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, 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.

  6. 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.

  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. Delayed switching applied to memristor neural networks

    SciTech Connect (OSTI)

    Wang, Frank Z.; Yang Xiao; Lim Guan; Helian Na; Wu Sining; Guo Yike; Rashid, Md Mamunur

    2012-04-01

    Magnetic flux and electric charge are linked in a memristor. We reported recently that a memristor has a peculiar effect in which the switching takes place with a time delay because a memristor possesses a certain inertia. This effect was named the ''delayed switching effect.'' In this work, we elaborate on the importance of delayed switching in a brain-like computer using memristor neural networks. The effect is used to control the switching of a memristor synapse between two neurons that fire together (the Hebbian rule). A theoretical formula is found, and the design is verified by a simulation. We have also built an experimental setup consisting of electronic memristive synapses and electronic neurons.

  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. Neural Networks for Modeling and Control of Particle Accelerators

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

    Edelen, A. L.; Biedron, S. G.; Chase, B. E.; Edstrom, D.; Milton, S. V.; Stabile, P.

    2016-04-01

    Myriad nonlinear and complex physical phenomena are host to particle accelerators. They often involve a multitude of interacting systems, are subject to tight performance demands, and should be able to run for extended periods of time with minimal interruptions. Often times, traditional control techniques cannot fully meet these requirements. One promising avenue is to introduce machine learning and sophisticated control techniques inspired by artificial intelligence, particularly in light of recent theoretical and practical advances in these fields. Within machine learning and artificial intelligence, neural networks are particularly well-suited to modeling, control, and diagnostic analysis of complex, nonlinear, and time-varying systems,more » as well as systems with large parameter spaces. Consequently, the use of neural network-based modeling and control techniques could be of significant benefit to particle accelerators. For the same reasons, particle accelerators are also ideal test-beds for these techniques. Moreover, many early attempts to apply neural networks to particle accelerators yielded mixed results due to the relative immaturity of the technology for such tasks. For the purpose of this paper is to re-introduce neural networks to the particle accelerator community and report on some work in neural network control that is being conducted as part of a dedicated collaboration between Fermilab and Colorado State University (CSU). We also describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.« less

  19. 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.

  20. 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.

  1. Real-time neural network earthquake profile predictor

    DOE Patents [OSTI]

    Leach, Richard R.; Dowla, Farid U.

    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.

  2. 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.

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

    Office of Scientific and Technical Information (OSTI)

    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 ...

  4. Neural network guided search control in partial order planning

    SciTech Connect (OSTI)

    Zimmerman, T.

    1996-12-31

    The development of efficient search control methods is an active research topic in the field of planning. Investigation of a planning program integrated with a neural network (NN) that assists in search control is underway, and has produced promising preliminary results.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. Application of neural networks to waste site screening

    SciTech Connect (OSTI)

    Dabiri, A.E.; Kraft, T.; Hilton, J.M.

    1993-03-01

    Waste site screening requires knowledge of the actual concentrations of hazardous materials and rates of flow around and below the site with time. The present approach to site screening consists primarily of drilling, boreholes near contaminated site and chemically analyzing the extracted physical samples and processing the data. In addition, hydraulic and geochemical soil properties are obtained so that numerical simulation models can be used to interpret and extrapolate the field data. The objective of this work is to investigate the feasibility of using neural network techniques to reduce the cost of waste site screening. A successful technique may lead to an ability to reduce the number of boreholes and the number of samples analyzed from each borehole to properly screen the waste site. The analytic tool development described here is inexpensive because it makes use of neural network techniques that can interpolate rapidly and which can learn how to analyze data rather than having to be explicitly programmed. In the following sections, data collection and data analyses will be described, followed by a section on different neural network techniques used. The results will be presented and compared with mathematical model. Finally, the last section will summarize the research work performed and make several recommendations for future work.

  16. 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.

  17. The application of neural networks with artificial intelligence technique in the modeling of industrial processes

    SciTech Connect (OSTI)

    Saini, K. K.; Saini, Sanju

    2008-10-07

    Neural networks are a relatively new artificial intelligence technique that emulates the behavior of biological neural systems in digital software or hardware. These networks can 'learn', automatically, complex relationships among data. This feature makes the technique very useful in modeling processes for which mathematical modeling is difficult or impossible. The work described here outlines some examples of the application of neural networks with artificial intelligence technique in the modeling of industrial processes.

  18. 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).

  19. An application of neural networks to process and materials control

    SciTech Connect (OSTI)

    Howell, J.A.; Whiteson, R. )

    1991-01-01

    Process control consists of two basic elements: a model of the process and knowledge of the desired control algorithm. In some cases the level of the control algorithm is merely supervisory, as in an alarm-reporting or anomaly-detection system. If the model of the process is known, then a set of equations may often be solved explicitly to provide the control algorithm. Otherwise, the model has to be discovered through empirical studies. Neural networks have properties that make them useful in this application. The problems of anomaly detection in nuclear materials control systems fits well into this general control framework. To successfully model a process with a neutral network, a good set of observable must be chosen. These observable just in some sense adequately span the space of representable events, so that a signature metric can be built for normal operation. In this way, a non-normal event, one that does not fit within the signature, can be detected. In this paper, the authors discuss the issues involved in applying a neural network model to anomaly detection in materials control systems.

