP in Brazil and over international links to Korea and Japan. Phoebus is a system that implements a new protocol and associated forwarding infrastructure for improving throughput in high-speed dynamic networks. It was developed to serve the needs of large DOE applications on high-performance networks. The idea underlying the Phoebus model is to embed Phoebus Gateways (PGs) in the network as on-ramps to dynamic circuit networks. The gateways act as protocol translators that allow legacy applications to use dedicated paths with high performance.

  4. The importance of input variables to a neural network fault-diagnostic system for nuclear power plants

    SciTech Connect (OSTI)

    Lanc, T.L.

    1992-01-01

    This thesis explores safety enhancement for nuclear power plants. Emergency response systems currently in use depend mainly on automatic systems engaging when certain parameters go beyond a pre-specified safety limit. Often times the operator has little or no opportunity to react since a fast scram signal shuts down the reactor smoothly and efficiently. These accidents are of interest to technical support personnel since examining the conditions that gave rise to these situations help determine causality. In many other cases an automated fault-diagnostic advisor would be a valuable tool in assisting the technicians and operators to determine what just happened and why.

  5. The importance of input variables to a neural network fault-diagnostic system for nuclear power plants

    SciTech Connect (OSTI)

    Lanc, T.L.

    1992-12-31

    This thesis explores safety enhancement for nuclear power plants. Emergency response systems currently in use depend mainly on automatic systems engaging when certain parameters go beyond a pre-specified safety limit. Often times the operator has little or no opportunity to react since a fast scram signal shuts down the reactor smoothly and efficiently. These accidents are of interest to technical support personnel since examining the conditions that gave rise to these situations help determine causality. In many other cases an automated fault-diagnostic advisor would be a valuable tool in assisting the technicians and operators to determine what just happened and why.

  6. Creative Environmental Networks | Open Energy Information

    Open Energy Info (EERE)

    Environmental Networks Jump to: navigation, search Name: Creative Environmental Networks Place: United Kingdom Zip: CR7 7JG Sector: Biomass, Renewable Energy, Services Product:...

  7. Networks, smart grids: new model for synchronization

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Networks, smart grids: new model for synchronization Networks, smart grids: new model for synchronization Researchers developed a surprisingly simple mathematical model that ...

  8. Better Buildings Network View December 2015

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    ... Email the details to Better Buildings Residential Network support. Social Media Spotlight: ... Better Buildings Residential Network is social media and in your materials. pleased to ...

  9. Better Buildings Residential Network Orientation Webinar | Department...

    Broader source: Energy.gov (indexed) [DOE]

    May 14, 2015. Call Slides and Discussion Summary More Documents & Publications Better Buildings Residential Network Orientation Webinar Better Buildings Residential Network...

  10. Better Buildings Residential Network Orientation | Department...

    Broader source: Energy.gov (indexed) [DOE]

    Summary, March 27, 2014. Call Slides and Summary More Documents & Publications Better Buildings Residential Network Orientation Webinar Better Buildings Residential Network...

  11. Structure Learning in Power Distribution Networks (Technical...

    Office of Scientific and Technical Information (OSTI)

    Structure Learning in Power Distribution Networks Citation Details In-Document Search Title: Structure Learning in Power Distribution Networks You are accessing a document from ...

  12. Clean Economy Network | Open Energy Information

    Open Energy Info (EERE)

    Network Jump to: navigation, search Name: Clean Economy Network Place: Washington, Washington, DC Zip: 20004 Product: Washingt (DC-based advocacy group focused on clean energy and...

  13. Rural Innovations Network | Open Energy Information

    Open Energy Info (EERE)

    Network Jump to: navigation, search Name: Rural Innovations Network Place: India Sector: Services Product: General Financial & Legal Services ( Charity Non-profit Association...

  14. Residential Energy Services Network (RESNET) Conference | Department...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Residential Energy Services Network (RESNET) Conference Residential Energy Services Network (RESNET) Conference February 29, 2016 9:00AM EST to March 2, 2016 5:0

  15. Indigenous Environmental Network | Open Energy Information

    Open Energy Info (EERE)

    Indigenous Environmental Network Name: Indigenous Environmental Network Address: PO Box 485 Place: Bemidji, MN Year Founded: 1990 Phone Number: (218) 751-4967 Website:...

  16. Silver Spring Networks Inc | Open Energy Information

    Open Energy Info (EERE)

    Spring Networks Inc Jump to: navigation, search Name: Silver Spring Networks Inc Place: Redwood City, California Zip: 94063 Product: California-based, developer of utility...

  17. Mesoscale Simulations of Coarsening in GB Networks

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Mukul Kumar is the Principal Investigator for Mesoscale Simulations of Coarsening in GB Networks LLNL BES Programs Highlight Mesoscale Simulations of Coarsening in GB Networks The...

  18. Grencubator. Ukrainian energy innovation network | Open Energy...

    Open Energy Info (EERE)

    Grencubator. Ukrainian energy innovation network Jump to: navigation, search Name: Greencubator. Ukrainian energy innovation network Place: Kyiv, Ukraine Number of Employees: 1-10...

  19. EA-1964: National Ecological Observation Network (NEON)

    Broader source: Energy.gov [DOE]

    The National Science Foundation (NSF) prepared an EA that evaluated potential environmental impacts of the proposed National Ecological Observation Network (NEON), a continental-scale network of...

  20. Towards A Network-of-Networks Framework for Cyber Security

    SciTech Connect (OSTI)

    Halappanavar, Mahantesh; Choudhury, Sutanay; Hogan, Emilie A.; Hui, Peter SY; Johnson, John R.; Ray, Indrajit; Holder, Lawrence B.

    2013-06-07

    Networks-of-networks (NoN) is a graph-theoretic model of interdependent networks that have distinct dynamics at each network (layer). By adding special edges to represent relationships between nodes in different layers, NoN provides a unified mechanism to study interdependent systems intertwined in a complex relationship. While NoN based models have been proposed for cyber-physical systems, in this paper we build towards a three-layer NoN model for an enterprise cyber system. Each layer captures a different facet of a cyber system. We then discuss the potential benefits of graph-theoretic analysis enabled from such a model. Our goal is to provide a novel and powerful tool for modeling and analyzing problems in cyber security.

  1. Biomass Rapid Analysis Network (BRAN)

    SciTech Connect (OSTI)

    Not Available

    2003-10-01

    Helping the emerging biotechnology industry develop new tools and methods for real-time analysis of biomass feedstocks, process intermediates and The Biomass Rapid Analysis Network is designed to fast track the development of modern tools and methods for biomass analysis to accelerate the development of the emerging industry. The network will be led by industry and organized and coordinated through the National Renewable Energy Lab. The network will provide training and other activities of interest to BRAN members. BRAN members will share the cost and work of rapid analysis method development, validate the new methods, and work together to develop the training for the future biomass conversion workforce.

  2. EIA - Natural Gas Pipeline Network - Network Configuration & System Design

    U.S. Energy Information Administration (EIA) Indexed Site

    Network Configuration & System Design About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates Network Configuration and System Design Overview | Transmission/Storage | Design Criteria | Importance of Storage| Overall Pipeline System Configuration Overview A principal requirement of the natural gas transmission system is that it be capable of meeting the peak demand of its shippers who have contracts for firm service. To meet this

  3. The use of neural nets for matching compressors with diesel engines

    SciTech Connect (OSTI)

    Nelson, S.A. II; Filipi, Z.S.; Assanis, D.N.

    1996-12-31

    A technique which uses trained neural nets to model the compressor in the context of a turbocharged diesel engine simulation is introduced. This technique replaces the usual interpolation of compressor maps with the evaluation of a smooth mathematical function, thus providing engine simulations with greater robustness and flexibility. Following presentation of the methodology, the proposed neural net technique is validated against data from a truck type, 6-cylinder, 14 liter diesel engine. Furthermore, with the introduction of an additional parameter, the proposed neural net can be trained to simulate an entire family of compressors. As a demonstration, five compressors of different sizes are represented with the neural net model, and used for matching calculations with intercooled and non-intercooled engine configurations at different speeds. This novel approach readily allows for evaluation of various options prior to prototype production, and is thus a powerful design tool for selection of the best compressor for a given diesel engine system.

  4. Ethanol-induced impairment of polyamine homeostasis – A potential cause of neural tube defect and intrauterine growth restriction in fetal alcohol syndrome

    SciTech Connect (OSTI)

    Haghighi Poodeh, Saeid; Alhonen, Leena; Salonurmi, Tuire; Savolainen, Markku J.

    2014-03-28

    Highlights: • Polyamine pools in embryonic and extraembryonic tissues are developmentally regulated. • Alcohol administration perturbs polyamine levels in the tissues with various patterns. • Total absence of polyamines in the embryo head at 9.5 dpc is critical for development. • The deficiency is associated with reduction in endothelial cell sprouting in the head. • Retarded migration of neural crest cells may cause development of neural tube defect. - Abstract: Introduction: Polyamines play a fundamental role during embryogenesis by regulating cell growth and proliferation and by interacting with RNA, DNA and protein. The polyamine pools are regulated by metabolism and uptake from exogenous sources. The use of certain inhibitors of polyamine synthesis causes similar defects to those seen in alcohol exposure e.g. retarded embryo growth and endothelial cell sprouting. Methods: CD-1 mice received two intraperitoneal injections of 3 g/kg ethanol at 4 h intervals 8.75 days post coitum (dpc). The fetal head, trunk, yolk sac and placenta were collected at 9.5 and 12.5 dpc and polyamine concentrations were determined. Results: No measurable quantity of polyamines could be detected in the embryo head at 9.5 dpc, 12 h after ethanol exposure. Putrescine was not detectable in the trunk of the embryo at that time, whereas polyamines in yolk sac and placenta were at control level. Polyamine deficiency was associated with slow cell growth, reduction in endothelial cell sprouting, an altered pattern of blood vessel network formation and consequently retarded migration of neural crest cells and growth restriction. Discussion: Our results indicate that the polyamine pools in embryonic and extraembryonic tissues are developmentally regulated. Alcohol administration, at the critical stage, perturbs polyamine levels with various patterns, depending on the tissue and its developmental stage. The total absence of polyamines in the embryo head at 9.5 dpc may explain why this stage is so vulnerable to the development of neural tube defect, and growth restriction, the findings previously observed in fetal alcohol syndrome.

  5. Program for Online Network Inversion

    Energy Science and Technology Software Center (OSTI)

    2009-12-21

    PONI determines the source location of a contamination incident in a water distribution network. PONI uses large scale optimization methods to predict likely source locations by reconciling the differences between observations and numerical predictions of possible contamination incidents.

  6. Networking and Solar Technical Assistance

    Broader source: Energy.gov [DOE]

    The SunShot Initiative provides state and local decision-makers with timely and actionable resources, peer networks, and technical assistance to lower local market barriers and establish best...

  7. ARM - Field Campaign - COSMOS Network

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    or call us at 1-888-ARM-DATA. Send Campaign : COSMOS Network 2010.08.05 - 2013.08.01 Lead Scientist : Marek Zreda For data sets, see below. Abstract Cosmic-ray soil moisture...

  8. Better Buildings Network View | February 2015 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    5 Better Buildings Network View | February 2015 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF icon Better Buildings Network View February 2015 More Documents & Publications Better Buildings Network View | June 2015 Nothing But Networking for Residential Network Members Better Buildings Network View | November 2014

  9. Transactional Network | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Transactional Network Transactional Network Lead Performer: Pacific Northwest National Laboratory - Richland, WA Project Partners: -- Lawrence Berkeley National Laboratory - Berkeley, CA -- Oak Ridge National Laboratory - Oak Ridge, TN -- Transformative Wave - Kent, WA -- Emerson - St. Louis, MO -- NorthWrite - Minneapolis, MN -- EnerNOC - Baltimore, MD DOE Funding: $625,000 Cost Share: N/A Project website: http://transactionalnetwork.pnnl.gov/ Project Term: Jan. 2013 - 2016 Project Objective

  10. Fact Sheet: Better Buildings Residential Network

    Broader source: Energy.gov [DOE]

    Fact Sheet: Better Buildings Residential Network, increasing the number of American Homes that are energy efficient.

  11. Better Buildings Residential Network Orientation Webinar

    Broader source: Energy.gov [DOE]

    Better Buildings Residential Network Orientation Webinar, call slides and discussion summary, September 11, 2014.

  12. Anomaly Detection in Dynamic Networks

    SciTech Connect (OSTI)

    Turcotte, Melissa

    2014-10-14

    Anomaly detection in dynamic communication networks has many important security applications. These networks can be extremely large and so detecting any changes in their structure can be computationally challenging; hence, computationally fast, parallelisable methods for monitoring the network are paramount. For this reason the methods presented here use independent node and edge based models to detect locally anomalous substructures within communication networks. As a first stage, the aim is to detect changes in the data streams arising from node or edge communications. Throughout the thesis simple, conjugate Bayesian models for counting processes are used to model these data streams. A second stage of analysis can then be performed on a much reduced subset of the network comprising nodes and edges which have been identified as potentially anomalous in the first stage. The first method assumes communications in a network arise from an inhomogeneous Poisson process with piecewise constant intensity. Anomaly detection is then treated as a changepoint problem on the intensities. The changepoint model is extended to incorporate seasonal behavior inherent in communication networks. This seasonal behavior is also viewed as a changepoint problem acting on a piecewise constant Poisson process. In a static time frame, inference is made on this extended model via a Gibbs sampling strategy. In a sequential time frame, where the data arrive as a stream, a novel, fast Sequential Monte Carlo (SMC) algorithm is introduced to sample from the sequence of posterior distributions of the change points over time. A second method is considered for monitoring communications in a large scale computer network. The usage patterns in these types of networks are very bursty in nature and don’t fit a Poisson process model. For tractable inference, discrete time models are considered, where the data are aggregated into discrete time periods and probability models are fitted to the communication counts. In a sequential analysis, anomalous behavior is then identified from outlying behavior with respect to the fitted predictive probability models. Seasonality is again incorporated into the model and is treated as a changepoint model on the transition probabilities of a discrete time Markov process. Second stage analytics are then developed which combine anomalous edges to identify anomalous substructures in the network.

  13. The Science DMZ: A Network Design Pattern

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Science DMZ: A Network Design Pattern for Data-Intensive Science Eli Dart Energy Sciences Network Lawrence Berkeley National Laboratory Berkeley, CA 94720 eddart@lbl.gov Lauren Rotman Energy Sciences Network Lawrence Berkeley National Laboratory Berkeley, CA 94720 lbrotman@lbl.gov Brian Tierney Energy Sciences Network Lawrence Berkeley National Laboratory Berkeley, CA 94720 bltierney@lbl.gov Mary Hester Energy Sciences Network Lawrence Berkeley National Laboratory Berkeley, CA 94720

  14. Virtual Private Network (VPN) | Argonne National Laboratory

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Virtual Private Network (VPN) Use a VPN connection for secure access to Argonne's internal networks. To protect Argonne's computing networks, resources, and data, many applications and computing resources on Laboratory networks are not available from offsite without the use of a Virtual Private Network (VPN) connection.The use of a VPN connection allows services to pass through an encrypted "tunnel" to and from the Laboratory, thus giving authenticated users offsite access to internal

  15. Flexible network wireless transceiver and flexible network telemetry transceiver

    DOE Patents [OSTI]

    Brown, Kenneth D.

    2008-08-05

    A transceiver for facilitating two-way wireless communication between a baseband application and other nodes in a wireless network, wherein the transceiver provides baseband communication networking and necessary configuration and control functions along with transmitter, receiver, and antenna functions to enable the wireless communication. More specifically, the transceiver provides a long-range wireless duplex communication node or channel between the baseband application, which is associated with a mobile or fixed space, air, water, or ground vehicle or other platform, and other nodes in the wireless network or grid. The transceiver broadly comprises a communication processor; a flexible telemetry transceiver including a receiver and a transmitter; a power conversion and regulation mechanism; a diplexer; and a phased array antenna system, wherein these various components and certain subcomponents thereof may be separately enclosed and distributable relative to the other components and subcomponents.

