WorldWideScience

Sample records for component neural systems

  1. Estimation of Leakage Ratio Using Principal Component Analysis and Artificial Neural Network in Water Distribution Systems

    Directory of Open Access Journals (Sweden)

    Dongwoo Jang

    2018-03-01

    Full Text Available Leaks in a water distribution network (WDS constitute losses of water supply caused by pipeline failure, operational loss, and physical factors. This has raised the need for studies on the factors affecting the leakage ratio and estimation of leakage volume in a water supply system. In this study, principal component analysis (PCA and artificial neural network (ANN were used to estimate the volume of water leakage in a WDS. For the study, six main effective parameters were selected and standardized data obtained through the Z-score method. The PCA-ANN model was devised and the leakage ratio was estimated. An accuracy assessment was performed to compare the measured leakage ratio to that of the simulated model. The results showed that the PCA-ANN method was more accurate for estimating the leakage ratio than a single ANN simulation. In addition, the estimation results differed according to the number of neurons in the ANN model’s hidden layers. In this study, an ANN with multiple hidden layers was found to be the best method for estimating the leakage ratio with 12–12 neurons. This suggested approaches to improve the accuracy of leakage ratio estimation, as well as a scientific approach toward the sustainable management of water distribution systems.

  2. EBaLM-THP - A neural network thermohydraulic prediction model of advanced nuclear system components

    International Nuclear Information System (INIS)

    Ridluan, Artit; Manic, Milos; Tokuhiro, Akira

    2009-01-01

    In lieu of the worldwide energy demand, economics and consensus concern regarding climate change, nuclear power - specifically near-term nuclear power plant designs are receiving increased engineering attention. However, as the nuclear industry is emerging from a lull in component modeling and analyses, optimization for example using ANN has received little research attention. This paper presents a neural network approach, EBaLM, based on a specific combination of two training algorithms, error-back propagation (EBP), and Levenberg-Marquardt (LM), applied to a problem of thermohydraulics predictions (THPs) of advanced nuclear heat exchangers (HXs). The suitability of the EBaLM-THP algorithm was tested on two different reference problems in thermohydraulic design analysis; that is, convective heat transfer of supercritical CO 2 through a single tube, and convective heat transfer through a printed circuit heat exchanger (PCHE) using CO 2 . Further, comparison of EBaLM-THP and a polynomial fitting approach was considered. Within the defined reference problems, the neural network approach generated good results in both cases, in spite of highly fluctuating trends in the dataset used. In fact, the neural network approach demonstrated cumulative measure of the error one to three orders of magnitude smaller than that produce via polynomial fitting of 10th order

  3. Evolvable synthetic neural system

    Science.gov (United States)

    Curtis, Steven A. (Inventor)

    2009-01-01

    An evolvable synthetic neural system includes an evolvable neural interface operably coupled to at least one neural basis function. Each neural basis function includes an evolvable neural interface operably coupled to a heuristic neural system to perform high-level functions and an autonomic neural system to perform low-level functions. In some embodiments, the evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy.

  4. Component Neural Systems for the Creation of Emotional Memories during Free Viewing of a Complex, Real-World Event.

    Science.gov (United States)

    Botzung, Anne; Labar, Kevin S; Kragel, Philip; Miles, Amanda; Rubin, David C

    2010-01-01

    To investigate the neural systems that contribute to the formation of complex, self-relevant emotional memories, dedicated fans of rival college basketball teams watched a competitive game while undergoing functional magnetic resonance imaging (fMRI). During a subsequent recognition memory task, participants were shown video clips depicting plays of the game, stemming either from previously-viewed game segments (targets) or from non-viewed portions of the same game (foils). After an old-new judgment, participants provided emotional valence and intensity ratings of the clips. A data driven approach was first used to decompose the fMRI signal acquired during free viewing of the game into spatially independent components. Correlations were then calculated between the identified components and post-scanning emotion ratings for successfully encoded targets. Two components were correlated with intensity ratings, including temporal lobe regions implicated in memory and emotional functions, such as the hippocampus and amygdala, as well as a midline fronto-cingulo-parietal network implicated in social cognition and self-relevant processing. These data were supported by a general linear model analysis, which revealed additional valence effects in fronto-striatal-insular regions when plays were divided into positive and negative events according to the fan's perspective. Overall, these findings contribute to our understanding of how emotional factors impact distributed neural systems to successfully encode dynamic, personally-relevant event sequences.

  5. Component neural systems for the creation of emotional memories during free viewing of a complex, real-world event

    Directory of Open Access Journals (Sweden)

    Anne Botzung

    2010-05-01

    Full Text Available To investigate the neural systems that contribute to the formation of complex, self-relevant emotional memories, dedicated fans of rival college basketball teams watched a competitive game while undergoing functional magnetic resonance imaging (fMRI. During a subsequent recognition memory task, participants were shown video clips depicting plays of the game, stemming either from previously-viewed game segments (targets or from non-viewed portions of the same game (foils. After an old-new judgment, participants provided emotional valence and intensity ratings of the clips. A data driven approach was first used to decompose the fMRI signal acquired during free viewing of the game into spatially independent components. Correlations were then calculated between the identified components and post-scanning emotion ratings for successfully encoded targets. Two components were correlated with intensity ratings, including temporal lobe regions implicated in memory and emotional functions, such as the hippocampus and amygdala, as well as a midline fronto-cingulo-parietal network implicated in social cognition and self-relevant processing. These data were supported by a general linear model analysis, which revealed additional valence effects in fronto-striatal-insular regions when plays were divided into positive and negative events according to the fan’s perspective. Overall, these findings contribute to our understanding of how emotional factors impact distributed neural systems to successfully encode dynamic, personally-relevant event sequences.

  6. Neural components of altruistic punishment

    Directory of Open Access Journals (Sweden)

    Emily eDu

    2015-02-01

    Full Text Available Altruistic punishment, which occurs when an individual incurs a cost to punish in response to unfairness or a norm violation, may play a role in perpetuating cooperation. The neural correlates underlying costly punishment have only recently begun to be explored. Here we review the current state of research on the neural basis of altruism from the perspectives of costly punishment, emphasizing the importance of characterizing elementary neural processes underlying a decision to punish. In particular, we emphasize three cognitive processes that contribute to the decision to altruistically punish in most scenarios: inequity aversion, cost-benefit calculation, and social reference frame to distinguish self from others. Overall, we argue for the importance of understanding the neural correlates of altruistic punishment with respect to the core computations necessary to achieve a decision to punish.

  7. Neural Systems Laboratory

    Data.gov (United States)

    Federal Laboratory Consortium — As part of the Electrical and Computer Engineering Department and The Institute for System Research, the Neural Systems Laboratory studies the functionality of the...

  8. Evolvable Neural Software System

    Science.gov (United States)

    Curtis, Steven A.

    2009-01-01

    The Evolvable Neural Software System (ENSS) is composed of sets of Neural Basis Functions (NBFs), which can be totally autonomously created and removed according to the changing needs and requirements of the software system. The resulting structure is both hierarchical and self-similar in that a given set of NBFs may have a ruler NBF, which in turn communicates with other sets of NBFs. These sets of NBFs may function as nodes to a ruler node, which are also NBF constructs. In this manner, the synthetic neural system can exhibit the complexity, three-dimensional connectivity, and adaptability of biological neural systems. An added advantage of ENSS over a natural neural system is its ability to modify its core genetic code in response to environmental changes as reflected in needs and requirements. The neural system is fully adaptive and evolvable and is trainable before release. It continues to rewire itself while on the job. The NBF is a unique, bilevel intelligence neural system composed of a higher-level heuristic neural system (HNS) and a lower-level, autonomic neural system (ANS). Taken together, the HNS and the ANS give each NBF the complete capabilities of a biological neural system to match sensory inputs to actions. Another feature of the NBF is the Evolvable Neural Interface (ENI), which links the HNS and ANS. The ENI solves the interface problem between these two systems by actively adapting and evolving from a primitive initial state (a Neural Thread) to a complicated, operational ENI and successfully adapting to a training sequence of sensory input. This simulates the adaptation of a biological neural system in a developmental phase. Within the greater multi-NBF and multi-node ENSS, self-similar ENI s provide the basis for inter-NBF and inter-node connectivity.

  9. The neural components of empathy: Predicting daily prosocial behavior

    OpenAIRE

    Morelli, Sylvia A.; Rameson, Lian T.; Lieberman, Matthew D.

    2012-01-01

    Previous neuroimaging studies on empathy have not clearly identified neural systems that support the three components of empathy: affective congruence, perspective-taking, and prosocial motivation. These limitations stem from a focus on a single emotion per study, minimal variation in amount of social context provided, and lack of prosocial motivation assessment. In the current investigation, 32 participants completed a functional magnetic resonance imaging session assessing empathic response...

  10. Towards modeling of combined cooling, heating and power system with artificial neural network for exergy destruction and exergy efficiency prognostication of tri-generation components

    International Nuclear Information System (INIS)

    Taghavifar, Hadi; Anvari, Simin; Saray, Rahim Khoshbakhti; Khalilarya, Shahram; Jafarmadar, Samad; Taghavifar, Hamid

    2015-01-01

    The current study is an attempt to address the investigation of the CCHP (combined cooling, heating and power) system when 10 input variables were chosen to analyze 10 most important objective output parameters. Moreover, ANN (artificial neural network) was successfully applied on the tri-generation system on account of its capability to predict responses with great confidence. The results of sensitivity analysis were considered as foundation for selecting the most suitable and potent input parameters of the supposed cycle. Furthermore, the best ANN topology was attained based on the least amount of MSE and number of iterations. Consequently, the trainlm (Levenberg–Marquardt) training approach with 10-9-10 configuration has been exploited for ANN modeling in order to give the best output correspondence. The maximum MRE = 1.75% (mean relative error) and minimum R 2  = 0.984 represents the reliability and outperformance of the developed ANN over common conventional thermodynamic analysis carried out by EES (engineering equation solver) software. - Highlights: • Exergy analysis is undertaken for CCHP components based on operative factors. • ANN tool is applied to obtained database from thermodynamic analyses session. • The best ANN topology is detected at 10-9-10 with trainlm learning algorithm. • The input and output layer parameters were selected based on sensitivity analysis.

  11. 2-component heating systems

    Energy Technology Data Exchange (ETDEWEB)

    Radtke, W

    1987-03-01

    The knowledge accumulated only recently of the damage to buildings and the hazards of formaldehyde, radon and hydrocarbons has been inducing louder calls for ventilation, which, on their part, account for the fact that increasing importance is being attached to the controlled ventilation of buildings. Two-component heating systems provide for fresh air and thermal comfort in one. While the first component uses fresh air blown directly and controllably into the rooms, the second component is similar to the Roman hypocaustic heating systems, meaning that heated outer air is circulating under the floor, thus providing for hot surfaces and thermal comfort. Details concerning the two-component heating system are presented along with systems diagrams, diagrams of the heating system and tables identifying the respective costs. Descriptions are given of the two systems components, the fast heat-up, the two-component made, the change of air, heat recovery and control systems. Comparative evaluations determine the differences between two-component heating systems and other heating systems. Conclusive remarks are dedicated to energy conservation and comparative evaluations of costs. (HWJ).

  12. neural control system

    International Nuclear Information System (INIS)

    Elshazly, A.A.E.

    2002-01-01

    Automatic power stabilization control is the desired objective for any reactor operation , especially, nuclear power plants. A major problem in this area is inevitable gap between a real plant ant the theory of conventional analysis and the synthesis of linear time invariant systems. in particular, the trajectory tracking control of a nonlinear plant is a class of problems in which the classical linear transfer function methods break down because no transfer function can represent the system over the entire operating region . there is a considerable amount of research on the model-inverse approach using feedback linearization technique. however, this method requires a prices plant model to implement the exact linearizing feedback, for nuclear reactor systems, this approach is not an easy task because of the uncertainty in the plant parameters and un-measurable state variables . therefore, artificial neural network (ANN) is used either in self-tuning control or in improving the conventional rule-based exper system.the main objective of this thesis is to suggest an ANN, based self-learning controller structure . this method is capable of on-line reinforcement learning and control for a nuclear reactor with a totally unknown dynamics model. previously, researches are based on back- propagation algorithm . back -propagation (BP), fast back -propagation (FBP), and levenberg-marquardt (LM), algorithms are discussed and compared for reinforcement learning. it is found that, LM algorithm is quite superior

  13. Neurally dissociable cognitive components of reading deficits in subacute stroke.

    Science.gov (United States)

    Boukrina, Olga; Barrett, A M; Alexander, Edward J; Yao, Bing; Graves, William W

    2015-01-01

    According to cognitive models of reading, words are processed by interacting orthographic (spelling), phonological (sound), and semantic (meaning) information. Despite extensive study of the neural basis of reading in healthy participants, little group data exist on patients with reading deficits from focal brain damage pointing to critical neural systems for reading. Here, we report on one such study. We have performed neuropsychological testing and magnetic resonance imaging on 11 patients with left-hemisphere stroke (picture or word choices to a target based on meaning), phonology (matching word choices to a target based on rhyming), and orthography (a two-alternative forced choice of the most plausible non-word). They also read aloud pseudowords and words with high or low levels of usage frequency, imageability, and spelling-sound consistency. As predicted by the cognitive model, when averaged across patients, the influence of semantics was most salient for low-frequency, low-consistency words, when phonological decoding is especially difficult. Qualitative subtraction analyses revealed lesion sites specific to phonological processing. These areas were consistent with those shown previously to activate for phonology in healthy participants, including supramarginal, posterior superior temporal, middle temporal, inferior frontal gyri, and underlying white matter. Notable divergence between this analysis and previous functional imaging is the association of lesions in the mid-fusiform gyrus and anterior temporal lobe with phonological reading deficits. This study represents progress toward identifying brain lesion-deficit relationships in the cognitive components of reading. Such correspondences are expected to help not only better understand the neural mechanisms of reading, but may also help tailor reading therapy to individual neurocognitive deficit profiles.

  14. Artificial Neural Network Analysis System

    Science.gov (United States)

    2001-02-27

    Contract No. DASG60-00-M-0201 Purchase request no.: Foot in the Door-01 Title Name: Artificial Neural Network Analysis System Company: Atlantic... Artificial Neural Network Analysis System 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Powell, Bruce C 5d. PROJECT NUMBER 5e. TASK NUMBER...34) 27-02-2001 Report Type N/A Dates Covered (from... to) ("DD MON YYYY") 28-10-2000 27-02-2001 Title and Subtitle Artificial Neural Network Analysis

  15. Electronic components and systems

    CERN Document Server

    Dennis, W H

    2013-01-01

    Electronic Components and Systems focuses on the principles and processes in the field of electronics and the integrated circuit. Covered in the book are basic aspects and physical fundamentals; different types of materials involved in the field; and passive and active electronic components such as capacitors, inductors, diodes, and transistors. Also covered in the book are topics such as the fabrication of semiconductors and integrated circuits; analog circuitry; digital logic technology; and microprocessors. The monograph is recommended for beginning electrical engineers who would like to kn

  16. The neural components of empathy: Predicting daily prosocial behavior

    Science.gov (United States)

    Rameson, Lian T.; Lieberman, Matthew D.

    2014-01-01

    Previous neuroimaging studies on empathy have not clearly identified neural systems that support the three components of empathy: affective congruence, perspective-taking, and prosocial motivation. These limitations stem from a focus on a single emotion per study, minimal variation in amount of social context provided, and lack of prosocial motivation assessment. In the current investigation, 32 participants completed a functional magnetic resonance imaging session assessing empathic responses to individuals experiencing painful, anxious, and happy events that varied in valence and amount of social context provided. They also completed a 14-day experience sampling survey that assessed real-world helping behaviors. The results demonstrate that empathy for positive and negative emotions selectively activates regions associated with positive and negative affect, respectively. In addition, the mirror system was more active during empathy for context-independent events (pain), whereas the mentalizing system was more active during empathy for context-dependent events (anxiety, happiness). Finally, the septal area, previously linked to prosocial motivation, was the only region that was commonly activated across empathy for pain, anxiety, and happiness. Septal activity during each of these empathic experiences was predictive of daily helping. These findings suggest that empathy has multiple input pathways, produces affect-congruent activations, and results in septally mediated prosocial motivation. PMID:22887480

  17. The neural components of empathy: predicting daily prosocial behavior.

    Science.gov (United States)

    Morelli, Sylvia A; Rameson, Lian T; Lieberman, Matthew D

    2014-01-01

    Previous neuroimaging studies on empathy have not clearly identified neural systems that support the three components of empathy: affective congruence, perspective-taking, and prosocial motivation. These limitations stem from a focus on a single emotion per study, minimal variation in amount of social context provided, and lack of prosocial motivation assessment. In the current investigation, 32 participants completed a functional magnetic resonance imaging session assessing empathic responses to individuals experiencing painful, anxious, and happy events that varied in valence and amount of social context provided. They also completed a 14-day experience sampling survey that assessed real-world helping behaviors. The results demonstrate that empathy for positive and negative emotions selectively activates regions associated with positive and negative affect, respectively. In addition, the mirror system was more active during empathy for context-independent events (pain), whereas the mentalizing system was more active during empathy for context-dependent events (anxiety, happiness). Finally, the septal area, previously linked to prosocial motivation, was the only region that was commonly activated across empathy for pain, anxiety, and happiness. Septal activity during each of these empathic experiences was predictive of daily helping. These findings suggest that empathy has multiple input pathways, produces affect-congruent activations, and results in septally mediated prosocial motivation.

  18. Neural systems for control

    National Research Council Canada - National Science Library

    Omidvar, Omid; Elliott, David L

    1997-01-01

    ... is reprinted with permission from A. Barto, "Reinforcement Learning," Handbook of Brain Theory and Neural Networks, M.A. Arbib, ed.. The MIT Press, Cambridge, MA, pp. 804-809, 1995. Chapter 4, Figures 4-5 and 7-9 and Tables 2-5, are reprinted with permission, from S. Cho, "Map Formation in Proprioceptive Cortex," International Jour...

  19. Principal component analysis for neural electron/jet discrimination in highly segmented calorimeters

    International Nuclear Information System (INIS)

    Vassali, M.R.; Seixas, J.M.

    2001-01-01

    A neural electron/jet discriminator based on calorimetry is developed for the second-level trigger system of the ATLAS detector. As preprocessing of the calorimeter information, a principal component analysis is performed on each segment of the two sections (electromagnetic and hadronic) of the calorimeter system, in order to reduce significantly the dimension of the input data space and fully explore the detailed energy deposition profile, which is provided by the highly-segmented calorimeter system. It is shown that projecting calorimeter data onto 33 segmented principal components, the discrimination efficiency of the neural classifier reaches 98.9% for electrons (with only 1% of false alarm probability). Furthermore, restricting data projection onto only 9 components, an electron efficiency of 99.1% is achieved (with 3% of false alarm), which confirms that a fast triggering system may be designed using few components

  20. Bidirectional neural interface: Closed-loop feedback control for hybrid neural systems.

    Science.gov (United States)

    Chou, Zane; Lim, Jeffrey; Brown, Sophie; Keller, Melissa; Bugbee, Joseph; Broccard, Frédéric D; Khraiche, Massoud L; Silva, Gabriel A; Cauwenberghs, Gert

    2015-01-01

    Closed-loop neural prostheses enable bidirectional communication between the biological and artificial components of a hybrid system. However, a major challenge in this field is the limited understanding of how these components, the two separate neural networks, interact with each other. In this paper, we propose an in vitro model of a closed-loop system that allows for easy experimental testing and modification of both biological and artificial network parameters. The interface closes the system loop in real time by stimulating each network based on recorded activity of the other network, within preset parameters. As a proof of concept we demonstrate that the bidirectional interface is able to establish and control network properties, such as synchrony, in a hybrid system of two neural networks more significantly more effectively than the same system without the interface or with unidirectional alternatives. This success holds promise for the application of closed-loop systems in neural prostheses, brain-machine interfaces, and drug testing.

  1. Intelligent neural network diagnostic system

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2010-01-01

    Recently, artificial neural network (ANN) has made a significant mark in the domain of diagnostic applications. Neural networks are used to implement complex non-linear mappings (functions) using simple elementary units interrelated through connections with adaptive weights. The performance of the ANN is mainly depending on their topology structure and weights. Some systems have been developed using genetic algorithm (GA) to optimize the topology of the ANN. But, they suffer from some limitations. They are : (1) The computation time requires for training the ANN several time reaching for the average weight required, (2) Slowness of GA for optimization process and (3) Fitness noise appeared in the optimization of ANN. This research suggests new issues to overcome these limitations for finding optimal neural network architectures to learn particular problems. This proposed methodology is used to develop a diagnostic neural network system. It has been applied for a 600 MW turbo-generator as a case of real complex systems. The proposed system has proved its significant performance compared to two common methods used in the diagnostic applications.

  2. Power Transformer Differential Protection Based on Neural Network Principal Component Analysis, Harmonic Restraint and Park's Plots

    OpenAIRE

    Tripathy, Manoj

    2012-01-01

    This paper describes a new approach for power transformer differential protection which is based on the wave-shape recognition technique. An algorithm based on neural network principal component analysis (NNPCA) with back-propagation learning is proposed for digital differential protection of power transformer. The principal component analysis is used to preprocess the data from power system in order to eliminate redundant information and enhance hidden pattern of differential current to disc...

  3. Neural circuit components of the Drosophila OFF motion vision pathway.

    Science.gov (United States)

    Meier, Matthias; Serbe, Etienne; Maisak, Matthew S; Haag, Jürgen; Dickson, Barry J; Borst, Alexander

    2014-02-17

    Detecting the direction of visual motion is an essential task of the early visual system. The Reichardt detector has been proven to be a faithful description of the underlying computation in insects. A series of recent studies addressed the neural implementation of the Reichardt detector in Drosophila revealing the overall layout in parallel ON and OFF channels, its input neurons from the lamina (L1→ON, and L2→OFF), and the respective output neurons to the lobula plate (ON→T4, and OFF→T5). While anatomical studies showed that T4 cells receive input from L1 via Mi1 and Tm3 cells, the neurons connecting L2 to T5 cells have not been identified so far. It is, however, known that L2 contacts, among others, two neurons, called Tm2 and L4, which show a pronounced directionality in their wiring. We characterized the visual response properties of both Tm2 and L4 neurons via Ca(2+) imaging. We found that Tm2 and L4 cells respond with an increase in activity to moving OFF edges in a direction-unselective manner. To investigate their participation in motion vision, we blocked their output while recording from downstream tangential cells in the lobula plate. Silencing of Tm2 and L4 completely abolishes the response to moving OFF edges. Our results demonstrate that both cell types are essential components of the Drosophila OFF motion vision pathway, prior to the computation of directionality in the dendrites of T5 cells. Copyright © 2014 Elsevier Ltd. All rights reserved.

  4. Demixed principal component analysis of neural population data.

    Science.gov (United States)

    Kobak, Dmitry; Brendel, Wieland; Constantinidis, Christos; Feierstein, Claudia E; Kepecs, Adam; Mainen, Zachary F; Qi, Xue-Lian; Romo, Ranulfo; Uchida, Naoshige; Machens, Christian K

    2016-04-12

    Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of single neuron responses can obscure what information these areas represent and how it is represented. Here we demonstrate the advantages of a new dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes population activity into a few components. In addition to systematically capturing the majority of the variance of the data, dPCA also exposes the dependence of the neural representation on task parameters such as stimuli, decisions, or rewards. To illustrate our method we reanalyze population data from four datasets comprising different species, different cortical areas and different experimental tasks. In each case, dPCA provides a concise way of visualizing the data that summarizes the task-dependent features of the population response in a single figure.

  5. Classification of fault diagnosis in a gear wheel by used probabilistic neural network, fast Fourier transform and principal component analysis

    Directory of Open Access Journals (Sweden)

    Piotr CZECH

    2007-01-01

    Full Text Available This paper presents the results of an experimental application of artificial neural network as a classifier of the degree of cracking of a tooth root in a gear wheel. The neural classifier was based on the artificial neural network of Probabilistic Neural Network type (PNN. The input data for the classifier was in a form of matrix composedof statistical measures, obtained from fast Fourier transform (FFT and principal component analysis (PCA. The identified model of toothed gear transmission, operating in a circulating power system, served for generation of the teaching and testing set applied for the experiment.

  6. PWR system simulation and parameter estimation with neural networks

    International Nuclear Information System (INIS)

    Akkurt, Hatice; Colak, Uener

    2002-01-01

    A detailed nonlinear model for a typical PWR system has been considered for the development of simulation software. Each component in the system has been represented by appropriate differential equations. The SCILAB software was used for solving nonlinear equations to simulate steady-state and transient operational conditions. Overall system has been constructed by connecting individual components to each other. The validity of models for individual components and overall system has been verified. The system response against given transients have been analyzed. A neural network has been utilized to estimate system parameters during transients. Different transients have been imposed in training and prediction stages with neural networks. Reactor power and system reactivity during the transient event have been predicted by the neural network. Results show that neural networks estimations are in good agreement with the calculated response of the reactor system. The maximum errors are within ±0.254% for power and between -0.146 and 0.353% for reactivity prediction cases. Steam generator parameters, pressure and water level, are also successfully predicted by the neural network employed in this study. The noise imposed on the input parameters of the neural network deteriorates the power estimation capability whereas the reactivity estimation capability is not significantly affected

  7. PWR system simulation and parameter estimation with neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Akkurt, Hatice; Colak, Uener E-mail: uc@nuke.hacettepe.edu.tr

    2002-11-01

    A detailed nonlinear model for a typical PWR system has been considered for the development of simulation software. Each component in the system has been represented by appropriate differential equations. The SCILAB software was used for solving nonlinear equations to simulate steady-state and transient operational conditions. Overall system has been constructed by connecting individual components to each other. The validity of models for individual components and overall system has been verified. The system response against given transients have been analyzed. A neural network has been utilized to estimate system parameters during transients. Different transients have been imposed in training and prediction stages with neural networks. Reactor power and system reactivity during the transient event have been predicted by the neural network. Results show that neural networks estimations are in good agreement with the calculated response of the reactor system. The maximum errors are within {+-}0.254% for power and between -0.146 and 0.353% for reactivity prediction cases. Steam generator parameters, pressure and water level, are also successfully predicted by the neural network employed in this study. The noise imposed on the input parameters of the neural network deteriorates the power estimation capability whereas the reactivity estimation capability is not significantly affected.

  8. The LILARTI neural network system

    Energy Technology Data Exchange (ETDEWEB)

    Allen, J.D. Jr.; Schell, F.M.; Dodd, C.V.

    1992-10-01

    The material of this Technical Memorandum is intended to provide the reader with conceptual and technical background information on the LILARTI neural network system of detail sufficient to confer an understanding of the LILARTI method as it is presently allied and to facilitate application of the method to problems beyond the scope of this document. Of particular importance in this regard are the descriptive sections and the Appendices which include operating instructions, partial listings of program output and data files, and network construction information.

  9. Machine and component residual life estimation through the application of neural networks

    International Nuclear Information System (INIS)

    Herzog, M.A.; Marwala, T.; Heyns, P.S.

    2009-01-01

    This paper concerns the use of neural networks for predicting the residual life of machines and components. In addition, the advantage of using condition-monitoring data to enhance the predictive capability of these neural networks was also investigated. A number of neural network variations were trained and tested with the data of two different reliability-related datasets. The first dataset represents the renewal case where the failed unit is repaired and restored to a good-as-new condition. Data were collected in the laboratory by subjecting a series of similar test pieces to fatigue loading with a hydraulic actuator. The average prediction error of the various neural networks being compared varied from 431 to 841 s on this dataset, where test pieces had a characteristic life of 8971 s. The second dataset were collected from a group of pumps used to circulate a water and magnetite solution within a plant. The data therefore originated from a repaired system affected by reliability degradation. When optimized, the multi-layer perceptron neural networks trained with the Levenberg-Marquardt algorithm and the general regression neural network produced a sum-of-squares error within 11.1% of each other for the renewal dataset. The small number of inputs and poorly mapped input space on the second dataset meant that much larger errors were recorded on some of the test data. The potential for using neural networks for residual life prediction and the advantage of incorporating condition-based data into the model was nevertheless proven for both examples

  10. Soft Sensor of Vehicle State Estimation Based on the Kernel Principal Component and Improved Neural Network

    Directory of Open Access Journals (Sweden)

    Haorui Liu

    2016-01-01

    Full Text Available In the car control systems, it is hard to measure some key vehicle states directly and accurately when running on the road and the cost of the measurement is high as well. To address these problems, a vehicle state estimation method based on the kernel principal component analysis and the improved Elman neural network is proposed. Combining with nonlinear vehicle model of three degrees of freedom (3 DOF, longitudinal, lateral, and yaw motion, this paper applies the method to the soft sensor of the vehicle states. The simulation results of the double lane change tested by Matlab/SIMULINK cosimulation prove the KPCA-IENN algorithm (kernel principal component algorithm and improved Elman neural network to be quick and precise when tracking the vehicle states within the nonlinear area. This algorithm method can meet the software performance requirements of the vehicle states estimation in precision, tracking speed, noise suppression, and other aspects.

  11. SNR coolant system components

    International Nuclear Information System (INIS)

    De Haas Van Dorsser, A.H.; Mausbeck, H.

    1976-01-01

    The DEBENELUX prototype fast reactor power plant SNR 300 at Kalkar has a loop-type heat transfer system similar to that of the prototype LMFBR plants in the USA and Japan. There exist three 257 MW/sub th/ primary sodium loops, each with a hot leg centrifugal pump and three 85.6 MW/sub th/ intermediate heat exchangers in parallel. From there the heat is transferred to the steam generators via three secondary sodium loops with one cold leg sodium circulating pump in each. At a nominal reactor outlet temperature of 819 0 K and a turbine inlet power of 771 MW/sub th/ super heated steam of 166 bar and 733 0 K is produced, giving rise to a plant rating of 327 MW/sub e/ gross. The primary and secondary loops are described in detail

  12. Extracting functional components of neural dynamics with Independent Component Analysis and inverse Current Source Density.

    Science.gov (United States)

    Lęski, Szymon; Kublik, Ewa; Swiejkowski, Daniel A; Wróbel, Andrzej; Wójcik, Daniel K

    2010-12-01

    Local field potentials have good temporal resolution but are blurred due to the slow spatial decay of the electric field. For simultaneous recordings on regular grids one can reconstruct efficiently the current sources (CSD) using the inverse Current Source Density method (iCSD). It is possible to decompose the resultant spatiotemporal information about the current dynamics into functional components using Independent Component Analysis (ICA). We show on test data modeling recordings of evoked potentials on a grid of 4 × 5 × 7 points that meaningful results are obtained with spatial ICA decomposition of reconstructed CSD. The components obtained through decomposition of CSD are better defined and allow easier physiological interpretation than the results of similar analysis of corresponding evoked potentials in the thalamus. We show that spatiotemporal ICA decompositions can perform better for certain types of sources but it does not seem to be the case for the experimental data studied. Having found the appropriate approach to decomposing neural dynamics into functional components we use the technique to study the somatosensory evoked potentials recorded on a grid spanning a large part of the forebrain. We discuss two example components associated with the first waves of activation of the somatosensory thalamus. We show that the proposed method brings up new, more detailed information on the time and spatial location of specific activity conveyed through various parts of the somatosensory thalamus in the rat.

  13. Reliability analysis of C-130 turboprop engine components using artificial neural network

    Science.gov (United States)

    Qattan, Nizar A.

    turbine under actual operating conditions, which can be used by aircraft operators for assessing system and component failures and customizing the maintenance programs recommended by the manufacturer.

  14. Learning in Artificial Neural Systems

    Science.gov (United States)

    Matheus, Christopher J.; Hohensee, William E.

    1987-01-01

    This paper presents an overview and analysis of learning in Artificial Neural Systems (ANS's). It begins with a general introduction to neural networks and connectionist approaches to information processing. The basis for learning in ANS's is then described, and compared with classical Machine learning. While similar in some ways, ANS learning deviates from tradition in its dependence on the modification of individual weights to bring about changes in a knowledge representation distributed across connections in a network. This unique form of learning is analyzed from two aspects: the selection of an appropriate network architecture for representing the problem, and the choice of a suitable learning rule capable of reproducing the desired function within the given network. The various network architectures are classified, and then identified with explicit restrictions on the types of functions they are capable of representing. The learning rules, i.e., algorithms that specify how the network weights are modified, are similarly taxonomized, and where possible, the limitations inherent to specific classes of rules are outlined.

  15. Component reliability for electronic systems

    CERN Document Server

    Bajenescu, Titu-Marius I

    2010-01-01

    The main reason for the premature breakdown of today's electronic products (computers, cars, tools, appliances, etc.) is the failure of the components used to build these products. Today professionals are looking for effective ways to minimize the degradation of electronic components to help ensure longer-lasting, more technically sound products and systems. This practical book offers engineers specific guidance on how to design more reliable components and build more reliable electronic systems. Professionals learn how to optimize a virtual component prototype, accurately monitor product reliability during the entire production process, and add the burn-in and selection procedures that are the most appropriate for the intended applications. Moreover, the book helps system designers ensure that all components are correctly applied, margins are adequate, wear-out failure modes are prevented during the expected duration of life, and system interfaces cannot lead to failure.

  16. Use of neural networks to monitor power plant components

    International Nuclear Information System (INIS)

    Ikonomopoulos, A.; Tsoukalas, L.H.

    1992-01-01

    A new methodology is presented for nondestructive evaluation (NDE) of check valve performance and degradation. Artificial neural network (ANN) technology is utilized for processing frequency domain signatures of check valves operating in a nuclear power plant (NPP). Acoustic signatures obtained from different locations on a check valve are transformed from the time domain to the frequency domain and then used as input to a pretrained neural network. The neural network has been trained with data sets corresponding to normal operation, therefore establishing a basis for check valve satisfactory performance. Results obtained from the proposed methodology demonstrate the ability of neural networks to perform accurate and quick evaluations of check valve performance

  17. Component Reification in Systems Modelling

    DEFF Research Database (Denmark)

    Bendisposto, Jens; Hallerstede, Stefan

    When modelling concurrent or distributed systems in Event-B, we often obtain models where the structure of the connected components is specified by constants. Their behaviour is specified by the non-deterministic choice of event parameters for events that operate on shared variables. From a certain......? These components may still refer to shared variables. Events of these components should not refer to the constants specifying the structure. The non-deterministic choice between these components should not be via parameters. We say the components are reified. We need to address how the reified components get...... reflected into the original model. This reflection should indicate the constraints on how to connect the components....

  18. Identifying apple surface defects using principal components analysis and artifical neural networks

    Science.gov (United States)

    Artificial neural networks and principal components were used to detect surface defects on apples in near-infrared images. Neural networks were trained and tested on sets of principal components derived from columns of pixels from images of apples acquired at two wavelengths (740 nm and 950 nm). I...

  19. Neural Control of the Immune System

    Science.gov (United States)

    Sundman, Eva; Olofsson, Peder S.

    2014-01-01

    Neural reflexes support homeostasis by modulating the function of organ systems. Recent advances in neuroscience and immunology have revealed that neural reflexes also regulate the immune system. Activation of the vagus nerve modulates leukocyte cytokine production and alleviates experimental shock and autoimmune disease, and recent data have…

  20. Feature selection for neural network based defect classification of ceramic components using high frequency ultrasound.

    Science.gov (United States)

    Kesharaju, Manasa; Nagarajah, Romesh

    2015-09-01

    The motivation for this research stems from a need for providing a non-destructive testing method capable of detecting and locating any defects and microstructural variations within armour ceramic components before issuing them to the soldiers who rely on them for their survival. The development of an automated ultrasonic inspection based classification system would make possible the checking of each ceramic component and immediately alert the operator about the presence of defects. Generally, in many classification problems a choice of features or dimensionality reduction is significant and simultaneously very difficult, as a substantial computational effort is required to evaluate possible feature subsets. In this research, a combination of artificial neural networks and genetic algorithms are used to optimize the feature subset used in classification of various defects in reaction-sintered silicon carbide ceramic components. Initially wavelet based feature extraction is implemented from the region of interest. An Artificial Neural Network classifier is employed to evaluate the performance of these features. Genetic Algorithm based feature selection is performed. Principal Component Analysis is a popular technique used for feature selection and is compared with the genetic algorithm based technique in terms of classification accuracy and selection of optimal number of features. The experimental results confirm that features identified by Principal Component Analysis lead to improved performance in terms of classification percentage with 96% than Genetic algorithm with 94%. Copyright © 2015 Elsevier B.V. All rights reserved.

  1. Neural neworks in a management information systems

    Directory of Open Access Journals (Sweden)

    Jana Weinlichová

    2009-01-01

    Full Text Available For having retrospection for all over the data which are used, analyzed, evaluated and for a future incident predictions are used Management Information Systems and Business Intelligence. In case of not to be able to apply standard methods of data processing there can be with benefit applied an Artificial Intelligence. In this article will be referred to proofed abilities of Neural Networks. The Neural Networks is supported by many software products related to provide effective solution of manager issues. Those products are given as primary support for manager issues solving. We were tried to find reciprocally between products using Neural Networks and between Management Information Systems for finding a real possibility of applying Neural Networks as a direct part of Management Information Systems (MIS. In the article are presented possibilities to apply Neural Networks on different types of tasks in MIS.

  2. Echoes in correlated neural systems

    International Nuclear Information System (INIS)

    Helias, M; Tetzlaff, T; Diesmann, M

    2013-01-01

    Correlations are employed in modern physics to explain microscopic and macroscopic phenomena, like the fractional quantum Hall effect and the Mott insulator state in high temperature superconductors and ultracold atoms. Simultaneously probed neurons in the intact brain reveal correlations between their activity, an important measure to study information processing in the brain that also influences the macroscopic signals of neural activity, like the electroencephalogram (EEG). Networks of spiking neurons differ from most physical systems: the interaction between elements is directed, time delayed, mediated by short pulses and each neuron receives events from thousands of neurons. Even the stationary state of the network cannot be described by equilibrium statistical mechanics. Here we develop a quantitative theory of pairwise correlations in finite-sized random networks of spiking neurons. We derive explicit analytic expressions for the population-averaged cross correlation functions. Our theory explains why the intuitive mean field description fails, how the echo of single action potentials causes an apparent lag of inhibition with respect to excitation and how the size of the network can be scaled while maintaining its dynamical state. Finally, we derive a new criterion for the emergence of collective oscillations from the spectrum of the time-evolution propagator. (paper)

  3. Neural systems for tactual memories.

    Science.gov (United States)

    Bonda, E; Petrides, M; Evans, A

    1996-04-01

    1. The aim of this study was to investigate the neural systems involved in the memory processing of experiences through touch. 2. Regional cerebral blood flow was measured with positron emission tomography by means of the water bolus H2(15)O methodology in human subjects as they performed tasks involving different levels of tactual memory. In one of the experimental tasks, the subjects had to palpate nonsense shapes to match each one to a previously learned set, thus requiring constant reference to long-term memory. The other experimental task involved judgements of the recent recurrence of shapes during the scanning period. A set of three control tasks was used to control for the type of exploratory movements and sensory processing inherent in the two experimental tasks. 3. Comparisons of the distribution of activity between the experimental and the control tasks were carried out by means of the subtraction method. In relation to the control conditions, the two experimental tasks requiring memory resulted in significant changes within the posteroventral insula and the central opercular region. In addition, the task requiring recall from long-term memory yielded changes in the perirhinal cortex. 4. The above findings demonstrated that a ventrally directed parietoinsular pathway, leading to the posteroventral insula and the perirhinal cortex, constitutes a system by which long-lasting representations of tactual experiences are formed. It is proposed that the posteroventral insula is involved in tactual feature analysis, by analogy with the similar role of the inferotemporal cortex in vision, whereas the perirhinal cortex is further involved in the integration of these features into long-lasting representations of somatosensory experiences.

  4. Real-time classification of signals from three-component seismic sensors using neural nets

    Science.gov (United States)

    Bowman, B. C.; Dowla, F.

    1992-05-01

    Adaptive seismic data acquisition systems with capabilities of signal discrimination and event classification are important in treaty monitoring, proliferation, and earthquake early detection systems. Potential applications include monitoring underground chemical explosions, as well as other military, cultural, and natural activities where characteristics of signals change rapidly and without warning. In these applications, the ability to detect and interpret events rapidly without falling behind the influx of the data is critical. We developed a system for real-time data acquisition, analysis, learning, and classification of recorded events employing some of the latest technology in computer hardware, software, and artificial neural networks methods. The system is able to train dynamically, and updates its knowledge based on new data. The software is modular and hardware-independent; i.e., the front-end instrumentation is transparent to the analysis system. The software is designed to take advantage of the multiprocessing environment of the Unix operating system. The Unix System V shared memory and static RAM protocols for data access and the semaphore mechanism for interprocess communications were used. As the three-component sensor detects a seismic signal, it is displayed graphically on a color monitor using X11/Xlib graphics with interactive screening capabilities. For interesting events, the triaxial signal polarization is computed, a fast Fourier Transform (FFT) algorithm is applied, and the normalized power spectrum is transmitted to a backpropagation neural network for event classification. The system is currently capable of handling three data channels with a sampling rate of 500 Hz, which covers the bandwidth of most seismic events. The system has been tested in laboratory setting with artificial events generated in the vicinity of a three-component sensor.

  5. Neural neworks in a management information systems

    OpenAIRE

    Jana Weinlichová; Michael Štencl

    2009-01-01

    For having retrospection for all over the data which are used, analyzed, evaluated and for a future incident predictions are used Management Information Systems and Business Intelligence. In case of not to be able to apply standard methods of data processing there can be with benefit applied an Artificial Intelligence. In this article will be referred to proofed abilities of Neural Networks. The Neural Networks is supported by many software products related to provide effective solution of ma...

  6. Identification of nonlinear dynamics in power plant components using neural networks

    International Nuclear Information System (INIS)

    Parlos, A.G.; Fernandez, B.; Tsai, W.K.