  20. 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....

  1. 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.

  2. 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

  3. 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

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. Flaws Identification Using Eddy Current Differential Transducer and Artificial Neural Networks

    SciTech Connect (OSTI)

    Chady, T.; Lopato, P.

    2006-03-06

    In this paper we present a multi-frequency excitation eddy current differential transducer and dynamic neural models which were used to detect and identify artificial flaws in thin conducting plates. Plates are made of Inconel600. EDM notches have relative depth from 10% to 80% and length from 2 mm to 7 mm. All flaws were located on the opposite surface of the examined specimen. Measured signals were used as input for training and verifying dynamic neural networks with a moving window. Wide range of ANN (Artificial Neural Network) structures are examined for different window length and different number of frequency components in excitation signal. Observed trends are presented in this paper.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. THE CHOICE OF OPTIMAL STRUCTURE OF ARTIFICIAL NEURAL NETWORK CLASSIFIER INTENDED FOR CLASSIFICATION OF WELDING FLAWS

    SciTech Connect (OSTI)

    Sikora, R.; Chady, T.; Baniukiewicz, P.; Caryk, M.; Piekarczyk, B.

    2010-02-22

    Nondestructive testing and evaluation are under continuous development. Currently researches are concentrated on three main topics: advancement of existing methods, introduction of novel methods and development of artificial intelligent systems for automatic defect recognition (ADR). Automatic defect classification algorithm comprises of two main tasks: creating a defect database and preparing a defect classifier. Here, the database was built using defect features that describe all geometrical and texture properties of the defect. Almost twenty carefully selected features calculated for flaws extracted from real radiograms were used. The radiograms were obtained from shipbuilding industry and they were verified by qualified operator. Two weld defect's classifiers based on artificial neural networks were proposed and compared. First model consisted of one neural network model, where each output neuron corresponded to different defect group. The second model contained five neural networks. Each neural network had one neuron on output and was responsible for detection of defects from one group. In order to evaluate the effectiveness of the neural networks classifiers, the mean square errors were calculated for test radiograms and compared.

  17. Computing single step operators of logic programming in radial basis function neural networks

    SciTech Connect (OSTI)

    Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong

    2014-07-10

    Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (T{sub p}:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.

  18. Development of a Neural Network Simulator for Studying the Constitutive Behavior of Structural Composite Materials

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

    Na, Hyuntae; Lee, Seung-Yub; Üstündag, Ersan; Ross, Sarah L.; Ceylan, Halil; Gopalakrishnan, Kasthurirangan

    2013-01-01

    This paper introduces a recent development and application of a noncommercial artificial neural network (ANN) simulator with graphical user interface (GUI) to assist in rapid data modeling and analysis in the engineering diffraction field. The real-time network training/simulation monitoring tool has been customized for the study of constitutive behavior of engineering materials, and it has improved data mining and forecasting capabilities of neural networks. This software has been used to train and simulate the finite element modeling (FEM) data for a fiber composite system, both forward and inverse. The forward neural network simulation precisely reduplicates FEM results several orders ofmore » magnitude faster than the slow original FEM. The inverse simulation is more challenging; yet, material parameters can be meaningfully determined with the aid of parameter sensitivity information. The simulator GUI also reveals that output node size for materials parameter and input normalization method for strain data are critical train conditions in inverse network. The successful use of ANN modeling and simulator GUI has been validated through engineering neutron diffraction experimental data by determining constitutive laws of the real fiber composite materials via a mathematically rigorous and physically meaningful parameter search process, once the networks are successfully trained from the FEM database.« less

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. Study of t anti-t production in tau jets channel at CDFII using neural networks

    SciTech Connect (OSTI)

    Amerio, Silvia; /Trento U.

    2005-12-01

    CDF (Collider Detector at Fermilab) is a particle detector located at Fermi National Laboratories, near Chicago. it allows to study decay products of p{bar p} collisions at center-of-mass energy of 1.96 TeV. During its first period of data taking (RunI), CDF observed for the first time the top quark (1995). The current period of data taking (RunII) is devoted to precise measurements of top properties and to search for new physics. This thesis work is about the top decay channel named {tau} + jets. A t{bar t} pair decays in two W bosons and two b quarks. In a {tau} + jets event, one out of the two W decays into two jets of hadrons, while the other produces a {tau} lepton and a neutrino; the {tau} decays semileptonically in one or more charged and neutral pions while b quarks hadronize producing two jets of particles. Thus the final state of a {tau} + jets event has this specific signature: five jets, one {tau}-like, i.e. narrow and with low track multiplicity, two from b quarks, two from a W boson and a large amount of missing energy from two {tau} neutrinos. They search for this signal in 311 pb{sup -1} of data collected with TOP{_}MULTIJET trigger. They use neural networks to separate signal from background and on the selected sample they perform a t{bar t} production cross section measurement. The thesis is structured as follows: in Chapter 1 they outline the physics of top and {tau}, concentrating on their discovery, production mechanisms and current physics results involving them. Chapter 2 is devoted to the description of the experimental setup: the accelerator complex first and CDF detector then. The trigger system is described in Chapter 3, while Chapter 4 shows how particles are reconstructed exploiting information from different CDF subdetectors. With Chapter 5 they begin to present their analysis: we use a feed forward neural network based on a minimization algorithm developed in Trento University, called Reactive Taboo Search (RTS), especially designed

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. A probablistic neural network classification system for signal and image processing

    SciTech Connect (OSTI)

    Bowman, B.