  16. Better Buildings Network View | April 2014 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    4 Better Buildings Network View | April 2014 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF icon Better Buildings Network View April 2014 More Documents & Publications Better Buildings Network View | December 2014 Better Buildings Residential Network Orientation Webinar Better Buildings Network View | May

  17. Better Buildings Network View | April 2015 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    5 Better Buildings Network View | April 2015 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF icon Better Buildings Network View April 2015 More Documents & Publications Better Buildings Network View | May 2015 Better Buildings Network View | March 2015 Better Buildings Network View | July-August

  18. Better Buildings Network View | April 2016 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    6 Better Buildings Network View | April 2016 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF icon Better Buildings Network View April 2016 More Documents & Publications Better Buildings Network View | March 2016 Better Buildings Network View | January 2016 Better Buildings Network View | February 2016

  19. Better Buildings Network View | February 2014 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    4 Better Buildings Network View | February 2014 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF icon Better Buildings Network View February 2014 More Documents & Publications Better Buildings Network View | January 2014 Better Buildings Network View | May 2015 Better Buildings Network View | June 2015

  20. Better Buildings Network View | February 2016 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    6 Better Buildings Network View | February 2016 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF icon Better Buildings Network View February 2016 More Documents & Publications Better Buildings Network View | March 2016 Better Buildings Network View | June 2014 Better Buildings Network View | April 2016

  1. Better Buildings Network View | January 2014 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    4 Better Buildings Network View | January 2014 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF icon Better Buildings Network View January 2014 More Documents & Publications Better Buildings Network View | February 2015 Better Buildings Network View | May 2015 Better Buildings Network View | September 2014

  2. Better Buildings Network View | January 2016 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    6 Better Buildings Network View | January 2016 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF icon Better Buildings Network View January 2016 More Documents & Publications Better Buildings Network View | October 2015 Better Buildings Network View | April 2016 Better Buildings Network View | December

  3. Better Buildings Network View | June 2014 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    4 Better Buildings Network View | June 2014 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF icon Better Buildings Network View June 2014 More Documents & Publications Better Buildings Network View | June 2015 Better Buildings Network View | July-August 2014 Better Buildings Network View | April 2014

  4. Better Buildings Network View | March 2015 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    5 Better Buildings Network View | March 2015 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF icon Better Buildings Network View March 2015 More Documents & Publications Better Buildings Network View | January 2015 Better Buildings Network View | December 2014 Better Buildings Network View | April 2015

  5. Better Buildings Network View | March 2016 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    6 Better Buildings Network View | March 2016 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF icon Better Buildings Network View March 2016 More Documents & Publications Better Buildings Network View | April 2016 Better Buildings Network View | February 2016 Better Buildings Network View | January 2016

  6. Better Buildings Network View | November 2014 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    4 Better Buildings Network View | November 2014 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF icon Better Buildings Network View November 2014 More Documents & Publications Better Buildings Network View | July-August 2014 Better Buildings Residential Network Orientation Webinar Better Buildings Network View | December 2014

  7. Better Buildings Network View | October 2015 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    5 Better Buildings Network View | October 2015 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF icon Better Buildings Network View October 2015 More Documents & Publications Better Buildings Network View | January 2016 Better Buildings Network View | April 2016 Better Buildings Network View | November

  8. Virtualized Network Control. Final Report

    SciTech Connect (OSTI)

    Ghani, Nasir

    2013-02-01

    This document is the final report for the Virtualized Network Control (VNC) project, which was funded by the United States Department of Energy (DOE) Office of Science. This project was also informally referred to as Advanced Resource Computation for Hybrid Service and TOpology NEtworks (ARCHSTONE). This report provides a summary of the project's activities, tasks, deliverable, and accomplishments. It also provides a summary of the documents, software, and presentations generated as part of this projects activities. Namely, the Appendix contains an archive of the deliverables, documents, and presentations generated a part of this project.

  9. Network user`s guide

    SciTech Connect (OSTI)

    McGrady, P.W.

    1994-12-01

    NETWORK is a FORTRAN code used to model process flow systems in the gaseous diffusion plants at Portsmouth, Ohio and Paducah, Kentucky, operated by the United States Enrichment Corporation. It can handle a wide range of components and several different types of controllers. NETWORK can be run in either a steady-state mode or a transient mode. In the transient mode many different types of perturbations may be modeled. It is currently being used to model taking a cell off-stream in a gaseous diffusion plant. A brief description of the code is given, and process equipment models and input data are discussed.

  10. Intelligent Control via Wireless Sensor Networks for Advanced Coal Combustion Systems

    SciTech Connect (OSTI)

    Aman Behal; Sunil Kumar; Goodarz Ahmadi

    2007-08-05

    Numerical Modeling of Solid Gas Flow, System Identification for purposes of modeling and control, and Wireless Sensor and Actor Network design were pursued as part of this project. Time series input-output data was obtained from NETL's Morgantown CFB facility courtesy of Dr. Lawrence Shadle. It was run through a nonlinear kernel estimator and nonparametric models were obtained for the system. Linear and first-order nonlinear kernels were then utilized to obtain a state-space description of the system. Neural networks were trained that performed better at capturing the plant dynamics. It is possible to use these networks to find a plant model and the inversion of this model can be used to control the system. These models allow one to compare with physics based models whose parameters can then be determined by comparing them against the available data based model. On a parallel track, Dr. Kumar designed an energy-efficient and reliable transport protocol for wireless sensor and actor networks, where the sensors could be different types of wireless sensors used in CFB based coal combustion systems and actors are more powerful wireless nodes to set up a communication network while avoiding the data congestion. Dr. Ahmadi's group studied gas solid flow in a duct. It was seen that particle concentration clearly shows a preferential distribution. The particles strongly interact with the turbulence eddies and are concentrated in narrow bands that are evolving with time. It is believed that observed preferential concentration is due to the fact that these particles are flung out of eddies by centrifugal force.

  11. Application of Radial Basis Functional Link Networks to Exploration for Proterozoic Mineral Deposits in Central Iran

    SciTech Connect (OSTI)

    Behnia, Pouran [Geological Survey of Iran, Geomatics Department (Iran, Islamic Republic of)], E-mail: pouranb@yahoo.com

    2007-06-15

    The metallogeny of Central Iran is characterized mainly by the presence of several iron, apatite, and uranium deposits of Proterozoic age. Radial Basis Function Link Networks (RBFLN) were used as a data-driven method for GIS-based predictive mapping of Proterozoic mineralization in this area. To generate the input data for RBFLN, the evidential maps comprising stratigraphic, structural, geophysical, and geochemical data were used. Fifty-eight deposits and 58 'nondeposits' were used to train the network. The operations for the application of neural networks employed in this study involve both multiclass and binary representation of evidential maps. Running RBFLN on different input data showed that an increase in the number of evidential maps and classes leads to a larger classification sum of squared error (SSE). As a whole, an increase in the number of iterations resulted in the improvement of training SSE. The results of applying RBFLN showed that a successful classification depends on the existence of spatially well distributed deposits and nondeposits throughout the study area.

  12. BER Science Network Requirements (Technical Report) | SciTech...

    Office of Scientific and Technical Information (OSTI)

    Technical Report: BER Science Network Requirements Citation Details In-Document Search Title: BER Science Network Requirements The Energy Sciences Network (ESnet) is the primary ...

  13. Prediction and Control of Network Cascade: Example of Power Grid...

    Office of Scientific and Technical Information (OSTI)

    Conference: Prediction and Control of Network Cascade: Example of Power Grid or Networking ... Citation Details In-Document Search Title: Prediction and Control of Network Cascade: ...

  14. Austin Solar Energy Entrepreneurs Network | Open Energy Information

    Open Energy Info (EERE)

    Entrepreneurs Network Jump to: navigation, search Logo: Austin Solar Energy Entrepreneurs Network Name: Austin Solar Energy Entrepreneurs Network Place: Austin, Texas Zip: 78701...

  15. UNEP-Southeast Asia Climate Change Network | Open Energy Information

    Open Energy Info (EERE)

    Southeast Asia Climate Change Network Jump to: navigation, search Logo: UNEP-Southeast Asia Climate Change Network Name UNEP-Southeast Asia Climate Change Network AgencyCompany...

  16. About the Better Buildings Residential Network | Department of...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    About the Better Buildings Residential Network About the Better Buildings Residential Network The Better Buildings Residential Network connects energy efficiency programs and ...

  17. Better Buildings Residential Network: Lessons Learned: Peer Exchange...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Network: Lessons Learned: Peer Exchange Calls Better Buildings Residential Network: Lessons Learned: Peer Exchange Calls Better Buildings Residential Network: Lessons Learned: Peer...

  18. Better Buildings Network View | September 2015 | Department of...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Better Buildings Network View | September 2015 Better Buildings Network View | September 2015 The Better Buildings Network View monthly newsletter from the U.S. Department of...

  19. Better Buildings Network View | November 2015 | Department of...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    5 Better Buildings Network View | November 2015 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF...

  20. United Nations Energy Knowledge Network (UN-Energy) | Open Energy...

    Open Energy Info (EERE)

    Energy Knowledge Network (UN-Energy) Jump to: navigation, search Logo: United Nations Energy Knowledge Network (UN-Energy) Name: United Nations Energy Knowledge Network (UN-Energy)...

  1. Better Buildings Network View | April 2014 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    4 Better Buildings Network View | April 2014 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF ...

  2. Better Buildings Network View | January 2016 | Department of...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    6 Better Buildings Network View | January 2016 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF ...

  3. Better Buildings Network View | February 2015 | Department of...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    5 Better Buildings Network View | February 2015 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF ...

  4. Better Buildings Network View | December 2015 | Department of...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    5 Better Buildings Network View | December 2015 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF ...

  5. Better Buildings Network View | June 2014 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    4 Better Buildings Network View | June 2014 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF icon ...

  6. Better Buildings Network View | January 2015 | Department of...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    5 Better Buildings Network View | January 2015 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF ...

  7. Better Buildings Network View | May 2015 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    5 Better Buildings Network View | May 2015 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF icon ...

  8. Better Buildings Network View | February 2014 | Department of...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    4 Better Buildings Network View | February 2014 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF ...

  9. Better Buildings Network View | November 2014 | Department of...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    4 Better Buildings Network View | November 2014 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF ...

  10. Better Buildings Network View | July-August 2014 | Department...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    4 Better Buildings Network View | July-August 2014 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. ...

  11. Better Buildings Network View | February 2016 | Department of...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    6 Better Buildings Network View | February 2016 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF ...

  12. Better Buildings Network View | May 2014 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    4 Better Buildings Network View | May 2014 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF icon ...

  13. Better Buildings Network View | March 2014 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    4 Better Buildings Network View | March 2014 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF ...

  14. Better Buildings Network View | April 2015 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    5 Better Buildings Network View | April 2015 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF ...

  15. Better Buildings Network View | December 2014 | Department of...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    4 Better Buildings Network View | December 2014 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF ...

  16. Better Buildings Network View | March 2015 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    5 Better Buildings Network View | March 2015 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF ...

  17. Better Buildings Network View | January 2014 | Department of...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    4 Better Buildings Network View | January 2014 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF ...

  18. Better Buildings Network View | October 2015 | Department of...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    5 Better Buildings Network View | October 2015 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF ...

  19. Better Buildings Network View | March 2016 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    6 Better Buildings Network View | March 2016 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF ...

  20. Better Buildings Network View | June 2015 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    5 Better Buildings Network View | June 2015 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF icon ...

  1. Better Buildings Network View | July-August 2015 | Department...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    5 Better Buildings Network View | July-August 2015 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. ...

  2. Renewable Energy Business Network - Boston | Open Energy Information

    Open Energy Info (EERE)

    Network - Boston Jump to: navigation, search Name: Clean Economy Network FoundationRenewable Energy Business Network - Boston Address: 55 Union Street Place: Boston, MA Zip: 02108...

  3. Organization of growing random networks

    SciTech Connect (OSTI)

    Krapivsky, P. L.; Redner, S.

    2001-06-01

    The organizational development of growing random networks is investigated. These growing networks are built by adding nodes successively, and linking each to an earlier node of degree k with an attachment probability A{sub k}. When A{sub k} grows more slowly than linearly with k, the number of nodes with k links, N{sub k}(t), decays faster than a power law in k, while for A{sub k} growing faster than linearly in k, a single node emerges which connects to nearly all other nodes. When A{sub k} is asymptotically linear, N{sub k}(t){similar_to}tk{sup {minus}{nu}}, with {nu} dependent on details of the attachment probability, but in the range 2{lt}{nu}{lt}{infinity}. The combined age and degree distribution of nodes shows that old nodes typically have a large degree. There is also a significant correlation in the degrees of neighboring nodes, so that nodes of similar degree are more likely to be connected. The size distributions of the in and out components of the network with respect to a given node{emdash}namely, its {open_quotes}descendants{close_quotes} and {open_quotes}ancestors{close_quotes}{emdash}are also determined. The in component exhibits a robust s{sup {minus}2} power-law tail, where s is the component size. The out component has a typical size of order lnt, and it provides basic insights into the genealogy of the network.

  4. Host Event Based Network Monitoring

    SciTech Connect (OSTI)

    Jonathan Chugg

    2013-01-01

    The purpose of INLs research on this project is to demonstrate the feasibility of a host event based network monitoring tool and the effects on host performance. Current host based network monitoring tools work on polling which can miss activity if it occurs between polls. Instead of polling, a tool could be developed that makes use of event APIs in the operating system to receive asynchronous notifications of network activity. Analysis and logging of these events will allow the tool to construct the complete real-time and historical network configuration of the host while the tool is running. This research focused on three major operating systems commonly used by SCADA systems: Linux, WindowsXP, and Windows7. Windows 7 offers two paths that have minimal impact on the system and should be seriously considered. First is the new Windows Event Logging API, and, second, Windows 7 offers the ALE API within WFP. Any future work should focus on these methods.

  5. Global Renewable Energy Network | Open Energy Information

    Open Energy Info (EERE)

    Renewable Energy Network (GReEN) Name: Global Renewable Energy Network (GReEN) Address: P.O. Box 1999 Place: Massapequa, NY Zip: 11758 Region: Northeast - NY NJ CT PA Area Number...

  6. Energy Materials Network | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy Materials Network LightMat LightMat Established as part of the Energy Materials Network, the mission of the Lightweight Materials National Lab Consortium (LightMat) is to ...

  7. Computer network control plane tampering monitor

    DOE Patents [OSTI]

    Michalski, John T.; Tarman, Thomas D.; Black, Stephen P.; Torgerson, Mark D.

    2010-06-08

    A computer network control plane tampering monitor that detects unauthorized alteration of a label-switched path setup for an information packet intended for transmission through a computer network.

  8. Better Buildings Residential Network (BBRN) Orientation Call...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    ... Network Vermont Energy Investment Corporation Virginia Energy Sense Walker-Miller Energy Services WECC West Michigan Environmental Action Council ...

  9. Better Buildings Residential Network Orientation Webinar Call...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    ... Network Vermont Energy Investment Corporation Virginia Energy Sense Walker-Miller Energy Services West Michigan Environmental Action Council William J. ...