    1990-01-01

    Advances in digital computer technology have enabled widespread implementation of closed-loop digital control systems in a variety of industries. In some instances, however, the complexity of the plant and the uncertainty associated with the parameters involved in the mathematical modeling narrow the range of applicability of most systematic control system design methodologies. A multiyear project has been initiated to assess the feasibility of the artificial neural networks (ANNs) technology for computerized enhanced diagnostics and control of nuclear power plant components. At this stage of the project, a new methodology, based on backpropagation learning, has been developed for identifying the nonlinear dynamic systems from a set of input-output data known as the training set

  7. Statistics of Shared Components in Complex Component Systems

    Science.gov (United States)

    Mazzolini, Andrea; Gherardi, Marco; Caselle, Michele; Cosentino Lagomarsino, Marco; Osella, Matteo

    2018-04-01

    Many complex systems are modular. Such systems can be represented as "component systems," i.e., sets of elementary components, such as LEGO bricks in LEGO sets. The bricks found in a LEGO set reflect a target architecture, which can be built following a set-specific list of instructions. In other component systems, instead, the underlying functional design and constraints are not obvious a priori, and their detection is often a challenge of both scientific and practical importance, requiring a clear understanding of component statistics. Importantly, some quantitative invariants appear to be common to many component systems, most notably a common broad distribution of component abundances, which often resembles the well-known Zipf's law. Such "laws" affect in a general and nontrivial way the component statistics, potentially hindering the identification of system-specific functional constraints or generative processes. Here, we specifically focus on the statistics of shared components, i.e., the distribution of the number of components shared by different system realizations, such as the common bricks found in different LEGO sets. To account for the effects of component heterogeneity, we consider a simple null model, which builds system realizations by random draws from a universe of possible components. Under general assumptions on abundance heterogeneity, we provide analytical estimates of component occurrence, which quantify exhaustively the statistics of shared components. Surprisingly, this simple null model can positively explain important features of empirical component-occurrence distributions obtained from large-scale data on bacterial genomes, LEGO sets, and book chapters. Specific architectural features and functional constraints can be detected from occurrence patterns as deviations from these null predictions, as we show for the illustrative case of the "core" genome in bacteria.

  8. Statistics of Shared Components in Complex Component Systems

    Directory of Open Access Journals (Sweden)

    Andrea Mazzolini

    2018-04-01

    Full Text Available Many complex systems are modular. Such systems can be represented as “component systems,” i.e., sets of elementary components, such as LEGO bricks in LEGO sets. The bricks found in a LEGO set reflect a target architecture, which can be built following a set-specific list of instructions. In other component systems, instead, the underlying functional design and constraints are not obvious a priori, and their detection is often a challenge of both scientific and practical importance, requiring a clear understanding of component statistics. Importantly, some quantitative invariants appear to be common to many component systems, most notably a common broad distribution of component abundances, which often resembles the well-known Zipf’s law. Such “laws” affect in a general and nontrivial way the component statistics, potentially hindering the identification of system-specific functional constraints or generative processes. Here, we specifically focus on the statistics of shared components, i.e., the distribution of the number of components shared by different system realizations, such as the common bricks found in different LEGO sets. To account for the effects of component heterogeneity, we consider a simple null model, which builds system realizations by random draws from a universe of possible components. Under general assumptions on abundance heterogeneity, we provide analytical estimates of component occurrence, which quantify exhaustively the statistics of shared components. Surprisingly, this simple null model can positively explain important features of empirical component-occurrence distributions obtained from large-scale data on bacterial genomes, LEGO sets, and book chapters. Specific architectural features and functional constraints can be detected from occurrence patterns as deviations from these null predictions, as we show for the illustrative case of the “core” genome in bacteria.

  9. Coherent systems with multistate components

    International Nuclear Information System (INIS)

    Caldarola, L.

    1980-01-01

    The basic rules of the Boolean algebra with restrictions on variables are briefly recalled. This special type of Boolean algebra allows one to handle fault trees of systems made of multistate (two or more than two states) components. Coherent systems are defined in the case of multistate components. This definition is consistent with that originally suggested by Barlow in the case of binary (two states) components. The basic properties of coherence are described and discussed. Coherent Boolean functions are also defined. It is shown that these functions are irredundant, that is they have only one base which is at the same time complete and irredundant. However, irredundant functions are not necessarily coherent. Finally a simplified algorithm for the calculation of the base of a coherent function is described. In the case that the function is not coherent, the algorithm can be used to reduce the size of the normal disjunctive form of the function. This in turn eases the application of the Nelson algorithm to calculate the complete base of the function. The simplified algorithm has been built in the computer program MUSTAFA-1. In a sample case the use of this algorithm caused a reduction of the CPU time by a factor of about 20. (orig.)

  10. Neural control of magnetic suspension systems

    Science.gov (United States)

    Gray, W. Steven

    1993-01-01

    The purpose of this research program is to design, build and test (in cooperation with NASA personnel from the NASA Langley Research Center) neural controllers for two different small air-gap magnetic suspension systems. The general objective of the program is to study neural network architectures for the purpose of control in an experimental setting and to demonstrate the feasibility of the concept. The specific objectives of the research program are: (1) to demonstrate through simulation and experimentation the feasibility of using neural controllers to stabilize a nonlinear magnetic suspension system; (2) to investigate through simulation and experimentation the performance of neural controllers designs under various types of parametric and nonparametric uncertainty; (3) to investigate through simulation and experimentation various types of neural architectures for real-time control with respect to performance and complexity; and (4) to benchmark in an experimental setting the performance of neural controllers against other types of existing linear and nonlinear compensator designs. To date, the first one-dimensional, small air-gap magnetic suspension system has been built, tested and delivered to the NASA Langley Research Center. The device is currently being stabilized with a digital linear phase-lead controller. The neural controller hardware is under construction. Two different neural network paradigms are under consideration, one based on hidden layer feedforward networks trained via back propagation and one based on using Gaussian radial basis functions trained by analytical methods related to stability conditions. Some advanced nonlinear control algorithms using feedback linearization and sliding mode control are in simulation studies.

  11. Neurally-dissociable cognitive components of reading deficits in subacute stroke

    Directory of Open Access Journals (Sweden)

    Olga eBoukrina

    2015-05-01

    Full Text Available According to cognitive models of reading, words are processed by interacting orthographic (spelling, phonological (sound and semantic (meaning information. Despite extensive study of the neural basis of reading in healthy participants, little group data exist on patients with reading deficits from focal brain damage pointing to critical neural systems for reading. Here we report on one such study. We have performed neuropsychological testing and MRI on 11 patients with left-hemisphere stroke (<= 5 weeks post stroke. Patients completed tasks assessing cognitive components of reading such as semantics (matching picture or word choices to a target based on meaning, phonology (matching word choices to a target based on rhyming, and orthography (a two-alternative forced choice of the most plausible nonword. They also read aloud pseudowords and words with high or low levels of usage frequency, imageability, and spelling-sound consistency. As predicted by the cognitive model, when averaged across patients, the influence of semantics was most salient for low-frequency, low-consistency words, when phonological decoding is especially difficult. Qualitative subtraction analyses revealed lesion sites specific to phonological processing. These areas were consistent with those shown previously to activate for phonology in healthy participants, including supramarginal, posterior superior temporal, middle temporal, inferior frontal gyri, and underlying white matter. Notable divergence between this analysis and previous functional imaging is the association of lesions in the mid-fusiform gyrus and anterior temporal lobe with phonological reading deficits. This study represents progress toward identifying brain lesion-deficit relationships in the cognitive components of reading. Such correspondences are expected to help not only better understand the neural mechanisms of reading, but may also help tailor reading therapy to individual neurocognitive deficit

  12. Application of neural networks in CRM systems

    Directory of Open Access Journals (Sweden)

    Bojanowska Agnieszka

    2017-01-01

    Full Text Available The central aim of this study is to investigate how to apply artificial neural networks in Customer Relationship Management (CRM. The paper presents several business applications of neural networks in software systems designed to aid CRM, e.g. in deciding on the profitability of building a relationship with a given customer. Furthermore, a framework for a neural-network based CRM software tool is developed. Building beneficial relationships with customers is generating considerable interest among various businesses, and is often mentioned as one of the crucial objectives of enterprises, next to their key aim: to bring satisfactory profit. There is a growing tendency among businesses to invest in CRM systems, which together with an organisational culture of a company aid managing customer relationships. It is the sheer amount of gathered data as well as the need for constant updating and analysis of this breadth of information that may imply the suitability of neural networks for the application in question. Neural networks exhibit considerably higher computational capabilities than sequential calculations because the solution to a problem is obtained without the need for developing a special algorithm. In the majority of presented CRM applications neural networks constitute and are presented as a managerial decision-taking optimisation tool.

  13. Component Control System for a Vehicle

    Science.gov (United States)

    Fraser-Chanpong, Nathan (Inventor); Spain, Ivan (Inventor); Dawson, Andrew D. (Inventor); Bluethmann, William J. (Inventor); Lee, Chunhao J. (Inventor); Vitale, Robert L. (Inventor); Guo, Raymond (Inventor); Waligora, Thomas M. (Inventor); Akinyode, Akinjide Akinniyi (Inventor); Reed, Ryan M. (Inventor)

    2016-01-01

    A vehicle includes a chassis, a modular component, and a central operating system. The modular component is supported by the chassis. The central operating system includes a component control system, a primary master controller, and a secondary master controller. The component control system is configured for controlling the modular component. The primary and secondary master controllers are in operative communication with the component control system. The primary and secondary master controllers are configured to simultaneously transmit commands to the component control system. The component control system is configured to accept commands from the secondary master controller only when a fault occurs in the primary master controller.

  14. Modelization of cooling system components

    Energy Technology Data Exchange (ETDEWEB)

    Copete, Monica; Ortega, Silvia; Vaquero, Jose Carlos; Cervantes, Eva [Westinghouse Electric (Spain)

    2010-07-01

    In the site evaluation study for licensing a new nuclear power facility, the criteria involved could be grouped in health and safety, environment, socio-economics, engineering and cost-related. These encompass different aspects such as geology, seismology, cooling system requirements, weather conditions, flooding, population, and so on. The selection of the cooling system is function of different parameters as the gross electrical output, energy consumption, available area for cooling system components, environmental conditions, water consumption, and others. Moreover, in recent years, extreme environmental conditions have been experienced and stringent water availability limits have affected water use permits. Therefore, modifications or alternatives of current cooling system designs and operation are required as well as analyses of the different possibilities of cooling systems to optimize energy production taking into account water consumption among other important variables. There are two basic cooling system configurations: - Once-through or Open-cycle; - Recirculating or Closed-cycle. In a once-through cooling system (or open-cycle), water from an external water sources passes through the steam cycle condenser and is then returned to the source at a higher temperature with some level of contaminants. To minimize the thermal impact to the water source, a cooling tower may be added in a once-through system to allow air cooling of the water (with associated losses on site due to evaporation) prior to returning the water to its source. This system has a high thermal efficiency, and its operating and capital costs are very low. So, from an economical point of view, the open-cycle is preferred to closed-cycle system, especially if there are no water limitations or environmental restrictions. In a recirculating system (or closed-cycle), cooling water exits the condenser, goes through a fixed heat sink, and is then returned to the condenser. This configuration

  15. Component codification and identification systems

    International Nuclear Information System (INIS)

    Pannenbaecker, K.

    1977-01-01

    The lecture covers the codification in power stations during the erection phase and commercial operation phase. A diagram gives a survey. There are three basic-codifications for application; 1) Kraftwerk-Kennzeichen-System (KKS) for marking each component in orientated systems, for marking electrical orientated positions in cubicals, switch gears etc. and for marking rooms in buildings; 2) Ordnungssystem (OS) for cost calculation and ordering; 3) Unterlagenarten-Schluessel (UAS) for letters, reports etc. and for documentation. The OS is developed on the principle of cost account number and is therefore close to the organization of each supplier and his special form of design and constrution. KKS has only to mark hardware. Therefore all German owners, consultants, authorities and suppliers develop KKS together and conform to it in DIN 407119. (ORU) [de

  16. Cooling system for auxiliary reactor component

    International Nuclear Information System (INIS)

    Fujihira, Tomoko.

    1991-01-01

    A cooling system for auxiliary reactor components comprises three systems, that is, two systems of reactor component cooling water systems (RCCW systems) and a high pressure component cooling water system (HPCCW system). Connecting pipelines having partition valves are intervened each in a cooling water supply pipeline to an emmergency component of each of the RCCW systems, a cooling water return pipeline from the emmergency component of each of the RCCW systems, a cooling water supply pipeline to each of the emmergency components of one of the RCCW system and the HPCCW system and a cooling water return pipeline from each of the emmergency components of one of the RCCW system and the HPCCW system. With such constitution, cooling water can be supplied also to the emmergency components in the stand-by system upon periodical inspection or ISI, thereby enabling to improve the backup performance of the emmergency cooling system. (I.N.)

  17. Spiking neural P systems with multiple channels.

    Science.gov (United States)

    Peng, Hong; Yang, Jinyu; Wang, Jun; Wang, Tao; Sun, Zhang; Song, Xiaoxiao; Luo, Xiaohui; Huang, Xiangnian

    2017-11-01

    Spiking neural P systems (SNP systems, in short) are a class of distributed parallel computing systems inspired from the neurophysiological behavior of biological spiking neurons. In this paper, we investigate a new variant of SNP systems in which each neuron has one or more synaptic channels, called spiking neural P systems with multiple channels (SNP-MC systems, in short). The spiking rules with channel label are introduced to handle the firing mechanism of neurons, where the channel labels indicate synaptic channels of transmitting the generated spikes. The computation power of SNP-MC systems is investigated. Specifically, we prove that SNP-MC systems are Turing universal as both number generating and number accepting devices. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. The magnet components database system

    International Nuclear Information System (INIS)

    Baggett, M.J.; Leedy, R.; Saltmarsh, C.; Tompkins, J.C.

    1990-01-01

    The philosophy, structure, and usage MagCom, the SSC magnet components database, are described. The database has been implemented in Sybase (a powerful relational database management system) on a UNIX-based workstation at the Superconducting Super Collider Laboratory (SSCL); magnet project collaborators can access the database via network connections. The database was designed to contain the specifications and measured values of important properties for major materials, plus configuration information (specifying which individual items were used in each cable, coil, and magnet) and the test results on completed magnets. These data will facilitate the tracking and control of the production process as well as the correlation of magnet performance with the properties of its constituents. 3 refs., 10 figs

  19. The magnet components database system

    International Nuclear Information System (INIS)

    Baggett, M.J.; Leedy, R.; Saltmarsh, C.; Tompkins, J.C.

    1990-01-01

    The philosophy, structure, and usage of MagCom, the SSC magnet components database, are described. The database has been implemented in Sybase (a powerful relational database management system) on a UNIX-based workstation at the Superconducting Super Collider Laboratory (SSCL); magnet project collaborators can access the database via network connections. The database was designed to contain the specifications and measured values of important properties for major materials, plus configuration information (specifying which individual items were used in each cable, coil, and magnet) and the test results on completed magnets. The data will facilitate the tracking and control of the production process as well as the correlation of magnet performance with the properties of its constituents. 3 refs., 9 figs

  20. Evaluating neural networks and artificial intelligence systems

    Science.gov (United States)

    Alberts, David S.

    1994-02-01

    Systems have no intrinsic value in and of themselves, but rather derive value from the contributions they make to the missions, decisions, and tasks they are intended to support. The estimation of the cost-effectiveness of systems is a prerequisite for rational planning, budgeting, and investment documents. Neural network and expert system applications, although similar in their incorporation of a significant amount of decision-making capability, differ from each other in ways that affect the manner in which they can be evaluated. Both these types of systems are, by definition, evolutionary systems, which also impacts their evaluation. This paper discusses key aspects of neural network and expert system applications and their impact on the evaluation process. A practical approach or methodology for evaluating a certain class of expert systems that are particularly difficult to measure using traditional evaluation approaches is presented.

  1. Integrated Neural Flight and Propulsion Control System

    Science.gov (United States)

    Kaneshige, John; Gundy-Burlet, Karen; Norvig, Peter (Technical Monitor)

    2001-01-01

    This paper describes an integrated neural flight and propulsion control system. which uses a neural network based approach for applying alternate sources of control power in the presence of damage or failures. Under normal operating conditions, the system utilizes conventional flight control surfaces. Neural networks are used to provide consistent handling qualities across flight conditions and for different aircraft configurations. Under damage or failure conditions, the system may utilize unconventional flight control surface allocations, along with integrated propulsion control, when additional control power is necessary for achieving desired flight control performance. In this case, neural networks are used to adapt to changes in aircraft dynamics and control allocation schemes. Of significant importance here is the fact that this system can operate without emergency or backup flight control mode operations. An additional advantage is that this system can utilize, but does not require, fault detection and isolation information or explicit parameter identification. Piloted simulation studies were performed on a commercial transport aircraft simulator. Subjects included both NASA test pilots and commercial airline crews. Results demonstrate the potential for improving handing qualities and significantly increasing survivability rates under various simulated failure conditions.

  2. Coupling component systems towards systems of systems

    OpenAIRE

    Autran , Frédéric; Auzelle , Jean-Philippe; Cattan , Denise; Garnier , Jean-Luc; Luzeaux , Dominique; Mayer , Frédérique; Peyrichon , Marc; Ruault , Jean-René

    2008-01-01

    International audience; Systems of systems (SoS) are a hot topic in our "fully connected global world". Our aim is not to provide another definition of what SoS are, but rather to focus on the adequacy of reusing standard system architecting techniques within this approach in order to improve performance, fault detection and safety issues in large-scale coupled systems that definitely qualify as SoS, whatever the definition is. A key issue will be to secure the availability of the services pr...

  3. Fractionating the neural correlates of individual working memory components underlying arithmetic problem solving skills in children

    Science.gov (United States)

    Metcalfe, Arron W. S.; Ashkenazi, Sarit; Rosenberg-Lee, Miriam; Menon, Vinod

    2013-01-01

    Baddeley and Hitch’s multi-component working memory (WM) model has played an enduring and influential role in our understanding of cognitive abilities. Very little is known, however, about the neural basis of this multi-component WM model and the differential role each component plays in mediating arithmetic problem solving abilities in children. Here, we investigate the neural basis of the central executive (CE), phonological (PL) and visuo-spatial (VS) components of WM during a demanding mental arithmetic task in 7–9 year old children (N=74). The VS component was the strongest predictor of math ability in children and was associated with increased arithmetic complexity-related responses in left dorsolateral and right ventrolateral prefrontal cortices as well as bilateral intra-parietal sulcus and supramarginal gyrus in posterior parietal cortex. Critically, VS, CE and PL abilities were associated with largely distinct patterns of brain response. Overlap between VS and CE components was observed in left supramarginal gyrus and no overlap was observed between VS and PL components. Our findings point to a central role of visuo-spatial WM during arithmetic problem-solving in young grade-school children and highlight the usefulness of the multi-component Baddeley and Hitch WM model in fractionating the neural correlates of arithmetic problem solving during development. PMID:24212504

  4. Fractionating the neural correlates of individual working memory components underlying arithmetic problem solving skills in children.

    Science.gov (United States)

    Metcalfe, Arron W S; Ashkenazi, Sarit; Rosenberg-Lee, Miriam; Menon, Vinod

    2013-10-01

    Baddeley and Hitch's multi-component working memory (WM) model has played an enduring and influential role in our understanding of cognitive abilities. Very little is known, however, about the neural basis of this multi-component WM model and the differential role each component plays in mediating arithmetic problem solving abilities in children. Here, we investigate the neural basis of the central executive (CE), phonological (PL) and visuo-spatial (VS) components of WM during a demanding mental arithmetic task in 7-9 year old children (N=74). The VS component was the strongest predictor of math ability in children and was associated with increased arithmetic complexity-related responses in left dorsolateral and right ventrolateral prefrontal cortices as well as bilateral intra-parietal sulcus and supramarginal gyrus in posterior parietal cortex. Critically, VS, CE and PL abilities were associated with largely distinct patterns of brain response. Overlap between VS and CE components was observed in left supramarginal gyrus and no overlap was observed between VS and PL components. Our findings point to a central role of visuo-spatial WM during arithmetic problem-solving in young grade-school children and highlight the usefulness of the multi-component Baddeley and Hitch WM model in fractionating the neural correlates of arithmetic problem solving during development. Copyright © 2013 Elsevier Ltd. All rights reserved.

  5. System and method for determining stability of a neural system

    Science.gov (United States)

    Curtis, Steven A. (Inventor)

    2011-01-01

    Disclosed are methods, systems, and computer-readable media for determining stability of a neural system. The method includes tracking a function world line of an N element neural system within at least one behavioral space, determining whether the tracking function world line is approaching a psychological stability surface, and implementing a quantitative solution that corrects instability if the tracked function world line is approaching the psychological stability surface.

  6. Direct process estimation from tomographic data using artificial neural systems

    Science.gov (United States)

    Mohamad-Saleh, Junita; Hoyle, Brian S.; Podd, Frank J.; Spink, D. M.

    2001-07-01

    The paper deals with the goal of component fraction estimation in multicomponent flows, a critical measurement in many processes. Electrical capacitance tomography (ECT) is a well-researched sensing technique for this task, due to its low-cost, non-intrusion, and fast response. However, typical systems, which include practicable real-time reconstruction algorithms, give inaccurate results, and existing approaches to direct component fraction measurement are flow-regime dependent. In the investigation described, an artificial neural network approach is used to directly estimate the component fractions in gas-oil, gas-water, and gas-oil-water flows from ECT measurements. A 2D finite- element electric field model of a 12-electrode ECT sensor is used to simulate ECT measurements of various flow conditions. The raw measurements are reduced to a mutually independent set using principal components analysis and used with their corresponding component fractions to train multilayer feed-forward neural networks (MLFFNNs). The trained MLFFNNs are tested with patterns consisting of unlearned ECT simulated and plant measurements. Results included in the paper have a mean absolute error of less than 1% for the estimation of various multicomponent fractions of the permittivity distribution. They are also shown to give improved component fraction estimation compared to a well known direct ECT method.

  7. Combined principal component preprocessing and n-tuple neural networks for improved classification

    DEFF Research Database (Denmark)

    Høskuldsson, Agnar; Linneberg, Christian

    2000-01-01

    We present a combined principal component analysis/neural network scheme for classification. The data used to illustrate the method consist of spectral fluorescence recordings from seven different production facilities, and the task is to relate an unknown sample to one of these seven factories....... The data are first preprocessed by performing an individual principal component analysis on each of the seven groups of data. The components found are then used for classifying the data, but instead of making a single multiclass classifier, we follow the ideas of turning a multiclass problem into a number...... of two-class problems. For each possible pair of classes we further apply a transformation to the calculated principal components in order to increase the separation between the classes. Finally we apply the so-called n-tuple neural network to the transformed data in order to give the classification...

  8. Analysis of complex systems using neural networks

    International Nuclear Information System (INIS)

    Uhrig, R.E.

    1992-01-01

    The application of neural networks, alone or in conjunction with other advanced technologies (expert systems, fuzzy logic, and/or genetic algorithms), to some of the problems of complex engineering systems has the potential to enhance the safety, reliability, and operability of these systems. Typically, the measured variables from the systems are analog variables that must be sampled and normalized to expected peak values before they are introduced into neural networks. Often data must be processed to put it into a form more acceptable to the neural network (e.g., a fast Fourier transformation of the time-series data to produce a spectral plot of the data). Specific applications described include: (1) Diagnostics: State of the Plant (2) Hybrid System for Transient Identification, (3) Sensor Validation, (4) Plant-Wide Monitoring, (5) Monitoring of Performance and Efficiency, and (6) Analysis of Vibrations. Although specific examples described deal with nuclear power plants or their subsystems, the techniques described can be applied to a wide variety of complex engineering systems

  9. Collaborative Recurrent Neural Networks forDynamic Recommender Systems

    Science.gov (United States)

    2016-11-22

    JMLR: Workshop and Conference Proceedings 63:366–381, 2016 ACML 2016 Collaborative Recurrent Neural Networks for Dynamic Recommender Systems Young...an unprece- dented scale. Although such activity logs are abundantly available, most approaches to recommender systems are based on the rating...Recurrent Neural Network, Recommender System , Neural Language Model, Collaborative Filtering 1. Introduction As ever larger parts of the population

  10. IMPLEMENTATION OF NEURAL - CRYPTOGRAPHIC SYSTEM USING FPGA

    Directory of Open Access Journals (Sweden)

    KARAM M. Z. OTHMAN

    2011-08-01

    Full Text Available Modern cryptography techniques are virtually unbreakable. As the Internet and other forms of electronic communication become more prevalent, electronic security is becoming increasingly important. Cryptography is used to protect e-mail messages, credit card information, and corporate data. The design of the cryptography system is a conventional cryptography that uses one key for encryption and decryption process. The chosen cryptography algorithm is stream cipher algorithm that encrypt one bit at a time. The central problem in the stream-cipher cryptography is the difficulty of generating a long unpredictable sequence of binary signals from short and random key. Pseudo random number generators (PRNG have been widely used to construct this key sequence. The pseudo random number generator was designed using the Artificial Neural Networks (ANN. The Artificial Neural Networks (ANN providing the required nonlinearity properties that increases the randomness statistical properties of the pseudo random generator. The learning algorithm of this neural network is backpropagation learning algorithm. The learning process was done by software program in Matlab (software implementation to get the efficient weights. Then, the learned neural network was implemented using field programmable gate array (FPGA.

  11. Simulating neural systems with Xyce.

    Energy Technology Data Exchange (ETDEWEB)

    Schiek, Richard Louis; Thornquist, Heidi K.; Mei, Ting; Warrender, Christina E.; Aimone, James Bradley; Teeter, Corinne; Duda, Alex M.

    2012-12-01

    Sandias parallel circuit simulator, Xyce, can address large scale neuron simulations in a new way extending the range within which one can perform high-fidelity, multi-compartment neuron simulations. This report documents the implementation of neuron devices in Xyce, their use in simulation and analysis of neuron systems.

  12. Compensating for Channel Fading in DS-CDMA Communication Systems Employing ICA Neural Network Detectors

    Directory of Open Access Journals (Sweden)

    David Overbye

    2005-06-01

    Full Text Available In this paper we examine the impact of channel fading on the bit error rate of a DS-CDMA communication system. The system employs detectors that incorporate neural networks effecting methods of independent component analysis (ICA, subspace estimation of channel noise, and Hopfield type neural networks. The Rayleigh fading channel model is used. When employed in a Rayleigh fading environment, the ICA neural network detectors that give superior performance in a flat fading channel did not retain this superior performance. We then present a new method of compensating for channel fading based on the incorporation of priors in the ICA neural network learning algorithms. When the ICA neural network detectors were compensated using the incorporation of priors, they give significantly better performance than the traditional detectors and the uncompensated ICA detectors. Keywords: CDMA, Multi-user Detection, Rayleigh Fading, Multipath Detection, Independent Component Analysis, Prior Probability Hebbian Learning, Natural Gradient

  13. Identification of the neural component of torque during manually-applied spasticity assessments in children with cerebral palsy

    NARCIS (Netherlands)

    Bar-On, L.; Desloovere, K.; Molenaers, G.; Harlaar, J.; Kindt, T.; Aertbelien, E.

    2014-01-01

    Clinical assessment of spasticity is compromised by the difficulty to distinguish neural from non-neural components of increased joint torque. Quantifying the contributions of each of these components is crucial to optimize the selection of anti-spasticity treatments such as botulinum toxin (BTX).

  14. An Artificial Neural Network Controller for Intelligent Transportation Systems Applications

    Science.gov (United States)

    1996-01-01

    An Autonomous Intelligent Cruise Control (AICC) has been designed using a feedforward artificial neural network, as an example for utilizing artificial neural networks for nonlinear control problems arising in intelligent transportation systems appli...

  15. Dynamic artificial neural networks with affective systems.

    Directory of Open Access Journals (Sweden)

    Catherine D Schuman

    Full Text Available Artificial neural networks (ANNs are processors that are trained to perform particular tasks. We couple a computational ANN with a simulated affective system in order to explore the interaction between the two. In particular, we design a simple affective system that adjusts the threshold values in the neurons of our ANN. The aim of this paper is to demonstrate that this simple affective system can control the firing rate of the ensemble of neurons in the ANN, as well as to explore the coupling between the affective system and the processes of long term potentiation (LTP and long term depression (LTD, and the effect of the parameters of the affective system on its performance. We apply our networks with affective systems to a simple pole balancing example and briefly discuss the effect of affective systems on network performance.

  16. Dynamical systems, attractors, and neural circuits.

    Science.gov (United States)

    Miller, Paul

    2016-01-01

    Biology is the study of dynamical systems. Yet most of us working in biology have limited pedagogical training in the theory of dynamical systems, an unfortunate historical fact that can be remedied for future generations of life scientists. In my particular field of systems neuroscience, neural circuits are rife with nonlinearities at all levels of description, rendering simple methodologies and our own intuition unreliable. Therefore, our ideas are likely to be wrong unless informed by good models. These models should be based on the mathematical theories of dynamical systems since functioning neurons are dynamic-they change their membrane potential and firing rates with time. Thus, selecting the appropriate type of dynamical system upon which to base a model is an important first step in the modeling process. This step all too easily goes awry, in part because there are many frameworks to choose from, in part because the sparsely sampled data can be consistent with a variety of dynamical processes, and in part because each modeler has a preferred modeling approach that is difficult to move away from. This brief review summarizes some of the main dynamical paradigms that can arise in neural circuits, with comments on what they can achieve computationally and what signatures might reveal their presence within empirical data. I provide examples of different dynamical systems using simple circuits of two or three cells, emphasizing that any one connectivity pattern is compatible with multiple, diverse functions.

  17. Neural System Prediction and Identification Challenge

    Directory of Open Access Journals (Sweden)

    Ioannis eVlachos

    2013-12-01

    Full Text Available Can we infer the function of a biological neural network (BNN if we know the connectivity and activity of all its constituent neurons? This question is at the core of neuroscience and, accordingly, various methods have been developed to record the activity and connectivity of as many neurons as possible. Surprisingly, there is no theoretical or computational demonstration that neuronal activity and connectivity are indeed sufficient to infer the function of a BNN. Therefore, we pose the Neural Systems Identification and Prediction Challenge (nuSPIC. We provide the connectivity and activity of all neurons and invite participants (i to infer the functions implemented (hard-wired in spiking neural networks (SNNs by stimulating and recording the activity of neurons and, (ii to implement predefined mathematical/biological functions using SNNs. The nuSPICs can be accessed via a web-interface to the NEST simulator and the user is not required to know any specific programming language. Furthermore, the nuSPICs can be used as a teaching tool. Finally, nuSPICs use the crowd-sourcing model to address scientific issues. With this computational approach we aim to identify which functions can be inferred by systematic recordings of neuronal activity and connectivity. In addition, nuSPICs will help the design and application of new experimental paradigms based on the structure of the SNN and the presumed function which is to be discovered.

  18. Neural system prediction and identification challenge.

    Science.gov (United States)

    Vlachos, Ioannis; Zaytsev, Yury V; Spreizer, Sebastian; Aertsen, Ad; Kumar, Arvind

    2013-01-01

    Can we infer the function of a biological neural network (BNN) if we know the connectivity and activity of all its constituent neurons?This question is at the core of neuroscience and, accordingly, various methods have been developed to record the activity and connectivity of as many neurons as possible. Surprisingly, there is no theoretical or computational demonstration that neuronal activity and connectivity are indeed sufficient to infer the function of a BNN. Therefore, we pose the Neural Systems Identification and Prediction Challenge (nuSPIC). We provide the connectivity and activity of all neurons and invite participants (1) to infer the functions implemented (hard-wired) in spiking neural networks (SNNs) by stimulating and recording the activity of neurons and, (2) to implement predefined mathematical/biological functions using SNNs. The nuSPICs can be accessed via a web-interface to the NEST simulator and the user is not required to know any specific programming language. Furthermore, the nuSPICs can be used as a teaching tool. Finally, nuSPICs use the crowd-sourcing model to address scientific issues. With this computational approach we aim to identify which functions can be inferred by systematic recordings of neuronal activity and connectivity. In addition, nuSPICs will help the design and application of new experimental paradigms based on the structure of the SNN and the presumed function which is to be discovered.

  19. Gyrotron: an ECH system component

    International Nuclear Information System (INIS)

    Loring, C.M.; Eason, H.O.; Kimrey, H.D.; White, T.L.; Jory, H.R.; Evans, S.J.

    1981-01-01

    The gyrotron, or electron-cyclotron maser, in the form of a gyromonotron, is being developed as a source of millimeter wave energy for fusion plasma heating. The characteristics of this high power, high efficiency electron tube are described in terms of the requirements for the beam power supply system, the mechanical support system, the cooling system, the focusing and tuning magnets, and the waveguide system. Requirements of power level and transmission efficiency dictate the use of oversize waveguide. The implications, both to the user and to the interaction mechanisms in the gyrotron, of the use of oversize waveguide are treated. The effects of variations of various operating parameters upon the gyrotron's power output and stability are also discussed. Data from gyrotron development and system operation are used where appropriate

  20. Fluvial facies reservoir productivity prediction method based on principal component analysis and artificial neural network

    Directory of Open Access Journals (Sweden)

    Pengyu Gao

    2016-03-01

    Full Text Available It is difficult to forecast the well productivity because of the complexity of vertical and horizontal developments in fluvial facies reservoir. This paper proposes a method based on Principal Component Analysis and Artificial Neural Network to predict well productivity of fluvial facies reservoir. The method summarizes the statistical reservoir factors and engineering factors that affect the well productivity, extracts information by applying the principal component analysis method and approximates arbitrary functions of the neural network to realize an accurate and efficient prediction on the fluvial facies reservoir well productivity. This method provides an effective way for forecasting the productivity of fluvial facies reservoir which is affected by multi-factors and complex mechanism. The study result shows that this method is a practical, effective, accurate and indirect productivity forecast method and is suitable for field application.

  1. Two component systems: physiological effect of a third component.

    Directory of Open Access Journals (Sweden)

    Baldiri Salvado

    Full Text Available Signal transduction systems mediate the response and adaptation of organisms to environmental changes. In prokaryotes, this signal transduction is often done through Two Component Systems (TCS. These TCS are phosphotransfer protein cascades, and in their prototypical form they are composed by a kinase that senses the environmental signals (SK and by a response regulator (RR that regulates the cellular response. This basic motif can be modified by the addition of a third protein that interacts either with the SK or the RR in a way that could change the dynamic response of the TCS module. In this work we aim at understanding the effect of such an additional protein (which we call "third component" on the functional properties of a prototypical TCS. To do so we build mathematical models of TCS with alternative designs for their interaction with that third component. These mathematical models are analyzed in order to identify the differences in dynamic behavior inherent to each design, with respect to functionally relevant properties such as sensitivity to changes in either the parameter values or the molecular concentrations, temporal responsiveness, possibility of multiple steady states, or stochastic fluctuations in the system. The differences are then correlated to the physiological requirements that impinge on the functioning of the TCS. This analysis sheds light on both, the dynamic behavior of synthetically designed TCS, and the conditions under which natural selection might favor each of the designs. We find that a third component that modulates SK activity increases the parameter space where a bistable response of the TCS module to signals is possible, if SK is monofunctional, but decreases it when the SK is bifunctional. The presence of a third component that modulates RR activity decreases the parameter space where a bistable response of the TCS module to signals is possible.

  2. Neural networks for feedback feedforward nonlinear control systems.

    Science.gov (United States)

    Parisini, T; Zoppoli, R

    1994-01-01

    This paper deals with the problem of designing feedback feedforward control strategies to drive the state of a dynamic system (in general, nonlinear) so as to track any desired trajectory joining the points of given compact sets, while minimizing a certain cost function (in general, nonquadratic). Due to the generality of the problem, conventional methods are difficult to apply. Thus, an approximate solution is sought by constraining control strategies to take on the structure of multilayer feedforward neural networks. After discussing the approximation properties of neural control strategies, a particular neural architecture is presented, which is based on what has been called the "linear-structure preserving principle". The original functional problem is then reduced to a nonlinear programming one, and backpropagation is applied to derive the optimal values of the synaptic weights. Recursive equations to compute the gradient components are presented, which generalize the classical adjoint system equations of N-stage optimal control theory. Simulation results related to nonlinear nonquadratic problems show the effectiveness of the proposed method.

  3. Hybrid solar lighting distribution systems and components

    Science.gov (United States)

    Muhs, Jeffrey D [Lenoir City, TN; Earl, Dennis D [Knoxville, TN; Beshears, David L [Knoxville, TN; Maxey, Lonnie C [Powell, TN; Jordan, John K [Oak Ridge, TN; Lind, Randall F [Lenoir City, TN

    2011-07-05

    A hybrid solar lighting distribution system and components having at least one hybrid solar concentrator, at least one fiber receiver, at least one hybrid luminaire, and a light distribution system operably connected to each hybrid solar concentrator and each hybrid luminaire. A controller operates all components.

  4. Neural systems for preparatory control of imitation.

    Science.gov (United States)

    Cross, Katy A; Iacoboni, Marco

    2014-01-01

    Humans have an automatic tendency to imitate others. Previous studies on how we control these tendencies have focused on reactive mechanisms, where inhibition of imitation is implemented after seeing an action. This work suggests that reactive control of imitation draws on at least partially specialized mechanisms. Here, we examine preparatory imitation control, where advance information allows control processes to be employed before an action is observed. Drawing on dual route models from the spatial compatibility literature, we compare control processes using biological and non-biological stimuli to determine whether preparatory imitation control recruits specialized neural systems that are similar to those observed in reactive imitation control. Results indicate that preparatory control involves anterior prefrontal, dorsolateral prefrontal, posterior parietal and early visual cortices regardless of whether automatic responses are evoked by biological (imitative) or non-biological stimuli. These results indicate both that preparatory control of imitation uses general mechanisms, and that preparatory control of imitation draws on different neural systems from reactive imitation control. Based on the regions involved, we hypothesize that preparatory control is implemented through top-down attentional biasing of visual processing.

  5. Online Vibration Monitoring of a Water Pump Machine to Detect Its Malfunction Components Based on Artificial Neural Network

    Science.gov (United States)

    Rahmawati, P.; Prajitno, P.

    2018-04-01

    Vibration monitoring is a measurement instrument used to identify, predict, and prevent failures in machine instruments[6]. This is very needed in the industrial applications, cause any problem with the equipment or plant translates into economical loss and they are mostly monitored component off-line[2]. In this research, a system has been developed to detect the malfunction of the components of Shimizu PS-128BT water pump machine, such as capacitor, bearing and impeller by online measurements. The malfunction components are detected by taking vibration data using a Micro-Electro-Mechanical System(MEMS)-based accelerometer that are acquired by using Raspberry Pi microcomputer and then the data are converted into the form of Relative Power Ratio(RPR). In this form the signal acquired from different components conditions have different patterns. The collected RPR used as the base of classification process for recognizing the damage components of the water pump that are conducted by Artificial Neural Network(ANN). Finally, the damage test result will be sent via text message using GSM module that are connected to Raspberry Pi microcomputer. The results, with several measurement readings, with each reading in 10 minutes duration for each different component conditions, all cases yield 100% of accuracies while in the case of defective capacitor yields 90% of accuracy.

  6. OCSEGen: Open Components and Systems Environment Generator

    Science.gov (United States)

    Tkachuk, Oksana

    2014-01-01

    To analyze a large system, one often needs to break it into smaller components.To analyze a component or unit under analysis, one needs to model its context of execution, called environment, which represents the components with which the unit interacts. Environment generation is a challenging problem, because the environment needs to be general enough to uncover unit errors, yet precise enough to make the analysis tractable. In this paper, we present a tool for automated environment generation for open components and systems. The tool, called OCSEGen, is implemented on top of the Soot framework. We present the tool's current support and discuss its possible future extensions.

  7. Digital Components in Swedish NPP Power Systems

    International Nuclear Information System (INIS)

    Karlsson, Mattias; Eriksson, Tage

    2015-01-01

    Swedish nuclear power plants have over the last 20 years of operation modernised or exchanged several systems and components of the electrical power system. Within these works, new components based on digital technology have been employed in order to realize functionality that was previously achieved by using electro-mechanical or analogue technology. Components and systems such as relay protection, rectifiers, inverters, variable speed drives and diesel-generator sets are today equipped with digital components. Several of the systems and components fulfil functions with a safety-role in the NPP. Recently, however, a number of incidents have occurred which highlight deficiencies in the design or HMI of the equipment, which warrants questions whether there are generic problems with some applications of digital components that needs to be addressed. The use of digital components has presented cost effective solutions, or even the only available solution on the market enabling a modernisation. The vast majority of systems using digital components have been operating without problems and often contribute to improved safety but the challenge of non-detectable, or non-identifiable, failure modes remain. In this paper, the extent to which digital components are used in Swedish NPP power systems will be presented including a description of typical applications. Based on data from maintenance records and fault reports, as well as interviews with designers and maintenance personnel, the main areas where problems have been encountered and where possible risks have been identified will be described. The paper intends to investigate any 'tell-tales' that could give signals of unwanted behaviour. Furthermore, particular benefits experienced by using digital components will be highlighted. The paper will also discuss the safety relevance of these findings and suggest measures to improve safety in the application of digital components in power systems. (authors)

  8. Neural networks and principle component analysis approaches to predict pile capacity in sand

    Directory of Open Access Journals (Sweden)

    Benali A

    2018-01-01

    Full Text Available Determination of pile bearing capacity from the in-situ tests has developed considerably due to the significant development of their technology. The project presented in this paper is a combination of two approaches, artificial neural networks and main component analyses that allow the development of a neural network model that provides a more accurate prediction of axial load bearing capacity based on the SPT test data. The retropropagation multi-layer perceptron with Bayesian regularization (RB was used in this model. This was established by the incorporation of about 260 data, obtained from the published literature, of experimental programs for large displacement driven piles. The PCA method is proposed for compression and suppression of the correlation between these data. This will improve the performance of generalization of the model.

  9. Bioelectric signal classification using a recurrent probabilistic neural network with time-series discriminant component analysis.

    Science.gov (United States)

    Hayashi, Hideaki; Shima, Keisuke; Shibanoki, Taro; Kurita, Yuichi; Tsuji, Toshio

    2013-01-01

    This paper outlines a probabilistic neural network developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower-dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model that incorporates a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into a neural network so that parameters can be obtained appropriately as network coefficients according to backpropagation-through-time-based training algorithm. The network is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. In the experiments conducted during the study, the validity of the proposed network was demonstrated for EEG signals.

