The myth of science-based predictive modeling.
Hemez, F. M. (François M.)
2004-01-01
A key aspect of science-based predictive modeling is the assessment of prediction credibility. This publication argues that the credibility of a family of models and their predictions must combine three components: (1) the fidelity of predictions to test data; (2) the robustness of predictions to variability, uncertainty, and lack-of-knowledge; and (3) the prediction accuracy of models in cases where measurements are not available. Unfortunately, these three objectives are antagonistic. A recently published Theorem that demonstrates the irrevocable trade-offs between fidelity-to-data, robustness-to-uncertainty, and confidence in prediction is summarized. High-fidelity models cannot be made increasingly robust to uncertainty and lack-of-knowledge. Similarly, robustness-to-uncertainty can only be improved at the cost of reducing the confidence in prediction. The concept of confidence in prediction relies on a metric for total uncertainty, capable of aggregating different representations of uncertainty (probabilistic or not). The discussion is illustrated with an engineering application where a family of models is developed to predict the acceleration levels obtained when impacts of varying levels propagate through layers of crushable hyper-foam material of varying thicknesses. Convex modeling is invoked to represent a severe lack-of-knowledge about the constitutive material behavior. The analysis produces intervals of performance metrics from which the total uncertainty and confidence levels are estimated. Finally, performance, robustness and confidence are extrapolated throughout the validation domain to assess the predictive power of the family of models away from tested configurations.
QMC Simulations DataBase for Predictive Theory and Modeling ...
Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)
CO monoxide adsorbs on the Pt (111) surface CO monoxide adsorbs on the Pt (111) surface. One application of the QMC Simulations Database for the Predictive Modeling and Theory...
Liu, Y. A.
Predictive Modeling of Large-Scale Commercial Water Desalination Plants: Data-Based Neural Network for developing predictive models for large-scale commercial water desalination plants by (1) a data (MSF) and reverse osmosis (RO) desalination plants in the world. Our resulting neural network
An approach to model validation and model-based prediction -- polyurethane foam case study.
Dowding, Kevin J.; Rutherford, Brian Milne
2003-07-01
Enhanced software methodology and improved computing hardware have advanced the state of simulation technology to a point where large physics-based codes can be a major contributor in many systems analyses. This shift toward the use of computational methods has brought with it new research challenges in a number of areas including characterization of uncertainty, model validation, and the analysis of computer output. It is these challenges that have motivated the work described in this report. Approaches to and methods for model validation and (model-based) prediction have been developed recently in the engineering, mathematics and statistical literatures. In this report we have provided a fairly detailed account of one approach to model validation and prediction applied to an analysis investigating thermal decomposition of polyurethane foam. A model simulates the evolution of the foam in a high temperature environment as it transforms from a solid to a gas phase. The available modeling and experimental results serve as data for a case study focusing our model validation and prediction developmental efforts on this specific thermal application. We discuss several elements of the ''philosophy'' behind the validation and prediction approach: (1) We view the validation process as an activity applying to the use of a specific computational model for a specific application. We do acknowledge, however, that an important part of the overall development of a computational simulation initiative is the feedback provided to model developers and analysts associated with the application. (2) We utilize information obtained for the calibration of model parameters to estimate the parameters and quantify uncertainty in the estimates. We rely, however, on validation data (or data from similar analyses) to measure the variability that contributes to the uncertainty in predictions for specific systems or units (unit-to-unit variability). (3) We perform statistical analyses and hypothesis tests as a part of the validation step to provide feedback to analysts and modelers. Decisions on how to proceed in making model-based predictions are made based on these analyses together with the application requirements. Updating modifying and understanding the boundaries associated with the model are also assisted through this feedback. (4) We include a ''model supplement term'' when model problems are indicated. This term provides a (bias) correction to the model so that it will better match the experimental results and more accurately account for uncertainty. Presumably, as the models continue to develop and are used for future applications, the causes for these apparent biases will be identified and the need for this supplementary modeling will diminish. (5) We use a response-modeling approach for our predictions that allows for general types of prediction and for assessment of prediction uncertainty. This approach is demonstrated through a case study supporting the assessment of a weapons response when subjected to a hydrocarbon fuel fire. The foam decomposition model provides an important element of the response of a weapon system in this abnormal thermal environment. Rigid foam is used to encapsulate critical components in the weapon system providing the needed mechanical support as well as thermal isolation. Because the foam begins to decompose at temperatures above 250 C, modeling the decomposition is critical to assessing a weapons response. In the validation analysis it is indicated that the model tends to ''exaggerate'' the effect of temperature changes when compared to the experimental results. The data, however, are too few and to restricted in terms of experimental design to make confident statements regarding modeling problems. For illustration, we assume these indications are correct and compensate for this apparent bias by constructing a model supplement term for use in the model-based predictions. Several hypothetical prediction problems are created and addressed. Hypothetical problems are used because no guidance was provided concern
VISUALIZING MODEL-BASED PREDICTIVE CONTROLLERS StephanieGuerlain Greg JamjesonandPeter Bullemer
Virginia, University of
control ayd optimize large sections of a petrochemical process;yqng a predictive model. However, current-based predictive controllers (MPC) are becoming very popular in petrochemical refineries, as they simultaneously
Predictive models of safety based on audit findings: Part 1: Model development and reliability
Wu, Changxu (Sean)
tools to carry out an ergonomic evaluation of maintenance and inspection operations. It was validated, we developed a Human Factors/Ergonomics classifi- cation framework based on HFACS model (Shappell to proceed with prediction validity testing in Part 2. Ó 2012 Elsevier Ltd and The Ergonomics Society. All
Daigle, Matthew
, and availability. Prognos- tics deals with determining the health state of compo- nents, and projecting) predictions. Model-based prognos- tics approaches perform these tasks with the aid of a model that captures
Interval Methods for Sensitivity-Based Model-Predictive Control of
Kearfott, R. Baker
Interval Methods for Sensitivity-Based Model-Predictive Control of Solid Oxide Fuel Cell Systems and experiment for the thermal subprocess of a high-temperature solid oxide fuel cell system. Keywords: Interval analysis, model-predictive control, sensitivity analysis, tracking control, solid oxide fuel cells AMS
Prediction Models for a Smart Home based Health Care System Vikramaditya R. Jakkula1
Cook, Diane J.
Prediction Models for a Smart Home based Health Care System Vikramaditya R. Jakkula1 , Diane J health care. Smart health care systems at home can be used to provide such solutions. A technology a prediction model in an intelligent smart home system can be used for identifying health trends over time
A data-based approach for multivariate model predictive control performance monitoring$
Chen, Sheng
A data-based approach for multivariate model predictive control performance monitoring$ Xuemin Tian of Petroleum (Hua Dong), Donying, Shandong 257061, China b School of Electronics and Computer Science by J. Zhang Available online 20 October 2010 Keywords: Model predictive control Performance monitoring
Comparison between JET Profile Data and the Predictions of a Transport Model Based on ITG and Trapped Electron Modes
OPERATOR INTERACTION WITH MODEL-BASED PREDICTIVE CONTROLLERS IN PETROCHEMICAL REFINING
Virginia, University of
OPERATOR INTERACTION WITH MODEL-BASED PREDICTIVE CONTROLLERS IN PETROCHEMICAL REFINING Greg A in process control to the more thoroughly studied Flight Management System (FMS) employed in airline cockpits and challenging task. Keywords: Cognitive task analysis; Process control; Predictive control; Optimization
Hybrid Model Predictive Control Based on Wireless Sensor Feedback: An Experimental Study
Johansson, Karl Henrik
Hybrid Model Predictive Control Based on Wireless Sensor Feedback: An Experimental Study Alberto based on measurements collected by a wireless sensor network. The proposed setup is the prototype of an industrial application in which a remote station controls the process via wireless network links
Prediction of Functional Sites Based on the Fuzzy Oil Drop Model
Skolnick, Jeff
Prediction of Functional Sites Based on the Fuzzy Oil Drop Model Michal Brylin´ski1,2 , Katarzyna, Astronomy and Applied Computer Science, Jagiellonian University, Krako´w, Poland, 4 Institute of Medical Oil Drop model. PLoS Comput Biol 3(5): e94. doi:10.1371/journal.pcbi.0030094 Introduction Because
A Novel Industry Grade Dataset for Fault Prediction based on Model-Driven Developed
A Novel Industry Grade Dataset for Fault Prediction based on Model-Driven Developed Automotive a novel industry dataset on static software and change metrics for Matlab/Simulink models and their corresponding auto-generated C source code. The data set comprises data of three automotive projects developed
Pedram, Massoud
Trace-Based Analysis and Prediction of Cloud Computing User Behavior Using the Fractal Modeling and technology. In this paper, we investigate the characteristics of the cloud computing requests received the alpha- stable distribution. Keywords- cloud computing; alpha-stable distribution; fractional order
Scenario-Based Fault-Tolerant Model Predictive Control for Diesel-Electric Marine Power Plant
Johansen, Tor Arne
Scenario-Based Fault-Tolerant Model Predictive Control for Diesel-Electric Marine Power Plant Email: torstein.bo@itk.ntnu.no, tor.arne.johansen@itk.ntnu.no Abstract--Diesel-electric propulsion generation control, Ma- rine safety, Optimal control. I. INTRODUCTION Diesel electric propulsion is a system
2014Science About the cover: A new transcriptomics-based model accurately predicts how much
2014Science Frontiers #12;About the cover: A new transcriptomics-based model accurately predicts's Environmental Molecular Sciences Laboratory: Making Isoprene from Biomass Material Using Bacillus Species. Pacific Northwest National Laboratory (PNNL) is a U.S. Department of Energy (DOE), Office of Science
Sanandaji, Borhan M.
Physically Based Model-Predictive Control for SOFC Stacks and Systems Tyrone L. Vincent, Borhan output tra- jectory. The process is demonstrated for a tubular SOFC stack that could be used, solid-oxide fuel cells (SOFC) must deliver power profiles that meet the demands of transient loads
Linear Model-Based Predictive Control of the LHC 1.8 K Cryogenic Loop
Blanco-Viñuela, E; De Prada-Moraga, C
1999-01-01
The LHC accelerator will employ 1800 superconducting magnets (for guidance and focusing of the particle beams) in a pressurized superfluid helium bath at 1.9 K. This temperature is a severely constrained control parameter in order to avoid the transition from the superconducting to the normal state. Cryogenic processes are difficult to regulate due to their highly non-linear physical parameters (heat capacity, thermal conductance, etc.) and undesirable peculiarities like non self-regulating process, inverse response and variable dead time. To reduce the requirements on either temperature sensor or cryogenic system performance, various control strategies have been investigated on a reduced-scale LHC prototype built at CERN (String Test). Model Based Predictive Control (MBPC) is a regulation algorithm based on the explicit use of a process model to forecast the plant output over a certain prediction horizon. This predicted controlled variable is used in an on-line optimization procedure that minimizes an approp...
BFEPM:Best Fit Energy Prediction Modeling Based on CPU Utilization Xiao Zhang, Jianjun Lu, Xiao Qin
Qin, Xiao
BFEPM:Best Fit Energy Prediction Modeling Based on CPU Utilization Xiao Zhang, Jianjun Lu, Xiao Qin BFEPM, a best fit energy prediction model. It choose best model based on the power consumption benchmark Engineering Auburn University Auburn, AL USA 36849-5347 Email: xqin@auburn.edu Abstract--Energy cost becomes
A voxel-based finite element model for the prediction of bladder deformation
Chai Xiangfei; Herk, Marcel van; Hulshof, Maarten C. C. M.; Bel, Arjan [Radiation Oncology Department, Academic Medical Center, University of Amsterdam, 1105 AZ Amsterdam (Netherlands); Radiation Oncology Department, Netherlands Cancer Institute, 1066 CX Amsterdam (Netherlands); Radiation Oncology Department, Academic Medical Center, University of Amsterdam, 1105 AZ Amsterdam (Netherlands)
2012-01-15
Purpose: A finite element (FE) bladder model was previously developed to predict bladder deformation caused by bladder filling change. However, two factors prevent a wide application of FE models: (1) the labor required to construct a FE model with high quality mesh and (2) long computation time needed to construct the FE model and solve the FE equations. In this work, we address these issues by constructing a low-resolution voxel-based FE bladder model directly from the binary segmentation images and compare the accuracy and computational efficiency of the voxel-based model used to simulate bladder deformation with those of a classical FE model with a tetrahedral mesh. Methods: For ten healthy volunteers, a series of MRI scans of the pelvic region was recorded at regular intervals of 10 min over 1 h. For this series of scans, the bladder volume gradually increased while rectal volume remained constant. All pelvic structures were defined from a reference image for each volunteer, including bladder wall, small bowel, prostate (male), uterus (female), rectum, pelvic bone, spine, and the rest of the body. Four separate FE models were constructed from these structures: one with a tetrahedral mesh (used in previous study), one with a uniform hexahedral mesh, one with a nonuniform hexahedral mesh, and one with a low-resolution nonuniform hexahedral mesh. Appropriate material properties were assigned to all structures and uniform pressure was applied to the inner bladder wall to simulate bladder deformation from urine inflow. Performance of the hexahedral meshes was evaluated against the performance of the standard tetrahedral mesh by comparing the accuracy of bladder shape prediction and computational efficiency. Results: FE model with a hexahedral mesh can be quickly and automatically constructed. No substantial differences were observed between the simulation results of the tetrahedral mesh and hexahedral meshes (<1% difference in mean dice similarity coefficient to manual contours and <0.02 cm difference in mean standard deviation of residual errors). The average equation solving time (without manual intervention) for the first two types of hexahedral meshes increased to 2.3 h and 2.6 h compared to the 1.1 h needed for the tetrahedral mesh, however, the low-resolution nonuniform hexahedral mesh dramatically decreased the equation solving time to 3 min without reducing accuracy. Conclusions: Voxel-based mesh generation allows fast, automatic, and robust creation of finite element bladder models directly from binary segmentation images without user intervention. Even the low-resolution voxel-based hexahedral mesh yields comparable accuracy in bladder shape prediction and more than 20 times faster in computational speed compared to the tetrahedral mesh. This approach makes it more feasible and accessible to apply FE method to model bladder deformation in adaptive radiotherapy.
Towards Accurate and Practical Predictive Models of Active-Vision-Based Visual Search
Hornof, Anthony
which permit increasingly realistic and accurate predictions for visual human-computer interaction tasks not practical. For as long as human-computer interaction has been studied, researchers have been working@cs.uoregon.edu ABSTRACT Being able to predict the performance of interface designs using models of human cognition
Predictive models of safety based on audit findings: Part 2: Measurement of model validity
Wu, Changxu (Sean)
prediction Neural network Aviation maintenance a b s t r a c t Part 1 of this study sequence developed predictors of future safety performance in the aviation maintenance field. Ó 2013 Elsevier Ltd, using monthly data on safety performance regarding the maintenance activities of two different airlines
ghMulti-Level Approach for Model-Based Predictive Control (MPC) in Buildings: A Preliminary Overview
Candanedo, J. A.; Dehkordi, V. R.
2013-01-01
Model-based predictive control (MPC) has emerged in recent years as a promising approach to building operation. MPC uses models of the system(s) under control -and knowledge about future disturbances- to select an optimal set of actions. Despite its...
Abdel-Aal, Radwan E.
and boiler operating conditions. Prediction performance compares favourably with neural network models for future work to further improve performance. Index Terms: Mercury speciation, Flue gases, Boiler emissions activities are coal-fired electric utility boilers, where speciation depends on the operating conditions
Paris-Sud XI, Université de
Short-term Wind Power Prediction for Offshore Wind Farms - Evaluation of Fuzzy-Neural Network Based of offshore farms and their secure integration to the grid. Modeling the behavior of large wind farms presents the new considerations that have to be made when dealing with large offshore wind farms
Johnson, J. D.; Oberkampf, William Louis; Helton, Jon Craig (Arizona State University, Tempe, AZ); Storlie, Curtis B. (North Carolina State University, Raleigh, NC)
2006-10-01
Evidence theory provides an alternative to probability theory for the representation of epistemic uncertainty in model predictions that derives from epistemic uncertainty in model inputs, where the descriptor epistemic is used to indicate uncertainty that derives from a lack of knowledge with respect to the appropriate values to use for various inputs to the model. The potential benefit, and hence appeal, of evidence theory is that it allows a less restrictive specification of uncertainty than is possible within the axiomatic structure on which probability theory is based. Unfortunately, the propagation of an evidence theory representation for uncertainty through a model is more computationally demanding than the propagation of a probabilistic representation for uncertainty, with this difficulty constituting a serious obstacle to the use of evidence theory in the representation of uncertainty in predictions obtained from computationally intensive models. This presentation describes and illustrates a sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory. Preliminary trials indicate that the presented strategy can be used to propagate uncertainty representations based on evidence theory in analysis situations where naive sampling-based (i.e., unsophisticated Monte Carlo) procedures are impracticable due to computational cost.
Supervised Learning Based Model for Predicting Variability-Induced Timing Errors
Gupta, Rajesh
combat variations in hardware and workload by increasing conservative guardbanding that leads, for a given amount of guardband reduction. The proposed methodology enables a model-based rule method the robustness of our modeling methodology by considering various operating voltage and temperature corners. Our
Model Predictive Control-based Optimal Coordination of Distributed Energy Resources
Mayhorn, Ebony T.; Kalsi, Karanjit; Lian, Jianming; Elizondo, Marcelo A.
2013-01-07
Distributed energy resources, such as renewable energy resources (wind, solar), energy storage and demand response, can be used to complement conventional generators. The uncertainty and variability due to high penetration of wind makes reliable system operations and controls challenging, especially in isolated systems. In this paper, an optimal control strategy is proposed to coordinate energy storage and diesel generators to maximize wind penetration while maintaining system economics and normal operation performance. The goals of the optimization problem are to minimize fuel costs and maximize the utilization of wind while considering equipment life of generators and energy storage. Model predictive control (MPC) is used to solve a look-ahead dispatch optimization problem and the performance is compared to an open loop look-ahead dispatch problem. Simulation studies are performed to demonstrate the efficacy of the closed loop MPC in compensating for uncertainties and variability caused in the system.
Model Predictive Control-based Optimal Coordination of Distributed Energy Resources
Mayhorn, Ebony T.; Kalsi, Karanjit; Lian, Jianming; Elizondo, Marcelo A.
2013-04-03
Distributed energy resources, such as renewable energy resources (wind, solar), energy storage and demand response, can be used to complement conventional generators. The uncertainty and variability due to high penetration of wind makes reliable system operations and controls challenging, especially in isolated systems. In this paper, an optimal control strategy is proposed to coordinate energy storage and diesel generators to maximize wind penetration while maintaining system economics and normal operation performance. The goals of the optimization problem are to minimize fuel costs and maximize the utilization of wind while considering equipment life of generators and energy storage. Model predictive control (MPC) is used to solve a look-ahead dispatch optimization problem and the performance is compared to an open loop look-ahead dispatch problem. Simulation studies are performed to demonstrate the efficacy of the closed loop MPC in compensating for uncertainties and variability caused in the system.
Lall, Pradeep; Wei, Junchao; Davis, J Lynn
2014-06-24
Abstract— Solid-state lighting (SSL) luminaires containing light emitting diodes (LEDs) have the potential of seeing excessive temperatures when being transported across country or being stored in non-climate controlled warehouses. They are also being used in outdoor applications in desert environments that see little or no humidity but will experience extremely high temperatures during the day. This makes it important to increase our understanding of what effects high temperature exposure for a prolonged period of time will have on the usability and survivability of these devices. Traditional light sources “burn out” at end-of-life. For an incandescent bulb, the lamp life is defined by B50 life. However, the LEDs have no filament to “burn”. The LEDs continually degrade and the light output decreases eventually below useful levels causing failure. Presently, the TM-21 test standard is used to predict the L70 life of LEDs from LM-80 test data. Several failure mechanisms may be active in a LED at a single time causing lumen depreciation. The underlying TM-21 Model may not capture the failure physics in presence of multiple failure mechanisms. Correlation of lumen maintenance with underlying physics of degradation at system-level is needed. In this paper, Kalman Filter (KF) and Extended Kalman Filters (EKF) have been used to develop a 70-percent Lumen Maintenance Life Prediction Model for LEDs used in SSL luminaires. Ten-thousand hour LM-80 test data for various LEDs have been used for model development. System state at each future time has been computed based on the state space at preceding time step, system dynamics matrix, control vector, control matrix, measurement matrix, measured vector, process noise and measurement noise. The future state of the lumen depreciation has been estimated based on a second order Kalman Filter model and a Bayesian Framework. Life prediction of L70 life for the LEDs used in SSL luminaires from KF and EKF based models have been compared with the TM-21 model predictions and experimental data.
Grimm, Lars J., E-mail: Lars.grimm@duke.edu; Ghate, Sujata V.; Yoon, Sora C.; Kim, Connie [Department of Radiology, Duke University Medical Center, Box 3808, Durham, North Carolina 27710 (United States)] [Department of Radiology, Duke University Medical Center, Box 3808, Durham, North Carolina 27710 (United States); Kuzmiak, Cherie M. [Department of Radiology, University of North Carolina School of Medicine, 2006 Old Clinic, CB No. 7510, Chapel Hill, North Carolina 27599 (United States)] [Department of Radiology, University of North Carolina School of Medicine, 2006 Old Clinic, CB No. 7510, Chapel Hill, North Carolina 27599 (United States); Mazurowski, Maciej A. [Duke University Medical Center, Box 2731 Medical Center, Durham, North Carolina 27710 (United States)] [Duke University Medical Center, Box 2731 Medical Center, Durham, North Carolina 27710 (United States)
2014-03-15
Purpose: The purpose of this study is to explore Breast Imaging-Reporting and Data System (BI-RADS) features as predictors of individual errors made by trainees when detecting masses in mammograms. Methods: Ten radiology trainees and three expert breast imagers reviewed 100 mammograms comprised of bilateral medial lateral oblique and craniocaudal views on a research workstation. The cases consisted of normal and biopsy proven benign and malignant masses. For cases with actionable abnormalities, the experts recorded breast (density and axillary lymph nodes) and mass (shape, margin, and density) features according to the BI-RADS lexicon, as well as the abnormality location (depth and clock face). For each trainee, a user-specific multivariate model was constructed to predict the trainee's likelihood of error based on BI-RADS features. The performance of the models was assessed using area under the receive operating characteristic curves (AUC). Results: Despite the variability in errors between different trainees, the individual models were able to predict the likelihood of error for the trainees with a mean AUC of 0.611 (range: 0.502–0.739, 95% Confidence Interval: 0.543–0.680,p < 0.002). Conclusions: Patterns in detection errors for mammographic masses made by radiology trainees can be modeled using BI-RADS features. These findings may have potential implications for the development of future educational materials that are personalized to individual trainees.
Understanding Uncertainties in Model-Based Predictions of Aedes aegypti Population Dynamics
Lloyd, Alun
million cases of dengue fever per year, 500,000 cases of dengue hemorrhagic fever (DHF) or dengue shock of America Abstract Background: Aedes aegypti is one of the most important mosquito vectors of human disease/Principal Findings: This study quantifies uncertainties in the predicted mosquito population dynamics
Harrison, T.D.
1981-05-01
Thermal performance predictions based on test data are presented for the Polisolar Model POL solar collector, with glass reflector surfaces, for three output temperatures at five cities in the United States.
Bayesian Methods in Nutrition Epidemiology and Regression-based Predictive Models in Healthcare
Zhang, Saijuan
2012-02-14
This dissertation has mainly two parts. In the first part, we propose a bivariate nonlinear multivariate measurement error model to understand the distribution of dietary intake and extend it to a multivariate model to capture dietary patterns...
crosswind flight [18], which essentially consists in extracting power from the airflow by flying an airfoil generation based on crosswind flight over conventional wind turbines is that higher altitude can be reached
Mirzahosseini et. al. ANN-Based Prediction Model for Rutting Propensity of Asphalt Mixtures1
Fernandez, Thomas
in a repeated load permanent deformation test.21 Dynamic creep test is one of the best tools for assessing of load cycles is the most23 important output of the dynamic creep test. This curve includes primary based on volumetric proportioning of the asphalt mixture. This method does16 not include any direct test
Mishra, Srikanta; Schuetter, Jared
2014-11-01
We compare two approaches for building a statistical proxy model (metamodel) for CO? geologic sequestration from the results of full-physics compositional simulations. The first approach involves a classical Box-Behnken or Augmented Pairs experimental design with a quadratic polynomial response surface. The second approach used a space-filling maxmin Latin Hypercube sampling or maximum entropy design with the choice of five different meta-modeling techniques: quadratic polynomial, kriging with constant and quadratic trend terms, multivariate adaptive regression spline (MARS) and additivity and variance stabilization (AVAS). Simulations results for CO? injection into a reservoir-caprock system with 9 design variables (and 97 samples) were used to generate the data for developing the proxy models. The fitted models were validated with using an independent data set and a cross-validation approach for three different performance metrics: total storage efficiency, CO? plume radius and average reservoir pressure. The Box-Behnken–quadratic polynomial metamodel performed the best, followed closely by the maximin LHS–kriging metamodel.
DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)
Ali, Ashehad A.; Medlyn, Belinda E.; Aubier, Thomas G.; Crous, Kristine Y.; Reich, Peter B.
2015-10-06
Differential species responses to atmospheric CO2 concentration (Ca) could lead to quantitative changes in competition among species and community composition, with flow-on effects for ecosystem function. However, there has been little theoretical analysis of how elevated Ca (eCa) will affect plant competition, or how composition of plant communities might change. Such theoretical analysis is needed for developing testable hypotheses to frame experimental research. Here, we investigated theoretically how plant competition might change under eCa by implementing two alternative competition theories, resource use theory and resource capture theory, in a plant carbon and nitrogen cycling model. The model makes several novelmore »predictions for the impact of eCa on plant community composition. Using resource use theory, the model predicts that eCa is unlikely to change species dominance in competition, but is likely to increase coexistence among species. Using resource capture theory, the model predicts that eCa may increase community evenness. Collectively, both theories suggest that eCa will favor coexistence and hence that species diversity should increase with eCa. Our theoretical analysis leads to a novel hypothesis for the impact of eCa on plant community composition. In this study, the hypothesis has potential to help guide the design and interpretation of eCa experiments.« less
Model Predictive Control Wind Turbines
Model Predictive Control of Wind Turbines Martin Klauco Kongens Lyngby 2012 IMM-MSc-2012-65 #12;Summary Wind turbines are the biggest part of the green energy industry. Increasing interest control strategies. Control strategy has a significant impact on the wind turbine operation on many levels
Introduction Earthquake prediction research is based on
Haak, Hein
126 Introduction Earthquake prediction research is based on understanding the long-term behaviour the 1934 event as an anomaly, a prediction was issued in 1985 that the next earthquake in this series would occur before 19931) . The Parkfield prediction was the only scientific earthquake prediction officially
Papalambros, Panos
MODEL PREDICTIVE CONTROL OF A MICROGRID WITH PLUG-IN VEHICLES: ERROR MODELING AND THE ROLE) for a microgrid with plug-in vehicles. A predictive model is de- veloped based on a hub model of the microgrid INTRODUCTION Recently, the control of electrical microgrids has been the focus of research efforts. A microgrid
Fenning, David P.
2013-04-10
Synchrotron-based X-ray fluorescence microscopy is applied to study the evolution of iron silicide precipitates during phosphorus diffusion gettering and low-temperature annealing. Heavily Fe-contaminated ingot border ...
Model Predictive Control for Energy Efficient Buildings
Ma, Yudong
2012-01-01
Model Predictive Control and Thermal Storage: a Simple 3.3of Building Thermal Storage”. In: ASHRAE Transactions 96.2 (and Passive Building Thermal Storage”. In: International
Hou, Zhangshuan; Makarov, Yuri V.; Samaan, Nader A.; Etingov, Pavel V.
2013-03-19
Given the multi-scale variability and uncertainty of wind generation and forecast errors, it is a natural choice to use time-frequency representation (TFR) as a view of the corresponding time series represented over both time and frequency. Here we use wavelet transform (WT) to expand the signal in terms of wavelet functions which are localized in both time and frequency. Each WT component is more stationary and has consistent auto-correlation pattern. We combined wavelet analyses with time series forecast approaches such as ARIMA, and tested the approach at three different wind farms located far away from each other. The prediction capability is satisfactory -- the day-ahead prediction of errors match the original error values very well, including the patterns. The observations are well located within the predictive intervals. Integrating our wavelet-ARIMA (‘stochastic’) model with the weather forecast model (‘deterministic’) will improve our ability significantly to predict wind power generation and reduce predictive uncertainty.
Progress towards a PETN Lifetime Prediction Model
Burnham, A K; Overturf III, G E; Gee, R; Lewis, P; Qiu, R; Phillips, D; Weeks, B; Pitchimani, R; Maiti, A; Zepeda-Ruiz, L; Hrousis, C
2006-09-11
Dinegar (1) showed that decreases in PETN surface area causes EBW detonator function times to increase. Thermal aging causes PETN to agglomerate, shrink, and densify indicating a ''sintering'' process. It has long been a concern that the formation of a gap between the PETN and the bridgewire may lead to EBW detonator failure. These concerns have led us to develop a model to predict the rate of coarsening that occurs with age for thermally driven PETN powder (50% TMD). To understand PETN contributions to detonator aging we need three things: (1) Curves describing function time dependence on specific surface area, density, and gap. (2) A measurement of the critical gap distance for no fire as a function of density and surface area for various wire configurations. (3) A model describing how specific surface area, density and gap change with time and temperature. We've had good success modeling high temperature surface area reduction and function time increase using a phenomenological deceleratory kinetic model based on a distribution of parallel nth-order reactions having evenly spaced activation energies where weighing factors of the reactions follows a Gaussian distribution about the reaction with the mean activation energy (Figure 1). Unfortunately, the mean activation energy derived from this approach is high (typically {approx}75 kcal/mol) so that negligible sintering is predicted for temperatures below 40 C. To make more reliable predictions, we've established a three-part effort to understand PETN mobility. First, we've measured the rates of step movement and pit nucleation as a function of temperature from 30 to 50 C for single crystals. Second, we've measured the evaporation rate from single crystals and powders from 105 to 135 C to obtain an activation energy for evaporation. Third, we've pursued mechanistic kinetic modeling of surface mobility, evaporation, and ripening.
Predictive Capability Maturity Model for computational modeling and simulation.
Oberkampf, William Louis; Trucano, Timothy Guy; Pilch, Martin M.
2007-10-01
The Predictive Capability Maturity Model (PCMM) is a new model that can be used to assess the level of maturity of computational modeling and simulation (M&S) efforts. The development of the model is based on both the authors experience and their analysis of similar investigations in the past. The perspective taken in this report is one of judging the usefulness of a predictive capability that relies on the numerical solution to partial differential equations to better inform and improve decision making. The review of past investigations, such as the Software Engineering Institute's Capability Maturity Model Integration and the National Aeronautics and Space Administration and Department of Defense Technology Readiness Levels, indicates that a more restricted, more interpretable method is needed to assess the maturity of an M&S effort. The PCMM addresses six contributing elements to M&S: (1) representation and geometric fidelity, (2) physics and material model fidelity, (3) code verification, (4) solution verification, (5) model validation, and (6) uncertainty quantification and sensitivity analysis. For each of these elements, attributes are identified that characterize four increasing levels of maturity. Importantly, the PCMM is a structured method for assessing the maturity of an M&S effort that is directed toward an engineering application of interest. The PCMM does not assess whether the M&S effort, the accuracy of the predictions, or the performance of the engineering system satisfies or does not satisfy specified application requirements.
Paris-Sud XI, Université de
A Parallel Statistical Learning Approach to the Prediction of Building Energy Consumption Based consumption of buildings based on historical performances is an important approach to achieve energy (SVMs), Prediction, Model, Energy Efficiency, Parallel Computing. 1. INTRODUCTION Building energy
Mishra, Srikanta; Jin, Larry; He, Jincong; Durlofsky, Louis
2015-06-30
Reduced-order models provide a means for greatly accelerating the detailed simulations that will be required to manage CO2 storage operations. In this work, we investigate the use of one such method, POD-TPWL, which has previously been shown to be effective in oil reservoir simulation problems. This method combines trajectory piecewise linearization (TPWL), in which the solution to a new (test) problem is represented through a linearization around the solution to a previously-simulated (training) problem, with proper orthogonal decomposition (POD), which enables solution states to be expressed in terms of a relatively small number of parameters. We describe the application of POD-TPWL for CO2-water systems simulated using a compositional procedure. Stanford’s Automatic Differentiation-based General Purpose Research Simulator (AD-GPRS) performs the full-order training simulations and provides the output (derivative matrices and system states) required by the POD-TPWL method. A new POD-TPWL capability introduced in this work is the use of horizontal injection wells that operate under rate (rather than bottom-hole pressure) control. Simulation results are presented for CO2 injection into a synthetic aquifer and into a simplified model of the Mount Simon formation. Test cases involve the use of time-varying well controls that differ from those used in training runs. Results of reasonable accuracy are consistently achieved for relevant well quantities. Runtime speedups of around a factor of 370 relative to full- order AD-GPRS simulations are achieved, though the preprocessing needed for POD-TPWL model construction corresponds to the computational requirements for about 2.3 full-order simulation runs. A preliminary treatment for POD-TPWL modeling in which test cases differ from training runs in terms of geological parameters (rather than well controls) is also presented. Results in this case involve only small differences between training and test runs, though they do demonstrate that the approach is able to capture basic solution trends. The impact of some of the detailed numerical treatments within the POD-TPWL formulation is considered in an Appendix. ii
In silico modeling to predict drug-induced phospholipidosis
Choi, Sydney S.; Kim, Jae S.; Valerio, Luis G., E-mail: luis.valerio@fda.hhs.gov; Sadrieh, Nakissa
2013-06-01
Drug-induced phospholipidosis (DIPL) is a preclinical finding during pharmaceutical drug development that has implications on the course of drug development and regulatory safety review. A principal characteristic of drugs inducing DIPL is known to be a cationic amphiphilic structure. This provides evidence for a structure-based explanation and opportunity to analyze properties and structures of drugs with the histopathologic findings for DIPL. In previous work from the FDA, in silico quantitative structure–activity relationship (QSAR) modeling using machine learning approaches has shown promise with a large dataset of drugs but included unconfirmed data as well. In this study, we report the construction and validation of a battery of complementary in silico QSAR models using the FDA's updated database on phospholipidosis, new algorithms and predictive technologies, and in particular, we address high performance with a high-confidence dataset. The results of our modeling for DIPL include rigorous external validation tests showing 80–81% concordance. Furthermore, the predictive performance characteristics include models with high sensitivity and specificity, in most cases above ? 80% leading to desired high negative and positive predictivity. These models are intended to be utilized for regulatory toxicology applied science needs in screening new drugs for DIPL. - Highlights: • New in silico models for predicting drug-induced phospholipidosis (DIPL) are described. • The training set data in the models is derived from the FDA's phospholipidosis database. • We find excellent predictivity values of the models based on external validation. • The models can support drug screening and regulatory decision-making on DIPL.
GIS-BASED PREDICTION OF HURRICANE FLOOD INUNDATION
JUDI, DAVID; KALYANAPU, ALFRED; MCPHERSON, TIMOTHY; BERSCHEID, ALAN
2007-01-17
A simulation environment is being developed for the prediction and analysis of the inundation consequences for infrastructure systems from extreme flood events. This decision support architecture includes a GIS-based environment for model input development, simulation integration tools for meteorological, hydrologic, and infrastructure system models and damage assessment tools for infrastructure systems. The GIS-based environment processes digital elevation models (30-m from the USGS), land use/cover (30-m NLCD), stream networks from the National Hydrography Dataset (NHD) and soils data from the NRCS (STATSGO) to create stream network, subbasins, and cross-section shapefiles for drainage basins selected for analysis. Rainfall predictions are made by a numerical weather model and ingested in gridded format into the simulation environment. Runoff hydrographs are estimated using Green-Ampt infiltration excess runoff prediction and a 1D diffusive wave overland flow routing approach. The hydrographs are fed into the stream network and integrated in a dynamic wave routing module using the EPA's Storm Water Management Model (SWMM) to predict flood depth. The flood depths are then transformed into inundation maps and exported for damage assessment. Hydrologic/hydraulic results are presented for Tropical Storm Allison.
Grossman, Robert
The Management and Mining of Multiple Predictive Models Using the Predictive Modeling Markup Language (PMML) Robert Grossman National Center for Data Mining, University of Illinois at Chicago & Magnify, Inc. Stuart Bailey, Ashok Ramu and Balinder Malhi National Center for Data Mining University
Autonomous Helicopter Formation using Model Predictive Control
Sastry, S. Shankar
Autonomous Helicopter Formation using Model Predictive Control Hoam Chung and S. Shankar Sastry for teams of helicopters. However, the potential for accidents is greatly increased when helicopter teams to the problem of helicopter formations comprised of heterogenous vehicles. The disturbance attenuation property
Designing Smart Environments: A Paradigm Based on Learning and Prediction
Cook, Diane J.