  10. Social Network and Communications Institutional Change Principle |

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Department of Energy Network and Communications Institutional Change Principle Social Network and Communications Institutional Change Principle Federal agencies can use social networks and communications to spark and reinforce behavior change for meeting sustainability goals. This principle is based on research findings showing that people and institutions often are strongly influenced by the behaviors and expectations of others. Methods The social network and communications behavior change

  11. Fermilab | Science at Fermilab | Computing | Networking

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Detectors and Computing Detectors and Computing Computing Networking Physicists are constantly exchanging information, within Fermilab and between Fermilab and collaborating institutions. They do this from the design phase of an experiment to long after they have finished collecting data. To move huge amounts of data from one place to another, Fermilab needs high-performance networking. For years, Fermilab has been the largest user of Energy Services Network, or ESnet, a network the Department

  12. Networks in Buildings: Which Path Forward?

    SciTech Connect (OSTI)

    Nordman, Bruce

    2008-08-17

    To date, digital networks have principally been installed for connecting information technology devices, with more modest use in consumer electronics, security, and large building control systems. The next 20 years will see much greater deployment of networks in buildings of all types, and across all end uses. Most of these are likely to be introduced primarily for reasons other than energy efficiency, and add energy use for network interfaces and network products. Widespread networking could easily lead to increased energy use, and experience with IT and CE networks suggests this may be likely. Active engagement by energy efficiency professionals in the architecture and design of future networks could lead to their being a large and highly cost-effective tool for efficiency. However, network standards are complex and take many years to develop and negotiate so that lack of action on this in the near term may foreclose important opportunities for years or decades to come. Digital networks need to be common globally, providing another challenge to building systems and elements that are more commonly designed only for national or regional markets. Key future networks are lighting, climate control, and security/presence. This paper reviews some examples of past network designs and use and the lessons they hold for future building networks. It also highlights key needed areas for research, policy, and standards development.

  13. Trace Replay and Network Simulation Tool

    Energy Science and Technology Software Center (OSTI)

    2015-03-23

    TraceR is a trace reply tool built upon the ROSS-based CODES simulation framework. TraceR can be used for predicting network performances and understanding network behavior by simulating messaging in High Performance Computing applications on interconnection networks.

  14. Network Security Mechanisms Utilizing Dynamic Network Address Translation LDRD Project

    SciTech Connect (OSTI)

    PRICE, CARRIE M.; STANTON, ERIC; LEE, ERIK J.; MICHALSKI, JOHN T.; CHUA, KUAN SEAH; WONG, YIP HENG; TAN, CHUNG PHENG

    2002-11-01

    A new protocol technology is just starting to emerge from the laboratory environment. Its stated purpose is to provide an additional means in which networks, and the services that reside on them, can be protected from adversarial compromise. This report has a two-fold objective. First is to provide the reader with an overview of this emerging Dynamic Defenses technology using Dynamic Network Address Translation (Dynat). This ''structure overview'' is concentrated in the body of the report, and describes the important attributes of the technology. The second objective is to provide a framework that can be used to help in the classification and assessment of the different types of dynamic defense technologies along with some related capabilities and limitations. This information is primarily contained in the appendices.

  15. WIST Talk: The Art of Networking video | Argonne National Laboratory

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    WIST Talk: The Art of Networking video Share

  16. The Network Completion Problem: Inferring Missing Nodes and Edges in Networks

    SciTech Connect (OSTI)

    Kim, M; Leskovec, J

    2011-11-14

    Network structures, such as social networks, web graphs and networks from systems biology, play important roles in many areas of science and our everyday lives. In order to study the networks one needs to first collect reliable large scale network data. While the social and information networks have become ubiquitous, the challenge of collecting complete network data still persists. Many times the collected network data is incomplete with nodes and edges missing. Commonly, only a part of the network can be observed and we would like to infer the unobserved part of the network. We address this issue by studying the Network Completion Problem: Given a network with missing nodes and edges, can we complete the missing part? We cast the problem in the Expectation Maximization (EM) framework where we use the observed part of the network to fit a model of network structure, and then we estimate the missing part of the network using the model, re-estimate the parameters and so on. We combine the EM with the Kronecker graphs model and design a scalable Metropolized Gibbs sampling approach that allows for the estimation of the model parameters as well as the inference about missing nodes and edges of the network. Experiments on synthetic and several real-world networks show that our approach can effectively recover the network even when about half of the nodes in the network are missing. Our algorithm outperforms not only classical link-prediction approaches but also the state of the art Stochastic block modeling approach. Furthermore, our algorithm easily scales to networks with tens of thousands of nodes.

  17. Integrated Analysis of Protein Complexes and Regulatory Networks...

    Office of Scientific and Technical Information (OSTI)

    and Regulatory Networks Involved in Anaerobic Energy Metabolism of Shewanella ... and Regulatory Networks Involved in Anaerobic Energy Metabolism of Shewanella ...

  18. Better Buildings Residential Network Reporting and Benefits Template

    Broader source: Energy.gov [DOE]

    Better Buildings Residential Network Reporting and Benefits Template, from the U.S. Department of Energy Better Buildings Residential Network.

  19. Better Buildings Residential Network Peer Exchange Call Series...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Clean Energy Works (Network Member) Anna Markowski, Elevate Energy (Network Member) ... information. 24 Lessons Learned: Anna Markowski, Community Projects Manager ...

  20. Better Buildings Residential Network Reporting and Benefits FAQ

    Broader source: Energy.gov [DOE]

    Better Buildings Residential Network Reporting and Benefits FAQ, from the U.S. Department of Energy Better Buildings Residential Network.

  1. On Building Inexpensive Network Capabilities

    SciTech Connect (OSTI)

    Shue, Craig A; Kalafut, Prof. Andrew; Allman, Mark; Taylor, Curtis R

    2011-01-01

    There are many deployed approaches for blocking unwanted traffic, either once it reaches the recipient's network, or closer to its point of origin. One of these schemes is based on the notion of traffic carrying capabilities that grant access to a network and/or end host. However, leveraging capabilities results in added complexity and additional steps in the communication process: Before communication starts a remote host must be vetted and given a capability to use in the subsequent communication. In this paper, we propose a lightweight mechanism that turns the answers provided by DNS name resolution---which Internet communication broadly depends on anyway---into capabilities. While not achieving an ideal capability system, we show the mechanism can be built from commodity technology and is therefore a pragmatic way to gain some of the key benefits of capabilities without requiring new infrastructure.

  2. Better Buildings Network View | December 2014 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    4 Better Buildings Network View | December 2014 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF icon Better Buildings Network View December 2014 More Documents & Publications Better Buildings Network View | February 2014 Better Buildings Network View | November 2014 Lessons Learned: Peer Exchange Calls -- No. 3

  3. Better Buildings Network View | December 2015 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    5 Better Buildings Network View | December 2015 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF icon Better Buildings Network View December 2015 More Documents & Publications BBRN Factsheet: Case Study: Community Engagement Better Buildings Network View | July-August 2015 Better Buildings Residential Network Orientation Webinar

  4. Better Buildings Network View | May 2015 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    5 Better Buildings Network View | May 2015 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF icon Better Buildings Network View May 2015 More Documents & Publications Better Buildings Network View | June 2015 Home Performance with ENERGY STAR - 2014 BTO Peer Review Better Buildings Network View | April 2015

  5. Better Buildings Network View | October 2014 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    October 2014 Better Buildings Network View | October 2014 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF icon Better Buildings Network View October 2014 More Documents & Publications Better Buildings Network View | September 2014 Better Buildings Network View | December 2014

  6. Membership Criteria: Better Buildings Residential Network | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy Membership Criteria: Better Buildings Residential Network Membership Criteria: Better Buildings Residential Network Membership Criteria: Better Buildings Residential Network of the U.S. Department of Energy. PDF icon Membership Criteria More Documents & Publications Better Buildings Residential Network Orientation Better Buildings Residential Network Reporting and Benefits FAQ How Can the Network Meet Your Needs?

  7. Quantifying evolvability in small biological networks

    SciTech Connect (OSTI)

    Nemenman, Ilya; Mugler, Andrew; Ziv, Etay; Wiggins, Chris H

    2008-01-01

    The authors introduce a quantitative measure of the capacity of a small biological network to evolve. The measure is applied to a stochastic description of the experimental setup of Guet et al. (Science 2002, 296, pp. 1466), treating chemical inducers as functional inputs to biochemical networks and the expression of a reporter gene as the functional output. The authors take an information-theoretic approach, allowing the system to set parameters that optimise signal processing ability, thus enumerating each network's highest-fidelity functions. All networks studied are highly evolvable by the measure, meaning that change in function has little dependence on change in parameters. Moreover, each network's functions are connected by paths in the parameter space along which information is not significantly lowered, meaning a network may continuously change its functionality without completely losing it along the way. This property further underscores the evolvability of the networks.

  8. Network

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    by more powerful supercomputers, global collaborations that can involve thousands of researchers, and specialized facilities like the Large Hadron Collider and digital sky surveys. ...

  9. Network

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    datasets reach their destinations in record time. Moving Datasets Equal to 20 Billion Books Experiments and simulations can produce enormous data sets that need to be...

  10. Diagnosing Anomalous Network Performance with Confidence

    SciTech Connect (OSTI)

    Settlemyer, Bradley W; Hodson, Stephen W; Kuehn, Jeffery A; Poole, Stephen W

    2011-04-01

    Variability in network performance is a major obstacle in effectively analyzing the throughput of modern high performance computer systems. High performance interconnec- tion networks offer excellent best-case network latencies; how- ever, highly parallel applications running on parallel machines typically require consistently high levels of performance to adequately leverage the massive amounts of available computing power. Performance analysts have usually quantified network performance using traditional summary statistics that assume the observational data is sampled from a normal distribution. In our examinations of network performance, we have found this method of analysis often provides too little data to under- stand anomalous network performance. Our tool, Confidence, instead uses an empirically derived probability distribution to characterize network performance. In this paper we describe several instances where the Confidence toolkit allowed us to understand and diagnose network performance anomalies that we could not adequately explore with the simple summary statis- tics provided by traditional measurement tools. In particular, we examine a multi-modal performance scenario encountered with an Infiniband interconnection network and we explore the performance repeatability on the custom Cray SeaStar2 interconnection network after a set of software and driver updates.

  11. Cross-linked structure of network evolution

    SciTech Connect (OSTI)

    Bassett, Danielle S.; Wymbs, Nicholas F.; Grafton, Scott T.; Porter, Mason A.; CABDyN Complexity Centre, University of Oxford, Oxford, OX1 1HP ; Mucha, Peter J.; Department of Applied Physical Sciences, University of North Carolina, Chapel Hill, North Carolina 27599

    2014-03-15

    We study the temporal co-variation of network co-evolution via the cross-link structure of networks, for which we take advantage of the formalism of hypergraphs to map cross-link structures back to network nodes. We investigate two sets of temporal network data in detail. In a network of coupled nonlinear oscillators, hyperedges that consist of network edges with temporally co-varying weights uncover the driving co-evolution patterns of edge weight dynamics both within and between oscillator communities. In the human brain, networks that represent temporal changes in brain activity during learning exhibit early co-evolution that then settles down with practice. Subsequent decreases in hyperedge size are consistent with emergence of an autonomous subgraph whose dynamics no longer depends on other parts of the network. Our results on real and synthetic networks give a poignant demonstration of the ability of cross-link structure to uncover unexpected co-evolution attributes in both real and synthetic dynamical systems. This, in turn, illustrates the utility of analyzing cross-links for investigating the structure of temporal networks.

  12. Zone routing in a torus network

    DOE Patents [OSTI]

    Chen, Dong; Heidelberger, Philip; Kumar, Sameer

    2013-01-29

    A system for routing data in a network comprising a network logic device at a sending node for determining a path between the sending node and a receiving node, wherein the network logic device sets one or more selection bits and one or more hint bits within the data packet, a control register for storing one or more masks, wherein the network logic device uses the one or more selection bits to select a mask from the control register and the network logic device applies the selected mask to the hint bits to restrict routing of the data packet to one or more routing directions for the data packet within the network and selects one of the restricted routing directions from the one or more routing directions and sends the data packet along a link in the selected routing direction toward the receiving node.

  13. Networks, smart grids: new model for synchronization

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    May » Networks, smart grids: new model for synchronization Networks, smart grids: new model for synchronization Researchers developed a surprisingly simple mathematical model that accurately predicts synchronization as a function of the parameters and the topology of the underlying network. May 21, 2013 High voltage transmission lines carry electrical power. High voltage transmission lines carry electrical power. The researchers envision that their method could be applied to assess

  14. Green Power Network Newsletter | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Network Newsletter Green Power Network Newsletter As a subscriber to the Green Power Network email newsletter, you will receive a monthly e-mail summarizing news and request for proposal postings from the past month. You will also be notified periodically of upcoming events and opportunities, publications, and other analyses related to green power markets. Partner Agency: U.S. Department of Energy Resource Type: Newsletter Stakeholder Group(s): Residential, Commercial, Rural, Financial, State

  15. Better Buildings Residential Network Social Media Toolkit

    Energy Savers [EERE]

    Social Media Toolkit BETTER BUILDINGS RESIDENTIAL NETWORK Learn more at betterbuildings.energy.gov/bbrn 1 T his Better Buildings Residential Network toolkit can be used to help residential energy efficiency programs learn to engage potential customers through social media. Social media can build brand awareness concerning home energy upgrades and the entities working on them, which can lead to more energy upgrade projects taking place in the long run. Residential Network members provided input

  16. Sensors, Controls, & Transactional Network Reports | Department...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Network Reports Buildings-to-Grid Technical Opportunities: Introduction and Vision (Mar 2014) Buildings-to-Grid Technical Opportunities: From the Buildings Perspective (Mar...

  17. Solar Action Network | Open Energy Information

    Open Energy Info (EERE)

    Jump to: navigation, search Name: Solar Action Network Address: PO Box 15546 Place: San Luis Obispo, California Zip: 93401 Phone Number: 5058476527 Website:...

  18. Organizations and Networks | Open Energy Information

    Open Energy Info (EERE)

    Network (CLEAN) CLEAN aims to improve communication and coordination by bringing together national and international organizations that are assisting developing countries with...

  19. SkyPilot Networks | Open Energy Information

    Open Energy Info (EERE)

    California Product: US-based provider of broadband wireless solutions to utilities, public service agencies and municipalities. References: SkyPilot Networks1 This article...

  20. Better Buildings Network View, April 2015

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    ... systems Better Buildings Network View financial sector. ... Sunnovations, Inc., is a McLean, Virginia-based firm offering ... as featured in the new Resource Corner Designing ...

  1. Better Buildings Residential Network Case Study: Partnerships

    Broader source: Energy.gov [DOE]

    Better Buildings Residential Network Case Study: Partnerships, from the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy.

  2. Social Network and Communications Institutional Change Principle...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Methods for social networking and communicating include many internal or external media channels, such as websites, blogs, newsletters, and emails. Communities of practice, which ...

  3. National Idling Reduction Network News - September 2012

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    ...egies-for-a-Sustainable-Multi- Modal-Transportation-Network.aspx San Joaquin Valley Air Pollution Control District Technology Advancement Program (TAP) Demonstration Projects 4 ...

  4. Engine Combustion Network (ECN): Global sensitivity analysis...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    10 Date Published June 2015 Keywords diesel, Engine Combustion Network, global sensitivity ... The uncertainty in the fuel temperature was found to have a profound influence on the ...

  5. Aries Network Performance Counters Monitoring Library

    Energy Science and Technology Software Center (OSTI)

    2014-09-04

    AriesNCL is a library to monitor and record network router tile performance counters on the Aries router of Cray's Cascade/XC30 platform.

  6. Energy Systems Network ESN | Open Energy Information

    Open Energy Info (EERE)

    associations and breakthroughs in cleantech which help promote growth in the local economy. References: Energy Systems Network (ESN)1 This article is a stub. You can help...

  7. Rainforest Action Network RAN | Open Energy Information

    Open Energy Info (EERE)

    pressure corporations into publicly adopting policies that protect rainforests and the human rights of those living in those areas. References: Rainforest Action Network (RAN)1...

  8. Networking and Information Technology Research and Development...

    Broader source: Energy.gov (indexed) [DOE]

    This Supplement to the President's Fiscal Year (FY) 2011 Budget provides a technical summary of the budget request for the Networking and Information Technology Research and ...