  10. Structured Performance Analysis for Component Based Systems

    OpenAIRE

    Salmi , N.; Moreaux , Patrice; Ioualalen , M.

    2012-01-01

    International audience; The Component Based System (CBS) paradigm is now largely used to design software systems. In addition, performance and behavioural analysis remains a required step for the design and the construction of efficient systems. This is especially the case of CBS, which involve interconnected components running concurrent processes. % This paper proposes a compositional method for modeling and structured performance analysis of CBS. Modeling is based on Stochastic Well-formed...

  11. Theory of Neural Information Processing Systems

    International Nuclear Information System (INIS)

    Galla, Tobias

    2006-01-01

    It is difficult not to be amazed by the ability of the human brain to process, to structure and to memorize information. Even by the toughest standards the behaviour of this network of about 10 11 neurons qualifies as complex, and both the scientific community and the public take great interest in the growing field of neuroscience. The scientific endeavour to learn more about the function of the brain as an information processing system is here a truly interdisciplinary one, with important contributions from biology, computer science, physics, engineering and mathematics as the authors quite rightly point out in the introduction of their book. The role of the theoretical disciplines here is to provide mathematical models of information processing systems and the tools to study them. These models and tools are at the centre of the material covered in the book by Coolen, Kuehn and Sollich. The book is divided into five parts, providing basic introductory material on neural network models as well as the details of advanced techniques to study them. A mathematical appendix complements the main text. The range of topics is extremely broad, still the presentation is concise and the book well arranged. To stress the breadth of the book let me just mention a few keywords here: the material ranges from the basics of perceptrons and recurrent network architectures to more advanced aspects such as Bayesian learning and support vector machines; Shannon's theory of information and the definition of entropy are discussed, and a chapter on Amari's information geometry is not missing either. Finally the statistical mechanics chapters cover Gardner theory and the replica analysis of the Hopfield model, not without being preceded by a brief introduction of the basic concepts of equilibrium statistical physics. The book also contains a part on effective theories of the macroscopic dynamics of neural networks. Many dynamical aspects of neural networks are usually hard to find in the

  12. Systems with randomly failing repairable components

    DEFF Research Database (Denmark)

    Der Kiureghian, Armen; Ditlevsen, Ove Dalager; Song, Junho

    2005-01-01

    Closed-form expressions are derived for the steady-state availability, mean rate of failure, mean duration of downtime and reliability of a general system with randomly and independently failing repairable components. Component failures are assumed to be homogeneous Poisson events in time and rep...

  13. Olfactory systems and neural circuits that modulate predator odor fear

    Directory of Open Access Journals (Sweden)

    Lorey K. Takahashi

    2014-03-01

    Full Text Available When prey animals detect the odor of a predator a constellation of fear-related autonomic, endocrine, and behavioral responses rapidly occur to facilitate survival. How olfactory sensory systems process predator odor and channel that information to specific brain circuits is a fundamental issue that is not clearly understood. However, research in the last 15 years has begun to identify some of the essential features of the sensory detection systems and brain structures that underlie predator odor fear. For instance, the main (MOS and accessory olfactory systems (AOS detect predator odors and different types of predator odors are sensed by specific receptors located in either the MOS or AOS. However, complex predator chemosignals may be processed by both the MOS and AOS, which complicate our understanding of the specific neural circuits connected directly and indirectly from the MOS and AOS to activate the physiological and behavioral components of unconditioned and conditioned fear. Studies indicate that brain structures including the dorsal periaqueductal gray, paraventricular nucleus of the hypothalamus, and the medial amygdala appear to be broadly involved in predator odor induced autonomic activity and hypothalamic-pituitary-adrenal stress hormone secretion. The medial amygdala also plays a key role in predator odor unconditioned fear behavior and retrieval of contextual fear memory associated with prior predator odor experiences. Other neural structures including the bed nucleus of the stria terminalis and the ventral hippocampus appear prominently involve in predator odor fear behavior. The basolateral amygdala, medial hypothalamic nuclei, and medial prefrontal cortex are also activated by some but not all predator odors. Future research that characterizes how distinct predator odors are uniquely processed in olfactory systems and neural circuits will provide significant insights into the differences of how diverse predator odors activate

  14. Olfactory systems and neural circuits that modulate predator odor fear

    Science.gov (United States)

    Takahashi, Lorey K.

    2014-01-01

    When prey animals detect the odor of a predator a constellation of fear-related autonomic, endocrine, and behavioral responses rapidly occur to facilitate survival. How olfactory sensory systems process predator odor and channel that information to specific brain circuits is a fundamental issue that is not clearly understood. However, research in the last 15 years has begun to identify some of the essential features of the sensory detection systems and brain structures that underlie predator odor fear. For instance, the main (MOS) and accessory olfactory systems (AOS) detect predator odors and different types of predator odors are sensed by specific receptors located in either the MOS or AOS. However, complex predator chemosignals may be processed by both the MOS and AOS, which complicate our understanding of the specific neural circuits connected directly and indirectly from the MOS and AOS to activate the physiological and behavioral components of unconditioned and conditioned fear. Studies indicate that brain structures including the dorsal periaqueductal gray (DPAG), paraventricular nucleus (PVN) of the hypothalamus, and the medial amygdala (MeA) appear to be broadly involved in predator odor induced autonomic activity and hypothalamic-pituitary-adrenal (HPA) stress hormone secretion. The MeA also plays a key role in predator odor unconditioned fear behavior and retrieval of contextual fear memory associated with prior predator odor experiences. Other neural structures including the bed nucleus of the stria terminalis and the ventral hippocampus (VHC) appear prominently involved in predator odor fear behavior. The basolateral amygdala (BLA), medial hypothalamic nuclei, and medial prefrontal cortex (mPFC) are also activated by some but not all predator odors. Future research that characterizes how distinct predator odors are uniquely processed in olfactory systems and neural circuits will provide significant insights into the differences of how diverse predator

  15. Simulation of an industrial wastewater treatment plant using artificial neural networks and principal components analysis

    Directory of Open Access Journals (Sweden)

    Oliveira-Esquerre K.P.

    2002-01-01

    Full Text Available This work presents a way to predict the biochemical oxygen demand (BOD of the output stream of the biological wastewater treatment plant at RIPASA S/A Celulose e Papel, one of the major pulp and paper plants in Brazil. The best prediction performance is achieved when the data are preprocessed using principal components analysis (PCA before they are fed to a backpropagated neural network. The influence of input variables is analyzed and satisfactory prediction results are obtained for an optimized situation.

  16. Solid State Lighting Reliability Components to Systems

    CERN Document Server

    Fan, XJ

    2013-01-01

    Solid State Lighting Reliability: Components to Systems begins with an explanation of the major benefits of solid state lighting (SSL) when compared to conventional lighting systems including but not limited to long useful lifetimes of 50,000 (or more) hours and high efficacy. When designing effective devices that take advantage of SSL capabilities the reliability of internal components (optics, drive electronics, controls, thermal design) take on critical importance. As such a detailed discussion of reliability from performance at the device level to sub components is included as well as the integrated systems of SSL modules, lamps and luminaires including various failure modes, reliability testing and reliability performance. This book also: Covers the essential reliability theories and practices for current and future development of Solid State Lighting components and systems Provides a systematic overview for not only the state-of-the-art, but also future roadmap and perspectives of Solid State Lighting r...

  17. Hot gas path component cooling system

    Science.gov (United States)

    Lacy, Benjamin Paul; Bunker, Ronald Scott; Itzel, Gary Michael

    2014-02-18

    A cooling system for a hot gas path component is disclosed. The cooling system may include a component layer and a cover layer. The component layer may include a first inner surface and a second outer surface. The second outer surface may define a plurality of channels. The component layer may further define a plurality of passages extending generally between the first inner surface and the second outer surface. Each of the plurality of channels may be fluidly connected to at least one of the plurality of passages. The cover layer may be situated adjacent the second outer surface of the component layer. The plurality of passages may be configured to flow a cooling medium to the plurality of channels and provide impingement cooling to the cover layer. The plurality of channels may be configured to flow cooling medium therethrough, cooling the cover layer.

  18. Neural Network for Optimization of Existing Control Systems

    DEFF Research Database (Denmark)

    Madsen, Per Printz

    1995-01-01

    The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems.......The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems....

  19. NHI Component Technical Readiness Evaluation System

    International Nuclear Information System (INIS)

    Sherman, S.; Wilson, Dane F.; Pawel, Steven J.

    2007-01-01

    A decision process for evaluating the technical readiness or maturity of components (i.e., heat exchangers, chemical reactors, valves, etc.) for use by the U.S. DOE Nuclear Hydrogen Initiative is described. This system is used by the DOE NHI to assess individual components in relation to their readiness for pilot-scale and larger-scale deployment and to drive the research and development work needed to attain technical maturity. A description of the evaluation system is provided, and examples are given to illustrate how it is used to assist in component R and D decisions.

  20. The Effects of GABAergic Polarity Changes on Episodic Neural Network Activity in Developing Neural Systems

    Directory of Open Access Journals (Sweden)

    Wilfredo Blanco

    2017-09-01

    Full Text Available Early in development, neural systems have primarily excitatory coupling, where even GABAergic synapses are excitatory. Many of these systems exhibit spontaneous episodes of activity that have been characterized through both experimental and computational studies. As development progress the neural system goes through many changes, including synaptic remodeling, intrinsic plasticity in the ion channel expression, and a transformation of GABAergic synapses from excitatory to inhibitory. What effect each of these, and other, changes have on the network behavior is hard to know from experimental studies since they all happen in parallel. One advantage of a computational approach is that one has the ability to study developmental changes in isolation. Here, we examine the effects of GABAergic synapse polarity change on the spontaneous activity of both a mean field and a neural network model that has both glutamatergic and GABAergic coupling, representative of a developing neural network. We find some intuitive behavioral changes as the GABAergic neurons go from excitatory to inhibitory, shared by both models, such as a decrease in the duration of episodes. We also find some paradoxical changes in the activity that are only present in the neural network model. In particular, we find that during early development the inter-episode durations become longer on average, while later in development they become shorter. In addressing this unexpected finding, we uncover a priming effect that is particularly important for a small subset of neurons, called the “intermediate neurons.” We characterize these neurons and demonstrate why they are crucial to episode initiation, and why the paradoxical behavioral change result from priming of these neurons. The study illustrates how even arguably the simplest of developmental changes that occurs in neural systems can present non-intuitive behaviors. It also makes predictions about neural network behavioral changes

  1. A simple mechanical system for studying adaptive oscillatory neural networks

    DEFF Research Database (Denmark)

    Jouffroy, Guillaume; Jouffroy, Jerome

    Central Pattern Generators (CPG) are oscillatory systems that are responsible for generating rhythmic patterns at the origin of many biological activities such as for example locomotion or digestion. These systems are generally modelled as recurrent neural networks whose parameters are tuned so...... that the network oscillates in a suitable way, this tuning being a non trivial task. It also appears that the link with the physical body that these oscillatory entities control has a fundamental importance, and it seems that most bodies used for experimental validation in the literature (walking robots, lamprey...... a brief description of the Roller-Racer, we present as a preliminary study an RNN-based feed-forward controller whose parameters are obtained through the well-known teacher forcing learning algorithm, extended to learn signals with a continuous component....

  2. Extracting the Behaviorally Relevant Stimulus: Unique Neural Representation of Farnesol, a Component of the Recruitment Pheromone of Bombus terrestris.

    Directory of Open Access Journals (Sweden)

    Martin F Strube-Bloss

    Full Text Available To trigger innate behavior, sensory neural networks are pre-tuned to extract biologically relevant stimuli. Many male-female or insect-plant interactions depend on this phenomenon. Especially communication among individuals within social groups depends on innate behaviors. One example is the efficient recruitment of nest mates by successful bumblebee foragers. Returning foragers release a recruitment pheromone in the nest while they perform a 'dance' behavior to activate unemployed nest mates. A major component of this pheromone is the sesquiterpenoid farnesol. How farnesol is processed and perceived by the olfactory system, has not yet been identified. It is much likely that processing farnesol involves an innate mechanism for the extraction of relevant information to trigger a fast and reliable behavioral response. To test this hypothesis, we used population response analyses of 100 antennal lobe (AL neurons recorded in alive bumblebee workers under repeated stimulation with four behaviorally different, but chemically related odorants (geraniol, citronellol, citronellal and farnesol. The analysis identified a unique neural representation of the recruitment pheromone component compared to the other odorants that are predominantly emitted by flowers. The farnesol induced population activity in the AL allowed a reliable separation of farnesol from all other chemically related odor stimuli we tested. We conclude that the farnesol induced population activity may reflect a predetermined representation within the AL-neural network allowing efficient and fast extraction of a behaviorally relevant stimulus. Furthermore, the results show that population response analyses of multiple single AL-units may provide a powerful tool to identify distinct representations of behaviorally relevant odors.

  3. Thermally activated, single component epoxy systems

    KAUST Repository

    Unruh, David A.

    2011-08-23

    A single component epoxy system in which the resin and hardener components found in many two-component epoxies are combined onto the same molecule is described. The single molecule precursor to the epoxy resin contains both multiple epoxide moieties and a diamine held latent by thermally degradable carbamate linkages. These bis-carbamate "single molecule epoxies" have an essentially infinite shelf life and access a significant range in curing temperatures related to the structure of the carbamate linkages used. © 2011 American Chemical Society.

  4. Thermally activated, single component epoxy systems

    KAUST Repository

    Unruh, David A.; Pastine, Stefan J.; Moreton, Jessica C.; Frechet, Jean

    2011-01-01

    A single component epoxy system in which the resin and hardener components found in many two-component epoxies are combined onto the same molecule is described. The single molecule precursor to the epoxy resin contains both multiple epoxide moieties and a diamine held latent by thermally degradable carbamate linkages. These bis-carbamate "single molecule epoxies" have an essentially infinite shelf life and access a significant range in curing temperatures related to the structure of the carbamate linkages used. © 2011 American Chemical Society.

  5. The ctenophore genome and the evolutionary origins of neural systems

    NARCIS (Netherlands)

    Moroz, Leonid L.; Kocot, Kevin M.; Citarella, Mathew R.; Dosung, Sohn; Norekian, Tigran P.; Povolotskaya, Inna S.; Grigorenko, Anastasia P.; Dailey, Christopher; Berezikov, Eugene; Buckley, Katherine M.; Ptitsyn, Andrey; Reshetov, Denis; Mukherjee, Krishanu; Moroz, Tatiana P.; Bobkova, Yelena; Yu, Fahong; Kapitonov, Vladimir V.; Jurka, Jerzy; Bobkov, Yuri V.; Swore, Joshua J.; Girardo, David O.; Fodor, Alexander; Gusev, Fedor; Sanford, Rachel; Bruders, Rebecca; Kittler, Ellen; Mills, Claudia E.; Rast, Jonathan P.; Derelle, Romain; Solovyev, Victor V.; Kondrashov, Fyodor A.; Swalla, Billie J.; Sweedler, Jonathan V.; Rogaev, Evgeny I.; Halanych, Kenneth M.; Kohn, Andrea B.

    2014-01-01

    The origins of neural systems remain unresolved. In contrast to other basal metazoans, ctenophores (comb jellies) have both complex nervous and mesoderm-derived muscular systems. These holoplanktonic predators also have sophisticated ciliated locomotion, behaviour and distinct development. Here we

  6. Spiking Neural P Systems with Communication on Request.

    Science.gov (United States)

    Pan, Linqiang; Păun, Gheorghe; Zhang, Gexiang; Neri, Ferrante

    2017-12-01

    Spiking Neural [Formula: see text] Systems are Neural System models characterized by the fact that each neuron mimics a biological cell and the communication between neurons is based on spikes. In the Spiking Neural [Formula: see text] systems investigated so far, the application of evolution rules depends on the contents of a neuron (checked by means of a regular expression). In these [Formula: see text] systems, a specified number of spikes are consumed and a specified number of spikes are produced, and then sent to each of the neurons linked by a synapse to the evolving neuron. [Formula: see text]In the present work, a novel communication strategy among neurons of Spiking Neural [Formula: see text] Systems is proposed. In the resulting models, called Spiking Neural [Formula: see text] Systems with Communication on Request, the spikes are requested from neighboring neurons, depending on the contents of the neuron (still checked by means of a regular expression). Unlike the traditional Spiking Neural [Formula: see text] systems, no spikes are consumed or created: the spikes are only moved along synapses and replicated (when two or more neurons request the contents of the same neuron). [Formula: see text]The Spiking Neural [Formula: see text] Systems with Communication on Request are proved to be computationally universal, that is, equivalent with Turing machines as long as two types of spikes are used. Following this work, further research questions are listed to be open problems.

  7. Component reliability criticality or importance metrics for systems with degrading components

    NARCIS (Netherlands)

    Peng, H.; Coit, D.W.; Feng, Q.

    2012-01-01

    This paper proposes two new importance measures: one new importance measure for systems with -independent degrading components, and another one for systems with -correlated degrading components. Importance measures in previous research are inadequate for systems with degrading components because

  8. Bifurcation and chaos in neural excitable system

    International Nuclear Information System (INIS)

    Jing Zhujun; Yang Jianping; Feng Wei

    2006-01-01

    In this paper, we investigate the dynamical behaviors of neural excitable system without periodic external current (proposed by Chialvo [Generic excitable dynamics on a two-dimensional map. Chaos, Solitons and Fractals 1995;5(3-4):461-79] and with periodic external current as system's parameters vary. The existence and stability of three fixed points, bifurcation of fixed points, the conditions of existences of fold bifurcation, flip bifurcation and Hopf bifurcation are derived by using bifurcation theory and center manifold theorem. The chaotic existence in the sense of Marotto's definition of chaos is proved. We then give the numerical simulated results (using bifurcation diagrams, computations of Maximum Lyapunov exponent and phase portraits), which not only show the consistence with the analytic results but also display new and interesting dynamical behaviors, including the complete period-doubling and inverse period-doubling bifurcation, symmetry period-doubling bifurcations of period-3 orbit, simultaneous occurrence of two different routes (invariant cycle and period-doubling bifurcations) to chaos for a given bifurcation parameter, sudden disappearance of chaos at one critical point, a great abundance of period windows (period 2 to 10, 12, 19, 20 orbits, and so on) in transient chaotic regions with interior crises, strange chaotic attractors and strange non-chaotic attractor. In particular, the parameter k plays a important role in the system, which can leave the chaotic behavior or the quasi-periodic behavior to period-1 orbit as k varies, and it can be considered as an control strategy of chaos by adjusting the parameter k. Combining the existing results in [Generic excitable dynamics on a two-dimensional map. Chaos, Solitons and Fractals 1995;5(3-4):461-79] with the new results reported in this paper, a more complete description of the system is now obtained

  9. Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning

    Science.gov (United States)

    Xu, Tao; Xiao, Na; Zhai, Xiaolong; Chan, Pak Kwan; Tin, Chung

    2018-02-01

    Objective. Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). Approach. The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. Main results. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. Significance. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

  10. Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning.

    Science.gov (United States)

    Xu, Tao; Xiao, Na; Zhai, Xiaolong; Kwan Chan, Pak; Tin, Chung

    2018-02-01

    Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

  11. Component aging evaluation with expert systems

    International Nuclear Information System (INIS)

    Wiesemann, J.S.; Maguire, H.T. Jr.

    1988-01-01

    The age degradation of components involves a complex relationship between a variety of variables. These relationships are typically modeled using probabilistic and deterministic analyses. These methods depend upon a formal understanding of the underlying degradation mechanisms and a database of experience which allows statistical analyses to extract numerical trends. At present, not all age degradation mechanisms are adequately modeled and available data for age degradation is in most cases insufficient. In addition, these methods tend to focus upon answers to isolated questions (e.g., What is the component failure rate?) rather than the more pertinent questions concerning operations and maintenance (e.g., should the component be replaced at the next outage). Fortunately, knowledge in the form of personal experience does exist which allows plant personnel to make decisions concerning operations and maintenance. This knowledge can be modeled using expert systems. This paper discusses CAGES (Component Aging Expert System). It combines expert rules (heuristics), probabilistic models, and deterministic models to make evaluations of component aging; predict the implications for component life extension, operational readiness, maintenance effectiveness, and safety, and make recommendations for maintenance and operation

  12. Diesel engine management systems and components

    CERN Document Server

    2014-01-01

    This reference book provides a comprehensive insight into todays diesel injection systems and electronic control. It focusses on minimizing emissions and exhaust-gas treatment. Innovations by Bosch in the field of diesel-injection technology have made a significant contribution to the diesel boom. Calls for lower fuel consumption, reduced exhaust-gas emissions and quiet engines are making greater demands on the engine and fuel-injection systems. Contents History of the diesel engine.- Areas of use for diesel engines.- Basic principles of the diesel engine.- Fuels: Diesel fuel.- Fuels: Alternative fuels.- Cylinder-charge control systems.- Basic principles of diesel fuel-injection.- Overview of diesel fuel-injection systems.- Fuel supply to the low pressure stage.- Overview of discrete cylinder systems.- Unit injector system.- Unit pump system.- Overview of common-rail systems.- High pressure components of the common-rail system.- Injection nozzles.- Nozzle holders.- High pressure lines.- Start assist systems.-...

  13. Perspective : component tracking on the Nova system

    International Nuclear Information System (INIS)

    MacDonald, S.

    1999-01-01

    The issue of introducing Component Tracking as a service to natural gas producers, shippers and straddle plant operators was discussed. Approximately 39 companies in the industry were contacted by consultants at Nova Gas Transmission in an effort to assess if introducing this service would add value to individual producers. The numerous implications that may have to be dealt with if Component Tracking is introduced were also described. Component Tracking would provide an equitable approach to the allocation of molecules in the gas stream, and could provide producers with the ability to avoid capital outlay in field plants by alternatively contracting for recovery of the liquids at the straddle plants. Component Tracking is to be voluntary and each shipper would be able to decide whether to utilize the service at each of their receipt points onto the Nova system

  14. A modular neural network scheme applied to fault diagnosis in electric power systems.

    Science.gov (United States)

    Flores, Agustín; Quiles, Eduardo; García, Emilio; Morant, Francisco; Correcher, Antonio

    2014-01-01

    This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.

  15. A Modular Neural Network Scheme Applied to Fault Diagnosis in Electric Power Systems

    Directory of Open Access Journals (Sweden)

    Agustín Flores

    2014-01-01

    Full Text Available This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.

  16. Short-term synaptic plasticity and heterogeneity in neural systems

    Science.gov (United States)

    Mejias, J. F.; Kappen, H. J.; Longtin, A.; Torres, J. J.

    2013-01-01

    We review some recent results on neural dynamics and information processing which arise when considering several biophysical factors of interest, in particular, short-term synaptic plasticity and neural heterogeneity. The inclusion of short-term synaptic plasticity leads to enhanced long-term memory capacities, a higher robustness of memory to noise, and irregularity in the duration of the so-called up cortical states. On the other hand, considering some level of neural heterogeneity in neuron models allows neural systems to optimize information transmission in rate coding and temporal coding, two strategies commonly used by neurons to codify information in many brain areas. In all these studies, analytical approximations can be made to explain the underlying dynamics of these neural systems.

  17. Integrating the pastoral component in agricultural systems

    Directory of Open Access Journals (Sweden)

    Paulo César de Faccio Carvalho

    2018-03-01

    Full Text Available ABSTRACT This paper aims to discuss the impact of the introduction of pastures and grazing animals in agricultural systems. For the purposes of this manuscript, we focus on within-farm integrated crop-livestock systems (ICLS, typical of Southern Brazil. These ICLS are designed to create and enhance the synergisms and emergent properties have arisen from agricultural areas where livestock activities are integrated with crops. We show that the introduction of the crop component will affect less the preceding condition than the introduction of the livestock component. While the introduction of crops in pastoral systems represents increasing diversity of the plant component, the introduction of animals would represent the entry of new flows and interactions within the system. Thus, given the new complexity levels achieved from the introduction of grazing, the probability of arising emergent properties is theoretically much higher. However, grazing management is vital in determining the success or failure of such initiative. The grazing intensity practiced during the pasture phase would affect the canopy structure and the forage availability to animals. In adequate and moderate grazing intensities, it is possible to affirm that livestock combined with crops (ICLS has a potential positive impact. As important as the improvements that grazing animals can generate to the soil-plant components, the economic resilience remarkably increases when pasture rotations are introduced compared with purely agriculture systems, particularly in climate-risk situations. Thus, the integration of the pastoral component can enhance the sustainable intensification of food production, but it modifies simple, pure agricultural systems into more complex and knowledge-demanding production systems.

  18. Shades of grey; Assessing the contribution of the magno- and parvocellular systems to neural processing of the retinal input in the human visual system from the influence of neural population size and its discharge activity on the VEP.

    Science.gov (United States)

    Marcar, Valentine L; Baselgia, Silvana; Lüthi-Eisenegger, Barbara; Jäncke, Lutz

    2018-03-01

    Retinal input processing in the human visual system involves a phasic and tonic neural response. We investigated the role of the magno- and parvocellular systems by comparing the influence of the active neural population size and its discharge activity on the amplitude and latency of four VEP components. We recorded the scalp electric potential of 20 human volunteers viewing a series of dartboard images presented as a pattern reversing and pattern on-/offset stimulus. These patterns were designed to vary both neural population size coding the temporal- and spatial luminance contrast property and the discharge activity of the population involved in a systematic manner. When the VEP amplitude reflected the size of the neural population coding the temporal luminance contrast property of the image, the influence of luminance contrast followed the contrast response function of the parvocellular system. When the VEP amplitude reflected the size of the neural population responding to the spatial luminance contrast property the image, the influence of luminance contrast followed the contrast response function of the magnocellular system. The latencies of the VEP components examined exhibited the same behavior across our stimulus series. This investigation demonstrates the complex interplay of the magno- and parvocellular systems on the neural response as captured by the VEP. It also demonstrates a linear relationship between stimulus property, neural response, and the VEP and reveals the importance of feedback projections in modulating the ongoing neural response. In doing so, it corroborates the conclusions of our previous study.

  19. Neural Network for Principal Component Analysis with Applications in Image Compression

    Directory of Open Access Journals (Sweden)

    Luminita State

    2007-04-01

    Full Text Available Classical feature extraction and data projection methods have been extensively investigated in the pattern recognition and exploratory data analysis literature. Feature extraction and multivariate data projection allow avoiding the "curse of dimensionality", improve the generalization ability of classifiers and significantly reduce the computational requirements of pattern classifiers. During the past decade a large number of artificial neural networks and learning algorithms have been proposed for solving feature extraction problems, most of them being adaptive in nature and well-suited for many real environments where adaptive approach is required. Principal Component Analysis, also called Karhunen-Loeve transform is a well-known statistical method for feature extraction, data compression and multivariate data projection and so far it has been broadly used in a large series of signal and image processing, pattern recognition and data analysis applications.

  20. Common and dissociable neural correlates associated with component processes of inductive reasoning.

    Science.gov (United States)

    Jia, Xiuqin; Liang, Peipeng; Lu, Jie; Yang, Yanhui; Zhong, Ning; Li, Kuncheng

    2011-06-15

    The ability to draw numerical inductive reasoning requires two key cognitive processes, identification and extrapolation. This study aimed to identify the neural correlates of both component processes of numerical inductive reasoning using event-related fMRI. Three kinds of tasks: rule induction (RI), rule induction and application (RIA), and perceptual judgment (Jud) were solved by twenty right-handed adults. Our results found that the left superior parietal lobule (SPL) extending into the precuneus and left dorsolateral prefrontal cortex (DLPFC) were commonly recruited in the two components. It was also observed that the fronto-parietal network was more specific to identification, whereas the striatal-thalamic network was more specific to extrapolation. The findings suggest that numerical inductive reasoning is mediated by the coordination of multiple brain areas including the prefrontal, parietal, and subcortical regions, of which some are more specific to demands on only one of these two component processes, whereas others are sensitive to both. Copyright © 2011 Elsevier Inc. All rights reserved.

  1. Artificial neural network combined with principal component analysis for resolution of complex pharmaceutical formulations.

    Science.gov (United States)

    Ioele, Giuseppina; De Luca, Michele; Dinç, Erdal; Oliverio, Filomena; Ragno, Gaetano

    2011-01-01

    A chemometric approach based on the combined use of the principal component analysis (PCA) and artificial neural network (ANN) was developed for the multicomponent determination of caffeine (CAF), mepyramine (MEP), phenylpropanolamine (PPA) and pheniramine (PNA) in their pharmaceutical preparations without any chemical separation. The predictive ability of the ANN method was compared with the classical linear regression method Partial Least Squares 2 (PLS2). The UV spectral data between 220 and 300 nm of a training set of sixteen quaternary mixtures were processed by PCA to reduce the dimensions of input data and eliminate the noise coming from instrumentation. Several spectral ranges and different numbers of principal components (PCs) were tested to find the PCA-ANN and PLS2 models reaching the best determination results. A two layer ANN, using the first four PCs, was used with log-sigmoid transfer function in first hidden layer and linear transfer function in output layer. Standard error of prediction (SEP) was adopted to assess the predictive accuracy of the models when subjected to external validation. PCA-ANN showed better prediction ability in the determination of PPA and PNA in synthetic samples with added excipients and pharmaceutical formulations. Since both components are characterized by low absorptivity, the better performance of PCA-ANN was ascribed to the ability in considering all non-linear information from noise or interfering excipients.

  2. Dopamine system: Manager of neural pathways

    Directory of Open Access Journals (Sweden)

    Simon eHong

    2013-12-01

    Full Text Available There are a growing number of roles that midbrain dopamine (DA neurons assume, such as, reward, aversion, alerting and vigor. Here I propose a theory that may be able to explain why the suggested functions of DA came about. It has been suggested that largely parallel cortico-basal ganglia-thalamo-cortico loops exist to control different aspects of behavior. I propose that (1 the midbrain DA system is organized in a similar manner, with different groups of DA neurons corresponding to these parallel neural pathways (NPs. The DA system can be viewed as the manager of these parallel NPs in that it recruits and activates only the task-relevant NPs when they are needed. It is likely that the functions of those NPs that have been consistently activated by the corresponding DA groups are facilitated. I also propose that (2 there are two levels of DA roles: the How and What roles. The How role is encoded in tonic and phasic DA neuron firing patterns and gives a directive to its target NP: how vigorously its function needs to be carried out. The tonic DA firing is to maintain a certain level of DA in the target NPs to support their expected behavioral and mental functions; it is only when a sudden unexpected boost or suppression of activity is required by the relevant target NP that DA neurons in the corresponding NP act in a phasic manner. The What role is the implementational aspect of the role of DA in the target NP, such as binding to D1 receptors to boost working memory. This What aspect of DA explains why DA seems to assume different functions depending on the region of the brain in which it is involved. In terms of the role of the lateral habenula (LHb, the LHb is expected to suppress maladaptive behaviors and mental processes by controlling the DA system. The demand-based smart management by the DA system may have given animals an edge in evolution with adaptive behaviors and a better survival rate in resource-scarce situations.

  3. BUSINESS PROCESS MANAGEMENT SYSTEMS TECHNOLOGY COMPONENTS ANALYSIS

    Directory of Open Access Journals (Sweden)

    Andrea Giovanni Spelta

    2007-05-01

    Full Text Available The information technology that supports the implementation of the business process management appproach is called Business Process Management System (BPMS. The main components of the BPMS solution framework are process definition repository, process instances repository, transaction manager, conectors framework, process engine and middleware. In this paper we define and characterize the role and importance of the components of BPMS's framework. The research method adopted was the case study, through the analysis of the implementation of the BPMS solution in an insurance company called Chubb do Brasil. In the case study, the process "Manage Coinsured Events"" is described and characterized, as well as the components of the BPMS solution adopted and implemented by Chubb do Brasil for managing this process.

  4. Diagnostic Neural Network Systems for the Electronic Circuits

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2014-01-01

    Neural Networks is one of the most important artificial intelligent approaches for solving the diagnostic processes. This research concerns with uses the neural networks for diagnosis of the electronic circuits. Modern electronic systems contain both the analog and digital circuits. But, diagnosis of the analog circuits suffers from great complexity due to their nonlinearity. To overcome this problem, the proposed system introduces a diagnostic system that uses the neural network to diagnose both the digital and analog circuits. So, it can face the new requirements for the modern electronic systems. A fault dictionary method was implemented in the system. Experimental results are presented on three electronic systems. They are: artificial kidney, wireless network and personal computer systems. The proposed system has improved the performance of the diagnostic systems when applied for these practical cases

  5. Vein matching using artificial neural network in vein authentication systems

    Science.gov (United States)

    Noori Hoshyar, Azadeh; Sulaiman, Riza

    2011-10-01

    Personal identification technology as security systems is developing rapidly. Traditional authentication modes like key; password; card are not safe enough because they could be stolen or easily forgotten. Biometric as developed technology has been applied to a wide range of systems. According to different researchers, vein biometric is a good candidate among other biometric traits such as fingerprint, hand geometry, voice, DNA and etc for authentication systems. Vein authentication systems can be designed by different methodologies. All the methodologies consist of matching stage which is too important for final verification of the system. Neural Network is an effective methodology for matching and recognizing individuals in authentication systems. Therefore, this paper explains and implements the Neural Network methodology for finger vein authentication system. Neural Network is trained in Matlab to match the vein features of authentication system. The Network simulation shows the quality of matching as 95% which is a good performance for authentication system matching.

  6. System for Cooling of Electronic Components

    Science.gov (United States)

    Vasil'ev, L. L.; Grakovich, L. P.; Dragun, L. A.; Zhuravlev, A. S.; Olekhnovich, V. A.; Rabetskii, M. I.

    2017-01-01

    Results of computational and experimental investigations of heat pipes having a predetermined thermal resistance and a system based on these pipes for air cooling of electronic components and diode assemblies of lasers are presented. An efficient compact cooling system comprising heat pipes with an evaporator having a capillary coating of a caked copper powder and a condenser having a developed outer finning, has been deviced. This system makes it possible to remove, to the ambient air, a heat flow of power more than 300 W at a temperature of 40-50°C.

  7. INDIA’S ELECTRICITY DEMAND FORECAST USING REGRESSION ANALYSIS AND ARTIFICIAL NEURAL NETWORKS BASED ON PRINCIPAL COMPONENTS

    Directory of Open Access Journals (Sweden)

    S. Saravanan

    2012-07-01

    Full Text Available Power System planning starts with Electric load (demand forecasting. Accurate electricity load forecasting is one of the most important challenges in managing supply and demand of the electricity, since the electricity demand is volatile in nature; it cannot be stored and has to be consumed instantly. The aim of this study deals with electricity consumption in India, to forecast future projection of demand for a period of 19 years from 2012 to 2030. The eleven input variables used are Amount of CO2 emission, Population, Per capita GDP, Per capita gross national income, Gross Domestic savings, Industry, Consumer price index, Wholesale price index, Imports, Exports and Per capita power consumption. A new methodology based on Artificial Neural Networks (ANNs using principal components is also used. Data of 29 years used for training and data of 10 years used for testing the ANNs. Comparison made with multiple linear regression (based on original data and the principal components and ANNs with original data as input variables. The results show that the use of ANNs with principal components (PC is more effective.

  8. Computational neuroanatomy: ontology-based representation of neural components and connectivity.

    Science.gov (United States)

    Rubin, Daniel L; Talos, Ion-Florin; Halle, Michael; Musen, Mark A; Kikinis, Ron

    2009-02-05

    A critical challenge in neuroscience is organizing, managing, and accessing the explosion in neuroscientific knowledge, particularly anatomic knowledge. We believe that explicit knowledge-based approaches to make neuroscientific knowledge computationally accessible will be helpful in tackling this challenge and will enable a variety of applications exploiting this knowledge, such as surgical planning. We developed ontology-based models of neuroanatomy to enable symbolic lookup, logical inference and mathematical modeling of neural systems. We built a prototype model of the motor system that integrates descriptive anatomic and qualitative functional neuroanatomical knowledge. In addition to modeling normal neuroanatomy, our approach provides an explicit representation of abnormal neural connectivity in disease states, such as common movement disorders. The ontology-based representation encodes both structural and functional aspects of neuroanatomy. The ontology-based models can be evaluated computationally, enabling development of automated computer reasoning applications. Neuroanatomical knowledge can be represented in machine-accessible format using ontologies. Computational neuroanatomical approaches such as described in this work could become a key tool in translational informatics, leading to decision support applications that inform and guide surgical planning and personalized care for neurological disease in the future.

  9. FFTF Heat Transport System (HTS) component and system design

    International Nuclear Information System (INIS)

    Young, M.W.; Edwards, P.A.

    1980-01-01

    The FFTF Heat Transport Systems and Components designs have been completed and successfully tested at isothermal conditions up to 427 0 C (800 0 F). General performance has been as predicted in the design analyses. Operational flexibility and reliability have been outstanding throughout the test program. The components and systems have been demonstrated ready to support reactor powered operation testing planned later in 1980

  10. A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis.

    Science.gov (United States)

    Hayashi, Hideaki; Shibanoki, Taro; Shima, Keisuke; Kurita, Yuichi; Tsuji, Toshio

    2015-12-01

    This paper proposes a probabilistic neural network (NN) developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model with a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into an NN, which is named a time-series discriminant component network (TSDCN), so that parameters of dimensionality reduction and classification can be obtained simultaneously as network coefficients according to a backpropagation through time-based learning algorithm with the Lagrange multiplier method. The TSDCN is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. The validity of the TSDCN is demonstrated for high-dimensional artificial data and electroencephalogram signals in the experiments conducted during the study.

  11. Repetition-related reductions in neural activity reveal component processes of mental simulation.

    Science.gov (United States)

    Szpunar, Karl K; St Jacques, Peggy L; Robbins, Clifford A; Wig, Gagan S; Schacter, Daniel L

    2014-05-01

    In everyday life, people adaptively prepare for the future by simulating dynamic events about impending interactions with people, objects and locations. Previous research has consistently demonstrated that a distributed network of frontal-parietal-temporal brain regions supports this ubiquitous mental activity. Nonetheless, little is known about the manner in which specific regions of this network contribute to component features of future simulation. In two experiments, we used a functional magnetic resonance (fMR)-repetition suppression paradigm to demonstrate that distinct frontal-parietal-temporal regions are sensitive to processing the scenarios or what participants imagined was happening in an event (e.g., medial prefrontal, posterior cingulate, temporal-parietal and middle temporal cortices are sensitive to the scenarios associated with future social events), people (medial prefrontal cortex), objects (inferior frontal and premotor cortices) and locations (posterior cingulate/retrosplenial, parahippocampal and posterior parietal cortices) that typically constitute simulations of personal future events. This pattern of results demonstrates that the neural substrates of these component features of event simulations can be reliably identified in the context of a task that requires participants to simulate complex, everyday future experiences.

  12. An Implantable Wireless Neural Interface System for Simultaneous Recording and Stimulation of Peripheral Nerve with a Single Cuff Electrode.

    Science.gov (United States)

    Shon, Ahnsei; Chu, Jun-Uk; Jung, Jiuk; Kim, Hyungmin; Youn, Inchan

    2017-12-21

    Recently, implantable devices have become widely used in neural prostheses because they eliminate endemic drawbacks of conventional percutaneous neural interface systems. However, there are still several issues to be considered: low-efficiency wireless power transmission; wireless data communication over restricted operating distance with high power consumption; and limited functionality, working either as a neural signal recorder or as a stimulator. To overcome these issues, we suggest a novel implantable wireless neural interface system for simultaneous neural signal recording and stimulation using a single cuff electrode. By using widely available commercial off-the-shelf (COTS) components, an easily reconfigurable implantable wireless neural interface system was implemented into one compact module. The implantable device includes a wireless power consortium (WPC)-compliant power transmission circuit, a medical implant communication service (MICS)-band-based radio link and a cuff-electrode path controller for simultaneous neural signal recording and stimulation. During in vivo experiments with rabbit models, the implantable device successfully recorded and stimulated the tibial and peroneal nerves while communicating with the external device. The proposed system can be modified for various implantable medical devices, especially such as closed-loop control based implantable neural prostheses requiring neural signal recording and stimulation at the same time.

  13. An Implantable Wireless Neural Interface System for Simultaneous Recording and Stimulation of Peripheral Nerve with a Single Cuff Electrode

    Directory of Open Access Journals (Sweden)

    Ahnsei Shon

    2017-12-01

    Full Text Available Recently, implantable devices have become widely used in neural prostheses because they eliminate endemic drawbacks of conventional percutaneous neural interface systems. However, there are still several issues to be considered: low-efficiency wireless power transmission; wireless data communication over restricted operating distance with high power consumption; and limited functionality, working either as a neural signal recorder or as a stimulator. To overcome these issues, we suggest a novel implantable wireless neural interface system for simultaneous neural signal recording and stimulation using a single cuff electrode. By using widely available commercial off-the-shelf (COTS components, an easily reconfigurable implantable wireless neural interface system was implemented into one compact module. The implantable device includes a wireless power consortium (WPC-compliant power transmission circuit, a medical implant communication service (MICS-band-based radio link and a cuff-electrode path controller for simultaneous neural signal recording and stimulation. During in vivo experiments with rabbit models, the implantable device successfully recorded and stimulated the tibial and peroneal nerves while communicating with the external device. The proposed system can be modified for various implantable medical devices, especially such as closed-loop control based implantable neural prostheses requiring neural signal recording and stimulation at the same time.

  14. Use of neural networks in the analysis of complex systems

    International Nuclear Information System (INIS)

    Uhrig, R.E.