    1994-11-15

    The Acoustical Heart Valve Analysis Package is a system for signal and image processing and classification. It is being developed in both Matlab and C, to provide an interactive, interpreted environment, and has been optimized for large scale matrix operations. It has been used successfully to classify acoustic signals from implanted prosthetic heart valves in human patients, and will be integrated into a commercial Heart Valve Screening Center. The system uses several standard signal processing algorithms, as well as supervised learning techniques using the probabilistic neural network (PNN). Although currently used for the acoustic heart valve application, the algorithms and modular design allow it to be used for other applications, as well. We will describe the signal classification system, and show results from a set of test valves.

  14. 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)

  15. NSDann2BS, a neutron spectrum unfolding code based on neural networks technology and two bonner spheres

    SciTech Connect (OSTI)

    Ortiz-Rodriguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Solis Sanches, L. O.; Miranda, R. Castaneda; Cervantes Viramontes, J. M.; Vega-Carrillo, H. R.

    2013-07-03

    In this work a neutron spectrum unfolding code, based on artificial intelligence technology is presented. The code called ''Neutron Spectrometry and Dosimetry with Artificial Neural Networks and two Bonner spheres'', (NSDann2BS), was designed in a graphical user interface under the LabVIEW programming environment. The main features of this code are to use an embedded artificial neural network architecture optimized with the ''Robust design of artificial neural networks methodology'' and to use two Bonner spheres as the only piece of information. In order to build the code here presented, once the net topology was optimized and properly trained, knowledge stored at synaptic weights was extracted and using a graphical framework build on the LabVIEW programming environment, the NSDann2BS code was designed. This code is friendly, intuitive and easy to use for the end user. The code is freely available upon request to authors. To demonstrate the use of the neural net embedded in the NSDann2BS code, the rate counts of {sup 252}Cf, {sup 241}AmBe and {sup 239}PuBe neutron sources measured with a Bonner spheres system.

  16. 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.

  17. Evaluating the performance of two neutron spectrum unfolding codes based on iterative procedures and artificial neural networks

    SciTech Connect (OSTI)

    Ortiz-Rodriguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Solis Sanches, L. O.; Miranda, R. Castaneda; Cervantes Viramontes, J. M.; Vega-Carrillo, H. R.

    2013-07-03

    In this work the performance of two neutron spectrum unfolding codes based on iterative procedures and artificial neural networks is evaluated. The first one code based on traditional iterative procedures and called Neutron spectrometry and dosimetry from the Universidad Autonoma de Zacatecas (NSDUAZ) use the SPUNIT iterative algorithm and was designed to unfold neutron spectrum and calculate 15 dosimetric quantities and 7 IAEA survey meters. The main feature of this code is the automated selection of the initial guess spectrum trough a compendium of neutron spectrum compiled by the IAEA. The second one code known as Neutron spectrometry and dosimetry with artificial neural networks (NDSann) is a code designed using neural nets technology. The artificial intelligence approach of neural net does not solve mathematical equations. By using the knowledge stored at synaptic weights on a neural net properly trained, the code is capable to unfold neutron spectrum and to simultaneously calculate 15 dosimetric quantities, needing as entrance data, only the rate counts measured with a Bonner spheres system. Similarities of both NSDUAZ and NSDann codes are: they follow the same easy and intuitive user's philosophy and were designed in a graphical interface under the LabVIEW programming environment. Both codes unfold the neutron spectrum expressed in 60 energy bins, calculate 15 dosimetric quantities and generate a full report in HTML format. Differences of these codes are: NSDUAZ code was designed using classical iterative approaches and needs an initial guess spectrum in order to initiate the iterative procedure. In NSDUAZ, a programming routine was designed to calculate 7 IAEA instrument survey meters using the fluence-dose conversion coefficients. NSDann code use artificial neural networks for solving the ill-conditioned equation system of neutron spectrometry problem through synaptic weights of a properly trained neural network. Contrary to iterative procedures, in neural

  18. 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

  19. Comparison of a Recurrent Neural Network PV System Model with a Traditional Component-Based PV System Model

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

    a Recurrent Neural Network PV System Model with a Traditional Component-Based PV System Model Daniel Riley, Sandia National Laboratories, Albuquerque, New Mexico, USA | Ganesh K. Venayagamoorthy, Missouri University of Science and Technology, Rolla, Missouri, USA Abstract Traditional PV system modeling approaches require system components to be tested in order to determine performance parameters. In some cases, system owners may wish to predict system performance, but lack the parameters

  20. 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.