  9. Network Requirements Workshop - Documents and Background Materials

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    of the science programs that ESnet serves. The case study is a network-centric narrative describing the science from two different perspectives - instruments and...

  10. Vihaan Networks Limited VNL | Open Energy Information

    Open Energy Info (EERE)

    to: navigation, search Name: Vihaan Networks Limited (VNL) Place: Gurgaon, Haryana, India Zip: 122015 Sector: Solar Product: Developer of solar-powered GSM system for rural...

  11. January 2016 National Idling Reduction Network News

    Broader source: Energy.gov [DOE]

    The National Idling Reduction Network brings together trucking and transit companies; railroads; ports; equipment manufacturers; Federal, state, and local government agencies (including regulators)...

  12. Analysis of TPV Network Losses (a Presentation)

    SciTech Connect (OSTI)

    DM DePoy; MW Dashiell; DD Rahner; LR Danielson; JE Oppenlander; JL Vell; RJ Wehrer

    2004-12-08

    This talk focuses on the theoretical analysis of electrical losses associated with electrically networking large numbers of TPV cells to produce high power TPV power generators.

  13. How Can the Network Meet Your Needs?

    Broader source: Energy.gov [DOE]

    Better Buildings Residential Program Peer Exchange Call: How Can the Network Meet Your Needs? Call Slides and Meeting Summary, February 27, 2014.

  14. Better Buildings Network View July 2015

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    ... Help your Twitter followers and Facebook friends beat the heat efficiently by sharing DOE's home cooling infographic with your social media network. Quotable "The difference was ...

  15. BetterBuildings Network View | December 2014

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    ... Photo: Jonathan House Clean Energy Works Taps Smart Market to Help Homeowners Beat the "Brrr" and Save Better Buildings Residential Network member Clean Energy Works (CEW) is ...

  16. Better Buildings Network View, April 2016

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Check out Residential Network member the Community Home Energy Retrofit Project's (Claremont, CA) Facebook page for more inspiration and read the Community Engagement Case Study ...

  17. Better Buildings Residential Network | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Residential Network Members Residential Resources Download the Social Media Toolkit. New ... Successful Quality Assurance and Quality Control Programs (101) January 28, 2016 Einstein ...

  18. Better Buildings Network View, May 2015

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    ... now Leveraging Seasonal Opportunities for Marketing Better Buildings Network View making. ... Prize (GUEP) participants are going digital to encourage people across the United ...

  19. Better Buildings Residential Network Data & Evaluations Peer...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    your program to do an evaluation? Has your program ... Other? 8% 5 Better Buildings Residential Network ... http:www.efficiencymaine.comaboutlibraryreports 12 Program ...

  20. Better Buildings Residential Network Membership Form, January...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Network members to create new resources to help them overcome implementation challenges Opportunities for shared, voluntary program benchmarking for comparing efforts...

  1. Broadband Energy Networks Inc | Open Energy Information

    Open Energy Info (EERE)

    Darby, Pennsylvania Zip: 19082 Product: Provides automated equipment and usage monitoring systems for energy management. References: Broadband Energy Networks Inc1 This article...

  2. Better Buildings Network View, March 2014

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    ... Better Buildings Residential Network members can use the series talking points and accompanying infographic to promote home energy assesments to customers. "The Energy Alliance ...

  3. Epidemic Percolation Networks, Epidemic Outcomes, and Interventions

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Kenah, Eben; Miller, Joel C.

    2011-01-01

    Epidemic percolation networks (EPNs) are directed random networks that can be used to analyze stochastic “Susceptible-Infectious-Removed” (SIR) and “Susceptible-Exposed-Infectious-Removed” (SEIR) epidemic models, unifying and generalizing previous uses of networks and branching processes to analyze mass-action and network-based S(E)IR models. This paper explains the fundamental concepts underlying the definition and use of EPNs, using them to build intuition about the final outcomes of epidemics. We then show how EPNs provide a novel and useful perspective on the design of vaccination strategies.

  4. Solar Instructor Training Network | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Instructor Training Network in 2009 to address the critical need for high-quality, local, accessible training in solar energy system design, installation, sales, and inspection. ...

  5. ESnet: a Production IPv6 Network

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    IPv6 Network In anticipation of the scalability problems with IPv4 (the current Internet Protocol), the Internet Engineering Task Force (IETF) has produced a comprehensive...

  6. March 2016 National Idling Reduction Network News

    Broader source: Energy.gov [DOE]

    The National Idling Reduction Network brings together trucking and transit companies; railroads; ports; equipment manufacturers; Federal, state, and local government agencies (including regulators)...

  7. Matching network for RF plasma source

    DOE Patents [OSTI]

    Pickard, Daniel S.; Leung, Ka-Ngo

    2007-11-20

    A compact matching network couples an RF power supply to an RF antenna in a plasma generator. The simple and compact impedance matching network matches the plasma load to the impedance of a coaxial transmission line and the output impedance of an RF amplifier at radio frequencies. The matching network is formed of a resonantly tuned circuit formed of a variable capacitor and an inductor in a series resonance configuration, and a ferrite core transformer coupled to the resonantly tuned circuit. This matching network is compact enough to fit in existing compact focused ion beam systems.

  8. Understanding Sampling Network Coverage Maddalena, Damian; Hoffman...

    Office of Scientific and Technical Information (OSTI)

    and quantitative representativeness maps of individual and combined networks. ORNL Climate Change Science Institute (CCSI), Oak Ridge National Laboratory (ORNL), Oak Rdige,...

  9. Electronic networking and sustainable development

    SciTech Connect (OSTI)

    Daudpota, Q.I.

    1995-12-01

    To increase the capacity of institutions in various countries to implement the ambitious plans of Agenda 21, the United Nations Development Programme (UNDP) set up the Sustainable Development Networking Programme (SDNP) to help the process of sustainable development nationally and globally. Started initially in 15 developing countries, SDNPs are considered as a medium for individuals, organizations and governments to communicate ideas, share information resources, and exchange experiences among each other and globally to learn the appropriate ways to solve our ecological problems. The paper will review the idea of SDNPs globally and will describe in detail its successful implementation in Pakistan. In a country with, hitherto, virtually no electronic mail service, the SDNP has shown how its provision has had a significant impact on obtaining useful information on environmental problems, and in one case has helped save lives. SDNP Pakistan has made an effort to demonstrate the benefits of electronic communications to wide range users in the country. Some of these will be described. It is suggested how electronic networks linking organizations and people in the developing world with experts, organizations and data sources internationally, can greatly assist developmental effort globally.

  10. Direct imaging of neural currents using ultra-low field magnetic resonance techniques

    DOE Patents [OSTI]

    Volegov, Petr L.; Matlashov, Andrei N.; Mosher, John C.; Espy, Michelle A.; Kraus, Jr., Robert H.

    2009-08-11

    Using resonant interactions to directly and tomographically image neural activity in the human brain using magnetic resonance imaging (MRI) techniques at ultra-low field (ULF), the present inventors have established an approach that is sensitive to magnetic field distributions local to the spin population in cortex at the Larmor frequency of the measurement field. Because the Larmor frequency can be readily manipulated (through varying B.sub.m), one can also envision using ULF-DNI to image the frequency distribution of the local fields in cortex. Such information, taken together with simultaneous acquisition of MEG and ULF-NMR signals, enables non-invasive exploration of the correlation between local fields induced by neural activity in cortex and more `distant` measures of brain activity such as MEG and EEG.

  11. Better Buildings Network View | November 2015 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    November 2015 Better Buildings Network View | November 2015 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF icon Better Buildings Network View November 2015 More Documents & Publications Lessons Learned: Peer Exchange Calls -- No. 3 Better Buildings Residential Network Membership Form

  12. Better Buildings Residential Network Membership Form | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Membership Form Better Buildings Residential Network Membership Form Membership form from the U.S. Department of Energy's Better Buildings Residential Network. File BBRN Membership Form More Documents & Publications Better Buildings Residential Network Orientation Fact Sheet: Better Buildings Residential Network Membership Criteria: Better Buildings Residential Network

  13. Better Buildings Residential Network Orientation | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Orientation Better Buildings Residential Network Orientation Better Buildings Residential Network (BBRN) Orientation Call Slides and Summary, March 27, 2014. PDF icon Call Slides and Summary More Documents & Publications Better Buildings Residential Network Orientation Webinar Better Buildings Residential Network Orientation Webinar How Can the Network Meet Your Needs?

  14. Better Buildings Network View | September 2015 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Network View | September 2015 Better Buildings Network View | September 2015 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF icon Better Buildings Network View September 2015 More Documents & Publications TTWG Licensing Guide ITP Aluminum: Technical Working Group on Inert Anode Technologies EIS-0333: Draft Environmental Impact Statement

  15. Inhibition of glycogen synthase kinase-3 enhances the differentiation and reduces the proliferation of adult human olfactory epithelium neural precursors

    SciTech Connect (OSTI)

    Manceur, Aziza P.; Donnelly Centre, University of Toronto, Toronto, Ontario ; Tseng, Michael; Department of Psychiatry, University of Toronto, Toronto, ON; Institute of Medical Science, University of Toronto, Toronto, ON ; Holowacz, Tamara; Witterick, Ian; Department of Otolaryngology, Head and Neck Surgery, University of Toronto, ON ; Weksberg, Rosanna; The Hospital for Sick Children, Research Institute, Program in Genetics and Genomic Biology, Toronto, Ontario Canada ; McCurdy, Richard D.; Warsh, Jerry J.; Department of Psychiatry, University of Toronto, Toronto, ON; Institute of Medical Science, University of Toronto, Toronto, ON ; Audet, Julie; Donnelly Centre, University of Toronto, Toronto, Ontario

    2011-09-10

    The olfactory epithelium (OE) contains neural precursor cells which can be easily harvested from a minimally invasive nasal biopsy, making them a valuable cell source to study human neural cell lineages in health and disease. Glycogen synthase kinase-3 (GSK-3) has been implicated in the etiology and treatment of neuropsychiatric disorders and also in the regulation of murine neural precursor cell fate in vitro and in vivo. In this study, we examined the impact of decreased GSK-3 activity on the fate of adult human OE neural precursors in vitro. GSK-3 inhibition was achieved using ATP-competitive (6-bromoindirubin-3'-oxime and CHIR99021) or substrate-competitive (TAT-eIF2B) inhibitors to eliminate potential confounding effects on cell fate due to off-target kinase inhibition. GSK-3 inhibitors decreased the number of neural precursor cells in OE cell cultures through a reduction in proliferation. Decreased proliferation was not associated with a reduction in cell survival but was accompanied by a reduction in nestin expression and a substantial increase in the expression of the neuronal differentiation markers MAP1B and neurofilament (NF-M) after 10 days in culture. Taken together, these results suggest that GSK-3 inhibition promotes the early stages of neuronal differentiation in cultures of adult human neural precursors and provide insights into the mechanisms by which alterations in GSK-3 signaling affect adult human neurogenesis, a cellular process strongly suspected to play a role in the etiology of neuropsychiatric disorders.

  16. Neural and Synaptic Defects in slytherin a Zebrafish Model for Human Congenital Disorders of Glycosylation

    SciTech Connect (OSTI)

    Y Song; J Willer; P Scherer; J Panzer; A Kugath; E Skordalakes; R Gregg; G Willer; R Balice-Gordon

    2011-12-31

    Congenital disorder of glycosylation type IIc (CDG IIc) is characterized by mental retardation, slowed growth and severe immunodeficiency, attributed to the lack of fucosylated glycoproteins. While impaired Notch signaling has been implicated in some aspects of CDG IIc pathogenesis, the molecular and cellular mechanisms remain poorly understood. We have identified a zebrafish mutant slytherin (srn), which harbors a missense point mutation in GDP-mannose 4,6 dehydratase (GMDS), the rate-limiting enzyme in protein fucosylation, including that of Notch. Here we report that some of the mechanisms underlying the neural phenotypes in srn and in CGD IIc are Notch-dependent, while others are Notch-independent. We show, for the first time in a vertebrate in vivo, that defects in protein fucosylation leads to defects in neuronal differentiation, maintenance, axon branching, and synapse formation. Srn is thus a useful and important vertebrate model for human CDG IIc that has provided new insights into the neural phenotypes that are hallmarks of the human disorder and has also highlighted the role of protein fucosylation in neural development.

  17. Catalog of gene expression in adult neural stem cells and their in vivo microenvironment

    SciTech Connect (OSTI)

    Williams, Cecilia; Wirta, Valtteri; Meletis, Konstantinos; Wikstroem, Lilian; Carlsson, Leif; Frisen, Jonas; Lundeberg, Joakim . E-mail: joakim.lundeberg@biotech.kth.se

    2006-06-10

    Stem cells generally reside in a stem cell microenvironment, where cues for self-renewal and differentiation are present. However, the genetic program underlying stem cell proliferation and multipotency is poorly understood. Transcriptome analysis of stem cells and their in vivo microenvironment is one way of uncovering the unique stemness properties and provides a framework for the elucidation of stem cell function. Here, we characterize the gene expression profile of the in vivo neural stem cell microenvironment in the lateral ventricle wall of adult mouse brain and of in vitro proliferating neural stem cells. We have also analyzed an Lhx2-expressing hematopoietic-stem-cell-like cell line in order to define the transcriptome of a well-characterized and pure cell population with stem cell characteristics. We report the generation, assembly and annotation of 50,792 high-quality 5'-end expressed sequence tag sequences. We further describe a shared expression of 1065 transcripts by all three stem cell libraries and a large overlap with previously published gene expression signatures for neural stem/progenitor cells and other multipotent stem cells. The sequences and cDNA clones obtained within this framework provide a comprehensive resource for the analysis of genes in adult stem cells that can accelerate future stem cell research.

  18. Nanofluidic interfaces in microfluidic networks

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Millet, Larry J.; Doktycz, Mitchel John; Retterer, Scott T.

    2015-09-24

    The integration of nano- and microfluidic technologies enables the construction of tunable interfaces to physical and biological systems across relevant length scales. The ability to perform chemical manipulations of miniscule sample volumes is greatly enhanced through these technologies and extends the ability to manipulate and sample the local fluidic environments at subcellular, cellular and community or tissue scales. Here we describe the development of a flexible surface micromachining process for the creation of nanofluidic channel arrays integrated within SU-8 microfluidic networks. The use of a semi-porous, silicon rich, silicon nitride structural layer allows rapid release of the sacrificial silicon dioxidemore » during the nanochannel fabrication. Nanochannel openings that form the interface to biological samples are customized using focused ion beam milling. The compatibility of these interfaces with on-chip microbial culture is demonstrated.« less

  19. Energy efficient sensor network implementations

    SciTech Connect (OSTI)

    Frigo, Janette R; Raby, Eric Y; Brennan, Sean M; Kulathumani, Vinod; Rosten, Ed; Wolinski, Christophe; Wagner, Charles; Charot, Francois

    2009-01-01

    In this paper, we discuss a low power embedded sensor node architecture we are developing for distributed sensor network systems deployed in a natural environment. In particular, we examine the sensor node for energy efficient processing-at-the-sensor. We analyze the following modes of operation; event detection, sleep(wake-up), data acquisition, data processing modes using low power, high performance embedded technology such as specialized embedded DSP processors and a low power FPGAs at the sensing node. We use compute intensive sensor node applications: an acoustic vehicle classifier (frequency domain analysis) and a video license plate identification application (learning algorithm) as a case study. We report performance and total energy usage for our system implementations and discuss the system architecture design trade offs.