    1992-01-01

    The application of neural networks, alone or in conjunction with other advanced technologies (expert systems, fuzzy logic, and/or genetic algorithms) to some of the problems of complex engineering systems has the potential to enhance the safety reliability and operability of these systems. The work described here deals with complex systems or parts of such systems that can be isolated from the total system. Typically, the measured variables from the systems are analog variables that must be sampled and normalized to expected peak values before they are introduced into neural networks. Often data must be processed to put it into a form more acceptable to the neural network. The neural networks are usually simulated on modern high-speed computers that carry out the calculations serially. However, it is possible to implement neural networks using specially designed microchips where the network calculations are truly carried out in parallel, thereby providing virtually instantaneous outputs for each set of inputs. Specific applications described include: Diagnostics: State of the Plant; Hybrid System for Transient Identification; Detection of Change of Mode in Complex Systems; Sensor Validation; Plant-Wide Monitoring; Monitoring of Performance and Efficiency; and Analysis of Vibrations. Although the specific examples described deal with nuclear power plants or their subsystems, the techniques described can be applied to a wide variety of complex engineering systems

  15. Genetic learning in rule-based and neural systems

    Science.gov (United States)

    Smith, Robert E.

    1993-01-01

    The design of neural networks and fuzzy systems can involve complex, nonlinear, and ill-conditioned optimization problems. Often, traditional optimization schemes are inadequate or inapplicable for such tasks. Genetic Algorithms (GA's) are a class of optimization procedures whose mechanics are based on those of natural genetics. Mathematical arguments show how GAs bring substantial computational leverage to search problems, without requiring the mathematical characteristics often necessary for traditional optimization schemes (e.g., modality, continuity, availability of derivative information, etc.). GA's have proven effective in a variety of search tasks that arise in neural networks and fuzzy systems. This presentation begins by introducing the mechanism and theoretical underpinnings of GA's. GA's are then related to a class of rule-based machine learning systems called learning classifier systems (LCS's). An LCS implements a low-level production-system that uses a GA as its primary rule discovery mechanism. This presentation illustrates how, despite its rule-based framework, an LCS can be thought of as a competitive neural network. Neural network simulator code for an LCS is presented. In this context, the GA is doing more than optimizing and objective function. It is searching for an ecology of hidden nodes with limited connectivity. The GA attempts to evolve this ecology such that effective neural network performance results. The GA is particularly well adapted to this task, given its naturally-inspired basis. The LCS/neural network analogy extends itself to other, more traditional neural networks. Conclusions to the presentation discuss the implications of using GA's in ecological search problems that arise in neural and fuzzy systems.

  16. Marketing information system - concept and components

    OpenAIRE

    Domazet Ivana S.

    2003-01-01

    Current conditions for the carrying out of business activities are being characterized by an intensive changing of the business surrounding, as well as by a need for a flexible adapting to the newly-created conditions. Dynamisation of economic ambience, based on the principles of a propulsive marketing economy is affirming a business philosophy which will be based on requests of the strategic marketing. Vital component of every successful company is information system, which helps data acquis...

  17. Bio-inspired spiking neural network for nonlinear systems control.

    Science.gov (United States)

    Pérez, Javier; Cabrera, Juan A; Castillo, Juan J; Velasco, Juan M

    2018-08-01

    Spiking neural networks (SNN) are the third generation of artificial neural networks. SNN are the closest approximation to biological neural networks. SNNs make use of temporal spike trains to command inputs and outputs, allowing a faster and more complex computation. As demonstrated by biological organisms, they are a potentially good approach to designing controllers for highly nonlinear dynamic systems in which the performance of controllers developed by conventional techniques is not satisfactory or difficult to implement. SNN-based controllers exploit their ability for online learning and self-adaptation to evolve when transferred from simulations to the real world. SNN's inherent binary and temporary way of information codification facilitates their hardware implementation compared to analog neurons. Biological neural networks often require a lower number of neurons compared to other controllers based on artificial neural networks. In this work, these neuronal systems are imitated to perform the control of non-linear dynamic systems. For this purpose, a control structure based on spiking neural networks has been designed. Particular attention has been paid to optimizing the structure and size of the neural network. The proposed structure is able to control dynamic systems with a reduced number of neurons and connections. A supervised learning process using evolutionary algorithms has been carried out to perform controller training. The efficiency of the proposed network has been verified in two examples of dynamic systems control. Simulations show that the proposed control based on SNN exhibits superior performance compared to other approaches based on Neural Networks and SNNs. Copyright © 2018 Elsevier Ltd. All rights reserved.

  18. Anomaly detection in an automated safeguards system using neural networks

    International Nuclear Information System (INIS)

    Whiteson, R.; Howell, J.A.

    1992-01-01

    An automated safeguards system must be able to detect an anomalous event, identify the nature of the event, and recommend a corrective action. Neural networks represent a new way of thinking about basic computational mechanisms for intelligent information processing. In this paper, we discuss the issues involved in applying a neural network model to the first step of this process: anomaly detection in materials accounting systems. We extend our previous model to a 3-tank problem and compare different neural network architectures and algorithms. We evaluate the computational difficulties in training neural networks and explore how certain design principles affect the problems. The issues involved in building a neural network architecture include how the information flows, how the network is trained, how the neurons in a network are connected, how the neurons process information, and how the connections between neurons are modified. Our approach is based on the demonstrated ability of neural networks to model complex, nonlinear, real-time processes. By modeling the normal behavior of the processes, we can predict how a system should be behaving and, therefore, detect when an abnormality occurs

  19. Representation of neural networks as Lotka-Volterra systems

    International Nuclear Information System (INIS)

    Moreau, Yves; Vandewalle, Joos; Louies, Stephane; Brenig, Leon

    1999-01-01

    We study changes of coordinates that allow the representation of the ordinary differential equations describing continuous-time recurrent neural networks into differential equations describing predator-prey models--also called Lotka-Volterra systems. We transform the equations for the neural network first into quasi-monomial form, where we express the vector field of the dynamical system as a linear combination of products of powers of the variables. In practice, this transformation is possible only if the activation function is the hyperbolic tangent or the logistic sigmoied. From this quasi-monomial form, we can directly transform the system further into Lotka-Volterra equations. The resulting Lotka-Volterra system is of higher dimension than the original system, but the behavior of its first variables is equivalent to the behavior of the original neural network

  20. System Identification, Prediction, Simulation and Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1997-01-01

    a Gauss-Newton search direction is applied. 3) Amongst numerous model types, often met in control applications, only the Non-linear ARMAX (NARMAX) model, representing input/output description, is examined. A simulated example confirms that a neural network has the potential to perform excellent System......The intention of this paper is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: 1) Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. 2) Amongst numerous training algorithms, only the Recursive Prediction Error Method using...

  1. Neural network training by Kalman filtering in process system monitoring

    International Nuclear Information System (INIS)

    Ciftcioglu, Oe.

    1996-03-01

    Kalman filtering approach for neural network training is described. Its extended form is used as an adaptive filter in a nonlinear environment of the form a feedforward neural network. Kalman filtering approach generally provides fast training as well as avoiding excessive learning which results in enhanced generalization capability. The network is used in a process monitoring application where the inputs are measurement signals. Since the measurement errors are also modelled in Kalman filter the approach yields accurate training with the implication of accurate neural network model representing the input and output relationships in the application. As the process of concern is a dynamic system, the input source of information to neural network is time dependent so that the training algorithm presents an adaptive form for real-time operation for the monitoring task. (orig.)

  2. Thermochemical modelling of multi-component systems

    International Nuclear Information System (INIS)

    Sundman, B.; Gueneau, C.

    2015-01-01

    Computational thermodynamic, also known as the Calphad method, is a standard tool in industry for the development of materials and improving processes and there is an intense scientific development of new models and databases. The calculations are based on thermodynamic models of the Gibbs energy for each phase as a function of temperature, pressure and constitution. Model parameters are stored in databases that are developed in an international scientific collaboration. In this way, consistent and reliable data for many properties like heat capacity, chemical potentials, solubilities etc. can be obtained for multi-component systems. A brief introduction to this technique is given here and references to more extensive documentation are provided. (authors)

  3. Thermal photovoltaic solar integrated system analysis using neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Ashhab, S. [Hashemite Univ., Zarqa (Jordan). Dept. of Mechanical Engineering

    2007-07-01

    The energy demand in Jordan is primarily met by petroleum products. As such, the development of renewable energy systems is quite attractive. In particular, solar energy is a promising renewable energy source in Jordan and has been used for food canning, paper production, air-conditioning and sterilization. Artificial neural networks (ANNs) have received significant attention due to their capabilities in forecasting, modelling of complex nonlinear systems and control. ANNs have been used for forecasting solar energy. This paper presented a study that examined a thermal photovoltaic solar integrated system that was built in Jordan. Historical input-output system data that was collected experimentally was used to train an ANN that predicted the collector, PV module, pump and total efficiencies. The model predicted the efficiencies well and can therefore be utilized to find the operating conditions of the system that will produce the maximum system efficiencies. The paper provided a description of the photovoltaic solar system including equations for PV module efficiency; pump efficiency; and total efficiency. The paper also presented data relevant to the system performance and neural networks. The results of a neural net model were also presented based on the thermal PV solar integrated system data that was collected. It was concluded that the neural net model of the thermal photovoltaic solar integrated system set the background for achieving the best system performance. 10 refs., 6 figs.

  4. Neural network-based model reference adaptive control system.

    Science.gov (United States)

    Patino, H D; Liu, D

    2000-01-01

    In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a sigma-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.

  5. High speed digital interfacing for a neural data acquisition system

    Directory of Open Access Journals (Sweden)

    Bahr Andreas

    2016-09-01

    Full Text Available Diseases like schizophrenia and genetic epilepsy are supposed to be caused by disorders in the early development of the brain. For the further investigation of these relationships a custom designed application specific integrated circuit (ASIC was developed that is optimized for the recording from neonatal mice [Bahr A, Abu-Saleh L, Schroeder D, Krautschneider W. 16 Channel Neural Recording Integrated Circuit with SPI Interface and Error Correction Coding. Proc. 9th BIOSTEC 2016. Biodevices: Rome, Italy, 2016; 1: 263; Bahr A, Abu-Saleh L, Schroeder D, Krautschneider W. Development of a neural recording mixed signal integrated circuit for biomedical signal acquisition. Biomed Eng Biomed Tech Abstracts 2015; 60(S1: 298–299; Bahr A, Abu-Saleh L, Schroeder D, Krautschneider WH. 16 Channel Neural Recording Mixed Signal ASIC. CDNLive EMEA 2015 Conference Proceedings, 2015.]. To enable the live display of the neural signals a multichannel neural data acquisition system with live display functionality is presented. It implements a high speed data transmission from the ASIC to a computer with a live display functionality. The system has been successfully implemented and was used in a neural recording of a head-fixed mouse.

  6. Nonlinear signal processing using neural networks: Prediction and system modelling

    Energy Technology Data Exchange (ETDEWEB)

    Lapedes, A.; Farber, R.

    1987-06-01

    The backpropagation learning algorithm for neural networks is developed into a formalism for nonlinear signal processing. We illustrate the method by selecting two common topics in signal processing, prediction and system modelling, and show that nonlinear applications can be handled extremely well by using neural networks. The formalism is a natural, nonlinear extension of the linear Least Mean Squares algorithm commonly used in adaptive signal processing. Simulations are presented that document the additional performance achieved by using nonlinear neural networks. First, we demonstrate that the formalism may be used to predict points in a highly chaotic time series with orders of magnitude increase in accuracy over conventional methods including the Linear Predictive Method and the Gabor-Volterra-Weiner Polynomial Method. Deterministic chaos is thought to be involved in many physical situations including the onset of turbulence in fluids, chemical reactions and plasma physics. Secondly, we demonstrate the use of the formalism in nonlinear system modelling by providing a graphic example in which it is clear that the neural network has accurately modelled the nonlinear transfer function. It is interesting to note that the formalism provides explicit, analytic, global, approximations to the nonlinear maps underlying the various time series. Furthermore, the neural net seems to be extremely parsimonious in its requirements for data points from the time series. We show that the neural net is able to perform well because it globally approximates the relevant maps by performing a kind of generalized mode decomposition of the maps. 24 refs., 13 figs.

  7. Control Strategy Based on Wavelet Transform and Neural Network for Hybrid Power System

    Directory of Open Access Journals (Sweden)

    Y. D. Song

    2013-01-01

    Full Text Available This paper deals with an energy management of a hybrid power generation system. The proposed control strategy for the energy management is based on the combination of wavelet transform and neural network arithmetic. The hybrid system in this paper consists of an emulated wind turbine generator, PV panels, DC and AC loads, lithium ion battery, and super capacitor, which are all connected on a DC bus with unified DC voltage. The control strategy is responsible for compensating the difference between the generated power from the wind and solar generators and the demanded power by the loads. Wavelet transform decomposes the power difference into smoothed component and fast fluctuated component. In consideration of battery protection, the neural network is introduced to calculate the reference power of battery. Super capacitor (SC is controlled to regulate the DC bus voltage. The model of the hybrid system is developed in detail under Matlab/Simulink software environment.

  8. Microfluidic systems for stem cell-based neural tissue engineering.

    Science.gov (United States)

    Karimi, Mahdi; Bahrami, Sajad; Mirshekari, Hamed; Basri, Seyed Masoud Moosavi; Nik, Amirala Bakhshian; Aref, Amir R; Akbari, Mohsen; Hamblin, Michael R

    2016-07-05

    Neural tissue engineering aims at developing novel approaches for the treatment of diseases of the nervous system, by providing a permissive environment for the growth and differentiation of neural cells. Three-dimensional (3D) cell culture systems provide a closer biomimetic environment, and promote better cell differentiation and improved cell function, than could be achieved by conventional two-dimensional (2D) culture systems. With the recent advances in the discovery and introduction of different types of stem cells for tissue engineering, microfluidic platforms have provided an improved microenvironment for the 3D-culture of stem cells. Microfluidic systems can provide more precise control over the spatiotemporal distribution of chemical and physical cues at the cellular level compared to traditional systems. Various microsystems have been designed and fabricated for the purpose of neural tissue engineering. Enhanced neural migration and differentiation, and monitoring of these processes, as well as understanding the behavior of stem cells and their microenvironment have been obtained through application of different microfluidic-based stem cell culture and tissue engineering techniques. As the technology advances it may be possible to construct a "brain-on-a-chip". In this review, we describe the basics of stem cells and tissue engineering as well as microfluidics-based tissue engineering approaches. We review recent testing of various microfluidic approaches for stem cell-based neural tissue engineering.

  9. Marketing information system - concept and components

    Directory of Open Access Journals (Sweden)

    Domazet Ivana S.

    2003-01-01

    Full Text Available Current conditions for the carrying out of business activities are being characterized by an intensive changing of the business surrounding, as well as by a need for a flexible adapting to the newly-created conditions. Dynamisation of economic ambience, based on the principles of a propulsive marketing economy is affirming a business philosophy which will be based on requests of the strategic marketing. Vital component of every successful company is information system, which helps data acquisition and analysis, and its transfer into information, which is then forwarded to users and management. Bearing that in mind, we have created marketing information system, which collects relevant data, analyses it and then produces information, which can be used in planning, implementation and control, those making your company more efficient.

  10. Thermal Components Boost Performance of HVAC Systems

    Science.gov (United States)

    2012-01-01

    As the International Space Station (ISS) travels 17,500 miles per hour, normal is having a constant sensation of free-falling. Normal is no rain, but an extreme amount of shine.with temperatures reaching 250 F when facing the Sun. Thanks to a number of advanced control systems onboard the ISS, however, the interior of the station remains a cool, comfortable, normal environment where astronauts can live and work for extended periods of time. There are two main control systems on the ISS that make it possible for humans to survive in space: the Thermal Control System (TCS) and the Environmental Control and Life Support system. These intricate assemblies work together to supply water and oxygen, regulate temperature and pressure, maintain air quality, and manage waste. Through artificial means, these systems create a habitable environment for the space station s crew. The TCS constantly works to regulate the temperature not only for astronauts, but for the critical instruments and machines inside the spacecraft as well. To do its job, the TCS encompasses several components and systems both inside and outside of the ISS. Inside the spacecraft, a liquid heat-exchange process mechanically pumps fluids in closed-loop circuits to collect, transport, and reject heat. Outside the ISS, an external system circulates anhydrous ammonia to transport heat and cool equipment, and radiators release the heat into space. Over the years, NASA has worked with a variety of partners.public and private, national and international. to develop and refine the most complex thermal control systems ever built for spacecraft, including the one on the ISS.

  11. Micro rapid prototyping system for micro components

    International Nuclear Information System (INIS)

    Li Xiaochun; Choi Hongseok; Yang Yong

    2002-01-01

    Similarities between silicon-based micro-electro-mechanical systems (MEMS) and Shape Deposition Manufacturing (SDM) processes are obvious: both integrate additive and subtractive processes and use part and sacrificial materials to obtain functional structures. These MEMS techniques are two-dimensional (2-D) processes for a limited number of materials while SDM enables the building of parts that have traditionally been impossible to fabricate because of their complex shapes or of their variety in materials. This work presents initial results on the development of a micro rapid prototyping system that adapts SDM methodology to micro-fabrication. This system is designed to incorporate microdeposition and laser micromachining. In the hope of obtaining a precise microdeposition, an ultrasonic-based micro powder-feeding mechanism was developed in order to form thin patterns of dry powders that can be cladded or sintered onto a substrate by a micro-sized laser beam. Furthermore, experimental results on laser micromachining using a laser beam with a wavelength of 355 nm are also presented. After further improvement, the developed micro manufacturing system could take computer-aided design (CAD) output to reproduce 3-D heterogeneous micro-components from a wide selection of materials

  12. Application of neural networks to software quality modeling of a very large telecommunications system.

    Science.gov (United States)

    Khoshgoftaar, T M; Allen, E B; Hudepohl, J P; Aud, S J

    1997-01-01

    Society relies on telecommunications to such an extent that telecommunications software must have high reliability. Enhanced measurement for early risk assessment of latent defects (EMERALD) is a joint project of Nortel and Bell Canada for improving the reliability of telecommunications software products. This paper reports a case study of neural-network modeling techniques developed for the EMERALD system. The resulting neural network is currently in the prototype testing phase at Nortel. Neural-network models can be used to identify fault-prone modules for extra attention early in development, and thus reduce the risk of operational problems with those modules. We modeled a subset of modules representing over seven million lines of code from a very large telecommunications software system. The set consisted of those modules reused with changes from the previous release. The dependent variable was membership in the class of fault-prone modules. The independent variables were principal components of nine measures of software design attributes. We compared the neural-network model with a nonparametric discriminant model and found the neural-network model had better predictive accuracy.

  13. Adaptive Synchronization of Memristor-based Chaotic Neural Systems

    Directory of Open Access Journals (Sweden)

    Xiaofang Hu

    2014-11-01

    Full Text Available Chaotic neural networks consisting of a great number of chaotic neurons are able to reproduce the rich dynamics observed in biological nervous systems. In recent years, the memristor has attracted much interest in the efficient implementation of artificial synapses and neurons. This work addresses adaptive synchronization of a class of memristor-based neural chaotic systems using a novel adaptive backstepping approach. A systematic design procedure is presented. Simulation results have demonstrated the effectiveness of the proposed adaptive synchronization method and its potential in practical application of memristive chaotic oscillators in secure communication.

  14. Robust fault detection of wind energy conversion systems based on dynamic neural networks.

    Science.gov (United States)

    Talebi, Nasser; Sadrnia, Mohammad Ali; Darabi, Ahmad

    2014-01-01

    Occurrence of faults in wind energy conversion systems (WECSs) is inevitable. In order to detect the occurred faults at the appropriate time, avoid heavy economic losses, ensure safe system operation, prevent damage to adjacent relevant systems, and facilitate timely repair of failed components; a fault detection system (FDS) is required. Recurrent neural networks (RNNs) have gained a noticeable position in FDSs and they have been widely used for modeling of complex dynamical systems. One method for designing an FDS is to prepare a dynamic neural model emulating the normal system behavior. By comparing the outputs of the real system and neural model, incidence of the faults can be identified. In this paper, by utilizing a comprehensive dynamic model which contains both mechanical and electrical components of the WECS, an FDS is suggested using dynamic RNNs. The presented FDS detects faults of the generator's angular velocity sensor, pitch angle sensors, and pitch actuators. Robustness of the FDS is achieved by employing an adaptive threshold. Simulation results show that the proposed scheme is capable to detect the faults shortly and it has very low false and missed alarms rate.

  15. Conductivity of two-component systems

    Energy Technology Data Exchange (ETDEWEB)

    Kuijper, A. de; Hofman, J.P.; Waal, J.A. de [Shell Research BV, Rijswijk (Netherlands). Koninklijke/Shell Exploratie en Productie Lab.; Sandor, R.K.J. [Shell International Petroleum Maatschappij, The Hague (Netherlands)

    1996-01-01

    The authors present measurements and computer simulation results on the electrical conductivity of nonconducting grains embedded in a conductive brine host. The shapes of the grains ranged from prolate-ellipsoidal (with an axis ratio of 5:1) through spherical to oblate-ellipsoidal (with an axis ratio of 1:5). The conductivity was studied as a function of porosity and packing, and Archie`s cementation exponent was found to depend on porosity. They used spatially regular and random configurations with aligned and nonaligned packings. The experimental results agree well with the computer simulation data. This data set will enable extensive tests of models for calculating the anisotropic conductivity of two-component systems.

  16. Integrated evolutionary computation neural network quality controller for automated systems

    Energy Technology Data Exchange (ETDEWEB)

    Patro, S.; Kolarik, W.J. [Texas Tech Univ., Lubbock, TX (United States). Dept. of Industrial Engineering

    1999-06-01

    With increasing competition in the global market, more and more stringent quality standards and specifications are being demands at lower costs. Manufacturing applications of computing power are becoming more common. The application of neural networks to identification and control of dynamic processes has been discussed. The limitations of using neural networks for control purposes has been pointed out and a different technique, evolutionary computation, has been discussed. The results of identifying and controlling an unstable, dynamic process using evolutionary computation methods has been presented. A framework for an integrated system, using both neural networks and evolutionary computation, has been proposed to identify the process and then control the product quality, in a dynamic, multivariable system, in real-time.

  17. Temporal neural networks and transient analysis of complex engineering systems

    Science.gov (United States)

    Uluyol, Onder

    A theory is introduced for a multi-layered Local Output Gamma Feedback (LOGF) neural network within the paradigm of Locally-Recurrent Globally-Feedforward neural networks. It is developed for the identification, prediction, and control tasks of spatio-temporal systems and allows for the presentation of different time scales through incorporation of a gamma memory. It is initially applied to the tasks of sunspot and Mackey-Glass series prediction as benchmarks, then it is extended to the task of power level control of a nuclear reactor at different fuel cycle conditions. The developed LOGF neuron model can also be viewed as a Transformed Input and State (TIS) Gamma memory for neural network architectures for temporal processing. The novel LOGF neuron model extends the static neuron model by incorporating into it a short-term memory structure in the form of a digital gamma filter. A feedforward neural network made up of LOGF neurons can thus be used to model dynamic systems. A learning algorithm based upon the Backpropagation-Through-Time (BTT) approach is derived. It is applicable for training a general L-layer LOGF neural network. The spatial and temporal weights and parameters of the network are iteratively optimized for a given problem using the derived learning algorithm.

  18. Optical neural network system for pose determination of spinning satellites

    Science.gov (United States)

    Lee, Andrew; Casasent, David

    1990-01-01

    An optical neural network architecture and algorithm based on a Hopfield optimization network are presented for multitarget tracking. This tracker utilizes a neuron for every possible target track, and a quadratic energy function of neural activities which is minimized using gradient descent neural evolution. The neural net tracker is demonstrated as part of a system for determining position and orientation (pose) of spinning satellites with respect to a robotic spacecraft. The input to the system is time sequence video from a single camera. Novelty detection and filtering are utilized to locate and segment novel regions from the input images. The neural net multitarget tracker determines the correspondences (or tracks) of the novel regions as a function of time, and hence the paths of object (satellite) parts. The path traced out by a given part or region is approximately elliptical in image space, and the position, shape and orientation of the ellipse are functions of the satellite geometry and its pose. Having a geometric model of the satellite, and the elliptical path of a part in image space, the three-dimensional pose of the satellite is determined. Digital simulation results using this algorithm are presented for various satellite poses and lighting conditions.

  19. Lithofacies identification using multiple adaptive resonance theory neural networks and group decision expert system

    Science.gov (United States)

    Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.; Rocky, Durrans S.

    2000-01-01

    Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.

  20. Silicon synaptic transistor for hardware-based spiking neural network and neuromorphic system

    Science.gov (United States)

    Kim, Hyungjin; Hwang, Sungmin; Park, Jungjin; Park, Byung-Gook

    2017-10-01

    Brain-inspired neuromorphic systems have attracted much attention as new computing paradigms for power-efficient computation. Here, we report a silicon synaptic transistor with two electrically independent gates to realize a hardware-based neural network system without any switching components. The spike-timing dependent plasticity characteristics of the synaptic devices are measured and analyzed. With the help of the device model based on the measured data, the pattern recognition capability of the hardware-based spiking neural network systems is demonstrated using the modified national institute of standards and technology handwritten dataset. By comparing systems with and without inhibitory synapse part, it is confirmed that the inhibitory synapse part is an essential element in obtaining effective and high pattern classification capability.

  1. Forecasting of UV-Vis absorbance time series using artificial neural networks combined with principal component analysis.

    Science.gov (United States)

    Plazas-Nossa, Leonardo; Hofer, Thomas; Gruber, Günter; Torres, Andres

    2017-02-01

    This work proposes a methodology for the forecasting of online water quality data provided by UV-Vis spectrometry. Therefore, a combination of principal component analysis (PCA) to reduce the dimensionality of a data set and artificial neural networks (ANNs) for forecasting purposes was used. The results obtained were compared with those obtained by using discrete Fourier transform (DFT). The proposed methodology was applied to four absorbance time series data sets composed by a total number of 5705 UV-Vis spectra. Absolute percentage errors obtained by applying the proposed PCA/ANN methodology vary between 10% and 13% for all four study sites. In general terms, the results obtained were hardly generalizable, as they appeared to be highly dependent on specific dynamics of the water system; however, some trends can be outlined. PCA/ANN methodology gives better results than PCA/DFT forecasting procedure by using a specific spectra range for the following conditions: (i) for Salitre wastewater treatment plant (WWTP) (first hour) and Graz West R05 (first 18 min), from the last part of UV range to all visible range; (ii) for Gibraltar pumping station (first 6 min) for all UV-Vis absorbance spectra; and (iii) for San Fernando WWTP (first 24 min) for all of UV range to middle part of visible range.

  2. A Computational Model of Torque Generation: Neural, Contractile, Metabolic and Musculoskeletal Components

    Science.gov (United States)

    Callahan, Damien M.; Umberger, Brian R.; Kent-Braun, Jane A.

    2013-01-01

    The pathway of voluntary joint torque production includes motor neuron recruitment and rate-coding, sarcolemmal depolarization and calcium release by the sarcoplasmic reticulum, force generation by motor proteins within skeletal muscle, and force transmission by tendon across the joint. The direct source of energetic support for this process is ATP hydrolysis. It is possible to examine portions of this physiologic pathway using various in vivo and in vitro techniques, but an integrated view of the multiple processes that ultimately impact joint torque remains elusive. To address this gap, we present a comprehensive computational model of the combined neuromuscular and musculoskeletal systems that includes novel components related to intracellular bioenergetics function. Components representing excitatory drive, muscle activation, force generation, metabolic perturbations, and torque production during voluntary human ankle dorsiflexion were constructed, using a combination of experimentally-derived data and literature values. Simulation results were validated by comparison with torque and metabolic data obtained in vivo. The model successfully predicted peak and submaximal voluntary and electrically-elicited torque output, and accurately simulated the metabolic perturbations associated with voluntary contractions. This novel, comprehensive model could be used to better understand impact of global effectors such as age and disease on various components of the neuromuscular system, and ultimately, voluntary torque output. PMID:23405245

  3. Classical Conditioning with Pulsed Integrated Neural Networks: Circuits and System

    DEFF Research Database (Denmark)

    Lehmann, Torsten

    1998-01-01

    In this paper we investigate on-chip learning for pulsed, integrated neural networks. We discuss the implementational problems the technology imposes on learning systems and we find that abiologically inspired approach using simple circuit structures is most likely to bring success. We develop a ...... chip to solve simple classical conditioning tasks, thus verifying the design methodologies put forward in the paper....

  4. Neural network based system for script identification in Indian ...

    Indian Academy of Sciences (India)

    2016-08-26

    Aug 26, 2016 ... The paper describes a neural network-based script identification system which can be used in the machine reading of documents written in English, Hindi and Kannada language scripts. Script identification is a basic requirement in automation of document processing, in multi-script, multi-lingual ...

  5. Development of a hybrid system of artificial neural networks and ...

    African Journals Online (AJOL)

    Development of a hybrid system of artificial neural networks and artificial bee colony algorithm for prediction and modeling of customer choice in the market. ... attempted to present a new method for the modeling and prediction of customer choice in the market using the combination of artificial intelligence and data mining.

  6. PERFORMANCE OF ALTERNATIVE COMPONENT PRICING SYSTEMS FOR PORK

    OpenAIRE

    Brorsen, B. Wade; Akridge, Jay T.; Boland, Michael A.; Mauney, Sean; Forrest, John C.

    1998-01-01

    One method of implementing value-based marketing is a component pricing system. This research develops and evaluates alternative component pricing systems for pork. Two electronic technologies for estimating carcass components (optical probe and electromagnetic scanner) were evaluated on two sets of data representing different populations. Model accuracy increased as additional components were added.

  7. Neural mechanisms of selective attention in the somatosensory system.

    Science.gov (United States)

    Gomez-Ramirez, Manuel; Hysaj, Kristjana; Niebur, Ernst

    2016-09-01

    Selective attention allows organisms to extract behaviorally relevant information while ignoring distracting stimuli that compete for the limited resources of their central nervous systems. Attention is highly flexible, and it can be harnessed to select information based on sensory modality, within-modality feature(s), spatial location, object identity, and/or temporal properties. In this review, we discuss the body of work devoted to understanding mechanisms of selective attention in the somatosensory system. In particular, we describe the effects of attention on tactile behavior and corresponding neural activity in somatosensory cortex. Our focus is on neural mechanisms that select tactile stimuli based on their location on the body (somatotopic-based attention) or their sensory feature (feature-based attention). We highlight parallels between selection mechanisms in touch and other sensory systems and discuss several putative neural coding schemes employed by cortical populations to signal the behavioral relevance of sensory inputs. Specifically, we contrast the advantages and disadvantages of using a gain vs. spike-spike correlation code for representing attended sensory stimuli. We favor a neural network model of tactile attention that is composed of frontal, parietal, and subcortical areas that controls somatosensory cells encoding the relevant stimulus features to enable preferential processing throughout the somatosensory hierarchy. Our review is based on data from noninvasive electrophysiological and imaging data in humans as well as single-unit recordings in nonhuman primates. Copyright © 2016 the American Physiological Society.

  8. Frequency-difference-dependent stochastic resonance in neural systems

    Science.gov (United States)

    Guo, Daqing; Perc, Matjaž; Zhang, Yangsong; Xu, Peng; Yao, Dezhong

    2017-08-01

    Biological neurons receive multiple noisy oscillatory signals, and their dynamical response to the superposition of these signals is of fundamental importance for information processing in the brain. Here we study the response of neural systems to the weak envelope modulation signal, which is superimposed by two periodic signals with different frequencies. We show that stochastic resonance occurs at the beat frequency in neural systems at the single-neuron as well as the population level. The performance of this frequency-difference-dependent stochastic resonance is influenced by both the beat frequency and the two forcing frequencies. Compared to a single neuron, a population of neurons is more efficient in detecting the information carried by the weak envelope modulation signal at the beat frequency. Furthermore, an appropriate fine-tuning of the excitation-inhibition balance can further optimize the response of a neural ensemble to the superimposed signal. Our results thus introduce and provide insights into the generation and modulation mechanism of the frequency-difference-dependent stochastic resonance in neural systems.

  9. Fault diagnosis system of electromagnetic valve using neural network filter

    International Nuclear Information System (INIS)

    Hayashi, Shoji; Odaka, Tomohiro; Kuroiwa, Jousuke; Ogura, Hisakazu

    2008-01-01

    This paper is concerned with the gas leakage fault detection of electromagnetic valve using a neural network filter. In modern plants, the ability to detect and identify gas leakage faults is becoming increasingly important. The main difficulty in detecting gas leakage faults by sound signals lies in the fact that the practical plants are usually very noisy. To solve this difficulty, a neural network filter is used to eliminate background noise and raise the signal noise ratio of the sound signal. The background noise is assumed as a dynamic system, and an accurate mathematical model of the dynamic system can be established using a neural network filter. The predicted error between predicted values and practical ones constitutes the output of the filter. If the predicted error is zero, then there is no leakage. If the predicted error is greater than a certain value, then there is a leakage fault. Through application to practical pneumatic systems, it is verified that the neural network filter was effective in gas leakage detection. (author)

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

    Science.gov (United States)

    Reifman, Jaques; Wei, Thomas Y. C.

    1995-01-01

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

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

    Science.gov (United States)

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

    1995-08-15

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

  12. Application of artificial neural networks to evaluate weld defects of nuclear components

    International Nuclear Information System (INIS)

    Amin, E.S.

    2007-01-01

    Artificial neural networks (ANNs) are computational representations based on the biological neural architecture of the brain. ANNs have been successfully applied to a wide range of engineering and scientific applications, such as signal, image processing and data analysis. Although Radiographic testing is widely used for welding defects, it is unsuccessful in identifying some welding defects because of the nature of image formation and quality. Neoteric algorithms have been used for the purpose of weld defects identifications in radiographic images to replace the expert knowledge. The application of artificial neural networks in noise detection of radiographic films is used. Radial Basis (RB) and learning vector quantization (LVQ) were applied. The method shows good performance in weld defects recognition and classification problems.

  13. Synthesis of recurrent neural networks for dynamical system simulation.

    Science.gov (United States)

    Trischler, Adam P; D'Eleuterio, Gabriele M T

    2016-08-01

    We review several of the most widely used techniques for training recurrent neural networks to approximate dynamical systems, then describe a novel algorithm for this task. The algorithm is based on an earlier theoretical result that guarantees the quality of the network approximation. We show that a feedforward neural network can be trained on the vector-field representation of a given dynamical system using backpropagation, then recast it as a recurrent network that replicates the original system's dynamics. After detailing this algorithm and its relation to earlier approaches, we present numerical examples that demonstrate its capabilities. One of the distinguishing features of our approach is that both the original dynamical systems and the recurrent networks that simulate them operate in continuous time. Copyright © 2016 Elsevier Ltd. All rights reserved.

  14. Distributed Adaptive Neural Control for Stochastic Nonlinear Multiagent Systems.

    Science.gov (United States)

    Wang, Fang; Chen, Bing; Lin, Chong; Li, Xuehua

    2016-11-14

    In this paper, a consensus tracking problem of nonlinear multiagent systems is investigated under a directed communication topology. All the followers are modeled by stochastic nonlinear systems in nonstrict feedback form, where nonlinearities and stochastic disturbance terms are totally unknown. Based on the structural characteristic of neural networks (in Lemma 4), a novel distributed adaptive neural control scheme is put forward. The raised control method not only effectively handles unknown nonlinearities in nonstrict feedback systems, but also copes with the interactions among agents and coupling terms. Based on the stochastic Lyapunov functional method, it is indicated that all the signals of the closed-loop system are bounded in probability and all followers' outputs are convergent to a neighborhood of the output of leader. At last, the efficiency of the control method is testified by a numerical example.

  15. Three neural network based sensor systems for environmental monitoring

    International Nuclear Information System (INIS)

    Keller, P.E.; Kouzes, R.T.; Kangas, L.J.

    1994-05-01

    Compact, portable systems capable of quickly identifying contaminants in the field are of great importance when monitoring the environment. One of the missions of the Pacific Northwest Laboratory is to examine and develop new technologies for environmental restoration and waste management at the Hanford Site. In this paper, three prototype sensing systems are discussed. These prototypes are composed of sensing elements, data acquisition system, computer, and neural network implemented in software, and are capable of automatically identifying contaminants. The first system employs an array of tin-oxide gas sensors and is used to identify chemical vapors. The second system employs an array of optical sensors and is used to identify the composition of chemical dyes in liquids. The third system contains a portable gamma-ray spectrometer and is used to identify radioactive isotopes. In these systems, the neural network is used to identify the composition of the sensed contaminant. With a neural network, the intense computation takes place during the training process. Once the network is trained, operation consists of propagating the data through the network. Since the computation involved during operation consists of vector-matrix multiplication and application of look-up tables unknown samples can be rapidly identified in the field

  16. Neural multigrid for gauge theories and other disordered systems

    International Nuclear Information System (INIS)

    Baeker, M.; Kalkreuter, T.; Mack, G.; Speh, M.

    1992-09-01

    We present evidence that multigrid works for wave equations in disordered systems, e.g. in the presence of gauge fields, no matter how strong the disorder, but one needs to introduce a 'neural computations' point of view into large scale simulations: First, the system must learn how to do the simulations efficiently, then do the simulation (fast). The method can also be used to provide smooth interpolation kernels which are needed in multigrid Monte Carlo updates. (orig.)

  17. Neural computing thermal comfort index for HVAC systems

    International Nuclear Information System (INIS)

    Atthajariyakul, S.; Leephakpreeda, T.

    2005-01-01

    The primary purpose of a heating, ventilating and air conditioning (HVAC) system within a building is to make occupants comfortable. Without real time determination of human thermal comfort, it is not feasible for the HVAC system to yield controlled conditions of the air for human comfort all the time. This paper presents a practical approach to determine human thermal comfort quantitatively via neural computing. The neural network model allows real time determination of the thermal comfort index, where it is not practical to compute the conventional predicted mean vote (PMV) index itself in real time. The feed forward neural network model is proposed as an explicit function of the relation of the PMV index to accessible variables, i.e. the air temperature, wet bulb temperature, globe temperature, air velocity, clothing insulation and human activity. An experiment in an air conditioned office room was done to demonstrate the effectiveness of the proposed methodology. The results show good agreement between the thermal comfort index calculated from the neural network model in real time and those calculated from the conventional PMV model

  18. ARTIFICIAL NEURAL NETWORKS BASED GEARS MATERIAL SELECTION HYBRID INTELLIGENT SYSTEM

    Institute of Scientific and Technical Information of China (English)

    X.C. Li; W.X. Zhu; G. Chen; D.S. Mei; J. Zhang; K.M. Chen

    2003-01-01

    An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples,the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.

  19. Reliability for systems of degrading components with distinct component shock sets

    International Nuclear Information System (INIS)

    Song, Sanling; Coit, David W.; Feng, Qianmei

    2014-01-01

    This paper studies reliability for multi-component systems subject to dependent competing risks of degradation wear and random shocks, with distinct shock sets. In practice, many systems are exposed to distinct and different types of shocks that can be categorized according to their sizes, function, affected components, etc. Previous research primarily focuses on simple systems with independent failure processes, systems with independent component time-to-failure, or components that share the same shock set or type of shocks. In our new model, we classify random shocks into different sets based on their sizes or function. Shocks with specific sizes or function can selectively affect one or more components in the system but not necessarily all components. Additionally the shocks from the different shock sets can arrive at different rates and have different relative magnitudes. Preventive maintenance (PM) optimization is conducted for the system with different component shock sets. Decision variables for two different maintenance scheduling problems, the PM replacement time interval, and the PM inspection time interval, are determined by minimizing a defined system cost rate. Sensitivity analysis is performed to provide insight into the behavior of the proposed maintenance policies. These models can be applied directly or customized for many complex systems that experience dependent competing failure processes with different component shock sets. A MEMS (Micro-electro mechanical systems) oscillator is a typical system subject to dependent and competing failure processes, and it is used as a numerical example to illustrate our new reliability and maintenance models

  20. Parameter estimation in space systems using recurrent neural networks

    Science.gov (United States)

    Parlos, Alexander G.; Atiya, Amir F.; Sunkel, John W.

    1991-01-01

    The identification of time-varying parameters encountered in space systems is addressed, using artificial neural systems. A hybrid feedforward/feedback neural network, namely a recurrent multilayer perception, is used as the model structure in the nonlinear system identification. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard back-propagation-learning algorithm is modified and it is used for both the off-line and on-line supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying parameters of nonlinear dynamic systems is investigated by estimating the mass properties of a representative large spacecraft. The changes in the spacecraft inertia are predicted using a trained neural network, during two configurations corresponding to the early and late stages of the spacecraft on-orbit assembly sequence. The proposed on-line mass properties estimation capability offers encouraging results, though, further research is warranted for training and testing the predictive capabilities of these networks beyond nominal spacecraft operations.

  1. Analysis of the DWPF glass pouring system using neural networks

    International Nuclear Information System (INIS)

    Calloway, T.B. Jr.; Jantzen, C.M.

    1997-01-01

    Neural networks were used to determine the sensitivity of 39 selected Melter/Melter Off Gas and Melter Feed System process parameters as related to the Defense Waste Processing Facility (DWPF) Melter Pour Spout Pressure during the overall analysis and resolution of the DWPF glass production and pouring issues. Two different commercial neural network software packages were used for this analysis. Models were developed and used to determine the critical parameters which accurately describe the DWPF Pour Spout Pressure. The model created using a low-end software package has a root mean square error of ± 0.35 inwc ( 2 = 0.77) with respect to the plant data used to validate and test the model. The model created using a high-end software package has a R 2 = 0.97 with respect to the plant data used to validate and test the model. The models developed for this application identified the key process parameters which contribute to the control of the DWPF Melter Pour Spout pressure during glass pouring operations. The relative contribution and ranking of the selected parameters was determined using the modeling software. Neural network computing software was determined to be a cost-effective software tool for process engineers performing troubleshooting and system performance monitoring activities. In remote high-level waste processing environments, neural network software is especially useful as a replacement for sensors which have failed and are costly to replace. The software can be used to accurately model critical remotely installed plant instrumentation. When the instrumentation fails, the software can be used to provide a soft sensor to replace the actual sensor, thereby decreasing the overall operating cost. Additionally, neural network software tools require very little training and are especially useful in mining or selecting critical variables from the vast amounts of data collected from process computers

  2. Neural Computations in a Dynamical System with Multiple Time Scales.

    Science.gov (United States)

    Mi, Yuanyuan; Lin, Xiaohan; Wu, Si

    2016-01-01

    Neural systems display rich short-term dynamics at various levels, e.g., spike-frequency adaptation (SFA) at the single-neuron level, and short-term facilitation (STF) and depression (STD) at the synapse level. These dynamical features typically cover a broad range of time scales and exhibit large diversity in different brain regions. It remains unclear what is the computational benefit for the brain to have such variability in short-term dynamics. In this study, we propose that the brain can exploit such dynamical features to implement multiple seemingly contradictory computations in a single neural circuit. To demonstrate this idea, we use continuous attractor neural network (CANN) as a working model and include STF, SFA and STD with increasing time constants in its dynamics. Three computational tasks are considered, which are persistent activity, adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, and hence cannot be implemented by a single dynamical feature or any combination with similar time constants. However, with properly coordinated STF, SFA and STD, we show that the network is able to implement the three computational tasks concurrently. We hope this study will shed light on the understanding of how the brain orchestrates its rich dynamics at various levels to realize diverse cognitive functions.