Designing Smart Environments: A Paradigm Based on Learning and Prediction Sajal K. Das and Diane J This chapter proposes a learning and prediction based paradigm for designing smart home environments and prediction based paradigm optimizes goal functions of smart home environments such as minimizing maintenance
USING A PHYSIOLOGICAL MODEL FOR PREDICTION OF THERAPY EFFECTS IN
Long, William J.
. Long, Shapur Naimi, M. G. Criscitiello, Robert Jayes M.I.T. Laboratory for Computer Science, Cambridge, based on signal flow analysis, for predicting hemodynamic changes using a model of physiological Library of Medicine. 2 #12; 1 Introduction As the variety of diagnostic and therapeutic modalities
Model Predictive Control of a Wind Lars Christian Henriksen
wind turbines is on the sea as their is a more stable wind. These water based wind farms are confined locations to become potential wind farms. This thesis investigates control of both wind turbines mountedModel Predictive Control of a Wind Turbine Lars Christian Henriksen Kongens Lyngby 2007 IMM
Predictive modeling of thermoelastic energy dissipation in tunable MEMS mirrors
Yi, Yun-Bo
Predictive modeling of thermoelastic energy dissipation in tunable MEMS mirrors Houwen Tang is of significant importance in many microelectromechanical sys- tem MEMS applications. Thermoelastic damping can such as MEMS mirrors. We deal with the simulation and analysis of thermoelastic damping of MEMS mirrors based
RESIDUAL PREDICTION BASED ON UNIT SELECTION David Sundermann1,2,3
Black, Alan W
RESIDUAL PREDICTION BASED ON UNIT SELECTION David S¨undermann1,2,3 , Harald H¨oge1 , Antonio Recently, we presented a study on residual prediction tech- niques that can be applied to voice conversion based on lin- ear transformation or hidden Markov model-based speech synthesis. Our voice conversion
Disease Prediction Models and Operational Readiness
Corley, Courtney D.; Pullum, Laura L.; Hartley, David M.; Benedum, Corey M.; Noonan, Christine F.; Rabinowitz, Peter M.; Lancaster, Mary J.
2014-03-19
INTRODUCTION: The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. One of the primary goals of this research was to characterize the viability of biosurveillance models to provide operationally relevant information for decision makers to identify areas for future research. Two critical characteristics differentiate this work from other infectious disease modeling reviews. First, we reviewed models that attempted to predict the disease event, not merely its transmission dynamics. Second, we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011). Methods: We searched dozens of commercial and government databases and harvested Google search results for eligible models utilizing terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche-modeling, The publication date of search results returned are bound by the dates of coverage of each database and the date in which the search was performed, however all searching was completed by December 31, 2010. This returned 13,767 webpages and 12,152 citations. After de-duplication and removal of extraneous material, a core collection of 6,503 items was established and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. Next, PNNL’s IN-SPIRE visual analytics software was used to cross-correlate these publications with the definition for a biosurveillance model resulting in the selection of 54 documents that matched the criteria resulting Ten of these documents, However, dealt purely with disease spread models, inactivation of bacteria, or the modeling of human immune system responses to pathogens rather than predicting disease events. As a result, we systematically reviewed 44 papers and the results are presented in this analysis.
Building risk prediction models -with a focus on Genome-Wide Association Studies
Brent, Roger
Kooperberg Charles Kooperberg Predictive models for GWAS #12;Risk prediction models Based on data: (Di , Xi1;Selection of predictors. Selection of predictors on the same data as training and/or evaluating models can data to evaluate your model as is part of your cross-validation procedure biases your results
Gamma-Ray Pulsars: Models and Predictions
Alice K. Harding
2000-12-12
Pulsed emission from gamma-ray pulsars originates inside the magnetosphere, from radiation by charged particles accelerated near the magnetic poles or in the outer gaps. In polar cap models, the high energy spectrum is cut off by magnetic pair production above an energy that is dependent on the local magnetic field strength. While most young pulsars with surface fields in the range B = 10^{12} - 10^{13} G are expected to have high energy cutoffs around several GeV, the gamma-ray spectra of old pulsars having lower surface fields may extend to 50 GeV. Although the gamma-ray emission of older pulsars is weaker, detecting pulsed emission at high energies from nearby sources would be an important confirmation of polar cap models. Outer gap models predict more gradual high-energy turnovers at around 10 GeV, but also predict an inverse Compton component extending to TeV energies. Detection of pulsed TeV emission, which would not survive attenuation at the polar caps, is thus an important test of outer gap models. Next-generation gamma-ray telescopes sensitive to GeV-TeV emission will provide critical tests of pulsar acceleration and emission mechanisms.
Predicting Protein Folding Kinetics via Temporal Logic Model Checking: Extended
Langmead, Christopher James
Predicting Protein Folding Kinetics via Temporal Logic Model Checking: Extended Abstract Abstract. We present a novel approach for predicting protein folding kinetics using techniques from checking. We tested our method on 19 test proteins. The quantitative predictions regarding folding rates
Roadmap Toward a Predictive Performance-based Commercial Energy Code
Rosenberg, Michael I.; Hart, Philip R.
2014-10-01
Energy codes have provided significant increases in building efficiency over the last 38 years, since the first national energy model code was published in late 1975. The most commonly used path in energy codes, the prescriptive path, appears to be reaching a point of diminishing returns. The current focus on prescriptive codes has limitations including significant variation in actual energy performance depending on which prescriptive options are chosen, a lack of flexibility for designers and developers, and the inability to handle control optimization that is specific to building type and use. This paper provides a high level review of different options for energy codes, including prescriptive, prescriptive packages, EUI Target, outcome-based, and predictive performance approaches. This paper also explores a next generation commercial energy code approach that places a greater emphasis on performance-based criteria. A vision is outlined to serve as a roadmap for future commercial code development. That vision is based on code development being led by a specific approach to predictive energy performance combined with building specific prescriptive packages that are designed to be both cost-effective and to achieve a desired level of performance. Compliance with this new approach can be achieved by either meeting the performance target as demonstrated by whole building energy modeling, or by choosing one of the prescriptive packages.
Illustrating the future prediction of performance based on computer...
Office of Scientific and Technical Information (OSTI)
Illustrating the future prediction of performance based on computer code, physical experiments, and critical performance parameter samples Citation Details In-Document Search...
A statistically predictive model for future monsoon failure in India
Levermann, Anders
A statistically predictive model for future monsoon failure in India Jacob Schewe1,2 and Anders Information #12;A statistically predictive model for future monsoon failure in India 2 mm/day numberofyears 0 statistically predictive model for future monsoon failure in India 4 30 o S 15o S 0 o 15o N 30o N A dry May B
Huang, C.; Song, Y.; Luo, X.
2006-01-01
Based on the Block model for predicting vertical temperature distribution in a large space, this paper describes an improved Gebhart-Block model for predicting vertical temperature distribution of a large space with natural ventilation...
Statistical surrogate models for prediction of high-consequence...
Office of Scientific and Technical Information (OSTI)
Technical Report: Statistical surrogate models for prediction of high-consequence climate change. Citation Details In-Document Search Title: Statistical surrogate models for...
A System for Online Power Prediction in Virtualized Environments Using Gaussian Mixture Models
Simunic, Tajana
A System for Online Power Prediction in Virtualized Environments Using Gaussian Mixture Models In this paper we present a system for online power prediction in vir- tualized environments. It is based dynamically by our system to predict both the physical machine and per VM level power consumption. A real
Predicting the past: archaeological predictive modeling in Central Texas
Werner, Corey M
2002-01-01
Texas has a well-stratified assemblage of Clovis artifacts. The discovery of additional sites like the Gault site could provide valuable information to resolve the debate. Two logistic regression models are created to locate areas with a high...
Settlement Prediction, Gas Modeling and Slope Stability Analysis
Politècnica de Catalunya, Universitat
Settlement Prediction, Gas Modeling and Slope Stability Analysis in Coll Cardús Landfill Li Yu UNIVERSIDAD POLITÉCNICA DE CATALUÑA April, 2007 GEOMODELS #12;Introduction to Coll Cardús landfill Prediction of settlement in Coll Cardús landfill 1) Settlement prediction by empirical method 2) Settlement prediction
Internally Electrodynamic Particle Model: Its Experimental Basis and Its Predictions
Zheng-Johansson, J X
2008-01-01
The internally electrodynamic (IED) particle model was derived based on overall experimental observations, with the IED process itself being built directly on three experimental facts, a) electric charges present with all material particles, b) an accelerated charge generates electromagnetic waves according to Maxwell's equations and Planck energy equation and c)source motion produces Doppler effect. A set of well-known basic particle equations and properties become predictable based on first-principles solutions for the IED particles; several key solutions achieved will be outlined, including the de Broglie phase wave, de Broglie relations, Schr\\"odinger equation, mass, mass-energy relation, Newton's law of gravity, single particle self interference, and electromagnetic radiation and absorption; these equations or properties have long been broadly experimentally validated or demonstrated. The IED solution also predicts the Doebner-Goldin equation which emerges to represent a form of long-sought quantum wave ...
Model Predictability-Form Lorenz System to Operational Ocean and
Chu, Peter C.
Model Predictability- Form Lorenz System to Operational Ocean and Atmospheric Models Peter C Chu. Poberezhny, 2002: Power law decay in model predictability skill. Geophysical Research Letters, 29 (15), 10 Six Months Four-Times Daily Data From July 9, 1998 for Verification #12;Model Generated Velocity
Prediction of interest rate using CKLS model with stochastic parameters
Ying, Khor Chia [Faculty of Computing and Informatics, Multimedia University, Jalan Multimedia, 63100 Cyberjaya, Selangor (Malaysia); Hin, Pooi Ah [Sunway University Business School, No. 5, Jalan Universiti, Bandar Sunway, 47500 Subang Jaya, Selangor (Malaysia)
2014-06-19
The Chan, Karolyi, Longstaff and Sanders (CKLS) model is a popular one-factor model for describing the spot interest rates. In this paper, the four parameters in the CKLS model are regarded as stochastic. The parameter vector ?{sup (j)} of four parameters at the (J+n)-th time point is estimated by the j-th window which is defined as the set consisting of the observed interest rates at the j?-th time point where j?j??j+n. To model the variation of ?{sup (j)}, we assume that ?{sup (j)} depends on ?{sup (j?m)}, ?{sup (j?m+1)},…, ?{sup (j?1)} and the interest rate r{sub j+n} at the (j+n)-th time point via a four-dimensional conditional distribution which is derived from a [4(m+1)+1]-dimensional power-normal distribution. Treating the (j+n)-th time point as the present time point, we find a prediction interval for the future value r{sub j+n+1} of the interest rate at the next time point when the value r{sub j+n} of the interest rate is given. From the above four-dimensional conditional distribution, we also find a prediction interval for the future interest rate r{sub j+n+d} at the next d-th (d?2) time point. The prediction intervals based on the CKLS model with stochastic parameters are found to have better ability of covering the observed future interest rates when compared with those based on the model with fixed parameters.
Solar cycle prediction using precursors and flux transport models
R. Cameron; M. Schuessler
2006-12-22
We study the origin of the predictive skill of some methods to forecast the strength of solar activity cycles. A simple flux transport model for the azimuthally averaged radial magnetic field at the solar surface is used, which contains a source term describing the emergence of new flux based on observational sunspot data. We consider the magnetic flux diffusing over the equator as a predictor, since this quantity is directly related to the global dipole field from which a Babcock-Leighton dynamo generates the toroidal field for the next activity cycle. If the source is represented schematically by a narrow activity belt drifting with constant speed over a fixed range of latitudes between activity minima, our predictor shows considerable predictive skill with correlation coefficients up to 0.95 for past cycles. However, the predictive skill is completely lost when the actually observed emergence latitudes are used. This result originates from the fact that the precursor amplitude is determined by the sunspot activity a few years before solar minimum. Since stronger cycles tend to rise faster to their maximum activity (known as the Waldmeier effect), the temporal overlapping of cycles leads to a shift of the minimum epochs that depends on the strength of the following cycle. This information is picked up by precursor methods and also by our flux transport model with a schematic source. Therefore, their predictive skill does not require a memory, i.e., a physical connection between the surface manifestations of subsequent activity cycles.
Autobiography based prediction in a situated Ladislau Boloni
Bölöni, Ladislau L
Autobiography based prediction in a situated AGI agent Ladislau B¨ol¨oni Dept. of Electrical memorizes its personal autobiography in an unprocessed narrative form. When a prediction is needed, the agent aligns story-lines from the autobiography with the current story, extends them into the future
An Optimization-Based Framework for Combinatorial Prediction Market Design
Chen, Yiling
An Optimization-Based Framework for Combinatorial Prediction Market Design Jacob Abernethy UC framework for the design of efficient prediction markets over very large outcome spaces. 1 Introduction) is then C(q)/qi, and is denoted pi(q). The market designer is free to choose any differentiable cost
Prediction of regionalized car insurance risks based on control variates
Schmidt, Volker
Prediction of regionalized car insurance risks based on control variates Marcus C. Christiansen, Christian Hirsch, Volker Schmidt October 1, 2013 Abstract We show how regional prediction of car insurance compute such predictors and consider an application to German car insurance data. 1 Introduction
A Real-time Framework for Model Predictive Control of Continuous-Time Nonlinear Systems
Sontag, Eduardo
for piecewise constant NMPC of continuous-time processes. Index Terms-- nonlinear model predictive control, real-time optimization, optimal control, piecewise constant control I. INTRODUCTION Model predictive control (MPC horizon, open-loop optimal control problem. The unprecedented industrial success of MPC ap- proaches based
RESIDUAL PREDICTION BASED ON UNIT SELECTION David Sundermann1,2
Suendermann, David
RESIDUAL PREDICTION BASED ON UNIT SELECTION David S¨undermann1,2 , Harald H¨oge1 , Antonio tech- niques that can be applied to voice conversion based on lin- ear transformation or hidden Markov model-based speech synthesis. Our voice conversion experiments showed that none of the six compared
Defect site prediction based upon statistical analysis of fault signatures
Trinka, Michael Robert
2004-09-30
Good failure analysis is the ability to determine the site of a circuit defect quickly and accurately. We propose a method for defect site prediction that is based on a site's probability of excitation, making no assumptions about the type...
Model-based tomographic reconstruction
Chambers, David H.; Lehman, Sean K.; Goodman, Dennis M.
2012-06-26
A model-based approach to estimating wall positions for a building is developed and tested using simulated data. It borrows two techniques from geophysical inversion problems, layer stripping and stacking, and combines them with a model-based estimation algorithm that minimizes the mean-square error between the predicted signal and the data. The technique is designed to process multiple looks from an ultra wideband radar array. The processed signal is time-gated and each section processed to detect the presence of a wall and estimate its position, thickness, and material parameters. The floor plan of a building is determined by moving the array around the outside of the building. In this paper we describe how the stacking and layer stripping algorithms are combined and show the results from a simple numerical example of three parallel walls.
Markovian Models for Electrical Load Prediction in Smart Buildings
California at Santa Barbara, University of
Markovian Models for Electrical Load Prediction in Smart Buildings Muhammad Kumail Haider, Asad,13100004,ihsan.qazi}@lums.edu.pk Abstract. Developing energy consumption models for smart buildings is important develop parsimo- nious Markovian models of smart buildings for different periods in a day for predicting
Predictive Models of Li-ion Battery Lifetime (Presentation) Smith...
Office of Scientific and Technical Information (OSTI)
Predictive Models of Li-ion Battery Lifetime (Presentation) Smith, K.; Wood, E.; Santhanagopalan, S.; Kim, G.; Shi, Y.; Pesaran, A. 25 ENERGY STORAGE; 33 ADVANCED PROPULSION...
Climate Prediction: The Limits of Ocean Models
Stone, Peter H.
We identify three major areas of ignorance which limit predictability in current ocean GCMs. One is the very crude representation of subgrid-scale mixing processes. These processes are parameterized with coefficients whose ...
Lapp, Tiffany Rae, 1979-
2004-01-01
This thesis presents the design and implementation of a model predictive control based trajectory optimization method for Nap-of-the-Earth (NOE) flight. A NOE trajectory reference is generated over a subspace of the terrain. ...
On Predicting the Solar Cycle using Mean-Field Models
Paul J. Bushby; Steven M. Tobias
2007-04-18
We discuss the difficulties of predicting the solar cycle using mean-field models. Here we argue that these difficulties arise owing to the significant modulation of the solar activity cycle, and that this modulation arises owing to either stochastic or deterministic processes. We analyse the implications for predictability in both of these situations by considering two separate solar dynamo models. The first model represents a stochastically-perturbed flux transport dynamo. Here even very weak stochastic perturbations can give rise to significant modulation in the activity cycle. This modulation leads to a loss of predictability. In the second model, we neglect stochastic effects and assume that generation of magnetic field in the Sun can be described by a fully deterministic nonlinear mean-field model -- this is a best case scenario for prediction. We designate the output from this deterministic model (with parameters chosen to produce chaotically modulated cycles) as a target timeseries that subsequent deterministic mean-field models are required to predict. Long-term prediction is impossible even if a model that is correct in all details is utilised in the prediction. Furthermore, we show that even short-term prediction is impossible if there is a small discrepancy in the input parameters from the fiducial model. This is the case even if the predicting model has been tuned to reproduce the output of previous cycles. Given the inherent uncertainties in determining the transport coefficients and nonlinear responses for mean-field models, we argue that this makes predicting the solar cycle using the output from such models impossible.
Switching Between Discrete and Continuous Models To Predict Genetic Activity
Weld, Daniel S.
Molecular biologists use a variety of models when they predict the behavior of genetic systems. A discrete model of the behavior of individual macromolecular elements forms the foundation for their theory of each system. ...
A METHOD FOR IDENTIFYING REPETITION STRUCTURE IN MUSICAL AUDIO BASED ON TIME SERIES PREDICTION
Dixon, Simon
A METHOD FOR IDENTIFYING REPETITION STRUCTURE IN MUSICAL AUDIO BASED ON TIME SERIES PREDICTION This paper investigates techniques for determining the repeti- tion structure of musical audio. In particular. To this end, we propose a novel approach based on multivari- ate time series modelling of audio features
A DETERMINISTIC PREDICTION MODEL FOR THE AMERICAN GAME OF FOOTBALL
Weaver, Adam Lee
A DETERMINISTIC PREDICTION MODEL FOR THE AMERICAN GAME OF FOOTBALL John Am Trono, Saint Michael's College Introduction This article describes a simulation model of the sport known as footballs It was created to predict results of post season football games, most notably college bowl games. By constructing
Predicting Protein Folding Kinetics via Temporal Logic Model Checking
Predicting Protein Folding Kinetics via Temporal Logic Model Checking Christopher James Langmead award from the U.S. Department of Energy. #12;Keywords: protein folding, model checking, temporal logic #12;Abstract We present a novel approach for predicting protein folding kinetics using techniques from
Development of a fourth generation predictive capability maturity model.
Hills, Richard Guy; Witkowski, Walter R.; Urbina, Angel; Rider, William J.; Trucano, Timothy Guy
2013-09-01
The Predictive Capability Maturity Model (PCMM) is an expert elicitation tool designed to characterize and communicate completeness of the approaches used for computational model definition, verification, validation, and uncertainty quantification associated for an intended application. The primary application of this tool at Sandia National Laboratories (SNL) has been for physics-based computational simulations in support of nuclear weapons applications. The two main goals of a PCMM evaluation are 1) the communication of computational simulation capability, accurately and transparently, and 2) the development of input for effective planning. As a result of the increasing importance of computational simulation to SNL's mission, the PCMM has evolved through multiple generations with the goal to provide more clarity, rigor, and completeness in its application. This report describes the approach used to develop the fourth generation of the PCMM.
Broader source: Energy.gov [DOE]
NREL, under the Physics of Reliability: Evaluating Design Insights for Component Technologies in Solar (PREDICTS) Program will be developing a physics-based computational degradation model to assess the kinetic oxidation rates; realistic model light attenuation and transport; and multi-layer treatment with variable properties Simulation based experimental design.
Wishart, Gordon C.; Azzato, Elizabeth M.; Greenberg, David C.; Rashbass, Jem; Kearins, Olive; Lawrence, Gill; Caldas, Carlos; Pharoah, Paul D. P.
2010-01-06
Abstract Introduction The aim of this study was to develop and validate a prognostication model to predict overall and breast cancer specific survival for women treated for early breast cancer in the UK. Methods Using the Eastern Cancer Registration...
LHC diphoton Higgs signal predicted by little Higgs models
Wang Lei; Yang Jinmin
2011-10-01
Little Higgs theory naturally predicts a light Higgs boson whose most important discovery channel at the LHC is the diphoton signal pp{yields}h{yields}{gamma}{gamma}. In this work, we perform a comparative study for this signal in some typical little Higgs models, namely, the littlest Higgs model, two littlest Higgs models with T-parity (named LHT-I and LHT-II), and the simplest little Higgs models. We find that compared with the standard model prediction, the diphoton signal rate is always suppressed and the suppression extent can be quite different for different models. The suppression is mild (< or approx. 10%) in the littlest Higgs model but can be quite severe ({approx_equal}90%) in other three models. This means that discovering the light Higgs boson predicted by the little Higgs theory through the diphoton channel at the LHC will be more difficult than discovering the standard model Higgs boson.
Modeling probability distributions with predictive state representations
Wiewiora, Eric Walter
2008-01-01
Discovery is the process of choosing the core tests, whose success probabilities will become the state of the learned model.
Vladislavleva, Katya; Neumann, Frank; Wagner, Markus
2011-01-01
Wind energy plays an increasing role in the supply of energy world-wide. The energy output of a wind farm is highly dependent on the weather condition present at the wind farm. If the output can be predicted more accurately, energy suppliers can coordinate the collaborative production of different energy sources more efficiently to avoid costly overproductions. With this paper, we take a computer science perspective on energy prediction based on weather data and analyze the important parameters as well as their correlation on the energy output. To deal with the interaction of the different parameters we use symbolic regression based on the genetic programming tool DataModeler. Our studies are carried out on publicly available weather and energy data for a wind farm in Australia. We reveal the correlation of the different variables for the energy output. The model obtained for energy prediction gives a very reliable prediction of the energy output for newly given weather data.
TESTS OF 1-D TRANSPORT MODELS, AND THEIR PREDICTIONS FOR ITER
Vlad, Gregorio
. INTRODUCTION Predictions of ITER based on validated 1-D transport models would provide: 1) a physical research programs. Many transport models have been partially tested against tokamak data [1]. In order to establish how well each model represents the wide range of existing tokamak data we have developed the ITER
Detection and Prediction of Errors in EPCs of the SAP Reference Model
van der Aalst, Wil
as a blueprint for roll-out projects of SAP's ERP system. It reflects Version18 4.6 of SAP R/3 which was marketedDetection and Prediction of Errors in EPCs of the SAP Reference Model J. Mendling a, H.M.W. Verbeek provide empirical evidence for these questions based on the SAP reference model. This model collection
Internally Electrodynamic Particle Model: Its Experimental Basis and Its Predictions
J. X. Zheng-Johansson
2010-07-13
The internally electrodynamic (IED) particle model was derived based on overall experimental observations, with the IED process itself being built directly on three experimental facts, a) electric charges present with all material particles, b) an accelerated charge generates electromagnetic waves according to Maxwell's equations and Planck energy equation and c) source motion produces Doppler effect. A set of well-known basic particle equations and properties become predictable based on first principles solutions for the IED process; several key solutions achieved are outlined, including the de Broglie phase wave, de Broglie relations, Schr\\"odinger equation, mass, Einstein mass-energy relation, Newton's law of gravity, single particle self interference, and electromagnetic radiation and absorption; these equations and properties have long been broadly experimentally validated or demonstrated. A specific solution also predicts the Doebner-Goldin equation which emerges to represent a form of long-sought quantum wave equation including gravity. A critical review of the key experiments is given which suggests that the IED process underlies the basic particle equations and properties not just sufficiently but also necessarily.
Media Sharing based on Colocation Prediction in Urban Transport
Hand, Steven
Media Sharing based on Colocation Prediction in Urban Transport Liam McNamara Dept. of Computer Science University College London London, WC1E 6BT, UK l.mcnamara@cs.ucl.ac.uk Cecilia Mascolo Computer Science University College London London, WC1E 6BT, UK l.capra@cs.ucl.ac.uk ABSTRACT People living
Representing Temporal Knowledge for Case-Based Prediction
Aamodt, Agnar
well drilling. 1 Introduction Most current CBR systems represent episodes as distinct snap. Our focus is on prediction problems for avoiding faulty situations. Based on a well-established theory-intensive CBR system Creek. The paper presents the theoretical foundation of the method, the representation
Model Predictive Control for Energy Efficient Buildings
Ma, Yudong
2012-01-01
based on mass and energy conservation law is developed andbased on mass and energy conservation laws, and the buildingmass and internal energy conservation laws, m ? CHW S ? m ?
MOLECULAR MODELING OF PROTEINS AND MATHEMATICAL PREDICTION OF PROTEIN STRUCTURE
Neumaier, Arnold
MOLECULAR MODELING OF PROTEINS AND MATHEMATICAL PREDICTION OF PROTEIN STRUCTURE ARNOLD NEUMAIERcalled protein folding problem. The static aspect is concerned with how to predict the folded (native, tertiary) structure of a protein, given its sequence of amino acids. The dynamic aspect asks about the possible
MOLECULAR MODELING OF PROTEINS AND MATHEMATICAL PREDICTION OF PROTEIN STRUCTURE
Neumaier, Arnold
MOLECULAR MODELING OF PROTEINS AND MATHEMATICAL PREDICTION OF PROTEIN STRUCTURE ARNOLD NEUMAIER-called protein folding problem. The static aspect is concerned with how to predict the folded (native, tertiary) structure of a protein, given its sequence of amino acids. The dynamic aspect asks about the possible
Model Formulation and Predictions for a Pyrotechnically Actuated Pin Puller*
for the simulated firing of an NSI into 1) a pin puller device, 2) a 10 cm3 closed vessel, and 3) an apparatus known as the Dynamic Test Device. The predictions are compared with experiments. The pressure magnitudes and time scales of pressure rise and decay are predicted well by the model. Introduction Pyrotechnically actuated
High Level antitative Hardware Prediction Modeling using Statistical methods
Bertels, Koen
essential to have efficient prediction models to drive early HW-SW partitioning and co-design. In this paper development and HW-SW co-design. Given an application composed of different kernels, in order to map one-level language description as input, enabling hardware prediction in the early design stages. We calibrate
Open problem: Dynamic Relational Models for Improved Hazardous Weather Prediction
McGovern, Amy
Open problem: Dynamic Relational Models for Improved Hazardous Weather Prediction Amy McGovern1 dis- covery methods for use on mesoscale weather data. Severe weather phenomena such as tornados, thun, current techniques for predicting severe weather are tied to specific characteristics of the radar systems
Data Assimilation for Idealised Mathematical Models of Numerical Weather Prediction
Wirosoetisno, Djoko
Data Assimilation for Idealised Mathematical Models of Numerical Weather Prediction Supervisors). Background: Numerical Weather Prediction (NWP) has seen significant gains in accuracy in recent years due in weather dynamics, e.g., the asymptotic balance seen in high and low pressure systems. Aims of the project
A predictive ocean oil spill model
Sanderson, J.; Barnette, D.; Papodopoulos, P.; Schaudt, K.; Szabo, D.
1996-07-01
This is the final report of a two-year, Laboratory-Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). Initially, the project focused on creating an ocean oil spill model and working with the major oil companies to compare their data with the Los Alamos global ocean model. As a result of this initial effort, Los Alamos worked closely with the Eddy Joint Industry Project (EJIP), a consortium oil and gas producing companies in the US. The central theme of the project was to use output produced from LANL`s global ocean model to look in detail at ocean currents in selected geographic areas of the world of interest to consortium members. Once ocean currents are well understood this information could be used to create oil spill models, improve offshore exploration and drilling equipment, and aid in the design of semi-permanent offshore production platforms.
Colliding cascades model for earthquake prediction
2000-10-12
on a direct cascade that would deliver energy from the largest size scales ... The general objective of the colliding cascades model has been to reproduce the ..... earthquake and critical phase transitions studied in statistical physics, where the
Conformal Higgs model: predicted dark energy density
R. K. Nesbet
2014-11-03
Postulated universal Weyl conformal scaling symmetry provides an alternative to the $\\Lambda$CDM paradigm for cosmology. Recent applications to galactic rotation velocities, Hubble expansion, and a model of dark galactic halos explain qualitative phenomena and fit observed data without invoking dark matter. Significant revision of theory relevant to galactic collisions and clusters is implied, but not yet tested. Dark energy is found to be a consequence of conformal symmetry for the Higgs scalar field of electroweak physics. The present paper tests this implication. The conformal Higgs model acquires a gravitational effect described by a modified Friedmann cosmic evolution equation, shown to fit cosmological data going back to the cosmic microwave background epoch. The tachyonic mass parameter of the Higgs model becomes dark energy in the Friedmann equation. A dynamical model of this parameter, analogous to the Higgs mechanism for gauge boson mass, is derived and tested here. An approximate calculation yields a result consistent with the empirical magnitude inferred from Hubble expansion.
Predictive capacity planning modeling with tactical and strategic applications
Zeppieri, Michael A. (Michael Anthony), 1975-
2004-01-01
The focus of my internship was the development of a predictive capacity planning model to characterize the storage requirements and space utilization for Amazon's Campbellsville (SDF) Fulfillment Center (FC). Amazon currently ...
Land Surface Model Data Assimilation for Atmospheric Prediction
Walker, Jeff
predictions from different models even when using the same parameters, inputs, and initial conditions (Houser remote sensing studies, using visible, thermal infrared (surface temperature) and microwave (passive and active) electromagnetic radiation. Of these, passive microwave soil moisture measurement has been
Hierarchical Bayesian Models for Predicting The Spread of Ecological Processes
Hierarchical Bayesian Models for Predicting The Spread of Ecological Processes Christopher K. Wikle Department of Statistics, University of Missouri To appear: Ecology June 10, 2002 Key Words: Bayesian, Diffusion, Forecast, Hierarchical, House Finch, Invasive, Malthu- sian, State Space, Uncertainty Abstract
Interactive software for model predictive control with simultaneous identification
Echeverria Del Rio, Pablo
2000-01-01
This thesis is a unified practical framework in the theory of Model Predictive Control with Simultaneous Identification. The ability to change and visualize parameters on-line makes this toolbox attractive for control ...
- tation properties and wind conditions based on Rothermel's model; a series of observations of the fire;1. INTRODUCTION Computer-based wildfire spread modeling has emerged during the past two decades as a powerful toolTowards predictive simulation of wildfire spread at regional scale using ensemble-based data
Predicting species invasions using ecological niche modeling
Peterson, A. Townsend; Vieglais, David A.
2001-05-01
) and commission (including niche space not ,lctually occupied by the 'pecies). Each algorithm for modeling specIes' ecological niches involves a specific com binatiol1 of errors of omission ,md commission. A rel.ltively new approach, called the (;enetic...
Greenberg, Albert
Iterative Multivariate Regression Model for Correlated Responses Prediction S. Tom Au, Guangqin Ma- tive procedure to model multiple responses prediction into correlated multivariate predicting scheme, which is always favorable for responses separations in our multivariate prediction. We also point out
Mathiesen, Patrick; Collier, Craig; Kleissl, Jan
2013-01-01
of numerical weather prediction solar irradiance forecasts numerical weather prediction model for solar irradiance weather prediction for intra?day solar forecasting in the
Predictive models of circulating fluidized bed combustors
Gidaspow, D.
1992-07-01
Steady flows influenced by walls cannot be described by inviscid models. Flows in circulating fluidized beds have significant wall effects. Particles in the form of clusters or layers can be seen to run down the walls. Hence modeling of circulating fluidized beds (CFB) without a viscosity is not possible. However, in interpreting Equations (8-1) and (8-2) it must be kept in mind that CFB or most other two phase flows are never in a true steady state. Then the viscosity in Equations (8-1) and (8-2) may not be the true fluid viscosity to be discussed next, but an Eddy type viscosity caused by two phase flow oscillations usually referred to as turbulence. In view of the transient nature of two-phase flow, the drag and the boundary layer thickness may not be proportional to the square root of the intrinsic viscosity but depend upon it to a much smaller extent. As another example, liquid-solid flow and settling of colloidal particles in a lamella electrosettler the settling process is only moderately affected by viscosity. Inviscid flow with settling is a good first approximation to this electric field driven process. The physical meaning of the particulate phase viscosity is described in detail in the chapter on kinetic theory. Here the conventional derivation resented in single phase fluid mechanics is generalized to multiphase flow.
Bayesian calibration of a k -turbulence model for predictive jet-in-crossflow simulations
Ray, Jaideep
Bayesian calibration of a k - turbulence model for predictive jet-in-crossflow simulations Jaideep skill in jet-in-crossflow simulations. The method is based on the hypotheses that (1) informative features of jet-in-crossflow interactions and (2) one can construct surrogates of RANS models
Results on Three predictions on July 2012 Federal Elections in Mexico based on past regularities
Hernández-Saldaña, H
2013-01-01
July 2012 Presidential Election in Mexico has been the third occasion that the PREP, the Previous Electoral Results Program, works. PREP results give the voter turnout based in electoral certificates of each polling station that arrives to the capture centres. In the previous ones some statistical regularities had been observed, three of them were selected to made predictions and published in \\texttt{arXiv:1207.0078 [physics.soc-ph]}. Two of the predictions were completely fulfilled and the third one was not measured since the electoral authorities changed the information in the data base for the 2012 process. The two confirmed predictions by actual measures are: (ii) The Partido Revolucionario Institucional is a sprinter and have a better performance in polling station which arrive late in the process. (iii) Distribution of vote of this party is well described by a smooth function named a Daisy model. A Gamma distribution, but compatible with a Daisy model, fits the distribution as well.
Experimental Validation of Stochastic Wireless Urban Channel Model: Estimation and Prediction
Kuruganti, Phani Teja [ORNL] [ORNL; Ma, Xiao [ORNL] [ORNL; Djouadi, Seddik M [ORNL] [ORNL
2012-01-01
Stochastic differential equations (SDE) can be used to describe the time-varying nature of wireless channels. This paper validates a long-term fading channel model for estimation and prediction from solely using measured received signal strength measurements. Such channel models can be used for optimizing wireless networks deployed for industrial automation, public access, and communication. This paper uses two different sets of received signal measurement data to estimate an predict the signal strength based on past measurements. The realworld performance of the estimation and prediction algorithm is demonstrated.
Modeling Social Cues: Effective Features for Predicting Listener Nods
Zhu, Xiaojin "Jerry"
Modeling Social Cues: Effective Features for Predicting Listener Nods Faisal Khan, Bilge Mutlu, we present preliminary work in modeling a particular communicative mechanism--listener nods observations of verbal and nonverbal cues from the speaker and listener nods and a hidden sub- structure
Chemical and Biological Engineering Model Predictive Control: Background
Grossmann, Ignacio E.
== - - = -- --- = DC C V F CC B k V F k Ckk V F A Bs s AsAfs s As s = f1 = f2 etcCAux Asss C f x f A , 1 ,1 1 11Chemical and Biological Engineering Model Predictive Control: Background B. Wayne Bequette "windup" problems Does not explicitly require a process model #12;Chemical and Biological Engineering
Classical Cepheid Pulsation Models. III. The Predictable Scenario
G. Bono; V. Castellani; M. Marconi
1999-08-02
Within the current uncertainties in the treatment of the coupling between pulsation and convection, limiting amplitude, nonlinear, convective models appear the only viable approach for providing theoretical predictions about the intrinsic properties of radial pulsators. In this paper we present the results of a comprehensive set of Cepheid models computed within such theoretical framework for selected assumptions on their original chemical composition.
A Network-Based Approach to Understanding and Predicting Diseases
Chawla, Nitesh V.
and emergent behavior over time. Our analysis reveals important insights with implications for modeling increases in cost of health care for the United States. Contributions: The aforementioned issues model to assess disease risk for individuals based on medical history. We evaluate the ability of our
Wood, Robert
GMAO Clouds in Global Models Annual mean Control run Atmospheric models #12;CGCM Problems: NOAA CFS Model CFS Errors SST Prec CLD · The CFS model has significant errors in the SEP · There is a meridional) · These errors adversely affect the skill of CFS climate forecasts (ENSO). What model developments are required
Lepton Flavor Violation in Predictive SUSY-GUT Models
Albright, Carl H.; /Northern Illinois U. /Fermilab; Chen, Mu-Chun; /UC, Irvine
2008-02-01
There have been many theoretical models constructed which aim to explain the neutrino masses and mixing patterns. While many of the models will be eliminated once more accurate determinations of the mixing parameters, especially sin{sup 2} 2{theta}{sub 13}, are obtained, charged lepton flavor violation (LFV) experiments are able to differentiate even further among the models. In this paper, they investigate various rare LFV processes, such as {ell}{sub i} {yields} {ell}{sub j} + {gamma} and {mu} - e conversion, in five predictive SUSY SO(10) models and their allowed soft SUSY breaking parameter space in the constrained minimal SUSY standard model (CMSSM). Utilizing the WMAP dark matter constraints, they obtain lower bounds on the branching ratios of these rare processes and find that at least three of the five models they consider give rise to predictions for {mu} {yields} e + {gamma} that will be tested by the MEG collaboration at PSI. in addition, the next generation {mu} - e conversion experiment has sensitivity to the predictions of all five models, making it an even more robust way to test these models. While generic studies have emphasized the dependence of the branching ratios of these rare processes on the reactor neutrino angle, {theta}{sub 13}, and the mass of the heaviest right-handed neutrino, M{sub 3}, they find very massive M{sub 3} is more significant than large {theta}{sub 13} in leading to branching ratios near to the present upper limits.