  20. Biological and Environmental Research Network Requirements

    SciTech Connect (OSTI)

    Balaji, V.; Boden, Tom; Cowley, Dave; Dart, Eli; Dattoria, Vince; Desai, Narayan; Egan, Rob; Foster, Ian; Goldstone, Robin; Gregurick, Susan; Houghton, John; Izaurralde, Cesar; Johnston, Bill; Joseph, Renu; Kleese-van Dam, Kerstin; Lipton, Mary; Monga, Inder; Pritchard, Matt; Rotman, Lauren; Strand, Gary; Stuart, Cory; Tatusova, Tatiana; Tierney, Brian; Thomas, Brian; Williams, Dean N.; Zurawski, Jason

    2013-09-01

    The Energy Sciences Network (ESnet) is the primary provider of network connectivity for the U.S. Department of Energy (DOE) Office of Science (SC), the single largest supporter of basic research in the physical sciences in the United States. In support of SC programs, ESnet regularly updates and refreshes its understanding of the networking requirements of the instruments, facilities, scientists, and science programs that it serves. This focus has helped ESnet be a highly successful enabler of scientific discovery for over 25 years. In November 2012, ESnet and the Office of Biological and Environmental Research (BER) of the DOE SC organized a review to characterize the networking requirements of the programs funded by the BER program office. Several key findings resulted from the review. Among them: 1) The scale of data sets available to science collaborations continues to increase exponentially. This has broad impact, both on the network and on the computational and storage systems connected to the network. 2) Many science collaborations require assistance to cope with the systems and network engineering challenges inherent in managing the rapid growth in data scale. 3) Several science domains operate distributed facilities that rely on high-performance networking for success. Key examples illustrated in this report include the Earth System Grid Federation (ESGF) and the Systems Biology Knowledgebase (KBase). This report expands on these points, and addresses others as well. The report contains a findings section as well as the text of the case studies discussed at the review.

  1. Analysis of complex networks using aggressive abstraction.

    SciTech Connect (OSTI)

    Colbaugh, Richard; Glass, Kristin.; Willard, Gerald

    2008-10-01

    This paper presents a new methodology for analyzing complex networks in which the network of interest is first abstracted to a much simpler (but equivalent) representation, the required analysis is performed using the abstraction, and analytic conclusions are then mapped back to the original network and interpreted there. We begin by identifying a broad and important class of complex networks which admit abstractions that are simultaneously dramatically simplifying and property preserving - we call these aggressive abstractions -- and which can therefore be analyzed using the proposed approach. We then introduce and develop two forms of aggressive abstraction: 1.) finite state abstraction, in which dynamical networks with uncountable state spaces are modeled using finite state systems, and 2.) onedimensional abstraction, whereby high dimensional network dynamics are captured in a meaningful way using a single scalar variable. In each case, the property preserving nature of the abstraction process is rigorously established and efficient algorithms are presented for computing the abstraction. The considerable potential of the proposed approach to complex networks analysis is illustrated through case studies involving vulnerability analysis of technological networks and predictive analysis for social processes.

  2. Advanced mobile networking, sensing, and controls.

    SciTech Connect (OSTI)

    Feddema, John Todd; Kilman, Dominique Marie; Byrne, Raymond Harry; Young, Joseph G.; Lewis, Christopher L.; Van Leeuwen, Brian P.; Robinett, Rush D. III; Harrington, John J.

    2005-03-01

    This report describes an integrated approach for designing communication, sensing, and control systems for mobile distributed systems. Graph theoretic methods are used to analyze the input/output reachability and structural controllability and observability of a decentralized system. Embedded in each network node, this analysis will automatically reconfigure an ad hoc communication network for the sensing and control task at hand. The graph analysis can also be used to create the optimal communication flow control based upon the spatial distribution of the network nodes. Edge coloring algorithms tell us that the minimum number of time slots in a planar network is equal to either the maximum number of adjacent nodes (or degree) of the undirected graph plus some small number. Therefore, the more spread out that the nodes are, the fewer number of time slots are needed for communication, and the smaller the latency between nodes. In a coupled system, this results in a more responsive sensor network and control system. Network protocols are developed to propagate this information, and distributed algorithms are developed to automatically adjust the number of time slots available for communication. These protocols and algorithms must be extremely efficient and only updated as network nodes move. In addition, queuing theory is used to analyze the delay characteristics of Carrier Sense Multiple Access (CSMA) networks. This report documents the analysis, simulation, and implementation of these algorithms performed under this Laboratory Directed Research and Development (LDRD) effort.

  3. Peeking Network States with Clustered Patterns

    SciTech Connect (OSTI)

    Kim, Jinoh; Sim, Alex

    2015-10-20

    Network traffic monitoring has long been a core element for effec- tive network management and security. However, it is still a chal- lenging task with a high degree of complexity for comprehensive analysis when considering multiple variables and ever-increasing traffic volumes to monitor. For example, one of the widely con- sidered approaches is to scrutinize probabilistic distributions, but it poses a scalability concern and multivariate analysis is not gen- erally supported due to the exponential increase of the complexity. In this work, we propose a novel method for network traffic moni- toring based on clustering, one of the powerful deep-learning tech- niques. We show that the new approach enables us to recognize clustered results as patterns representing the network states, which can then be utilized to evaluate “similarity” of network states over time. In addition, we define a new quantitative measure for the similarity between two compared network states observed in dif- ferent time windows, as a supportive means for intuitive analysis. Finally, we demonstrate the clustering-based network monitoring with public traffic traces, and show that the proposed approach us- ing the clustering method has a great opportunity for feasible, cost- effective network monitoring.

  4. Network architecture functional description and design

    SciTech Connect (OSTI)

    Stans, L.; Bencoe, M.; Brown, D.; Kelly, S.; Pierson, L.; Schaldach, C.

    1989-05-25

    This report provides a top level functional description and design for the development and implementation of the central network to support the next generation of SNL, Albuquerque supercomputer in a UNIX{reg sign} environment. It describes the network functions and provides an architecture and topology.

  5. Instantiating a Global Network Measurement Framework

    SciTech Connect (OSTI)

    Tierney, Brian L.; Boote, Jeff; Boyd, Eric; Brown, Aaron; Grigoriev, Maxim; Metzger, Joe; Swany, Martin; Zekauskas, Matt; Zurawski, Jason

    2008-12-15

    perfSONAR is a web services-based infrastructure for collecting and publishing network performance monitoring. A primary goal of perfSONAR is making it easier to solve end-to-end performance problems on paths crossing several networks. It contains a set of services delivering performance measurements in a federated environment. These services act as an intermediate layer, between the performance measurement tools and the diagnostic or visualization applications. This layer is aimed at making and exchanging performance measurements across multiple networks and multiple user communities, using well-defined protocols. This paper summarizes the key perfSONAR components, and describes how they are deployed by the US-LHC community to monitor the networks distributing LHC data from CERN. All monitoring data described herein is publicly available, and we hope the availability of this data via a standard schema will inspire others to contribute to the effort by building network data analysis applications that use perfSONAR.

  6. High-speed, intra-system networks

    SciTech Connect (OSTI)

    Quinn, Heather M; Graham, Paul S; Manuzzato, Andrea; Fairbanks, Tom; Dallmann, Nicholas; Desgeorges, Rose

    2010-06-28

    Recently, engineers have been studying on-payload networks for fast communication paths. Using intra-system networks as a means to connect devices together allows for a flexible payload design that does not rely on dedicated communication paths between devices. In this manner, the data flow architecture of the system can be dynamically reconfigured to allow data routes to be optimized for the application or configured to route around devices that are temporarily or permanently unavailable. To use intra-system networks, devices will need network controllers and switches. These devices are likely to be affected by single-event effects, which could affect data communication. In this paper we will present radiation data and performance analysis for using a Broadcom network controller in a neutron environment.

  7. DOE Science Networking Challenge: Roadmap to 2008

    SciTech Connect (OSTI)

    R. Roy Whitney; Larry Price

    2003-06-01

    This report establishes a roadmap for a new approach to the DOE Science Networking and Services needed for science in the U.S. Department of Energy in the 21st century. It has become increasingly clear 2 that the network provided for DOE science in the past will not be adequate to keep that science competitive in the future. This roadmap, if implemented and followed during the next five years, will solve that problem. The past 5 years have seen a broad and general movement toward the assumption of and reliance on networked systems in all of the large new initiatives for DOE science. It is clear that the success of science depends increasingly on the ability of scientists to move large amounts of data, access computing and data resources, and collaborate in real time from multiple remote locations. It is also abundantly clear that business-as-usual in the network and information services that underpin the scientific collaborations will fall woefully short of what is needed. New capabilities such as computational and data grids, high-speed wireless networking, super-high-speed metro-scale networks, and cheap gigabit Ethernet have arrived in turn and have been enthusiastically incorporated into the arsenal of science, each permitting substantial new collaborative abilities and efficiencies. However, sophisticated structures and services using basic network connections can be used effectively only if the network infrastructure itself provides the necessary environment. Increasingly, the network must become a collaborative information exchange, with a core of higher-level services supported by network providers in addition to basic bandwidth and connectivity.

  8. E-print Network : Main View : Deep Federated Search

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    javascript. Home About Contact Us Help E-print Network Search Powered By Deep Web Technologies New Search Preferences E-print Network E-print Network Skip to main content FAQ *...

  9. Benefits of Better Buildings Residential Network Reporting | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy Benefits of Better Buildings Residential Network Reporting Benefits of Better Buildings Residential Network Reporting Better Buildings Residential Network All-Member Peer Exchange Call: Member Reporting and Benefits, Call Slides and Discussion Summary, May 22, 2014. PDF icon Call Slides and Discussion Summary More Documents & Publications Better Buildings Residential Network Orientation Better Buildings Residential Network Orientation Webinar Nothing But Networking for Residential

  10. ASEM Green Independent Power Producers Network | Open Energy...

    Open Energy Info (EERE)

    ASEM Green Independent Power Producers Network Jump to: navigation, search Name: ASEM Green Independent Power Producers Network Place: Germany Sector: Renewable Energy Product: A...

  11. CARISMA: A Networking Project for High Temperature PEMFC MEA...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    CARISMA: A Networking Project for High Temperature PEMFC MEA Activities in Europe CARISMA: A Networking Project for High Temperature PEMFC MEA Activities in Europe This...

  12. Active Network Management (Smart Grid Project) | Open Energy...

    Open Energy Info (EERE)

    Network Management (Smart Grid Project) Jump to: navigation, search Project Name Active Network Management Country United Kingdom Coordinates 55.378052, -3.435973 Loading...

  13. A State-Wide Research Network for Alzheimer's Disease (Technical...

    Office of Scientific and Technical Information (OSTI)

    Technical Report: A State-Wide Research Network for Alzheimer's Disease Citation Details In-Document Search Title: A State-Wide Research Network for Alzheimer's Disease The ...

  14. Better Buildings Residential Network Orientation Webinar | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy May 14, 2015. PDF icon Call Slides and Discussion Summary More Documents & Publications Better Buildings Residential Network Orientation Webinar Better Buildings Residential Network Orientation

  15. Wireless Sensor Network for Electric Transmission Line Monitoring...

    Office of Scientific and Technical Information (OSTI)

    Technical Report: Wireless Sensor Network for Electric Transmission Line Monitoring Citation Details In-Document Search Title: Wireless Sensor Network for Electric Transmission ...

  16. Blue Gene/Q Network Performance Counters Monitoring Library

    Energy Science and Technology Software Center (OSTI)

    2015-03-12

    BGQNCL is a library to monitor and record network performance counters on the 5D torus interconnection network of IBM's Blue Gene/Q platform.

  17. Clean Economy Network-Rockies | Open Energy Information

    Open Energy Info (EERE)

    Economy Network-Rockies Jump to: navigation, search Name: Clean Economy Network-Rockies Place: Denver, CO Region: Rockies Area Website: rockies.cleaneconomynetwork.or Coordinates:...

  18. V-125: Cisco Connected Grid Network Management System Multiple...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    5: Cisco Connected Grid Network Management System Multiple Vulnerabilities V-125: Cisco Connected Grid Network Management System Multiple Vulnerabilities April 3, 2013 - 1:44am...

  19. Solar Energy Sources SES Solar Inc formerly Electric Network...

    Open Energy Info (EERE)

    SES Solar Inc formerly Electric Network com Jump to: navigation, search Name: Solar Energy Sources - SES Solar Inc (formerly Electric Network.com) Place: Vancouver, British...

  20. Fracture Network and Fluid Flow Imaging for EGS Applications...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Network and Fluid Flow Imaging for EGS Applications from Multi-Dimensional Electrical Resistivity Structure Fracture Network and Fluid Flow Imaging for EGS Applications from ...

  1. High Energy Physics and Nuclear Physics Network Requirements...

    Office of Scientific and Technical Information (OSTI)

    Technical Report: High Energy Physics and Nuclear Physics Network Requirements Citation Details In-Document Search Title: High Energy Physics and Nuclear Physics Network ...

  2. A mobile-agent based wireless sensing network for structural...

    Office of Scientific and Technical Information (OSTI)

    A mobile-agent based wireless sensing network for structural monitoring applications Citation Details In-Document Search Title: A mobile-agent based wireless sensing network for ...

  3. Better Buildings Network View | September 2014 | Department of...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    September 2014 Better Buildings Network View | September 2014 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential...

  4. Representativeness-based Sampling Network Design for the State...

    Office of Scientific and Technical Information (OSTI)

    Representativeness-based Sampling Network Design for the State of Alaska Citation Details In-Document Search Title: Representativeness-based Sampling Network Design for the State...

  5. Representativeness based Sampling Network Design for the State...

    Office of Scientific and Technical Information (OSTI)

    Representativeness based Sampling Network Design for the State of Alaska Title: Representativeness-based Sampling Network Design for the State of Alaska Authors: Forrest M. Hoffman...

  6. Representativeness-Based Sampling Network Design for the State...

    Office of Scientific and Technical Information (OSTI)

    Journal Article: Representativeness-Based Sampling Network Design for the State of Alaska Citation Details In-Document Search Title: Representativeness-Based Sampling Network...

  7. Better Buildings Network View | October 2014 | Department of...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    October 2014 Better Buildings Network View | October 2014 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential ...

  8. V-036: EMC Smarts Network Configuration Manager Database Authenticatio...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    V-036: EMC Smarts Network Configuration Manager Database Authentication Bypass ... Tomcat and JBOSS. Addthis Related Articles V-120: EMC Smarts Network Configuration ...

  9. Computer System, Cluster, and Networking Summer Institute Program...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    System, Cluster, and Networking Summer Institute Program Description The Computer System, Cluster, and Networking Summer Institute (CSCNSI) is a focused technical enrichment ...

  10. E-print Network home page -- Energy, science, and technology...

    Office of Scientific and Technical Information (OSTI)

    Energy, science, and technology for the research community Enter Search Terms Search Advanced Search The E-print Network is . . . . . . a vast, integrated network of electronic ...

  11. DNS as a Covert Channel Within Protected Networks | Department...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    DNS as a Covert Channel Within Protected Networks DNS as a Covert Channel Within Protected Networks This whitepaper discusses ways to detect DNS exfiltration attempts based on ...

  12. V-120: EMC Smarts Network Configuration Manager Java RMI Access...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    0: EMC Smarts Network Configuration Manager Java RMI Access Control Flaw Lets Remote Users Gain Full Control V-120: EMC Smarts Network Configuration Manager Java RMI Access Control...

  13. Renewable Energy Network of Entrepreneurs in Western New York...

    Open Energy Info (EERE)

    York Jump to: navigation, search Logo: Renewable Energy Network of Entrepreneurs in Western New York Name: Renewable Energy Network of Entrepreneurs in Western New York Address:...

  14. World Renewable Energy Network WREN | Open Energy Information

    Open Energy Info (EERE)

    Renewable Energy Network WREN Jump to: navigation, search Name: World Renewable Energy Network (WREN) Place: Brighton, United Kingdom Zip: BN2 1YH Sector: Renewable Energy Product:...

  15. News Release: Advanced Network Toolkit for Assessments and Remote...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    for network mapping can be costly, and these tools can disrupt control system function. ... tools (e.g., traceroute, Nmap), network device configuration files, traffic logs, etc. ...