  1. Neural Network Based State of Health Diagnostics for an Automated Radioxenon Sampler/Analyzer

    SciTech Connect (OSTI)

    Keller, Paul E.; Kangas, Lars J.; Hayes, James C.; Schrom, Brian T.; Suarez, Reynold; Hubbard, Charles W.; Heimbigner, Tom R.; McIntyre, Justin I.

    2009-05-13

    Artificial neural networks (ANNs) are used to determine the state-of-health (SOH) of the Automated Radioxenon Analyzer/Sampler (ARSA). ARSA is a gas collection and analysis system used for non-proliferation monitoring in detecting radioxenon released during nuclear tests. SOH diagnostics are important for automated, unmanned sensing systems so that remote detection and identification of problems can be made without onsite staff. Both recurrent and feed-forward ANNs are presented. The recurrent ANN is trained to predict sensor values based on current valve states, which control air flow, so that with only valve states the normal SOH sensor values can be predicted. Deviation between modeled value and actual is an indication of a potential problem. The feed-forward ANN acts as a nonlinear version of principal components analysis (PCA) and is trained to replicate the normal SOH sensor values. Because of ARSA’s complexity, this nonlinear PCA is better able to capture the relationships among the sensors than standard linear PCA and is applicable to both sensor validation and recognizing off-normal operating conditions. Both models provide valuable information to detect impending malfunctions before they occur to avoid unscheduled shutdown. Finally, the ability of ANN methods to predict the system state is presented.

  2. 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.

  3. 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).

  4. Measurement of the top pair production cross section at CDF using neural networks

    SciTech Connect (OSTI)

    Marginean, Radu

    2004-11-01

    In the Tevatron accelerator at Fermilab protons and antiprotons are collided at a 1.96 TeV center of mass energy. CDF and D0 are the two experiments currently operating at the Tevatron. At these energies top quark is mostly produced via strong interactions as a top anti-top pair (t{bar t}). The top quark has an extremely short lifetime and according to the Standard Model it decays with {approx} 100% probability into a b quark and a W boson. In the ''lepton+jets'' channel, the signal from top pair production is detected for those events where one of the two W bosons decays hadronically in two quarks which we see as jets in the detector, and the other W decays into an electrically charged lepton and a neutrino. A relatively unambiguous identification in the detector is possible when we require that the charged lepton must be an electron or muon of either charge. The neutrino does not interact in the detector and its presence is inferred from an imbalance in the transverse energy of the event. They present a measurement of the top pair production cross section in p{bar p} collisions at 1.96 TeV, from a data sample collected at CDF between March 2002 and September 2003 with an integrated luminosity of 193.5 pb{sup -1}. In order to bring the signal to background ratio at manageable levels, measurements in this channel traditionally use precision tracking information to identify at least one secondary vertex produced in the decay of a long lived b hadron. A different approach is taken here. Because of the large mass of the top quark, t{bar t} events tend to be more spherical and more energetic than most of the background processes which otherwise mimic the t{bar t} signature in the ''lepton+jets'' channel. A number of energy based and event shape variables can be used to statistically discriminate between signal and background events. Monte Carlo simulation is used to model the kinematics of t{bar t} and most of the background processes. A neural network technique is

  5. 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.

  6. 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

  7. 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.

  8. The Calculation Of Absorbing Thin Film Optical Constants And Electronic Structure From Photometric Measures On Domain IR-VIS-UV Using Neural Networks

    SciTech Connect (OSTI)

    Bourouis, Chahrazed; Meddour, Ahcene; Moussaoui, Abdelkrim

    2008-09-23

    In this paper a new method using the combination of Neural Networks and the Newton-Raphson algorithm is developped. The technique consists of the use of the solution obtained by Newton-Raphson algorithm between 0.5 and 2.1eV for pure manganese (Mn) and for the amorphous metallic alloy Al{sub 88}Mn{sub 12}, to construct two parts of datasets; the first one is used for training the neural network and the second one for the validation tests. The validated neural network model is applied to the determination of optical constants of the two materials Mn and Al{sub 88}Mn{sub 12} in the range of 0.5 and 6.2eV (IR-VIS-UV). The results obtained over all the studied energy range are used to trace back to dielectric function, optical absorption and electronic structure of the same material. By using the partial solution obtained by Newton-Raphson as a database of the neural network prediction model, it is shown that the obtained results are in accordance with those of the literature which consolidate the efficiency of the suggested approach.

  9. 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).