A physics-based emissions model for aircraft gas turbine combustors
Allaire, Douglas L
2006-01-01
In this thesis, a physics-based model of an aircraft gas turbine combustor is developed for predicting NO. and CO emissions. The objective of the model is to predict the emissions of current and potential future gas turbine ...
Goldsby, Michael E.; Mayo, Jackson R.; Bhattacharyya, Arnab; Armstrong, Robert C.; Vanderveen, Keith
2008-09-01
The goal of this research was to examine foundational methods, both computational and theoretical, that can improve the veracity of entity-based complex system models and increase confidence in their predictions for emergent behavior. The strategy was to seek insight and guidance from simplified yet realistic models, such as cellular automata and Boolean networks, whose properties can be generalized to production entity-based simulations. We have explored the usefulness of renormalization-group methods for finding reduced models of such idealized complex systems. We have prototyped representative models that are both tractable and relevant to Sandia mission applications, and quantified the effect of computational renormalization on the predictive accuracy of these models, finding good predictivity from renormalized versions of cellular automata and Boolean networks. Furthermore, we have theoretically analyzed the robustness properties of certain Boolean networks, relevant for characterizing organic behavior, and obtained precise mathematical constraints on systems that are robust to failures. In combination, our results provide important guidance for more rigorous construction of entity-based models, which currently are often devised in an ad-hoc manner. Our results can also help in designing complex systems with the goal of predictable behavior, e.g., for cybersecurity.
MBGP IN MODELLING AND PREDICTION Carlos OliverMorales
Fernandez, Thomas
(MBGP) encoding. Having multiple branches representing an individual allows us to get simpler), relative humidity (H), solar radiation (R) and wind speed (V) and direction (D) were recorded. The time). Cost function was predictive errorbased metric. For each experiment, 20 runs were evaluated in order
What is the Recent Controversy in Evaluating Risk Prediction Models
Brent, Roger
What is the Recent Controversy in Evaluating Risk Prediction Models All About? Margaret Sullivan Pepe #12;Controversy about Risk Reclassification Techniques · Purpose: To evaluate the addition cases controls C-index = P(riskevent > risknonevent) · Should not be used to evaluate or compare risk
Reference wind farm selection for regional wind power prediction models
Paris-Sud XI, Université de
1 Reference wind farm selection for regional wind power prediction models Nils Siebert George.siebert@ensmp.fr, georges.kariniotakis@ensmp.fr Abstract Short-term wind power forecasting is recognized today as a major requirement for a secure and economic integration of wind generation in power systems. This paper deals
The origins of computer weather prediction and climate modeling
Lynch, Peter [Meteorology and Climate Centre, School of Mathematical Sciences, University College Dublin, Belfield (Ireland)], E-mail: Peter.Lynch@ucd.ie
2008-03-20
Numerical simulation of an ever-increasing range of geophysical phenomena is adding enormously to our understanding of complex processes in the Earth system. The consequences for mankind of ongoing climate change will be far-reaching. Earth System Models are capable of replicating climate regimes of past millennia and are the best means we have of predicting the future of our climate. The basic ideas of numerical forecasting and climate modeling were developed about a century ago, long before the first electronic computer was constructed. There were several major practical obstacles to be overcome before numerical prediction could be put into practice. A fuller understanding of atmospheric dynamics allowed the development of simplified systems of equations; regular radiosonde observations of the free atmosphere and, later, satellite data, provided the initial conditions; stable finite difference schemes were developed; and powerful electronic computers provided a practical means of carrying out the prodigious calculations required to predict the changes in the weather. Progress in weather forecasting and in climate modeling over the past 50 years has been dramatic. In this presentation, we will trace the history of computer forecasting through the ENIAC integrations to the present day. The useful range of deterministic prediction is increasing by about one day each decade, and our understanding of climate change is growing rapidly as Earth System Models of ever-increasing sophistication are developed.
Model Predictive Control of Residential Energy Systems Using
Knobloch,Jürgen
network infrastructure and can lead to a degradation of power quality and even outages. In responseModel Predictive Control of Residential Energy Systems Using Energy Storage & Controllable Loads degree of freedom leads to improved performance. 1 Introduction Widespread uptake of local electricity
Flood Prevention of the Demer using Model Predictive Control
Flood Prevention of the Demer using Model Predictive Control Toni Barjas Blanco, ,1 Patrick Willems Abstract: In order to prevent flooding of a river system the local water administration of the Demer reduced the damage and frequency of flooding events, simulations have shown that a better usage
A distributed accelerated gradient algorithm for distributed model predictive
Como, Giacomo
is applied to the power reference tracking problem of a hydro power valley (HPV) system. The applied, Distributed model predictive control 1. Introduction Hydro power plants generate electricity from potential river or a water body system to generate the power at different stages. Currently, hydro power is one
Penetration rate prediction for percussive drilling via dry friction model
Krivtsov, Anton M.
Penetration rate prediction for percussive drilling via dry friction model Anton M. Krivtsov a of percussive drilling assuming a dry friction mechanism to explain the experimentally observed drop in pene in drilling research is a fall of pene- tration rate for higher static loads. This is known both
Ensemble climate predictions using climate models and observational constraints
REVIEW Ensemble climate predictions using climate models and observational constraints BY PETER A. STOTT 1,* AND CHRIS E. FOREST 2 1 Hadley Centre for Climate Change (Reading Unit), Meteorology Building, Massachusetts Institute of Technology, Cambridge, MA 02139, USA Two different approaches are described
An Energy Based Fatigue Life Prediction Framework for In-Service Structural Components
H. Ozaltun; M. H.H. Shen; T. George; C. Cross
2011-06-01
An energy based fatigue life prediction framework has been developed for calculation of remaining fatigue life of in service gas turbine materials. The purpose of the life prediction framework is to account aging effect caused by cyclic loadings on fatigue strength of gas turbine engines structural components which are usually designed for very long life. Previous studies indicate the total strain energy dissipated during a monotonic fracture process and a cyclic process is a material property that can be determined by measuring the area underneath the monotonic true stress-strain curve and the sum of the area within each hysteresis loop in the cyclic process, respectively. The energy-based fatigue life prediction framework consists of the following entities: (1) development of a testing procedure to achieve plastic energy dissipation per life cycle and (2) incorporation of an energy-based fatigue life calculation scheme to determine the remaining fatigue life of in-service gas turbine materials. The accuracy of the remaining fatigue life prediction method was verified by comparison between model approximation and experimental results of Aluminum 6061-T6. The comparison shows promising agreement, thus validating the capability of the framework to produce accurate fatigue life prediction.
Predicting solar cycle 24 with a solar dynamo model
Arnab Rai Choudhuri; Piyali Chatterjee; Jie Jiang
2007-01-18
Whether the upcoming cycle 24 of solar activity will be strong or not is being hotly debated. The solar cycle is produced by a complex dynamo mechanism. We model the last few solar cycles by `feeding' observational data of the Sun's polar magnetic field into our solar dynamo model. Our results fit the observed sunspot numbers of cycles 21-23 extremely well and predict that cycle~24 will be about 35% weaker than cycle~23.
Predicting Solar Flares by Data Assimilation in Avalanche Models. I. Model Design and Validation
Eric Bélanger; Alain Vincent; Paul Charbonneau
2007-08-14
Data assimilation techniques, developed in the last two decades mainly for weather prediction, produce better forecasts by taking advantage of both theoretical/numerical models and real-time observations. In this paper, we explore the possibility of applying the data-assimilation techniques known as 4D-VAR to the prediction of solar flares. We do so in the context of a continuous version of the classical cellular-automaton-based self-organized critical avalanche models of solar flares introduced by Lu and Hamilton (Astrophys. J., 380, L89, 1991). Such models, although a priori far removed from the physics of magnetic reconnection and magneto-hydrodynamical evolution of coronal structures, nonetheless reproduce quite well the observed statistical distribution of flare characteristics. We report here on a large set of data assimilation runs on synthetic energy release time series. Our results indicate that, despite the unpredictable (and unobservable) stochastic nature of the driving/triggering mechanism within the avalanche model, 4D-VAR succeeds in producing optimal initial conditions that reproduce adequately the time series of energy released by avalanches/flares. This is an essential first step towards forecasting real flares.
ScoPred--Scalable User-Directed Performance Prediction Using Complexity Modeling and Historical Data
Feitelson, Dror
complexity models, good prediction accuracy can be obtained. 1 Introduction The typical approach in parallel, partic
A multivariate quadrature based moment method for supersonic combustion modeling
Raman, Venkat
QMOM is then used for studying an experimental Mach 2.2 supersonic cavity based combustor. Development of predictiveA multivariate quadrature based moment method for supersonic combustion modeling Pratik Donde models for supersonic combustion is a critical step in design and development of scramjet engines
Mining Behavior Based Safety Data to Predict Safety Performance
Jeffrey C. Joe
2010-06-01
The Idaho National Laboratory (INL) operates a behavior based safety program called Safety Observations Achieve Results (SOAR). This peer-to-peer observation program encourages employees to perform in-field observations of each other's work practices and habits (i.e., behaviors). The underlying premise of conducting these observations is that more serious accidents are prevented from occurring because lower level “at risk” behaviors are identified and corrected before they can propagate into culturally accepted “unsafe” behaviors that result in injuries or fatalities. Although the approach increases employee involvement in safety, the premise of the program has not been subject to sufficient empirical evaluation. The INL now has a significant amount of SOAR data on these lower level “at risk” behaviors. This paper describes the use of data mining techniques to analyze these data to determine whether they can predict if and when a more serious accident will occur.
A KNOWLEDGE-BASED APPROACH TO PROTEIN LOCAL STRUCTURE PREDICTION*
Wong, Limsoon
prediction method that assigns a measure called the local match rate to each position of an amino acid its amino acid sequence. Local structure prediction helps improve the per- formance of both profile@iis.sinica.edu.tw 1 #12;structures, predicting protein local structures from amino acid sequences is much more
On the Predictiveness of Single-Field Inflationary Models
C. P. Burgess; Subodh P. Patil; Michael Trott
2015-07-20
We re-examine the predictiveness of single-field inflationary models and discuss how an unknown UV completion can complicate determining inflationary model parameters from observations, even from precision measurements. Besides the usual naturalness issues associated with having a shallow inflationary potential, we describe another issue for inflation, namely, unknown UV physics modifies the running of Standard Model (SM) parameters and thereby introduces uncertainty into the potential inflationary predictions. We illustrate this point using the minimal Higgs Inflationary scenario, which is arguably the most predictive single-field model on the market, because its predictions for $A_s$, $r$ and $n_s$ are made using only one new free parameter beyond those measured in particle physics experiments, and run up to the inflationary regime. We find that this issue can already have observable effects. At the same time, this UV-parameter dependence in the Renormalization Group allows Higgs Inflation to occur (in principle) for a slightly larger range of Higgs masses. We comment on the origin of the various UV scales that arise at large field values for the SM Higgs, clarifying cut off scale arguments by further developing the formalism of a non-linear realization of $\\rm SU_L(2) \\times U(1)$ in curved space. We discuss the interesting fact that, outside of Higgs Inflation, the effect of a non-minimal coupling to gravity, even in the SM, results in a non-linear EFT for the Higgs sector. Finally, we briefly comment on post BICEP2 attempts to modify the Higgs Inflation scenario.
SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity
Bejerano, Gill
SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity Qingyuan Zhao Stanford: Algorithms; Experimentation. Keywords: information diffusion; cascade prediction; self-exciting point process
Neutrino minimal standard model predictions for neutrinoless double beta decay
Bezrukov, F. [Institute for Nuclear Research of the Russian Academy of Sciences, 60th October Anniversary prospect 7a, Moscow 117312 (Russian Federation) and Institut de Theorie des Phenomenes Physiques, Ecole Polytechnique Federale de Lausanne, CH-1015 Lausanne (Switzerland)
2005-10-01
Prediction of the effective Majorana mass for neutrinoless double {beta} decay in a simple extension of the standard model ({nu}MSM) is given. The model adds three right-handed neutrinos with masses smaller than the electroweak scale and explains dark matter of the Universe. This leads to constraints 1.3
Evaluation of a mathematical model in predicting intake of growing and finishing cattle
Bourg, Brandi Marie
2009-05-15
energy (ME) value was conducted. A meta-analysis of growing and finishing steers evaluated to model’s accuracy in predicting DMR of individually fed steers, and the relationships between several model-predicted variables and actual performance...
Predictive models for power dissipation in optical transceivers
Butler, Katherine, 1981-
2004-01-01
Power dissipation in optical networks is a significant problem for the telecommunications industry. The optical transceiver was selected as a representative device of the network, and a component based power model is ...
Predicting Land-Ice Retreat and Sea-Level Rise with the Community Earth System Model
Lipscomb, William
2012-06-19
Coastal stakeholders need defensible predictions of 21st century sea-level rise (SLR). IPCC assessments suggest 21st century SLR of {approx}0.5 m under aggressive emission scenarios. Semi-empirical models project SLR of {approx}1 m or more by 2100. Although some sea-level contributions are fairly well constrained by models, others are highly uncertain. Recent studies suggest a potential large contribution ({approx}0.5 m/century) from the marine-based West Antarctic Ice Sheet, linked to changes in Southern Ocean wind stress. To assess the likelihood of fast retreat of marine ice sheets, we need coupled ice-sheet/ocean models that do not yet exist (but are well under way). CESM is uniquely positioned to provide integrated, physics based sea-level predictions.
Incorporating Control Performance Tuning into Economic Model Predictive Control
Olanrewaju, Olumuyiwa I.; Maciejowski, Jan M.
2015-01-01
[1] A. Singh, J. Forbes, P. Vermeer, and S. Woo, “Model-based real-time optimization of automotive gasoline blending operations,” Journal of Process Control, vol. 10, no. 1, pp. 43 – 58, 2000. [2] A. Toumi and S. Engell, “Optimization-based control...
Prediction-based Strategies for Energy Saving in Object Tracking Sensor Networks
Lee, Wang-Chien
Prediction-based Strategies for Energy Saving in Object Tracking Sensor Networks Yingqi Xu Julian are sometimes predictable, we pro- pose a Prediction-based Energy Saving scheme, called PES, to re- duce on PES through extensive simulation. Our results show that PES can save significant energy under various
DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)
Anderson-Cook, Christine M.; Morzinski, Jerome; Blecker, Kenneth D.
2015-08-19
Understanding the impact of production, environmental exposure and age characteristics on the reliability of a population is frequently based on underlying science and empirical assessment. When there is incomplete science to prescribe which inputs should be included in a model of reliability to predict future trends, statistical model/variable selection techniques can be leveraged on a stockpile or population of units to improve reliability predictions as well as suggest new mechanisms affecting reliability to explore. We describe a five-step process for exploring relationships between available summaries of age, usage and environmental exposure and reliability. The process involves first identifying potential candidatemore »inputs, then second organizing data for the analysis. Third, a variety of models with different combinations of the inputs are estimated, and fourth, flexible metrics are used to compare them. As a result, plots of the predicted relationships are examined to distill leading model contenders into a prioritized list for subject matter experts to understand and compare. The complexity of the model, quality of prediction and cost of future data collection are all factors to be considered by the subject matter experts when selecting a final model.« less
Model Predictive Control of Integrated Gasification Combined Cycle Power Plants
B. Wayne Bequette; Priyadarshi Mahapatra
2010-08-31
The primary project objectives were to understand how the process design of an integrated gasification combined cycle (IGCC) power plant affects the dynamic operability and controllability of the process. Steady-state and dynamic simulation models were developed to predict the process behavior during typical transients that occur in plant operation. Advanced control strategies were developed to improve the ability of the process to follow changes in the power load demand, and to improve performance during transitions between power levels. Another objective of the proposed work was to educate graduate and undergraduate students in the application of process systems and control to coal technology. Educational materials were developed for use in engineering courses to further broaden this exposure to many students. ASPENTECH software was used to perform steady-state and dynamic simulations of an IGCC power plant. Linear systems analysis techniques were used to assess the steady-state and dynamic operability of the power plant under various plant operating conditions. Model predictive control (MPC) strategies were developed to improve the dynamic operation of the power plants. MATLAB and SIMULINK software were used for systems analysis and control system design, and the SIMULINK functionality in ASPEN DYNAMICS was used to test the control strategies on the simulated process. Project funds were used to support a Ph.D. student to receive education and training in coal technology and the application of modeling and simulation techniques.
Yu, Z.; Peldszus, S.; Huck, P.M. [University of Waterloo, Waterloo, ON (Canada). NSERC Chair in Water Treatment
2009-03-01
The adsorption of two representative pharmaceutically active compounds (PhACs) naproxen and carbamazepine and one endocrine disrupting compound (EDC) nonylphenol was studied in pilot-scale granular activated carbon (GAC) adsorbers using post-sedimentation (PS) water from a full-scale drinking water treatment plant. The GAC adsorbents were coal-based Calgon Filtrasorb 400 and coconut shell-based PICA CTIF TE. Acidic naproxen broke through fastest while nonylphenol was removed best, which was consistent with the degree to which fouling affected compound removals. Model predictions and experimental data were generally in good agreement for all three compounds, which demonstrated the effectiveness and robustness of the pore and surface diffusion model (PSDM) used in combination with the time-variable parameter approach for predicting removals at environmentally relevant concentrations (i.e., ng/L range). Sensitivity analyses suggested that accurate determination of film diffusion coefficients was critical for predicting breakthrough for naproxen and carbamazepine, in particular when high removals are targeted. Model simulations demonstrated that GAC carbon usage rates (CURs) for naproxen were substantially influenced by the empty bed contact time (EBCT) at the investigated conditions. Model-based comparisons between GAC CURs and minimum CURs for powdered activated carbon (PAC) applications suggested that PAC would be most appropriate for achieving 90% removal of naproxen, whereas GAC would be more suitable for nonylphenol. 25 refs., 4 figs., 1 tab.
Predictive modeling of reactive wetting and metal joining.
van Swol, Frank B.
2013-09-01
The performance, reproducibility and reliability of metal joints are complex functions of the detailed history of physical processes involved in their creation. Prediction and control of these processes constitutes an intrinsically challenging multi-physics problem involving heating and melting a metal alloy and reactive wetting. Understanding this process requires coupling strong molecularscale chemistry at the interface with microscopic (diffusion) and macroscopic mass transport (flow) inside the liquid followed by subsequent cooling and solidification of the new metal mixture. The final joint displays compositional heterogeneity and its resulting microstructure largely determines the success or failure of the entire component. At present there exists no computational tool at Sandia that can predict the formation and success of a braze joint, as current capabilities lack the ability to capture surface/interface reactions and their effect on interface properties. This situation precludes us from implementing a proactive strategy to deal with joining problems. Here, we describe what is needed to arrive at a predictive modeling and simulation capability for multicomponent metals with complicated phase diagrams for melting and solidification, incorporating dissolutive and composition-dependent wetting.
Crucial stages of protein folding through a solvable model: Predicting target sites
Cecconi, Fabio
Crucial stages of protein folding through a solvable model: Predicting target sites for enzyme. Keywords: Protein-folding modeling; prediction of key folding sites; HIV-1 protease; drug resistance One
Results from baseline tests of the SPRE I and comparison with code model predictions
Cairelli, J.E.; Geng, S.M.; Skupinski, R.C.
1994-09-01
The Space Power Research Engine (SPRE), a free-piston Stirling engine with linear alternator, is being tested at the NASA Lewis Research Center as part of the Civil Space Technology Initiative (CSTI) as a candidate for high capacity space power. This paper presents results of base-line engine tests at design and off-design operating conditions. The test results are compared with code model predictions.
SNO+: predictions from standard solar models and spin flavour precession
Marco Picariello; João Pulido; S. Andringa; N. F. Barros; J. Maneira
2007-10-22
Time variability of the solar neutrino flux especially in the low and intermediate energy sector remains an open question and, if it exists, it is likely to be originated from the magnetic moment transition from active to light sterile neutrinos at times of intense solar activity and magnetic field. We examine the prospects for the SNO+ experiment to address this important issue and to distinguish between the two classes of solar models which are currently identified as corresponding to a high (SSM I) and a low (SSM II) heavy element abundance. We also evaluate the predictions from these two models for the Chlorine experiment event rate in the standard LMA and LMA+Spin Flavour Precession (SFP) scenarios. It is found that after three years of SNO+ data taking, the pep flux measurement will be able to discriminate between the standard LMA and LMA+SFP scenarios, independently of which is the correct solar model. If the LMA rate is measured, SFP with $B_0 \\sim 280kG$ for the resonant $\\Delta m^2_{01}$ can be excluded at more than $4\\sigma$. A low rate would signal new physics, excluding all the 90% allowed range of the standard LMA solution at 3$\\sigma$, and a time variability would be a strong signature of the SFP model. The CNO fluxes are the ones for which the two SSM predictions exhibit the largest differences, so their measurement at SNO+ will be important to favour one or the other. The distinction will be clearer after LMA or SFP are confirmed with pep, but still, a CNO measurement at the level of SSM I/LMA will disfavour SSM II at about $3 \\sigma$. We conclude that consistency between future pep and CNO flux measurements at SNO+ and Chlorine would either favour an LMA+SFP scenario or favour SSM II over SSM I.
Prediction-based trajectory tracking of External Gas Recirculation for turbocharged SI Engines
Prediction-based trajectory tracking of External Gas Recirculation for turbocharged SI Engines with direct injection, turbocharger and Variable Valve Timing (VVT) actuators [21]. Such a setup
Haves, Phillip
2010-01-01
13] Wetter, M.. 2009. “Modelica?based Modeling and 14] Wetter, M.. 2009. “Modelica?based Modeling and modeling language Modelica. Steady state models of
Reliability analysis and prediction of mixed mode load using Markov Chain Model
Nikabdullah, N.; Singh, S. S. K.; Alebrahim, R.; Azizi, M. A.; K, Elwaleed A.; Noorani, M. S. M.
2014-06-19
The aim of this paper is to present the reliability analysis and prediction of mixed mode loading by using a simple two state Markov Chain Model for an automotive crankshaft. The reliability analysis and prediction for any automotive component or structure is important for analyzing and measuring the failure to increase the design life, eliminate or reduce the likelihood of failures and safety risk. The mechanical failures of the crankshaft are due of high bending and torsion stress concentration from high cycle and low rotating bending and torsional stress. The Markov Chain was used to model the two states based on the probability of failure due to bending and torsion stress. In most investigations it revealed that bending stress is much serve than torsional stress, therefore the probability criteria for the bending state would be higher compared to the torsion state. A statistical comparison between the developed Markov Chain Model and field data was done to observe the percentage of error. The reliability analysis and prediction was derived and illustrated from the Markov Chain Model were shown in the Weibull probability and cumulative distribution function, hazard rate and reliability curve and the bathtub curve. It can be concluded that Markov Chain Model has the ability to generate near similar data with minimal percentage of error and for a practical application; the proposed model provides a good accuracy in determining the reliability for the crankshaft under mixed mode loading.
How Computational Models Predict the Behavior of Complex Systems John Symons 1
Boschetti, Fabio
How Computational Models Predict the Behavior of Complex Systems John Symons 1 Fabio Boschetti2,3 1 of prediction in the use of computational models in science. We focus on the consequences of the irreversibility of computational models and on the conditional or ceteris paribus, nature of the kinds of their predictions
Beating the bookie: A look at statistical models for prediction of football matches
Langseth, Helge
Beating the bookie: A look at statistical models for prediction of football matches Helge LANGSETH, Norway Abstract. In this paper we look at statistical models for predicting the outcome of football. Keywords. Association football, statistical models, predictions, betting 1. Introduction Association
Colliding cascades model for earthquake prediction Andrei Gabrielov,1,2
Gabrielov, Andrei
Colliding cascades model for earthquake prediction Andrei Gabrielov,1,2 Ilya Zaliapin,3 William I Lafayette, IN 47907-1395, USA 3 International Institute of Earthquake Prediction Theory and Mathematical model of seismicity, and their performance in the prediction of major model earthquakes is evaluated
Crowdtuning: systematizing auto-tuning using predictive modeling and
Paris-Sud XI, Université de
in a public repository to initiate systematic, reproducible and collaborative R&D with a new publication model, reliability and cost. We present our novel long-term holistic and practical solution to this problem basedTuning.org for collaborative explanation, top-down complexity reduction, incremental problem decomposition and detection
Image Analysis Model-Based Methods
Wolfe, Patrick J.
Model-Based Methods Comparing and Evaluating Models Summary Further Reading Data Collection ScientificImage Analysis Model-Based Methods Comparing and Evaluating Models Summary Further Reading Fully Low-Count Image Analysis #12;Image Analysis Model-Based Methods Comparing and Evaluating Models
Climate predictions: the chaos and complexity in climate models
Dragutin T. Mihailovi?; Gordan Mimi?; Ilija Arseni?
2013-10-15
Some issues which are relevant for the recent state in climate modeling have been considered. A detailed overview of literature related to this subject is given. The concept in modeling of climate, as a complex system, seen through Godel's Theorem and Rosen's definition of complexity and predictability is discussed. It is pointed out to occurrence of chaos in computing the environmental interface temperature from the energy balance equation given in a difference form. A coupled system of equations, often used in climate models is analyzed. It is shown that the Lyapunov exponent mostly has positive values allowing presence of chaos in this systems. The horizontal energy exchange between environmental interfaces, which is described by the dynamics of driven coupled oscillators, is analyzed. Their behavior and synchronization, when a perturbation is introduced in the system, as a function of the coupling parameters, the logistic parameter and the parameter of exchange, was studied calculating the Lyapunov exponent under simulations with the closed contour of N=100 environmental interfaces. Finally, we have explored possible differences in complexities of two global and two regional climate models using their output time series by applying the algorithm for calculating the Kolmogorov complexity.
Spatial predictive distribution for precipitation based on numerical weather predictions (NWP)
Steinsland, Ingelin
for precipitation based on NWP #12;Motivation, hydro power production How much water comes when? With uncertainty
Lygeros, John
of High Performance Hybrid Race Cars Background The power unit of a high performance hybrid race carPrerequisites Control Systems, System Modeling, Optimal Control, Model Predictive Control, (Engine consists of an internal combustion engine (ICE) and a kinetic energy recovery system (KERS). The time
Hybrid coupled models of the tropical Paci c | II ENSO prediction
Hsieh, William
Hybrid coupled models of the tropical Paci#12;c | II ENSO prediction by Youmin Tang 1 , William W: ytang@cims.nyu.edu #12; Abstract Two hybrid coupled models (HCMs), a dynamical ocean model coupled Introduction Models for ENSO prediction can be categorized into purely statistical models, hybrid coupled
ORIGINAL ARTICLE Task scheduling with ANN-based temperature prediction
operational temperatures for reducing energy cost. Consequently, resource management with thermal, researchers have shown that workload man- agement, focused on a data center's thermal properties, effectively reduces temperatures within a data center. In this paper, we propose a method to predict a workload
Optimal Control of Distributed Energy Resources using Model Predictive Control
Mayhorn, Ebony T.; Kalsi, Karanjit; Elizondo, Marcelo A.; Zhang, Wei; Lu, Shuai; Samaan, Nader A.; Butler-Purry, Karen
2012-07-22
In an isolated power system (rural microgrid), Distributed Energy Resources (DERs) such as renewable energy resources (wind, solar), energy storage and demand response can be used to complement fossil fueled generators. The uncertainty and variability due to high penetration of wind makes reliable system operations and controls challenging. In this paper, an optimal control strategy is proposed to coordinate energy storage and diesel generators to maximize wind penetration while maintaining system economics and normal operation. The problem is formulated as a multi-objective optimization problem with the goals of minimizing fuel costs and changes in power output of diesel generators, minimizing costs associated with low battery life of energy storage and maintaining system frequency at the nominal operating value. Two control modes are considered for controlling the energy storage to compensate either net load variability or wind variability. Model predictive control (MPC) is used to solve the aforementioned problem and the performance is compared to an open-loop look-ahead dispatch problem. Simulation studies using high and low wind profiles, as well as, different MPC prediction horizons demonstrate the efficacy of the closed-loop MPC in compensating for uncertainties in wind and demand.
Adaptive model predictive process control using neural networks
Buescher, Kevin L. (Los Alamos, NM); Baum, Christopher C. (Mazomanie, WI); Jones, Roger D. (Espanola, NM)
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.
Adaptive model predictive process control using neural networks
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.
Surussavadee, Chinnawat
2007-01-01
This thesis develops and validates the MM5/TBSCAT/F([lambda]) model, composed of a mesoscale numerical weather prediction (NWP) model (MM5), a two-stream radiative transfer model (TBSCAT), and electromagnetic models for ...
A Statistical Prediction-based Scheme for Energy-aware Multimedia Data Streaming
Bhandarkar, Suchendra "Suchi" M.
A Statistical Prediction-based Scheme for Energy-aware Multimedia Data Streaming Yong Wei Surendar multimedia objects has fueled the demand of mobile streaming multimedia. A necessary criterion for the mass prediction-based client-side strategies to reduce the wireless network interface card (WNIC) energy
A FIRST PRINCIPLES BASED METHOD FOR THE PREDICTION OF LOADING OVER FIXED AND ROTARY WING GEOMETRIES
A FIRST PRINCIPLES BASED METHOD FOR THE PREDICTION OF LOADING OVER FIXED AND ROTARY WING GEOMETRIES Lakshmi N. Sankar and Mert Berkman School of Aerospace Engineering Georgia Institute of Technology-principles based techniques for the prediction of fixed and rotary wing wake geometry are described
The Case For Prediction-based Best-effort Real-time Systems
The Case For Prediction-based Best-effort Real-time Systems Peter A. Dinda Bruce Lowekamp Loukas and Distributed Real- Time Systems (WPDRTS '99) Abstract We propose a prediction-based best-effort real significant examples, an earthquake visualization tool and a GIS map display tool, and show how they could
Martin, Luis; Marchante, Ruth; Cony, Marco; Zarzalejo, Luis F.; Polo, Jesus; Navarro, Ana
2010-10-15
Due to strong increase of solar power generation, the predictions of incoming solar energy are acquiring more importance. Photovoltaic and solar thermal are the main sources of electricity generation from solar energy. In the case of solar thermal energy plants with storage energy system, its management and operation need reliable predictions of solar irradiance with the same temporal resolution as the temporal capacity of the back-up system. These plants can work like a conventional power plant and compete in the energy stock market avoiding intermittence in electricity production. This work presents a comparisons of statistical models based on time series applied to predict half daily values of global solar irradiance with a temporal horizon of 3 days. Half daily values consist of accumulated hourly global solar irradiance from solar raise to solar noon and from noon until dawn for each day. The dataset of ground solar radiation used belongs to stations of Spanish National Weather Service (AEMet). The models tested are autoregressive, neural networks and fuzzy logic models. Due to the fact that half daily solar irradiance time series is non-stationary, it has been necessary to transform it to two new stationary variables (clearness index and lost component) which are used as input of the predictive models. Improvement in terms of RMSD of the models essayed is compared against the model based on persistence. The validation process shows that all models essayed improve persistence. The best approach to forecast half daily values of solar irradiance is neural network models with lost component as input, except Lerida station where models based on clearness index have less uncertainty because this magnitude has a linear behaviour and it is easier to simulate by models. (author)
Young, R. Michael
are built with traditional metrics of complexity, code churn, and fault history. We have performed to the code [17]. Hence, complexity metrics and code churn metrics have been used for fault prediction [5, 17 fault prediction metrics complexity, code churn, and fault history metrics for vulnerability
Numerical and analytical modeling of sanding onset prediction
Yi, Xianjie
2004-09-30
To provide technical support for sand control decision-making, it is necessary to predict the production condition at which sand production occurs. Sanding onset prediction involves simulating the stress state on the surface of an oil/gas producing...
The Dark Gravity model predictions for Gravity Probe B
Frederic Henry-Couannier
2007-10-23
The previous version of this article gave erroneous predictions. The correct uptodate predictions can be found in the section devoted to gravitomagnetism in the living review of the Dark Gravity theory: gr-qc/0610079 The most natural prediction is zero frame dragging and the same geodetic effect as predicted by GR. However, a straightforward extension of the theory could lead to the same frame-dragging as in GR.
Physics-based models of the plasmasphere
Jordanova, Vania K; Pierrard, Vivane; Goldstein, Jerry; Andr'e, Nicolas; Lemaire, Joseph F; Liemohn, Mike W; Matsui, H
2008-01-01
We describe recent progress in physics-based models of the plasmasphere using the Auid and the kinetic approaches. Global modeling of the dynamics and inAuence of the plasmasphere is presented. Results from global plasmasphere simulations are used to understand and quantify (i) the electric potential pattern and evolution during geomagnetic storms, and (ii) the inAuence of the plasmasphere on the excitation of electromagnetic ion cyclotron (ElvIIC) waves a.nd precipitation of energetic ions in the inner magnetosphere. The interactions of the plasmasphere with the ionosphere a.nd the other regions of the magnetosphere are pointed out. We show the results of simulations for the formation of the plasmapause and discuss the inAuence of plasmaspheric wind and of ultra low frequency (ULF) waves for transport of plasmaspheric material. Theoretical formulations used to model the electric field and plasma distribution in the plasmasphere are given. Model predictions are compared to recent CLUSTER and MAGE observations, but also to results of earlier models and satellite observations.
ZEPHYR THE PREDICTION MODELS T.S. Nielsen, H. Madsen, H. Aa. Nielsen
models and methods for predicting the wind power output from wind farms. The system is being developed are evaluated for five wind farms in Denmark as well as one wind farm in Spain. It is shown that the predictions farms the Prediktor model developed at Risø and the Wind Power Prediction Tool (WPPT) developed at IMM
Selection of Ground Motion Prediction Equations for the Global Earthquake Model
Paris-Sud XI, Université de
1 Selection of Ground Motion Prediction Equations for the Global Earthquake Model Jonathan P are developed. Keywords: Engineering seismology, ground-motion prediction, site effects, Global Earthquake Model.EERI, and Peter J. Stafford, h) M.EERI Ground-motion prediction equations (GMPEs) relate ground-motion intensity
Wurstner, S.K.; Freshley, M.D.
1994-12-01
A ground-water flow model was used to predict water level decline in selected wells in the operating areas (100, 200, 300, and 400 Areas) and the 600 Area. To predict future water levels, the unconfined aquifer system was stimulated with the two-dimensional version of a ground-water model of the Hanford Site, which is based on the Coupled Fluid, Energy, and Solute Transport (CFEST) Code in conjunction with the Geographic Information Systems (GIS) software package. The model was developed using the assumption that artificial recharge to the unconfined aquifer system from Site operations was much greater than any natural recharge from precipitation or from the basalt aquifers below. However, artificial recharge is presently decreasing and projected to decrease even more in the future. Wells currently used for monitoring at the Hanford Site are beginning to go dry or are difficult to sample, and as the water table declines over the next 5 to 10 years, a larger number of wells is expected to be impacted. The water levels predicted by the ground-water model were compared with monitoring well completion intervals to determine which wells will become dry in the future. Predictions of wells that will go dry within the next 5 years have less uncertainty than predictions for wells that will become dry within 5 to 10 years. Each prediction is an estimate based on assumed future Hanford Site operating conditions and model assumptions.
On flare predictability based on sunspot group evolution
Korsos, Marianna; Erdelyi, Robert; Baranyi, Tunde
2015-01-01
The forecast method introduced by Kors\\'os et al.(2014) is generalised from the horizontal magnetic gradient (GM), defined between two opposite polarity spots, to all spots within an appropriately defined region close to the magnetic neutral line of an active region. This novel approach is not limited to searching for the largest GM of two single spots as in previous methods. Instead, the pre-flare conditions of the evolution of spot groups is captured by the introduction of the weighted horizontal magnetic gradient, or W_GM. This new proxy enables the potential of forecasting flares stronger than M5. The improved capability includes (i) the prediction of flare onset time and (ii) an assessment whether a flare is followed by another event within about 18 hours. The prediction of onset time is found to be more accurate here. A linear relationship is established between the duration of converging motion and the time elapsed from the moment of closest position to that of the flare onset of opposite polarity spot...
Economic Model Predictive Control of Nonlinear Process Systems Using Empirical Models
ALANQAR, ANAS WAEL
2015-01-01
4 Application to a Chemical Process Example 5 Conclusionsnonlinear processes. Chemical Engineering Science 2003, 58,based on Wiener models. Chemical Engineering Science 1998,
Application of the cumulative risk model in predicting school readiness in Head Start children
Rodriguez-Escobar, Olga Lydia
2009-05-15
This study investigates the degree to which the cumulative risk index predicted school readiness in a Head Start population. In general, the reviewed studies indicated the cumulative risk model was efficacious in predicting adverse developmental...