  3. Statistical Physics of Neural Systems with Nonadditive Dendritic Coupling

    Directory of Open Access Journals (Sweden)

    David Breuer

    2014-03-01

    Full Text Available How neurons process their inputs crucially determines the dynamics of biological and artificial neural networks. In such neural and neural-like systems, synaptic input is typically considered to be merely transmitted linearly or sublinearly by the dendritic compartments. Yet, single-neuron experiments report pronounced supralinear dendritic summation of sufficiently synchronous and spatially close-by inputs. Here, we provide a statistical physics approach to study the impact of such nonadditive dendritic processing on single-neuron responses and the performance of associative-memory tasks in artificial neural networks. First, we compute the effect of random input to a neuron incorporating nonlinear dendrites. This approach is independent of the details of the neuronal dynamics. Second, we use those results to study the impact of dendritic nonlinearities on the network dynamics in a paradigmatic model for associative memory, both numerically and analytically. We find that dendritic nonlinearities maintain network convergence and increase the robustness of memory performance against noise. Interestingly, an intermediate number of dendritic branches is optimal for memory functionality.

  4. Neural network-based expert system for severe accident management

    International Nuclear Information System (INIS)

    Klopp, G.T.; Silverman, E.B.

    1992-01-01

    This paper presents the results of the second phase of a three-phase Severe Accident Management expert system program underway at Commonwealth Edison Company (CECo). Phase I successfully demonstrated the feasibility of Artificial Neural Networks to support several of the objectives of severe accident management. Simulated accident scenarios were generated by the Modular Accident Analysis Program (MAAP) code currently in use by CECo as part of their Individual Plant Evaluations (IPE)/Accident Management Program. The primary objectives of the second phase were to develop and demonstrate four capabilities of neural networks with respect to nuclear power plant severe accident monitoring and prediction. The results of this work would form the foundation of a demonstration system which included expert system performance features. These capabilities included the ability to: (1) Predict the time available prior to support plate (and reactor vessel) failure; (2) Calculate the time remaining until recovery actions were too late to prevent core damage; (3) Predict future parameter values of each of the MAAP parameter variables; and (4) Detect simulated sensor failure and provide best-value estimates for further processing in the presence of a sensor failure. A variety of accident scenarios for the Zion and Dresden plants were used to train and test the neural network expert system. These included large and small break LOCAs as well as a range of transient events. 3 refs., 1 fig., 1 tab

  5. Evolutionary Computation and Its Applications in Neural and Fuzzy Systems

    Directory of Open Access Journals (Sweden)

    Biaobiao Zhang

    2011-01-01

    Full Text Available Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the universally accepted neo-Darwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.

  6. Neural Mechanisms and Information Processing in Recognition Systems

    Directory of Open Access Journals (Sweden)

    Mamiko Ozaki

    2014-10-01

    Full Text Available Nestmate recognition is a hallmark of social insects. It is based on the match/mismatch of an identity signal carried by members of the society with that of the perceiving individual. While the behavioral response, amicable or aggressive, is very clear, the neural systems underlying recognition are not fully understood. Here we contrast two alternative hypotheses for the neural mechanisms that are responsible for the perception and information processing in recognition. We focus on recognition via chemical signals, as the common modality in social insects. The first, classical, hypothesis states that upon perception of recognition cues by the sensory system the information is passed as is to the antennal lobes and to higher brain centers where the information is deciphered and compared to a neural template. Match or mismatch information is then transferred to some behavior-generating centers where the appropriate response is elicited. An alternative hypothesis, that of “pre-filter mechanism”, posits that the decision as to whether to pass on the information to the central nervous system takes place in the peripheral sensory system. We suggest that, through sensory adaptation, only alien signals are passed on to the brain, specifically to an “aggressive-behavior-switching center”, where the response is generated if the signal is above a certain threshold.

  7. A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment.

    Directory of Open Access Journals (Sweden)

    Yongcheng Li

    Full Text Available We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning. Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot's performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks.

  8. A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment.

    Science.gov (United States)

    Li, Yongcheng; Sun, Rong; Wang, Yuechao; Li, Hongyi; Zheng, Xiongfei

    2016-01-01

    We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning). Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle) to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot's performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks.

  9. Nonlinear dynamical system approaches towards neural prosthesis

    International Nuclear Information System (INIS)

    Torikai, Hiroyuki; Hashimoto, Sho

    2011-01-01

    An asynchronous discrete-state spiking neurons is a wired system of shift registers that can mimic nonlinear dynamics of an ODE-based neuron model. The control parameter of the neuron is the wiring pattern among the registers and thus they are suitable for on-chip learning. In this paper an asynchronous discrete-state spiking neuron is introduced and its typical nonlinear phenomena are demonstrated. Also, a learning algorithm for a set of neurons is presented and it is demonstrated that the algorithm enables the set of neurons to reconstruct nonlinear dynamics of another set of neurons with unknown parameter values. The learning function is validated by FPGA experiments.

  10. Integrating Artificial Immune, Neural and Endrocine Systems in Autonomous Sailing Robots

    Science.gov (United States)

    2010-09-24

    system - Development of an adaptive hormone system capable of changing operation and control of the neural network depending on changing enviromental ...and control of the neural network depending on changing enviromental conditions • First basic design of the MOOP and a simple neural-endocrine based

  11. Sympathetic neural modulation of the immune system

    International Nuclear Information System (INIS)

    Madden, K.S.

    1989-01-01

    One route by which the central nervous system communicates with lymphoid organs in the periphery is through the sympathetic nervous system (SNS). To study SNS regulation of immune activity in vivo, selective removal of peripheral noradrenergic nerve fibers was achieved by administration of the neurotoxic drug, 6-hydroxydopamine (6-OHDA), to adult mice. To assess SNS influence on lymphocyte proliferation in vitro, uptake of 125 iododeoxyuridine ( 125 IUdR), a DNA precursor, was measured following 6-OHDA treatment. Sympathectomy prior to epicutaneous immunization with TNCB did not alter draining lymph nodes (LN) cell proliferation, whereas 6-OHDA treatment before footpad immunization with KLH reduced DNA synthesis in popliteal LN by 50%. In mice which were not deliberately immunized, sympathectomy stimulated 125 IUdR uptake inguinal and axillary LN, spleen, and bone marrow. In vitro, these LN and spleen cells exhibited decreased proliferation responses to the T cell mitogen, concanavalin A (Con A), whereas lipopolysaccharide (LPS)-stimulated IgG secretion was enhanced. Studies examining 51 Cr-labeled lymphocyte trafficking to LN suggested that altered cell migration may play a part in sympathectomy-induced changes in LN cell function

  12. Bifurcation analysis on a generalized recurrent neural network with two interconnected three-neuron components

    International Nuclear Information System (INIS)

    Hajihosseini, Amirhossein; Maleki, Farzaneh; Rokni Lamooki, Gholam Reza

    2011-01-01

    Highlights: → We construct a recurrent neural network by generalizing a specific n-neuron network. → Several codimension 1 and 2 bifurcations take place in the newly constructed network. → The newly constructed network has higher capabilities to learn periodic signals. → The normal form theorem is applied to investigate dynamics of the network. → A series of bifurcation diagrams is given to support theoretical results. - Abstract: A class of recurrent neural networks is constructed by generalizing a specific class of n-neuron networks. It is shown that the newly constructed network experiences generic pitchfork and Hopf codimension one bifurcations. It is also proved that the emergence of generic Bogdanov-Takens, pitchfork-Hopf and Hopf-Hopf codimension two, and the degenerate Bogdanov-Takens bifurcation points in the parameter space is possible due to the intersections of codimension one bifurcation curves. The occurrence of bifurcations of higher codimensions significantly increases the capability of the newly constructed recurrent neural network to learn broader families of periodic signals.

  13. Neural feedback linearization adaptive control for affine nonlinear systems based on neural network estimator

    Directory of Open Access Journals (Sweden)

    Bahita Mohamed

    2011-01-01

    Full Text Available In this work, we introduce an adaptive neural network controller for a class of nonlinear systems. The approach uses two Radial Basis Functions, RBF networks. The first RBF network is used to approximate the ideal control law which cannot be implemented since the dynamics of the system are unknown. The second RBF network is used for on-line estimating the control gain which is a nonlinear and unknown function of the states. The updating laws for the combined estimator and controller are derived through Lyapunov analysis. Asymptotic stability is established with the tracking errors converging to a neighborhood of the origin. Finally, the proposed method is applied to control and stabilize the inverted pendulum system.

  14. Applications of Advanced Electromagnetics Components and Systems

    CERN Document Server

    Kouzaev, Guennadi A

    2013-01-01

    This text, directed to the microwave engineers and Master and PhD students, is on the use of electromagnetics to the development and design of advanced integrated components distinguished by their extended field of applications. The results of hundreds of authors scattered in numerous journals and conference proceedings are carefully reviewed and classed.  Several chapters are to refresh the knowledge of readers in advanced electromagnetics. New techniques are represented by compact electromagnetic–quantum equations which can be used in modeling of microwave-quantum integrated circuits of future In addition, a topological method to the boundary value problem analysis is considered with the results and examples.  One extended chapter is for the development and design of integrated components for extended bandwidth applications, and the technology and electromagnetic issues of silicon integrated transmission lines, transitions, filters, power dividers, directional couplers, etc are considered. Novel prospec...

  15. Requirements for containment system components in CANDU nuclear power plants

    International Nuclear Information System (INIS)

    1988-02-01

    This Standard specifies the requirements and establishes the rules for design, fabrication, and installation of pressure-retaining containment system components. In this Standard the term 'components' includes non registered items

  16. Requirements for containment system components in CANDU nuclear power plants

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1988-02-01

    This Standard specifies the requirements and establishes the rules for design, fabrication, and installation of pressure-retaining containment system components. In this Standard the term `components` includes non registered items.

  17. Components of Maternal Healthcare Delivery System Contributing to ...

    African Journals Online (AJOL)

    Components of Maternal Healthcare Delivery System Contributing to Maternal Deaths ... transcripts were analyzed using a directed approach to content analysis. Excerpts were categorized according to three main components of the maternal ...

  18. Wavelet low- and high-frequency components as features for predicting stock prices with backpropagation neural networks

    Directory of Open Access Journals (Sweden)

    Salim Lahmiri

    2014-07-01

    Full Text Available This paper presents a forecasting model that integrates the discrete wavelet transform (DWT and backpropagation neural networks (BPNN for predicting financial time series. The presented model first uses the DWT to decompose the financial time series data. Then, the obtained approximation (low-frequency and detail (high-frequency components after decomposition of the original time series are used as input variables to forecast future stock prices. Indeed, while high-frequency components can capture discontinuities, ruptures and singularities in the original data, low-frequency components characterize the coarse structure of the data, to identify the long-term trends in the original data. As a result, high-frequency components act as a complementary part of low-frequency components. The model was applied to seven datasets. For all of the datasets, accuracy measures showed that the presented model outperforms a conventional model that uses only low-frequency components. In addition, the presented model outperforms both the well-known auto-regressive moving-average (ARMA model and the random walk (RW process.

  19. A low-cost multichannel wireless neural stimulation system for freely roaming animals

    Science.gov (United States)

    Alam, Monzurul; Chen, Xi; Fernandez, Eduardo

    2013-12-01

    Objectives. Electrical stimulation of nerve tissue and recording of neural activity are the basis of many therapies and neural prostheses. Conventional stimulation systems have a number of practical limitations, especially in experiments involving freely roaming subjects. Our main objective was to develop a modular, versatile and inexpensive multichannel wireless system able to overcome some of these constraints. Approach. We have designed and implemented a new multichannel wireless neural stimulator based on commercial components. The system is small (2 cm × 4 cm × 0.5 cm) and light in weight (9 g) which allows it to be easily carried in a small backpack. To test and validate the performance and reliability of the whole system we conducted several bench tests and in vivo experiments. Main results. The performance and accuracy of the stimulator were comparable to commercial threaded systems. Stimulation sequences can be constructed on-the-fly with 251 selectable current levels (from 0 to 250 µA) with 1 µA step resolution. The pulse widths and intervals can be as long as 65 ms in 2 µs time resolution. The system covers approximately 10 m of transmission range in a regular laboratory environment and 100 m in free space (line of sight). Furthermore it provides great flexibility for experiments since it allows full control of the stimulator and the stimulation parameters in real time. When there is no stimulation, the device automatically goes into low-power sleep mode to preserve battery power. Significance. We introduce the design of a powerful multichannel wireless stimulator assembled from commercial components. Key features of the system are their reliability, robustness and small size. The system has a flexible design that can be modified straightforwardly to tailor it to any specific experimental need. Furthermore it can be effortlessly adapted for use with any kind of multielectrode arrays.

  20. Fuzzy-Neural Automatic Daylight Control System

    Directory of Open Access Journals (Sweden)

    Grif H. Şt.

    2011-12-01

    Full Text Available The paper presents the design and the tuning of a CMAC controller (Cerebellar Model Articulation Controller implemented in an automatic daylight control application. After the tuning process of the controller, the authors studied the behavior of the automatic lighting control system (ALCS in the presence of luminance disturbances. The luminance disturbances were produced by the authors in night conditions and day conditions as well. During the night conditions, the luminance disturbances were produced by turning on and off a halogen desk lamp. During the day conditions the luminance disturbances were produced in two ways: by daylight contributions changes achieved by covering and uncovering a part of the office window and by turning on and off a halogen desk lamp. During the day conditions the luminance disturbances, produced by turning on and off the halogen lamp, have a smaller amplitude than those produced during the night conditions. The luminance disturbance during the night conditions was a helpful tool to select the proper values of the learning rate for CMAC controller. The luminance disturbances during the day conditions were a helpful tool to demonstrate the right setting of the CMAC controller.

  1. Gasoline engine management systems and components

    CERN Document Server

    2015-01-01

    The call for environmentally compatible and economical vehicles necessitates immense efforts to develop innovative engine concepts. Technical concepts such as gasoline direct injection helped to save fuel up to 20 % and reduce CO2-emissions. Descriptions of the cylinder-charge control, fuel injection, ignition and catalytic emission-control systems provides comprehensive overview of today´s gasoline engines. This book also describes emission-control systems and explains the diagnostic systems. The publication provides information on engine-management-systems and emission-control regulations. Contents History of the automobile.- Basics of the gasoline engine.- Fuels.- Cylinder-charge control systems.- Gasoline injection systems over the years.- Fuel supply.- Manifold fuel injection.- Gasoline direct injection.- Operation of gasoline engines on natural gas.- Ignition systems over the years.- Inductive ignition systems.- Ignition coils.- Spark plugs.- Electronic control.- Sensors.- Electronic control unit.- Exh...

  2. Application of Component Technology to E-commerce System

    Institute of Scientific and Technical Information of China (English)

    ZHU Jianfeng

    2004-01-01

    At present E-commerce system tends to become more complex, and traditional system designing methods can not fufil the need of E-commerce system, thus requiring an effective methodas solution. With this concern, this paper introduces some concepts of component technology, then brings forward the new connotation and basic features of component technology through the analysis of its technological character. This paper finally discusses the application of component technology to E-commerce system.

  3. The development of component-based information systems

    CERN Document Server

    Cesare, Sergio de; Macredie, Robert

    2015-01-01

    This work provides a comprehensive overview of research and practical issues relating to component-based development information systems (CBIS). Spanning the organizational, developmental, and technical aspects of the subject, the original research included here provides fresh insights into successful CBIS technology and application. Part I covers component-based development methodologies and system architectures. Part II analyzes different aspects of managing component-based development. Part III investigates component-based development versus commercial off-the-shelf products (COTS), includi

  4. Reliability analysis of a consecutive r-out-of-n: F system based on neural networks

    International Nuclear Information System (INIS)

    Habib, Aziz; Alsieidi, Ragab; Youssef, Ghada

    2009-01-01

    In this paper, we present a generalized Markov reliability and fault-tolerant model, which includes the effects of permanent fault and intermittent fault for reliability evaluations based on neural network techniques. The reliability of a consecutive r-out-of-n: F system was obtained with a three-layer connected neural network represents a discrete time state reliability Markov model of the system. Such that we fed the neural network with the desired reliability of the system under design. Then we extracted the parameters of the system from the neural weights at the convergence of the neural network to the desired reliability. Finally, we obtain simulation results.

  5. Adaptive neural networks control for camera stabilization with active suspension system

    Directory of Open Access Journals (Sweden)

    Feng Zhao

    2015-08-01

    Full Text Available The camera always suffers from image instability on the moving vehicle due to unintentional vibrations caused by road roughness. This article presents an adaptive neural network approach mixed with linear quadratic regulator control for a quarter-car active suspension system to stabilize the image captured area of the camera. An active suspension system provides extra force through the actuator which allows it to suppress vertical vibration of sprung mass. First, to deal with the road disturbance and the system uncertainties, radial basis function neural network is proposed to construct the map between the state error and the compensation component, which can correct the optimal state-feedback control law. The weights matrix of radial basis function neural network is adaptively tuned online. Then, the closed-loop stability and asymptotic convergence performance is guaranteed by Lyapunov analysis. Finally, the simulation results demonstrate that the proposed controller effectively suppresses the vibration of the camera and enhances the stabilization of the entire camera, where different excitations are considered to validate the system performance.

  6. Dynamics of a neural system with a multiscale architecture

    Science.gov (United States)

    Breakspear, Michael; Stam, Cornelis J

    2005-01-01

    The architecture of the brain is characterized by a modular organization repeated across a hierarchy of spatial scales—neurons, minicolumns, cortical columns, functional brain regions, and so on. It is important to consider that the processes governing neural dynamics at any given scale are not only determined by the behaviour of other neural structures at that scale, but also by the emergent behaviour of smaller scales, and the constraining influence of activity at larger scales. In this paper, we introduce a theoretical framework for neural systems in which the dynamics are nested within a multiscale architecture. In essence, the dynamics at each scale are determined by a coupled ensemble of nonlinear oscillators, which embody the principle scale-specific neurobiological processes. The dynamics at larger scales are ‘slaved’ to the emergent behaviour of smaller scales through a coupling function that depends on a multiscale wavelet decomposition. The approach is first explicated mathematically. Numerical examples are then given to illustrate phenomena such as between-scale bifurcations, and how synchronization in small-scale structures influences the dynamics in larger structures in an intuitive manner that cannot be captured by existing modelling approaches. A framework for relating the dynamical behaviour of the system to measured observables is presented and further extensions to capture wave phenomena and mode coupling are suggested. PMID:16087448

  7. Artificial Neural Network for Location Estimation in Wireless Communication Systems

    Directory of Open Access Journals (Sweden)

    Chien-Sheng Chen

    2012-03-01

    Full Text Available In a wireless communication system, wireless location is the technique used to estimate the location of a mobile station (MS. To enhance the accuracy of MS location prediction, we propose a novel algorithm that utilizes time of arrival (TOA measurements and the angle of arrival (AOA information to locate MS when three base stations (BSs are available. Artificial neural networks (ANN are widely used techniques in various areas to overcome the problem of exclusive and nonlinear relationships. When the MS is heard by only three BSs, the proposed algorithm utilizes the intersections of three TOA circles (and the AOA line, based on various neural networks, to estimate the MS location in non-line-of-sight (NLOS environments. Simulations were conducted to evaluate the performance of the algorithm for different NLOS error distributions. The numerical analysis and simulation results show that the proposed algorithms can obtain more precise location estimation under different NLOS environments.

  8. Artificial neural network for location estimation in wireless communication systems.

    Science.gov (United States)

    Chen, Chien-Sheng

    2012-01-01

    In a wireless communication system, wireless location is the technique used to estimate the location of a mobile station (MS). To enhance the accuracy of MS location prediction, we propose a novel algorithm that utilizes time of arrival (TOA) measurements and the angle of arrival (AOA) information to locate MS when three base stations (BSs) are available. Artificial neural networks (ANN) are widely used techniques in various areas to overcome the problem of exclusive and nonlinear relationships. When the MS is heard by only three BSs, the proposed algorithm utilizes the intersections of three TOA circles (and the AOA line), based on various neural networks, to estimate the MS location in non-line-of-sight (NLOS) environments. Simulations were conducted to evaluate the performance of the algorithm for different NLOS error distributions. The numerical analysis and simulation results show that the proposed algorithms can obtain more precise location estimation under different NLOS environments.

  9. Semi-empirical neural network models of controlled dynamical systems

    Directory of Open Access Journals (Sweden)

    Mihail V. Egorchev

    2017-12-01

    Full Text Available A simulation approach is discussed for maneuverable aircraft motion as nonlinear controlled dynamical system under multiple and diverse uncertainties including knowledge imperfection concerning simulated plant and its environment exposure. The suggested approach is based on a merging of theoretical knowledge for the plant with training tools of artificial neural network field. The efficiency of this approach is demonstrated using the example of motion modeling and the identification of the aerodynamic characteristics of a maneuverable aircraft. A semi-empirical recurrent neural network based model learning algorithm is proposed for multi-step ahead prediction problem. This algorithm sequentially states and solves numerical optimization subproblems of increasing complexity, using each solution as initial guess for subsequent subproblem. We also consider a procedure for representative training set acquisition that utilizes multisine control signals.

  10. Development of an accident diagnosis system using a dynamic neural network for nuclear power plants

    International Nuclear Information System (INIS)

    Lee, Seung Jun; Kim, Jong Hyun; Seong, Poong Hyun

    2004-01-01

    In this work, an accident diagnosis system using the dynamic neural network is developed. In order to help the plant operators to quickly identify the problem, perform diagnosis and initiate recovery actions ensuring the safety of the plant, many operator support system and accident diagnosis systems have been developed. Neural networks have been recognized as a good method to implement an accident diagnosis system. However, conventional accident diagnosis systems that used neural networks did not consider a time factor sufficiently. If the neural network could be trained according to time, it is possible to perform more efficient and detailed accidents analysis. Therefore, this work suggests a dynamic neural network which has different features from existing dynamic neural networks. And a simple accident diagnosis system is implemented in order to validate the dynamic neural network. After training of the prototype, several accident diagnoses were performed. The results show that the prototype can detect the accidents correctly with good performances

  11. Power system protection 1 principles and components

    CERN Document Server

    Association, Electricity Training

    1995-01-01

    The worldwide growth in demand for electricity has forced the pace of developments in electrical power system design to meet consumer needs for reliable, secure and cheap supplies. Power system protection, as a technology essential to high quality supply, is widely recognised as a specialism of growing and often critical importance, in which power system needs and technological progress have combined to result in rapid developments in policy and practice in recent years. In the United Kingdom, the need for appropriate training in power system protection was recognised in the early 1960s with t

  12. Recent progresses of neural network unsupervised learning: I. Independent component analyses generalizing PCA

    Science.gov (United States)

    Szu, Harold H.

    1999-03-01

    The early vision principle of redundancy reduction of 108 sensor excitations is understandable from computer vision viewpoint toward sparse edge maps. It is only recently derived using a truly unsupervised learning paradigm of artificial neural networks (ANN). In fact, the biological vision, Hubel- Wiesel edge maps, is reproduced seeking the underlying independent components analyses (ICA) among 102 image samples by maximizing the ANN output entropy (partial)H(V)/(partial)[W] equals (partial)[W]/(partial)t. When a pair of newborn eyes or ears meet the bustling and hustling world without supervision, they seek ICA by comparing 2 sensory measurements (x1(t), x2(t))T equalsV X(t). Assuming a linear and instantaneous mixture model of the external world X(t) equals [A] S(t), where both the mixing matrix ([A] equalsV [a1, a2] of ICA vectors and the source percentages (s1(t), s2(t))T equalsV S(t) are unknown, we seek the independent sources approximately equals [I] where the approximated sign indicates that higher order statistics (HOS) may not be trivial. Without a teacher, the ANN weight matrix [W] equalsV [w1, w2] adjusts the outputs V(t) equals tanh([W]X(t)) approximately equals [W]X(t) until no desired outputs except the (Gaussian) 'garbage' (neither YES '1' nor NO '-1' but at linear may-be range 'origin 0') defined by Gaussian covariance G equals [I] equals [W][A] the internal knowledge representation [W], as the inverse of the external world matrix [A]-1. To unify IC, PCA, ANN & HOS theories since 1991 (advanced by Jutten & Herault, Comon, Oja, Bell-Sejnowski, Amari-Cichocki, Cardoso), the LYAPONOV function L(v1,...,vn, w1,...wn,) equals E(v1,...,vn) - H(w1,...wn) is constructed as the HELMHOTZ free energy to prove both convergences of supervised energy E and unsupervised entropy H learning. Consequently, rather using the faithful but dumb computer: 'GARBAGE-IN, GARBAGE-OUT,' the smarter neurocomputer will be equipped with an unsupervised learning that extracts

  13. DIGITAL SUBSTATION COMPONENT SYSTEM "SMART GRID"

    Directory of Open Access Journals (Sweden)

    V.I. Vasilchenko

    2014-12-01

    Full Text Available New production technologies of modern control systems have moved from the stage of research and experimentation into the stage of practical use. Modern communication standards for the exchange of information are developed and introduced. Digital devices, protectors and automation are widely used. There has been substantial development of hardware and software of control systems.

  14. Novel Devices and Components for THz Systems

    Science.gov (United States)

    2014-04-25

    effectively . The cage rod system is again 30 mm...often used to reflect RF radiation, or create Faraday cages , this concept can be applied to the FPI mirrors. Wire meshes will simulate solid conductors...Single peice construction should resist leaning more effectively . The cage rod system is again 30 mm. 175 wire-mesh-mirrors. Fixing the rocking

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

    Energy Technology Data Exchange (ETDEWEB)

    Ondrej Linda; Milos Manic; Timothy R. McJunkin

    2011-08-01

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

  16. Reactor component inventory system at FFTF

    International Nuclear Information System (INIS)

    Ordonez, C.R.; Redekopp, R.D.; Reed, E.A.

    1985-02-01

    A reliable inventory control system was developed at the Fast Flux Test Facility (FFTF) to keep track of the occupancy of 900 refueling facility locations, to compile historical data on the movement of each reactor assembly, and to simulate assembly moves. The simulate capability is valuable because it allows verification of documents before they are issued for use in the plant, and eliminates the possibility of planning illegal or impossible moves. The system is installed on a UNIVAC 1100 computer and is maintained using a data base management system by Sperry Univac called MAPPER

  17. Expert system for accelerator single-freedom nonlinear components

    International Nuclear Information System (INIS)

    Wang Sheng; Xie Xi; Liu Chunliang

    1995-01-01

    An expert system by Arity Prolog is developed for accelerator single-freedom nonlinear components. It automatically yields any order approximate analytical solutions for various accelerator single-freedom nonlinear components. As an example, the eighth order approximate analytical solution is derived by this expert system for a general accelerator single-freedom nonlinear component, showing that the design of the expert system is successful

  18. Radial basis function (RBF) neural network control for mechanical systems design, analysis and Matlab simulation

    CERN Document Server

    Liu, Jinkun

    2013-01-01

    Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MATLAB simulation of stable adaptive RBF neural control strategies. In this book, a broad range of implementable neural network control design methods for mechanical systems are presented, such as robot manipulators, inverted pendulums, single link flexible joint robots, motors, etc. Advanced neural network controller design methods and their stability analysis are explored. The book provides readers with the fundamentals of neural network control system design.   This book is intended for the researchers in the fields of neural adaptive control, mechanical systems, Matlab simulation, engineering design, robotics and automation. Jinkun Liu is a professor at Beijing University of Aeronautics and Astronauti...

  19. Artificial heart system thermal insulation component development

    International Nuclear Information System (INIS)

    Svedberg, R.C.; Buckman, R.W. Jr.

    1975-01-01

    A concentric cup vacuum multifoil insulation system has been selected by virtue of its size, weight, and thermal performance to insulate the hot radioisotope portion of the thermal converter of an artificial implantable heart system. A factor of 2 improvement in thermal performance, based on the heat loss per number of foil layers (minimum system weight and volume) has been realized over conventional spiral wrapped multifoil vacuum insulation. This improvement is the result of the concentric cup construction to maintain a uniform interfoil spacing and the elimination of corner heat losses. Based on external insulation system dimensions (surface area in contact with host body), heat losses of 0.019 W/ cm 2 at 1140 0 K (1600 0 F) and 0.006 W/cm 2 at 920 0 K (1200 0 F) have been achieved. Factors which influence thermal performance of the nickel foil concentric cup insulation system include the number of cups, configuration and method of application of zirconia (ZrO 2 ) spacer material, system pressure, emittance of the cups, and operating temperature

  20. Neural Computations in a Dynamical System with Multiple Time Scales

    Directory of Open Access Journals (Sweden)

    Yuanyuan Mi

    2016-09-01

    Full Text Available Neural systems display rich short-term dynamics at various levels, e.g., spike-frequencyadaptation (SFA at single neurons, and short-term facilitation (STF and depression (STDat neuronal synapses. These dynamical features typically covers a broad range of time scalesand exhibit large diversity in different brain regions. It remains unclear what the computationalbenefit for the brain to have such variability in short-term dynamics is. In this study, we proposethat the brain can exploit such dynamical features to implement multiple seemingly contradictorycomputations in a single neural circuit. To demonstrate this idea, we use continuous attractorneural network (CANN as a working model and include STF, SFA and STD with increasing timeconstants in their dynamics. Three computational tasks are considered, which are persistent activity,adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, andhence cannot be implemented by a single dynamical feature or any combination with similar timeconstants. However, with properly coordinated STF, SFA and STD, we show that the network isable to implement the three computational tasks concurrently. We hope this study will shed lighton the understanding of how the brain orchestrates its rich dynamics at various levels to realizediverse cognitive functions.

  1. Neural network application to aircraft control system design

    Science.gov (United States)

    Troudet, Terry; Garg, Sanjay; Merrill, Walter C.

    1991-01-01

    The feasibility of using artificial neural network as control systems for modern, complex aerospace vehicles is investigated via an example aircraft control design study. The problem considered is that of designing a controller for an integrated airframe/propulsion longitudinal dynamics model of a modern fighter aircraft to provide independent control of pitch rate and airspeed responses to pilot command inputs. An explicit model following controller using H infinity control design techniques is first designed to gain insight into the control problem as well as to provide a baseline for evaluation of the neurocontroller. Using the model of the desired dynamics as a command generator, a multilayer feedforward neural network is trained to control the vehicle model within the physical limitations of the actuator dynamics. This is achieved by minimizing an objective function which is a weighted sum of tracking errors and control input commands and rates. To gain insight in the neurocontrol, linearized representations of the nonlinear neurocontroller are analyzed along a commanded trajectory. Linear robustness analysis tools are then applied to the linearized neurocontroller models and to the baseline H infinity based controller. Future areas of research identified to enhance the practical applicability of neural networks to flight control design.

  2. Neural network application to aircraft control system design

    Science.gov (United States)

    Troudet, Terry; Garg, Sanjay; Merrill, Walter C.

    1991-01-01

    The feasibility of using artificial neural networks as control systems for modern, complex aerospace vehicles is investigated via an example aircraft control design study. The problem considered is that of designing a controller for an integrated airframe/propulsion longitudinal dynamics model of a modern fighter aircraft to provide independent control of pitch rate and airspeed responses to pilot command inputs. An explicit model following controller using H infinity control design techniques is first designed to gain insight into the control problem as well as to provide a baseline for evaluation of the neurocontroller. Using the model of the desired dynamics as a command generator, a multilayer feedforward neural network is trained to control the vehicle model within the physical limitations of the actuator dynamics. This is achieved by minimizing an objective function which is a weighted sum of tracking errors and control input commands and rates. To gain insight in the neurocontrol, linearized representations of the nonlinear neurocontroller are analyzed along a commanded trajectory. Linear robustness analysis tools are then applied to the linearized neurocontroller models and to the baseline H infinity based controller. Future areas of research are identified to enhance the practical applicability of neural networks to flight control design.

  3. Modular and coordinated expression of immune system regulatory and signaling components in the developing and adult nervous system.

    Science.gov (United States)

    Monzón-Sandoval, Jimena; Castillo-Morales, Atahualpa; Crampton, Sean; McKelvey, Laura; Nolan, Aoife; O'Keeffe, Gerard; Gutierrez, Humberto

    2015-01-01

    During development, the nervous system (NS) is assembled and sculpted through a concerted series of neurodevelopmental events orchestrated by a complex genetic programme. While neural-specific gene expression plays a critical part in this process, in recent years, a number of immune-related signaling and regulatory components have also been shown to play key physiological roles in the developing and adult NS. While the involvement of individual immune-related signaling components in neural functions may reflect their ubiquitous character, it may also reflect a much wider, as yet undescribed, genetic network of immune-related molecules acting as an intrinsic component of the neural-specific regulatory machinery that ultimately shapes the NS. In order to gain insights into the scale and wider functional organization of immune-related genetic networks in the NS, we examined the large scale pattern of expression of these genes in the brain. Our results show a highly significant correlated expression and transcriptional clustering among immune-related genes in the developing and adult brain, and this correlation was the highest in the brain when compared to muscle, liver, kidney and endothelial cells. We experimentally tested the regulatory clustering of immune system (IS) genes by using microarray expression profiling in cultures of dissociated neurons stimulated with the pro-inflammatory cytokine TNF-alpha, and found a highly significant enrichment of immune system-related genes among the resulting differentially expressed genes. Our findings strongly suggest a coherent recruitment of entire immune-related genetic regulatory modules by the neural-specific genetic programme that shapes the NS.

  4. Neural Network Based Intrusion Detection System for Critical Infrastructures

    Energy Technology Data Exchange (ETDEWEB)

    Todd Vollmer; Ondrej Linda; Milos Manic

    2009-07-01

    Resiliency and security in control systems such as SCADA and Nuclear plant’s in today’s world of hackers and malware are a relevant concern. Computer systems used within critical infrastructures to control physical functions are not immune to the threat of cyber attacks and may be potentially vulnerable. Tailoring an intrusion detection system to the specifics of critical infrastructures can significantly improve the security of such systems. The IDS-NNM – Intrusion Detection System using Neural Network based Modeling, is presented in this paper. The main contributions of this work are: 1) the use and analyses of real network data (data recorded from an existing critical infrastructure); 2) the development of a specific window based feature extraction technique; 3) the construction of training dataset using randomly generated intrusion vectors; 4) the use of a combination of two neural network learning algorithms – the Error-Back Propagation and Levenberg-Marquardt, for normal behavior modeling. The presented algorithm was evaluated on previously unseen network data. The IDS-NNM algorithm proved to be capable of capturing all intrusion attempts presented in the network communication while not generating any false alerts.

  5. Neural Network Target Identification System for False Alarm Reduction

    Science.gov (United States)

    Ye, David; Edens, Weston; Lu, Thomas T.; Chao, Tien-Hsin

    2009-01-01

    A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feed forward back propagation neural network (NN) is then trained to classify each feature vector and remove false positives. This paper discusses the test of the system performance and parameter optimizations process which adapts the system to various targets and datasets. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar image dataset.

  6. Automated system for load flow prediction in power substations using artificial neural networks

    Directory of Open Access Journals (Sweden)

    Arlys Michel Lastre Aleaga

    2015-09-01

    Full Text Available The load flow is of great importance in assisting the process of decision making and planning of generation, distribution and transmission of electricity. Ignorance of the values in this indicator, as well as their inappropriate prediction, difficult decision making and efficiency of the electricity service, and can cause undesirable situations such as; the on demand, overheating of the components that make up a substation, and incorrect planning processes electricity generation and distribution. Given the need for prediction of flow of electric charge of the substations in Ecuador this research proposes the concept for the development of an automated prediction system employing the use of Artificial Neural Networks.

  7. Catalog of components for electric and hybrid vehicle propulsion systems

    Science.gov (United States)

    Eissler, H. C.

    1981-01-01

    This catalog of commercially available electric and hybrid vehicle propulsion system components is intended for designers and builders of these vehicles and contains 50 categories of components. These categories include those components used between the battery terminals and the output axle hub, as well as some auxiliary equipment. An index of the components and a listing of the suppliers and their addresses and phone numbers are included.

  8. Imprecise system reliability and component importance based on survival signature

    International Nuclear Information System (INIS)

    Feng, Geng; Patelli, Edoardo; Beer, Michael; Coolen, Frank P.A.

    2016-01-01

    The concept of the survival signature has recently attracted increasing attention for performing reliability analysis on systems with multiple types of components. It opens a new pathway for a structured approach with high computational efficiency based on a complete probabilistic description of the system. In practical applications, however, some of the parameters of the system might not be defined completely due to limited data, which implies the need to take imprecisions of component specifications into account. This paper presents a methodology to include explicitly the imprecision, which leads to upper and lower bounds of the survival function of the system. In addition, the approach introduces novel and efficient component importance measures. By implementing relative importance index of each component without or with imprecision, the most critical component in the system can be identified depending on the service time of the system. Simulation method based on survival signature is introduced to deal with imprecision within components, which is precise and efficient. Numerical example is presented to show the applicability of the approach for systems. - Highlights: • Survival signature is a novel way for system reliability and component importance • High computational efficiency based on a complete description of system. • Include explicitly the imprecision, which leads to bounds of the survival function. • A novel relative importance index is proposed as importance measure. • Allows to identify critical components depending on the service time of the system.

  9. System identification of an unmanned quadcopter system using MRAN neural

    Science.gov (United States)

    Pairan, M. F.; Shamsudin, S. S.

    2017-12-01

    This project presents the performance analysis of the radial basis function neural network (RBF) trained with Minimal Resource Allocating Network (MRAN) algorithm for real-time identification of quadcopter. MRAN’s performance is compared with the RBF with Constant Trace algorithm for 2500 input-output pair data sampling. MRAN utilizes adding and pruning hidden neuron strategy to obtain optimum RBF structure, increase prediction accuracy and reduce training time. The results indicate that MRAN algorithm produces fast training time and more accurate prediction compared with standard RBF. The model proposed in this paper is capable of identifying and modelling a nonlinear representation of the quadcopter flight dynamics.

  10. Advanced Reconnaissance System Component Reliability Study

    Science.gov (United States)

    1956-07-31

    dielectrics. Gaseous dielectrics such as sulphur hexafluoride and ’ fluorocarbons at two to three atmospheres. Fluorinated liquid dielectrics. 3) The...limits. (2) determine compatibility with varnish treatments, (3) compatibility in a complete insulation system. Mechanical and thermal limits of...of a varnish to have good • adhersion, provide an element of flexibility and be chemically compatible with’the wire it is impregnating.. Factors of

  11. Monitoring nuclear reactor systems using neural networks and fuzzy logic

    International Nuclear Information System (INIS)

    Ikonomopoulos, A.; Tsoukalas, L.H.; Uhrig, R.E.; Mullens, J.A.

    1991-01-01

    A new approach is presented that demonstrates the potential of trained artificial neural networks (ANNs) as generators of membership functions for the purpose of monitoring nuclear reactor systems. ANN's provide a complex-to-simple mapping of reactor parameters in a process analogous to that of measurement. Through such ''virtual measurements'' the value of parameters with operational significance, e.g., control-valve-disk-position, valve-line-up or performance can be determined. In the methodology presented the output of a virtual measuring device is a set of membership functions which independently represent different states of the system. Utilizing a fuzzy logic representation offers the advantage of describing the state of the system in a condensed form, developed through linguistic descriptions and convenient for application in monitoring, diagnostics and generally control algorithms. The developed methodology is applied to the problem of measuring the disk position of the secondary flow control valve of an experimental reactor using data obtained during a start-up. The enhanced noise tolerance of the methodology is clearly demonstrated as well as a method for selecting the actual output. The results suggest that it is possible to construct virtual measuring devices through artificial neural networks mapping dynamic time series to a set of membership functions and thus enhance the capability of monitoring systems. 8 refs., 11 figs., 1 tab

  12. Monitoring nuclear reactor systems using neural networks and fuzzy logic

    International Nuclear Information System (INIS)

    Ikonomopoulos, A.; Tsoukalas, L.H.; Uhrig, R.E.; Mullens, J.A.

    1992-01-01

    A new approach is presented that demonstrates the potential of trained artificial neural networks (ANNs) as generators of membership functions for the purpose of monitoring nuclear reactor systems. ANN's provide a complex-to-simple mapping of reactor parameters in a process analogous to that of measurement. Through such virtual measurements the value of parameters with operational significance, e.g., control-valve-disk-position, valve-line-up-or performance can be determined. In the methodology presented the output of virtual measuring device is a set of membership functions which independently represent different states of the system. Utilizing a fuzzy logic representation offers the advantage of describing the state of the system in a condensed form, developed through linguistic descriptions and convenient for application in monitoring, diagnostics and generally control algorithms. The developed methodology is applied to the problem of measuring the disk position of the secondary flow control is clearly demonstrated as well as a method for selecting the actual output. The results suggest that it is possible to construct virtual measuring devices through artificial neural networks mapping dynamic time series to a set of membership functions and thus enhance the capability of monitoring systems

  13. Algorithmic fault tree construction by component-based system modeling

    International Nuclear Information System (INIS)

    Majdara, Aref; Wakabayashi, Toshio

    2008-01-01

    Computer-aided fault tree generation can be easier, faster and less vulnerable to errors than the conventional manual fault tree construction. In this paper, a new approach for algorithmic fault tree generation is presented. The method mainly consists of a component-based system modeling procedure an a trace-back algorithm for fault tree synthesis. Components, as the building blocks of systems, are modeled using function tables and state transition tables. The proposed method can be used for a wide range of systems with various kinds of components, if an inclusive component database is developed. (author)

  14. System Risk Balancing Profiles: Software Component

    Science.gov (United States)

    Kelly, John C.; Sigal, Burton C.; Gindorf, Tom

    2000-01-01

    The Software QA / V&V guide will be reviewed and updated based on feedback from NASA organizations and others with a vested interest in this area. Hardware, EEE Parts, Reliability, and Systems Safety are a sample of the future guides that will be developed. Cost Estimates, Lessons Learned, Probability of Failure and PACTS (Prevention, Avoidance, Control or Test) are needed to provide a more complete risk management strategy. This approach to risk management is designed to help balance the resources and program content for risk reduction for NASA's changing environment.