  10. Using a Neural Network to Determine the Hatch Status of the AERI at the ARM North Slope of Alaska Site

    SciTech Connect (OSTI)

    Zwink, AB; Turner, DD

    2012-03-19

    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 (Knuteson et al. 2004). Limit switches connected with the hatch controller provide a signal of the hatch state: open, closed, undetermined (typically associated with the hatch being between fully open or fully closed during the instrument's sky view period), or an error condition. The instrument then records the state of the hatch with the radiance data so that samples taken when the hatch is not open can be removed from any subsequent analysis. However, the hatch controller suffered a multi-year failure for the AERI located at the ARM North Slope of Alaska (NSA) Central Facility in Barrow, Alaska, from July 2006-February 2008. The failure resulted in misreporting the state of the hatch in the 'hatchOpen' field within the AERI data files. With this error there is no simple solution to translate what was reported back to the correct hatch status, thereby making it difficult for an analysis to determine when the AERI was actually viewing the sky. As only the data collected when the hatch is fully open are scientifically useful, an algorithm was developed to determine whether the hatch was open or closed based on spectral radiance data from the AERI. Determining if the hatch is open or closed in a scene with low clouds is non-trivial, as low opaque clouds may look very similar spectrally as the closed hatch. This algorithm used a backpropagation neural network; these types of neural networks have been used with increasing frequency in atmospheric science applications.

  11. 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.

  12. 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.

  13. 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.

  14. 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...

  15. Study of Single Top Quark Production Using Bayesian Neural Networks With D0 Detector at the Tevatron

    SciTech Connect (OSTI)

    Joshi, Jyoti

    2012-01-01

    Top quark, the heaviest and most intriguing among the six known quarks, can be created via two independent production mechanisms in {\\pp} collisions. The primary mode, strong {\\ttbar} pair production from a $gtt$ vertex, was used by the {\\d0} and CDF collaborations to establish the existence of the top quark in March 1995. The second mode is the electroweak production of a single top quark or antiquark, which has been observed recently in March 2009. Since single top quarks are produced at hadron colliders through a $Wtb$ vertex, thereby provide a direct probe of the nature of $Wtb$ coupling and of the Cabibbo-Kobayashi-Maskawa matrix element, $V_{tb}$. So this mechanism provides a sensitive probe for several, standard model and beyond standard model, parameters such as anomalous $Wtb$ couplings. In this thesis, we measure the cross section of the electroweak produced top quark in three different production modes, $s+t$, $s$ and $t$-channels using a technique based on the Bayesian neural networks. This technique is applied for analysis of the 5.4 $fb^{-1}$ of data collected by the {\\d0} detector. From a comparison of the Bayesian neural networks discriminants between data and the signal-background model using Bayesian statistics, the cross sections of the top quark produced through the electroweak mechanism have been measured as: \\[\\sigma(p\\bar{p}→tb+X,tqb+X) = 3.11^{+0.77}_{-0.71}\\;\\rm pb\\] \\[\\sigma(p\\bar{p}→tb+X) = 0.72^{+0.44}_{-0.43}\\;\\rm pb\\] \\[\\sigma(p\\bar{p}→tqb+X) = 2.92^{+0.87}_{-0.73}\\;\\rm pb\\] % The $s+t$-channel has a gaussian significance of $4.7\\sigma$, the $s$-channel $0.9\\sigma$ and the $t$-channel~$4.7\\sigma$. The results are consistent with the standard model predictions within one standard deviation. By combining these results with the results for two other analyses (using different MVA techniques) improved results \\[\\sigma(p\\bar{p}→tb+X,tqb+X) = 3.43^{+0.73}_{-0.74}\\;\\rm pb\\] \\[\\sigma

  16. 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.

  17. 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:

  18. 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...

  19. 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.

  20. 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}.

  1. 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.

  2. 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)

  3. 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)

  4. Network

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

    ESnet About ESnet Our Mission The Network ESnet History Governance & Policies 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 About ESnet A Platform for Science Discovery The Energy Sciences Network (ESnet) is a high-performance, unclassified network built to

  5. 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.

  6. 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).

  7. 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.

  8. 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...

  9. 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.

  10. Neural-net based real-time economic dispatch for thermal power plants

    SciTech Connect (OSTI)

    Djukanovic, M.; Milosevic, B.; Calovic, M.; Sobajic, D.J.

    1996-12-01

    This paper proposes the application of artificial neural networks to real-time optimal generation dispatch of thermal units. The approach can take into account the operational requirements and network losses. The proposed economic dispatch uses an artificial neural network (ANN) for generation of penalty factors, depending on the input generator powers and identified system load change. Then, a few additional iterations are performed within an iterative computation procedure for the solution of coordination equations, by using reference-bus penalty-factors derived from the Newton-Raphson load flow. A coordination technique for environmental and economic dispatch of pure thermal systems, based on the neural-net theory for simplified solution algorithms and improved man-machine interface is introduced. Numerical results on two test examples show that the proposed algorithm can efficiently and accurately develop optimal and feasible generator output trajectories, by applying neural-net forecasts of system load patterns.

  11. 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...

  12. 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.

  13. 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

  14. 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)

  15. 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. ...

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

    Office of Scientific and Technical Information (OSTI)

    Chronic, Multi-Contact, Neural Interface for Deep Brain Stimulation Citation Details In-Document Search Title: Chronic, Multi-Contact, Neural Interface for Deep Brain Stimulation ...

  17. 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 ; ...

  18. 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.

  19. 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.

  20. Nothing But Networking for Residential Network Members

    Broader source: Energy.gov [DOE]

    Better Buildings Residential Network Peer Exchange Call: Nothing But Networking for Residential Network Members, Call Slides and Discussion Summary, March 12, 2015.