  16. Network for ab initio Many-body Methods: Development, Education...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Miguel Morales is Prinicipal Investigator on Network for ab initio Many-body Methods: Development, Education and Training. Network for ab initio Many-body Methods: Development,...

  17. Emerson Network Power Co Ltd | Open Energy Information

    Open Energy Info (EERE)

    Power Co Ltd Jump to: navigation, search Name: Emerson Network Power Co Ltd Place: Canada Product: Power network developer. Involved in fuel cell power research consortia....

  18. Microsoft Word - Johnston.IOS.Network Communication as a Service...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    ... or capability, changes in network capacity (augments, or outages), or routing changes. ... Long term (months to years) traffic trend monitoring supports planning future network ...

  19. EIS-0525: Nationwide Public Safety Broadband Network Programmatic...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    25: Nationwide Public Safety Broadband Network Programmatic Environmental Impact Statement for the Eastern United States EIS-0525: Nationwide Public Safety Broadband Network ...

  20. Recommended Practices Guide For Securing ZigBee Wireless Networks...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Practices Guide For Securing ZigBee Wireless Networks in Process Control System Environments Recommended Practices Guide For Securing ZigBee Wireless Networks in Process Control ...

  1. Software security for a network storage service

    SciTech Connect (OSTI)

    Haynes, R.A.; Kelly, S.M.

    1992-09-01

    In 1991, Sandia National Laboratories acquired a Network Storage Service (NSS) as a result of a fully competitive procurement. The Network Storage Service, which provides access to over a terabyte of data storage in a two-tiered hierarchy, had minimal software security features. Before the NSS could be placed into production, it had to be accredited by the Department of Energy, Sandia`s accrediting authority. Sandia was faced with implementing security features to allow the NSS to be operated in its secure computing network, which is a single security clearance, multiple data security level environment. This paper describes the software security design alternatives that were considered and what was ultimately implemented.

  2. Software security for a network storage service

    SciTech Connect (OSTI)

    Haynes, R.A.; Kelly, S.M.

    1992-01-01

    In 1991, Sandia National Laboratories acquired a Network Storage Service (NSS) as a result of a fully competitive procurement. The Network Storage Service, which provides access to over a terabyte of data storage in a two-tiered hierarchy, had minimal software security features. Before the NSS could be placed into production, it had to be accredited by the Department of Energy, Sandia's accrediting authority. Sandia was faced with implementing security features to allow the NSS to be operated in its secure computing network, which is a single security clearance, multiple data security level environment. This paper describes the software security design alternatives that were considered and what was ultimately implemented.

  3. Intrusion detection and monitoring for wireless networks.

    SciTech Connect (OSTI)

    Thomas, Eric D.; Van Randwyk, Jamie A.; Lee, Erik J.; Stephano, Amanda; Tabriz, Parisa; Pelon, Kristen; McCoy, Damon (University of Colorado, Boulder); Lodato, Mark; Hemingway, Franklin; Custer, Ryan P.; Averin, Dimitry; Franklin, Jason; Kilman, Dominique Marie

    2005-11-01

    Wireless computer networks are increasing exponentially around the world. They are being implemented in both the unlicensed radio frequency (RF) spectrum (IEEE 802.11a/b/g) and the licensed spectrum (e.g., Firetide [1] and Motorola Canopy [2]). Wireless networks operating in the unlicensed spectrum are by far the most popular wireless computer networks in existence. The open (i.e., proprietary) nature of the IEEE 802.11 protocols and the availability of ''free'' RF spectrum have encouraged many producers of enterprise and common off-the-shelf (COTS) computer networking equipment to jump into the wireless arena. Competition between these companies has driven down the price of 802.11 wireless networking equipment and has improved user experiences with such equipment. The end result has been an increased adoption of the equipment by businesses and consumers, the establishment of the Wi-Fi Alliance [3], and widespread use of the Alliance's ''Wi-Fi'' moniker to describe these networks. Consumers use 802.11 equipment at home to reduce the burden of running wires in existing construction, facilitate the sharing of broadband Internet services with roommates or neighbors, and increase their range of ''connectedness''. Private businesses and government entities (at all levels) are deploying wireless networks to reduce wiring costs, increase employee mobility, enable non-employees to access the Internet, and create an added revenue stream to their existing business models (coffee houses, airports, hotels, etc.). Municipalities (Philadelphia; San Francisco; Grand Haven, MI) are deploying wireless networks so they can bring broadband Internet access to places lacking such access; offer limited-speed broadband access to impoverished communities; offer broadband in places, such as marinas and state parks, that are passed over by traditional broadband providers; and provide themselves with higher quality, more complete network coverage for use by emergency responders and other municipal agencies. In short, these Wi-Fi networks are being deployed everywhere. Much thought has been and is being put into evaluating cost-benefit analyses of wired vs. wireless networks and issues such as how to effectively cover an office building or municipality, how to efficiently manage a large network of wireless access points (APs), and how to save money by replacing an Internet service provider (ISP) with 802.11 technology. In comparison, very little thought and money are being focused on wireless security and monitoring for security purposes.

  4. Visual Matrix Clustering of Social Networks

    SciTech Connect (OSTI)

    Wong, Pak C.; Mackey, Patrick S.; Foote, Harlan P.; May, Richard A.

    2013-07-01

    The prevailing choices to graphically represent a social network in todays literature are a node-link graph layout and an adjacency matrix. Both visualization techniques have unique strengths and weaknesses when applied to different domain applications. In this article, we focus our discussion on adjacency matrix and how to turn the matrix-based visualization technique from merely showing pairwise associations among network actors (or graph nodes) to depicting clusters of a social network. We also use node-link layouts to supplement the discussion.

  5. Web100-based Network Diagnostic Tool

    Energy Science and Technology Software Center (OSTI)

    2003-03-20

    NDT is a client/server based network diagnostic tool developed to aid in finding network performance and configuration problems. The tool measures data transfer rates between two internet hosts (client and server). It also gathers detailed TCP statistical variable counters supplied by the Web100 modified server and uses these TCP variables to compute the theoretical performance rate between the two internet hosts. It then compares these analytical results with the measured results to determine if performancemore » or configuration problems exist and translates these results into plain text messages to aid users and network operators in resolving reported problems.« less

  6. Characterization of the Weatherization Assistance Program network

    SciTech Connect (OSTI)

    Mihlmester, P.E.; Koehler, W.C. Jr.; Beyer, M.A. . Applied Management Sciences Div.); Brown, M.A. ); Beschen, D.A. Jr. . Office of Weatherization Assistance Programs)

    1992-02-01

    The Characterization of the Weatherization Assistance Program (WAP) Network was designed to describe the national network of State and local agencies that provide WAP services to qualifying low-income households. The objective of this study was to profile the current WAP network. To achieve the objective, two national surveys were conducted: one survey collected data from 49 State WAP agencies (including the coterminous 48 States and the District of Columbia), and the second survey collected data from 920 (or 81 percent) of the local WAP agencies.

  7. Silicon-embedded copper nanostructure network for high energy storage

    DOE Patents [OSTI]

    Yu, Tianyue

    2016-03-15

    Provided herein are nanostructure networks having high energy storage, electrochemically active electrode materials including nanostructure networks having high energy storage, as well as electrodes and batteries including the nanostructure networks having high energy storage. According to various implementations, the nanostructure networks have high energy density as well as long cycle life. In some implementations, the nanostructure networks include a conductive network embedded with electrochemically active material. In some implementations, silicon is used as the electrochemically active material. The conductive network may be a metal network such as a copper nanostructure network. Methods of manufacturing the nanostructure networks and electrodes are provided. In some implementations, metal nanostructures can be synthesized in a solution that contains silicon powder to make a composite network structure that contains both. The metal nanostructure growth can nucleate in solution and on silicon nanostructure surfaces.

  8. Better Buildings Network View February 2016

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Connecticut Green Bank, NY Green Bank, four other banks with similar missions, and two ... Learn more about the Green Bank Network. Boost Your Business Skills at the 2016 ACI ...

  9. Solar Instructor Training Network Frequently Asked Questions

    Broader source: Energy.gov [DOE]

    These frequently asked questions (FAQs) relate to the solar instructor training network. This project was launched by the U.S. Department of Energy (DOE) Solar Energy Technologies Program (SETP or...

  10. Advanced Scientific Computing Research Network Requirements

    SciTech Connect (OSTI)

    Bacon, Charles; Bell, Greg; Canon, Shane; Dart, Eli; Dattoria, Vince; Goodwin, Dave; Lee, Jason; Hicks, Susan; Holohan, Ed; Klasky, Scott; Lauzon, Carolyn; Rogers, Jim; Shipman, Galen; Skinner, David; Tierney, Brian

    2013-03-08

    The Energy Sciences Network (ESnet) is the primary provider of network connectivity for the U.S. Department of Energy (DOE) Office of Science (SC), the single largest supporter of basic research in the physical sciences in the United States. In support of SC programs, ESnet regularly updates and refreshes its understanding of the networking requirements of the instruments, facilities, scientists, and science programs that it serves. This focus has helped ESnet to be a highly successful enabler of scientific discovery for over 25 years. In October 2012, ESnet and the Office of Advanced Scientific Computing Research (ASCR) of the DOE SC organized a review to characterize the networking requirements of the programs funded by the ASCR program office. The requirements identified at the review are summarized in the Findings section, and are described in more detail in the body of the report.

  11. Dynamics on modular networks with heterogeneous correlations

    SciTech Connect (OSTI)

    Melnik, Sergey; Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, Oxford OX2 6GG; CABDyN Complexity Centre, University of Oxford, Oxford OX1 1HP ; Porter, Mason A.; CABDyN Complexity Centre, University of Oxford, Oxford OX1 1HP ; Mucha, Peter J.; Institute for Advanced Materials, Nanoscience and Technology, University of North Carolina, Chapel Hill, North Carolina 27599-3216 ; Gleeson, James P.

    2014-06-15

    We develop a new ensemble of modular random graphs in which degree-degree correlations can be different in each module, and the inter-module connections are defined by the joint degree-degree distribution of nodes for each pair of modules. We present an analytical approach that allows one to analyze several types of binary dynamics operating on such networks, and we illustrate our approach using bond percolation, site percolation, and the Watts threshold model. The new network ensemble generalizes existing models (e.g., the well-known configuration model and Lancichinetti-Fortunato-Radicchi networks) by allowing a heterogeneous distribution of degree-degree correlations across modules, which is important for the consideration of nonidentical interacting networks.

  12. Network Optimization Models (RNAS and ATOM) | NISAC

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    been used to study policy options concerning the movement of toxic chemicals by rail. Air Transport Optimization Model (ATOM) The TOM is a network-optimization model designed to...

  13. Southeast Energy Efficiency Alliance Launches Finance Network...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Photo of two hands holding dollar bills shaped like a house. The Southeast Energy ... This new network will leverage U.S. Department of Energy (DOE) loan capital to engage a ...

  14. Global Energy Network Institute | Open Energy Information

    Open Energy Info (EERE)

    inlineLabel":"","visitedicon":"" Hide Map References: Global Energy Network Institute Web Site1 This article is a stub. You can help OpenEI by expanding it. Global Energy...

  15. June 2015 National Idling Reduction Network News

    Broader source: Energy.gov [DOE]

    This is the June 2015 edition of the National Idling Reduction Network News, an e-newsletter that reports solicitations for funding, regulatory changes, awards and recognition, reports and other resources of interest, upcoming meetings and events, and manufacturers’ announcements.

  16. December 2015 National Idling Reduction Network News

    Broader source: Energy.gov [DOE]

    This is the December 2015 edition of the National Idling Reduction Network News, an e-newsletter that reports solicitations for funding, regulatory changes, awards and recognition, reports and other resources of interest, upcoming meetings and events, and manufacturers’ announcements.

  17. About the Better Buildings Residential Network

    Broader source: Energy.gov [DOE]

    The Better Buildings Residential Network connects energy efficiency programs and partners to share best practices and learn from one another to increase the number of homes that are energy efficient.

  18. Better Buildings Residential Network Membership Form | Department...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Residential Network Recommended Instructions for Downloading and Saving Membership Form Adobe Reader 6.0 or higher is required to view and print PDF forms. To obtain the latest...

  19. Data Network Weather Service Reporting - Final Report

    SciTech Connect (OSTI)

    Michael Frey

    2012-08-30

    A final report is made of a three-year effort to develop a new forecasting paradigm for computer network performance. This effort was made in co-ordination with Fermi Lab's construction of e-Weather Center.

  20. EPA National Environmental Information Exchange Network Grant...

    Broader source: Energy.gov (indexed) [DOE]

    5 5:00PM EST U.S. Environmental Protection Agency The U.S. Environmental Protection Agency is accepting applications for the National Environmental Information Exchange Network...

  1. Data and Networking | Argonne Leadership Computing Facility

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Tape storage is used to archive data from completed projects. Disk Storage: The Blue GeneQ data systems consist of 384 IO nodes that connect to 16 storage area networks...

  2. Joint Implementation Network (JIN) | Open Energy Information

    Open Energy Info (EERE)

    2.2 JIN Programs 3 References About Joint Implementation Network (JIN) was established in 1995 as knowledge centre for climate change policy issues in general and the concept of...

  3. Effective Protocols for Mobile Communications and Networking

    SciTech Connect (OSTI)

    Espinoza, J.; Sholander, P.; Van Leeuwen, B,

    1998-12-01

    This report examines methods of mobile communications with an emphasis on mobile computing and wireless communications. Many of the advances in communications involve the use of Internet Protocol (IP), Asynchronous Transfer Mode (ATM), and ad hoc network protocols. However, many of the advances in these protocols have been focused on wired communications. Recently much focus has been directed at advancing communication technology in the area of mobile wireless networks. This report discusses various protocols used in mobile communications and proposes a number of extensions to existing protocols. A detailed discussion is also included on desirable protocol characteristics and evaluation criteria. In addition, the report includes a discussion on several network simulation tools that maybe used to evaluate network protocols.

  4. Synchronization in networks of spatially extended systems

    SciTech Connect (OSTI)

    Filatova, Anastasiya E.; Hramov, Alexander E.; Koronovskii, Alexey A.; Boccaletti, Stefano

    2008-06-15

    Synchronization processes in networks of spatially extended dynamical systems are analytically and numerically studied. We focus on the relevant case of networks whose elements (or nodes) are spatially extended dynamical systems, with the nodes being connected with each other by scalar signals. The stability of the synchronous spatio-temporal state for a generic network is analytically assessed by means of an extension of the master stability function approach. We find an excellent agreement between the theoretical predictions and the data obtained by means of numerical calculations. The efficiency and reliability of this method is illustrated numerically with networks of beam-plasma chaotic systems (Pierce diodes). We discuss also how the revealed regularities are expected to take place in other relevant physical and biological circumstances.

  5. E-print Network : User Account

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    New Search | My Selections (0) | | | | Alerts | E-print Network Create User Account User Name: Email Address: I want to: Always receive emails Receive emails if there are new...

  6. December 2014 National Idling Reduction Network News

    Broader source: Energy.gov [DOE]

    This is the December 2014 edition of the National Idling Reduction Network News, an e-newsletter that reports solicitations for funding, regulatory changes, awards and recognition, reports and other resources of interest, upcoming meetings and events, and manufacturers’ announcements.

  7. November 2015 National Idling Reduction Network News

    Broader source: Energy.gov [DOE]

    This is the November 2015 edition of the National Idling Reduction Network News, an e-newsletter that reports solicitations for funding, regulatory changes, awards and recognition, reports and other resources of interest, upcoming meetings and events, and manufacturers’ announcements.