  15. Multilayer electronic component systems and methods of manufacture

    Science.gov (United States)

    Thompson, Dane (Inventor); Wang, Guoan (Inventor); Kingsley, Nickolas D. (Inventor); Papapolymerou, Ioannis (Inventor); Tentzeris, Emmanouil M. (Inventor); Bairavasubramanian, Ramanan (Inventor); DeJean, Gerald (Inventor); Li, RongLin (Inventor)

    2010-01-01

    Multilayer electronic component systems and methods of manufacture are provided. In this regard, an exemplary system comprises a first layer of liquid crystal polymer (LCP), first electronic components supported by the first layer, and a second layer of LCP. The first layer is attached to the second layer by thermal bonds. Additionally, at least a portion of the first electronic components are located between the first layer and the second layer.

  16. Phase transitions in glassy systems via convolutional neural networks

    Science.gov (United States)

    Fang, Chao

    Machine learning is a powerful approach commonplace in industry to tackle large data sets. Most recently, it has found its way into condensed matter physics, allowing for the first time the study of, e.g., topological phase transitions and strongly-correlated electron systems. The study of spin glasses is plagued by finite-size effects due to the long thermalization times needed. Here we use convolutional neural networks in an attempt to detect a phase transition in three-dimensional Ising spin glasses. Our results are compared to traditional approaches.

  17. NEURAL NETWORK SYSTEM FOR DIAGNOSTICS OF AVIATION DESIGNATION PRODUCTS

    Directory of Open Access Journals (Sweden)

    В. Єременко

    2011-02-01

    Full Text Available In the article for solving the classification problem of the technical state of the  object, proposed to use a hybrid neural network with a Kohonen layer and multilayer perceptron. The information-measuring system can be used for standardless diagnostics, cluster analysis and to classify the products which made from composite materials. The advantage of this architecture is flexibility, high performance, ability to use different methods for collecting diagnostic information about unit under test, high reliability of information processing

  18. Fault Tolerant Neural Network for ECG Signal Classification Systems

    Directory of Open Access Journals (Sweden)

    MERAH, M.

    2011-08-01

    Full Text Available The aim of this paper is to apply a new robust hardware Artificial Neural Network (ANN for ECG classification systems. This ANN includes a penalization criterion which makes the performances in terms of robustness. Specifically, in this method, the ANN weights are normalized using the auto-prune method. Simulations performed on the MIT ? BIH ECG signals, have shown that significant robustness improvements are obtained regarding potential hardware artificial neuron failures. Moreover, we show that the proposed design achieves better generalization performances, compared to the standard back-propagation algorithm.

  19. Optimizing Markovian modeling of chaotic systems with recurrent neural networks

    International Nuclear Information System (INIS)

    Cechin, Adelmo L.; Pechmann, Denise R.; Oliveira, Luiz P.L. de

    2008-01-01

    In this paper, we propose a methodology for optimizing the modeling of an one-dimensional chaotic time series with a Markov Chain. The model is extracted from a recurrent neural network trained for the attractor reconstructed from the data set. Each state of the obtained Markov Chain is a region of the reconstructed state space where the dynamics is approximated by a specific piecewise linear map, obtained from the network. The Markov Chain represents the dynamics of the time series in its statistical essence. An application to a time series resulted from Lorenz system is included

  20. Principal Component Analysis Coupled with Artificial Neural Networks—A Combined Technique Classifying Small Molecular Structures Using a Concatenated Spectral Database

    Directory of Open Access Journals (Sweden)

    Mihail Lucian Birsa

    2011-10-01

    Full Text Available In this paper we present several expert systems that predict the class identity of the modeled compounds, based on a preprocessed spectral database. The expert systems were built using Artificial Neural Networks (ANN and are designed to predict if an unknown compound has the toxicological activity of amphetamines (stimulant and hallucinogen, or whether it is a nonamphetamine. In attempts to circumvent the laws controlling drugs of abuse, new chemical structures are very frequently introduced on the black market. They are obtained by slightly modifying the controlled molecular structures by adding or changing substituents at various positions on the banned molecules. As a result, no substance similar to those forming a prohibited class may be used nowadays, even if it has not been specifically listed. Therefore, reliable, fast and accessible systems capable of modeling and then identifying similarities at molecular level, are highly needed for epidemiological, clinical, and forensic purposes. In order to obtain the expert systems, we have preprocessed a concatenated spectral database, representing the GC-FTIR (gas chromatography-Fourier transform infrared spectrometry and GC-MS (gas chromatography-mass spectrometry spectra of 103 forensic compounds. The database was used as input for a Principal Component Analysis (PCA. The scores of the forensic compounds on the main principal components (PCs were then used as inputs for the ANN systems. We have built eight PC-ANN systems (principal component analysis coupled with artificial neural network with a different number of input variables: 15 PCs, 16 PCs, 17 PCs, 18 PCs, 19 PCs, 20 PCs, 21 PCs and 22 PCs. The best expert system was found to be the ANN network built with 18 PCs, which accounts for an explained variance of 77%. This expert system has the best sensitivity (a rate of classification C = 100% and a rate of true positives TP = 100%, as well as a good selectivity (a rate of true negatives TN

  1. Examination of neural systems sub-serving facebook "addiction".

    Science.gov (United States)

    Turel, Ofir; He, Qinghua; Xue, Gui; Xiao, Lin; Bechara, Antoine

    2014-12-01

    Because addictive behaviors typically result from violated homeostasis of the impulsive (amygdala-striatal) and inhibitory (prefrontal cortex) brain systems, this study examined whether these systems sub-serve a specific case of technology-related addiction, namely Facebook "addiction." Using a go/no-go paradigm in functional MRI settings, the study examined how these brain systems in 20 Facebook users (M age = 20.3 yr., SD = 1.3, range = 18-23) who completed a Facebook addiction questionnaire, responded to Facebook and less potent (traffic sign) stimuli. The findings indicated that at least at the examined levels of addiction-like symptoms, technology-related "addictions" share some neural features with substance and gambling addictions, but more importantly they also differ from such addictions in their brain etiology and possibly pathogenesis, as related to abnormal functioning of the inhibitory-control brain system.

  2. Component-Based Approach in Learning Management System Development

    Science.gov (United States)

    Zaitseva, Larisa; Bule, Jekaterina; Makarov, Sergey

    2013-01-01

    The paper describes component-based approach (CBA) for learning management system development. Learning object as components of e-learning courses and their metadata is considered. The architecture of learning management system based on CBA being developed in Riga Technical University, namely its architecture, elements and possibilities are…

  3. Neural systems supporting and affecting economically relevant behavior

    Directory of Open Access Journals (Sweden)

    Braeutigam S

    2012-05-01

    Full Text Available Sven BraeutigamOxford Centre for Human Brain Activity, University of Oxford, Oxford, United KingdomAbstract: For about a hundred years, theorists and traders alike have tried to unravel and understand the mechanisms and hidden rules underlying and perhaps determining economically relevant behavior. This review focuses on recent developments in neuroeconomics, where the emphasis is placed on two directions of research: first, research exploiting common experiences of urban inhabitants in industrialized societies to provide experimental paradigms with a broader real-life content; second, research based on behavioral genetics, which provides an additional dimension for experimental control and manipulation. In addition, possible limitations of state-of-the-art neuroeconomics research are addressed. It is argued that observations of neuronal systems involved in economic behavior converge to some extent across the technologies and paradigms used. Conceptually, the data available as of today raise the possibility that neuroeconomic research might provide evidence at the neuronal level for the existence of multiple systems of thought and for the importance of conflict. Methodologically, Bayesian approaches in particular may play an important role in identifying mechanisms and establishing causality between patterns of neural activity and economic behavior.Keywords: neuroeconomics, behavioral genetics, decision-making, consumer behavior, neural system

  4. Feature-based component model for design of embedded systems

    Science.gov (United States)

    Zha, Xuan Fang; Sriram, Ram D.

    2004-11-01

    An embedded system is a hybrid of hardware and software, which combines software's flexibility and hardware real-time performance. Embedded systems can be considered as assemblies of hardware and software components. An Open Embedded System Model (OESM) is currently being developed at NIST to provide a standard representation and exchange protocol for embedded systems and system-level design, simulation, and testing information. This paper proposes an approach to representing an embedded system feature-based model in OESM, i.e., Open Embedded System Feature Model (OESFM), addressing models of embedded system artifacts, embedded system components, embedded system features, and embedded system configuration/assembly. The approach provides an object-oriented UML (Unified Modeling Language) representation for the embedded system feature model and defines an extension to the NIST Core Product Model. The model provides a feature-based component framework allowing the designer to develop a virtual embedded system prototype through assembling virtual components. The framework not only provides a formal precise model of the embedded system prototype but also offers the possibility of designing variation of prototypes whose members are derived by changing certain virtual components with different features. A case study example is discussed to illustrate the embedded system model.

  5. Using Pulse Width Modulation for Wireless Transmission of Neural Signals in Multichannel Neural Recording Systems

    Science.gov (United States)

    Yin, Ming; Ghovanloo, Maysam

    2013-01-01

    We have used a well-known technique in wireless communication, pulse width modulation (PWM) of time division multiplexed (TDM) signals, within the architecture of a novel wireless integrated neural recording (WINeR) system. We have evaluated the performance of the PWM-based architecture and indicated its accuracy and potential sources of error through detailed theoretical analysis, simulations, and measurements on a setup consisting of a 15-channel WINeR prototype as the transmitter and two types of receivers; an Agilent 89600 vector signal analyzer and a custom wideband receiver, with 36 and 75 MHz of maximum bandwidth, respectively. Furthermore, we present simulation results from a realistic MATLAB-Simulink model of the entire WINeR system to observe the system behavior in response to changes in various parameters. We have concluded that the 15-ch WINeR prototype, which is fabricated in a 0.5-μm standard CMOS process and consumes 4.5 mW from ±1.5 V supplies, can acquire and wirelessly transmit up to 320 k-samples/s to a 75-MHz receiver with 8.4 bits of resolution, which is equivalent to a wireless data rate of ~ 2.26 Mb/s. PMID:19497823

  6. A Gamma Memory Neural Network for System Identification

    Science.gov (United States)

    Motter, Mark A.; Principe, Jose C.

    1992-01-01

    A gamma neural network topology is investigated for a system identification application. A discrete gamma memory structure is used in the input layer, providing delayed values of both the control inputs and the network output to the input layer. The discrete gamma memory structure implements a tapped dispersive delay line, with the amount of dispersion regulated by a single, adaptable parameter. The network is trained using static back propagation, but captures significant features of the system dynamics. The system dynamics identified with the network are the Mach number dynamics of the 16 Foot Transonic Tunnel at NASA Langley Research Center, Hampton, Virginia. The training data spans an operating range of Mach numbers from 0.4 to 1.3.

  7. Artificial neural network analysis of triple effect absorption refrigeration systems

    Energy Technology Data Exchange (ETDEWEB)

    Hajizadeh Aghdam, A. [Department of Mechanical Engineering, Islamic Azad University (Iran, Islamic Republic of)], email: a.hajizadeh@iaukashan.ac.ir; Nazmara, H.; Farzaneh, B. [Department of Mechanical Engineering, University of Tabriz (Iran, Islamic Republic of)], email: h.nazmara@nioec.org, email: b_farzaneh_ms@yahoo.com

    2011-07-01

    In this study, artificial neural networks are utilized to predict the performance of triple effect series and parallel flow absorption refrigeration systems, with lithium bromide/water as the working fluid. Important parameters such as high generator and evaporator temperatures were varied and their effects on the performance characteristics of the refrigeration unit were observed. Absorption refrigeration systems make energy savings possible because they can use heat energy to produce cooling, in place of the electricity used for conventional vapour compression chillers. In addition, non-conventional sources of energy (such as solar, waste heat, and geothermal) can be utilized as their primary energy input. Moreover, absorption units use environmentally friendly working fluid pairs instead of CFCs and HCFCs, which affect the ozone layer. Triple effect absorption cycles were analysed. Results apply for both series and parallel flow systems. A relative preference for parallel-flow over series-flow is also shown.

  8. Blood hyperviscosity identification with reflective spectroscopy of tongue tip based on principal component analysis combining artificial neural network.

    Science.gov (United States)

    Liu, Ming; Zhao, Jing; Lu, XiaoZuo; Li, Gang; Wu, Taixia; Zhang, LiFu

    2018-05-10

    With spectral methods, noninvasive determination of blood hyperviscosity in vivo is very potential and meaningful in clinical diagnosis. In this study, 67 male subjects (41 health, and 26 hyperviscosity according to blood sample analysis results) participate. Reflectance spectra of subjects' tongue tips is measured, and a classification method bases on principal component analysis combined with artificial neural network model is built to identify hyperviscosity. Hold-out and Leave-one-out methods are used to avoid significant bias and lessen overfitting problem, which are widely accepted in the model validation. To measure the performance of the classification, sensitivity, specificity, accuracy and F-measure are calculated, respectively. The accuracies with 100 times Hold-out method and 67 times Leave-one-out method are 88.05% and 97.01%, respectively. Experimental results indicate that the built classification model has certain practical value and proves the feasibility of using spectroscopy to identify hyperviscosity by noninvasive determination.

  9. Ranking of risk significant components for the Davis-Besse Component Cooling Water System

    International Nuclear Information System (INIS)

    Seniuk, P.J.

    1994-01-01

    Utilities that run nuclear power plants are responsible for testing pumps and valves, as specified by the American Society of Mechanical Engineers (ASME) that are required for safe shutdown, mitigating the consequences of an accident, and maintaining the plant in a safe condition. These inservice components are tested according to ASME Codes, either the earlier requirements of the ASME Boiler and Pressure Vessel Code, Section XI, or the more recent requirements of the ASME Operation and Maintenance Code, Section IST. These codes dictate test techniques and frequencies regardless of the component failure rate or significance of failure consequences. A probabilistic risk assessment or probabilistic safety assessment may be used to evaluate the component importance for inservice test (IST) risk ranking, which is a combination of failure rate and failure consequences. Resources for component testing during the normal quarterly verification test or postmaintenance test are expensive. Normal quarterly testing may cause component unavailability. Outage testing may increase outage cost with no real benefit. This paper identifies the importance ranking of risk significant components in the Davis-Besse component cooling water system. Identifying the ranking of these risk significant IST components adds technical insight for developing the appropriate test technique and test frequency

  10. Speaker diarization system using HXLPS and deep neural network

    Directory of Open Access Journals (Sweden)

    V. Subba Ramaiah

    2018-03-01

    Full Text Available In general, speaker diarization is defined as the process of segmenting the input speech signal and grouped the homogenous regions with regard to the speaker identity. The main idea behind this system is that it is able to discriminate the speaker signal by assigning the label of the each speaker signal. Due to rapid growth of broadcasting and meeting, the speaker diarization is burdensome to enhance the readability of the speech transcription. In order to solve this issue, Holoentropy with the eXtended Linear Prediction using autocorrelation Snapshot (HXLPS and deep neural network (DNN is proposed for the speaker diarization system. The HXLPS extraction method is newly developed by incorporating the Holoentropy with the XLPS. Once we attain the features, the speech and non-speech signals are detected by the Voice Activity Detection (VAD method. Then, i-vector representation of every segmented signal is obtained using Universal Background Model (UBM model. Consequently, DNN is utilized to assign the label for the speaker signal which is then clustered according to the speaker label. The performance is analysed using the evaluation metrics, such as tracking distance, false alarm rate and diarization error rate. The outcome of the proposed method ensures the better diarization performance by achieving the lower DER of 1.36% based on lambda value and DER of 2.23% depends on the frame length. Keywords: Speaker diarization, HXLPS feature extraction, Voice activity detection, Deep neural network, Speaker clustering, Diarization Error Rate (DER

  11. Classification Technique for Ultrasonic Weld Inspection Signals using a Neural Network based on 2-dimensional fourier Transform and Principle Component Analysis

    International Nuclear Information System (INIS)

    Kim, Jae Joon

    2004-01-01

    Neural network-based signal classification systems are increasingly used in the analysis of large volumes of data obtained in NDE applications. Ultrasonic inspection methods on the other hand are commonly used in the nondestructive evaluation of welds to detect flaws. An important characteristic of ultrasonic inspection is the ability to identify the type of discontinuity that gives rise to a peculiar signal. Standard techniques rely on differences in individual A-scans to classify the signals. This paper proposes an ultrasonic signal classification technique based on the information tying in the neighboring signals. The approach is based on a 2-dimensional Fourier transform and the principal component analysis to generate a reduced dimensional feature vector for classification. Results of applying the technique to data obtained from the inspection of actual steel welds are presented

  12. Generalized synthetic aperture radar automatic target recognition by convolutional neural network with joint use of two-dimensional principal component analysis and support vector machine

    Science.gov (United States)

    Zheng, Ce; Jiang, Xue; Liu, Xingzhao

    2017-10-01

    Convolutional neural network (CNN), as a vital part of the deep learning research field, has shown powerful potential for automatic target recognition (ATR) of synthetic aperture radar (SAR). However, the high complexity caused by the deep structure of CNN makes it difficult to generalize. An improved form of CNN with higher generalization capability and less probability of overfitting, which further improves the efficiency and robustness of the SAR ATR system, is proposed. The convolution layers of CNN are combined with a two-dimensional principal component analysis algorithm. Correspondingly, the kernel support vector machine is utilized as the classifier layer instead of the multilayer perceptron. The verification experiments are implemented using the moving and stationary target acquisition and recognition database, and the results validate the efficiency of the proposed method.

  13. Visualization of suspicious lesions in breast MRI based on intelligent neural systems

    Science.gov (United States)

    Twellmann, Thorsten; Lange, Oliver; Nattkemper, Tim Wilhelm; Meyer-Bäse, Anke

    2006-05-01

    Intelligent medical systems based on supervised and unsupervised artificial neural networks are applied to the automatic visualization and classification of suspicious lesions in breast MRI. These systems represent an important component of future sophisticated computer-aided diagnosis systems and enable the extraction of spatial and temporal features of dynamic MRI data stemming from patients with confirmed lesion diagnosis. By taking into account the heterogenity of the cancerous tissue, these techniques reveal the malignant, benign and normal kinetic signals and and provide a regional subclassification of pathological breast tissue. Intelligent medical systems are expected to have substantial implications in healthcare politics by contributing to the diagnosis of indeterminate breast lesions by non-invasive imaging.

  14. Multi-level predictive maintenance for multi-component systems

    International Nuclear Information System (INIS)

    Nguyen, Kim-Anh; Do, Phuc; Grall, Antoine

    2015-01-01

    In this paper, a novel predictive maintenance policy with multi-level decision-making is proposed for multi-component system with complex structure. The main idea is to propose a decision-making process considered on two levels: system level and component one. The goal of the decision rules at the system level is to address if preventive maintenance actions are needed regarding the predictive reliability of the system. At component level the decision rules aim at identifying optimally a group of several components to be preventively maintained when preventive maintenance is trigged due to the system level decision. Selecting optimal components is based on a cost-based group improvement factor taking into account the predictive reliability of the components, the economic dependencies as well as the location of the components in the system. Moreover, a cost model is developed to find the optimal maintenance decision variables. A 14-component system is finally introduced to illustrate the use and the performance of the proposed predictive maintenance policy. Different sensitivity analysis are also investigated and discussed. Indeed, the proposed policy provides more flexibility in maintenance decision-making for complex structure systems, hence leading to significant profits in terms of maintenance cost when compared with existing policies. - Highlights: • A predictive maintenance policy for complex structure systems is proposed. • Multi-level decision process based on prognostic results is proposed. • A cost-based group importance measure is introduced for decision-making. • Both positive and negative dependencies between components are investigated. • A cost model and Monte Carlo simulation are developed for optimization process.

  15. The Location GNSS Modules for the Components of Proteus System

    Science.gov (United States)

    Brzostowski, K.; Darakchiev, R.; Foks-Ryznar, A.; Sitek, P.

    2012-01-01

    The Proteus system - the Integrated Mobile System for Counterterrorism and Rescue Operations is a complex innovative project. To assure the best possible localization of mobile components of the system, many different Global Navigation Satellite System (GNSS) modules were taken into account. In order to chose the best solution many types of tests were done. Full results and conclusions are presented in this paper. The idea of measurements was to test modules in GPS Standard Positioning Service (SPS) with EGNOS system specification according to certain algorithms. The tests had to answer the question: what type of GNSS modules should be used on different components with respect to specific usage of Proteus system. The second goal of tests was to check the solution quality of integrated GNSS/INS (Inertial Navigation System) and its possible usage in some Proteus system components.

  16. Hybrid case-neural network (CNN) diagnostic system

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2010-01-01

    recently, the mobile health care has a great attention for the researcher and people all over the world. Case based reasoning (CBR) systems have proved their performance as world wide web (WWW) medical diagnostic systems. They were preferred rather than different reasoning approaches due to their high performance and results' explanation. But, their operations require a complex knowledge acquisition and management processes. On the other hand, it is found that, artificial neural network (ANN) has a great acceptance as a classifier methodology using a little amount of knowledge. But, ANN lacks of an explanation capability .The present research introduces a new web-based hybrid diagnostic system that can use the ANN inside the CBR , cycle.It can provide higher performance for the web diagnostic systems. Besides, the proposed system can be used as a web diagnostic system. It can be applied for diagnosis different types of systems in several domains. It has been applied in diagnosis of the cancer diseases that has a great spreading in recent years as a case of study . However, the suggested system has proved its acceptance in the manner.

  17. Stability Analysis of Neural Networks-Based System Identification

    Directory of Open Access Journals (Sweden)

    Talel Korkobi

    2008-01-01

    Full Text Available This paper treats some problems related to nonlinear systems identification. A stability analysis neural network model for identifying nonlinear dynamic systems is presented. A constrained adaptive stable backpropagation updating law is presented and used in the proposed identification approach. The proposed backpropagation training algorithm is modified to obtain an adaptive learning rate guarantying convergence stability. The proposed learning rule is the backpropagation algorithm under the condition that the learning rate belongs to a specified range defining the stability domain. Satisfying such condition, unstable phenomena during the learning process are avoided. A Lyapunov analysis leads to the computation of the expression of a convenient adaptive learning rate verifying the convergence stability criteria. Finally, the elaborated training algorithm is applied in several simulations. The results confirm the effectiveness of the CSBP algorithm.

  18. The exploitation of neural networks in automotive engine management systems

    Energy Technology Data Exchange (ETDEWEB)

    Shayler, P.J.; Goodman, M. [University of Nottingham (United Kingdom); Ma, T. [Ford Motor Company, Dagenham (United Kingdom). Research and Engineering Centre

    2000-07-01

    The use of electronic engine control systems on spark ignition engines has enabled a high degree of performance optimisation to be achieved. The range of functions performed by these systems, and the level of performance demanded, is rising and thus so are development times and costs. Neural networks have attracted attention as having the potential to simplify software development and improve the performance of this software. The scope and nature of possible applications is described. In particular, the pattern recognition and classification abilities of networks are applied to crankshaft speed fluctuation data for engine-fault diagnosis, and multidimensional mapping capabilities are investigated as an alternative to large 'lookup' tables and calibration functions. (author)

  19. Safety classification of nuclear power plant systems, structures and components

    International Nuclear Information System (INIS)

    1992-01-01

    The Safety Classification principles used for the systems, structures and components of a nuclear power plant are detailed in the guide. For classification, the nuclear power plant is divided into structural and operational units called systems. Every structure and component under control is included into some system. The Safety Classes are 1, 2 and 3 and the Class EYT (non-nuclear). Instructions how to assign each system, structure and component to an appropriate safety class are given in the guide. The guide applies to new nuclear power plants and to the safety classification of systems, structures and components designed for the refitting of old nuclear power plants. The classification principles and procedures applying to the classification document are also given

  20. Availability, reliability and downtime of systems with repairable components

    DEFF Research Database (Denmark)

    Kiureghian, Armen Der; Ditlevsen, Ove Dalager; Song, J.

    2007-01-01

    Closed-form expressions are derived for the steady-state availability, mean rate of failure, mean duration of downtime and lower bound reliability of a general system with randomly and independently failing repairable components. Component failures are assumed to be homogeneous Poisson events in ...

  1. Application of neural networks to connectional expert system for identification of transients in nuclear power plants

    International Nuclear Information System (INIS)

    Cheon, Se Woo; Kim, Wan Joo; Chang, Soon Heung; Roh, Myung Sub

    1991-01-01

    The Back-propagation Neural Network (BPN) algorithm is applied to connectionist expert system for the identification of BWR transients. Several powerful features of neural network-based expert systems over traditional rule-based expert systems are described. The general mapping capability of the neural networks enables to identify transients easily. A number of case studies were performed with emphasis on the applicability of the neural networks to the diagnostic domain. It is revealed that the BPN algorithm can identify transients properly, even when incomplete or untrained symptoms are given. It is also shown that multiple transients are easily identified

  2. Neural-network hybrid control for antilock braking systems.

    Science.gov (United States)

    Lin, Chih-Min; Hsu, C F

    2003-01-01

    The antilock braking systems are designed to maximize wheel traction by preventing the wheels from locking during braking, while also maintaining adequate vehicle steerability; however, the performance is often degraded under harsh road conditions. In this paper, a hybrid control system with a recurrent neural network (RNN) observer is developed for antilock braking systems. This hybrid control system is comprised of an ideal controller and a compensation controller. The ideal controller, containing an RNN uncertainty observer, is the principal controller; and the compensation controller is a compensator for the difference between the system uncertainty and the estimated uncertainty. Since for dynamic response the RNN has capabilities superior to the feedforward NN, it is utilized for the uncertainty observer. The Taylor linearization technique is employed to increase the learning ability of the RNN. In addition, the on-line parameter adaptation laws are derived based on a Lyapunov function, so the stability of the system can be guaranteed. Simulations are performed to demonstrate the effectiveness of the proposed NN hybrid control system for antilock braking control under various road conditions.

  3. Verifying Embedded Systems using Component-based Runtime Observers

    DEFF Research Database (Denmark)

    Guan, Wei; Marian, Nicolae; Angelov, Christo K.

    against formally specified properties. This paper presents a component-based design method for runtime observers, which are configured from instances of prefabricated reusable components---Predicate Evaluator (PE) and Temporal Evaluator (TE). The PE computes atomic propositions for the TE; the latter...... is a reconfigurable component processing a data structure, representing the state transition diagram of a non-deterministic state machine, i.e. a Buchi automaton derived from a system property specified in Linear Temporal Logic (LTL). Observer components have been implemented using design models and design patterns...

  4. BOOK REVIEW: Theory of Neural Information Processing Systems

    Science.gov (United States)

    Galla, Tobias

    2006-04-01

    It is difficult not to be amazed by the ability of the human brain to process, to structure and to memorize information. Even by the toughest standards the behaviour of this network of about 1011 neurons qualifies as complex, and both the scientific community and the public take great interest in the growing field of neuroscience. The scientific endeavour to learn more about the function of the brain as an information processing system is here a truly interdisciplinary one, with important contributions from biology, computer science, physics, engineering and mathematics as the authors quite rightly point out in the introduction of their book. The role of the theoretical disciplines here is to provide mathematical models of information processing systems and the tools to study them. These models and tools are at the centre of the material covered in the book by Coolen, Kühn and Sollich. The book is divided into five parts, providing basic introductory material on neural network models as well as the details of advanced techniques to study them. A mathematical appendix complements the main text. The range of topics is extremely broad, still the presentation is concise and the book well arranged. To stress the breadth of the book let me just mention a few keywords here: the material ranges from the basics of perceptrons and recurrent network architectures to more advanced aspects such as Bayesian learning and support vector machines; Shannon's theory of information and the definition of entropy are discussed, and a chapter on Amari's information geometry is not missing either. Finally the statistical mechanics chapters cover Gardner theory and the replica analysis of the Hopfield model, not without being preceded by a brief introduction of the basic concepts of equilibrium statistical physics. The book also contains a part on effective theories of the macroscopic dynamics of neural networks. Many dynamical aspects of neural networks are usually hard to find in the

  5. Defense Strategies for Asymmetric Networked Systems with Discrete Components

    Directory of Open Access Journals (Sweden)

    Nageswara S. V. Rao

    2018-05-01

    Full Text Available We consider infrastructures consisting of a network of systems, each composed of discrete components. The network provides the vital connectivity between the systems and hence plays a critical, asymmetric role in the infrastructure operations. The individual components of the systems can be attacked by cyber and physical means and can be appropriately reinforced to withstand these attacks. We formulate the problem of ensuring the infrastructure performance as a game between an attacker and a provider, who choose the numbers of the components of the systems and network to attack and reinforce, respectively. The costs and benefits of attacks and reinforcements are characterized using the sum-form, product-form and composite utility functions, each composed of a survival probability term and a component cost term. We present a two-level characterization of the correlations within the infrastructure: (i the aggregate failure correlation function specifies the infrastructure failure probability given the failure of an individual system or network, and (ii the survival probabilities of the systems and network satisfy first-order differential conditions that capture the component-level correlations using multiplier functions. We derive Nash equilibrium conditions that provide expressions for individual system survival probabilities and also the expected infrastructure capacity specified by the total number of operational components. We apply these results to derive and analyze defense strategies for distributed cloud computing infrastructures using cyber-physical models.

  6. Defense Strategies for Asymmetric Networked Systems with Discrete Components.

    Science.gov (United States)

    Rao, Nageswara S V; Ma, Chris Y T; Hausken, Kjell; He, Fei; Yau, David K Y; Zhuang, Jun

    2018-05-03

    We consider infrastructures consisting of a network of systems, each composed of discrete components. The network provides the vital connectivity between the systems and hence plays a critical, asymmetric role in the infrastructure operations. The individual components of the systems can be attacked by cyber and physical means and can be appropriately reinforced to withstand these attacks. We formulate the problem of ensuring the infrastructure performance as a game between an attacker and a provider, who choose the numbers of the components of the systems and network to attack and reinforce, respectively. The costs and benefits of attacks and reinforcements are characterized using the sum-form, product-form and composite utility functions, each composed of a survival probability term and a component cost term. We present a two-level characterization of the correlations within the infrastructure: (i) the aggregate failure correlation function specifies the infrastructure failure probability given the failure of an individual system or network, and (ii) the survival probabilities of the systems and network satisfy first-order differential conditions that capture the component-level correlations using multiplier functions. We derive Nash equilibrium conditions that provide expressions for individual system survival probabilities and also the expected infrastructure capacity specified by the total number of operational components. We apply these results to derive and analyze defense strategies for distributed cloud computing infrastructures using cyber-physical models.

  7. Investigations on Incipient Fault Diagnosis of Power Transformer Using Neural Networks and Adaptive Neurofuzzy Inference System

    Directory of Open Access Journals (Sweden)

    Nandkumar Wagh

    2014-01-01

    Full Text Available Continuity of power supply is of utmost importance to the consumers and is only possible by coordination and reliable operation of power system components. Power transformer is such a prime equipment of the transmission and distribution system and needs to be continuously monitored for its well-being. Since ratio methods cannot provide correct diagnosis due to the borderline problems and the probability of existence of multiple faults, artificial intelligence could be the best approach. Dissolved gas analysis (DGA interpretation may provide an insight into the developing incipient faults and is adopted as the preliminary diagnosis tool. In the proposed work, a comparison of the diagnosis ability of backpropagation (BP, radial basis function (RBF neural network, and adaptive neurofuzzy inference system (ANFIS has been investigated and the diagnosis results in terms of error measure, accuracy, network training time, and number of iterations are presented.

  8. Process and apparatus for fractionating close-boiling components of a multi-component system

    International Nuclear Information System (INIS)

    Tsao, U.

    1983-01-01

    A process and apparatus are described for the fractionation of close-boiling components of a multi-component system comprising at least two fractionation columns A, B in series having a plurality of equilibrium stages in which the vapor stream from a downstream fractionation column B is compressed by a compressor and passed into a lower portion of a preceding fractionation column A and a liquid bottom stream from any one of said columns except the last is expanded by an orifice sufficiently to convey the resulting liquid-vapor mixture to the upper portion of the next fractionation column B. In a particularly preferred embodiment, the compressed overhead vapor stream is passed in heat transfer relationship to a liquid stream withdrawn from the preceding fractionation column A prior to introduction into the lower portion of such preceding fractionation column A. In one of the claims, the multi-component close-boiling system is a deuterium oxide-water solution. (author)

  9. Neural mechanism of facilitation system during physical fatigue.

    Directory of Open Access Journals (Sweden)

    Masaaki Tanaka

    Full Text Available An enhanced facilitation system caused by motivational input plays an important role in supporting performance during physical fatigue. We tried to clarify the neural mechanisms of the facilitation system during physical fatigue using magnetoencephalography (MEG and a classical conditioning technique. Twelve right-handed volunteers participated in this study. Participants underwent MEG recording during the imagery of maximum grips of the right hand guided by metronome sounds for 10 min. Thereafter, fatigue-inducing maximum handgrip trials were performed for 10 min; the metronome sounds were started 5 min after the beginning of the handgrip trials. The metronome sounds were used as conditioned stimuli and maximum handgrip trials as unconditioned stimuli. The next day, they were randomly assigned to two groups in a single-blinded, two-crossover fashion to undergo two types of MEG recordings, that is, for the control and motivation sessions, during the imagery of maximum grips of the right hand guided by metronome sounds for 10 min. The alpha-band event-related desynchronizations (ERDs of the motivation session relative to the control session within the time windows of 500 to 700 and 800 to 900 ms after the onset of handgrip cue sounds were identified in the sensorimotor areas. In addition, the alpha-band ERD within the time window of 400 to 500 ms was identified in the right dorsolateral prefrontal cortex (Brodmann's area 46. The ERD level in the right dorsolateral prefrontal cortex was positively associated with that in the sensorimotor areas within the time window of 500 to 700 ms. These results suggest that the right dorsolateral prefrontal cortex is involved in the neural substrates of the facilitation system and activates the sensorimotor areas during physical fatigue.

  10. Applying of component system development in object methodology

    Directory of Open Access Journals (Sweden)

    Milan Mišovič

    2013-01-01

    Full Text Available In the last three decades, the concept and implementation of component-based architectures have been promoted in software systems creation. Increasingly complex demands are placed on the software component systems, in particular relating to the dynamic properties. The emergence of such requirements has been gradually enforced by the practice of development and implementation of these systems, especially for information systems software.Just the information systems (robust IS of different types require that target software meets their requirements. Among other things, we mean primarily the adaptive processes of different domains, high distributives due to the possibilities of the Internet 2.0, acceptance of high integrity of life domains (process, data and communications integrity, scalability, and flexible adaptation to process changes, a good context for external devices and transparent structure of the sub-process modules and architectural units.Of course, the target software of required qualities and the type robust cannot be a monolith. As commonly known, development of design toward information systems software has clearly come to the need for the software composition of completely autonomous, but cooperating architectural units that communicate with each other using messages of prescribed formats.Although for such units there were often used the so called subsystems and modules, see (Jac, Boo, Rumbo, 1998 and (Arlo, Neus, 2007, their abstraction being gradually enacted as the term component. In other words, the subsystems and modules are specific types of components.In (Král, Žeml, 2000 and (Král, Žeml, 2003 there are considered two types of target software of information systems. The first type – there are SWC (Software Components, composed of permanently available components, which are thought as services – Confederate software. The second type – SWA (Software Alliance, called semi Confederate, formed during the run-time of the

  11. PRODIAG: Combined expert system/neural network for process fault diagnosis. Volume 1, Theory

    Energy Technology Data Exchange (ETDEWEB)

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

    1995-09-01

    The function of the PRODIAG code is to diagnose on-line the root cause of a thermal-hydraulic (T-H) system transient with trace back to the identification of the malfunctioning component using the T-H instrumentation signals exclusively. The code methodology is based on the Al techniques of automated reasoning/expert systems (ES) and artificial neural networks (ANN). The research and development objective is to develop a generic code methodology which would be plant- and T-H-system-independent. For the ES part the only plant or T-H system specific code requirements would be implemented through input only and at that only through a Piping and Instrumentation Diagram (PID) database. For the ANN part the only plant or T-H system specific code requirements would be through the ANN training data for normal component characteristics and the same PID database information. PRODIAG would, therefore, be generic and portable from T-H system to T-H system and from plant to plant without requiring any code-related modifications except for the PID database and the ANN training with the normal component characteristics. This would give PRODIAG the generic feature which numerical simulation plant codes such as TRAC or RELAP5 have. As the code is applied to different plants and different T-H systems, only the connectivity information, the operating conditions and the normal component characteristics are changed, and the changes are made entirely through input. Verification and validation of PRODIAG would, be T-H system independent and would be performed only ``once``.

  12. System diagnostics using qualitative analysis and component functional classification

    International Nuclear Information System (INIS)

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

    1993-01-01

    A method for detecting and identifying faulty component candidates during off-normal operations of nuclear power plants involves the qualitative analysis of macroscopic imbalances in the conservation equations of mass, energy and momentum in thermal-hydraulic control volumes associated with one or more plant components and the functional classification of components. The qualitative analysis of mass and energy is performed through the associated equations of state, while imbalances in momentum are obtained by tracking mass flow rates which are incorporated into a first knowledge base. The plant components are functionally classified, according to their type, as sources or sinks of mass, energy and momentum, depending upon which of the three balance equations is most strongly affected by a faulty component which is incorporated into a second knowledge base. Information describing the connections among the components of the system forms a third knowledge base. The method is particularly adapted for use in a diagnostic expert system to detect and identify faulty component candidates in the presence of component failures and is not limited to use in a nuclear power plant, but may be used with virtually any type of thermal-hydraulic operating system. 5 figures

  13. Ventilation systems and components of nuclear power plants

    International Nuclear Information System (INIS)

    1997-01-01

    The most important radiation and nuclear safety requirements for the design and manufacture of nuclear power plant ventilation systems and components are presented in the guide. Also the regulatory activities of the Finnish Centre for Radiation and Nuclear Safety (STUK) as regards the ventilation systems and components are explained. Documents and data which shall be submitted to STUK during the various phases of the regulatory procedure relating to the design, construction, commissioning and operation of the nuclear power plants are presented. (13 refs.)

  14. Investigation on Prefabricated Building System Skilled Component Installers

    OpenAIRE

    Nurul Rezuana Buyung; Md Azree Othuman Mydin; Abdul Naser Abdul Ghani

    2013-01-01

    In the face of an increasingly challenging era of globalization, skills and new equipments which includes prefabricated building components, known as Industrialized Building System (IBS) has been introduced towards achievement of sustainable construction. IBS is a construction system in which the components are manufactured in a factory, on or off site, positioned and assembled into complete structures with minimal additional site work. IBS requires high construction precisi...

  15. Neural systems language: a formal modeling language for the systematic description, unambiguous communication, and automated digital curation of neural connectivity.

    Science.gov (United States)

    Brown, Ramsay A; Swanson, Larry W

    2013-09-01

    Systematic description and the unambiguous communication of findings and models remain among the unresolved fundamental challenges in systems neuroscience. No common descriptive frameworks exist to describe systematically the connective architecture of the nervous system, even at the grossest level of observation. Furthermore, the accelerating volume of novel data generated on neural connectivity outpaces the rate at which this data is curated into neuroinformatics databases to synthesize digitally systems-level insights from disjointed reports and observations. To help address these challenges, we propose the Neural Systems Language (NSyL). NSyL is a modeling language to be used by investigators to encode and communicate systematically reports of neural connectivity from neuroanatomy and brain imaging. NSyL engenders systematic description and communication of connectivity irrespective of the animal taxon described, experimental or observational technique implemented, or nomenclature referenced. As a language, NSyL is internally consistent, concise, and comprehensible to both humans and computers. NSyL is a promising development for systematizing the representation of neural architecture, effectively managing the increasing volume of data on neural connectivity and streamlining systems neuroscience research. Here we present similar precedent systems, how NSyL extends existing frameworks, and the reasoning behind NSyL's development. We explore NSyL's potential for balancing robustness and consistency in representation by encoding previously reported assertions of connectivity from the literature as examples. Finally, we propose and discuss the implications of a framework for how NSyL will be digitally implemented in the future to streamline curation of experimental results and bridge the gaps among anatomists, imagers, and neuroinformatics databases. Copyright © 2013 Wiley Periodicals, Inc.

  16. Computers as components principles of embedded computing system design

    CERN Document Server

    Wolf, Marilyn

    2012-01-01

    Computers as Components: Principles of Embedded Computing System Design, 3e, presents essential knowledge on embedded systems technology and techniques. Updated for today's embedded systems design methods, this edition features new examples including digital signal processing, multimedia, and cyber-physical systems. Author Marilyn Wolf covers the latest processors from Texas Instruments, ARM, and Microchip Technology plus software, operating systems, networks, consumer devices, and more. Like the previous editions, this textbook: Uses real processors to demonstrate both technology and tec

  17. Component processes of memory in alcoholism: pattern of compromise and neural substrates.

    Science.gov (United States)

    Pitel, Anne-Lise; Eustache, Francis; Beaunieux, Helene

    2014-01-01

    Initially, alcohol-related memory deficits were considered only through the prism of Korsakoff's syndrome (KS). It is now clear, however, that chronic alcohol consumption results in memory disorders in alcoholics without ostensible neurologic complications, such as Wernicke's encephalopathy and KS. Most of the principal memory components are affected, including working memory, episodic memory, semantic memory, perceptual memory, and procedural memory. The extent of those cognitive impairments depends on several factors, such as age, gender, nutritional status, and psychiatric comorbidity. While memory disorders, especially episodic memory deficits, are largely definitive in patients with KS, recovery of memory abilities has been described with abstinence in uncomplicated alcoholics. Neuropsychologic impairments, and especially memory disorders, must be evaluated at alcohol treatment entry because they could impede patients from benefiting fully from cognitive and behavioral treatment approaches for alcohol dependence. Screening of memory deficits could also enable clinicians to detect, among alcoholics without ostensible neurologic complications, those at risk of developing permanent and debilitating amnesia that features KS. © 2014 Elsevier B.V. All rights reserved.