  1. 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.

  2. 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

  3. 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

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

    Energy Savers [EERE]

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

  5. 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...

  6. 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.

  7. 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

  8. 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.

  9. Neural-net based coordinated stabilizing control for the exciter and governor loops of low head hydropower plants

    SciTech Connect (OSTI)

    Djukanovic, M.; Novicevic, M.; Dobrijevic, D.; Babic, B.; Sobajic, D.J.; Pao, Y.H. |

    1995-12-01

    This paper presents a design technique of a new adaptive optimal controller of the low head hydropower plant using artificial neural networks (ANN). The adaptive controller is to operate in real time to improve the generating unit transients through the exciter input, the guide vane position and the runner blade position. The new design procedure is based on self-organization and the predictive estimation capabilities of neural-nets implemented through the cluster-wise segmented associative memory scheme. The developed neural-net based controller (NNC) whose control signals are adjusted using the on-line measurements, can offer better damping effects for generator oscillations over a wide range of operating conditions than conventional controllers. Digital simulations of hydropower plant equipped with low head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, state-space optimal control and neural-net based control are presented. Results obtained on the non-linear mathematical model demonstrate that the effects of the NNC closely agree with those obtained using the state-space multivariable discrete-time optimal controllers.

  10. ESnet Network Operating System (ENOS)

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

    Experimental Network Testbeds Performance (perfSONAR) Software & Tools Development Data ... Blog ESnet Live Home Network R&D Software-Defined Networking (SDN) ENOS Network ...

  11. 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.

  12. Inhibition of Sirt1 promotes neural progenitors toward motoneuron...

    Office of Scientific and Technical Information (OSTI)

    Sirt1, to promote neural precursor cell (NPC) development during differentiation ... in the development of hESC-based cell therapy in motoneuron disease. less ...

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

    Office of Scientific and Technical Information (OSTI)

    Electrode-Immune System Interface Monitor through Neural Stimulation in American Cockroach ... of Science (DOE SC) Country of Publication: United States Language: English Subject: CHEM

  14. 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, ...

  15. 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

  16. 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...

  17. 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.

  18. 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.

  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. 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.

  1. 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.

  2. 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

  3. 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

  4. Metallic nanowire networks

    DOE Patents [OSTI]

    Song, Yujiang; Shelnutt, John A.

    2012-11-06

    A metallic nanowire network synthesized using chemical reduction of a metal ion source by a reducing agent in the presence of a soft template comprising a tubular inverse micellar network. The network of interconnected polycrystalline nanowires has a very high surface-area/volume ratio, which makes it highly suitable for use in catalytic applications.

  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. LBNL Transactional Network Applications

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

    Transactional Network Environmental Energy Technologies Division, Lawrence Berkeley National Laboratory LBNL Team: Mary Ann Piette, Richard Brown, Phil Price, Janie Page, Stephen Czarnecki, Anna Liao, Stephen Lanzisera, Jessica Granderson . LBNL Transactional Network Applications 2 | Building Technologies Office eere.energy.gov LBNL Transactional Network Applications Baseline Load Shape provides basis for measuring change in peak demand and energy use Demand Response Event Scheduler coordinates

  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. 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...

  9. The EB factory project. I. A fast, neural-net-based, general purpose light curve classifier optimized for eclipsing binaries

    SciTech Connect (OSTI)

    Paegert, Martin; Stassun, Keivan G.; Burger, Dan M.

    2014-08-01

    We describe a new neural-net-based light curve classifier and provide it with documentation as a ready-to-use tool for the community. While optimized for identification and classification of eclipsing binary stars, the classifier is general purpose, and has been developed for speed in the context of upcoming massive surveys such as the Large Synoptic Survey Telescope. A challenge for classifiers in the context of neural-net training and massive data sets is to minimize the number of parameters required to describe each light curve. We show that a simple and fast geometric representation that encodes the overall light curve shape, together with a chi-square parameter to capture higher-order morphology information results in efficient yet robust light curve classification, especially for eclipsing binaries. Testing the classifier on the ASAS light curve database, we achieve a retrieval rate of 98% and a false-positive rate of 2% for eclipsing binaries. We achieve similarly high retrieval rates for most other periodic variable-star classes, including RR Lyrae, Mira, and delta Scuti. However, the classifier currently has difficulty discriminating between different sub-classes of eclipsing binaries, and suffers a relatively low (∼60%) retrieval rate for multi-mode delta Cepheid stars. We find that it is imperative to train the classifier's neural network with exemplars that include the full range of light curve quality to which the classifier will be expected to perform; the classifier performs well on noisy light curves only when trained with noisy exemplars. The classifier source code, ancillary programs, a trained neural net, and a guide for use, are provided.

  10. 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.

  11. 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.

  12. 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

  13. 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...