Location Prediction in Social Media Based on Tie Strength
McGee, Jeffrey A
2013-04-29
We propose a novel network-based approach for location estimation in social media that integrates evidence of the social tie strength between users for improved location estimation. Concretely, we propose a location estimator – Friendly...
Narasimhan, C.S.L.; Verma, R.P. [Indian Oil Corporation Ltd., Faridabad (India)
1995-12-31
Modeling of hydrocracking kinetics capturing the chemistry of the process has been a continuous endeavor for the researchers. Very few approaches have been formulated so far, which either over simplify the problem or require large number of computation parameters for acceptable solution. The present paper proposes a novel and elegant approach based on continuum theory of lumping, which attempts to follow the process chemistry closely to model the complex hydrocracking kinetics for prediction of paraffins, naphthenes and aromatics (PNAs) in the product mixture. The model predictions match well with reported experimental results.
Model based dependability evaluation for automotive control functions
Schlingloff, Holger
Model based dependability evaluation for automotive control functions Sasa Vulinovic 1 , Bernd@informatik.hu-berlin.de Abstract In this paper, we study the evaluation of reliability for embedded functions in automotive. In order to assess fault tolerant designs for automotive software it is essential to be able to predict
Mechanism-based classification of PAH mixtures to predict carcinogenic potential
DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)
Tilton, Susan C.; Siddens, Lisbeth K.; Krueger, Sharon K.; Larkin, Andrew J.; Löhr, Christiane V.; Williams, David E.; Baird, William M.; Waters, Katrina M.
2015-04-22
We have previously shown that relative potency factors and DNA adduct measurements are inadequate for predicting carcinogenicity of certain polycyclic aromatic hydrocarbons (PAHs) and PAH mixtures, particularly those that function through alternate pathways or exhibit greater promotional activity compared to benzo[a]pyrene (BaP). Therefore, we developed a pathway based approach for classification of tumor outcome after dermal exposure to PAH/mixtures. FVB/N mice were exposed to dibenzo[def,p]chrysene (DBC), BaP or environmental PAH mixtures (Mix 1-3) following a two-stage initiation/promotion skin tumor protocol. Resulting tumor incidence could be categorized by carcinogenic potency as DBC>>BaP=Mix2=Mix3>Mix1=Control, based on statistical significance. Gene expression profiles measured inmore »skin of mice collected 12 h post-initiation were compared to tumor outcome for identification of short-term bioactivity profiles. A Bayesian integration model was utilized to identify biological pathways predictive of PAH carcinogenic potential during initiation. Integration of probability matrices from four enriched pathways (p« less
Babiker, Mustafa H.M.; Reilly, John M.; Mayer, Monika.; Eckaus, Richard S.; Sue Wing, Ian.; Hyman, Robert C.
The Emissions Prediction and Policy Analysis (EPPA) model is a component of the MIT Integrated Earth Systems Model (IGSM). Here, we provide an overview of the model accessible to a broad audience and present the detailed ...
A Forward Looking Version of the MIT Emissions Prediction and Policy Analysis (EPPA) Model
Babiker, Mustafa M.H.
This paper documents a forward looking multi-regional general equilibrium model developed from the latest version of the recursive-dynamic MIT Emissions Prediction and Policy Analysis (EPPA) model. The model represents ...
The MIT Emissions Prediction and Policy Analysis (EPPA) Model: Version 4
Paltsev, Sergey.
The Emissions Prediction and Policy Analysis (EPPA) model is the part of the MIT Integrated Global Systems Model (IGSM) that represents the human systems. EPPA is a recursive-dynamic multi-regional general equilibrium model ...
G. R. Odette; G. E. Lucas
2005-11-15
This final report on "In-Service Design & Performance Prediction of Advanced Fusion Material Systems by Computational Modeling and Simulation" (DE-FG03-01ER54632) consists of a series of summaries of work that has been published, or presented at meetings, or both. It briefly describes results on the following topics: 1) A Transport and Fate Model for Helium and Helium Management; 2) Atomistic Studies of Point Defect Energetics, Dynamics and Interactions; 3) Multiscale Modeling of Fracture consisting of: 3a) A Micromechanical Model of the Master Curve (MC) Universal Fracture Toughness-Temperature Curve Relation, KJc(T - To), 3b) An Embrittlement DTo Prediction Model for the Irradiation Hardening Dominated Regime, 3c) Non-hardening Irradiation Assisted Thermal and Helium Embrittlement of 8Cr Tempered Martensitic Steels: Compilation and Analysis of Existing Data, 3d) A Model for the KJc(T) of a High Strength NFA MA957, 3e) Cracked Body Size and Geometry Effects of Measured and Effective Fracture Toughness-Model Based MC and To Evaluations of F82H and Eurofer 97, 3-f) Size and Geometry Effects on the Effective Toughness of Cracked Fusion Structures; 4) Modeling the Multiscale Mechanics of Flow Localization-Ductility Loss in Irradiation Damaged BCC Alloys; and 5) A Universal Relation Between Indentation Hardness and True Stress-Strain Constitutive Behavior. Further details can be found in the cited references or presentations that generally can be accessed on the internet, or provided upon request to the authors. Finally, it is noted that this effort was integrated with our base program in fusion materials, also funded by the DOE OFES.
Valerio, Luis G. . E-mail: luis.valerio@FDA.HHS.gov; Arvidson, Kirk B.; Chanderbhan, Ronald F.; Contrera, Joseph F.
2007-07-01
Consistent with the U.S. Food and Drug Administration (FDA) Critical Path Initiative, predictive toxicology software programs employing quantitative structure-activity relationship (QSAR) models are currently under evaluation for regulatory risk assessment and scientific decision support for highly sensitive endpoints such as carcinogenicity, mutagenicity and reproductive toxicity. At the FDA's Center for Food Safety and Applied Nutrition's Office of Food Additive Safety and the Center for Drug Evaluation and Research's Informatics and Computational Safety Analysis Staff (ICSAS), the use of computational SAR tools for both qualitative and quantitative risk assessment applications are being developed and evaluated. One tool of current interest is MDL-QSAR predictive discriminant analysis modeling of rodent carcinogenicity, which has been previously evaluated for pharmaceutical applications by the FDA ICSAS. The study described in this paper aims to evaluate the utility of this software to estimate the carcinogenic potential of small, organic, naturally occurring chemicals found in the human diet. In addition, a group of 19 known synthetic dietary constituents that were positive in rodent carcinogenicity studies served as a control group. In the test group of naturally occurring chemicals, 101 were found to be suitable for predictive modeling using this software's discriminant analysis modeling approach. Predictions performed on these compounds were compared to published experimental evidence of each compound's carcinogenic potential. Experimental evidence included relevant toxicological studies such as rodent cancer bioassays, rodent anti-carcinogenicity studies, genotoxic studies, and the presence of chemical structural alerts. Statistical indices of predictive performance were calculated to assess the utility of the predictive modeling method. Results revealed good predictive performance using this software's rodent carcinogenicity module of over 1200 chemicals, comprised primarily of pharmaceutical, industrial and some natural products developed under an FDA-MDL cooperative research and development agreement (CRADA). The predictive performance for this group of dietary natural products and the control group was 97% sensitivity and 80% concordance. Specificity was marginal at 53%. This study finds that the in silico QSAR analysis employing this software's rodent carcinogenicity database is capable of identifying the rodent carcinogenic potential of naturally occurring organic molecules found in the human diet with a high degree of sensitivity. It is the first study to demonstrate successful QSAR predictive modeling of naturally occurring carcinogens found in the human diet using an external validation test. Further test validation of this software and expansion of the training data set for dietary chemicals will help to support the future use of such QSAR methods for screening and prioritizing the risk of dietary chemicals when actual animal data are inadequate, equivocal, or absent.
Transistor roadmap projection using predictive full-band atomistic modeling
Salmani-Jelodar, M., E-mail: m.salmani@gmail.com; Klimeck, G. [Network for Computational Nanotechnology and School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana 47907 (United States); Kim, S. [Intel Corporation, 2501 Northwest 229th Avenue, Hillsboro, Oregon 97124 (United States); Ng, K. [Semiconductor Research Corporation (SRC), 1101 Slater Rd, Durham, North Carolina 27703 (United States)
2014-08-25
In this letter, a full band atomistic quantum transport tool is used to predict the performance of double gate metal-oxide-semiconductor field-effect transistors (MOSFETs) over the next 15?years for International Technology Roadmap for Semiconductors (ITRS). As MOSFET channel lengths scale below 20?nm, the number of atoms in the device cross-sections becomes finite. At this scale, quantum mechanical effects play an important role in determining the device characteristics. These quantum effects can be captured with the quantum transport tool. Critical results show the ON-current degradation as a result of geometry scaling, which is in contrast to previous ITRS compact model calculations. Geometric scaling has significant effects on the ON-current by increasing source-to-drain (S/D) tunneling and altering the electronic band structure. By shortening the device gate length from 20?nm to 5.1?nm, the ratio of S/D tunneling current to the overall subthreshold OFF-current increases from 18% to 98%. Despite this ON-current degradation by scaling, the intrinsic device speed is projected to increase at a rate of at least 8% per year as a result of the reduction of the quantum capacitance.
Project Profile: Predictive Physico-Chemical Modeling of Intrinsic...
Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site
Advanced Reflector Materials NREL logo NREL, under the Physics of Reliability: Evaluating Design Insights for Component Technologies in Solar (PREDICTS) Program will be developing...
Eulerian CFD Models to Predict Thermophoretic Deposition of Soot...
Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site
This paper describes an Eulerian axisymmetric method in Fluent(R) to predict the overall heat transfer reduction of a surrogate tube due to thermophoretic deposition of submicron...
Failure Predictions for VHTR Core Components using a Probabilistic Contiuum Damage Mechanics Model
Fok, Alex
2013-10-30
The proposed work addresses the key research need for the development of constitutive models and overall failure models for graphite and high temperature structural materials, with the long-term goal being to maximize the design life of the Next Generation Nuclear Plant (NGNP). To this end, the capability of a Continuum Damage Mechanics (CDM) model, which has been used successfully for modeling fracture of virgin graphite, will be extended as a predictive and design tool for the core components of the very high- temperature reactor (VHTR). Specifically, irradiation and environmental effects pertinent to the VHTR will be incorporated into the model to allow fracture of graphite and ceramic components under in-reactor conditions to be modeled explicitly using the finite element method. The model uses a combined stress-based and fracture mechanics-based failure criterion, so it can simulate both the initiation and propagation of cracks. Modern imaging techniques, such as x-ray computed tomography and digital image correlation, will be used during material testing to help define the baseline material damage parameters. Monte Carlo analysis will be performed to address inherent variations in material properties, the aim being to reduce the arbitrariness and uncertainties associated with the current statistical approach. The results can potentially contribute to the current development of American Society of Mechanical Engineers (ASME) codes for the design and construction of VHTR core components.
The Ideal Evaluation of a Risk Prediction Model: A Randomized Clinical Trial
Brent, Roger
The Ideal Evaluation of a Risk Prediction Model: A Randomized Clinical Trial Holly Janes Fred Hutchinson Cancer Research Center 1/25 #12;Context Often a risk prediction model is developed to identify high risk subjects who can benefit from preventative therapy E.g. Framingham risk model to identify
An Analytical Model for Predicting the Remaining Battery Capacity of Lithium-Ion Batteries
Pedram, Massoud
An Analytical Model for Predicting the Remaining Battery Capacity of Lithium-Ion Batteries Peng cycle-life tends to shrink significantly. The capacities of commercial lithium-ion batteries fade by 10 prediction model to estimate the remaining capacity of a Lithium-Ion battery. The proposed analytical model
A Geologic Prediction Model For Tunneling By Photios G. Ioannou, A.M. ASCE
A Geologic Prediction Model For Tunneling By Photios G. Ioannou, A.M. ASCE Abstract: Geologic to inflated costs. This paper presents a general model for the probabilistic prediction of tunnel geology. The geologic conditions along the tunnel alignment are modeled by a set of geologic parameters (such as rock
Cheng, Jianlin Jack
Sequence-Based Prediction of Protein Folding Rates Using Contacts, Secondary Structures and Support, Columbia, Missouri * Corresponding author: chengji@missouri.edu Abstract Predicting protein folding rate is useful for understanding protein folding process and guiding protein design. Here we developed a method
Lässig, Michael
specificity of genomics-based protein-function prediction, although whether specific experimental interactome. Proc. Natl. Acad. Sci. U. S. A. 98, 45694574 9 Dwight, S.S. et al. (2002) Saccharomyces Genome for predicting proteinprotein interactions from genomic data. Science 302, 449453 17 Troyanskaya, O.G. et al
Comparisons of Exact Amplitude-Based Resummation Predictions and LHCb data
Aditi Mukhopadhyay; B. F. L. Ward
2015-07-12
We present the current status of the comparisons with the respective data of the predictions of our approach of exact amplitude-based resummation in quantum field theory as applied to precision QCD calculations as needed for LHC physics. The agreement between the theoretical predictions and the data exhibited continues to be encouraging.
Context-Based Pedestrian Path Prediction Julian Francisco Pieter Kooij1,2
Gavrila, Dariu M.
Context-Based Pedestrian Path Prediction Julian Francisco Pieter Kooij1,2 , Nicolas Schneider1.F.P.Kooij,D.M.Gavrila}@uva.nl Abstract. We present a novel Dynamic Bayesian Network for pedestrian path prediction in the intelligent) to anticipate changes in the pedestrian dynamics. Using computer vision, situational awareness is assessed
Will the Pedestrian Cross? Probabilistic Path Prediction Based on Learned Motion Features
Gavrila, Dariu M.
Will the Pedestrian Cross? Probabilistic Path Prediction Based on Learned Motion Features Christoph, The Netherlands Abstract. Future vehicle systems for active pedestrian safety will not only re- quire a high, we present a system for pedestrian action classification (walking vs. stopping) and path prediction
Frequency Prediction of Power Systems in FNET based on State Space Approach and Uncertain Basis
Li, Husheng
of oscillations [9]; when a significant disturbance occurs in a power system, the frequency will vary in time1 Frequency Prediction of Power Systems in FNET based on State Space Approach and Uncertain Basis and prediction of power frequency. Power frequency is one of the most essential parameters in the monitoring
Broader source: Energy.gov [DOE]
Modeling the Number of Ignitions Following an Earthquake: Developing Prediction Limits for Overdispersed Count Data Elizabeth J. Kelly and Raymond N. Tell
Liu, Huan
Second International Workshop on Social Computing, Behavioral Modeling, and Prediction Phoenix, Arizona March 31 - April 1, 2009 Phoenix, Arizona Proceedings published by Springer Social computing
Continuum-based Multiscale Computational Damage Modeling of Cementitous Composites
Kim, Sun-Myung
2011-08-08
, aggregates, and interfacial transition zone (ITZ) and interaction among components at meso-scale, and the interaction between reinforcements, such as fiber and carbon nanotubes (CNTs) and mortar matrix or the ITZ at nano scale in order to predict more... of Advisory Committee: Dr. Rashid K. Abu Al-Rub Based on continuum damage mechanics (CDM), an isotropic and anisotropic damage model coupled with a novel plasticity model for plain concrete is proposed in this research. Two different damage evolution laws...
Office of Energy Efficiency and Renewable Energy (EERE)
Presentation given by USAMP at 2014 DOE Hydrogen and Fuel Cells Program and Vehicle Technologies Office Annual Merit Review and Peer Evaluation Meeting about mechanistic-based ductility prediction...
Health Monitoring in an Agent-Based Smart Home by Activity Prediction
Cook, Diane J.
Health Monitoring in an Agent-Based Smart Home by Activity Prediction Sajal K. Das and Diane J objective of this paper is to investigate techniques for using agent-based smart home technologies-based smart home project funded by NSF. 1 Introduction and Motivation We live in an increasingly connected
Bulalo field, Philippines: Reservoir modeling for prediction of limits to sustainable generation
Strobel, Calvin J.
1993-01-28
The Bulalo geothermal field, located in Laguna province, Philippines, supplies 12% of the electricity on the island of Luzon. The first 110 MWe power plant was on line May 1979; current 330 MWe (gross) installed capacity was reached in 1984. Since then, the field has operated at an average plant factor of 76%. The National Power Corporation plans to add 40 MWe base load and 40 MWe standby in 1995. A numerical simulation model for the Bulalo field has been created that matches historic pressure changes, enthalpy and steam flash trends and cumulative steam production. Gravity modeling provided independent verification of mass balances and time rate of change of liquid desaturation in the rock matrix. Gravity modeling, in conjunction with reservoir simulation provides a means of predicting matrix dry out and the time to limiting conditions for sustainable levelized steam deliverability and power generation.
Hammes-Schiffer, Sharon
Model Proton-Coupled Electron Transfer Reactions in Solution: Predictions of Rates, Mechanisms isotope effects for proton-coupled electron transfer (PCET) reactions. These studies are based, the solvent is represented as a dielectric continuum, and the active electrons and transferring protons
that may flow out of a burned basin. This study develops a set of empirically-based models that predict soils (DeBano, 1981; Doerr et al., 2000; Letey, 2001; Woods et al., 2006) that reduce infiltration and increase overland flow and erosion through the production of rills and channels (Wells, 1987). Infiltration of
Parametric Urban Regulation Models for Predicting Development Performances
Kim, Jong Bum
2014-12-23
are significant indicators for predicting environmental footprints for the resource managements (Fischer and Guy, 2009; Lang, 1994; Punter, 1997). The prescriptive urban regulations such as FBC are less rigid in limiting density than the conventional zoning...
Burlatsky, S F; O'Neill, J; Atrazhev, V V; Varyukhin, A N; Dmitriev, D V; Erikhman, N S
2013-01-01
Under typical PEM fuel cell operating conditions, part of membrane electrode assembly is subjected to humidity cycling due to variation of inlet gas RH and/or flow rate. Cyclic membrane hydration/dehydration would cause cyclic swelling/shrinking of the unconstrained membrane. In a constrained membrane, it causes cyclic stress resulting in mechanical failure in the area adjacent to the gas inlet. A mathematical modeling framework for prediction of the lifetime of a PEM FC membrane subjected to hydration cycling is developed in this paper. The model predicts membrane lifetime as a function of RH cycling amplitude and membrane mechanical properties. The modeling framework consists of three model components: a fuel cell RH distribution model, a hydration/dehydration induced stress model that predicts stress distribution in the membrane, and a damage accrual model that predicts membrane life-time. Short descriptions of the model components along with overall framework are presented in the paper. The model was used...
Kitaev models based on unitary quantum groupoids
Chang, Liang, E-mail: liangchang@math.tamu.edu [Department of Mathematics, Texas A and M University, College Station, Texas 77843-3368 (United States)] [Department of Mathematics, Texas A and M University, College Station, Texas 77843-3368 (United States)
2014-04-15
We establish a generalization of Kitaev models based on unitary quantum groupoids. In particular, when inputting a Kitaev-Kong quantum groupoid H{sub C}, we show that the ground state manifold of the generalized model is canonically isomorphic to that of the Levin-Wen model based on a unitary fusion category C. Therefore, the generalized Kitaev models provide realizations of the target space of the Turaev-Viro topological quantum field theory based on C.
Peyret, Thomas [DSEST, Universite de Montreal, Canada H3T 1A8 (Canada); Poulin, Patrick [Consultant, 4009 rue Sylvia Daoust, Quebec City, Quebec, G1X 0A6 (Canada); Krishnan, Kannan, E-mail: kannan.krishnan@umontreal.ca [DSEST, Universite de Montreal, H3T 1A8 (Canada)
2010-12-15
The algorithms in the literature focusing to predict tissue:blood PC (P{sub tb}) for environmental chemicals and tissue:plasma PC based on total (K{sub p}) or unbound concentration (K{sub pu}) for drugs differ in their consideration of binding to hemoglobin, plasma proteins and charged phospholipids. The objective of the present study was to develop a unified algorithm such that P{sub tb}, K{sub p} and K{sub pu} for both drugs and environmental chemicals could be predicted. The development of the unified algorithm was accomplished by integrating all mechanistic algorithms previously published to compute the PCs. Furthermore, the algorithm was structured in such a way as to facilitate predictions of the distribution of organic compounds at the macro (i.e. whole tissue) and micro (i.e. cells and fluids) levels. The resulting unified algorithm was applied to compute the rat P{sub tb}, K{sub p} or K{sub pu} of muscle (n = 174), liver (n = 139) and adipose tissue (n = 141) for acidic, neutral, zwitterionic and basic drugs as well as ketones, acetate esters, alcohols, aliphatic hydrocarbons, aromatic hydrocarbons and ethers. The unified algorithm reproduced adequately the values predicted previously by the published algorithms for a total of 142 drugs and chemicals. The sensitivity analysis demonstrated the relative importance of the various compound properties reflective of specific mechanistic determinants relevant to prediction of PC values of drugs and environmental chemicals. Overall, the present unified algorithm uniquely facilitates the computation of macro and micro level PCs for developing organ and cellular-level PBPK models for both chemicals and drugs.
Flood control of the Demer by using Model Predictive Control Maarten Breckpot a,n
rainfall. Also hydraulic structures were built to control the discharges in the river and the water goingFlood control of the Demer by using Model Predictive Control Maarten Breckpot a,n , Oscar Mauricio 2013 Keywords: Model Predictive Control Flood control Kalman filter Open channel flow a b s t r a c
Genetic Algorithm for Predicting Protein Folding in the 2D HP Model
Emmerich, Michael
Genetic Algorithm for Predicting Protein Folding in the 2D HP Model A Parameter Tuning Case Study of a protein, predicting its tertiary structure is known as the protein folding problem. This problem has been. The protein folding problem in the HP model is to find a conformation (a folded sequence) with the lowest
Budny, Robert
predictions using the GYRO verified and experimentally validated trapped gyro-Landau fluid transport model JITER predictions using the GYRO verified and experimentally validated trapped gyro-Landau fluid transport model This article has been downloaded from IOPscience. Please scroll down to see the full text
The US National Multi-Model Ensemble ISI Prediction System Ben Kirtman (University of Miami)
Miami, University of
The US National Multi-Model Ensemble ISI Prediction System Ben Kirtman (University of Miami) The newly emerging US National Multi-Model Ensemble (NMME) sub-seasonal to interannual (ISI) prediction includes experimental real-time ISI forecasting that leverages existing CTB partner activities. The NMME
Model-based Risk Assessment What does model-based mean?
Stølen, Ketil
Model-based Risk Assessment What does model-based mean? Model-based means based on modelling keeping a good overview at the same time. What does Risk Assessment mean? A risk assessment is a process to change the system in order to reduce these indicated risks. Why Risk Assessment? IT-systems get bigger
Similarity-based semi-local estimation of EMOS models
Lerch, Sebastian
2015-01-01
Weather forecasts are typically given in the form of forecast ensembles obtained from multiple runs of numerical weather prediction models with varying initial conditions and physics parameterizations. Such ensemble predictions tend to be biased and underdispersive and thus require statistical postprocessing. In the ensemble model output statistics (EMOS) approach, a probabilistic forecast is given by a single parametric distribution with parameters depending on the ensemble members. This article proposes two semi-local methods for estimating the EMOS coefficients where the training data for a specific observation station are augmented with corresponding forecast cases from stations with similar characteristics. Similarities between stations are determined using either distance functions or clustering based on various features of the climatology, forecast errors, ensemble predictions and locations of the observation stations. In a case study on wind speed over Europe with forecasts from the Grand Limited Area...
Paris-Sud XI, Université de
Proton Exchange Membrane Fuel Cell degradation prediction based on Adaptive Neuro Fuzzy Inference online XX XX XXXX Keywords: Proton Exchange Membrane fuel cell degradation, Prognostic and Health nominal operating condition of a PEM fuel cell stack. It proposes a methodology based on Adaptive Neuro
Yunovich, M.; Thompson, N.G.
1998-12-31
During the past fifteen years corrosion inhibiting admixtures (CIAs) have become increasingly popular for protection of reinforced components of highway bridges and other structures from damage induced by chlorides. However, there remains considerable debate about the benefits of CIAs in concrete. A variety of testing methods to assess the performance of CIA have been reported in the literature, ranging from tests in simulated pore solutions to long-term exposures of concrete slabs. The paper reviews the published techniques and recommends the methods which would make up a comprehensive CIA effectiveness testing program. The results of this set of tests would provide the data which can be used to rank the presently commercially available CIA and future candidate formulations utilizing a proposed predictive model. The model is based on relatively short-term laboratory testing and considers several phases of a service life of a structure (corrosion initiation, corrosion propagation without damage, and damage to the structure).
Martin, A; Venkatesan, Dr V Prasanna
2011-01-01
Today in every organization financial analysis provides the basis for understanding and evaluating the results of business operations and delivering how well a business is doing. This means that the organizations can control the operational activities primarily related to corporate finance. One way that doing this is by analysis of bankruptcy prediction. This paper develops an ontological model from financial information of an organization by analyzing the Semantics of the financial statement of a business. One of the best bankruptcy prediction models is Altman Z-score model. Altman Z-score method uses financial rations to predict bankruptcy. From the financial ontological model the relation between financial data is discovered by using data mining algorithm. By combining financial domain ontological model with association rule mining algorithm and Zscore model a new business intelligence model is developed to predict the bankruptcy.
OCEAN PREDICTION WITH THE HYBRID COORDINATE OCEAN MODEL (HYCOM)
. of South Florida, Fugro-GEOS, ROFFS, Orbimage, Shell, ExxonMobil #12;414 ERIC P. CHASSIGNET ET AL-resolving, real-time global and basin-scale ocean prediction system in the context of the Global Ocean Data Assimilation Experiment (GODAE). Keywords: HYCOM, GODAE, LAS, data assimilation, metrics. 1. Introduction
A machine learning approach to modeling and predicting training effectiveness
Stimpson, Alexander J. (Alexander James)
2015-01-01
Developments in online and computer-based training (CBT) technologies have enabled improvements in efficiency, efficacy, and scalability of modern training programs. The use of computer-based methods in training programs ...
Variable horizon model predictive control: robustness and optimality
Shekhar, Rohan Chandra
2012-07-03
. . . . . . . . . . . . . . . . . . . . . . . . . . . 106 6.3 Kinematic vectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 6.4 Mechanism model showing generalised coordinates . . . . . . . . . . . . . . . . 109 6.5 Static balance of material failure forces... .1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 6.1.1 Nomenclature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 6.2 Mechanism Model...
Human walking model predicts joint mechanics, electromyography and mechanical economy
Endo, Ken
In this paper, we present an under-actuated model of human walking, comprising only a soleus muscle and flexion/extension monoarticular hip muscles. The remaining muscle groups of the human leg are modeled using quasi-passive, ...
An event-based approach to validating solar wind speed predictions: High-speed
California at Berkeley, University of
December 2005. [1] One of the primary goals of the Center for Integrated Space Weather Modeling (CISM for the Center for Integrated Space Weather Modeling (CISM) solar wind models and suggest an event-based approach-Sheeley-Arge Model [3] CISM is using the Wang-Sheeley-Arge model (WSA) [Arge and Pizzo, 2000; Arge et al., 2003
1 Abstract--Eventually, prediction of transformer thermal performance for dynamic loading will be made using models distilled from measure data, rather than models derived from transformer heat for measuring the acceptability of transformer thermal models. For a model to be acceptable, it must have
Increased Efficiency with Model Based Calibration | Department...
Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site
Calibration Increased Efficiency with Model Based Calibration Meeting future TIER 4 emission limits requires the integration of many new technology elements. deer09diewald.pdf...
Modeling uranium transport in acidic contaminated groundwater with base addition
Zhang, Fan [Institute of Tibetan Plateau Research, Chinese Academy of Sciences; Luo, Wensui [ORNL; Parker, Jack C. [University of Tennessee, Knoxville (UTK); Brooks, Scott C [ORNL; Watson, David B [ORNL; Jardine, Philip [University of Tennessee, Knoxville (UTK); Gu, Baohua [ORNL
2011-01-01
This study investigates reactive transport modeling in a column of uranium(VI)-contaminated sediments with base additions in the circulating influent. The groundwater and sediment exhibit oxic conditions with low pH, high concentrations of NO{sub 3}{sup -}, SO{sub 4}{sup 2-}, U and various metal cations. Preliminary batch experiments indicate that additions of strong base induce rapid immobilization of U for this material. In the column experiment that is the focus of the present study, effluent groundwater was titrated with NaOH solution in an inflow reservoir before reinjection to gradually increase the solution pH in the column. An equilibrium hydrolysis, precipitation and ion exchange reaction model developed through simulation of the preliminary batch titration experiments predicted faster reduction of aqueous Al than observed in the column experiment. The model was therefore modified to consider reaction kinetics for the precipitation and dissolution processes which are the major mechanism for Al immobilization. The combined kinetic and equilibrium reaction model adequately described variations in pH, aqueous concentrations of metal cations (Al, Ca, Mg, Sr, Mn, Ni, Co), sulfate and U(VI). The experimental and modeling results indicate that U(VI) can be effectively sequestered with controlled base addition due to sorption by slowly precipitated Al with pH-dependent surface charge. The model may prove useful to predict field-scale U(VI) sequestration and remediation effectiveness.
DISLOCATION GENERATION IN Si: A THERMO-MECHANICAL MODEL BASED ON MEASURABLE PARAMETERS*
Balzar, Davor
DISLOCATION GENERATION IN Si: A THERMO-MECHANICAL MODEL BASED ON MEASURABLE PARAMETERS* Bhushan for predicting dislocation distribution generated by thermal stresses in Si is described. We use that can minimize dislocation generation for improved solar cell performance. INTRODUCTION Dislocations
Nielson, Gregory N.; Barbastathis, George (Massachusetts Institute of Technology)
2005-07-01
A physical parameter based model for dielectric charge accumulation is proposed and used to predict the displacement versus applied voltage and pull-in response of an electrostatic MEMS wavelength selective integrated optical switch.
Event Based Low Frequency Impedance Modeling using Well Logs and Seismic Attributes
Mosegaard, Klaus
prediction of this specific attribute. However, quantitative reservoir characterization in chalk is severelyEvent Based Low Frequency Impedance Modeling using Well Logs and Seismic Attributes Radmila logs. Seismic inversion, a process of converting seismic data into relative impedance, provides
Nguyen, Ba Nghiep; Kunc, Vlastimil; Jin, Xiaoshi; Tucker III, Charles L.; Costa, Franco
2013-12-18
This article illustrates the predictive capabilities for long-fiber thermoplastic (LFT) composites that first simulate the injection molding of LFT structures by Autodesk® Simulation Moldflow® Insight (ASMI) to accurately predict fiber orientation and length distributions in these structures. After validating fiber orientation and length predictions against the experimental data, the predicted results are used by ASMI to compute distributions of elastic properties in the molded structures. In addition, local stress-strain responses and damage accumulation under tensile loading are predicted by an elastic-plastic damage model of EMTA-NLA, a nonlinear analysis tool implemented in ABAQUS® via user-subroutines using an incremental Eshelby-Mori-Tanaka approach. Predicted stress-strain responses up to failure and damage accumulations are compared to the experimental results to validate the model.
Comparison of Uncertainty of Two Precipitation Prediction Models...
Office of Scientific and Technical Information (OSTI)
Lab Technical Area 54 Meteorological inputs are an important part of subsurface flow and transport modeling. The choice of source for meteorological data used as inputs has...
Predictive Models of Li-ion Battery Lifetime (Presentation) (Conference) |
Office of Scientific and Technical Information (OSTI)
AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of NaturalDukeWakefield MunicipalTechnical Report:Speeding access toSmall Reactor forPatents -SciTech Connect Predictive
Predicting hurricane regional landfall rates: comparing local and basin-wide track model approaches
Hall, T; Hall, Tim; Jewson, Stephen
2006-01-01
We compare two methods for making predictions of the climatological distribution of the number of hurricanes making landfall along short sections of the North American coastline. The first method uses local data, and the second method uses a basin-wide track model. Using cross-validation we show that the basin-wide track model gives better predictions for almost all parts of the coastline. This is the first time such a comparison has been made, and is the first rigourous justification for the use of basin-wide track models for predicting hurricane landfall rates and hurricane risk.
Drover, Damion, Ryan
2011-12-01
One of the largest exports in the Southeast U.S. is forest products. Interest in biofuels using forest biomass has increased recently, leading to more research into better forest management BMPs. The USDA Forest Service, along with the Oak Ridge National Laboratory, University of Georgia and Oregon State University are researching the impacts of intensive forest management for biofuels on water quality and quantity at the Savannah River Site in South Carolina. Surface runoff of saturated areas, transporting excess nutrients and contaminants, is a potential water quality issue under investigation. Detailed maps of variable source areas and soil characteristics would therefore be helpful prior to treatment. The availability of remotely sensed and computed digital elevation models (DEMs) and spatial analysis tools make it easy to calculate terrain attributes. These terrain attributes can be used in models to predict saturated areas or other attributes in the landscape. With laser altimetry, an area can be flown to produce very high resolution data, and the resulting data can be resampled into any resolution of DEM desired. Additionally, there exist many maps that are in various resolutions of DEM, such as those acquired from the U.S. Geological Survey. Problems arise when using maps derived from different resolution DEMs. For example, saturated areas can be under or overestimated depending on the resolution used. The purpose of this study was to examine the effects of DEM resolution on the calculation of topographic wetness indices used to predict variable source areas of saturation, and to find the best resolutions to produce prediction maps of soil attributes like nitrogen, carbon, bulk density and soil texture for low-relief, humid-temperate forested hillslopes. Topographic wetness indices were calculated based on the derived terrain attributes, slope and specific catchment area, from five different DEM resolutions. The DEMs were resampled from LiDAR, which is a laser altimetry remote sensing method, obtained from the USDA Forest Service at Savannah River Site. The specific DEM resolutions were chosen because they are common grid cell sizes (10m, 30m, and 50m) used in mapping for management applications and in research. The finer resolutions (2m and 5m) were chosen for the purpose of determining how finer resolutions performed compared with coarser resolutions at predicting wetness and related soil attributes. The wetness indices were compared across DEMs and with each other in terms of quantile and distribution differences, then in terms of how well they each correlated with measured soil attributes. Spatial and non-spatial analyses were performed, and predictions using regression and geostatistics were examined for efficacy relative to each DEM resolution. Trends in the raw data and analysis results were also revealed.
Unbiased Statistical Comparison of Creep and Shrinkage Prediction Models
, important for designing durable and safe concrete structures. Statistical methods of standard and several to improper data sampling in the database, and then examines Bazant and Baweja's model B3, ACI model, CEB of least squares, which is the standard and the only statistically correct method, dictated by the maximum
Mass predictions of atomic nuclei in the infinite nuclear matter model
Nayak, R.C., E-mail: rcnayak00@yahoo.com [Department of Physics, Berhampur University, Berhampur-760 007 (India); Satpathy, L., E-mail: satpathy@iopb.res.in [Institute of Physics, Bhubaneswar-751 005 (India)
2012-07-15
We present here the mass excesses, binding energies, one- and two-neutron, one- and two-proton and {alpha}-particle separation energies of 6727 nuclei in the ranges 4{<=}Z{<=}120 and 8{<=}A{<=}303 calculated in the infinite nuclear matter model. Compared to our predictions of 1999 mass table, the present ones are obtained using larger data base of 2003 mass table of Wapstra and Audi and resorting to higher accuracy in the solutions of the {eta}-differential equations of the INM model. The local energy {eta}'s supposed to carry signature of the characteristic properties of nuclei are found to possess the predictive capability. In fact {eta}-systematics reveal new magic numbers in the drip-line regions giving rise to new islands of stability supported by relativistic mean field theoretic calculations. This is a manifestation of a new phenomenon where shell-effect overcomes the instability due to repulsive components of the nucleon-nucleon force broadening the stability peninsula. The two-neutron separation energy-systematics derived from the present mass predictions reveal a general new feature for the existence of islands of inversion in the exotic neutron-rich regions of nuclear landscape, apart from supporting the presently known islands around {sup 31}Na and {sup 62}Ti. The five global parameters representing the properties of infinite nuclear matter, the surface, the Coulomb and the pairing terms are retained as per our 1999 mass table. The root-mean-square deviation of the present mass-fit to 2198 known masses is 342 keV, while the mean deviation is 1.3 keV, reminiscent of no left-over systematic effects. This is a substantive improvement over our 1999 mass table having rms deviation of 401 keV and mean deviation of 9 keV for 1884 data nuclei.