  8. EIA - Natural Gas Pipeline Network - U.S. Natural Gas Pipeline Network Map

    U.S. Energy Information Administration (EIA) Indexed Site

    Network Map About U.S. Natural Gas Pipelines - Transporting Natural Gas based on data through 2007/2008 with selected updates U.S. Natural Gas Pipeline Network, 2009 U.S. Natural Gas Pipeline Network Map The EIA has determined that the informational map displays here do not raise security concerns, based on the application of the Federal Geographic Data Committee's Guidelines for Providing Appropriate Access to Geospatial Data in Response to Security Concerns

  9. Natural Gas Pipeline Network: Changing and Growing

    Reports and Publications (EIA)

    1996-01-01

    This chapter focuses upon the capabilities of the national natural gas pipeline network, examining how it has expanded during this decade and how it may expand further over the coming years. It also looks at some of the costs of this expansion, including the environmental costs which may be extensive. Changes in the network as a result of recent regional market shifts are also discussed.

  10. Efficient uncertainty propagation for network multiphysics systems.

    Office of Scientific and Technical Information (OSTI)

    (Journal Article) | SciTech Connect Journal Article: Efficient uncertainty propagation for network multiphysics systems. Citation Details In-Document Search Title: Efficient uncertainty propagation for network multiphysics systems. Authors: Phipps, Eric T. ; Wildey, Timothy Michael ; Constantine, Paul G. Publication Date: 2013-01-01 OSTI Identifier: 1063360 Report Number(s): SAND2013-0423J DOE Contract Number: AC04-94AL85000 Resource Type: Journal Article Resource Relation: Journal Name:

  11. On the exact evaluation of spin networks

    SciTech Connect (OSTI)

    Freidel, Laurent; Hnybida, Jeff; Department of Physics, University of Waterloo, Waterloo, Ontario N2L 3G1

    2013-11-15

    We introduce a fully coherent spin network amplitude whose expansion generates all SU(2) spin networks associated with a given graph. We then give an explicit evaluation of this amplitude for an arbitrary graph. We show how this coherent amplitude can be obtained from the specialization of a generating functional obtained by the contraction of parametrized intertwiners la Schwinger. We finally give the explicit evaluation of this generating functional for arbitrary graphs.

  12. Energy Materials Network News | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    News Energy Materials Network News Below are news stories and blog posts related to the Energy Materials Network (EMN) from the Energy Department and the Office of Energy Efficiency and Renewable Energy. Please see the Consortia and National Labs news page to learn more about the latest on the EMN consortia's funding opportunities, public-private partnership activities, and materials development capabilities and projects. April 1, 2016 New Energy Department-supported technologies under

  13. Network Requirements Workshop - Documents and Background Materials

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Requirements Review Reports Case Studies News & Publications ESnet News Publications and Presentations Galleries ESnet Awards and Honors Blog ESnet Live Home » Science Engagement » Science Requirements Reviews » Network Requirements Reviews » Documents and Background Materials Science Engagement Move your data Programs & Workshops Science Requirements Reviews Network Requirements Reviews Documents and Background Materials FAQ for Case Study Authors BER Requirements Review 2015 ASCR

  14. Downhole drilling network using burst modulation techniques

    DOE Patents [OSTI]

    Hall; David R. , Fox; Joe

    2007-04-03

    A downhole drilling system is disclosed in one aspect of the present invention as including a drill string and a transmission line integrated into the drill string. Multiple network nodes are installed at selected intervals along the drill string and are adapted to communicate with one another through the transmission line. In order to efficiently allocate the available bandwidth, the network nodes are configured to use any of numerous burst modulation techniques to transmit data.

  15. Network traffic analysis using dispersion patterns

    Energy Science and Technology Software Center (OSTI)

    2010-03-15

    The Verilog code us used to map a measurement solution on FPGA to analyze network traffic. It realizes a set of Bloom filters and counters, besides associated control logic that can quickly measure statistics like InDegree, OutDegree, Depth, in the context of Traffic Dispersion Graphs. Such patterns are helpful in classification of network activity, like Peer to Peer and Port-Scanning, in the traffic.

  16. A connecting network with fault tolerance capabilities

    SciTech Connect (OSTI)

    Ciminiera, L.; Serra, A.

    1986-06-01

    A new multistage interconnection network is presented in this paper. It is able to handle the communications between the connected devices correctly, even in the presence of fault(s) in the network. This goal is achieved by using redundant paths with a fast procedure able to dynamically reroute the message. It is also shown that the rerouting properties are still valid when broadcasting transmission is used.

  17. Redundancy and Error Resilience in Boolean Networks

    SciTech Connect (OSTI)

    Peixoto, Tiago P.

    2010-01-29

    We consider the effect of noise in sparse Boolean networks with redundant functions. We show that they always exhibit a nonzero error level, and the dynamics undergoes a phase transition from nonergodicity to ergodicity, as a function of noise, after which the system is no longer capable of preserving a memory of its initial state. We obtain upper bounds on the critical value of noise for networks of different sparsity.

  18. Global-Address Space Networking (GASNet) Library

    Energy Science and Technology Software Center (OSTI)

    2011-04-06

    GASNet (Global-Address Space Networking) is a language-independent, low-level networking layer that provides network-independent, high-performance communication primitives tailored for implementing parallel global address space SPMD languages such as UPC and Titanium. The interface is primarily intended as a compilation target and for use by runtime library writers (as opposed to end users), and the primary goals are high performance, interface portability, and expressiveness. GASNet is designed specifically to support high-performance, portable implementations of global address spacemore » languages on modern high-end communication networks. The interface provides the flexibility and extensibility required to express a wide variety of communication patterns without sacrificing performance by imposing large computational overheads in the interface. The design of the GASNet interface is partitioned into two layers to maximize porting ease without sacrificing performance: the lower level is a narrow but very general interface called the GASNet core API - the design is basedheavily on Active Messages, and is implemented directly on top of each individual network architecture. The upper level is a wider and more expressive interface called GASNet extended API, which provides high-level operations such as remote memory access and various collective operations. This release implements GASNet over MPI, the Quadrics "elan" API, the Myrinet "GM" API and the "LAPI" interface to the IBM SP switch. A template is provided for adding support for additional network interfaces.« less

  19. National Idling Reduction Network News - November 2009 | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy 09 National Idling Reduction Network News - November 2009 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon nov09_network_news.pdf More Documents & Publications National Idling Reduction Network News - January 2009 National Idling Reduction Network News - October

  20. National Idling Reduction Network News - November 2010 | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy 10 National Idling Reduction Network News - November 2010 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon nov10_network_news.pdf More Documents & Publications National Idling Reduction Network News - January 2011 National Idling Reduction Network News - January

  1. IEEE 342 Node Low Voltage Networked Test System

    SciTech Connect (OSTI)

    Schneider, Kevin P.; Phanivong, Phillippe K.; Lacroix, Jean-Sebastian

    2014-07-31

    The IEEE Distribution Test Feeders provide a benchmark for new algorithms to the distribution analyses community. The low voltage network test feeder represents a moderate size urban system that is unbalanced and highly networked. This is the first distribution test feeder developed by the IEEE that contains unbalanced networked components. The 342 node Low Voltage Networked Test System includes many elements that may be found in a networked system: multiple 13.2kV primary feeders, network protectors, a 120/208V grid network, and multiple 277/480V spot networks. This paper presents a brief review of the history of low voltage networks and how they evolved into the modern systems. This paper will then present a description of the 342 Node IEEE Low Voltage Network Test System and power flow results.

  2. National Idling Reduction Network News - April 2010 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    0 National Idling Reduction Network News - April 2010 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon apr10_network_news.pdf More Documents & Publications National Idling Reduction Network News - July 2010 National Idling Reduction Network News - May 2010 National Idling Reduction Network News - October 2009

  3. National Idling Reduction Network News - April 2011 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    1 National Idling Reduction Network News - April 2011 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon apr11_network_news.pdf More Documents & Publications National Idling Reduction Network News - August 2012 National Idling Reduction Network News - January 2013 National Idling Reduction Network News - June

  4. National Idling Reduction Network News - April 2012 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    2 National Idling Reduction Network News - April 2012 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon apr12_network_news.pdf More Documents & Publications National Idling Reduction Network News - February 2012 National Idling Reduction Network News - March 2012 National Idling Reduction Network News - October

  5. National Idling Reduction Network News - April 2013 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    3 National Idling Reduction Network News - April 2013 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon apr13_network_news.pdf More Documents & Publications National Idling Reduction Network News - October 2012 National Idling Reduction Network News - March 2014 National Idling Reduction Network News Compendium

  6. National Idling Reduction Network News - April 2014 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    4 National Idling Reduction Network News - April 2014 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon apr14_network_news.pdf More Documents & Publications National Idling Reduction Network News Compendium National Idling Reduction Network News - January 2014 National Idling Reduction Network News - December 2013

  7. National Idling Reduction Network News - August 2010 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    0 National Idling Reduction Network News - August 2010 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon aug10_network_news.pdf More Documents & Publications National Idling Reduction Network News - February 2011 National Idling Reduction Network News - May 2010 National Idling Reduction Network News - August

  8. National Idling Reduction Network News - August 2012 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    2 National Idling Reduction Network News - August 2012 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon aug12_network_news.pdf More Documents & Publications National Idling Reduction Network News - January 2013 National Idling Reduction Network News - March 2012 National Idling Reduction Network News - June 2012

  9. National Idling Reduction Network News - August 2013 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    3 National Idling Reduction Network News - August 2013 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon aug13_network_news.pdf More Documents & Publications National Idling Reduction Network News - October 2013 National Idling Reduction Network News - January 2013 National Idling Reduction Network News - August 2011

  10. National Idling Reduction Network News - December 2010 | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy 0 National Idling Reduction Network News - December 2010 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon dec10_network_news.pdf More Documents & Publications National Idling Reduction Network News - August 2011 National Idling Reduction Network News - May 2010 National Idling Reduction Network News - January 2013

  11. National Idling Reduction Network News - December 2012 | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy 2 National Idling Reduction Network News - December 2012 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon dec12_network_news.pdf More Documents & Publications National Idling Reduction Network News - June 2011 National Idling Reduction Network News - April 2011 National Idling Reduction Network News - July 2010

  12. National Idling Reduction Network News - December 2013 | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy 3 National Idling Reduction Network News - December 2013 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon dec13_network_news.pdf More Documents & Publications National Idling Reduction Network News - June 2012 National Idling Reduction Network News Compendium National Idling Reduction Network News - March 2014

  13. National Idling Reduction Network News - Early Spring 2009 | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy Early Spring 2009 National Idling Reduction Network News - Early Spring 2009 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon early_spring09_network_news.pdf More Documents & Publications National Idling Reduction Network News - January 2009 National Idling Reduction Network News - August 2009 National Idling Reduction Network News

  14. National Idling Reduction Network News - February 2010 | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy 0 National Idling Reduction Network News - February 2010 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon feb10_network_news.pdf More Documents & Publications National Idling Reduction Network News - March 2012 National Idling Reduction Network News - May 2010 National Idling Reduction Network News - July 2010

  15. National Idling Reduction Network News - February 2011 | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy 1 National Idling Reduction Network News - February 2011 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon feb11_network_news.pdf More Documents & Publications National Idling Reduction Network News - January 2011 National Idling Reduction Network News - November 2010 National Idling Reduction Network News - December 2011

  16. National Idling Reduction Network News - February 2012 | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy 2 National Idling Reduction Network News - February 2012 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon feb12_network_news.pdf More Documents & Publications National Idling Reduction Network News - August 2011 National Idling Reduction Network News - November 2011 National Idling Reduction Network News - March 2012

  17. National Idling Reduction Network News - February 2013 | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy 3 National Idling Reduction Network News - February 2013 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon feb13_network_news.pdf More Documents & Publications National Idling Reduction Network News - October 2013 National Idling Reduction Network News - April 2011 National Idling Reduction Network News Compendium

  18. National Idling Reduction Network News - January 2009 | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy 09 National Idling Reduction Network News - January 2009 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon jan09_network_news.pdf More Documents & Publications National Idling Reduction Network News - Early Spring 2009 National Idling Reduction Network News - October 2009 National Idling Reduction Network News - September 2009

  19. National Idling Reduction Network News - January 2010 | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy 0 National Idling Reduction Network News - January 2010 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon jan10_network_news.pdf More Documents & Publications National Idling Reduction Network News - February 2012 National Idling Reduction Network News - September 2010 National Idling Reduction Network News - March

  20. National Idling Reduction Network News - January 2011 | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy 1 National Idling Reduction Network News - January 2011 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon jan11_network_news.pdf More Documents & Publications National Idling Reduction Network News - February 2011 National Idling Reduction Network News - June 2011 National Idling Reduction Network News - August

  1. National Idling Reduction Network News - January 2012 | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy 2 National Idling Reduction Network News - January 2012 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon jan12_network_news.pdf More Documents & Publications National Idling Reduction Network News - May 2012 National Idling Reduction Network News - July 2011 National Idling Reduction Network News - February

  2. National Idling Reduction Network News - January 2013 | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy 3 National Idling Reduction Network News - January 2013 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon jan13_network_news.pdf More Documents & Publications National Idling Reduction Network News - February 2013 National Idling Reduction Network News - March 2013 National Idling Reduction Network News - August

  3. National Idling Reduction Network News - January 2014 | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy 4 National Idling Reduction Network News - January 2014 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon jan14_network_news.pdf More Documents & Publications National Idling Reduction Network News Compendium National Idling Reduction Network News - June 2012 National Idling Reduction Network News - January 2009

  4. National Idling Reduction Network News - July 2009 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    09 National Idling Reduction Network News - July 2009 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon july09_network_news.pdf More Documents & Publications National Idling Reduction Network News - May 2012 National Idling Reduction Network News - June 2011 National Idling Reduction Network News - November

  5. National Idling Reduction Network News - July 2010 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    0 National Idling Reduction Network News - July 2010 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon july10_network_news.pdf More Documents & Publications National Idling Reduction Network News - Early Spring 2009 National Idling Reduction Network News - June 2010 National Idling Reduction Network News - June

  6. National Idling Reduction Network News - July 2011 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    1 National Idling Reduction Network News - July 2011 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon july11_network_news.pdf More Documents & Publications National Idling Reduction Network News - June 2011 National Idling Reduction Network News - September 2009 National Idling Reduction Network News - April

  7. National Idling Reduction Network News - July 2012 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    2 National Idling Reduction Network News - July 2012 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon july12_network_news.pdf More Documents & Publications National Idling Reduction Network News - March 2011 National Idling Reduction Network News - May 2012 National Idling Reduction Network News - October 2012

  8. National Idling Reduction Network News - July 2013 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    3 National Idling Reduction Network News - July 2013 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon july13_network_news.pdf More Documents & Publications National Idling Reduction Network News - September 2012 National Idling Reduction Network News - June 2013 National Idling Reduction Network News - September 2013

  9. National Idling Reduction Network News - June 2011 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    1 National Idling Reduction Network News - June 2011 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon june11_network_news.pdf More Documents & Publications National Idling Reduction Network News - November 2011 National Idling Reduction Network News - October 2011 National Idling Reduction Network News - September 2012

  10. National Idling Reduction Network News - March 2010 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    0 National Idling Reduction Network News - March 2010 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon mar10_network_news.pdf More Documents & Publications National Idling Reduction Network News - July 2010 National Idling Reduction Network News - Early Spring 2009 National Idling Reduction Network News - May 2010

  11. National Idling Reduction Network News - March 2011 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    1 National Idling Reduction Network News - March 2011 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon mar11_network_news.pdf More Documents & Publications National Idling Reduction Network News - August 2011 National Idling Reduction Network News - June 2011 National Idling Reduction Network News - January 2011

  12. National Idling Reduction Network News - March 2012 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    2 National Idling Reduction Network News - March 2012 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon mar12_network_news.pdf More Documents & Publications National Idling Reduction Network News Compendium National Idling Reduction Network News - May 2012 National Idling Reduction Network News - January 2013