  14. Seven Deadliest Network Attacks

    SciTech Connect (OSTI)

    Prowell, Stacy J; Borkin, Michael; Kraus, Robert

    2010-05-01

    Do you need to keep up with the latest hacks, attacks, and exploits effecting networks? Then you need "Seven Deadliest Network Attacks". This book pinpoints the most dangerous hacks and exploits specific to networks, laying out the anatomy of these attacks including how to make your system more secure. You will discover the best ways to defend against these vicious hacks with step-by-step instruction and learn techniques to make your computer and network impenetrable. Attacks detailed in this book include: Denial of Service; War Dialing; Penetration 'Testing'; Protocol Tunneling; Spanning Tree Attacks; Man-in-the-Middle; and, Password Replay. Knowledge is power, find out about the most dominant attacks currently waging war on computers and networks globally. Discover the best ways to defend against these vicious attacks; step-by-step instruction shows you how. Institute countermeasures, don't be caught defenseless again, learn techniques to make your computer and network impenetrable.

  15. Reconfigureable network node

    DOE Patents [OSTI]

    Vanderveen, Keith B.; Talbot, Edward B.; Mayer, Laurence E.

    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.

  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. 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 and building new ones is essential to finding a job-especially for a dual career family that is new to the Los Alamos area. Networking is a proven and effective way to increase

  18. BER Science Network Requirements

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

    ... However, once this step is completed, the network transfers of data or documentation may not need the same level of protection accorded to the authentication credentials. For the ...

  19. LBNL Transactional Network Applications

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

    Load Shape provides basis for measuring change in peak demand and energy use Demand Response Event Scheduler coordinates DR signals from outside server with available network ...

  20. 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 ... Energy Storage Nuclear Power & Engineering Grid Modernization Battery Testing Nuclear Fuel ...

  1. Rooftop Unit Network Project

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

    part load performance - equipment maintenance * RTUs cannot easily interact with the ... Diagnostics - RTU Network Platform * Smart Monitoring and Diagnostics - Cloud * Autonomous ...

  2. Network Requirements Reviews

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

    Reviews Network Requirements Reviews Documents and Background Materials FAQ for Case Study Authors BER Requirements Review 2015 ASCR Requirements Review 2015 Previous...

  3. 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...

  4. Better Buildings Residential Network | Department of Energy

    Energy Savers [EERE]

    Residential Buildings Better Buildings Residential Network Better Buildings Residential Network Better Buildings Residential Network Explore Latest Peer Exchange Call Summaries ...

  5. 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...

  6. 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.

  7. Residential Network Members Unite to Form Green Bank Network...

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

    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 ...

  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. 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.

  11. 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.

  12. 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.

  13. 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.

  14. Energy Efficient Digital Networks

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

    and rising * About 7% of all U.S. electricity consumption -Much of this digitally networked already Our Future? Media room in high-end home Electronics are Different - Service ...

  15. Energy Materials Network Overview

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

    30 th , 2016 2 MGI - Framework New Material Innovations for Clean Energy 2X Faster and 2X Cheaper Predictive Simulation Across Scales Synthesis & Characterization Rapid Screening End Use Performance Process Scalability Process Control Real-time Characterization Reliability Validation Data Management & Informatics Coordinated resource network with a suite of capabilities for advanced materials R&D In Support of the Materials Genome Initiative (MGI) 3 Network Requirements 1. WORLD

  16. 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

  17. 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

  18. Network topology mapper

    DOE Patents [OSTI]

    Quist, Daniel A.; Gavrilov, Eugene M.; Fisk, Michael E.

    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.

  19. 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

  20. 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

  1. 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.

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

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

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

  3. 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...

  4. 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...

  5. 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...

  6. Solar Instructor Training Network | Department of Energy

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

    Instructor Training Network Solar Instructor Training Network The Solar Instructor Training Network promotes high-quality training in the installation of solar technologies. Nine ...

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

    Energy Savers [EERE]

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

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

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

    ... Residential Network (Residential Network) Better Buildings Residential Network: Connects energy efficiency programs and partners to share best practices to increase the ...

  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.; TeGrotenhuis, Ward E.; Whyatt, Greg A.

    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. Microsystem process networks

    DOE Patents [OSTI]

    Wegeng, Robert S.; TeGrotenhuis, Ward E.; Whyatt, Greg A.

    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.

  12. 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.

  13. BER Science Network Requirements

    SciTech Connect (OSTI)

    Alapaty, Kiran; Allen, Ben; Bell, Greg; Benton, David; Brettin, Tom; Canon, Shane; Dart, Eli; Cotter, Steve; Crivelli, Silvia; Carlson, Rich; Dattoria, Vince; Desai, Narayan; Egan, Richard; Tierney, Brian; Goodwin, Ken; Gregurick, Susan; Hicks, Susan; Johnston, Bill; de Jong, Bert; Kleese van Dam, Kerstin; Livny, Miron; Markowitz, Victor; McGraw, Jim; McCord, Raymond; Oehmen, Chris; Regimbal, Kevin; Shipman, Galen; Strand, Gary; Flick, Jeff; Turnbull, Susan; Williams, Dean; Zurawski, Jason

    2010-11-01

    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 2010 ESnet and the Office of Biological and Environmental Research, of the DOE Office of Science, organized a workshop to characterize the networking requirements of the science programs funded by BER. The requirements identified at the workshop are summarized and described in more detail in the case studies and the Findings section. A number of common themes emerged from the case studies and workshop discussions. One is that BER science, like many other disciplines, is becoming more and more distributed and collaborative in nature. Another common theme is that data set sizes are exploding. Climate Science in particular is on the verge of needing to manage exabytes of data, and Genomics is on the verge of a huge paradigm shift in the number of sites with sequencers and the amount of sequencer data being generated.