Tucker, Susan L., E-mail: sltucker@mdanderson.org [Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Li Minghuan [Department of Radiation Oncology, Shandong Cancer Hospital, Jinan, Shandong (China)] [Department of Radiation Oncology, Shandong Cancer Hospital, Jinan, Shandong (China); Xu Ting; Gomez, Daniel [Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas (United States)] [Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Yuan Xianglin [Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan (China)] [Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan (China); Yu Jinming [Department of Radiation Oncology, Shandong Cancer Hospital, Jinan, Shandong (China)] [Department of Radiation Oncology, Shandong Cancer Hospital, Jinan, Shandong (China); Liu Zhensheng; Yin Ming; Guan Xiaoxiang; Wang Lie; Wei Qingyi [Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, Texas (United States)] [Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Mohan, Radhe [Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas (United States)] [Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Vinogradskiy, Yevgeniy [University of Colorado School of Medicine, Aurora, Colorado (United States)] [University of Colorado School of Medicine, Aurora, Colorado (United States); Martel, Mary [Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas (United States)] [Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Liao Zhongxing [Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas (United States)] [Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas (United States)
2013-01-01
Purpose: To determine whether single-nucleotide polymorphisms (SNPs) in genes associated with DNA repair, cell cycle, transforming growth factor-{beta}, tumor necrosis factor and receptor, folic acid metabolism, and angiogenesis can significantly improve the fit of the Lyman-Kutcher-Burman (LKB) normal-tissue complication probability (NTCP) model of radiation pneumonitis (RP) risk among patients with non-small cell lung cancer (NSCLC). Methods and Materials: Sixteen SNPs from 10 different genes (XRCC1, XRCC3, APEX1, MDM2, TGF{beta}, TNF{alpha}, TNFR, MTHFR, MTRR, and VEGF) were genotyped in 141 NSCLC patients treated with definitive radiation therapy, with or without chemotherapy. The LKB model was used to estimate the risk of severe (grade {>=}3) RP as a function of mean lung dose (MLD), with SNPs and patient smoking status incorporated into the model as dose-modifying factors. Multivariate analyses were performed by adding significant factors to the MLD model in a forward stepwise procedure, with significance assessed using the likelihood-ratio test. Bootstrap analyses were used to assess the reproducibility of results under variations in the data. Results: Five SNPs were selected for inclusion in the multivariate NTCP model based on MLD alone. SNPs associated with an increased risk of severe RP were in genes for TGF{beta}, VEGF, TNF{alpha}, XRCC1 and APEX1. With smoking status included in the multivariate model, the SNPs significantly associated with increased risk of RP were in genes for TGF{beta}, VEGF, and XRCC3. Bootstrap analyses selected a median of 4 SNPs per model fit, with the 6 genes listed above selected most often. Conclusions: This study provides evidence that SNPs can significantly improve the predictive ability of the Lyman MLD model. With a small number of SNPs, it was possible to distinguish cohorts with >50% risk vs <10% risk of RP when they were exposed to high MLDs.
Li, Ruijiang; Jia, Xun; Zhao, Tianyu; Lamb, James; Yang, Deshan; Low, Daniel A; Jiang, Steve B
2010-01-01
Organ motion induced by respiration may cause clinically significant targeting errors and greatly degrade the effectiveness of conformal radiotherapy. It is therefore crucial to be able to model respiratory motion accurately. A recently proposed lung motion model based on principal component analysis (PCA) has been shown to be promising on a few patients. However, there is still a need to understand the underlying reason why it works. In this paper, we present a much deeper and detailed analysis of the PCA-based lung motion model. We provide the theoretical justification of the effectiveness of PCA in modeling lung motion. We also prove that under certain conditions, the PCA motion model is equivalent to 5D motion model, which is based on physiology and anatomy of the lung. The modeling power of PCA model was tested on clinical data and the average 3D error was found to be below 1 mm.
Corani, Giorgio
2005-01-01
Ecological Modelling 185 (2005) 513529 Air quality prediction in Milan: feed-forward neural December 2004; accepted 3 January 2005 Abstract Ozone and PM10 constitute the major concern for air quality of Milan. This paper addresses the problem of the prediction of such two pollutants, using to this end
Mixtures of Predictive Linear Gaussian Models for Nonlinear Stochastic Dynamical Systems
Baveja, Satinder Singh
Mixtures of Predictive Linear Gaussian Models for Nonlinear Stochastic Dynamical Systems David dynamical systems. The primary contribution of this work is to extend the PLG to nonlinear, stochastic- proves upon traditional linear dynamical system mod- els by using a predictive representation of state
Wind Speed Modelling and Short-term Predic-tion using Wavelets
Nason, Guy
prediction of the wind regime at a proposed wind farm site. Suppose a small amount of wind speed data hasWind Speed Modelling and Short-term Predic- tion using Wavelets Katherine Hunt and Guy P Nason@bristol.ac.uk Abstract The mathematical method of wavelets is explained and used to predict wind condi- tions using short
Scientific Programming 11 (2003) 159176 159 A performance-prediction model for PIC
Vlad, Gregorio
2003-01-01
Scientific Programming 11 (2003) 159176 159 IOS Press A performance-prediction model for PIC hierarchical workload decomposition strategies for particle in cell (PIC) codes on Clusters of Symmetric Multi of parallelization efficiency are compared with the predicted results. 1. Introduction Particle-in-cell (PIC
Prediction of Solid Polycyclic Aromatic Hydrocarbons Solubility in Water with the NRTL-PR Model
Paris-Sud XI, Université de
Prediction of Solid Polycyclic Aromatic Hydrocarbons Solubility in Water with the NRTL-PR Model of solid polycyclic aromatic hydrocarbons in water. For this purpose, we first validate our methodology for fluid phase equilibria predictions of aromatic hydrocarbons and gas (CO2, C2H6) mixtures. Finally, we
Predicting the net carbon exchanges of crop rotations in Europe with an agro-ecosystem model
Boyer, Edmond
Predicting the net carbon exchanges of crop rotations in Europe with an agro-ecosystem model S.Lehuger@art.admin.ch. Fax: (+41) 44 377 72 01. Phone: (+41) 44 377 75 13. hal-00414342,version2-1Sep2010 #12;Abstract Carbon and measuring land-atmosphere carbon exchanges from arable lands are important tasks to predict the influence
`TVLSI-00029-2003.R1 An Analytical Model for Predicting the Remaining Battery
Pedram, Massoud
`TVLSI-00029-2003.R1 1 An Analytical Model for Predicting the Remaining Battery Capacity of Lithium-Ion Batteries Peng Rong, Student Member, IEEE and Massoud Pedram, Fellow, IEEE Abstract -- Predicting the residual energy of the battery source that powers a portable electronic device is imperative in designing
Discrepancies in the Prediction of Solar Wind using Potential Field Source Surface Model: An
Zhao, Xuepu
Discrepancies in the Prediction of Solar Wind using Potential Field Source Surface Model. This inverse relation has been made use of in the prediction of solar wind speed at 1 AU using a potential between the magnetic flux tube expansion factor (FTE) at the source surface and the solar wind speed
Copula Based Hierarchical Bayesian Models
Ghosh, Souparno
2010-10-12
. Finally, we take up the important problem of modeling multivariate extreme values with copulas. We describe, in detail, how dependences can be induced in the block maxima approach and peak over threshold approach by an extreme value copula. We prove...
Robust Constrained Model Predictive Control using Linear Matrix Inequalities \\Lambda
Balakrishnan, Venkataramanan "Ragu"
dynamical systems, such as those encountered in chemical process control in the petrochemical, pulp process models as well as many performance criteria of significance to the process industries can
Robust Constrained Model Predictive Control using Linear Matrix Inequalities
Balakrishnan, Venkataramanan "Ragu"
, such as those encountered in chemical process control in the petrochemical, pulp and paper industries, several process models as well as many performance criteria of significance to the process industries can
Terminal Spacecraft Rendezvous and Capture with LASSO Model Predictive Control
Hartley, Edward N.; Gallieri, Marco; Maciejowski, Jan M.
2013-08-20
. Int. Conf. Instrumentation, Communication Information Technology, and Biomedical Engineering, Badung, 23–25 Nov, pp. 435–439. Kawai, F., Ito, H., Nakazawa, C., Matsui, T., Fukuyama, Y., Suzuki, R., and Aiyoshi, E. (2007), “Automatic Tuning for Model...
Optimal Model-Based Approaches for Predictive Inference in Biology
Knight, Jason Matthew
2015-05-04
Triff, Dr. Tim Hou, Robert Fuentes, Eunjoo Kim, Dr. Roger Zoh, Dr. Manasvi Shah, Jason Xingde Jiang, Dr. Sriram Sridharan, Dr. Mohammad Shahrokh, Dr. Esmaeil Gargari, Dr. Ting Chen, Dr. Chen Zhou, Dr. Youting Sun, Dr. Mohammad Yousefi-Rezaei, Dr. Lori...
Productivity prediction model based on Bayesian analysis and productivity console
Yun, Seok Jun
2005-08-29
-THEN rule ....................... 91 23 Detailed KB schema on plan ...................... 96 24 Productivity console shows a project level view ............ 100 25 Productivity console shows a team level view ............. 101 26 Format of the weekly status... activities has been accomplished, deter- mine the current productivity of individual, team and project, or discover if resources are adequate. Without the correct information, it becomes impossible to actively monitor project failures and identify appropriate...
Kumar, Aditya; Shi, Ruijie; Kumar, Rajeeva; Dokucu, Mustafa
2013-04-09
Control system and method for controlling an integrated gasification combined cycle (IGCC) plant are provided. The system may include a controller coupled to a dynamic model of the plant to process a prediction of plant performance and determine a control strategy for the IGCC plant over a time horizon subject to plant constraints. The control strategy may include control functionality to meet a tracking objective and control functionality to meet an optimization objective. The control strategy may be configured to prioritize the tracking objective over the optimization objective based on a coordinate transformation, such as an orthogonal or quasi-orthogonal projection. A plurality of plant control knobs may be set in accordance with the control strategy to generate a sequence of coordinated multivariable control inputs to meet the tracking objective and the optimization objective subject to the prioritization resulting from the coordinate transformation.
Joshi, Praveen Sudhakar
1999-01-01
Predictive Variable Structure and Fuzzy Logic based controllers for the same benchmark problem. Evaluation criteria consist of closed-loop system performance, activity level of the VFC nozzles, ease of controller synthesis, time required to synthesize...
Validity of the WEPP model for predicting infiltration on irrigated lands
Ngang, Fidelis Ndemah
1995-01-01
The objective of this research was to establish the validity of the hydrologic component of the YVEPP erosion model for predicting infiltration on irrigated lands. WEPP uses the Green and Ampt equation with ponding to compute infiltration...
Switching Strategy for Direct Model Predictive Control in Power Converter and Drive Applications
Noé, Reinhold
of permanent magnet synchronous motors with interior magnets (IPMSM). Index Terms--Direct Model Predictive Direct-MPC approaches, a more flexible gate-signal generation method which enables switching during
How GIS and fire indices can be used in developing a fire prediction model for Scotland
MacKinnon, Frances
2008-12-05
This project looks at how GIS and the six fire indices from the Canadian Forest Fire Weather Index System (FWI) could be used to aid in developing a fire prediction model for Scotland. Information on land cover type, ...
A predictive, size-dependent continuum model for dense granular flows
Henann, David Lee
Dense granular materials display a complicated set of flow properties, which differentiate them from ordinary fluids. Despite their ubiquity, no model has been developed that captures or predicts the complexities of granular ...
Bittle, Joshua A
2014-04-18
Attempting to bridge the gap between typical off-line engine simulations and online real-time control strategies a computationally efficient model has been created that predicts the combustion trajectory (path through the ?-T plane). To give...
ECOLOGICAL NICHE MODELING AS A PREDICTIVE TOOL: ASIATIC FRESHWATER FISHES IN NORTH AMERICA
Chen, Pingfu
2008-05-30
appropriately. After introduction, the most effective way is to predict their spread, to discover populations early, and to adopt measures to eradicate or at least contain them. This dissertation uses ecological niches modeling to estimate the ecological...
Prediction Capabilities of Vulnerability Discovery Models Omar H. Alhazmi, Colorado State University
Malaiya, Yashwant K.
Prediction Capabilities of Vulnerability Discovery Models Omar H. Alhazmi, Colorado State Discovery Models (VDMs) have been proposed to model vulnerability discovery and have has been fitted discovery process, presenting a static approach to estimating the initial values of one of the VDM
Modeling, Analysis, Predictions, and Projections Email: oar.cpo.mapp@noaa.gov
Earth system models to better simulate the climate system? Can we improve intraseasonal to seasonal mission, MAPP supports the development of advanced Earth system models that can predict climate variations, and the external research community. MAPP Objectives · Improve Earth system models · Achieve an integrated Earth
Prediction of tree diameter growth using quantile regression and mixed-effects models
Cao, Quang V.
of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA b School of Renewable Natural ResourcesPrediction of tree diameter growth using quantile regression and mixed-effects models Som B. Bohora is an important component of an individual-tree model. This function can be considered as a mixed-effects model
Using Trust-Based Information Aggregation for Predicting Security Level of Systems
Ray, Indrakshi
Using Trust-Based Information Aggregation for Predicting Security Level of Systems Siv Hilde Houmb1 level of a security solution using information sources who are trusted to varying degrees. We show how}@cs.colostate.edu Abstract. Sometimes developers must design innovative security solutions that have a rapid development
Using Trust-Based Information Aggregation for Predicting Security Level of Systems
Paris-Sud XI, Université de
Using Trust-Based Information Aggregation for Predicting Security Level of Systems Siv Hilde Houmb1 level of a security solution using information sources who are trusted to varying degrees. We show how.colostate.edu Abstract. Sometimes developers must design innovative security solutions that have a rapid development
History-based, Online, Battery Lifetime Prediction for Embedded and Mobile Devices
Krintz, Chandra
History-based, Online, Battery Lifetime Prediction for Embedded and Mobile Devices Ye Wen Rich approach on a widely used mobile device (HP iPAQ) running Linux, and compare it to two similar battery pre- diction technologies: ACPI and Smart Battery. We em- ploy twenty-two constant and variable workloads
A Novel Method for Early Software Quality Prediction Based on Support Vector Machine
Lyu, Michael R.
A Novel Method for Early Software Quality Prediction Based on Support Vector Machine Fei Xing1 development process imposes major impacts on the quality of software at every development stage; therefore, a common goal of each software development phase concerns how to improve software quality. Software quality
LS-SVM based spectral clustering and regression for predicting maintenance of industrial machines
,13 and maintenance operations can be fully automated and implemented in a cost14 effective way.15LS-SVM based spectral clustering and regression for predicting maintenance of industrial machines plays a key role in reducing production arrest, increasing the safety of plant operations
Efficient Mobile Content Delivery Based on Co-route Prediction in Urban Transport
mules. The routers in our scenario are only equipped with short range wireless network interfaces-and- forward routers based on vehicle mobility patterns and human regular movement behaviors; we also propose a router-centric prediction scheme that collects passenger historical trajectory information to determine
An Integrated Development Environment for Building Predictable Component-Based Embedded Systems
Becker, Steffen
Save-IDE An Integrated Development Environment for Building Predictable Component-Based Embedded-- In this paper we present an Integrated Development Environment Save-IDE, a toolset that embraces several tools-time properties, such as timing properties, and transforming the components to real-time execution elements. Save
S. F. Burlatsky; M. Gummalla; J. O'Neill; V. V. Atrazhev; A. N. Varyukhin; D. V. Dmitriev; N. S. Erikhman
2013-06-19
Under typical PEM fuel cell operating conditions, part of membrane electrode assembly is subjected to humidity cycling due to variation of inlet gas RH and/or flow rate. Cyclic membrane hydration/dehydration would cause cyclic swelling/shrinking of the unconstrained membrane. In a constrained membrane, it causes cyclic stress resulting in mechanical failure in the area adjacent to the gas inlet. A mathematical modeling framework for prediction of the lifetime of a PEM FC membrane subjected to hydration cycling is developed in this paper. The model predicts membrane lifetime as a function of RH cycling amplitude and membrane mechanical properties. The modeling framework consists of three model components: a fuel cell RH distribution model, a hydration/dehydration induced stress model that predicts stress distribution in the membrane, and a damage accrual model that predicts membrane life-time. Short descriptions of the model components along with overall framework are presented in the paper. The model was used for lifetime prediction of a GORE-SELECT membrane.
Physically-based demand modeling
Calloway, Terry Marshall
1980-01-01
nts on the demand. Of course the demand of a real a1r cond1t1oner has lower and upper bounds equal to 0 and 0 , respec- u tively. A constra1ned system can be simulated numerically, but there 1s no explicit system response formula s1m11ar... sect1on. It may now be instruct1ve to relate this model to that of Jones and Bri ce [5] . The average demand pred1 cted by their model is the expected value of the product of a load response factor 0 and a U sw1tching process H(t), which depends...
Lee, Jooyoung
Random Forest-Based Protein Model Quality Assessment (RFMQA) Using Structural Features and Potential Energy Terms Balachandran Manavalan, Juyong Lee, Jooyoung Lee* Center for In Silico Protein in protein structure prediction. In this study, we present the first application of random forest based model
Comparison of Uncertainty of Two Precipitation Prediction Models
Shield, Stephen
2015-01-01
Meteorological inputs are an important part of subsurface flow and transport modeling. The choice of source for meteorological data used as inputs has significant impacts on the results of subsurface flow and transport studies. One method to obtain the meteorological data required for flow and transport studies is the use of weather generating models. This paper compares the difference in performance of two weather generating models at Technical Area 54 of Los Alamos National Lab. Technical Area 54 is contains several waste pits for low-level radioactive waste and is the site for subsurface flow and transport studies. This makes the comparison of the performance of the two weather generators at this site particularly valuable.
Ontology-based Model Transformation Stephan Roser
Bauer, Bernhard
Ontology-based Model Transformation Stephan Roser Advisor: Bernhard Bauer Progamming of Distributed to achieve interoperability in modeling enterprises and application systems by semantic enrichment, which can abstract descriptions of systems, as they are used for model- and code- generation they are the key part
Xu, Wei; Sun, Xin; Li, Dongsheng; Ryu, Seun; Khaleel, Mohammad A.
2013-02-01
Quantitative understanding of the evolving thermal-mechanical properties of a multi-phase material hinges upon the availability of quantitative statistically representative microstructure descriptions. Questions then arise as to whether a two-dimensional (2D) or a three-dimensional (3D) representative volume element (RVE) should be considered as the statistically representative microstructure. Although 3D models are more representative than 2D models in general, they are usually computationally expensive and difficult to be reconstructed. In this paper, we evaluate the accuracy of a 2D RVE in predicting the property degradations induced by different degradation mechanisms with the multiphase solid oxide fuel cell (SOFC) anode material as an example. Both 2D and 3D microstructure RVEs of the anodes are adopted to quantify the effects of two different degradation mechanisms: humidity-induced electrochemical degradation and phosphorus poisoning induced structural degradation. The predictions of the 2D model are then compared with the available experimental measurements and the results from the 3D model. It is found that the 2D model, limited by its inability of reproducing the realistic electrical percolation, is unable to accurately predict the degradation of thermo-electrical properties. On the other hand, for the phosphorus poisoning induced structural degradation, both 2D and 3D microstructures yield similar results, indicating that the 2D model is capable of providing computationally efficient yet accurate results for studying the structural degradation within the anodes.
On the Predictive Uncertainty of a Distributed Hydrologic Model
Cho, Huidae
2009-05-15
of the San Jacinto River watershed. . . . . . . . . . . . . . 14 2 Barton Creek and Onion Creek watersheds. . . . . . . . . . . . . . . 15 3 Streamflow versus runoff for selected models out of the 54 cali- brated models...?99 SOL AWC Available water capacity of the soil layer (mm H2O/mm soil) 0.0?1.0 ESCO Soil evaporation compensation factor 0.01?1.0 GWQMN Threshold depth of water in the shallow aquifer re- quired for return flow to occur (mm H2O) 0?5000 GW REVAP...
Toward understanding predictability of climate: a linear stochastic modeling approach
Wang, Faming
2004-11-15
in examining a dynamical system. The origin and growth of small perturbations are often attributed to the 10 existence of unstable modes. In the limit of long times, the ?rst normal mode (least damped mode) dominates the response. The above classical stability... for the linear case. Recently, Neumaier and Schneider (2001) developed a procedure to estimate eigen- modes of high order autoregressive (AR) models, while (2.3) is basically an AR(1) model. Traditionally, the least damped eigenmodes are considered to be the most...
Singh, Kunwar P., E-mail: kpsingh_52@yahoo.com [Academy of Scientific and Innovative Research, Council of Scientific and Industrial Research, New Delhi (India); Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow 226 001 (India); Gupta, Shikha; Rai, Premanjali [Academy of Scientific and Innovative Research, Council of Scientific and Industrial Research, New Delhi (India); Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow 226 001 (India)
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 Brock–Dechert–Scheinkman statistics. Probabilistic neural network (PNN) and generalized regression neural network (GRNN) models were constructed for classification and function optimization problems using the carcinogenicity end point in rat. Validation of the models was performed using the internal and external procedures employing a wide series of statistical checks. PNN constructed using five descriptors rendered classification accuracy of 92.09% in complete rat data. The PNN model rendered classification accuracies of 91.77%, 80.70% and 92.08% in mouse, hamster and pesticide data, respectively. The GRNN constructed with nine descriptors yielded correlation coefficient of 0.896 between the measured and predicted carcinogenic potency with mean squared error (MSE) of 0.44 in complete rat data. The rat carcinogenicity model (GRNN) applied to the mouse and hamster data yielded correlation coefficient and MSE of 0.758, 0.71 and 0.760, 0.46, respectively. The results suggest for wide applicability of the inter-species models in predicting carcinogenic potency of chemicals. Both the PNN and GRNN (inter-species) models constructed here can be useful tools in predicting the carcinogenicity of new chemicals for regulatory purposes. - Graphical abstract: Figure (a) shows classification accuracies (positive and non-positive carcinogens) in rat, mouse, hamster, and pesticide data yielded by optimal PNN model. Figure (b) shows generalization and predictive abilities of the interspecies GRNN model to predict the carcinogenic potency of diverse chemicals. - Highlights: • Global robust models constructed for carcinogenicity prediction of diverse chemicals. • Tanimoto/BDS test revealed structural diversity of chemicals and nonlinearity in data. • PNN/GRNN successfully predicted carcinogenicity/carcinogenic potency of chemicals. • Developed interspecies PNN/GRNN models for carcinogenicity prediction. • Proposed models can be used as tool to predict carcinogenicity of new chemicals.
Distributional Analysis for Model Predictive Deferrable Load Control
Low, Steven H.
for demand response. There are two major categories of demand response, direct load control (DLC) and price-based demand response. See [1] for a discussion of the contrasts between these approaches. In this paper we focus on direct load control with the goal of using demand response to reduce variations
Bayesian System Identification and Response Predictions Robust to Modeling Uncertainty
Beck, James L.
.g. system ID, structural health monitoring, robust control, state &/or parameter estimation ) #12;33 Outline of seismic ground acceleration Finite element model with uncertain parameters Posterior analysis: During;55 System performance measure in the presence of uncertainty: Failure probability + - "Failure" t(t)iy i b i
Comparison of Thermal Properties Predicted by Interatomic Potential Models
Cai, Wei
). The state-of-the-art free energy methods are used to determine the melting points of these models within]. In the "free-energy" method, the Gibbs free energies of the solid and liquid phases are computed as functions of temperature, and the melting point is determined by their intersection point. The free energy method has been
NONLINEAR MODEL PREDICTIVE CONTROL WITH MOVING HORIZON STATE AND
Van den Hof, Paul
referred to as air pollution or "post-combustion" control systems). In this paper only the combustion - WITH APPLICATION TO MSW COMBUSTION M. Leskens , L.B.M. van Kessel , P.M.J. Van den Hof and O.H. Bosgra strategy are demonstrated by applying it to a model of a municipal solid waste (MSW) combustion plant under
New Tools in Non-Linear Modelling and Prediction
Jones, Antonia J.
networks . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 5 A case study: Thames River Valley 28 5.1 The Thames river valley region . . . . . . . . . . . . . . . . . . . 28 5.2 Model identification of attributes, a single run of the Gamma test typically takes a few seconds. Around this essentially simple
Global warming and climate change - predictive models for temperate and tropical regions
Malini, B.H.
1997-12-31
Based on the assumption of 4{degree}C increase of global temperature by the turn of 21st century due to the accumulation of greenhouse gases an attempt is made to study the possible variations in different climatic regimes. The predictive climatic water balance model for Hokkaido island of Japan (a temperate zone) indicates the possible occurrence of water deficit for two to three months, which is a unknown phenomenon in this region at present. Similarly, India which represents tropical region also will experience much drier climates with increased water deficit conditions. As a consequence, the thermal region of Hokkaido which at present is mostly Tundra and Micro thermal will change into a Meso thermal category. Similarly, the moisture regime which at present supports per humid (A2, A3 and A4) and Humid (B4) climates can support A1, B4, B3, B2 and B1 climates indicating a shift towards drier side of the climatic spectrum. Further, the predictive modes of both the regions have indicated increased evapotranspiration rates. Although there is not much of change in the overall thermal characteristics of the Indian region the moisture regime indicates a clear shift towards the aridity in the country.
Advanced Models and Controls for Prediction and Extension of Battery Lifetime (Presentation)
Smith, K.; Wood, E.; Santhanagopalan, S.; Kim, G.; Pesaran, A.
2014-02-01
Predictive models of capacity and power fade must consider a multiplicity of degradation modes experienced by Li-ion batteries in the automotive environment. Lacking accurate models and tests, lifetime uncertainty must presently be absorbed by overdesign and excess warranty costs. To reduce these costs and extend life, degradation models are under development that predict lifetime more accurately and with less test data. The lifetime models provide engineering feedback for cell, pack and system designs and are being incorporated into real-time control strategies.
Microstructure-based approach for predicting crack initiation and early growth in metals.
Cox, James V.; Emery, John M.; Brewer, Luke N.; Reedy, Earl David, Jr.; Puskar, Joseph David; Bartel, Timothy James; Dingreville, Remi P. M.; Foulk, James W., III; Battaile, Corbett Chandler; Boyce, Brad Lee
2009-09-01
Fatigue cracking in metals has been and is an area of great importance to the science and technology of structural materials for quite some time. The earliest stages of fatigue crack nucleation and growth are dominated by the microstructure and yet few models are able to predict the fatigue behavior during these stages because of a lack of microstructural physics in the models. This program has developed several new simulation tools to increase the microstructural physics available for fatigue prediction. In addition, this program has extended and developed microscale experimental methods to allow the validation of new microstructural models for deformation in metals. We have applied these developments to fatigue experiments in metals where the microstructure has been intentionally varied.
Rychard J. Bouwens; Laura Cayon; Joseph Silk
1997-09-13
We develop an idealized inside-out formation model for disk galaxies to include a realistic mix of galaxy types and luminosities that provides a fair match to the traditional observables. The predictions of our infall models are compared against identical models with no-size evolution by generating fully realistic simulations of the HDF, from which we recover the angular size distributions. We find that our infall models produce nearly identical angular size distributions to those of our no-size evolution models in the case of a Omega = 0 geometry but produce slightly smaller sizes in the case of a Omega = 1 geometry, a difference we associate with the fact that there is a different amount of cosmic time in our two models for evolving to relatively low redshifts (z \\approx 1-2). Our infall models also predict a slightly smaller (11% - 29%) number of large (disk scale lengths > 4 h_{50} ^{-1} kpc) galaxies at z \\approx 0.7 for the CFRS as well as different increases in the central surface brightness of the disks for early-type spirals, the infall model predicting an increase by 1.2 magnitudes out to z \\approx 2 (Omega = 0), 1 (Omega = 1), while our no-size evolution models predict an increase of only 0.5 magnitude. This result suggests that infall models could be important for explaining the 1.2-1.6 magnitude increase in surface brightness reported by Schade et al. (1995, 1996a, 1996b).
A Tutorial on Model Predictive Control for Spacecraft Rendezvous
Hartley, Edward N.
2015-05-26
by the linear inequalities Hox ? h0. For the chaser to remain outside of this set is a non- convex constraint, and imposing Hox(k) ? ho would be infeasible. If dim(ho) = nh, a workaround is to introduce an nh dimensional vector b(k) of binary variables, a... -based” 1?norm cost was used to improve robustness to uncertainties. The cost function is designed to be zero if the state is inside a hyper-cube ?b ? x ? b containing the setpoint, and a 1-norm penalty placed on the deviation s from this set: `(x, u) = ?Qs...
Predictive modeling of synergistic effects in nanoscale ion track formation
DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)
Zarkadoula, Eva; Pakarinen, Olli H.; Xue, Haizhou; Zhang, Yanwen; Weber, William J.
2015-08-05
Molecular dynamics techniques and the inelastic thermal spike model are used to study the coupled effects of inelastic energy loss due to 21 MeV Ni ion irradiation and pre-existing defects in SrTiO3. We determine the dependence on pre-existing defect concentration of nanoscale track formation occurring from the synergy between the inelastic energy loss and the pre-existing atomic defects. We show that the nanoscale ion tracks’ size can be controlled by the concentration of pre-existing disorder. This work identifies a major gap in fundamental understanding concerning the role played by defects in electronic energy dissipation and electron–lattice coupling.
Model predicts space weather and protects satellite hardware
Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)
AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach Home Room NewsInformationJessework uses concrete7 AssessmentBusinessAlternativeModel Verification
Prediction of Emerging Technologies Based on Analysis of the U.S. Patent Citation Network
Érdi, Péter; Somogyvári, Zoltán; Strandburg, Katherine; Tobochnik, Jan; Volf, Péter; Zalányi, László
2012-01-01
The network of patents connected by citations is an evolving graph, which provides a representation of the innovation process. A patent citing another implies that the cited patent reflects a piece of previously existing knowledge that the citing patent builds upon. A methodology presented here (i) identifies actual clusters of patents: i.e. technological branches, and (ii) gives predictions about the temporal changes of the structure of the clusters. A predictor, called the {citation vector}, is defined for characterizing technological development to show how a patent cited by other patents belongs to various industrial fields. The clustering technique adopted is able to detect the new emerging recombinations, and predicts emerging new technology clusters. The predictive ability of our new method is illustrated on the example of USPTO subcategory 11, Agriculture, Food, Textiles. A cluster of patents is determined based on citation data up to 1991, which shows significant overlap of the class 442 formed at th...
arXiv:1307.5640v2[math.OC]18Nov2013 The Scenario Approach for Stochastic Model Predictive
Frei, Christoph
or load mitigation for wind turbines. For such sys- tems, a new control method of Scenario-Based ModelarXiv:1307.5640v2[math.OC]18Nov2013 The Scenario Approach for Stochastic Model Predictive Control. In the presence of model uncertainties or disturbances, for many control applications it suffices to keep
Kusiak, Andrew
35 A. Kusiak, S. Shah, and B. Dixon, Data mining based decision-making approach for predicting, Melbourne, Australia, published by Elsevier, Amsterdam, The Netherlands, August 2003, pp. 35-39. DATA MINING interventions and the dialysis treatment prescription. In this research, a data mining approach is used
Scarrott, Carl
ENGXT +++= )F( Temperature at Channel (i,j) Fuel Irradiation for Channel (r,s) Direct and Neutron(.)?How to Model F(.)? l Effect of Fuel Irradiation on Temperatures l Direct Non-Linear Effect l Neutron Diffusion Region Cold Outer Region l Similar Behaviour Sharp Increase Constant l Weak Relationship l Scatter
Haves, Phillip; Hencey, Brandon; Borrell, Francesco; Elliot, John; Ma, Yudong; Coffey, Brian; Bengea, Sorin; Wetter, Michael
2010-06-29
A Model Predictive Control algorithm was developed for the UC Merced campus chilled water plant. Model predictive control (MPC) is an advanced control technology that has proven successful in the chemical process industry and other industries. The main goal of the research was to demonstrate the practical and commercial viability of MPC for optimization of building energy systems. The control algorithms were developed and implemented in MATLAB, allowing for rapid development, performance, and robustness assessment. The UC Merced chilled water plant includes three water-cooled chillers and a two million gallon chilled water storage tank. The tank is charged during the night to minimize on-peak electricity consumption and take advantage of the lower ambient wet bulb temperature. The control algorithms determined the optimal chilled water plant operation including chilled water supply (CHWS) temperature set-point, condenser water supply (CWS) temperature set-point and the charging start and stop times to minimize a cost function that includes energy consumption and peak electrical demand over a 3-day prediction horizon. A detailed model of the chilled water plant and simplified models of the buildings served by the plant were developed using the equation-based modeling language Modelica. Steady state models of the chillers, cooling towers and pumps were developed, based on manufacturers performance data, and calibrated using measured data collected and archived by the control system. A detailed dynamic model of the chilled water storage tank was also developed and calibrated. Simple, semi-empirical models were developed to predict the temperature and flow rate of the chilled water returning to the plant from the buildings. These models were then combined and simplified for use in a model predictive control algorithm that determines the optimal chiller start and stop times and set-points for the condenser water temperature and the chilled water supply temperature. The report describes the development and testing of the algorithm and evaluates the resulting performance, concluding with a discussion of next steps in further research. The experimental results show a small improvement in COP over the baseline policy but it is difficult to draw any strong conclusions about the energy savings potential for MPC with this system only four days of suitable experimental data were obtained once correct operation of the MPC system had been achieved. These data show an improvement in COP of 3.1% {+-} 2.2% relative to a baseline established immediately prior to the period when the MPC was run in its final form. This baseline includes control policy improvements that the plant operators learned by observing the earlier implementations of MPC, including increasing the temperature of the water supplied to the chiller condensers from the cooling towers. The process of data collection and model development, necessary for any MPC project, resulted in the team uncovering various problems with the chilled water system. Although it is difficult to quantify the energy savings resulting from these problems being remedied, they were likely on the same order as the energy savings from the MPC itself. Although the types of problems uncovered and the level of energy savings may differ significantly from other projects, some of the benefits of detecting and diagnosing problems are expected from the use of MPC for any chilled water plant. The degree of chiller loading was found to be a key factor for efficiency. It is more efficient to operate the chillers at or near full load. In order to maximize the chiller load, one would maximize the temperature difference across chillers and the chilled water flow rate through the chillers. Thus, the CHWS set-point and the chilled water flow-rate can be used to limit the chiller loading to prevent chiller surging. Since the flow rate has an upper bound and the CHWS set point has a lower bound, the chiller loading is constrained and often determined by the chilled water return temperature (CHWR). The CHWR temperature
Distributed Prognostics Based on Structural Model Decomposition
Daigle, Matthew
efficiency. Using a centrifugal pump as a case study, we perform a number of simulation-based experiments or probability Q volumetric flow T temperature r friction coefficient w wear parameter M model/submodel v
A Simplified Residential Base-Case Model
Do, S. L.; Choi, J. H.; Haberl, J. S.
2013-01-01
This study was for the DOE-2.1e program to develop a simplified residential ASHP house model in Houston, Texas. The house characteristics were based on the standard reference design and requirements as defined in Chapter ...
Comparison of LMA and LOW Solar Solution Predictions in an SO(10) GUT Model
Carl H. Albright; S. Geer
2002-02-15
Within the framework of an SO(10) GUT model that can accommodate both the LMA and LOW solar neutrino mixing solutions by appropriate choice of the right-handed Majorana matrix elements, we present explicit predictions for the neutrino oscillation parameters \\Delta m^2_{21}, \\sin^2 2\\theta_{12}, \\sin^2 2\\theta_{23}, \\sin^2 2\\theta_{13}, and \\delta_{CP}. Given the observed near maximality of the atmospheric mixing, the model favors the LMA solution and predicts that \\delta_{CP} is small. The suitability of Neutrino Superbeams and Neutrino Factories for precision tests of the two model versions is discussed.
Chen, Shu-Hua
Particulate air quality model predictions using prognostic vs. diagnostic meteorology in central a , Michael J. Kleeman c,* a Department of Land, Air and Water Resources, University of California, Davis, 1 Prognostic meteorological fields Data assimilation UCD/CIT air quality model California Regional Particulate
IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 8, NO. 6, DECEMBER 2000 665 Fuzzy Model Predictive Control
Huang, Yinlun
and petrochemical industries during the past decade. In MPC, a process dynamic model is used to predict future (FMPC) approach is introduced to design a control system for a highly nonlinear process. In this approach, a process system is described by a fuzzy convolution model that consists of a number of quasi
User-click Modeling for Understanding and Predicting Search-behavior
Yang, Qiang
. Categories and Subject Descriptors H.3.3 [Information Search and Retrieval]: General Terms AlgorithmsUser-click Modeling for Understanding and Predicting Search-behavior Yuchen Zhang1 , Weizhu Chen1 advances in search users' click modeling consider both users' search queries and click/skip behavior
Intercomparison of Single-Column Numerical Models for the Prediction of Radiation Fog
Intercomparison of Single-Column Numerical Models for the Prediction of Radiation Fog THIERRY-term forecasting of fog is a difficult issue that can have a large societal impact. Radiation fog appears layers of the atmosphere. Current NWP models poorly forecast the life cycle of fog, and improved NWP
Hamarneh, Ghassan
BIOMECHANICAL KIDNEY MODEL FOR PREDICTING TUMOR DISPLACEMENT IN THE PRESENCE OF EXTERNAL PRESSURE biomechanical model to simulate de- formations under additional external pressure load. A second CT scan that the biomechanical simula- tion improves by 29% the tumor localization. Index Terms-- Partial nephrectomy, image
Weston, Ken
Atomistic Modeling of Macromolecular Crowding Predicts Modest Increases in Protein Folding that macromolecular crowding can increase protein folding stability, but depending on details of the models (e.g., how on the effects of macro- molecular crowding on protein folding and binding stability has been reached. Crowders
Temporal Models for Groundwater Level Prediction in Regions of Maharashtra Dissertation Report
Sohoni, Milind
Temporal Models for Groundwater Level Prediction in Regions of Maharashtra Dissertation Report In this project work we perform analysis of groundwater level data in three districts of Maha- rashtra - Thane of these districts and developed seasonal models to represent the groundwater be- havior. Three different type
Critical Fracture Stress and Fracture Strain Models for the Prediction of Lower and
Ritchie, Robert
Critical Fracture Stress and Fracture Strain Models for the Prediction of Lower and Upper Shelf fracture stress and stress modified fracture strain models are utilized to describe the variation of lower and upper shelf fracture toughness with temperature and strain rate for two alloy steels used
Predicting Response to Political Blog Posts with Topic Models Language Technologies Institute
Cohen, William W.