  13. National Idling Reduction Network News - March 2013 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    3 National Idling Reduction Network News - March 2013 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon mar13_network_news.pdf More Documents & Publications National Idling Reduction Network News - August 2011 National Idling Reduction Network News - March 2012 National Idling Reduction Network News - June 2011

  14. National Idling Reduction Network News - March 2014 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    4 National Idling Reduction Network News - March 2014 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon mar14_network_news.pdf More Documents & Publications National Idling Reduction Network News - December 2013 National Idling Reduction Network News Compendium National Idling Reduction Network News - June 2012

  15. National Idling Reduction Network News - May 2010 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    0 National Idling Reduction Network News - May 2010 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon may10_network_news.pdf More Documents & Publications National Idling Reduction Network News - August 2011 National Idling Reduction Network News - December 2010 National Idling Reduction Network News - January 2013

  16. National Idling Reduction Network News - May 2011 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    1 National Idling Reduction Network News - May 2011 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon may11_network_news.pdf More Documents & Publications National Idling Reduction Network News - October 2011 National Idling Reduction Network News - March 2012 National Idling Reduction Network News - February 2011

  17. National Idling Reduction Network News - May 2013 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    3 National Idling Reduction Network News - May 2013 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon may13_network_news.pdf More Documents & Publications National Idling Reduction Network News - November 2012 National Idling Reduction Network News - November 2011 National Idling Reduction Network News - April 2013

  18. National Idling Reduction Network News - November 2011 | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy 1 National Idling Reduction Network News - November 2011 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon nov11_network_news.pdf More Documents & Publications National Idling Reduction Network News - August 2011 National Idling Reduction Network News - December 2010 National Idling Reduction Network News - August 2010

  19. National Idling Reduction Network News - November 2012 | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy 2 National Idling Reduction Network News - November 2012 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon nov12_network_news.pdf More Documents & Publications National Idling Reduction Network News - June 2011 National Idling Reduction Network News - May 2012 National Idling Reduction Network News - Early Spring 2009

  20. National Idling Reduction Network News - October 2009 | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy 09 National Idling Reduction Network News - October 2009 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon oct09_network_news.pdf More Documents & Publications National Idling Reduction Network News - January 2009 National Idling Reduction Network News - November 2009 National Idling Reduction Network News - August

  1. National Idling Reduction Network News - October 2010 | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy 10 National Idling Reduction Network News - October 2010 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon oct10_network_news.pdf More Documents & Publications National Idling Reduction Network News - July 2010 National Idling Reduction Network News - May 2010 National Idling Reduction Network News - October

  2. National Idling Reduction Network News - October 2011 | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy 1 National Idling Reduction Network News - October 2011 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon oct11_network_news.pdf More Documents & Publications National Idling Reduction Network News - May 2011 National Idling Reduction Network News - November 2011 National Idling Reduction Network News - August 2011

  3. National Idling Reduction Network News - October 2012 | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy 2 National Idling Reduction Network News - October 2012 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon oct12_network_news.pdf More Documents & Publications National Idling Reduction Network News - Early Spring 2009 National Idling Reduction Network News - June 2011 National Idling Reduction Network News - June 2009

  4. National Idling Reduction Network News - October 2013 | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy 3 National Idling Reduction Network News - October 2013 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon oct13_network_news.pdf More Documents & Publications National Idling Reduction Network News - February 2013 National Idling Reduction Network News - March 2013 National Idling Reduction Network News - May 2010

  5. National Idling Reduction Network News - September 2010 | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy 0 National Idling Reduction Network News - September 2010 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon sep10_network_news.pdf More Documents & Publications National Idling Reduction Network News - August 2010 National Idling Reduction Network News - May 2010 National Idling Reduction Network News - August 2011

  6. National Idling Reduction Network News - September 2012 | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy 2 National Idling Reduction Network News - September 2012 Newsletter with information on idling reduction regulations, idling reduction grants, idling reduction general news, summary of state ani-idling regulations, and upcoming meetings and events. PDF icon sep12_network_news.pdf More Documents & Publications National Idling Reduction Network News - July 2013 National Idling Reduction Network News - February 2012 National Idling Reduction Network News - April

  7. Better Buildings Network View | September 2014 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    September 2014 Better Buildings Network View | September 2014 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF icon Better Buildings Network View September 2014 More Documents & Publications Focus Series: OREGON-On Bill Financing Program: On-Bill Financing Brings Lenders and Homeowners On Board Better Buildings Network View | December 2014 On-Bill Financing for Energy E

  8. IPv6 Implementation at a Network Service Provider

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    IPv6 Implementation at a Network Service Provider 2010 Inter Agency IPv6 Information Exchange August 4, 2010 R. Kevin Oberman Sr. Network Engineer Lawrence Berkeley National Laboratory U.S. Department of Energy | Office of Science Who Are We? ESnet is the network provider for the Department of Energy's Office of Science * ESnet is a networking pioneer with nearly a quarter century of networking * Began as MFEnet in 1976 * Became ESnet with broader mission in 1986 * Started support of BGP4 and

  9. Alternative Fuels Data Center: New York Broadens Network for Electric

    Alternative Fuels and Advanced Vehicles Data Center [Office of Energy Efficiency and Renewable Energy (EERE)]

    Vehicle Charging New York Broadens Network for Electric Vehicle Charging to someone by E-mail Share Alternative Fuels Data Center: New York Broadens Network for Electric Vehicle Charging on Facebook Tweet about Alternative Fuels Data Center: New York Broadens Network for Electric Vehicle Charging on Twitter Bookmark Alternative Fuels Data Center: New York Broadens Network for Electric Vehicle Charging on Google Bookmark Alternative Fuels Data Center: New York Broadens Network for Electric

  10. Nanoporous Gold as a Neural Interface Coating: Effects of Topography, Surface Chemistry, and Feature Size

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Chapman, Christopher A. R.; Chen, Hao; Stamou, Marianna; Biener, Juergen; Biener, Monika M.; Lein, Pamela J.; Seker, Erkin

    2015-02-23

    We report that designing neural interfaces that maintain close physical coupling of neurons to an electrode surface remains a major challenge for both implantable and in vitro neural recording electrode arrays. Typically, low-impedance nanostructured electrode coatings rely on chemical cues from pharmaceuticals or surface-immobilized peptides to suppress glial scar tissue formation over the electrode surface (astrogliosis), which is an obstacle to reliable neuron–electrode coupling. Nanoporous gold (np-Au), produced by an alloy corrosion process, is a promising candidate to reduce astrogliosis solely through topography by taking advantage of its tunable length scale. In the present in vitro study on np-Au’s interactionmore » with cortical neuron–glia co-cultures, we demonstrate that the nanostructure of np-Au achieves close physical coupling of neurons by maintaining a high neuron-to-astrocyte surface coverage ratio. Atomic layer deposition-based surface modification was employed to decouple the effect of morphology from surface chemistry. Additionally, length scale effects were systematically studied by controlling the characteristic feature size of np-Au through variations in the dealloying conditions. In conclusion, our results show that np-Au nanotopography, not surface chemistry, reduces astrocyte surface coverage while maintaining high neuronal coverage and may enhance neuron–electrode coupling through nanostructure-mediated suppression of scar tissue formation.« less

  11. Cooperative UAV-Based Communications Backbone for Sensor Networks

    SciTech Connect (OSTI)

    Roberts, R S

    2001-10-07

    The objective of this project is to investigate the use of unmanned air vehicles (UAVs) as mobile, adaptive communications backbones for ground-based sensor networks. In this type of network, the UAVs provide communication connectivity to sensors that cannot communicate with each other because of terrain, distance, or other geographical constraints. In these situations, UAVs provide a vertical communication path for the sensors, thereby mitigating geographic obstacles often imposed on networks. With the proper use of UAVs, connectivity to a widely disbursed sensor network in rugged terrain is readily achieved. Our investigation has focused on networks where multiple cooperating UAVs are used to form a network backbone. The advantage of using multiple UAVs to form the network backbone is parallelization of sensor connectivity. Many widely spaced or isolated sensors can be connected to the network at once using this approach. In these networks, the UAVs logically partition the sensor network into sub-networks (subnets), with one UAV assigned per subnet. Partitioning the network into subnets allows the UAVs to service sensors in parallel thereby decreasing the sensor-to-network connectivity. A UAV services sensors in its subnet by flying a route (path) through the subnet, uplinking data collected by the sensors, and forwarding the data to a ground station. An additional advantage of using multiple UAVs in the network is that they provide redundancy in the communications backbone, so that the failure of a single UAV does not necessarily imply the loss of the network.

  12. Network Traffic Generator for Low-rate Small Network Equipment Software

    Energy Science and Technology Software Center (OSTI)

    2013-05-28

    Application that uses the Python low-level socket interface to pass network traffic between devices on the local side of a NAT router and the WAN side of the NAT router. This application is designed to generate traffic that complies with the Energy Star Small Network Equipment Test Method.

  13. STIMULUS: End-System Network Interface Controller for 100 Gb/s Wide Area Networks

    SciTech Connect (OSTI)

    Zarkesh-Ha, Payman

    2014-09-12

    The main goal of this research grant is to develop a system-level solution leveraging novel technologies that enable network communications at 100 Gb/s or beyond. University of New Mexico in collaboration with Acadia Optronics LLC has been working on this project to develop the 100 Gb/s Network Interface Controller (NIC) under this Department of Energy (DOE) grant.

  14. Parallel processing data network of master and slave transputers controlled by a serial control network

    DOE Patents [OSTI]

    Crosetto, D.B.

    1996-12-31

    The present device provides for a dynamically configurable communication network having a multi-processor parallel processing system having a serial communication network and a high speed parallel communication network. The serial communication network is used to disseminate commands from a master processor to a plurality of slave processors to effect communication protocol, to control transmission of high density data among nodes and to monitor each slave processor`s status. The high speed parallel processing network is used to effect the transmission of high density data among nodes in the parallel processing system. Each node comprises a transputer, a digital signal processor, a parallel transfer controller, and two three-port memory devices. A communication switch within each node connects it to a fast parallel hardware channel through which all high density data arrives or leaves the node. 6 figs.

  15. Parallel processing data network of master and slave transputers controlled by a serial control network

    DOE Patents [OSTI]

    Crosetto, Dario B.

    1996-01-01

    The present device provides for a dynamically configurable communication network having a multi-processor parallel processing system having a serial communication network and a high speed parallel communication network. The serial communication network is used to disseminate commands from a master processor (100) to a plurality of slave processors (200) to effect communication protocol, to control transmission of high density data among nodes and to monitor each slave processor's status. The high speed parallel processing network is used to effect the transmission of high density data among nodes in the parallel processing system. Each node comprises a transputer (104), a digital signal processor (114), a parallel transfer controller (106), and two three-port memory devices. A communication switch (108) within each node (100) connects it to a fast parallel hardware channel (70) through which all high density data arrives or leaves the node.

  16. Advance Network Reservation and Provisioning for Science

    SciTech Connect (OSTI)

    Balman, Mehmet; Chaniotakis, Evangelos; Shoshani, Arie; Sim, Alex

    2009-07-10

    We are witnessing a new era that offers new opportunities to conduct scientific research with the help of recent advancements in computational and storage technologies. Computational intensive science spans multiple scientific domains, such as particle physics, climate modeling, and bio-informatics simulations. These large-scale applications necessitate collaborators to access very large data sets resulting from simulations performed in geographically distributed institutions. Furthermore, often scientific experimental facilities generate massive data sets that need to be transferred to validate the simulation data in remote collaborating sites. A major component needed to support these needs is the communication infrastructure which enables high performance visualization, large volume data analysis, and also provides access to computational resources. In order to provide high-speed on-demand data access between collaborating institutions, national governments support next generation research networks such as Internet 2 and ESnet (Energy Sciences Network). Delivering network-as-a-service that provides predictable performance, efficient resource utilization and better coordination between compute and storage resources is highly desirable. In this paper, we study network provisioning and advanced bandwidth reservation in ESnet for on-demand high performance data transfers. We present a novel approach for path finding in time-dependent transport networks with bandwidth guarantees. We plan to improve the current ESnet advance network reservation system, OSCARS [3], by presenting to the clients, the possible reservation options and alternatives for earliest completion time and shortest transfer duration. The Energy Sciences Network (ESnet) provides high bandwidth connections between research laboratories and academic institutions for data sharing and video/voice communication. The ESnet On-Demand Secure Circuits and Advance Reservation System (OSCARS) establishes guaranteed bandwidth of secure virtual circuits at a certain time, for a certain bandwidth and length of time. Though OSCARS operates within the ESnet, it also supplies end-to-end provisioning between multiple autonomous network domains. OSCARS gets reservation requests through a standard web service interface, and conducts a Quality-of-service (QoS) path for bandwidth guarantees. Multi-protocol Label Switching (MPLS) and the Resource Reservation Protocol (RSVP) enable to create a virtual circuit using Label Switched Paths (LSP's). It contains three main components: a reservation manager, a bandwidth scheduler, and a path setup subsystem. The bandwidth scheduler needs to have information about the current and future states of the network topology in order to accomplish end-to-end bandwidth guaranteed paths.

  17. The Global Environment Radiation Monitoring Network (GERMON)

    SciTech Connect (OSTI)

    Zakheim, B.J.; Goellner, D.A.

    1994-12-31

    Following the Chernobyl accident in 1986, a group of experts from the World Health Organization (WHO) and the United Nations Environment Program (UNEP) met in France to discuss and develop the basic principles of a global environmental radiation monitoring network (GERMON). The basic functions of this network were to provide regular reports on environmental radiation levels and to be in a position to provide reliable and accurate radiation measurements on a quick and accurate radiation measurements on a quick turnaround basis in the event of a major radiation release. By 1992, although 58 countries had indicated an interest in becoming a part of the GERMON system, only 16 were providing data on a regular basis. This paper traces the history of GERMON from its inception in 1987 through its activities during 1993-4. It details the objectives of the network, describes functions, lists its participants, and presents obstacles in the current network. The paper examines the data requirements for radiological emergency preparedness and offers suggestions for the current system. The paper also describes the growing need for such a network. To add a domestic perspective, the authors present a summary of the environmental monitoring information system that was used by the NRC in 1986 in its analyses of the Chernobyl incident. Then we will use this 1986 experience to propose a method for the use of GERMON should a similar occasion arise in the future.

  18. Gigabit network technology. Final technical report

    SciTech Connect (OSTI)

    Davenport, C.M.C. [ed.

    1996-10-01

    Current digital networks are evolving toward distributed multimedia with a wide variety of applications with individual data rates ranging from kb/sec to tens and hundreds of Mb/sec. Link speed requirements are pushing into the Gb/sec range and beyond the envelop of electronic networking capabilities. There is a vast amount of untapped bandwidth available in the low-attenuation communication bands of an optical fiber. The capacity in one fiber thread is enough to carry more than two thousand times as much information as all the current radio and microwave frequencies. And while fiber optics has replaced copper wire as the transmission medium of choice, the communication capacity of conventional fiber optic networks is ultimately limited by electronic processing speeds.

  19. Better Buildings Network View | July-August 2015 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    5 Better Buildings Network View | July-August 2015 The Better Buildings Network View monthly newsletter from the U.S. Department of Energy's Better Buildings Residential Network. PDF icon Better Buildings Network View July-August 2015 More Documents & Publications Better Buildings Network View | May 2015 Better Buildings Network View | June 2015 Better Buildings Network View | April

  20. Position estimation of transceivers in communication networks

    DOE Patents [OSTI]

    Kent, Claudia A.; Dowla, Farid

    2008-06-03

    This invention provides a system and method using wireless communication interfaces coupled with statistical processing of time-of-flight data to locate by position estimation unknown wireless receivers. Such an invention can be applied in sensor network applications, such as environmental monitoring of water in the soil or chemicals in the air where the position of the network nodes is deemed critical. Moreover, the present invention can be arranged to operate in areas where a Global Positioning System (GPS) is not available, such as inside buildings, caves, and tunnels.