  18. Stochastic Neural Field Theory and the System-Size Expansion

    KAUST Repository

    Bressloff, Paul C.

    2010-01-01

    We analyze a master equation formulation of stochastic neurodynamics for a network of synaptically coupled homogeneous neuronal populations each consisting of N identical neurons. The state of the network is specified by the fraction of active or spiking neurons in each population, and transition rates are chosen so that in the thermodynamic or deterministic limit (N → ∞) we recover standard activity-based or voltage-based rate models. We derive the lowest order corrections to these rate equations for large but finite N using two different approximation schemes, one based on the Van Kampen system-size expansion and the other based on path integral methods. Both methods yield the same series expansion of the moment equations, which at O(1/N) can be truncated to form a closed system of equations for the first-and second-order moments. Taking a continuum limit of the moment equations while keeping the system size N fixed generates a system of integrodifferential equations for the mean and covariance of the corresponding stochastic neural field model. We also show how the path integral approach can be used to study large deviation or rare event statistics underlying escape from the basin of attraction of a stable fixed point of the mean-field dynamics; such an analysis is not possible using the system-size expansion since the latter cannot accurately determine exponentially small transitions. © by SIAM.

  19. Decoupling control of vehicle chassis system based on neural network inverse system

    Science.gov (United States)

    Wang, Chunyan; Zhao, Wanzhong; Luan, Zhongkai; Gao, Qi; Deng, Ke

    2018-06-01

    Steering and suspension are two important subsystems affecting the handling stability and riding comfort of the chassis system. In order to avoid the interference and coupling of the control channels between active front steering (AFS) and active suspension subsystems (ASS), this paper presents a composite decoupling control method, which consists of a neural network inverse system and a robust controller. The neural network inverse system is composed of a static neural network with several integrators and state feedback of the original chassis system to approach the inverse system of the nonlinear systems. The existence of the inverse system for the chassis system is proved by the reversibility derivation of Interactor algorithm. The robust controller is based on the internal model control (IMC), which is designed to improve the robustness and anti-interference of the decoupled system by adding a pre-compensation controller to the pseudo linear system. The results of the simulation and vehicle test show that the proposed decoupling controller has excellent decoupling performance, which can transform the multivariable system into a number of single input and single output systems, and eliminate the mutual influence and interference. Furthermore, it has satisfactory tracking capability and robust performance, which can improve the comprehensive performance of the chassis system.

  20. Components and system tests on the RFX toroidal power supply

    International Nuclear Information System (INIS)

    Toigo, V.; Zanotto, L.; Gaio, E.; Perna, M.; Bordignon, P.; Coffetti, A.; Novaro, R.; Bertolotto, P.; Rinaldi, E.; Villa, G.

    2005-01-01

    The paper deals with the component and system tests performed on the new toroidal power supply system of the RFX experiment. The high technological innovation of the system required a deep experimental characterization and validation campaign; special factory tests were performed on prototypes of single components aimed at verifying the most critical design aspects. Consequently an articulated series of tests were performed, based on a step-by-step approach to achieve the desired coordinate operation of the whole system. The test procedures and the most significant results are described in the paper

  1. Robustness of Component Models in Energy System Simulators

    DEFF Research Database (Denmark)

    Elmegaard, Brian

    2003-01-01

    During the development of the component-based energy system simulator DNA (Dynamic Network Analysis), several obstacles to easy use of the program have been observed. Some of these have to do with the nature of the program being based on a modelling language, not a graphical user interface (GUI......). Others have to do with the interaction between models of the nature of the substances in an energy system (e.g., fuels, air, flue gas), models of the components in a system (e.g., heat exchangers, turbines, pumps), and the solver for the system of equations. This paper proposes that the interaction...

  2. Implementation of neuromorphic systems: from discrete components to analog VLSI chips (testing and communication issues).

    Science.gov (United States)

    Dante, V; Del Giudice, P; Mattia, M

    2001-01-01

    We review a series of implementations of electronic devices aiming at imitating to some extent structure and function of simple neural systems, with particular emphasis on communication issues. We first provide a short overview of general features of such "neuromorphic" devices and the implications of setting up "tests" for them. We then review the developments directly related to our work at the Istituto Superiore di Sanità (ISS): a pilot electronic neural network implementing a simple classifier, autonomously developing internal representations of incoming stimuli; an output network, collecting information from the previous classifier and extracting the relevant part to be forwarded to the observer; an analog, VLSI (very large scale integration) neural chip implementing a recurrent network of spiking neurons and plastic synapses, and the test setup for it; a board designed to interface the standard PCI (peripheral component interconnect) bus of a PC with a special purpose, asynchronous bus for communication among neuromorphic chips; a short and preliminary account of an application-oriented device, taking advantage of the above communication infrastructure.

  3. Context sensitivity and ambiguity in component-based systems design

    Energy Technology Data Exchange (ETDEWEB)

    Bespalko, S.J.; Sindt, A.

    1997-10-01

    Designers of components-based, real-time systems need to guarantee to correctness of soft-ware and its output. Complexity of a system, and thus the propensity for error, is best characterized by the number of states a component can encounter. In many cases, large numbers of states arise where the processing is highly dependent on context. In these cases, states are often missed, leading to errors. The following are proposals for compactly specifying system states which allow the factoring of complex components into a control module and a semantic processing module. Further, the need for methods that allow for the explicit representation of ambiguity and uncertainty in the design of components is discussed. Presented herein are examples of real-world problems which are highly context-sensitive or are inherently ambiguous.

  4. Unsupervised neural spike sorting for high-density microelectrode arrays with convolutive independent component analysis.

    Science.gov (United States)

    Leibig, Christian; Wachtler, Thomas; Zeck, Günther

    2016-09-15

    Unsupervised identification of action potentials in multi-channel extracellular recordings, in particular from high-density microelectrode arrays with thousands of sensors, is an unresolved problem. While independent component analysis (ICA) achieves rapid unsupervised sorting, it ignores the convolutive structure of extracellular data, thus limiting the unmixing to a subset of neurons. Here we present a spike sorting algorithm based on convolutive ICA (cICA) to retrieve a larger number of accurately sorted neurons than with instantaneous ICA while accounting for signal overlaps. Spike sorting was applied to datasets with varying signal-to-noise ratios (SNR: 3-12) and 27% spike overlaps, sampled at either 11.5 or 23kHz on 4365 electrodes. We demonstrate how the instantaneity assumption in ICA-based algorithms has to be relaxed in order to improve the spike sorting performance for high-density microelectrode array recordings. Reformulating the convolutive mixture as an instantaneous mixture by modeling several delayed samples jointly is necessary to increase signal-to-noise ratio. Our results emphasize that different cICA algorithms are not equivalent. Spike sorting performance was assessed with ground-truth data generated from experimentally derived templates. The presented spike sorter was able to extract ≈90% of the true spike trains with an error rate below 2%. It was superior to two alternative (c)ICA methods (≈80% accurately sorted neurons) and comparable to a supervised sorting. Our new algorithm represents a fast solution to overcome the current bottleneck in spike sorting of large datasets generated by simultaneous recording with thousands of electrodes. Copyright © 2016 Elsevier B.V. All rights reserved.

  5. A shared neural substrate for mentalizing and the affective component of sentence comprehension.

    Directory of Open Access Journals (Sweden)

    Pierre-Yves Hervé

    Full Text Available Using event-related fMRI in a sample of 42 healthy participants, we compared the cerebral activity maps obtained when classifying spoken sentences based on the mental content of the main character (belief, deception or empathy or on the emotional tonality of the sentence (happiness, anger or sadness. To control for the effects of different syntactic constructions (such as embedded clauses in belief sentences, we subtracted from each map the BOLD activations obtained during plausibility judgments on structurally matching sentences, devoid of emotions or ToM. The obtained theory of mind (ToM and emotional speech comprehension networks overlapped in the bilateral temporo-parietal junction, posterior cingulate cortex, right anterior temporal lobe, dorsomedial prefrontal cortex and in the left inferior frontal sulcus. These regions form a ToM network, which contributes to the emotional component of spoken sentence comprehension. Compared with the ToM task, in which the sentences were enounced on a neutral tone, the emotional sentence classification task, in which the sentences were play-acted, was associated with a greater activity in the bilateral superior temporal sulcus, in line with the presence of emotional prosody. Besides, the ventromedial prefrontal cortex was more active during emotional than ToM sentence processing. This region may link mental state representations with verbal and prosodic emotional cues. Compared with emotional sentence classification, ToM was associated with greater activity in the caudate nucleus, paracingulate cortex, and superior frontal and parietal regions, in line with behavioral data showing that ToM sentence comprehension was a more demanding task.

  6. LEDA RF distribution system design and component test results

    International Nuclear Information System (INIS)

    Roybal, W.T.; Rees, D.E.; Borchert, H.L.; McCarthy, M.; Toole, L.

    1998-01-01

    The 350 MHz and 700 MHz RF distribution systems for the Low Energy Demonstration Accelerator (LEDA) have been designed and are currently being installed at Los Alamos National Laboratory. Since 350 MHz is a familiar frequency used at other accelerator facilities, most of the major high-power components were available. The 700 MHz, 1.0 MW, CW RF delivery system designed for LEDA is a new development. Therefore, high-power circulators, waterloads, phase shifters, switches, and harmonic filters had to be designed and built for this applications. The final Accelerator Production of Tritium (APT) RF distribution systems design will be based on much of the same technology as the LEDA systems and will have many of the RF components tested for LEDA incorporated into the design. Low power and high-power tests performed on various components of these LEDA systems and their results are presented here

  7. Commercial Off-the-Shelf (COTS) Components and Enterprise Component Information System (eCIS)

    Energy Technology Data Exchange (ETDEWEB)

    John Minihan; Ed Schmidt; Greg Enserro; Melissa Thompson

    2008-06-30

    The purpose of the project was to develop the processes for using commercial off-the-shelf (COTS) parts for WR production and to put in place a system for implementing the data management tools required to disseminate, store, track procurement, and qualify vendors. Much of the effort was devoted to determining if the use of COTS parts was possible. A basic question: How does the Nuclear Weapons Complex (NWC) begin to use COTS in the weapon Stockpile Life Extension Programs with high reliability, affordability, while managing risk at acceptable levels? In FY00, it was determined that a certain weapon refurbishment program could not be accomplished without the use of COTS components. The elements driving the use of COTS components included decreased cost, greater availability, and shorter delivery time. Key factors that required implementation included identifying the best suppliers and components, defining life cycles and predictions of obsolescence, testing the feasibility of using COTS components with a test contractor to ensure capability, as well as quality and reliability, and implementing the data management tools required to disseminate, store, track procurement, and qualify vendors. The primary effort of this project then was to concentrate on the risks involved in the use of COTS and address the issues of part and vendor selection, procurement and acceptance processes, and qualification of the parts via part and sample testing. The Enterprise Component Information System (eCIS) was used to manage the information generated by the COTS process. eCIS is a common interface for both the design and production of NWC components and systems integrating information between SNL National Laboratory (SNL) and the Kansas City Plant (KCP). The implementation of COTS components utilizes eCIS from part selection through qualification release. All part related data is linked across an unclassified network for access by both SNL and KCP personnel. The system includes not

  8. Calculation and experimental investigation of multi-component ceramic systems

    International Nuclear Information System (INIS)

    Rother, M.

    1994-12-01

    This work shows a way to combine thermodynamic calculations and experiments in order to get useful information on the constitution of metal/non-metal systems. Many data from literature are critically evaluated and used as a basis for experiments and calculations. The following multi-component systems are treated: 1. Multi-component systems of 'ceramic' materials with partially metallic bonding (carbides, nitrides, oxides, borides, carbonitrides, borocarbides, oxinitrides of the 4-8th transition group metals) 2. multi-component systems of non-metallic materials with dominant covalent bonding (SiC, Si 3 N 4 , SiB 6 , BN, Al 4 C 3 , Be 2 C) 3. multi-component systems of non-metallic materials with dominant heteropolar bonding (Al 2 O 3 , TiO 2 , BeO, SiO 2 , ZrO 2 ). The interactions between 1. and 2., 2. and 3., 1. and 3. are also considered. The latest commercially available programmes for the calculation of thermodynamical equilibria and phase diagrams are evaluated and compared considering their facilities and limits. New phase diagrams are presented for many presently unknown multi-component systems; partly known systems are completed on the basis of selected thermodynamic data. The calculations are verified by experimental investigations (metallurgical and powder technology methods). Altogether 690 systems are evaluated, 126 are calculated for the first time and 52 systems are experimentally verified. New data for 60 ternary phases are elaborated by estimating the data limits for the Gibbs energy values. A synthesis of critical evaluation of literature, calculations and experiments leads to new important information about equilibria and reaction behaviour in multi-component systems. This information is necessary to develop new stable and metastable materials. (orig./MM) [de

  9. NNETS - NEURAL NETWORK ENVIRONMENT ON A TRANSPUTER SYSTEM

    Science.gov (United States)

    Villarreal, J.

    1994-01-01

    The primary purpose of NNETS (Neural Network Environment on a Transputer System) is to provide users a high degree of flexibility in creating and manipulating a wide variety of neural network topologies at processing speeds not found in conventional computing environments. To accomplish this purpose, NNETS supports back propagation and back propagation related algorithms. The back propagation algorithm used is an implementation of Rumelhart's Generalized Delta Rule. NNETS was developed on the INMOS Transputer. NNETS predefines a Back Propagation Network, a Jordan Network, and a Reinforcement Network to assist users in learning and defining their own networks. The program also allows users to configure other neural network paradigms from the NNETS basic architecture. The Jordan network is basically a feed forward network that has the outputs connected to a pseudo input layer. The state of the network is dependent on the inputs from the environment plus the state of the network. The Reinforcement network learns via a scalar feedback signal called reinforcement. The network propagates forward randomly. The environment looks at the outputs of the network to produce a reinforcement signal that is fed back to the network. NNETS was written for the INMOS C compiler D711B version 1.3 or later (MS-DOS version). A small portion of the software was written in the OCCAM language to perform the communications routing between processors. NNETS is configured to operate on a 4 X 10 array of Transputers in sequence with a Transputer based graphics processor controlled by a master IBM PC 286 (or better) Transputer. A RGB monitor is required which must be capable of 512 X 512 resolution. It must be able to receive red, green, and blue signals via BNC connectors. NNETS is meant for experienced Transputer users only. The program is distributed on 5.25 inch 1.2Mb MS-DOS format diskettes. NNETS was developed in 1991. Transputer and OCCAM are registered trademarks of Inmos Corporation. MS

  10. Symptom based diagnostic system using artificial neural networks

    International Nuclear Information System (INIS)

    Santosh; Vinod, Gopika; Saraf, R.K.

    2003-01-01

    Nuclear power plant experiences a number of transients during its operations. In case of such an undesired plant condition generally known as an initiating event, the operator has to carry out diagnostic and corrective actions. The operator's response may be too late to mitigate or minimize the negative consequences in such scenarios. The objective of this work is to develop an operator support system based on artificial neural networks that will assist the operator to identify the initiating events at the earliest stages of their developments. A symptom based diagnostic system has been developed to investigate the initiating events. Neutral networks are utilized for carrying out the event identification by continuously monitoring process parameters. Whenever an event is detected, the system will display the necessary operator actions along with the initiating event. The system will also show the graphical trend of process parameters that are relevant to the event. This paper describes the features of the software that is used to monitor the reactor. (author)

  11. Adaptive Neural Control for a Class of Outputs Time-Delay Nonlinear Systems

    Directory of Open Access Journals (Sweden)

    Ruliang Wang

    2012-01-01

    Full Text Available This paper considers an adaptive neural control for a class of outputs time-delay nonlinear systems with perturbed or no. Based on RBF neural networks, the radius basis function (RBF neural networks is employed to estimate the unknown continuous functions. The proposed control guarantees that all closed-loop signals remain bounded. The simulation results demonstrate the effectiveness of the proposed control scheme.

  12. Predictive Control of Hydronic Floor Heating Systems using Neural Networks and Genetic Algorithms

    DEFF Research Database (Denmark)

    Vinther, Kasper; Green, Torben; Østergaard, Søren

    2017-01-01

    This paper presents the use a neural network and a micro genetic algorithm to optimize future set-points in existing hydronic floor heating systems for improved energy efficiency. The neural network can be trained to predict the impact of changes in set-points on future room temperatures. Additio...... space is not guaranteed. Evaluation of the performance of multiple neural networks is performed, using different levels of information, and optimization results are presented on a detailed house simulation model....

  13. Component configuration control system development at EBR-II

    International Nuclear Information System (INIS)

    Monson, L.R.; Stratton, R.C.

    1984-01-01

    One ofthe major programs being pursued by the EBR-II Division of Argonne National Laboratory is to improve the reliability of plant control and protection systems. This effort involves looking closely at the present state of the art and needs associated with plant diagnostic, control and protection systems. One of the areas of development at EBR-II involves a component configuration control system (CCCS). This system is a computerized control and planning aid for the nuclear power operator

  14. HTR-E project. High-temperature components and systems

    International Nuclear Information System (INIS)

    Breuil, E.; Exner, R.

    2002-01-01

    The HTR-E European project (four years project) is proposed for the 5th Framework Programme and concerns the technical developments needed for the innovative components of a modern HTR with a direct cycle. These components have been selected with reference to the present projects (GT-MHR, PBMR): (1) the helium turbine, the recuperator heat exchanger, the electro-magnetic bearings and the helium rotating seal; (2) the tribology. Sliding innovative components in helium environment are particularly concerned. (3) the helium purification system. Recommendations on impurities contents have to be provided in accordance with the materials proposed for the innovative components. The main outcomes expected from the HTR-E project are the design recommendations and identification of further R and D needs for these components. This will be based: (1) on experience feedback from European past helium test loops and reactors; (2) on design studies, thermal-hydraulic and structural analyses; (3) and on experimental tests

  15. Algebraic and adaptive learning in neural control systems

    Science.gov (United States)

    Ferrari, Silvia

    A systematic approach is developed for designing adaptive and reconfigurable nonlinear control systems that are applicable to plants modeled by ordinary differential equations. The nonlinear controller comprising a network of neural networks is taught using a two-phase learning procedure realized through novel techniques for initialization, on-line training, and adaptive critic design. A critical observation is that the gradients of the functions defined by the neural networks must equal corresponding linear gain matrices at chosen operating points. On-line training is based on a dual heuristic adaptive critic architecture that improves control for large, coupled motions by accounting for actual plant dynamics and nonlinear effects. An action network computes the optimal control law; a critic network predicts the derivative of the cost-to-go with respect to the state. Both networks are algebraically initialized based on prior knowledge of satisfactory pointwise linear controllers and continue to adapt on line during full-scale simulations of the plant. On-line training takes place sequentially over discrete periods of time and involves several numerical procedures. A backpropagating algorithm called Resilient Backpropagation is modified and successfully implemented to meet these objectives, without excessive computational expense. This adaptive controller is as conservative as the linear designs and as effective as a global nonlinear controller. The method is successfully implemented for the full-envelope control of a six-degree-of-freedom aircraft simulation. The results show that the on-line adaptation brings about improved performance with respect to the initialization phase during aircraft maneuvers that involve large-angle and coupled dynamics, and parameter variations.

  16. NSSS Component Control System Design of Integral Reactor

    International Nuclear Information System (INIS)

    Lee, Joon Koo; Kwon, Ho Je; Jeong, Kwong Il; Park, Heui Youn; Koo, In Soo

    2005-01-01

    MMIS(Man Machine Interface System) of an integral reactor is composed of a Control Room, Plant Protection System, Control System and Monitoring System which are related with the overall plant operation. MMIS is being developed with a new design concept and digital technology to reduce the Human Factor Error and improve the systems' safety, reliability and availability. And CCS(component control system) is also being developed with a new design concept and digital hardware technology A fully digitalized system and design concept are introduced in the NSSS CCS

  17. Applying of component system development in object methodology, case study

    Directory of Open Access Journals (Sweden)

    Milan Mišovič

    2013-01-01

    Full Text Available To create computarization target software as a component system has been a very strong requirement for the last 20 years of software developing. Finally, the architectural components are self-contained units, presenting not only partial and overall system behavior, but also cooperating with each other on the basis of their interfaces. Among others, components have allowed flexible modification of processes the behavior of which is the foundation of components behavior without changing the life of the component system. On the other hand, the component system makes it possible, at design time, to create numerous new connections between components and thus creating modified system behaviors. This all enables the company management to perform, at design time, required behavioral changes of processes in accordance with the requirements of changing production and market.The development of software which is generally referred to as SDP (Software Development Process contains two directions. The first one, called CBD (Component–Based Development, is dedicated to the development of component–based systems CBS (Component–based System, the second target is the development of software under the influence of SOA (Service–Oriented Architecture. Both directions are equipped with their different development methodologies. The subject of this paper is only the first direction and application of development of component–based systems in its object–oriented methodologies. The requirement of today is to carry out the development of component-based systems in the framework of developed object–oriented methodologies precisely in the way of a dominant style. In some of the known methodologies, however, this development is not completely transparent and is not even recognized as dominant. In some cases, it is corrected by the special meta–integration models of component system development into an object methodology.This paper presents a case study

  18. Fuel-cell-system and its components for mobile application

    Energy Technology Data Exchange (ETDEWEB)

    Venturi, Massimo [NuCellSys GmbH, Kirchheim/Teck-Nabern (Germany)

    2013-06-01

    In the past years the development of fuel cell systems for mobile applications has made significant progress in power density, performance and robustness. For a successful market introduction the cost of the fuel system powertrain needs to be competitive to diesel hybrid engine. The current development activities are therefore focusing on cost reduction. There are 3 major areas for cost reduction: functional integration, materials and design, supplier competitiveness and volume. Today unique fuel cell system components are developed by single suppliers, which lead to a monopoly. In the future the components will be developed at multiple suppliers to achieve a competitor situation, which will further reduce the component cost. Using all these cost reduction measures the fuel cell system will become a competitive alternative drive train. (orig.)

  19. Energy dependence of critical state of single-component systems

    International Nuclear Information System (INIS)

    Volchenkova, R.A.

    1985-01-01

    Equations of critical states of the single-component systems: Psub(cr)(/Psub(o)=(Tsub(cr)/Tsub(o))x0.73, Tsub(cr)=K(Tsub(boil))sup(1.116) and Hsub(cr)(/Hsub(B)=Tsub(sr)/Tsub(B))sup(1.48) where Tsub(B)=1K, Hsub(B)-2 kcal/g-at, K-dimension factor are presented. It is shown that the revealed dependence Hsub(cr)=H(Tsub(cr)) is an energy boundary of a liquid-vapour phase state of the single-component systems beyond limits of which difference between liquid and vapour phases vanishes in increasing the system energy content. The given equations of state are true for all the single-component systems and permit to consider physicomechanical properties of substances in dynamic state depending on external conditions. Critical temperatures and dependences for elements from the most fusible He to infusible W and Re have been calculated

  20. In silico dissection of Type VII Secretion System components across

    Indian Academy of Sciences (India)

    Type VII Secretion System (T7SS) is one of the factors involved in virulence of Mycobacteriun tuberculosis H37Rv. Numerous research efforts have been made in the last decade towards characterizing the components of this secretion system. An extensive genome-wide analysis through compilation of isolated information ...

  1. Integrity of neutron-absorbing components of LWR fuel systems

    International Nuclear Information System (INIS)

    Bailey, W.J.; Berting, F.M.

    1991-03-01

    A study of the integrity and behavior of neutron-absorbing components of light-water (LWR) fuel systems was performed by Pacific Northwest Laboratory (PNL) and sponsored by the US Department of Energy (DOE). The components studies include control blades (cruciforms) for boiling-water reactors (BWRs) and rod cluster control assemblies for pressurized-water reactors (PWRs). The results of this study can be useful for understanding the degradation of neutron-absorbing components and for waste management planning and repository design. The report includes examples of the types of degradation, damage, or failures that have been encountered. Conclusions and recommendations are listed. 84 refs

  2. Neural signal processing and closed-loop control algorithm design for an implanted neural recording and stimulation system.

    Science.gov (United States)

    Hamilton, Lei; McConley, Marc; Angermueller, Kai; Goldberg, David; Corba, Massimiliano; Kim, Louis; Moran, James; Parks, Philip D; Sang Chin; Widge, Alik S; Dougherty, Darin D; Eskandar, Emad N

    2015-08-01

    A fully autonomous intracranial device is built to continually record neural activities in different parts of the brain, process these sampled signals, decode features that correlate to behaviors and neuropsychiatric states, and use these features to deliver brain stimulation in a closed-loop fashion. In this paper, we describe the sampling and stimulation aspects of such a device. We first describe the signal processing algorithms of two unsupervised spike sorting methods. Next, we describe the LFP time-frequency analysis and feature derivation from the two spike sorting methods. Spike sorting includes a novel approach to constructing a dictionary learning algorithm in a Compressed Sensing (CS) framework. We present a joint prediction scheme to determine the class of neural spikes in the dictionary learning framework; and, the second approach is a modified OSort algorithm which is implemented in a distributed system optimized for power efficiency. Furthermore, sorted spikes and time-frequency analysis of LFP signals can be used to generate derived features (including cross-frequency coupling, spike-field coupling). We then show how these derived features can be used in the design and development of novel decode and closed-loop control algorithms that are optimized to apply deep brain stimulation based on a patient's neuropsychiatric state. For the control algorithm, we define the state vector as representative of a patient's impulsivity, avoidance, inhibition, etc. Controller parameters are optimized to apply stimulation based on the state vector's current state as well as its historical values. The overall algorithm and software design for our implantable neural recording and stimulation system uses an innovative, adaptable, and reprogrammable architecture that enables advancement of the state-of-the-art in closed-loop neural control while also meeting the challenges of system power constraints and concurrent development with ongoing scientific research designed

  3. Comparison of Component Frameworks for Real-Time Embedded Systems

    Czech Academy of Sciences Publication Activity Database

    Pop, T.; Hnětynka, P.; Hošek, P.; Malohlava, M.; Bureš, Tomáš

    2014-01-01

    Roč. 40, č. 1 (2014), s. 127-170 ISSN 0219-1377 Grant - others:GA AV ČR(CZ) GAP202/11/0312; GA UK(CZ) Project 378111; UK(CZ) SVV-2013- 267312 Keywords : component-based development * component frameworks * real-time and embedded systems Subject RIV: JC - Computer Hardware ; Software Impact factor: 1.782, year: 2014

  4. NNSYSID and NNCTRL Tools for system identification and control with neural networks

    DEFF Research Database (Denmark)

    Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad

    2001-01-01

    choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview......Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...... a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...

  5. NNSYSID and NNCTRL Tools for system identification and control with neural networks

    DEFF Research Database (Denmark)

    Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad

    2001-01-01

    a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...... choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview......Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...

  6. Non-invasive prediction of bloodstain age using the principal component and a back propagation artificial neural network

    Science.gov (United States)

    Sun, Huimin; Meng, Yaoyong; Zhang, Pingli; Li, Yajing; Li, Nan; Li, Caiyun; Guo, Zhiyou

    2017-09-01

    The age determination of bloodstains is an important and immediate challenge for forensic science. No reliable methods are currently available for estimating the age of bloodstains. Here we report a method for determining the age of bloodstains at different storage temperatures. Bloodstains were stored at 37 °C, 25 °C, 4 °C, and  -20 °C for 80 d. Bloodstains were measured using Raman spectroscopy at various time points. The principal component and a back propagation artificial neural network model were then established for estimating the age of the bloodstains. The results were ideal; the square of correlation coefficient was up to 0.99 (R 2  >  0.99) and the root mean square error of the prediction at lowest reached 55.9829 h. This method is real-time, non-invasive, non-destructive and highly efficiency. It may well prove that Raman spectroscopy is a promising tool for the estimation of the age of bloodstains.

  7. The Node Monitoring Component of a Scalable Systems Software Environment

    Energy Technology Data Exchange (ETDEWEB)

    Miller, Samuel James [Iowa State Univ., Ames, IA (United States)

    2006-01-01

    This research describes Fountain, a suite of programs used to monitor the resources of a cluster. A cluster is a collection of individual computers that are connected via a high speed communication network. They are traditionally used by users who desire more resources, such as processing power and memory, than any single computer can provide. A common drawback to effectively utilizing such a large-scale system is the management infrastructure, which often does not often scale well as the system grows. Large-scale parallel systems provide new research challenges in the area of systems software, the programs or tools that manage the system from boot-up to running a parallel job. The approach presented in this thesis utilizes a collection of separate components that communicate with each other to achieve a common goal. While systems software comprises a broad array of components, this thesis focuses on the design choices for a node monitoring component. We will describe Fountain, an implementation of the Scalable Systems Software (SSS) node monitor specification. It is targeted at aggregate node monitoring for clusters, focusing on both scalability and fault tolerance as its design goals. It leverages widely used technologies such as XML and HTTP to present an interface to other components in the SSS environment.

  8. Parsimonious classification of binary lacunarity data computed from food surface images using kernel principal component analysis and artificial neural networks.

    Science.gov (United States)

    Iqbal, Abdullah; Valous, Nektarios A; Sun, Da-Wen; Allen, Paul

    2011-02-01

    Lacunarity is about quantifying the degree of spatial heterogeneity in the visual texture of imagery through the identification of the relationships between patterns and their spatial configurations in a two-dimensional setting. The computed lacunarity data can designate a mathematical index of spatial heterogeneity, therefore the corresponding feature vectors should possess the necessary inter-class statistical properties that would enable them to be used for pattern recognition purposes. The objectives of this study is to construct a supervised parsimonious classification model of binary lacunarity data-computed by Valous et al. (2009)-from pork ham slice surface images, with the aid of kernel principal component analysis (KPCA) and artificial neural networks (ANNs), using a portion of informative salient features. At first, the dimension of the initial space (510 features) was reduced by 90% in order to avoid any noise effects in the subsequent classification. Then, using KPCA, the first nineteen kernel principal components (99.04% of total variance) were extracted from the reduced feature space, and were used as input in the ANN. An adaptive feedforward multilayer perceptron (MLP) classifier was employed to obtain a suitable mapping from the input dataset. The correct classification percentages for the training, test and validation sets were 86.7%, 86.7%, and 85.0%, respectively. The results confirm that the classification performance was satisfactory. The binary lacunarity spatial metric captured relevant information that provided a good level of differentiation among pork ham slice images. Copyright © 2010 The American Meat Science Association. Published by Elsevier Ltd. All rights reserved.

  9. Examining the neural correlates of depressive and motor symptoms in Parkinson's disease using Frequency Component Analysis (FCA)

    Science.gov (United States)

    Song, Xiaopeng; Hu, Xiao; Zhou, Shuqin; Liu, Weiguo; Liu, Yijun; Zhu, Huaiqiu; Gao, Jia-Hong

    2016-03-01

    Depression is prevalent among patients with Parkinson's disease (PD); however the pathophysiology of depression in PD is not well understood. In order to investigate how depression and motor impairments differentially and interactively affect specific brain regions in Parkinson's disease, we introduced a new data driven approach, namely Frequency Component Analysis (FCA), to decompose the resting-state functional magnetic resonance imaging data of 59 subjects with Parkinson's disease into different frequency bands. We then evaluated the main effects of motor severity and depression, and their interactive effects on the BOLD-fMRI signal oscillation energy in these specific frequency components. Our results show that the severity of motor symptoms is more negatively correlated with energy in the frequency band of 0.10-0.25Hz in the bilateral thalamus (THA), but more positively correlated with energy in the frequency band of 0.01-0.027Hz in the bilateral postcentral gyrus (PoCG). In contrast, the severity of depressive symptoms is more associated with the higher energy of the high frequency oscillations (>0.1Hz) but lower energy of 0.01-0.027Hz in the bilateral subgenual gyrus (SGC). Importantly, the interaction between motor and depressive symptoms is negatively correlated with the energy of high frequency oscillations (>0.1Hz) in the substantia nigra/ventral tegmental area (SN/VTA), left hippocampus (HIPP), left inferior orbital frontal cortex (OFC), and left temporoparietal junction (TPJ), but positively correlated with the energy of 0.02-0.05Hz in the left inferior OFC, left TPJ, left inferior temporal gyrus (ITG), and bilateral cerebellum. These results demonstrated that FCA was a promising method in interrogating the neurophysiological implications of different brain rhythms. Our findings further revealed the neural bases underlying the interactions as well the dissociations between motor and depressive symptoms in Parkinson's disease.

  10. On fault propagation in deterioration of multi-component systems

    International Nuclear Information System (INIS)

    Liang, Zhenglin; Parlikad, Ajith Kumar; Srinivasan, Rengarajan; Rasmekomen, Nipat

    2017-01-01

    In extant literature, deterioration dependence among components can be modelled as inherent dependence and induced dependence. We find that the two types of dependence may co-exist and interact with each other in one multi-component system. We refer to this phenomenon as fault propagation. In practice, a fault induced by the malfunction of a non-critical component may further propagate through the dependence amongst critical components. Such fault propagation scenario happens in industrial assets or systems (bridge deck, and heat exchanging system). In this paper, a multi-layered vector-valued continuous-time Markov chain is developed to capture the characteristics of fault propagation. To obtain the mathematical tractability, we derive a partitioning rule to aggregate states with the same characteristics while keeping the overall aging behaviour of the multi-component system. Although the detailed information of components is masked by aggregated states, lumpability is attainable with the partitioning rule. It means that the aggregated process is stochastically equivalent to the original one and retains the Markov property. We apply this model on a heat exchanging system in oil refinery company. The results show that fault propagation has a more significant impact on the system's lifetime comparing with inherent dependence and induced dependence. - Highlights: • We develop a vector value continuous-time Markov chain to model the meta-dependent characteristic of fault propagation. • A partitioning rule is derived to reduce the state space and attain lumpability. • The model is applied on analysing the impact of fault propagation in a heat exchanging system.

  11. Three-dimensional hydrogel cell culture systems for modeling neural tissue

    Science.gov (United States)

    Frampton, John

    designed for use as a tool to predict the transport and processing that occurs prior to drug uptake in the central nervous system (CNS), and to predict BBB permeability. Electrochemical techniques and immunohistochemistry were used to validate this model and provide detailed information about cellular organization and function. Electrochemical impedance spectroscopy (EIS) provided evidence that endothelial cells cultured in the presence of astrocytes formed tight junctions capable of occluding the flow of electrical current. In a second series of experiments, a microglia-astrocyte co-culture system was developed to assess the effects of glial cells on electrode impedance recorded from neural prosthetic devices in vitro. Impedance measurements were compared with confocal images to determine the effects of glial cell density and cell type on electrode performance. The results indicate that EIS data can be used to model components of the reactive cell responses in brain tissue, and that impedance measurements recorded in vitro can be compared to measurements recorded in vivo. Taken together, these results demonstrate that alginate hydrogels can be used for the creation of 3-D neural cell scaffolds, and that such cell scaffolds can be used to model a variety of three-dimensional neural tissues in vitro, that cannot be studied in 2-D cultures.

  12. Towards a Component Based Model for Database Systems

    Directory of Open Access Journals (Sweden)

    Octavian Paul ROTARU

    2004-02-01

    Full Text Available Due to their effectiveness in the design and development of software applications and due to their recognized advantages in terms of reusability, Component-Based Software Engineering (CBSE concepts have been arousing a great deal of interest in recent years. This paper presents and extends a component-based approach to object-oriented database systems (OODB introduced by us in [1] and [2]. Components are proposed as a new abstraction level for database system, logical partitions of the schema. In this context, the scope is introduced as an escalated property for transactions. Components are studied from the integrity, consistency, and concurrency control perspective. The main benefits of our proposed component model for OODB are the reusability of the database design, including the access statistics required for a proper query optimization, and a smooth information exchange. The integration of crosscutting concerns into the component database model using aspect-oriented techniques is also discussed. One of the main goals is to define a method for the assessment of component composition capabilities. These capabilities are restricted by the component’s interface and measured in terms of adaptability, degree of compose-ability and acceptability level. The above-mentioned metrics are extended from database components to generic software components. This paper extends and consolidates into one common view the ideas previously presented by us in [1, 2, 3].[1] Octavian Paul Rotaru, Marian Dobre, Component Aspects in Object Oriented Databases, Proceedings of the International Conference on Software Engineering Research and Practice (SERP’04, Volume II, ISBN 1-932415-29-7, pages 719-725, Las Vegas, NV, USA, June 2004.[2] Octavian Paul Rotaru, Marian Dobre, Mircea Petrescu, Integrity and Consistency Aspects in Component-Oriented Databases, Proceedings of the International Symposium on Innovation in Information and Communication Technology (ISIICT

  13. On the Universality and Non-Universality of Spiking Neural P Systems With Rules on Synapses.

    Science.gov (United States)

    Song, Tao; Xu, Jinbang; Pan, Linqiang

    2015-12-01

    Spiking neural P systems with rules on synapses are a new variant of spiking neural P systems. In the systems, the neuron contains only spikes, while the spiking/forgetting rules are moved on the synapses. It was obtained that such system with 30 neurons (using extended spiking rules) or with 39 neurons (using standard spiking rules) is Turing universal. In this work, this number is improved to 6. Specifically, we construct a Turing universal spiking neural P system with rules on synapses having 6 neurons, which can generate any set of Turing computable natural numbers. As well, it is obtained that spiking neural P system with rules on synapses having less than two neurons are not Turing universal: i) such systems having one neuron can characterize the family of finite sets of natural numbers; ii) the family of sets of numbers generated by the systems having two neurons is included in the family of semi-linear sets of natural numbers.

  14. Hybrid energy system evaluation in water supply system energy production: neural network approach

    Energy Technology Data Exchange (ETDEWEB)

    Goncalves, Fabio V.; Ramos, Helena M. [Civil Engineering Department, Instituto Superior Tecnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001, Lisbon (Portugal); Reis, Luisa Fernanda R. [Universidade de Sao Paulo, EESC/USP, Departamento de Hidraulica e Saneamento., Avenida do Trabalhador Saocarlense, 400, Sao Carlos-SP (Brazil)

    2010-07-01

    Water supply systems are large consumers of energy and the use of hybrid systems for green energy production is this new proposal. This work presents a computational model based on neural networks to determine the best configuration of a hybrid system to generate energy in water supply systems. In this study the energy sources to make this hybrid system can be the national power grid, micro-hydro and wind turbines. The artificial neural network is composed of six layers, trained to use data generated by a model of hybrid configuration and an economic simulator - CES. The reason for the development of an advanced model of forecasting based on neural networks is to allow rapid simulation and proper interaction with hydraulic and power model simulator - HPS. The results show that this computational model is useful as advanced decision support system in the design of configurations of hybrid power systems applied to water supply systems, improving the solutions in the development of its global energy efficiency.

  15. Adaptive fuzzy-neural-network control for maglev transportation system.

    Science.gov (United States)

    Wai, Rong-Jong; Lee, Jeng-Dao

    2008-01-01

    A magnetic-levitation (maglev) transportation system including levitation and propulsion control is a subject of considerable scientific interest because of highly nonlinear and unstable behaviors. In this paper, the dynamic model of a maglev transportation system including levitated electromagnets and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is developed first. Then, a model-based sliding-mode control (SMC) strategy is introduced. In order to alleviate chattering phenomena caused by the inappropriate selection of uncertainty bound, a simple bound estimation algorithm is embedded in the SMC strategy to form an adaptive sliding-mode control (ASMC) scheme. However, this estimation algorithm is always a positive value so that tracking errors introduced by any uncertainty will cause the estimated bound increase even to infinity with time. Therefore, it further designs an adaptive fuzzy-neural-network control (AFNNC) scheme by imitating the SMC strategy for the maglev transportation system. In the model-free AFNNC, online learning algorithms are designed to cope with the problem of chattering phenomena caused by the sign action in SMC design, and to ensure the stability of the controlled system without the requirement of auxiliary compensated controllers despite the existence of uncertainties. The outputs of the AFNNC scheme can be directly supplied to the electromagnets and LIM without complicated control transformations for relaxing strict constrains in conventional model-based control methodologies. The effectiveness of the proposed control schemes for the maglev transportation system is verified by numerical simulations, and the superiority of the AFNNC scheme is indicated in comparison with the SMC and ASMC strategies.

  16. An alternative respiratory sounds classification system utilizing artificial neural networks

    Directory of Open Access Journals (Sweden)

    Rami J Oweis

    2015-04-01

    Full Text Available Background: Computerized lung sound analysis involves recording lung sound via an electronic device, followed by computer analysis and classification based on specific signal characteristics as non-linearity and nonstationarity caused by air turbulence. An automatic analysis is necessary to avoid dependence on expert skills. Methods: This work revolves around exploiting autocorrelation in the feature extraction stage. All process stages were implemented in MATLAB. The classification process was performed comparatively using both artificial neural networks (ANNs and adaptive neuro-fuzzy inference systems (ANFIS toolboxes. The methods have been applied to 10 different respiratory sounds for classification. Results: The ANN was superior to the ANFIS system and returned superior performance parameters. Its accuracy, specificity, and sensitivity were 98.6%, 100%, and 97.8%, respectively. The obtained parameters showed superiority to many recent approaches. Conclusions: The promising proposed method is an efficient fast tool for the intended purpose as manifested in the performance parameters, specifically, accuracy, specificity, and sensitivity. Furthermore, it may be added that utilizing the autocorrelation function in the feature extraction in such applications results in enhanced performance and avoids undesired computation complexities compared to other techniques.