Predicting Response to Political Blog Posts with Topic Models Tae Yano Language Technologies Language Technologies Institute Carnegie Mellon University Pittsburgh, PA 15213, USA nasmith@cs.cmu.edu Abstract In this paper we model discussions in online po- litical weblogs (blogs). To do this, we extend La
An Efficient Genetic Algorithm for Predicting Protein Tertiary Structures in the 2D HP Model
Istrail, Sorin
, predicting its tertiary structure is known as the protein folding problem. This problem has been widely genetic algo- rithm for the protein folding problem under the HP model in the two-dimensional square Genetic Algorithm, Protein Folding Problem, 2D HP Model 1. INTRODUCTION Amino acids are the building
Bledsoe, Brian
The Nature Conservancy, Fort Collins, Colorado USA ABSTRACT Dams and water diversions can dramatically alter the hydraulic habitats of stream ecosystems. Predicting how water depth and velocity respond to flow alteration is possible using hydraulic models, such as Physical Habitat Simulation (PHABSIM); however, such models
Prediction of oxy-coal flame stand-off using high-fidelity thermochemical models
Prediction of oxy-coal flame stand-off using high-fidelity thermochemical models and the one Abstract An Eulerian one-dimensional turbulence (ODT) model is applied to simulate oxy-coal combustion temperature and mixing rate on oxy-coal flame is simulated and discussed where flame stand-off is used
Greitzer, Frank L.; Frincke, Deborah A.
2010-09-01
The purpose of this chapter is to motivate the combination of traditional cyber security audit data with psychosocial data, so as to move from an insider threat detection stance to one that enables prediction of potential insider presence. Two distinctive aspects of the approach are the objective of predicting or anticipating potential risks and the use of organizational data in addition to cyber data to support the analysis. The chapter describes the challenges of this endeavor and progress in defining a usable set of predictive indicators, developing a framework for integrating the analysis of organizational and cyber security data to yield predictions about possible insider exploits, and developing the knowledge base and reasoning capability of the system. We also outline the types of errors that one expects in a predictive system versus a detection system and discuss how those errors can affect the usefulness of the results.
Prediction of Liver Function by Using Magnetic Resonance-based Portal Venous Perfusion Imaging
Cao Yue; Wang Hesheng; Johnson, Timothy D.; Pan, Charlie; Hussain, Hero; Balter, James M.; Normolle, Daniel; Ben-Josef, Edgar; Ten Haken, Randall K.; Lawrence, Theodore S.; Feng, Mary
2013-01-01
Purpose: To evaluate whether liver function can be assessed globally and spatially by using volumetric dynamic contrast-enhanced magnetic resonance imaging MRI (DCE-MRI) to potentially aid in adaptive treatment planning. Methods and Materials: Seventeen patients with intrahepatic cancer undergoing focal radiation therapy (RT) were enrolled in institution review board-approved prospective studies to obtain DCE-MRI (to measure regional perfusion) and indocyanine green (ICG) clearance rates (to measure overall liver function) prior to, during, and at 1 and 2 months after treatment. The volumetric distribution of portal venous perfusion in the whole liver was estimated for each scan. We assessed the correlation between mean portal venous perfusion in the nontumor volume of the liver and overall liver function measured by ICG before, during, and after RT. The dose response for regional portal venous perfusion to RT was determined using a linear mixed effects model. Results: There was a significant correlation between the ICG clearance rate and mean portal venous perfusion in the functioning liver parenchyma, suggesting that portal venous perfusion could be used as a surrogate for function. Reduction in regional venous perfusion 1 month after RT was predicted by the locally accumulated biologically corrected dose at the end of RT (P<.0007). Regional portal venous perfusion measured during RT was a significant predictor for regional venous perfusion assessed 1 month after RT (P<.00001). Global hypovenous perfusion pre-RT was observed in 4 patients (3 patients with hepatocellular carcinoma and cirrhosis), 3 of whom had recovered from hypoperfusion, except in the highest dose regions, post-RT. In addition, 3 patients who had normal perfusion pre-RT had marked hypervenous perfusion or reperfusion in low-dose regions post-RT. Conclusions: This study suggests that MR-based volumetric hepatic perfusion imaging may be a biomarker for spatial distribution of liver function, which could aid in individualizing therapy, particularly for patients at risk for liver injury after RT.
9/26/2007 Model Based Testing 1 Model Based Testing
Browne, James C.
-based testing often cost-effective but requires certain skills within organization #12;9/26/2007 Model Based but transitions guided by probability distribution Â· State charts: UML diagram, shows states that system can of a state chart www.agilemodeling.com/style/stateChart Diagram.htm #12;9/26/2007 Model Based Testing 10
Almassalkhi, MR; Hiskens, IA
2015-01-01
The novel cascade-mitigation scheme developed in Part I of this paper is implemented within a receding-horizon model predictive control (MPC) scheme with a linear controller model. This present paper illustrates the MPC strategy with a case-study that is based on the IEEE RTS-96 network, though with energy storage and renewable generation added. It is shown that the MPC strategy alleviates temperature overloads on transmission lines by rescheduling generation, energy storage, and other network elements, while taking into account ramp-rate limits and network limitations. Resilient performance is achieved despite the use of a simplified linear controller model. The MPC scheme is compared against a base-case that seeks to emulate human operator behavior.
A probabilistic graphical model based stochastic input model construction
Wan, Jiang [Materials Process Design and Control Laboratory, Sibley School of Mechanical and Aerospace Engineering, 101 Frank H.T. Rhodes Hall, Cornell University, Ithaca, NY 14853-3801 (United States); Zabaras, Nicholas, E-mail: nzabaras@gmail.com [Materials Process Design and Control Laboratory, Sibley School of Mechanical and Aerospace Engineering, 101 Frank H.T. Rhodes Hall, Cornell University, Ithaca, NY 14853-3801 (United States); Center for Applied Mathematics, 657 Frank H.T. Rhodes Hall, Cornell University, Ithaca, NY 14853-3801 (United States)
2014-09-01
Model reduction techniques have been widely used in modeling of high-dimensional stochastic input in uncertainty quantification tasks. However, the probabilistic modeling of random variables projected into reduced-order spaces presents a number of computational challenges. Due to the curse of dimensionality, the underlying dependence relationships between these random variables are difficult to capture. In this work, a probabilistic graphical model based approach is employed to learn the dependence by running a number of conditional independence tests using observation data. Thus a probabilistic model of the joint PDF is obtained and the PDF is factorized into a set of conditional distributions based on the dependence structure of the variables. The estimation of the joint PDF from data is then transformed to estimating conditional distributions under reduced dimensions. To improve the computational efficiency, a polynomial chaos expansion is further applied to represent the random field in terms of a set of standard random variables. This technique is combined with both linear and nonlinear model reduction methods. Numerical examples are presented to demonstrate the accuracy and efficiency of the probabilistic graphical model based stochastic input models. - Highlights: • Data-driven stochastic input models without the assumption of independence of the reduced random variables. • The problem is transformed to a Bayesian network structure learning problem. • Examples are given in flows in random media.
D'Sousa, Rohan Joseph
2000-01-01
Predictions of rotordynamic-coefficients for annular honeycomb gas seals are compared using different friction-factor models. Analysis shows that the fundamental improvement in predicting the rotordynamic-coefficients ...
Project Profile: Physics-Based Reliability Models for Supercritical...
Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site
developed models to predict the reliability of hybrid gas bearing (HGB) and dry gas seal (DGS) components in the turboexpander of a supercritical CO2 turbine. The...
Mass-transport models to predict toxicity of inhaled gases in the upper respiratory tract
Hubal, E.A.C.; Fedkiw, P.S.; Kimbell, J.S. [North Carolina State Univ., Raleigh, NC (United States)
1996-04-01
Mass-transport (the movement of a chemical species) plays an important role in determining toxic responses of the upper respiratory tract (URT) to inhaled chemicals. Mathematical dosimetry models incorporate physical characteristics of mass transport and are used to predict quantitative uptake (absorption rate) and distribution of inhaled gases and vapors in the respiratory tract. Because knowledge of dose is an essential component of quantitative risk assessment, dosimetry modeling plays an important role in extrapolation of animal study results to humans. A survey of existing mathematical dosimetry models for the URT is presented, limitations of current models are discussed, and adaptations of existing models to produce a generally applicable model are suggested. Reviewed URT dosimetry models are categorized as early, lumped-parameter, and distributed-parameter models. Specific examples of other relevant modeling work are also presented. 35 refs., 11 figs., 1 tab.
Economic Model Predictive Control of Nonlinear Process Systems Using Empirical Models
ALANQAR, ANAS WAEL
2015-01-01
optimization and feedback control since it is a predictive control scheme that is formulated with an objective function representing the processoptimization and feedback control since it is a predictive control scheme that is formulated with an objective function representing the process/
A Predictive Model of Fragmentation using Adaptive Mesh Refinement and a Hierarchical Material Model
Koniges, A E; Masters, N D; Fisher, A C; Anderson, R W; Eder, D C; Benson, D; Kaiser, T B; Gunney, B T; Wang, P; Maddox, B R; Hansen, J F; Kalantar, D H; Dixit, P; Jarmakani, H; Meyers, M A
2009-03-03
Fragmentation is a fundamental material process that naturally spans spatial scales from microscopic to macroscopic. We developed a mathematical framework using an innovative combination of hierarchical material modeling (HMM) and adaptive mesh refinement (AMR) to connect the continuum to microstructural regimes. This framework has been implemented in a new multi-physics, multi-scale, 3D simulation code, NIF ALE-AMR. New multi-material volume fraction and interface reconstruction algorithms were developed for this new code, which is leading the world effort in hydrodynamic simulations that combine AMR with ALE (Arbitrary Lagrangian-Eulerian) techniques. The interface reconstruction algorithm is also used to produce fragments following material failure. In general, the material strength and failure models have history vector components that must be advected along with other properties of the mesh during remap stage of the ALE hydrodynamics. The fragmentation models are validated against an electromagnetically driven expanding ring experiment and dedicated laser-based fragmentation experiments conducted at the Jupiter Laser Facility. As part of the exit plan, the NIF ALE-AMR code was applied to a number of fragmentation problems of interest to the National Ignition Facility (NIF). One example shows the added benefit of multi-material ALE-AMR that relaxes the requirement that material boundaries must be along mesh boundaries.
Predicting ecological roles in the rhizosphere using metabolome and transportome modeling
Larsen, Peter E.; Collart, Frank R.; Dai, Yang; Blanchard, Jeffrey L.
2015-09-02
The ability to obtain complete genome sequences from bacteria in environmental samples, such as soil samples from the rhizosphere, has highlighted the microbial diversity and complexity of environmental communities. New algorithms to analyze genome sequence information in the context of community structure are needed to enhance our understanding of the specific ecological roles of these organisms in soil environments. We present a machine learning approach using sequenced Pseudomonad genomes coupled with outputs of metabolic and transportomic computational models for identifying the most predictive molecular mechanisms indicative of a Pseudomonad’s ecological role in the rhizosphere: a biofilm, biocontrol agent, promoter of plant growth, or plant pathogen. Computational predictions of ecological niche were highly accurate overall with models trained on transportomic model output being the most accurate (Leave One Out Validation F-scores between 0.82 and 0.89). The strongest predictive molecular mechanism features for rhizosphere ecological niche overlap with many previously reported analyses of Pseudomonad interactions in the rhizosphere, suggesting that this approach successfully informs a system-scale level understanding of how Pseudomonads sense and interact with their environments. The observation that an organism’s transportome is highly predictive of its ecological niche is a novel discovery and may have implications in our understanding microbial ecology. The framework developed here can be generalized to the analysis of any bacteria across a wide range of environments and ecological niches making this approach a powerful tool for providing insights into functional predictions from bacterial genomic data.
Predicting ecological roles in the rhizosphere using metabolome and transportome modeling
DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)
Larsen, Peter E.; Collart, Frank R.; Dai, Yang; Blanchard, Jeffrey L.
2015-09-02
The ability to obtain complete genome sequences from bacteria in environmental samples, such as soil samples from the rhizosphere, has highlighted the microbial diversity and complexity of environmental communities. New algorithms to analyze genome sequence information in the context of community structure are needed to enhance our understanding of the specific ecological roles of these organisms in soil environments. We present a machine learning approach using sequenced Pseudomonad genomes coupled with outputs of metabolic and transportomic computational models for identifying the most predictive molecular mechanisms indicative of a Pseudomonad’s ecological role in the rhizosphere: a biofilm, biocontrol agent, promoter ofmore »plant growth, or plant pathogen. Computational predictions of ecological niche were highly accurate overall with models trained on transportomic model output being the most accurate (Leave One Out Validation F-scores between 0.82 and 0.89). The strongest predictive molecular mechanism features for rhizosphere ecological niche overlap with many previously reported analyses of Pseudomonad interactions in the rhizosphere, suggesting that this approach successfully informs a system-scale level understanding of how Pseudomonads sense and interact with their environments. The observation that an organism’s transportome is highly predictive of its ecological niche is a novel discovery and may have implications in our understanding microbial ecology. The framework developed here can be generalized to the analysis of any bacteria across a wide range of environments and ecological niches making this approach a powerful tool for providing insights into functional predictions from bacterial genomic data.« less
Improving the Fanger model's thermal comfort predictions for naturally ventilated spaces
Truong, Phan Hue
2010-01-01
The Fanger model is the official thermal comfort model in U.S. and international standards and is based on the heat balance of the human body with the environment. This investigation focuses on re-specifying the parameters ...
A MULTISCALE, CELL-BASED FRAMEWORK FOR MODELING CANCER DEVELOPMENT
JIANG, YI
2007-01-16
Cancer remains to be one of the leading causes of death due to diseases. We use a systems approach that combines mathematical modeling, numerical simulation, in vivo and in vitro experiments, to develop a predictive model that medical researchers can use to study and treat cancerous tumors. The multiscale, cell-based model includes intracellular regulations, cellular level dynamics and intercellular interactions, and extracellular level chemical dynamics. The intracellular level protein regulations and signaling pathways are described by Boolean networks. The cellular level growth and division dynamics, cellular adhesion and interaction with the extracellular matrix is described by a lattice Monte Carlo model (the Cellular Potts Model). The extracellular dynamics of the signaling molecules and metabolites are described by a system of reaction-diffusion equations. All three levels of the model are integrated through a hybrid parallel scheme into a high-performance simulation tool. The simulation results reproduce experimental data in both avasular tumors and tumor angiogenesis. By combining the model with experimental data to construct biologically accurate simulations of tumors and their vascular systems, this model will enable medical researchers to gain a deeper understanding of the cellular and molecular interactions associated with cancer progression and treatment.
HASARD: A Model-Based Method for Quality Analysis of Software Architecture
Zhu, Hong
HASARD: A Model-Based Method for Quality Analysis of Software Architecture Hong Zhu Department for the analysis of software quality as entailed in software architectural designs. In this method, quality hazards architectural designs to predict their quality. 1.1 Motivation Software architecture is a structural model
Supersonic combustion studies using a multivariate quadrature based method for combustion modeling
Raman, Venkat
Supersonic combustion studies using a multivariate quadrature based method for combustion modeling function (PDF) of thermochemical variables can be used for accurately computing the combustion source term of predictive models for supersonic combustion is a critical step in design and development of scramjet engines
A DISLOCATION-BASED CLEAVAGE INITIATION MODEL FOR PRESSURE VESSEL
Cochran, Kristine B; Erickson, Marjorie A; Williams, Paul T; Klasky, Hilda B; Bass, Bennett Richard
2012-01-01
Efforts are under way to develop a theoretical, multi-scale model for the prediction of fracture toughness of ferritic steels in the ductile-to-brittle transition temperature (DBTT) region that accounts for temperature, irradiation, strain rate, and material condition (chemistry and heat treatment) effects. This new model is intended to address difficulties associated with existing empirically-derived models of the DBTT region that cannot be extrapolated to conditions for which data are unavailable. Dislocation distribution equations, derived from the theories of Yokobori et al., are incorporated to account for the local stress state prior to and following initiation of a microcrack from a second-phase particle. The new model is the basis for the DISlocation-based FRACture (DISFRAC) computer code being developed at the Oak Ridge National Laboratory (ORNL). The purpose of this code is to permit fracture safety assessments of ferritic structures with only tensile properties required as input. The primary motivation for the code is to assist in the prediction of radiation effects on nuclear reactor pressure vessels, in parallel with the EURATOM PERFORM 60 project.
Key challenges to model-based design : distinguishing model confidence from model validation
Flanagan, Genevieve (Genevieve Elise Cregar)
2012-01-01
Model-based design is becoming more prevalent in industry due to increasing complexities in technology while schedules shorten and budgets tighten. Model-based design is a means to substantiate good design under these ...
The impact of global nuclear mass model uncertainties on $r$-process abundance predictions
M. Mumpower; R. Surman; A. Aprahamian
2014-11-14
Rapid neutron capture or `$r$-process' nucleosynthesis may be responsible for half the production of heavy elements above iron on the periodic table. Masses are one of the most important nuclear physics ingredients that go into calculations of $r$-process nucleosynthesis as they enter into the calculations of reaction rates, decay rates, branching ratios and Q-values. We explore the impact of uncertainties in three nuclear mass models on $r$-process abundances by performing global monte carlo simulations. We show that root-mean-square (rms) errors of current mass models are large so that current $r$-process predictions are insufficient in predicting features found in solar residuals and in $r$-process enhanced metal poor stars. We conclude that the reduction of global rms errors below $100$ keV will allow for more robust $r$-process predictions.
Subtask 2.4 - Integration and Synthesis in Climate Change Predictive Modeling
Jaroslav Solc
2009-06-01
The Energy & Environmental Research Center (EERC) completed a brief evaluation of the existing status of predictive modeling to assess options for integration of our previous paleohydrologic reconstructions and their synthesis with current global climate scenarios. Results of our research indicate that short-term data series available from modern instrumental records are not sufficient to reconstruct past hydrologic events or predict future ones. On the contrary, reconstruction of paleoclimate phenomena provided credible information on past climate cycles and confirmed their integration in the context of regional climate history is possible. Similarly to ice cores and other paleo proxies, acquired data represent an objective, credible tool for model calibration and validation of currently observed trends. It remains a subject of future research whether further refinement of our results and synthesis with regional and global climate observations could contribute to improvement and credibility of climate predictions on a regional and global scale.
Dynamics of Cell Shape and Forces on Micropatterned Substrates Predicted by a Cellular Potts Model
Philipp J. Albert; Ulrich S. Schwarz
2014-05-19
Micropatterned substrates are often used to standardize cell experiments and to quantitatively study the relation between cell shape and function. Moreover, they are increasingly used in combination with traction force microscopy on soft elastic substrates. To predict the dynamics and steady states of cell shape and forces without any a priori knowledge of how the cell will spread on a given micropattern, here we extend earlier formulations of the two-dimensional cellular Potts model. The third dimension is treated as an area reservoir for spreading. To account for local contour reinforcement by peripheral bundles, we augment the cellular Potts model by elements of the tension-elasticity model. We first parameterize our model and show that it accounts for momentum conservation. We then demonstrate that it is in good agreement with experimental data for shape, spreading dynamics, and traction force patterns of cells on micropatterned substrates. We finally predict shapes and forces for micropatterns that have not yet been experimentally studied.
Flood regulation using nonlinear model predictive control Toni Barjas Blanco a,, Patrick Willems b
Flood regulation using nonlinear model predictive control Toni Barjas Blanco a,Ã, Patrick Willems b t In this paper the flood problem of the river Demer, a river located in Belgium, is discussed. First a simplified. & 2010 Elsevier Ltd. All rights reserved. 1. Introduction Flooding of rivers are a worldwide cause
Wake models are used to improve predictions of Annual Energy Production (AEP) of wind farms.
Daraio, Chiara
measurements in the ETHZ facility compare well with measurements at the Horns Rev offshore wind farm·Wake models are used to improve predictions of Annual Energy Production (AEP) of wind farms. ·Wake and wind turbine wakes in large windfarms offshore, Wind Energy 12, pp. 431-444, 2009. [2] L.P. Chamorro
A predictive analytical friction model from basic theories of interfaces, contacts and dislocations
Marks, Laurence D.
A predictive analytical friction model from basic theories of interfaces, contacts and dislocations of dislocation drag, contact mechanics, and interface theory. An analytic expression for the friction force still see use in basic discus- sions of the phenomenon [1]. Three basic observations have persisted
Predicting pesticide fate in the hive (part 2): development of a dynamic hive model
.g. bees, wax and honey). The proposed model is validated using empirical data on -fluvalinate residues in bees, wax and honey. It predicts with good approximation both the trends over time to measured data. A honeybee hive is a micro-ecosystem com- posed of several components (e.g. bees, wax, honey
PREV'AIR, a modeling platform for the air quality predictability study , C. Honor2
Menut, Laurent
PREV'AIR, a modeling platform for the air quality predictability study Menut L.1 , C. Honoré2 , L Ministère de l'écologie et du développement durable, Paris, France This platform is proposed by the PREV'AIR about PREV'AIR ? please send an e-mail to cecile.honore@ineris.fr 1. Introduction Since 2002, the PREV'AIR
Linear-quadratic model predictive control for urban traffic , Hai L. Vu a
Nazarathy, Yoni
Accepted 30 June 2013 Keywords: Model predictive control Intelligent transport system Congestion control- tion systems are driving the field of intelligent transport systems (ITS) into the twenty first century for large urban networks containing thousands of sensors and actuators. We demonstrate the essence of our
Variable Selection in Data Mining: Building a Predictive Model for Bankruptcy
Stine, Robert A.
Variable Selection in Data Mining: Building a Predictive Model for Bankruptcy Dean P. Foster and Robert A. Stine Department of Statistics The Wharton School of the University of Pennsylvania consequences of over-fitting (e.g. ?). Many in- teresting problems, particularly classification problems
Barber, Stuart
4 th World Congress on Industrial Process Tomography, Aizu, Japan Modelling and predicting flow of Statistics, University of Leeds, Leeds, LS2 9JT, UK, robert@maths.leeds.ac.uk ABSTRACT The aim of industrial without intruding into the industrial process, but produce highly correlated and noisy data, and hence
Towards a Generalized Regression Model for On-body Energy Prediction from Treadmill Walking
Sukhatme, Gaurav S.
Towards a Generalized Regression Model for On-body Energy Prediction from Treadmill Walking sensor data to energy expenditure is the ques- tion of normalizating across physiological parameters. Common approaches such as weight scaling require validation for each new population. An alternative
A comparison of various models in predicting ignition delay in single-particle coal combustion
by the varying properties and chemical structure of different coal types [2], and by the fact that the coal properties change significantly throughout a coal particle's lifetime in a combustor [35]. The coal particleA comparison of various models in predicting ignition delay in single-particle coal combustion
Flood Control with Model Predictive Control for River Systems with Water Reservoirs
Flood Control with Model Predictive Control for River Systems with Water Reservoirs Maarten consisting of multiple channels, gates, and a water reservoir. One controller is used in combination of measured water levels. It was observed that the influence of this estimator on the control performance
Flood control of rivers with nonlinear model predictive control and moving horizon estimation
]. Several studies can be found in literature where MPC is used to control water systems [15], [16] and [5Flood control of rivers with nonlinear model predictive control and moving horizon estimation control (MPC) in combination with moving horizon estimation (MHE) can more effectively be used for flood
A Novel Virtual Age Reliability Model for Time-to-Failure Prediction
Cotofana, Sorin
counts, devices approaching physical feature size limits and nuclear plant comparable power densityA Novel Virtual Age Reliability Model for Time-to-Failure Prediction Yao Wang, Sorin Cotofana their relatively short operating lifetime. To overcome the MTTF weakness, this paper proposes a novel virtual age
Qiu, Qinru
Binghamton University, State University of New York Binghamton, New York, USA {jlu5, sliu5, qwu, qqiuAccurate Modeling and Prediction of Energy Availability in Energy Harvesting Real-Time Embedded}@binghamton.edu Abstract -- Energy availability is the primary subject that drives the research innovations in energy
Model-predicted distribution of wind-induced internal wave energy in the world's oceans
Miami, University of
Model-predicted distribution of wind-induced internal wave energy in the world's oceans Naoki 9 July 2008; published 30 September 2008. [1] The distribution of wind-induced internal wave energy-scaled kinetic energy are all consistent with the available observations in the regions of significant wind
PREDICTION OF FOG EPISODES AT THE AIRPORT OF MADRID-BARAJAS USING DIFFERENT MODELING APPROACHES
Politècnica de Catalunya, Universitat
PREDICTION OF FOG EPISODES AT THE AIRPORT OF MADRID-BARAJAS USING DIFFERENT MODELING APPROACHES Meteorología (INM) has been investigating for some time the phenomena related to the formation of fog episodes between the development of fog and the establishment of katabatic flows in the region, generally under
Voltage Utilization in Model Predictive Control for Michael Leuer, Joachim Bocker
Noé, Reinhold
Voltage Utilization in Model Predictive Control for IPMSM Michael Leuer, Joachim B¨ocker Power (IPMSM). Besides the good dynamics, the utilization of the DC link voltage is important for these motor types. Since the MPC is able to utilize the available DC link voltage optimally, the MPC is superior
Adaptive Model Predictive Control of the Hybrid Dynamics of a Fuel Cell System.
Paris-Sud XI, Université de
Adaptive Model Predictive Control of the Hybrid Dynamics of a Fuel Cell System. M. Fiacchini, T operation of a fuel cell system is presented. The aim of the control design is to guarantee that the oxygen control to a fuel cell plant is presented. The fuel cell, located in the laboratory of the Department
Giurgiutiu, Victor
Structural health monitoring with piezoelectric wafer active sensors predictive modeling of the state of the art in structural health monitoring with piezoelectric wafer active sensors and follows with conclusions and suggestions for further work Key Words: structural health monitoring, SHM, nondestructive
Economic Nonlinear Model Predictive Control for the Optimization of Gas Pipeline Networks
Grossmann, Ignacio E.
Compressor 4 Commercial Industry Power Plant LDC 3 Suppliers, 12 Demand nodes, 5 Compressors Sinusoidal Flowrates Industry: N6,12,13,19,21 Commercial: N30,32,34,35 Power Plant: N4,25 LDC: N23 Pcontract = 500 kEconomic Nonlinear Model Predictive Control for the Optimization of Gas Pipeline Networks EWO
Prediction in District Heating Systems with cFIR models Pierre Pinson , Torben S. Nielsen, Henrik Aa. Nielsen, Lyngby, Denmark Abstract Current methodologies for the optimal operation of district heating systems regularization. Results are given for the test case of the Roskilde district heating system, over a period
Paul Smolen; Douglas A. Baxter; John H. Byrne
2012-08-03
Protein synthesis-dependent, late long-term potentiation (LTP) and depression (LTD) at glutamatergic hippocampal synapses are well characterized examples of long-term synaptic plasticity. Persistent increased activity of the enzyme protein kinase M (PKM) is thought essential for maintaining LTP. Additional spatial and temporal features that govern LTP and LTD induction are embodied in the synaptic tagging and capture (STC) and cross capture hypotheses. Only synapses that have been "tagged" by an stimulus sufficient for LTP and learning can "capture" PKM. A model was developed to simulate the dynamics of key molecules required for LTP and LTD. The model concisely represents relationships between tagging, capture, LTD, and LTP maintenance. The model successfully simulated LTP maintained by persistent synaptic PKM, STC, LTD, and cross capture, and makes testable predictions concerning the dynamics of PKM. The maintenance of LTP, and consequently of at least some forms of long-term memory, is predicted to require continual positive feedback in which PKM enhances its own synthesis only at potentiated synapses. This feedback underlies bistability in the activity of PKM. Second, cross capture requires the induction of LTD to induce dendritic PKM synthesis, although this may require tagging of a nearby synapse for LTP. The model also simulates the effects of PKM inhibition, and makes additional predictions for the dynamics of CaM kinases. Experiments testing the above predictions would significantly advance the understanding of memory maintenance.
Model Predictive Control for Starvation Prevention in a Hybrid Fuel Cell System1
Stefanopoulou, Anna
Model Predictive Control for Starvation Prevention in a Hybrid Fuel Cell System1 Ardalan Vahidi 2 current is drawn from a fuel cell, it is critical that the reacted oxygen is replenished rapidly. We formulate distribution of current demand between the fuel cell and the auxiliary source
Predicting Protein Folds with Structural Repeats Using a Chain Graph Model
Carbonell, Jaime
Predicting Protein Folds with Structural Repeats Using a Chain Graph Model Yan Liu yanliu, Carnegie Mellon University, Pittsburgh, PA 15213 USA Abstract Protein fold recognition is a key step to to accurately identify protein folds aris- ing from typical spatial arrangements of well-defined secondary
Three-body interactions improve the prediction of rate and mechanism in protein folding models
Plotkin, Steven S.
Three-body interactions improve the prediction of rate and mechanism in protein folding models M. R-body interactions on rate and mechanism in protein folding by using the results of molecular dynamics simulations that stabilize protein structures and govern protein folding mechanisms is a fundamental problem in molecular
Baer, Ferdinand
Optimizing Computations in Weather and Climate Prediction Models* F. BAER, BANGLIN ZHANG, AND BING scenarios for many time scales, more computer power than is currently available will be needed. One and sometimes with a biosphere included, are very complex and require so much computing power on available
Modeling Ideology and Predicting Policy Change with Social Media: Case of Same-Sex Marriage
Modeling Ideology and Predicting Policy Change with Social Media: Case of Same-Sex Marriage Amy X of important policy decisions. Focus- ing on the issue of same-sex marriage legalization, we exam- ine almost 2 million public Twitter posts related to same-sex marriage in the U.S. states over the course of 4 years
ARSA: A Sentiment-Aware Model for Predicting Sales Performance Using Blogs
Huang, Jimmy
ARSA: A Sentiment-Aware Model for Predicting Sales Performance Using Blogs Yang Liu1 , Xiangji, Toronto, Canada 2 School of Information Technology York University, Toronto, Canada yliu@cse.yorku.ca, jhuang@yorku.ca, aan@cse.yorku.ca, xhyu@yorku.ca ABSTRACT Due to its high popularity, Weblogs (or blogs
Model to Predict Temperature and Capillary Pressure Driven Water Transport in PEFCs After Shutdown
Mench, Matthew M.
Model to Predict Temperature and Capillary Pressure Driven Water Transport in PEFCs After Shutdown-912 Korea To enhance durability and cold-start performance of polymer electrolyte fuel cells PEFCs in the PEFC components after shutdown, which for the first time includes thermo-osmotic flow in the membrane
A LIFETIME PREDICTION MODEL FOR SINGLE CRYSTAL SUPERALLOYS SUBJECTED TO THERMOMECHANICAL
Paris-Sud XI, Université de
materials tensile, creep and LCF test data at different temperatures. Some parameters, independentA LIFETIME PREDICTION MODEL FOR SINGLE CRYSTAL SUPERALLOYS SUBJECTED TO THERMOMECHANICAL CREEP for Single Crystal Superalloys operated at high temperatures and subjected to creep, fatigue and oxidation
Towards a Predictive Model for Opal Exploration using a Spatio-temporal Data Mining Approach
Müller, Dietmar
Towards a Predictive Model for Opal Exploration using a Spatio-temporal Data Mining Approach Andrew depositional, unclassified Opal deposit 140°E120°E 20°S 40°S Winton Opalton Jundah Eromanga Quilpie Lightning Ridge White Cliffs Stuart Creek LambinaMintabie Coober Pedy Surat Basin Eromanga Basin Opal mining town
A comparison of various models in predicting ignition delay in single-particle coal combustion
A comparison of various models in predicting ignition delay in single-particle coal combustion November 2013 Accepted 7 January 2014 Available online xxxx Keywords: Coal Devolatilization Ignition delay a b s t r a c t In this paper, individual coal particle combustion under laminar conditions
Precipitation sensitivity to autoconversion rate in a Numerical Weather Prediction model
Marsham, John
1 Precipitation sensitivity to autoconversion rate in a Numerical Weather Prediction model Céline;2 Summary Aerosols are known to significantly affect cloud and precipitation patterns and intensity. The impact of changing cloud droplet number concentration (CDNC), on cloud and precipitation evolution can
Baker, Jack W.
Conditional Spectrum Computation Incorporating Multiple Causal Earthquakes and Ground-Motion Prediction Models by Ting Lin, Stephen C. Harmsen, Jack W. Baker, and Nicolas Luco Abstract The conditional uncertainties in all earthquake scenarios and resulting ground motions, as well as the epistemic uncertainties
Demonstrating and Validating a Next Generation Model-Based Controller...
Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site
a Next Generation Model-Based Controller for Fuel Efficient, Low Emissions Diesel Engines Demonstrating and Validating a Next Generation Model-Based Controller for Fuel...
Experiment-Based Model for the Chemical Interactions between...
Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site
Experiment-Based Model for the Chemical Interactions between Geothermal Rocks, Supercritical Carbon Dioxide and Water Experiment-Based Model for the Chemical Interactions between...
Zhang, Da-Lin
Analysis and prediction of hazard risks caused by tropical cyclones in Southern China with fuzzy 2011 Keywords: Combined weights Fuzzy mathematical models Hazard risk analysis Exceeded probability Tropical cyclones Grey prediction model a b s t r a c t A hazard-risk assessment model and a grey hazard
Vassiliadis, Dimitrios
, Modeling, and Prediction for Space Weather Environments Dimitris Vassiliadis Abstract--By now nonlinear dynamical models and neural net- works have been used to predict and model a wide variety of space weather. These developments have prompted the establishment of national space weather programs in the U.S. [21], [22
Vision-based macroscopic pedestrian models
Pierre Degond; Cécile Appert-Rolland; Julien Pettré; Guy Theraulaz
2013-07-08
We propose a hierarchy of kinetic and macroscopic models for a system consisting of a large number of interacting pedestrians. The basic interaction rules are derived from earlier work where the dangerousness level of an interaction with another pedestrian is measured in terms of the derivative of the bearing angle (angle between the walking direction and the line connecting the two subjects) and of the time-to-interaction (time before reaching the closest distance between the two subjects). A mean-field kinetic model is derived. Then, three different macroscopic continuum models are proposed. The first two ones rely on two different closure assumptions of the kinetic model, respectively based on a monokinetic and a von Mises-Fisher distribution. The third one is derived through a hydrodynamic limit. In each case, we discuss the relevance of the model for practical simulations of pedestrian crowds.
The Dirac Form Factor Predicts the Pauli Form Factor in the Endpoint Model
Sumeet Dagaonkar; Pankaj Jain; John P. Ralston
2015-03-24
We compute the momentum-transfer dependence of the proton Pauli form factor $F_{2}$ in the endpoint overlap model. We find the model correctly reproduces the scaling of the ratio of $F_{2}$ with the Dirac Form factor $F_{1}$ observed at the Jefferson Laboratory. The calculation uses the leading-power, leading twist Dirac structure of the quark light-cone wave function, and the same endpoint dependence previously determined from the Dirac form factor $F_{1}$. There are no parameters and no adjustable functions in the endpoint model's prediction for $F_{2}$. The model's predicted ratio $F_{2}(Q^{2})/F_{1}(Q^{2})$ is quite insensitive to the endpoint wave function, which explains why the observed ratio scales like $1/Q$ down to rather low momentum transfers. The endpoint model appears to be the only comprehensive model consistent with all form factor information as well as reproducing fixed-angle proton-proton scattering at large momentum transfer. Any one of the processes is capable of predicting the others.
Long-Fiber Thermoplastic Injection Molded Composites: from Process Modeling to Property Prediction
Nguyen, Ba Nghiep; Holbery, Jim D.; Johnson, Kenneth I.; Smith, Mark T.