  14. Energy Materials Network Workshop

    Office of Energy Efficiency and Renewable Energy (EERE)

    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.

  15. Residential Network Members Unite to Form Green Bank Network

    Broader source: Energy.gov [DOE]

    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.

  16. Software Defined Networking (SDN) Project

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

    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

  17. 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.

  18. 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:...

  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. Better Buildings Network View | February 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.

  1. Better Buildings Network View | November 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.

  2. 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.

  3. Better Buildings Network View | September 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.

  4. Better Buildings Network View | June 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.

  5. Better Buildings Network View | May 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.

  6. 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.

  7. Better Buildings Network View | October 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.

  8. Better Buildings Network View | October 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.

  9. Better Buildings Network View | January 2016

    Broader source: Energy.gov [DOE]

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

  10. Better Buildings Network View | February 2016

    Broader source: Energy.gov [DOE]

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

  11. Better Buildings Network View | January 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.

  12. 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.

  13. Exploiting Network Parallelism

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

    Exploiting Network Parallelism for Improving Data Transfer Performance Dan Gunter ∗ , Raj Kettimuthu † , Ezra Kissel ‡ , Martin Swany ‡ , Jun Yi § , Jason Zurawski ¶ ∗ Advanced Computing for Science Department, Lawrence Berkeley National Laboratory, Berkeley, CA † Mathematics and Computer Science Division, Argonne National Laboratory Argonne, IL ‡ School of Informatics and Computing, Indiana University, Bloomington, IN § Computation Institute, University of Chicago/Argonne

  14. Experimental Network Testbeds

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

    100G SDN Testbed Dark Fiber Testbed Test Circuit Service Testbed Results Current Testbed Research Previous Testbed Research 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 » Experimental

  15. 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.

  16. 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.

  17. Fuzzy neural order robust of the non-linear systems

    SciTech Connect (OSTI)

    Madour, F.; Benmahammed, K.

    2008-06-12

    This article introduces a controller at structure of a network multi-layer neurons specified by the fuzzy reasoning of Takagi-Sugeno (TS) order one, the weights of the network represent the standard deviations of the membership function. This controller is applied to the ordering of a reversed pendulum. Changes in the entries and the exit, as of the environment changes of operation are introduced in order to test the robustness of the designed controller.

  18. 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

  19. 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).

  20. 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.

  1. 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.

  2. 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

  3. 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.

  4. 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.

  5. 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.

  6. Network Upgrade for the SLC: PEP II Network

    SciTech Connect (OSTI)

    Crane, M.; Call, M.; Clark, S.; Coffman, F.; Himel, T.; Lahey, T.; Miller, E.; Sass, R.; /SLAC

    2011-09-09

    The PEP-II control system required a new network to support the system functions. This network, called CTLnet, is an FDDI/Ethernet based network using only TCP/IP protocols. An upgrade of the SLC Control System micro communications to use TCP/IP and SLCNET would allow all PEP-II control system nodes to use TCP/IP. CTLnet is private and separate from the SLAC public network. Access to nodes and control system functions is provided by multi-homed application servers with connections to both the private CTLnet and the SLAC public network. Monitoring and diagnostics are provided using a dedicated system. Future plans and current status information is included.

  7. 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.

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

    Office of Environmental Management (EM)

    newsletter from the U.S. Department of Energy's Better Buildings Residential Network. ... Better Buildings Network View | June 2015 Nothing But Networking for Residential Network ...

  9. Global interrupt and barrier networks

    DOE Patents [OSTI]

    Blumrich, Matthias A.; Chen, Dong; Coteus, Paul W.; Gara, Alan G.; Giampapa, Mark E; Heidelberger, Philip; Kopcsay, Gerard V.; Steinmacher-Burow, Burkhard D.; Takken, Todd E.

    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.

  10. 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.

  11. 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.

  12. 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.

  13. 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

  14. Network Information System

    SciTech Connect (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 are 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

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

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

    Office of Scientific and Technical Information (OSTI)

    inductions components at each time step and it is trained by 2-d measurements ... of the neural system returns the predicted value of the field H at the same time step. ...

  1. 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.

  2. 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:...

  3. 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...

  4. 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...

  5. 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

  6. Better Buildings Network View, April 2015

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

    ... Laboratory) announcement emails sent to Residential Network members or via the Residential Network Group on Home Energy Pros. To receive emails about upcoming calls, contact ...

  7. Better Buildings Network View, July 2014

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

    ... summaries through announcement emails sent to Residential Network members or via the Residential Network Group on Home Energy Pros. To receive emails about upcoming calls email ...

  8. 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 ...

  9. 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 ...

  10. 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...