2005-09-01
Recently, long-fiber filled thermoplastics have become a great interest to the automotive industry since these materials offer much better property performance (e.g. elastic moduli, strength, durability…) than their short-fiber analogues, and they can be processed through injection molding with some specific tool design. However, in order that long-fiber thermoplastic injection molded composites can be used efficiently for automotive applications, there is a tremendous need to develop process and constitutive models as well as computational tools to predict the microstructure of the as-formed composite, and its resulting properties and macroscopic responses from processing to the final product. The microstructure and properties of such a composite are governed by i) flow-induced fiber orientation, ii) fiber breakage during injection molding, and iii) processing conditions (e,g. pressure, mold and melt temperatures, mold geometries, injection speed, etc.). This paper highlights our efforts to address these challenging issues. The work is an integrated part of a research program supported by the US Department of Energy, which includes • The development of process models for long-fiber filled thermoplastics, • The construction of an interface between process modeling and property prediction as well as the development of new constitutive models to perform linear and nonlinear structural analyses, • Experimental characterization of model parameters and verification of the model predictions.
Model-Inspired Research. TES research uses modeling, prediction, and synthesis to identify
in Earth system models (ESMs). TES supports research to advance fundamental understanding of terrestrial-process models, ecosystem models, and the Community Earth System Model). This emphasis on the capture of advanced in Earth system models to increase the quality of climate model projections and to provide the scientific
Optimal Model-Based Production Planning
Grossmann, Ignacio E.
Given Refinery configuration: Process units Feedstock & Final Product Objective Select crude oils Hydrotreatment Gasoline blending Distillate blending Gas oil blending Cat Crack CDU crude1 crude2 butane Fuel gas1 Optimal Model-Based Production Planning for Refinery Operation Abdulrahman Alattas Advisor
Constraint-Based Planning and Scheduling Models
Grossmann, Ignacio E.
and Logistics Laboratory Adaptive traffic signal control [Traffic21, Heinz] The Robotics Institute, CarnegieConstraint-Based Planning and Scheduling Models Stephen F. Smith The Robotics Institute Carnegie Multi-Robot Coordination for Aircraft Assembly [Boeing] - Distributed management of joint plans
Towards an Empirically Based Parametric Explosion Spectral Model
Ford, S R; Walter, W R; Ruppert, S; Matzel, E; Hauk, T; Gok, R
2009-08-31
Small underground nuclear explosions need to be confidently detected, identified, and characterized in regions of the world where they have never before been tested. The focus of our work is on the local and regional distances (< 2000 km) and phases (Pn, Pg, Sn, Lg) necessary to see small explosions. We are developing a parametric model of the nuclear explosion seismic source spectrum that is compatible with the earthquake-based geometrical spreading and attenuation models developed using the Magnitude Distance Amplitude Correction (MDAC) techniques (Walter and Taylor, 2002). The explosion parametric model will be particularly important in regions without any prior explosion data for calibration. The model is being developed using the available body of seismic data at local and regional distances for past nuclear explosions at foreign and domestic test sites. Parametric modeling is a simple and practical approach for widespread monitoring applications, prior to the capability to carry out fully deterministic modeling. The achievable goal of our parametric model development is to be able to predict observed local and regional distance seismic amplitudes for event identification and yield determination in regions with incomplete or no prior history of underground nuclear testing. The relationship between the parametric equations and the geologic and containment conditions will assist in our physical understanding of the nuclear explosion source.
Celi, Leo Anthony G
2009-01-01
Introduction. Models for mortality prediction are traditionally developed from prospective multi-center observational studies involving a heterogeneous group of patients to optimize external validity. We hypothesize that ...
Integration of Landsat Imagery and an Inundation Model in Flood Assessment and Predictions
Ezer,Tal
regions. The topography data used by the model were based only on a subjective assessment from various data in shallow regions and flood zones where land- base data are not available. Keywords constraint to the development of such numerical models is the lack of suitable validation data sources [3
.8, a positive predictive value of 27.5% and a negative predictive value of 99.4%. CONCLUSIONS: The logisticThe use of a new logistic regression model for predicting the outcome of pregnancies of unknown, London UK. E-mail: gcondous@hotmail.com BACKGROUND: The aim of this study was to generate and evaluate
M. K. Parida; Sudhanwa Patra
2013-01-14
In TeV scale left-right symmetric models, new dominant predictions to neutrinoless double beta decay and light neutrino masses are in mutual contradiction because of large contribution to the latter through popular seesaw mechanisms. We show that in a class of left-right models with high-scale parity restoration, these results coexist without any contravention with neutrino oscillation data and the relevant formula for light neutrino masses is obtained via gauged inverse seesaw mechanism. The most dominant contribution to the double beta decay is shown to be via $W^-_L- W^-_R$ mediation involving both light and heavy neutrino exchanges, and the model predictions are found to discriminate whether the Dirac neutrino mass is of quark-lepton symmetric origin or without it. We also discuss associated lepton flavor violating decays.
Energy Band Model Based on Effective Mass
Viktor Ariel
2012-09-06
In this work, we demonstrate an alternative method of deriving an isotropic energy band model using a one-dimensional definition of the effective mass and experimentally observed dependence of mass on energy. We extend the effective mass definition to anti-particles and particles with zero rest mass. We assume an often observed linear dependence of mass on energy and derive a generalized non-parabolic energy-momentum relation. The resulting non-parabolicity leads to velocity saturation at high particle energies. We apply the energy band model to free relativistic particles and carriers in solid state materials and obtain commonly used dispersion relations and experimentally confirmed effective masses. We apply the model to zero rest mass particles in graphene and propose using the effective mass for photons. Therefore, it appears that the new energy band model based on the effective mass can be applied to relativistic particles and carriers in solid state materials.
Pérez-Andújar, Angélica [Department of Radiation Physics, Unit 1202, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030 (United States)] [Department of Radiation Physics, Unit 1202, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030 (United States); Zhang, Rui; Newhauser, Wayne [Department of Radiation Physics, Unit 1202, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030 and The University of Texas Graduate School of Biomedical Sciences at Houston, 6767 Bertner Avenue, Houston, Texas 77030 (United States)] [Department of Radiation Physics, Unit 1202, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030 and The University of Texas Graduate School of Biomedical Sciences at Houston, 6767 Bertner Avenue, Houston, Texas 77030 (United States)
2013-12-15
Purpose: Stray neutron radiation is of concern after radiation therapy, especially in children, because of the high risk it might carry for secondary cancers. Several previous studies predicted the stray neutron exposure from proton therapy, mostly using Monte Carlo simulations. Promising attempts to develop analytical models have also been reported, but these were limited to only a few proton beam energies. The purpose of this study was to develop an analytical model to predict leakage neutron equivalent dose from passively scattered proton beams in the 100-250-MeV interval.Methods: To develop and validate the analytical model, the authors used values of equivalent dose per therapeutic absorbed dose (H/D) predicted with Monte Carlo simulations. The authors also characterized the behavior of the mean neutron radiation-weighting factor, w{sub R}, as a function of depth in a water phantom and distance from the beam central axis.Results: The simulated and analytical predictions agreed well. On average, the percentage difference between the analytical model and the Monte Carlo simulations was 10% for the energies and positions studied. The authors found that w{sub R} was highest at the shallowest depth and decreased with depth until around 10 cm, where it started to increase slowly with depth. This was consistent among all energies.Conclusion: Simple analytical methods are promising alternatives to complex and slow Monte Carlo simulations to predict H/D values. The authors' results also provide improved understanding of the behavior of w{sub R} which strongly depends on depth, but is nearly independent of lateral distance from the beam central axis.
Simons, Jack
Hydrogen Atom Loss in Pyrimidine DNA Bases Induced by Low-Energy Electrons: Energetics Predicted In addition to inducing DNA strand breaks, low-energy electrons (LEEs) also have been shown to induce of a hydrogen atom from a DNA base-electron adduct initiates chemical modification of the base, which can cause
Model predictive adaptive control of process systems using recurrent neural networks
Parthasarathy, Sanjay
1993-01-01
) controller structure is used for the simulations. The feasibility of the approach is first demonstrated on a, piece-wise linearized model of the UTSG. It is found that the proposed model predictive adaptive PI controller significantly reduces the system set... Summary 41 41 42 45 49 53 54 V CASE-STUDY: THE U-TUBE STEAM GENERATOR LEVEL CONTROL PROBLEM WATER o6 V. 1 Introduction V. 2 Current Practice: The PID Controller 56 60 CHAPTER Page V. 3 Development of the Piece-wise Linearized Model ol...
PREDICTING WATER ACTIVITY IN ELECTROLYTE SOLUTIONS WITH THE CISTERNAS-LAM MODEL
REYNOLDS JG; GREER DA; DISSELKAMP RL
2011-03-01
Water activity is an important parameter needed to predict the solubility of hydrated salts in Hanford nuclear waste supernatants. A number of models available in the scientific literature predict water activity from electrolyte solution composition. The Cisternas-Lam model is one of those models and has several advantages for nuclear waste application. One advantage is that it has a single electrolyte specific parameter that is temperature independent. Thus, this parameter can be determined from very limited data and extrapolated widely. The Cisternas-Lam model has five coefficients that are used for all aqueous electrolytes. The present study aims to determine if there is a substantial improvement in making all six coefficients electrolyte specific. The Cisternas-Lam model was fit to data for six major electrolytes in Hanford nuclear waste supernatants. The model was first fit to all data to determine the five global coefficients, when they were held constant for all electrolytes it yielded a substantially better fit. Subsequently, the model was fit to each electrolyte dataset separately, where all six coefficients were allowed to be electrolyte specific. Treating all six coefficients as electrolyte specific did not make sufficient difference, given the complexity of applying the electrolyte specific parameters to multi-solute systems. Revised water specific parameters, optimized to the electrolytes relevant to Hanford waste, are also reported.
Model-based Prognostics with Concurrent Damage Progression Processes
Daigle, Matthew
and wear processes contribute to the overall component degradation. We develop a model- based prognostics-based model of a centrifugal pump that includes damage progression models, to which we apply our model Terms--model-based prognostics, particle filters, vari- ance control, centrifugal pumps I. INTRODUCTION
Wang, Chien.; Prinn, Ronald G.
The possible trends for atmospheric carbon monoxide in the next 100 yr have been illustrated using a coupled atmospheric chemistry and climate model driven by emissions predicted by a global economic development model. ...
Integration of GIS with Activity-Based Model in ATIS
Kwan, Mei-Po; Golledge, Reginald
1995-01-01
Integration of GIS with Activity-Based Model in ATIS Mei-Poregulation. Integration of GIS with Activity-Based Model inGrant UCTC Grant DTRS92-G-0009: GIS Data Model for DOT "A
Use of a biomechanical tongue model to predict the impact of tongue surgery on speech production
Buchaillard, Stéphanie; Perrier, Pascal; Payan, Yohan
2008-01-01
This paper presents predictions of the consequences of tongue surgery on speech production. For this purpose, a 3D finite element model of the tongue is used that represents this articulator as a deformable structure in which tongue muscles anatomy is realistically described. Two examples of tongue surgery, which are common in the treatment of cancers of the oral cavity, are modelled, namely a hemiglossectomy and a large resection of the mouth floor. In both cases, three kinds of possible reconstruction are simulated, assuming flaps with different stiffness. Predictions are computed for the cardinal vowels /i, a, u/ in the absence of any compensatory strategy, i.e. with the same motor commands as the one associated with the production of these vowels in non-pathological conditions. The estimated vocal tract area functions and the corresponding formants are compared to the ones obtained under normal conditions
An Elastic-Plastic and Strength Prediction Model for Injection-Molded Long-Fiber Thermoplastics
Nguyen, Ba Nghiep; Kunc, Vlastimil; Phelps, Jay; Tucker III, Charles L.; Bapanapalli, Satish K.
2008-09-01
This paper applies a recently developed model to predict the elastic-plastic stress/strain response and strength of injection-molded long-fiber thermoplastics (LFTs). The model combines a micro-macro constitutive modeling approach with experimental characterization and modeling of the composite microstructure to determine the composite stress/strain response and strength. Specifically, it accounts for elastic fibers embedded in a thermoplastic resin that exhibits the elastic-plastic behavior obeying the Ramberg-Osgood relation and J-2 deformation theory of plasticity. It also accounts for fiber length, orientation and volume fraction distributions in the composite formed by the injection-molding process. Injection-molded-long-glass-fiber/polypropylene (PP) specimens were prepared for mechanical characterization and testing. Fiber length, orientation, and volume fraction distributions were then measured at some selected locations for use in the computation. Fiber orientations in these specimens were also predicted using an anisotropic rotary diffusion model developed for LFTs. The stress-strain response of the as-formed composite was computed by an incremental procedure that uses the Eshelby’s equivalent inclusion method, the Mori-Tanaka assumption and a fiber orientation averaging technique. The model has been validated against the experimental stress-strain results obtained for these long-glass-fiber/PP specimens.
Energy Savings Through Application of Model Predictive Control to an Air Separation Facility
Hanson, T. C.; Scharf, P. F.
1996-01-01
signs for cryogenic air separation plants. Equally important is the adherence of operating conditions to their optimal values, a task assigned to the plant's control system. This paper addresses the application of Model Predictive Control (MPC... maintain the plant at an optimal operating state. REFERENCE 1. Daryanian, B., Bohn, R.E., and Tabors, R.D., "Op timal Demand-Side Response to Electricity Spot Prices for Storage-Type Customers", IEEE Transac tions on Power Systems, 4(3), 897...
A new thermodynamic model to predict wax deposition from crude oils
Loganathan, Narayanan
1993-01-01
. , 1926; Affens et al. , 1984), crystal morphology (Ferris and Cowles, 1945; Edwards, 1957), and physical properties of petroleum wax (Templin, 1956) have been studied in detail. Bem et ak (1980) studied wax deposition in North Sea submarine crude-oil...A NEW THERMODYNAMIC MODEL TO PREDICT WAX DEPOSITION FROM CRUDE OILS A Thesis by NARAYANAN LOGANATHAN Submitted to the Office of Graduate Studies of Texas A&M University in partial fullillment of the requirements for the degree of MASTER...
A comparison of general circulation model predictions to sand drift and dune orientations
Blumberg, D.G.; Greeley, R.
1996-12-01
The growing concern over climate change and decertification stresses the importance of aeolian process prediction. In this paper the use of a general circulation model to predict current aeolian features is examined. A GCM developed at NASA/Goddard Space Flight Center was used in conjunction with White`s aeolian sand flux model to produce a global potential aeolian transport map. Surface wind shear stress predictions were used from the output of a GCM simulation that was performed as part of the Atmospheric Model Intercomparison Project on 1979 climate conditions. The spatial resolution of this study (as driven by the GCM) is 4{degrees} X 5{degrees}; instantaneous 6-hourly wind stress data were saved by the GCM and used in this report. A global map showing potential sand transport was compared to drift potential directions as inferred from Landsat images from the 1980s for several sand seas and a coastal dune field. Generally, results show a good correlation between the simulated sand drift direction and the drift direction inferred for dune forms. Discrepancies between the drift potential and the drift inferred from images were found in the North American deserts and the Arabian peninsula. An attempt to predict the type of dune that would be formed in specific regions was not successful. The model could probably be further improved by incorporating soil moisture, surface roughness, and vegetation information for a better assessment of sand threshold conditions. The correlation may permit use of a GCM to analyze {open_quotes}fossil{close_quotes} dunes or to forecast aeolian processes. 48 refs., 8 figs.
Modeling of diffusive mass transport in micropores in cement based materials
Yamaguchi, Tetsuji, E-mail: yamaguchi.tetsuji@jaea.go.j [Japan Atomic Energy Agency, Shirakata, Tokai, Ibaraki 319-1195 (Japan); Negishi, Kumi [Japan Atomic Energy Agency, Shirakata, Tokai, Ibaraki 319-1195 (Japan); Taiheiyo Consultant Company Limited, 2-4-2, Osaku, Sakura, Chiba 285-8655 (Japan); Hoshino, Seiichi; Tanaka, Tadao [Japan Atomic Energy Agency, Shirakata, Tokai, Ibaraki 319-1195 (Japan)
2009-12-15
In order to predict long-term leaching behavior of cement constituents for safety assessments of radioactive waste disposal, we modeled diffusive mass transport in micropores in cement based materials. Based on available knowledge on the pore structure, we developed a transport porosity model that enables us to estimate effective porosity available for diffusion (transport porosity) in cement based materials. We microscopically examined the pore structure of hardened cement pastes to partially verify the model. Effective diffusivities of tritiated water in hardened cement pastes were also obtained experimentally, and were shown to be proportional to the estimated transport porosity.
Morris, Melody K
2012-01-01
Upon exposure to environmental cues, protein modifications form a complex signaling network that dictates cellular response. In this thesis, we develop methods for using continuous logic-based models to aide our understanding ...
Observational Tests and Predictive Stellar Evolution II: Non-standard Models
Patrick A. Young; David Arnett
2004-09-27
We examine contributions of second order physical processes to results of stellar evolution calculations amenable to direct observational testing. In the first paper in the series (Young et al. 2001) we established baseline results using only physics which are common to modern stellar evolution codes. In the current paper we establish how much of the discrepancy between observations and baseline models is due to particular elements of new physics. We then consider the impact of the observational uncertainties on the maximum predictive accuracy achievable by a stellar evolution code. The sun is an optimal case because of the precise and abundant observations and the relative simplicity of the underlying stellar physics. The Standard Model is capable of matching the structure of the sun as determined by helioseismology and gross surface observables to better than a percent. Given an initial mass and surface composition within the observational errors, and no additional constraints for which the models can be optimized, it is not possible to predict the sun's current state to better than ~7%. Convectively induced mixing in radiative regions, seen in multidimensional hydrodynamic simulations, dramatically improves the predictions for radii, luminosity, and apsidal motions of eclipsing binaries while simultaneously maintaining consistency with observed light element depletion and turnoff ages in young clusters (Young et al. 2003). Systematic errors in core size for models of massive binaries disappear with more complete mixing physics, and acceptable fits are achieved for all of the binaries without calibration of free parameters. The lack of accurate abundance determinations for binaries is now the main obstacle to improving stellar models using this type of test.
Developing algorithms for predicting protein-protein interactions of homology modeled proteins.
Martin, Shawn Bryan; Sale, Kenneth L.; Faulon, Jean-Loup Michel; Roe, Diana C.
2006-01-01
The goal of this project was to examine the protein-protein docking problem, especially as it relates to homology-based structures, identify the key bottlenecks in current software tools, and evaluate and prototype new algorithms that may be developed to improve these bottlenecks. This report describes the current challenges in the protein-protein docking problem: correctly predicting the binding site for the protein-protein interaction and correctly placing the sidechains. Two different and complementary approaches are taken that can help with the protein-protein docking problem. The first approach is to predict interaction sites prior to docking, and uses bioinformatics studies of protein-protein interactions to predict theses interaction site. The second approach is to improve validation of predicted complexes after docking, and uses an improved scoring function for evaluating proposed docked poses, incorporating a solvation term. This scoring function demonstrates significant improvement over current state-of-the art functions. Initial studies on both these approaches are promising, and argue for full development of these algorithms.
Haptics-based Volumetric Modeling Using Dynamic Spline-based Implicit Functions
Qin, Hong
Haptics-based Volumetric Modeling Using Dynamic Spline-based Implicit Functions Jing Hua Hong Qin-based volumetric modeling framework, which is founded upon volumetric implicit functions and powerful physics-based modeling. The volumetric implicit functions incorporate hierarchical B-splines, CSG-based functional
Bernard J. Wood Jonathan D. Blundy A predictive model for rare earth element partitioning
van Westrenen, Wim
of natural compositions. Propagating Dqf into the Brice model we obtain an expression for h3 o in terms and anhydrous silicate melt as a function of pressure , temperature and bulk composition . The model is based is the Young's Modulus of the site, is the gas constant and is in K. Values of iM2 obtained by ®tting
Model based control of a coke battery
Stone, P.M.; Srour, J.M.; Zulli, P. [BHP Research, Mulgrave (Australia). Melbourne Labs.; Cunningham, R.; Hockings, K. [BHP Steel, Pt Kembla, New South Wales (Australia). Coal and Coke Technical Development Group
1997-12-31
This paper describes a model-based strategy for coke battery control at BHP Steel`s operations in Pt Kembla, Australia. The strategy uses several models describing the battery thermal and coking behavior. A prototype controller has been installed on the Pt Kembla No. 6 Battery (PK6CO). In trials, the new controller has been well accepted by operators and has resulted in a clear improvement in battery thermal stability, with a halving of the standard deviation of average battery temperature. Along with other improvements to that battery`s operations, this implementation has contributed to a 10% decrease in specific battery energy consumption. A number of enhancements to the low level control systems on that battery are currently being undertaken in order to realize further benefits.
Machine Learning Based Online Performance Prediction for Runtime Parallelization and Task Scheduling
Li, J; Ma, X; Singh, K; Schulz, M; de Supinski, B R; McKee, S A
2008-10-09
With the emerging many-core paradigm, parallel programming must extend beyond its traditional realm of scientific applications. Converting existing sequential applications as well as developing next-generation software requires assistance from hardware, compilers and runtime systems to exploit parallelism transparently within applications. These systems must decompose applications into tasks that can be executed in parallel and then schedule those tasks to minimize load imbalance. However, many systems lack a priori knowledge about the execution time of all tasks to perform effective load balancing with low scheduling overhead. In this paper, we approach this fundamental problem using machine learning techniques first to generate performance models for all tasks and then applying those models to perform automatic performance prediction across program executions. We also extend an existing scheduling algorithm to use generated task cost estimates for online task partitioning and scheduling. We implement the above techniques in the pR framework, which transparently parallelizes scripts in the popular R language, and evaluate their performance and overhead with both a real-world application and a large number of synthetic representative test scripts. Our experimental results show that our proposed approach significantly improves task partitioning and scheduling, with maximum improvements of 21.8%, 40.3% and 22.1% and average improvements of 15.9%, 16.9% and 4.2% for LMM (a real R application) and synthetic test cases with independent and dependent tasks, respectively.
An Equilibrium-Based Model of Gas Reaction and Detonation
Trowbridge, L.D.
2000-04-01
During gaseous diffusion plant operations, conditions leading to the formation of flammable gas mixtures may occasionally arise. Currently, these could consist of the evaporative coolant CFC-114 and fluorinating agents such as F2 and ClF3. Replacement of CFC-114 with a non-ozone-depleting substitute is planned. Consequently, in the future, the substitute coolant must also be considered as a potential fuel in flammable gas mixtures. Two questions of practical interest arise: (1) can a particular mixture sustain and propagate a flame if ignited, and (2) what is the maximum pressure that can be generated by the burning (and possibly exploding) gas mixture, should it ignite? Experimental data on these systems, particularly for the newer coolant candidates, are limited. To assist in answering these questions, a mathematical model was developed to serve as a tool for predicting the potential detonation pressures and for estimating the composition limits of flammability for these systems based on empirical correlations between gas mixture thermodynamics and flammability for known systems. The present model uses the thermodynamic equilibrium to determine the reaction endpoint of a reactive gas mixture and uses detonation theory to estimate an upper bound to the pressure that could be generated upon ignition. The model described and documented in this report is an extended version of related models developed in 1992 and 1999.
A Simple Stand Growth Model Based on Canopy Dynamics and Biomechanics
Cao, Quang V.
A Simple Stand Growth Model Based on Canopy Dynamics and Biomechanics Thomas J. Dean, Mauricio be expressed in terms of wind drag. From this point of view, biomechanical principles determine the stem cross in a developing stand, biomechanics create a conceptual framework for predicting aboveground stem production
Soulami, Ayoub; Lavender, Curt A.; Paxton, Dean M.; Burkes, Douglas
2014-04-23
Pacific Northwest National Laboratory (PNNL) has been investigating manufacturing processes for the uranium-10% molybdenum (U-10Mo) alloy plate-type fuel for the U.S. high-performance research reactors. This work supports the Convert Program of the U.S. Department of Energy’s National Nuclear Security Administration (DOE/NNSA) Global Threat Reduction Initiative. This report documents modeling results of PNNL’s efforts to perform finite-element simulations to predict roll separating forces and rolling defects. Simulations were performed using a finite-element model developed using the commercial code LS-Dyna. Simulations of the hot rolling of U-10Mo coupons encapsulated in low-carbon steel have been conducted following two different schedules. Model predictions of the roll-separation force and roll-pack thicknesses at different stages of the rolling process were compared with experimental measurements. This report discusses various attributes of the rolled coupons revealed by the model (e.g., dog-boning and thickness non-uniformity).
Alyabes, Abdullah Fahad
2014-08-01
USING NON-UNIFORM MESH PARTITIONING BASED ON RAY DENSITY PREDICTION FOR THE PARALLEL WAVEFRONT CONSTRUCTION METHOD A Thesis by ABDULLAH FAHAD ALYABES Submitted to the Office of Graduate and Professional Studies of Texas A&M University in partial... Construction Method . . . . . . . . . . . . . . . . 4 2.3 Parallel Wavefront Construction Performance . . . . . . . . . . . . . 7 2.3.1 Wavefront Mesh Density Prediction . . . . . . . . . . . . . . . 10 2.3.2 Non-uniform Wavefront Mesh Partitioning...
Watney, W.L.
1994-12-01
Reservoirs in the Lansing-Kansas City limestone result from complex interactions among paleotopography (deposition, concurrent structural deformation), sea level, and diagenesis. Analysis of reservoirs and surface and near-surface analogs has led to developing a {open_quotes}strandline grainstone model{close_quotes} in which relative sea-level stabilized during regressions, resulting in accumulation of multiple grainstone buildups along depositional strike. Resulting stratigraphy in these carbonate units are generally predictable correlating to inferred topographic elevation along the shelf. This model is a valuable predictive tool for (1) locating favorable reservoirs for exploration, and (2) anticipating internal properties of the reservoir for field development. Reservoirs in the Lansing-Kansas City limestones are developed in both oolitic and bioclastic grainstones, however, re-analysis of oomoldic reservoirs provides the greatest opportunity for developing bypassed oil. A new technique, the {open_quotes}Super{close_quotes} Pickett crossplot (formation resistivity vs. porosity) and its use in an integrated petrophysical characterization, has been developed to evaluate extractable oil remaining in these reservoirs. The manual method in combination with 3-D visualization and modeling can help to target production limiting heterogeneities in these complex reservoirs and moreover compute critical parameters for the field such as bulk volume water. Application of this technique indicates that from 6-9 million barrels of Lansing-Kansas City oil remain behind pipe in the Victory-Northeast Lemon Fields. Petroleum geologists are challenged to quantify inferred processes to aid in developing rationale geologically consistent models of sedimentation so that acceptable levels of prediction can be obtained.
Biologically based multistage modeling of radiation effects
William Hazelton; Suresh Moolgavkar; E. Georg Luebeck
2005-08-30
This past year we have made substantial progress in modeling the contribution of homeostatic regulation to low-dose radiation effects and carcinogenesis. We have worked to refine and apply our multistage carcinogenesis models to explicitly incorporate cell cycle states, simple and complex damage, checkpoint delay, slow and fast repair, differentiation, and apoptosis to study the effects of low-dose ionizing radiation in mouse intestinal crypts, as well as in other tissues. We have one paper accepted for publication in ''Advances in Space Research'', and another manuscript in preparation describing this work. I also wrote a chapter describing our combined cell-cycle and multistage carcinogenesis model that will be published in a book on stochastic carcinogenesis models edited by Wei-Yuan Tan. In addition, we organized and held a workshop on ''Biologically Based Modeling of Human Health Effects of Low dose Ionizing Radiation'', July 28-29, 2005 at Fred Hutchinson Cancer Research Center in Seattle, Washington. We had over 20 participants, including Mary Helen Barcellos-Hoff as keynote speaker, talks by most of the low-dose modelers in the DOE low-dose program, experimentalists including Les Redpath (and Mary Helen), Noelle Metting from DOE, and Tony Brooks. It appears that homeostatic regulation may be central to understanding low-dose radiation phenomena. The primary effects of ionizing radiation (IR) are cell killing, delayed cell cycling, and induction of mutations. However, homeostatic regulation causes cells that are killed or damaged by IR to eventually be replaced. Cells with an initiating mutation may have a replacement advantage, leading to clonal expansion of these initiated cells. Thus we have focused particularly on modeling effects that disturb homeostatic regulation as early steps in the carcinogenic process. There are two primary considerations that support our focus on homeostatic regulation. First, a number of epidemiologic studies using multistage carcinogenesis models that incorporate the ''initiation, promotion, and malignant conversion'' paradigm of carcinogenesis are indicating that promotion of initiated cells is the most important cellular mechanism driving the shape of the age specific hazard for many types of cancer. Second, we have realized that many of the genes that are modified in early stages of the carcinogenic process contribute to one or more of four general cellular pathways that confer a promotional advantage to cells when these pathways are disrupted.
Towards a physics-based modelling of the electro-mechanical coupling in EAPs
Noy Cohen; Andreas Menzel; Gal deBotton
2015-02-03
Due to the increasing number of industrial applications of electro-active polymers (EAPs), there is a growing need for electromechanical models which accurately capture their behavior. To this end, we compare the predicted behavior of EAPs undergoing homogenous deformations according to three electromechanical models. The first model is a continuum based model composed of the mechanical Gent model and a linear relationship between the electric field and the polarization. The electrical and the mechanical responses according to the second model are based on the polymer microstructure, whereas the third model incorporates a neo-Hookean mechanical response and a microstructural based long-chains model for the electrical behavior. In the microstructural motivated models the integration from the microscopic to the macroscopic levels is accomplished by the micro-sphere technique. Four types of homogeneous boundary conditions are considered and the behaviors determined according to the three models are compared. The differences between the predictions of the models are discussed, highlighting the need for an in-depth investigation of the relations between the structure and the behaviors of the EAPs at microscopic level and their overall macroscopic response.
A Stochastic Reactor Based Virtual Engine Model Employing Detailed...
Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site
A Stochastic Reactor Based Virtual Engine Model Employing Detailed Chemistry for Kinetic Studies of In-Cylinder Combustion and Exhaust Aftertreatment A Stochastic Reactor Based...
Beltrami Energy Based Partitioning Model for Image Segmentation
An, Jing
2015-01-01
Beltrami Energy Based Partitioning Model for Imagemodel based on Beltrami energy, and made noticeable resultsMoreover, because Beltrami energy is fully constructed using
Model-Based Transient Calibration Optimization for Next Generation...
Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site
Based Transient Calibration Optimization for Next Generation Diesel Engines Model-Based Transient Calibration Optimization for Next Generation Diesel Engines 2005 Diesel Engine...
Carl H. Albright; S. Geer
2001-10-16
Within the framework of an SO(10) GUT model that can accommodate both the atmospheric and the LMA solar neutrino mixing solutions, we present explicit predictions for the neutrino oscillation parameters \\sin^2 2\\theta_{13}, \\sin^2 2\\theta_{12}, \\sin^2 2\\theta_{23}, and \\Delta m^2_{21}. Precise measurements of \\sin^2 2\\theta_{12} and \\Delta m^2_{21} by KamLAND can be used to precisely determine the GUT model parameters. We find that the model can then be tested at Neutrino Superbeams and Neutrino Factories with precision neutrino oscillation measurements of \\sin^2 2\\theta_{23}, \\sin^2 2\\theta_{13}, and the leptonic CP phase \\delta_{CP}.
Fronefield Crawford; Marek Demianski
2003-06-11
There are currently two well-accepted models that explain how pulsars exhibit glitches, sudden changes in their regular rotational spin-down. According to the starquake model, the glitch healing parameter, Q, which is measurable in some cases from pulsar timing, should be equal to the ratio of the moment of inertia of the superfluid core of a neutron star (NS) to its total moment of inertia. Measured values of the healing parameter from pulsar glitches can therefore be used in combination with realistic NS structure models as one test of the feasibility of the starquake model as a glitch mechanism. We have constructed NS models using seven representative equations of state of superdense matter to test whether starquakes can account for glitches observed in the Crab and Vela pulsars, for which the most extensive and accurate glitch data are available. We also present a compilation of all measured values of Q for Crab and Vela glitches to date which have been separately published in the literature. We have computed the fractional core moment of inertia for stellar models covering a range of NS masses and find that for stable NSs in the realistic mass range 1.4 +/- 0.2 solar masses, the fraction is greater than 0.55 in all cases. This range is not consistent with the observational restriction Q 0.7) are consistent with the starquake model predictions and support previous conclusions that starquakes can be the cause of Crab glitches.
Electrochemistry-based Battery Modeling for Prognostics Matthew Daigle1
Daigle, Matthew
Electrochemistry-based Battery Modeling for Prognostics Matthew Daigle1 and Chetan S. Kulkarni2 1- toring, diagnosis, and prognosis algorithms. In this work, we develop electrochemistry-based models
A Novel Analytical Model for Flexure-based Proportion Compliant
Li, Yangmin
A Novel Analytical Model for Flexure-based Proportion Compliant Mechanisms Qiaoling Meng Yangmin model for flexure-based proportion compliant mechanisms. The displacement and stiffness calculations geometric and material properties of the compliant mechanism. Displacement proportion, input stiffness
Physics-based statistical model and simulation method of RF propagation in urban environments
Pao, Hsueh-Yuan (San Jose, CA); Dvorak, Steven L. (Tucson, AZ)
2010-09-14
A physics-based statistical model and simulation/modeling method and system of electromagnetic wave propagation (wireless communication) in urban environments. In particular, the model is a computationally efficient close-formed parametric model of RF propagation in an urban environment which is extracted from a physics-based statistical wireless channel simulation method and system. The simulation divides the complex urban environment into a network of interconnected urban canyon waveguides which can be analyzed individually; calculates spectral coefficients of modal fields in the waveguides excited by the propagation using a database of statistical impedance boundary conditions which incorporates the complexity of building walls in the propagation model; determines statistical parameters of the calculated modal fields; and determines a parametric propagation model based on the statistical parameters of the calculated modal fields from which predictions of communications capability may be made.
Threshold Values for Identification of Contamination Predicted by Reduced-Order Models
DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)
Last, George V.; Murray, Christopher J.; Bott, Yi-Ju; Brown, Christopher F.
2014-12-31
The U.S. Department of Energy’s (DOE’s) National Risk Assessment Partnership (NRAP) Project is developing reduced-order models to evaluate potential impacts on underground sources of drinking water (USDWs) if CO2 or brine leaks from deep CO2 storage reservoirs. Threshold values, below which there would be no predicted impacts, were determined for portions of two aquifer systems. These threshold values were calculated using an interwell approach for determining background groundwater concentrations that is an adaptation of methods described in the U.S. Environmental Protection Agency’s Unified Guidance for Statistical Analysis of Groundwater Monitoring Data at RCRA Facilities.
DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)
Buceta, David; Tojo, Concha; Vukmirovic, Miomir B.; Deepak, F. Leonard; Lopez-Quintela, M. Arturo
2015-06-02
In this study, we present a theoretical model to predict the atomic structure of Au/Pt nanoparticles synthesized in microemulsions. Excellent concordance with the experimental results shows that the structure of the nanoparticles can be controlled at sub-nanometer resolution simply by changing the reactants concentration. The results of this study not only offer a better understanding of the complex mechanisms governing reactions in microemulsions, but open up a simple new way to synthesize bimetallic nanoparticles with ad-hoc controlled nanostructures.
PRACE Project Access for Seasonal Prediction with a high ResolUtion Climate modEl (SPRUCE)
climate variations six months ahead, by co mbining our best present tools and data with a high performance are of strong potential value, since society and key economic sectors (energy, agriculture, ...) have to base) and local predictability of temperature over Europe. We will then examine the predictability of summer heat
Istrail, Sorin
Lattice and Off-Lattice Side Chain Models of Protein Folding: Linear Time Structure Prediction This paper considers the protein structure prediction problem for lattice and off-lattice protein folding tools for reasoning about protein folding in unrestricted continuous space through anal- ogy. This paper
Baker, Jack W.
Regression models for predicting the probability of near-fault earthquake ground motion pulses to the earthquake magnitude, but other predictive parameters are also considered and discussed. Both empirical University, Stanford, CA, USA ABSTRACT: Near-fault earthquake ground motions containing large velocity pulses
California at Berkeley, University of
Discrepancies in the prediction of solar wind using potential field source surface model expansion factor (FTE) at the source surface and the solar wind speed (SWS) observed at Earth, which has been made use of in the prediction of solar wind speed near the Earth with reasonable accuracy. However
ModelBased Information Integration in a Neuroscience Mediator System
LudÃ¤scher, Bertram
ModelÂBased Information Integration in a Neuroscience Mediator System Bertram LudÂ¨ascher ? Amarnath
Broader source: Energy.gov [DOE]
Presentation given by Argonne National Laboratory at 2014 DOE Hydrogen and Fuel Cells Program and Vehicle Technologies Office Annual Merit Review and Peer Evaluation Meeting about trip prediction...
Modeling of stagnation-line nonequilibrium flows by means of quantum based collisional models
Munafò, A. Magin, T. E.
2014-09-15
The stagnation-line flow over re-entry bodies is analyzed by means of a quantum based collisional model which accounts for dissociation and energy transfer in N{sub 2}-N interactions. The physical model is based on a kinetic database developed at NASA Ames Research Center. The reduction of the kinetic mechanism is achieved by lumping the rovibrational energy levels of the N{sub 2} molecule in energy bins. The energy bins are treated as separate species, thus allowing for non-Boltzmann distributions of their populations. The governing equations are discretized in space by means of the Finite Volume method. A fully implicit time-integration is used to obtain steady-state solutions. The results show that the population of the energy bins strongly deviate from a Boltzmann distribution close to the shock wave and across the boundary layer. The sensitivity analysis to the number of energy bins reveals that accurate estimation of flow quantities (such as chemical composition and wall heat flux) can be obtained by using only 10 energy bins. A comparison with the predictions obtained by means of conventional multi-temperature models indicates that the former can lead to an overestimation of the wall heat flux, due to an inaccurate modeling of recombination in the boundary layer.
Bioenergetics-Based Modeling of Individual PCB Congeners in
McCarty, John P.
Bioenergetics-Based Modeling of Individual PCB Congeners in Nestling Tree Swallows from Two 14850 A bioenergetics-based model was used to simulate the accumulation of total PCBs and 20 PCB of sediment- associated contaminants to sediment-dwelling organisms. A bioenergetics-based model was developed
Institute for Software Technology Model-Based Testing
t Institute for Software Technology Model-Based Testing Ausgewählte Kapitel Softwaretechnologie 2 2013/14 B.K. Aichernig Model-Based Testing 1 / 38 #12;t Institute for Software Technology Testing-Based Testing 2 / 38 #12;t Institute for Software Technology Testing vs. Model Checking vs. Proving One proves
Kumaran, K.; Babu, V.
2009-04-15
In this numerical study, the influence of chemistry models on the predictions of supersonic combustion in a model combustor is investigated. To this end, 3D, compressible, turbulent, reacting flow calculations with a detailed chemistry model (with 37 reactions and 9 species) and the Spalart-Allmaras turbulence model have been carried out. These results are compared with earlier results obtained using single step chemistry. Hydrogen is used as the fuel and three fuel injection schemes, namely, strut, staged (i.e., strut and wall) and wall injection, are considered to evaluate the impact of the chemistry models on the flow field predictions. Predictions of the mass fractions of major species, minor species, dimensionless stagnation temperature, dimensionless static pressure rise and thrust percentage along the combustor length are presented and discussed. Overall performance metrics such as mixing efficiency and combustion efficiency are used to draw inferences on the nature (whether mixing- or kinetic-controlled) and the completeness of the combustion process. The predicted values of the dimensionless wall static pressure are compared with experimental data reported in the literature. The calculations show that multi step chemistry predicts higher and more wide spread heat release than what is predicted by single step chemistry. In addition, it is also shown that multi step chemistry predicts intricate details of the combustion process such as the ignition distance and induction distance. (author)
Kamal, Sameer A. (Sameer Ahmed)
2009-01-01
This thesis describes the use of a distributed hydrology model in conjunction with a Factor of Safety (FS) algorithm to predict dynamic landslide susceptibility for a humid basin in Puerto Rico. The Mameyes basin, located ...
Ko, Hanseo
1994-01-01
An analytical model is established to predict an electrostatically charged particle deposition as a function of particle size in fully-developed turbulent pipe flow. The convectivediffusion flux equation is solved for the particle concentration as a...
Wang, Jinrong
1996-01-01
-retrofit weather is generally different from the weather used for model development, the prediction error of the baseline model may be different from the fitting error. Daily and monthly baseline models were developed for a midsize commercial building with (i) dual...
Pushing with a physics-based model
Liu, Huan
2011-01-01
Humans often push when grasping or lifting is inconvenient or infeasible, because pushing requires fewer contacts and fights against only a fraction of the object's weight. However, pushing results are hard to predict, ...
Wu, Jiun-Yu
2011-10-21
researcher can either use the ad-hoc robust sandwich standard error estimators to correct the standard error estimates (Design-based approach) or perform multilevel analysis to model the multilevel data structure (Model-based approach) to analyze dependent...
Muendej, Krisanee
2004-11-15
Thirty convenience stores in College Station, Texas, have been selected as the samples for an energy consumption prediction. The predicted models assist facility energy managers for making decisions of energy demand/supply plans. The models...
Real-time capable first principle based modelling of tokamak turbulent transport
Breton, S; Felici, F; Imbeaux, F; Aniel, T; Artaud, J F; Baiocchi, B; Bourdelle, C; Camenen, Y; Garcia, J
2015-01-01
A real-time capable core turbulence tokamak transport model is developed. This model is constructed from the regularized nonlinear regression of quasilinear gyrokinetic transport code output. The regression is performed with a multilayer perceptron neural network. The transport code input for the neural network training set consists of five dimensions, and is limited to adiabatic electrons. The neural network model successfully reproduces transport fluxes predicted by the original quasilinear model, while gaining five orders of magnitude in computation time. The model is implemented in a real-time capable tokamak simulator, and simulates a 300s ITER discharge in 10s. This proof-of-principle for regression based transport models anticipates a significant widening of input space dimensionality and physics realism for future training sets. This aims to provide unprecedented computational speed coupled with first-principle based physics for real-time control and integrated modelling applications.
A market-power based model of business groups
Feenstra, Robert C; Huang, D S; Hamilton, G G
2003-01-01
complicated. In our model, business groups not only sellof Indian groups. 3. A Model of Business Groups We willa market-power based model of business groups. This We
Multiple Damage Progression Paths in Model-based Prognostics
Daigle, Matthew
the use of physics-based models. Compo- nent wear is driven by several different degradation phenom- ena uncertainty. We construct a detailed physics-based model of a centrifugal pump, to which we apply our model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2 PROGNOSTICS APPROACH . . . . . . . . . . . . . . . . . . . . . . . . 2 3 PUMP MODELING
Prediction-Based Recovery from Link Outages in On-Board Mobile Communication Networks
Libman, Lavy
, such as those pro- posed for (and employed in) public transport vehicles, users are connected to a local network transport routes, and their repetitive nature, allows a certain degree of prediction of impending link extension known as Freeze-TCP, we study how the performance of the protocol depends on the outage prediction
Stochastic Modeling and Simulation of Ground Motions for Performance-Based Earthquake Engineering
Rezaeian, Sanaz
2010-01-01
practice as predictions of future earthquake ground motionsprediction of the model parameters if the earthquake andearthquake and site characteristics is viable and consistent with existing prediction
Lall, Pradeep [Auburn Univ., Auburn, AL (United States); Wei, Junchao [Auburn Univ., Auburn, AL (United States); Sakalaukus, Peter [Auburn Univ., Auburn, AL (United States)
2014-06-22
A new method has been developed for assessment of the onset of degradation in solid state luminaires to classify failure mechanisms by using metrics beyond lumen degradation that are currently used for identification of failure. Luminous Flux output, Correlated Color Temperature Data on Philips LED Lamps has been gathered under 85°C/85%RH till lamp failure. Failure modes of the test population of the lamps have been studied to understand the failure mechanisms in 85°C/85%RH accelerated test. Results indicate that the dominant failure mechanism is the discoloration of the LED encapsulant inside the lamps which is the likely cause for the luminous flux degradation and the color shift. The acquired data has been used in conjunction with Bayesian Probabilistic Models to identify luminaires with onset of degradation much prior to failure through identification of decision boundaries between lamps with accrued damage and lamps beyond the failure threshold in the feature space. In addition luminaires with different failure modes have been classified separately from healthy pristine luminaires. The ?-? plots have been used to evaluate the robustness of the proposed methodology. Results show that the predicted degradation for the lamps tracks the true degradation observed during 85°C/85%RH during accelerated life test fairly closely within the ±20% confidence bounds. Correlation of model prediction with experimental results indicates that the presented methodology allows the early identification of the onset of failure much prior to development of complete failure distributions and can be used for assessing the damage state of SSLs in fairly large deployments. It is expected that, the new prediction technique will allow the development of failure distributions without testing till L70 life for the manifestation of failure.
SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity
Zhao, Qingyuan; He, Hera Y; Rajaraman, Anand; Leskovec, Jure
2015-01-01
Social networking websites allow users to create and share content. Big information cascades of post resharing can form as users of these sites reshare others' posts with their friends and followers. One of the central challenges in understanding such cascading behaviors is in forecasting information outbreaks, where a single post becomes widely popular by being reshared by many users. In this paper, we focus on predicting the final number of reshares of a given post. We build on the theory of self-exciting point processes to develop a statistical model that allows us to make accurate predictions. Our model requires no training or expensive feature engineering. It results in a simple and efficiently computable formula that allows us to answer questions, in real-time, such as: Given a post's resharing history so far, what is our current estimate of its final number of reshares? Is the post resharing cascade past the initial stage of explosive growth? And, which posts will be the most reshared in the future? We...
Phillips, T J; Potter, G L; Williamson, D L; Cederwall, R T; Boyle, J S; Fiorino, M; Hnilo, J J; Olson, J G; Xie, S; Yio, J J
2004-05-06
To significantly improve the simulation of climate by general circulation models (GCMs), systematic errors in representations of relevant processes must first be identified, and then reduced. This endeavor demands that the GCM parameterizations of unresolved processes, in particular, should be tested over a wide range of time scales, not just in climate simulations. Thus, a numerical weather prediction (NWP) methodology for evaluating model parameterizations and gaining insights into their behavior may prove useful, provided that suitable adaptations are made for implementation in climate GCMs. This method entails the generation of short-range weather forecasts by a realistically initialized climate GCM, and the application of six-hourly NWP analyses and observations of parameterized variables to evaluate these forecasts. The behavior of the parameterizations in such a weather-forecasting framework can provide insights on how these schemes might be improved, and modified parameterizations then can be tested in the same framework. In order to further this method for evaluating and analyzing parameterizations in climate GCMs, the U.S. Department of Energy is funding a joint venture of its Climate Change Prediction Program (CCPP) and Atmospheric Radiation Measurement (ARM) Program: the CCPP-ARM Parameterization Testbed (CAPT). This article elaborates the scientific rationale for CAPT, discusses technical aspects of its methodology, and presents examples of its implementation in a representative climate GCM.
Validation of model based active control of combustion instability
Fleifil, M.; Ghoneim, Z.; Ghoniem, A.F.
1998-07-01
The demand for efficient, company and clean combustion systems have spurred research into the fundamental mechanisms governing their performance and means of interactively changing their performance characteristics. Thermoacoustic instability which is frequently observed in combustion systems with high power density, when burning close to the lean flammability limit, or using exhaust gas recirculation to meet more stringent emissions regulations, etc. Its occurrence and/or means to mitigate them passively lead to performance degradation such as reduced combustion efficiency, high local heat transfer rates, increase in the mixture equivalence ratio or system failure due to structural damage. This paper reports on their study of the origin of thermoacoustic instability, its dependence on system parameters and the means of actively controlling it. The authors have developed an analytical model of thermoacoustic instability in premixed combustors. The model combines a heat release dynamics model constructed using the kinematics of a premixed flame stabilized behind a perforated plate with the linearized conservation equations governing the system acoustics. This formulation allows model based controller design. In order to test the performance of the analytical model, a numerical solution of the partial differential equations governing the system has been carried out using the principle of harmonic separation and focusing on the dominant unstable mode. This leads to a system of ODEs governing the thermofluid variables. Analytical predictions of the frequency and growth ate of the unstable mode are shown to be in good agreement with the numerical simulations as well s with those obtained using experimental identification techniques when applied to a laboratory combustor. The authors use these results to confirm the validity of the assumptions used in formulating the analytical model. A controller based on the minimization of a cost function using the LQR technique has been designed using the analytical model and implemented on a bench top laboratory combustor. The authors show that the controller is capable of suppressing the pressure oscillations in the combustor with a settling time much shorter than what had been attained before and without exciting secondary peaks.
Stress-induced patterns in ion-irradiated Silicon: a model based on anisotropic plastic flow
Scott A. Norris
2012-07-24
We present a model for the effect of stress on thin amorphous films that develop atop ion-irradiated silicon, based on the mechanism of ion-induced anisotropic plastic flow. Using only parameters directly measured or known to high accuracy, the model exhibits remarkably good agreement with the wavelengths of experimentally-observed patterns, and agrees qualitatively with limited data on ripple propagation speed. The predictions of the model are discussed in the context of other mechanisms recently theorized to explain the wavelengths, including extensive comparison with an alternate model of stress.
QoS prediction for web service compositions using kernel-based quantile estimation with online, such as WS-BPEL,2 focus on combining web services into aggregate services that satisfy the needs of clients planning web service can be created by composing services for hotel booking, airline booking, payment, etc
McCalley, James D.
A Predictive Maintenance Policy Based on the Blade of Offshore Wind Turbine Wenjin Zhu, Troyes onshore to offshore locations [1]. As offshore wind turbines are located at remote sites withlimited]. Operation and maintenance (O&M) costs of off-shore wind turbines contribute about 25-30% to the total energy
Thompson, Paul
Personalized prediction of brain fiber integrity in 396 young adults based on genotyping-derived measures may be useful for early, personalized risk assessment of impaired brain integrity. #12;#12; matter fiber integrity in the living brain, as measured by diffusion tensor imaging (DTI). We considered
Casillas, Danielle Courtney
2015-01-01
Angeles Ceria based inverse opals for thermochemical fuelCeria based inverse opals for thermochemical fuelCeria- based inverse opals are currently being investigated
Munoz-Cobo, Jose Luis [Universidad Politecnica de Valencia (Spain); Palomo, Maria Jose [Universidad Politecnica de Valencia (Spain); Herranz, Luis Enrique [CIEMAT (Spain)
2001-04-15
A mechanistic model is presented to predict the steam condensation on containment finned tubes in the presence of noncondensables (NCs). The total thermal resistance from bulk gas to coolant is formulated as a parallel combination of the convective and condensation gas resistances coupled in series to those of the condensate layer, the wall, and the coolant.The condensate layer thermal resistance is calculated by means of an Adamek-based model, while the gas mixture thermal resistance is formulated based on diffusion layer modeling.The model results are compared with the available experimental data of Wanniarachi and Rose for pure steam condensation on finned tubes and with the data of Mazzochi for condensation in the presence of NC gases.
Dutton, Robert W.
1 of 4 Abstract-- This paper presents a stochastic model for the observed signal of biosensors-based biosensors. The effects of scaling from macroscopic to microscopic regimes are also studied, which indicate to analyze the behavior of a DNA hybridization electronic detector1. Keywords--Biosensor, noise, scaling, DNA
Bittle, Joshua A.; Gao, Zhiming; Jacobs, Timothy J.
2013-01-01
A pseudo-multi-zone phenomenological model has been created with the ultimate goal of supporting efforts to enable broader commercialization of low temperature combustion modes in diesel engines. The benefits of low temperature combustion are the simultaneous reduction in soot and nitric oxide emissions and increased engine efficiency if combustion is properly controlled. Determining what qualifies as low temperature combustion for any given engine can be difficult without expensive emissions analysis equipment. This determination can be made off-line using computer models or through factory calibration procedures. This process could potentially be simplified if a real-time prediction model could be implemented to run for any engine platform this is the motivation for this study. The major benefit of this model is the ability for it to predict the combustion trajectory, i.e. local temperature and equivalence ratio in the burning zones. The model successfully captures all the expected trends based on the experimental data and even highlights an opportunity for simply using the average reaction temperature and equivalence ratio as an indicator of emissions levels alone - without solving formation sub-models. This general type of modeling effort is not new, but a major effort was made to minimize the calculation duration to enable implementation as an input to real-time next-cycle engine controller Instead of simply using the predicted engine out soot and NOx levels, control decisions could be made based on the trajectory. This has the potential to save large amounts of calibration time because with minor tuning (the model has only one automatically determined constant) it is hoped that the control algorithm would be generally applicable.
Model-Based Clustering for Expression Data via a Dirichlet Process Mixture Model
Dahl, David B.
10 Model-Based Clustering for Expression Data via a Dirichlet Process Mixture Model DAVID B. DAHL data based on a well-defined statistical model, specifically, a conjugate Dirichlet process mixture are generally not known. Model-based clustering procedures have been proposed for microarray data, including (1
Dr. Binh T. Pham; Grant L. Hawkes; Jeffrey J. Einerson
2012-10-01
As part of the Research and Development program for Next Generation High Temperature Reactors (HTR), a series of irradiation tests, designated as Advanced Gas-cooled Reactor (AGR), have been defined to support development and qualification of fuel design, fabrication process, and fuel performance under normal operation and accident conditions. The AGR tests employ fuel compacts placed in a graphite cylinder shrouded by a steel capsule and instrumented with thermocouples (TC) embedded in graphite blocks enabling temperature control. The data representing the crucial test fuel conditions (e.g., temperature, neutron fast fluence, and burnup) while impossible to obtain from direct measurements are calculated by physics and thermal models. The irradiation and post-irradiation examination (PIE) experimental data are used in model calibration effort to reduce the inherent uncertainty of simulation results. This paper is focused on fuel temperature predicted by the ABAQUS code’s finite element-based thermal models. The work follows up on a previous study, in which several statistical analysis methods were adapted, implemented in the NGNP Data Management and Analysis System (NDMAS), and applied for improving qualification of AGR-1 thermocouple data. The present work exercises the idea that the abnormal trends of measured data observed from statistical analysis may be caused by either measuring instrument deterioration or physical mechanisms in capsules that may have shifted the system thermal response. As an example, the uneven reduction of the control gas gap in Capsule 5 revealed by the capsule metrology measurements in PIE helps justify the reduction in TC readings instead of TC drift. This in turn prompts modification of thermal model to better fit with experimental data, thus help increase confidence, and in other word reduce model uncertainties in thermal simulation results of the AGR-1 test.
Modeling Heavy/Medium-Duty Fuel Consumption Based on Drive Cycle Properties
Wang, Lijuan; Duran, Adam; Gonder, Jeffrey; Kelly, Kenneth
2015-10-13
This paper presents multiple methods for predicting heavy/medium-duty vehicle fuel consumption based on driving cycle information. A polynomial model, a black box artificial neural net model, a polynomial neural network model, and a multivariate adaptive regression splines (MARS) model were developed and verified using data collected from chassis testing performed on a parcel delivery diesel truck operating over the Heavy Heavy-Duty Diesel Truck (HHDDT), City Suburban Heavy Vehicle Cycle (CSHVC), New York Composite Cycle (NYCC), and hydraulic hybrid vehicle (HHV) drive cycles. Each model was trained using one of four drive cycles as a training cycle and the other three as testing cycles. By comparing the training and testing results, a representative training cycle was chosen and used to further tune each method. HHDDT as the training cycle gave the best predictive results, because HHDDT contains a variety of drive characteristics, such as high speed, acceleration, idling, and deceleration. Among the four model approaches, MARS gave the best predictive performance, with an average absolute percent error of -1.84% over the four chassis dynamometer drive cycles. To further evaluate the accuracy of the predictive models, the approaches were first applied to real-world data. MARS outperformed the other three approaches, providing an average absolute percent error of -2.2% of four real-world road segments. The MARS model performance was then compared to HHDDT, CSHVC, NYCC, and HHV drive cycles with the performance from Future Automotive System Technology Simulator (FASTSim). The results indicated that the MARS method achieved a comparative predictive performance with FASTSim.
Prediction Space Weather Using an Asymmetric Cone Model for Halo CMEs
G. Michalek; N. Gopalswamy; S. Yashiro
2007-10-24
Halo coronal mass ejections (HCMEs) are responsible of the most severe geomagnetic storms. A prediction of their geoeffectiveness and travel time to Earth's vicinity is crucial to forecast space weather. Unfortunately coronagraphic observations are subjected to projection effects and do not provide true characteristics of CMEs. Recently, Michalek (2006, {\\it Solar Phys.}, {\\bf237}, 101) developed an asymmetric cone model to obtain the space speed, width and source location of HCMEs. We applied this technique to obtain the parameters of all front-sided HCMEs observed by the SOHO/LASCO experiment during a period from the beginning of 2001 until the end of 2002 (solar cycle 23). These parameters were applied for the space weather forecast. Our study determined that the space speeds are strongly correlated with the travel times of HCMEs within Earth's vicinity and with the magnitudes related to geomagnetic disturbances.
Watney, W.L.
1992-01-01
Interdisciplinary studies of the Upper Pennsylvanian Lansing and Kansas City groups have been undertaken in order to improve the geologic characterization of petroleum reservoirs and to develop a quantitative understanding of the processes responsible for formation of associated depositional sequences. To this end, concepts and methods of sequence stratigraphy are being used to define and interpret the three-dimensional depositional framework of the Kansas City Group. The investigation includes characterization of reservoir rocks in oil fields in western Kansas, description of analog equivalents in near-surface and surface sites in southeastern Kansas, and construction of regional structural and stratigraphic framework to link the site specific studies. Geologic inverse and simulation models are being developed to integrate quantitative estimates of controls on sedimentation to produce reconstructions of reservoir-bearing strata in an attempt to enhance our ability to predict reservoir characteristics.
Vessel tractography using an intensity based tensor model
Yanikoglu, Berrin
Vessel tractography using an intensity based tensor model Suheyla Cetin1 , Gozde Unal1 , Ali Demir1 method, which is based on an intensity-based tensor that fits to a vessel. Our model is initialized with a single seed point and it is ca- pable of capturing whole vessel tree by an automatic branch detection
Net Balanced Floorplanning Based on Elastic Energy Model
Nannarelli, Alberto
Net Balanced Floorplanning Based on Elastic Energy Model Wei Liu and Alberto Nannarelli Dept variations can introduce extra signal skew, it is desirable to have floorplans with balanced net delays based on the elastic energy model. The B*-tree, which is based on an ordered binary tree, is used
Development of a model for predicting transient hydrogen venting in 55-gallon drums
Apperson, Jason W; Clemmons, James S; Garcia, Michael D; Sur, John C; Zhang, Duan Z; Romero, Michael J
2008-01-01
Remote drum venting was performed on a population of unvented high activity drums (HAD) in the range of 63 to 435 plutonium equivalent Curies (PEC). These 55-gallon Transuranic (TRU) drums will eventually be shipped to the Waste Isolation Pilot Plant (WIPP). As a part of this process, the development of a calculational model was required to predict the transient hydrogen concentration response of the head space and polyethylene liner (if present) within the 55-gallon drum. The drum and liner were vented using a Remote Drum Venting System (RDVS) that provided a vent sampling path for measuring flammable hydrogen vapor concentrations and allow hydrogen to diffuse below lower flammability limit (LFL) concentrations. One key application of the model was to determine the transient behavior of hydrogen in the head space, within the liner, and the sensitivity to the number of holes made in the liner or number of filters. First-order differential mass transport equations were solved using Laplace transformations and numerically to verify the results. the Mathematica 6.0 computing tool was also used as a validation tool and for examining larger than two chamber systems. Results will be shown for a variety of configurations, including 85-gallon and 110-gallon overpack drums. The model was also validated against hydrogen vapor concentration assay measurements.
Duffy, Stephen
2013-09-09
This project will implement inelastic constitutive models that will yield the requisite stress-strain information necessary for graphite component design. Accurate knowledge of stress states (both elastic and inelastic) is required to assess how close a nuclear core component is to failure. Strain states are needed to assess deformations in order to ascertain serviceability issues relating to failure, e.g., whether too much shrinkage has taken place for the core to function properly. Failure probabilities, as opposed to safety factors, are required in order to capture the bariability in failure strength in tensile regimes. The current stress state is used to predict the probability of failure. Stochastic failure models will be developed that can accommodate possible material anisotropy. This work will also model material damage (i.e., degradation of mechanical properties) due to radiation exposure. The team will design tools for components fabricated from nuclear graphite. These tools must readily interact with finite element software--in particular, COMSOL, the software algorithm currently being utilized by the Idaho National Laboratory. For the eleastic response of graphite, the team will adopt anisotropic stress-strain relationships available in COMSO. Data from the literature will be utilized to characterize the appropriate elastic material constants.
Model-based Enhancement of Lighting Conditions in Image Sequences
Girod, Bernd
Model-based Enhancement of Lighting Conditions in Image Sequences Peter Eisert and Bernd Girod model-based technique for estimating and manipulating the lighting in an image sequence. The current scene lighting is estimated for each frame exploiting 3-D model information and by synthetic re-lighting
A Denotational Model for Component-Based Risk Analysis
Stølen, Ketil
A Denotational Model for Component-Based Risk Analysis Gyrd Brændeland1,2, , Atle Refsdal2 by traditional risk analysis methods. This paper ad- dresses this problem from a theoretical perspective by proposing a deno- tational model for component-based risk analysis. In order to model the probabilistic
Fuel Cell System Improvement for Model-Based Diagnosis Analysis
Paris-Sud XI, Université de
Fuel Cell System Improvement for Model-Based Diagnosis Analysis Philippe Fiani & Michel Batteux of a model of a fuel cell system, in order to make it usable for model- based diagnosis methods. A fuel cell for the fuel cell stack but also for the system environment. In this paper, we present an adapted library which
Zhang, Pengpeng; Yorke, Ellen; Hu, Yu-Chi; Mageras, Gig; Rimner, Andreas; Deasy, Joseph O.
2014-02-01
Purpose: We hypothesized that a treatment planning technique that incorporates predicted lung tumor regression into optimization, predictive treatment planning (PTP), could allow dose escalation to the residual tumor while maintaining coverage of the initial target without increasing dose to surrounding organs at risk (OARs). Methods and Materials: We created a model to estimate the geometric presence of residual tumors after radiation therapy using planning computed tomography (CT) and weekly cone beam CT scans of 5 lung cancer patients. For planning purposes, we modeled the dynamic process of tumor shrinkage by morphing the original planning target volume (PTV{sub orig}) in 3 equispaced steps to the predicted residue (PTV{sub pred}). Patients were treated with a uniform prescription dose to PTV{sub orig}. By contrast, PTP optimization started with the same prescription dose to PTV{sub orig} but linearly increased the dose at each step, until reaching the highest dose achievable to PTV{sub pred} consistent with OAR limits. This method is compared with midcourse adaptive replanning. Results: Initial parenchymal gross tumor volume (GTV) ranged from 3.6 to 186.5 cm{sup 3}. On average, the primary GTV and PTV decreased by 39% and 27%, respectively, at the end of treatment. The PTP approach gave PTV{sub orig} at least the prescription dose, and it increased the mean dose of the true residual tumor by an average of 6.0 Gy above the adaptive approach. Conclusions: PTP, incorporating a tumor regression model from the start, represents a new approach to increase tumor dose without increasing toxicities, and reduce clinical workload compared with the adaptive approach, although model verification using per-patient midcourse imaging would be prudent.
Draxl, C.; Churchfield, M.; Mirocha, J.; Lee, S.; Lundquist, J.; Michalakes, J.; Moriarty, P.; Purkayastha, A.; Sprague, M.; Vanderwende, B.
2014-06-01
Wind plant aerodynamics are influenced by a combination of microscale and mesoscale phenomena. Incorporating mesoscale atmospheric forcing (e.g., diurnal cycles and frontal passages) into wind plant simulations can lead to a more accurate representation of microscale flows, aerodynamics, and wind turbine/plant performance. Our goal is to couple a numerical weather prediction model that can represent mesoscale flow [specifically the Weather Research and Forecasting model] with a microscale LES model (OpenFOAM) that can predict microscale turbulence and wake losses.
Model-based Opponent Modelling in Domains Beyond the Prisoner's Dilemma Collin Rogowski
Kaminka, Gal A.
Model-based Opponent Modelling in Domains Beyond the Prisoner's Dilemma Collin Rogowski University of the model-based opponent modelling algorithm it-us-l* for domains be- yond the prisoner's dilemma- nent modelling algorithms are almost exclusivly done for the iterated prisoner's dilemma game (PD) [2
Prowell, I.; Robertson, A.; Jonkman, J.; Stewart, G. M.; Goupee, A. J.
2013-01-01
Realizing the critical importance the role physical experimental tests play in understanding the dynamics of floating offshore wind turbines, the DeepCwind consortium conducted a one-fiftieth-scale model test program where several floating wind platforms were subjected to a variety of wind and wave loading condition at the Maritime Research Institute Netherlands wave basin. This paper describes the observed behavior of a tension-leg platform, one of three platforms tested, and the systematic effort to predict the measured response with the FAST simulation tool using a model primarily based on consensus geometric and mass properties of the test specimen.
Lubliner, Howard
2011-12-31
While there have been numerous previous studies performed to develop the rural two-lane segment crash prediction models as part of the Highway Safety Manual (HSM), no previous study has been developed to validate the accuracy of the current model...
Logistic Growth Logistic growth is a simple model for predicting the size y(t) of a population as a
Feldman, Joel
Logistic Growth Logistic growth is a simple model for predicting the size y(t) of a population the differential equation y (t) = by(t) Logistic growth adds one more wrinkle to this model. It assumes available to each member decreases. This in turn causes the net birth rate b to decrease. In the logistic
Chen, Brian Y.
VASP-S: A Volumetric Analysis and Statistical Model for Predicting Steric Influences on Protein, are harder to find. To assist this process, we present VASP-S (Volumetric Analysis of Surface Properties with Statistics), an unsupervised volumetric analysis and statistical model for isolating statistically
Romero, Vicente Jose
2011-11-01
This report explores some important considerations in devising a practical and consistent framework and methodology for utilizing experiments and experimental data to support modeling and prediction. A pragmatic and versatile 'Real Space' approach is outlined for confronting experimental and modeling bias and uncertainty to mitigate risk in modeling and prediction. The elements of experiment design and data analysis, data conditioning, model conditioning, model validation, hierarchical modeling, and extrapolative prediction under uncertainty are examined. An appreciation can be gained for the constraints and difficulties at play in devising a viable end-to-end methodology. Rationale is given for the various choices underlying the Real Space end-to-end approach. The approach adopts and refines some elements and constructs from the literature and adds pivotal new elements and constructs. Crucially, the approach reflects a pragmatism and versatility derived from working many industrial-scale problems involving complex physics and constitutive models, steady-state and time-varying nonlinear behavior and boundary conditions, and various types of uncertainty in experiments and models. The framework benefits from a broad exposure to integrated experimental and modeling activities in the areas of heat transfer, solid and structural mechanics, irradiated electronics, and combustion in fluids and solids.
A test of an expert-based bird-habitat relationship model in South Carolina.
Kilgo, John, C.; Gartner, David, L.; Chapman, Brian, R.; Dunning, John, B., Jr.; Franzreb, Kathleen, E.; Gauthreaux, Sidney, A.; Greenberg, Catheryn, H.; Levey, Douglas, J.; Miller, Karl, V.; Pearson, Scott, F.
2002-01-01
Wildlife-habitat relationships models are used widely by land managers to provide information on which species are likely to occur in an area of interest and may be impacted by a proposed management activity. Few such models have been tested. Recent Avian census data from the Savannah River Site, South Carolina was used to validate BIRDHAB, a geographic information system (GIS) model developed by United States Forest Service resource managers to predict relative habitat quality for birds at the stand level on national forests in the southeastern United States. BIRDHAB is based on the species-habitat matrices presented by Hamel (1992).
Three predictions on July 2012 Federal Elections in Mexico based on past regularities
Hernández-Saldaña, H
2012-01-01
Electoral systems are subject of study for physicist and mathematicians in last years given place to a new area: sociophysics. Based on previous works of the author on the Mexican electoral processes in the new millennium, he found three characteristics appearing along the 2000 and 2006 preliminary dataset offered by the electoral authorities, named PREP: I) Error distributions are not Gaussian or Lorentzian, they are characterized for power laws at the center and asymmetric lobes at each side. II) The Partido Revolucionario Institucional (PRI) presented a change in the slope of the percentage of votes obtained when it go beyond the 70% of processed certificates; hence it have an improvement at the end of the electoral computation. III) The distribution of votes for the PRI is a smooth function well described by Daisy model distributions of rank $r$ in all the analyzed cases, presidential and congressional elections in 2000, 2003 and 2006. If all these characteristics are proper of the Mexican reality they sh...
Identifying at-risk employees: A behavioral model for predicting potential insider threats
Greitzer, Frank L.; Kangas, Lars J.; Noonan, Christine F.; Dalton, Angela C.
2010-09-01
A psychosocial model was developed to assess an employee’s behavior associated with an increased risk of insider abuse. The model is based on case studies and research literature on factors/correlates associated with precursor behavioral manifestations of individuals committing insider crimes. In many of these crimes, managers and other coworkers observed that the offenders had exhibited signs of stress, disgruntlement, or other issues, but no alarms were raised. Barriers to using such psychosocial indicators include the inability to recognize the signs and the failure to record the behaviors so that they could be assessed by a person experienced in psychosocial evaluations. We have developed a model using a Bayesian belief network with the help of human resources staff, experienced in evaluating behaviors in staff. We conducted an experiment to assess its agreement with human resources and management professionals, with positive results. If implemented in an operational setting, the model would be part of a set of management tools for employee assessment that can raise an alarm about employees who pose higher insider threat risks. In separate work, we combine this psychosocial model’s assessment with computer workstation behavior to raise the efficacy of recognizing an insider crime in the making.
Fox, Jared B.; Ghan, Steven J.
2004-03-01
Assessments of the effects of climate change typically require information at scales of 10 km or less. In regions with complex terrain, much of the spatial variability in climate (temperature, precipitation, and snow water) occurs on scales below 10 km. Since the typical global climate model simulations grid size is more than 200 km, it is necessary to develop models with much higher resolution. Unfortunately, no datasets currently produced are both highly accurate and provide data at a sufficiently high resolution. As a result, current global climate models are forced to ignore the important climate variations that occur below the 200 km scale. This predicament prompted the creation of a global hybrid dataset with information for precipitation, temperature, and relative humidity. The resulting dataset illustrated the importance of having high-resolution datasets and gives clear proof that regions with complex terrain require a fine resolution grid to give an accurate represent at ion of their climatology. For example, the Andes Mountains in Chile cause a temperature shift of more than 25C within the same area as a single 2.5 grid cell from the NCEP dataset. Fortunately the CRU, U.D., GPCP, and NCEP datasets, when hybridized, are able to provide both precision and satisfactory resolution with global coverage. This composite will enable the development of both high-resolution models and quality empirical downscaling methods--both of which are necessary for scientists to more accurately predict the effects of global climate change. Without accurate long-term forecasts, climatologists and policy makers will not have the tools they need to effectively reduce the negative effects human activity have on the earth.
Garten Jr, Charles T [ORNL
2012-01-01
Abstract. A simple, multi-compartment model was developed to predict soil carbon sequestration beneath switchgrass (Panicum virgatum) plantations in the southeastern United States. Soil carbon sequestration is an important component of sustainable switchgrass production for bioenergy because soil organic matter promotes water retention, nutrient supply, and soil properties that minimize erosion. A literature review was included for the purpose of model parameterization and five model-based experiments were conducted to predict how changes in environment (temperature) or crop management (cultivar, fertilization, and harvest efficiency) might affect soil carbon storage and nitrogen losses. Predictions of soil carbon sequestration were most sensitive to changes in annual biomass production, the ratio of belowground to aboveground biomass production, and temperature. Predictions of ecosystem nitrogen loss were most sensitive to changes in annual biomass production, the soil C/N ratio, and nitrogen remobilization efficiency (i.e., nitrogen cycling within the plant). Model-based experiments indicated that 1) soil carbon sequestration can be highly site specific depending on initial soil carbon stocks, temperature, and the amount of annual nitrogen fertilization, 2) response curves describing switchgrass yield as a function of annual nitrogen fertilization were important to model predictions, 3) plant improvements leading to greater belowground partitioning of biomass could increase soil carbon sequestration, 4) improvements in harvest efficiency have no indicated effects on soil carbon and nitrogen, but improve cumulative biomass yield, and 5) plant improvements that reduce organic matter decomposition rates could also increase soil carbon sequestration, even though the latter may not be consistent with desired improvements in plant tissue chemistry to maximize yields of cellulosic ethanol.
A physically based approach to modeling and animating a sailboat
Miniati, Maria Pia
2000-01-01
This thesis describes a method for modeling and animating a sailboat, by means of physically based techniques. Sailboats are excellent candidates for modeling and animating. Their dynamics have been well studied by nautical ...
White, Stephen
into Phosphatidylcholine Bilayer Interfaces Kalina Hristova and Stephen H. White*,§ Department of Physiology and Biophysics interfaces and n-octanol. A simplified version of the thermodynamic (4) cycle that forms the quantitative for the complete cycle opens the way for predicting the partitioning and se- condary structure of membrane
Content-Based Methods for Predicting Web-Site Demographic Attributes Santosh Kabbur
Minnesota, University of
, and different ways of aggregat- ing web-page level predictions that take into account the web's hyperlinked, occupation, etc.) about the audience of a web-site (i.e., the set of users viewing the web-pages) play the demographic attributes of a user or a web-site's audience by utilizing different features such as web-page
Static Model Analysis with Lattice-based Ontologies
Lickly, Ben
2012-01-01
3 Static Analysis 3.1 Heuristics-based tools . . . . .Ontology Framework for Static Model Analysis”. In: EMSOFT ’Murawski and Kwangkeun Yi. “Static Monotonicity Analysis for