  17. Flood forecasting within urban drainage systems using NARX neural network.

    Science.gov (United States)

    Abou Rjeily, Yves; Abbas, Oras; Sadek, Marwan; Shahrour, Isam; Hage Chehade, Fadi

    2017-11-01

    Urbanization activity and climate change increase the runoff volumes, and consequently the surcharge of the urban drainage systems (UDS). In addition, age and structural failures of these utilities limit their capacities, and thus generate hydraulic operation shortages, leading to flooding events. The large increase in floods within urban areas requires rapid actions from the UDS operators. The proactivity in taking the appropriate actions is a key element in applying efficient management and flood mitigation. Therefore, this work focuses on developing a flooding forecast system (FFS), able to alert in advance the UDS managers for possible flooding. For a forecasted storm event, a quick estimation of the water depth variation within critical manholes allows a reliable evaluation of the flood risk. The Nonlinear Auto Regressive with eXogenous inputs (NARX) neural network was chosen to develop the FFS as due to its calculation nature it is capable of relating water depth variation in manholes to rainfall intensities. The campus of the University of Lille is used as an experimental site to test and evaluate the FFS proposed in this paper.

  18. PERFORMANCE COMPARISON FOR INTRUSION DETECTION SYSTEM USING NEURAL NETWORK WITH KDD DATASET

    Directory of Open Access Journals (Sweden)

    S. Devaraju

    2014-04-01

    Full Text Available Intrusion Detection Systems are challenging task for finding the user as normal user or attack user in any organizational information systems or IT Industry. The Intrusion Detection System is an effective method to deal with the kinds of problem in networks. Different classifiers are used to detect the different kinds of attacks in networks. In this paper, the performance of intrusion detection is compared with various neural network classifiers. In the proposed research the four types of classifiers used are Feed Forward Neural Network (FFNN, Generalized Regression Neural Network (GRNN, Probabilistic Neural Network (PNN and Radial Basis Neural Network (RBNN. The performance of the full featured KDD Cup 1999 dataset is compared with that of the reduced featured KDD Cup 1999 dataset. The MATLAB software is used to train and test the dataset and the efficiency and False Alarm Rate is measured. It is proved that the reduced dataset is performing better than the full featured dataset.

  19. Review: the role of neural crest cells in the endocrine system.

    Science.gov (United States)

    Adams, Meghan Sara; Bronner-Fraser, Marianne

    2009-01-01

    The neural crest is a pluripotent population of cells that arises at the junction of the neural tube and the dorsal ectoderm. These highly migratory cells form diverse derivatives including neurons and glia of the sensory, sympathetic, and enteric nervous systems, melanocytes, and the bones, cartilage, and connective tissues of the face. The neural crest has long been associated with the endocrine system, although not always correctly. According to current understanding, neural crest cells give rise to the chromaffin cells of the adrenal medulla, chief cells of the extra-adrenal paraganglia, and thyroid C cells. The endocrine tumors that correspond to these cell types are pheochromocytomas, extra-adrenal paragangliomas, and medullary thyroid carcinomas. Although controversies concerning embryological origin appear to have mostly been resolved, questions persist concerning the pathobiology of each tumor type and its basis in neural crest embryology. Here we present a brief history of the work on neural crest development, both in general and in application to the endocrine system. In particular, we present findings related to the plasticity and pluripotency of neural crest cells as well as a discussion of several different neural crest tumors in the endocrine system.

  20. A neural network method for solving a system of linear variational inequalities

    International Nuclear Information System (INIS)

    Lan Hengyou; Cui Yishun

    2009-01-01

    In this paper, we transmute the solution for a new system of linear variational inequalities to an equilibrium point of neural networks, and by using analytic technique, some sufficient conditions are presented. Further, the estimation of the exponential convergence rates of the neural networks is investigated. The new and useful results obtained in this paper generalize and improve the corresponding results of recent works.

  1. Integrated Evaluation of Closed Loop Air Revitalization System Components

    Science.gov (United States)

    Murdock, K.

    2010-01-01

    NASA s vision and mission statements include an emphasis on human exploration of space, which requires environmental control and life support technologies. This Contractor Report (CR) describes the development and evaluation of an Air Revitalization System, modeling and simulation of the components, and integrated hardware testing with the goal of better understanding the inherent capabilities and limitations of this closed loop system. Major components integrated and tested included a 4-Bed Modular Sieve, Mechanical Compressor Engineering Development Unit, Temperature Swing Adsorption Compressor, and a Sabatier Engineering and Development Unit. The requisite methodolgy and technical results are contained in this CR.

  2. Manual on maintenance of systems and components important to safety

    International Nuclear Information System (INIS)

    1986-01-01

    The Manual should serve as guidance at the plant management level for the maintenance of systems and components important to safety. It includes a detailed description of management systems, administrative controls and procedures. The Annexes contain examples of documents and practices adopted by Operating Organizations of some Member States. It is not the intention of this Manual to address the technical problem of how to maintain a particular component but rather to cover the programmatic aspects of maintenance. It also contains some aspects of surveillance and verification activities. The Manual makes only general statements about radiation protection provisions in connection with maintenance; detailed guidance can be found in other IAEA documents

  3. Removal of Shippingport Station primary system components and piping

    International Nuclear Information System (INIS)

    LaGuardia, T.S.; Lipsett, S.M.

    1987-01-01

    The dismantling workscope for the Shippingport Station Decommissioning Project was divided into subtasks to permit the work to be subcontracted to the maximum extent practicable. Major subtasks were identified and described by Activity specifications which could then be grouped into logical work packages to be put out for bid. Two of the largest dismantling work packages, removal of piping and components, were grouped together and designated as Activity Specifications 4 and 5. TLG Services, Inc. and Cleveland Wrecking Company formed a Joint Venture to perform this work during a two-year period at a cost of approximately $7 million. The major portions of this dismantling workscope are described. The primary system components within this workscope consist of the stainless steel reactor coolant piping, check valves, reactor coolant pumps, steam generators, and reactor purification demineralizers and coolers. The work performed, the heavy rigging preparations and procedures, the cutting tools used, component draining/capping techniques to prevent spills, contamination containment, airborne control techniques, and lessons learned during the removal of these primary system components are described. Summaries of crew size and composition, labor hours, duration hours and radiation exposure to workers are provided and discussed briefly. The successful completion of this work is evidence of the engineering, planning, equipment, materials and labor pool available to remove large, radioactively contaminated components safely. This experience will help decommissioning planners to prepare for the removal of reactor components in future decommissioning

  4. Neural network models for biological waste-gas treatment systems.

    Science.gov (United States)

    Rene, Eldon R; Estefanía López, M; Veiga, María C; Kennes, Christian

    2011-12-15

    This paper outlines the procedure for developing artificial neural network (ANN) based models for three bioreactor configurations used for waste-gas treatment. The three bioreactor configurations chosen for this modelling work were: biofilter (BF), continuous stirred tank bioreactor (CSTB) and monolith bioreactor (MB). Using styrene as the model pollutant, this paper also serves as a general database of information pertaining to the bioreactor operation and important factors affecting gas-phase styrene removal in these biological systems. Biological waste-gas treatment systems are considered to be both advantageous and economically effective in treating a stream of polluted air containing low to moderate concentrations of the target contaminant, over a rather wide range of gas-flow rates. The bioreactors were inoculated with the fungus Sporothrix variecibatus, and their performances were evaluated at different empty bed residence times (EBRT), and at different inlet styrene concentrations (C(i)). The experimental data from these bioreactors were modelled to predict the bioreactors performance in terms of their removal efficiency (RE, %), by adequate training and testing of a three-layered back propagation neural network (input layer-hidden layer-output layer). Two models (BIOF1 and BIOF2) were developed for the BF with different combinations of easily measurable BF parameters as the inputs, that is concentration (gm(-3)), unit flow (h(-1)) and pressure drop (cm of H(2)O). The model developed for the CSTB used two inputs (concentration and unit flow), while the model for the MB had three inputs (concentration, G/L (gas/liquid) ratio, and pressure drop). Sensitivity analysis in the form of absolute average sensitivity (AAS) was performed for all the developed ANN models to ascertain the importance of the different input parameters, and to assess their direct effect on the bioreactors performance. The performance of the models was estimated by the regression

  5. Postnatal remodeling of the neural components of the epithelial-mesenchymal trophic unit in the proximal airways of infant rhesus monkeys exposed to ozone and allergen

    International Nuclear Information System (INIS)

    Larson, Shawnessy D.; Schelegle, Edward S.; Walby, William F.; Gershwin, Laural J.; Fanuccihi, Michelle V.; Evans, Michael J.; Joad, Jesse P.; Tarkington, Brian K.; Hyde, Dallas M.; Plopper, Charles G.

    2004-01-01

    Nerves and neuroendocrine cells located within the airway epithelium are ideally situated to sample a changing airway environment, to transmit that information to the central nervous system, and to promote trophic interactions between epithelial and mesenchymal cellular and acellular components. We tested the hypothesis that the environmental stresses of ozone (O 3 ) and house dust mite allergen (HDMA) in atopic infant rhesus monkeys alter the distribution of airway nerves. Midlevel bronchi and bronchioles from 6-month-old infant monkeys that inhaled filtered air (FA), house dust mite allergen HDMA, O 3 , or HDMA + O 3 for 11 episodes (5 days each, 0.5 ppm O 3 , 8 h/day followed by 9 days recovery) were examined using immunohistochemistry for the presence of Protein gene product 9.5 (PGP 9.5), a nonspecific neural indicator, and calcitonin gene-related peptide (CGRP). Along the axial path between the sixth and the seventh intrapulmonary airway generations, there were small significant (P 3 , while in monkeys exposed to HDMA + O 3 there was a greater significant (P 3 or HDMA + O 3 there was a significant increase in the number of PGP 9.5 positive/CGRP negative cells that were anchored to the basal lamina and emitted projections in primarily the lateral plain and often intertwined with projections and cell bodies of other similar cells. We conclude that repeated cycles of acute injury and repair associated with the episodic pattern of ozone and allergen exposure alter the normal development of neural innervation of the epithelial compartment and the appearance of a new population of undefined PGP 9.5 positive cells within the epithelium

  6. Postnatal remodeling of the neural components of the epithelial-mesenchymal trophic unit in the proximal airways of infant rhesus monkeys exposed to ozone and allergen.

    Science.gov (United States)

    Larson, Shawnessy D; Schelegle, Edward S; Walby, William F; Gershwin, Laural J; Fanuccihi, Michelle V; Evans, Michael J; Joad, Jesse P; Tarkington, Brian K; Hyde, Dallas M; Plopper, Charles G

    2004-02-01

    Nerves and neuroendocrine cells located within the airway epithelium are ideally situated to sample a changing airway environment, to transmit that information to the central nervous system, and to promote trophic interactions between epithelial and mesenchymal cellular and acellular components. We tested the hypothesis that the environmental stresses of ozone (O(3)) and house dust mite allergen (HDMA) in atopic infant rhesus monkeys alter the distribution of airway nerves. Midlevel bronchi and bronchioles from 6-month-old infant monkeys that inhaled filtered air (FA), house dust mite allergen HDMA, O(3), or HDMA + O(3) for 11 episodes (5 days each, 0.5 ppm O(3), 8 h/day followed by 9 days recovery) were examined using immunohistochemistry for the presence of Protein gene product 9.5 (PGP 9.5), a nonspecific neural indicator, and calcitonin gene-related peptide (CGRP). Along the axial path between the sixth and the seventh intrapulmonary airway generations, there were small significant (P < 0.05) decrements in the density of epithelial nerves in monkeys exposed to HDMA or O(3), while in monkeys exposed to HDMA + O(3) there was a greater significant (P < 0.05) reduction in epithelial innervation. In animals exposed to O(3) or HDMA + O(3) there was a significant increase in the number of PGP 9.5 positive/CGRP negative cells that were anchored to the basal lamina and emitted projections in primarily the lateral plain and often intertwined with projections and cell bodies of other similar cells. We conclude that repeated cycles of acute injury and repair associated with the episodic pattern of ozone and allergen exposure alter the normal development of neural innervation of the epithelial compartment and the appearance of a new population of undefined PGP 9.5 positive cells within the epithelium.

  7. Water reuse systems: A review of the principal components

    Science.gov (United States)

    Lucchetti, G.; Gray, G.A.

    1988-01-01

    Principal components of water reuse systems include ammonia removal, disease control, temperature control, aeration, and particulate filtration. Effective ammonia removal techniques include air stripping, ion exchange, and biofiltration. Selection of a particular technique largely depends on site-specific requirements (e.g., space, existing water quality, and fish densities). Disease control, although often overlooked, is a major problem in reuse systems. Pathogens can be controlled most effectively with ultraviolet radiation, ozone, or chlorine. Simple and inexpensive methods are available to increase oxygen concentration and eliminate gas supersaturation, these include commercial aerators, air injectors, and packed columns. Temperature control is a major advantage of reuse systems, but the equipment required can be expensive, particularly if water temperature must be rigidly controlled and ambient air temperature fluctuates. Filtration can be readily accomplished with a hydrocyclone or sand filter that increases overall system efficiency. Based on criteria of adaptability, efficiency, and reasonable cost, we recommend components for a small water reuse system.

  8. Modeling of the height control system using artificial neural networks

    Directory of Open Access Journals (Sweden)

    A. R Tahavvor

    2016-09-01

    Full Text Available Introduction Automation of agricultural and machinery construction has generally been enhanced by intelligent control systems due to utility and efficiency rising, ease of use, profitability and upgrading according to market demand. A broad variety of industrial merchandise are now supplied with computerized control systems of earth moving processes to be performed by construction and agriculture field vehicle such as grader, backhoe, tractor and scraper machines. A height control machine which is used in measuring base thickness is consisted of two mechanical and electronic parts. The mechanical part is consisted of conveyor belt, main body, electrical engine and invertors while the electronic part is consisted of ultrasonic, wave transmitter and receiver sensor, electronic board, control set, and microcontroller. The main job of these controlling devices consists of the topographic surveying, cutting and filling of elevated and spotted low area, and these actions fundamentally dependent onthe machine's ability in elevation and thickness measurement and control. In this study, machine was first tested and then some experiments were conducted for data collection. Study of system modeling in artificial neural networks (ANN was done for measuring, controlling the height for bases by input variable input vectors such as sampling time, probe speed, conveyer speed, sound wave speed and speed sensor are finally the maximum and minimum probe output vector on various conditions. The result reveals the capability of this procedure for experimental recognition of sensors' behavior and improvement of field machine control systems. Inspection, calibration and response, diagnosis of the elevation control system in combination with machine function can also be evaluated by some extra development of this system. Materials and Methods Designing and manufacture of the planned apparatus classified in three dissimilar, mechanical and electronic module, courses of

  9. An automatic chip structure optical inspection system for electronic components

    Science.gov (United States)

    Song, Zhichao; Xue, Bindang; Liang, Jiyuan; Wang, Ke; Chen, Junzhang; Liu, Yunhe

    2018-01-01

    An automatic chip structure inspection system based on machine vision is presented to ensure the reliability of electronic components. It consists of four major modules, including a metallographic microscope, a Gigabit Ethernet high-resolution camera, a control system and a high performance computer. An auto-focusing technique is presented to solve the problem that the chip surface is not on the same focusing surface under the high magnification of the microscope. A panoramic high-resolution image stitching algorithm is adopted to deal with the contradiction between resolution and field of view, caused by different sizes of electronic components. In addition, we establish a database to storage and callback appropriate parameters to ensure the consistency of chip images of electronic components with the same model. We use image change detection technology to realize the detection of chip images of electronic components. The system can achieve high-resolution imaging for chips of electronic components with various sizes, and clearly imaging for the surface of chip with different horizontal and standardized imaging for ones with the same model, and can recognize chip defects.

  10. Emergence of gamma motor activity in an artificial neural network model of the corticospinal system.

    Science.gov (United States)

    Grandjean, Bernard; Maier, Marc A

    2017-02-01

    Muscle spindle discharge during active movement is a function of mechanical and neural parameters. Muscle length changes (and their derivatives) represent its primary mechanical, fusimotor drive its neural component. However, neither the action nor the function of fusimotor and in particular of γ-drive, have been clearly established, since γ-motor activity during voluntary, non-locomotor movements remains largely unknown. Here, using a computational approach, we explored whether γ-drive emerges in an artificial neural network model of the corticospinal system linked to a biomechanical antagonist wrist simulator. The wrist simulator included length-sensitive and γ-drive-dependent type Ia and type II muscle spindle activity. Network activity and connectivity were derived by a gradient descent algorithm to generate reciprocal, known target α-motor unit activity during wrist flexion-extension (F/E) movements. Two tasks were simulated: an alternating F/E task and a slow F/E tracking task. Emergence of γ-motor activity in the alternating F/E network was a function of α-motor unit drive: if muscle afferent (together with supraspinal) input was required for driving α-motor units, then γ-drive emerged in the form of α-γ coactivation, as predicted by empirical studies. In the slow F/E tracking network, γ-drive emerged in the form of α-γ dissociation and provided critical, bidirectional muscle afferent activity to the cortical network, containing known bidirectional target units. The model thus demonstrates the complementary aspects of spindle output and hence γ-drive: i) muscle spindle activity as a driving force of α-motor unit activity, and ii) afferent activity providing continuous sensory information, both of which crucially depend on γ-drive.

  11. A novel neural-wavelet approach for process diagnostics and complex system modeling

    Science.gov (United States)

    Gao, Rong

    Neural networks have been effective in several engineering applications because of their learning abilities and robustness. However certain shortcomings, such as slow convergence and local minima, are always associated with neural networks, especially neural networks applied to highly nonlinear and non-stationary problems. These problems can be effectively alleviated by integrating a new powerful tool, wavelets, into conventional neural networks. The multi-resolution analysis and feature localization capabilities of the wavelet transform offer neural networks new possibilities for learning. A neural wavelet network approach developed in this thesis enjoys fast convergence rate with little possibility to be caught at a local minimum. It combines the localization properties of wavelets with the learning abilities of neural networks. Two different testbeds are used for testing the efficiency of the new approach. The first is magnetic flowmeter-based process diagnostics: here we extend previous work, which has demonstrated that wavelet groups contain process information, to more general process diagnostics. A loop at Applied Intelligent Systems Lab (AISL) is used for collecting and analyzing data through the neural-wavelet approach. The research is important for thermal-hydraulic processes in nuclear and other engineering fields. The neural-wavelet approach developed is also tested with data from the electric power grid. More specifically, the neural-wavelet approach is used for performing short-term and mid-term prediction of power load demand. In addition, the feasibility of determining the type of load using the proposed neural wavelet approach is also examined. The notion of cross scale product has been developed as an expedient yet reliable discriminator of loads. Theoretical issues involved in the integration of wavelets and neural networks are discussed and future work outlined.

  12. Artificial Neural Network-Based System for PET Volume Segmentation

    Directory of Open Access Journals (Sweden)

    Mhd Saeed Sharif

    2010-01-01

    Full Text Available Tumour detection, classification, and quantification in positron emission tomography (PET imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs, as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results.

  13. A new evolutionary system for evolving artificial neural networks.

    Science.gov (United States)

    Yao, X; Liu, Y

    1997-01-01

    This paper presents a new evolutionary system, i.e., EPNet, for evolving artificial neural networks (ANNs). The evolutionary algorithm used in EPNet is based on Fogel's evolutionary programming (EP). Unlike most previous studies on evolving ANN's, this paper puts its emphasis on evolving ANN's behaviors. Five mutation operators proposed in EPNet reflect such an emphasis on evolving behaviors. Close behavioral links between parents and their offspring are maintained by various mutations, such as partial training and node splitting. EPNet evolves ANN's architectures and connection weights (including biases) simultaneously in order to reduce the noise in fitness evaluation. The parsimony of evolved ANN's is encouraged by preferring node/connection deletion to addition. EPNet has been tested on a number of benchmark problems in machine learning and ANNs, such as the parity problem, the medical diagnosis problems, the Australian credit card assessment problem, and the Mackey-Glass time series prediction problem. The experimental results show that EPNet can produce very compact ANNs with good generalization ability in comparison with other algorithms.

  14. A neural network approach to the study of dynamics and structure of molecular systems

    International Nuclear Information System (INIS)

    Getino, C.; Sumpter, B.G.; Noid, D.W.

    1994-01-01

    Neural networks are used to study intramolecular energy flow in molecular systems (tetratomics to macromolecules), developing new techniques for efficient analysis of data obtained from molecular-dynamics and quantum mechanics calculations. Neural networks can map phase space points to intramolecular vibrational energies along a classical trajectory (example of complicated coordinate transformation), producing reasonably accurate values for any region of the multidimensional phase space of a tetratomic molecule. Neural network energy flow predictions are found to significantly enhance the molecular-dynamics method to longer time-scales and extensive averaging of trajectories for macromolecular systems. Pattern recognition abilities of neural networks can be used to discern phase space features. Neural networks can also expand model calculations by interpolation of costly quantum mechanical ab initio data, used to develop semiempirical potential energy functions

  15. Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network

    Energy Technology Data Exchange (ETDEWEB)

    Du, Zhimin; Jin, Xinqiao; Yang, Yunyu [School of Mechanical Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, Shanghai (China)

    2009-09-15

    Wavelet neural network, the integration of wavelet analysis and neural network, is presented to diagnose the faults of sensors including temperature, flow rate and pressure in variable air volume (VAV) systems to ensure well capacity of energy conservation. Wavelet analysis is used to process the original data collected from the building automation first. With three-level wavelet decomposition, the series of characteristic information representing various operation conditions of the system are obtained. In addition, neural network is developed to diagnose the source of the fault. To improve the diagnosis efficiency, three data groups based on several physical models or balances are classified and constructed. Using the data decomposed by three-level wavelet, the neural network can be well trained and series of convergent networks are obtained. Finally, the new measurements to diagnose are similarly processed by wavelet. And the well-trained convergent neural networks are used to identify the operation condition and isolate the source of the fault. (author)

  16. Radial basis function neural network for power system load-flow

    International Nuclear Information System (INIS)

    Karami, A.; Mohammadi, M.S.

    2008-01-01

    This paper presents a method for solving the load-flow problem of the electric power systems using radial basis function (RBF) neural network with a fast hybrid training method. The main idea is that some operating conditions (values) are needed to solve the set of non-linear algebraic equations of load-flow by employing an iterative numerical technique. Therefore, we may view the outputs of a load-flow program as functions of the operating conditions. Indeed, we are faced with a function approximation problem and this can be done by an RBF neural network. The proposed approach has been successfully applied to the 10-machine and 39-bus New England test system. In addition, this method has been compared with that of a multi-layer perceptron (MLP) neural network model. The simulation results show that the RBF neural network is a simpler method to implement and requires less training time to converge than the MLP neural network. (author)

  17. Green IT engineering components, networks and systems implementation

    CERN Document Server

    Kondratenko, Yuriy; Kacprzyk, Janusz

    2017-01-01

    This book presents modern approaches to improving the energy efficiency, safety and environmental performance of industrial processes and products, based on the application of advanced trends in Green Information Technologies (IT) Engineering to components, networks and complex systems (software, programmable and hardware components, communications, Cloud and IoT-based systems, as well as IT infrastructures). The book’s 16 chapters, prepared by authors from Greece, Malaysia, Russia, Slovakia, Ukraine and the United Kingdom, are grouped into four sections: (1) The Green Internet of Things, Cloud Computing and Data Mining, (2) Green Mobile and Embedded Control Systems, (3) Green Logic and FPGA Design, and (4) Green IT for Industry and Smart Grids. The book will motivate researchers and engineers from different IT domains to develop, implement and propagate green values in complex systems. Further, it will benefit all scientists and graduate students pursuing research in computer science with a focus on green ...

  18. Optimal maintenance of multi-component systems: a review

    NARCIS (Netherlands)

    R.P. Nicolai (Robin); R. Dekker (Rommert)

    2006-01-01

    textabstractIn this article we give an overview of the literature on multi-component maintenance optimization. We focus on work appearing since the 1991 survey "A survey of maintenance models for multi-unit systems" by Cho and Parlar. This paper builds forth on the review article by Dekker et al.

  19. Plant systems/components modularization study. Final report. [PWR

    Energy Technology Data Exchange (ETDEWEB)

    1977-07-01

    The final results are summarized of a Plant Systems/Components Modularization Study based on Stone and Webster's Pressurized Water Reactor Reference Design. The program has been modified to include evaluation of the most promising areas for modular consideration based on the level of the Sundesert Project engineering design completion and the feasibility of their incorporation into the plant construction effort.

  20. Predicting phase equilibria in one-component systems

    Science.gov (United States)

    Korchuganova, M. R.; Esina, Z. N.

    2015-07-01

    It is shown that Simon equation coefficients for n-alkanes and n-alcohols can be modeled using critical and triple point parameters. Predictions of the phase liquid-vapor, solid-vapor, and liquid-solid equilibria in one-component systems are based on the Clausius-Clapeyron relation, Van der Waals and Simon equations, and the principle of thermodynamic similarity.

  1. Plant systems/components modularization study. Final report

    International Nuclear Information System (INIS)

    1977-07-01

    The final results are summarized of a Plant Systems/Components Modularization Study based on Stone and Webster's Pressurized Water Reactor Reference Design. The program has been modified to include evaluation of the most promising areas for modular consideration based on the level of the Sundesert Project engineering design completion and the feasibility of their incorporation into the plant construction effort

  2. Simulating the DIRCM engagement: component and system level performance

    CSIR Research Space (South Africa)

    Willers, CJ

    2012-11-01

    Full Text Available , spectrally matched, pyrophoric. Jammers with arc lamps and steered (directional) beams. Early directional laser beams: soft- kill - jamming & dazzling. Single head, complex gimbals. Advanced direction- al laser beams: hard kill - power levels cause damage... countermeasures (DIRCM), employing high power lamps or lasers as sources of infrared energy. The larger aircraft self-protection scenario, comprising the missile, aircraft and DIRCM hardware is a complex system. In this system, each component presents major...

  3. Modelling safety of multistate systems with ageing components

    Energy Technology Data Exchange (ETDEWEB)

    Kołowrocki, Krzysztof; Soszyńska-Budny, Joanna [Gdynia Maritime University, Department of Mathematics ul. Morska 81-87, Gdynia 81-225 Poland (Poland)

    2016-06-08

    An innovative approach to safety analysis of multistate ageing systems is presented. Basic notions of the ageing multistate systems safety analysis are introduced. The system components and the system multistate safety functions are defined. The mean values and variances of the multistate systems lifetimes in the safety state subsets and the mean values of their lifetimes in the particular safety states are defined. The multi-state system risk function and the moment of exceeding by the system the critical safety state are introduced. Applications of the proposed multistate system safety models to the evaluation and prediction of the safty characteristics of the consecutive “m out of n: F” is presented as well.

  4. Modelling safety of multistate systems with ageing components

    International Nuclear Information System (INIS)

    Kołowrocki, Krzysztof; Soszyńska-Budny, Joanna

    2016-01-01

    An innovative approach to safety analysis of multistate ageing systems is presented. Basic notions of the ageing multistate systems safety analysis are introduced. The system components and the system multistate safety functions are defined. The mean values and variances of the multistate systems lifetimes in the safety state subsets and the mean values of their lifetimes in the particular safety states are defined. The multi-state system risk function and the moment of exceeding by the system the critical safety state are introduced. Applications of the proposed multistate system safety models to the evaluation and prediction of the safty characteristics of the consecutive “m out of n: F” is presented as well.

  5. Systems of independent Markov components and their transient behavior

    International Nuclear Information System (INIS)

    Keilson, J.

    1975-01-01

    The transient behavior of redundant systems of independent, repairable, memoryless (two state) components is studied. Four failure times for such systems are considered, each an exit time from the set of working states for initial system conditions of interest: the failure time from the perfect state, the post-recovery exit time, the ergodic exit time, and the quasi-stationary exit time. The structure of these failure time distributions and their interrelation are discussed and asymptotic estimates and bounds for their expectations are presented. When such systems have high reliability, the failure time distributions are approximately exponential and asymptotically equivalent

  6. A Fault Diagnosis Approach for the Hydraulic System by Artificial Neural Networks

    OpenAIRE

    Xiangyu He; Shanghong He

    2014-01-01

    Based on artificial neural networks, a fault diagnosis approach for the hydraulic system was proposed in this paper. Normal state samples were used as the training data to develop a dynamic general regression neural network (DGRNN) model. The trained DGRNN model then served as the fault determinant to diagnose test faults and the work condition of the hydraulic system was identified. Several typical faults of the hydraulic system were used to verify the fault diagnosis approach. Experiment re...

  7. An artificial neural network for modeling reliability, availability and maintainability of a repairable system

    International Nuclear Information System (INIS)

    Rajpal, P.S.; Shishodia, K.S.; Sekhon, G.S.

    2006-01-01

    The paper explores the application of artificial neural networks to model the behaviour of a complex, repairable system. A composite measure of reliability, availability and maintainability parameters has been proposed for measuring the system performance. The artificial neural network has been trained using past data of a helicopter transportation facility. It is used to simulate behaviour of the facility under various constraints. The insights obtained from results of simulation are useful in formulating strategies for optimal operation of the system

  8. Development of the disable software reporting system on the basis of the neural network

    Science.gov (United States)

    Gavrylenko, S.; Babenko, O.; Ignatova, E.

    2018-04-01

    The PE structure of malicious and secure software is analyzed, features are highlighted, binary sign vectors are obtained and used as inputs for training the neural network. A software model for detecting malware based on the ART-1 neural network was developed, optimal similarity coefficients were found, and testing was performed. The obtained research results showed the possibility of using the developed system of identifying malicious software in computer systems protection systems

  9. Component wall thinning and a corrosion-erosion monitoring system

    International Nuclear Information System (INIS)

    Bogard, T.; Batt, T.; Roarty, D.

    1989-01-01

    Since a 1986 incident involving failure of a piping elbow due to erosion-corrosion, the electric utility industry has been actively developing technology for implementing long term programs to address corrosion-erosion. This paper describes a typical corrosion-erosion monitoring program, the types of non-destructive examinations (NDE) performed on components, and the extensive NDE data obtained when the program is applied to components in a power plant. To facilitate evaluation of the NDE data on components, an automated NDE data manipulation and data display system is advisable and perhaps necessary due to the large amounts of NDE data typically obtained during a program. Such a comprehensive corrosion-erosion monitoring system (CEMS) needs to be integral with methods for selection of inspection locations and perform NDE data analysis to help in replace, repair, or run decisions. The structure for one CEMS is described which uses IBM PC compatible hardware and a set of software addressing most data evaluation and decision making needs. CEMS features include automated input/output for typical NDE devices, database structuring, graphics outputs including color 2-D or 3-D contour plots of components, trending and predictive evaluations for future inspection planning, EC severity determination, integration of piping isometrics and component properties, and desktop publishing capabilities

  10. Plutonium Finishing Plant (PFP) HVAC System Component Index; FINAL

    International Nuclear Information System (INIS)

    DICK, J.D.

    1999-01-01

    This document identities the components, design media, procedures and defines the critical characteristics of Commercial Grade Items necessary to ensure the HVAC system provides these functions. This document lists safety class (SC) and safety significant (SS) components for the Heating Ventilation Air Conditioning (HVAC) and specifies the critical characteristics for Commercial Grade Items (CGI), as required by HNF-PRO-268 and HNF-PRO-1819. These are the minimum specifications that the equipment must meet in order to properly perform its safety function. There may be several manufacturers or models that meet the critical characteristics for any one item

  11. Statistical mechanics of complex neural systems and high dimensional data

    International Nuclear Information System (INIS)

    Advani, Madhu; Lahiri, Subhaneil; Ganguli, Surya

    2013-01-01

    Recent experimental advances in neuroscience have opened new vistas into the immense complexity of neuronal networks. This proliferation of data challenges us on two parallel fronts. First, how can we form adequate theoretical frameworks for understanding how dynamical network processes cooperate across widely disparate spatiotemporal scales to solve important computational problems? Second, how can we extract meaningful models of neuronal systems from high dimensional datasets? To aid in these challenges, we give a pedagogical review of a collection of ideas and theoretical methods arising at the intersection of statistical physics, computer science and neurobiology. We introduce the interrelated replica and cavity methods, which originated in statistical physics as powerful ways to quantitatively analyze large highly heterogeneous systems of many interacting degrees of freedom. We also introduce the closely related notion of message passing in graphical models, which originated in computer science as a distributed algorithm capable of solving large inference and optimization problems involving many coupled variables. We then show how both the statistical physics and computer science perspectives can be applied in a wide diversity of contexts to problems arising in theoretical neuroscience and data analysis. Along the way we discuss spin glasses, learning theory, illusions of structure in noise, random matrices, dimensionality reduction and compressed sensing, all within the unified formalism of the replica method. Moreover, we review recent conceptual connections between message passing in graphical models, and neural computation and learning. Overall, these ideas illustrate how statistical physics and computer science might provide a lens through which we can uncover emergent computational functions buried deep within the dynamical complexities of neuronal networks. (paper)

  12. Neutron spectrometry and dosimetry by means of Bonner spheres system and artificial neural networks applying robust design of artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Martinez B, M.R.; Ortiz R, J.M.; Vega C, H.R. [UAZ, Av. Ramon Lopez Velarde No. 801, 98000 Zacatecas (Mexico)

    2006-07-01

    An Artificial Neural Network has been designed, trained and tested to unfold neutron spectra and simultaneously to calculate equivalent doses. A set of 187 neutron spectra compiled by the International Atomic Energy Agency and 13 equivalent doses were used in the artificial neural network designed, trained and tested. In order to design the neural network was used the robust design of artificial neural networks methodology, which assures that the quality of the neural networks takes into account from the design stage. Unless previous works, here, for first time a group of neural networks were designed and trained to unfold 187 neutron spectra and at the same time to calculate 13 equivalent doses, starting from the count rates coming from the Bonner spheres system by using a systematic and experimental strategy. (Author)

  13. Neutron spectrometry and dosimetry by means of Bonner spheres system and artificial neural networks applying robust design of artificial neural networks

    International Nuclear Information System (INIS)

    Martinez B, M.R.; Ortiz R, J.M.; Vega C, H.R.

    2006-01-01

    An Artificial Neural Network has been designed, trained and tested to unfold neutron spectra and simultaneously to calculate equivalent doses. A set of 187 neutron spectra compiled by the International Atomic Energy Agency and 13 equivalent doses were used in the artificial neural network designed, trained and tested. In order to design the neural network was used the robust design of artificial neural networks methodology, which assures that the quality of the neural networks takes into account from the design stage. Unless previous works, here, for first time a group of neural networks were designed and trained to unfold 187 neutron spectra and at the same time to calculate 13 equivalent doses, starting from the count rates coming from the Bonner spheres system by using a systematic and experimental strategy. (Author)

  14. Goal-directed behaviour and instrumental devaluation: a neural system-level computational model

    Directory of Open Access Journals (Sweden)

    Francesco Mannella

    2016-10-01

    Full Text Available Devaluation is the key experimental paradigm used to demonstrate the presence of instrumental behaviours guided by goals in mammals. We propose a neural system-level computational model to address the question of which brain mechanisms allow the current value of rewards to control instrumental actions. The model pivots on and shows the computational soundness of the hypothesis for which the internal representation of instrumental manipulanda (e.g., levers activate the representation of rewards (or `action-outcomes', e.g. foods while attributing to them a value which depends on the current internal state of the animal (e.g., satiation for some but not all foods. The model also proposes an initial hypothesis of the integrated system of key brain components supporting this process and allowing the recalled outcomes to bias action selection: (a the sub-system formed by the basolateral amygdala and insular cortex acquiring the manipulanda-outcomes associations and attributing the current value to the outcomes; (b the three basal ganglia-cortical loops selecting respectively goals, associative sensory representations, and actions; (c the cortico-cortical and striato-nigro-striatal neural pathways supporting the selection, and selection learning, of actions based on habits and goals. The model reproduces and integrates the results of different devaluation experiments carried out with control rats and rats with pre- and post-training lesions of the basolateral amygdala, the nucleus accumbens core, the prelimbic cortex, and the dorso-medial striatum. The results support the soundness of the hypotheses of the model and show its capacity to integrate, at the system-level, the operations of the key brain structures underlying devaluation. Based on its hypotheses and predictions, the model also represents an operational framework to support the design and analysis of new experiments on the motivational aspects of goal-directed behaviour.

  15. Qualification and issues with space flight laser systems and components

    Science.gov (United States)

    Ott, Melanie N.; Coyle, D. B.; Canham, John S.; Leidecker, Henning W., Jr.

    2006-02-01

    The art of flight quality solid-state laser development is still relatively young, and much is still unknown regarding the best procedures, components, and packaging required for achieving the maximum possible lifetime and reliability when deployed in the harsh space environment. One of the most important issues is the limited and unstable supply of quality, high power diode arrays with significant technological heritage and market lifetime. Since Spectra Diode Labs Inc. ended their involvement in the pulsed array business in the late 1990's, there has been a flurry of activity from other manufacturers, but little effort focused on flight quality production. This forces NASA, inevitably, to examine the use of commercial parts to enable space flight laser designs. System-level issues such as power cycling, operational derating, duty cycle, and contamination risks to other laser components are some of the more significant unknown, if unquantifiable, parameters that directly effect transmitter reliability. Designs and processes can be formulated for the system and the components (including thorough modeling) to mitigate risk based on the known failures modes as well as lessons learned that GSFC has collected over the past ten years of space flight operation of lasers. In addition, knowledge of the potential failure modes related to the system and the components themselves can allow the qualification testing to be done in an efficient yet, effective manner. Careful test plan development coupled with physics of failure knowledge will enable cost effect qualification of commercial technology. Presented here will be lessons learned from space flight experience, brief synopsis of known potential failure modes, mitigation techniques, and options for testing from the system level to the component level.

  16. Building 65 Hydraulic Systems Handbook: Components, Systems, and Applications

    Science.gov (United States)

    2016-04-01

    Dump Buttons OVERVIEW Pump Dump Buttons...hydraulic system? There are different types of dump buttons that control a hierarchy of flow paths. Some dump buttons are used to shut down a pump ...that branch. The use of this dump button is preferred over the Pump Dump Button when possible. Test Site Dump

  17. A New Controller to Enhance PV System Performance Based on Neural Network

    Directory of Open Access Journals (Sweden)

    Roshdy A AbdelRassoul

    2017-06-01

    Full Text Available In recent years, a radical increase of photovoltaic (PV power generators installation took place because of increased efficiency of solar cells, as well as the growth of manufacturing technology of solar panels. This paper shows the operation and modeling of photovoltaic systems, particularly designing neural controller to control the system. Neural controller is optimized using particle swarm optimization (PSO   leads to getting the best performance of the designed PV system. Using neural network the maximum overshoot and rise time obtained become 0.00001% and 0.1798 seconds, respectively also this paper introduce a comparison between some kind of controller for PV system.In recent years, a radical increase of photovoltaic (PV power generators installation took place because of increased efficiency of solar cells, as well as the growth of manufacturing technology of solar panels. This paper shows the operation and modeling of photovoltaic systems, particularly designing neural controller to control the system. Neural controller is optimized using particle swarm optimization (PSO   leads to getting the best performance of the designed PV system. Using neural network the maximum overshoot and rise time obtained become 0.00001% and 0.1798 seconds, respectively also this paper introduce a comparison between some kind of controller for PV system.

  18. Dynamic analysis of the radiolysis of binary component system

    International Nuclear Information System (INIS)

    Katayama, M.; Trumbore, C.N.

    1975-01-01

    Dynamic analysis was performed on a variety of combinations of components in the radiolysis of binary system, taking the hydrogen-producing reaction with hydrocarbon RH 2 as an example. A definite rule was able to be established from this analysis, which is useful for revealing the reaction mechanism. The combinations were as follows: 1) both components A and B do not interact but serve only as diluents, 2) A is a diluent, and B is a radical captor, 3) both A and B are radical captors, 4-1) A is a diluent, and B decomposes after the reception of the exciting energy of A, 4-2) A is a diluent, and B does not participate in decomposition after the reception of the exciting energy of A, 5-1) A is a radical captor, and B decomposes after the reception of the exciting energy of A, 5-2) A is a radical captor, and B does not participate in decomposition after the reception of the exciting energy of A, 6-1) both A and B decompose after the reception of the exciting energy of the partner component; and 6-2) both A and B do not decompose after the reception of the exciting energy of the partner component. According to the dynamical analysis of the above nine combinations, it can be pointed out that if excitation transfer participates, the similar phenomena to radical capture are presented apparently. It is desirable to measure the yield of radicals experimentally with the system which need not much consideration to the excitation transfer. Isotope substitution mixture system is conceived as one of such system. This analytical method was applied to the system containing cyclopentanone, such as cyclopentanone-cyclohexane system. (Iwakiri, K.)

  19. Passive components of NPP safety-related systems

    International Nuclear Information System (INIS)

    Ionaytis Romuald, R.; Bubnova Tatyana, A.

    2005-01-01

    This paper presents a new passive components with having drives: fast-response cutoff valves; modular actuators with opposite cocking pneumatic drives and actuation spring drives; voting electromagnetic valve units for control of pneumatic drives; passive initiators of actuation; visual diagnostics . All these devices have been developed and tested at mock-ups. This paper presents also the following direct-action passive safety components: modular pressure-relief safety valves; pilot safety valves with passive action; check valves with remote position indicator and after-tightening; modular inserts for limiting emergency coolant flow; vortex rectifier; critical weld fasteners; gas-liquid valves; fast-removable seal assembly; seal spring loaders; grooves for increasing hydraulic resistance. Replacement of active safety system components for passive ones improves the general reliability NPP by 1.5 or 2 orders of magnitudes. (authors)

  20. Identification of Complex Dynamical Systems with Neural Networks (2/2)

    CERN Multimedia

    CERN. Geneva

    2016-01-01

    The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...

  1. Identification of Complex Dynamical Systems with Neural Networks (1/2)

    CERN Multimedia

    CERN. Geneva

    2016-01-01

    The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...

  2. Computational neural network regression model for Host based Intrusion Detection System

    Directory of Open Access Journals (Sweden)

    Sunil Kumar Gautam

    2016-09-01

    Full Text Available The current scenario of information gathering and storing in secure system is a challenging task due to increasing cyber-attacks. There exists computational neural network techniques designed for intrusion detection system, which provide security to single machine and entire network's machine. In this paper, we have used two types of computational neural network models, namely, Generalized Regression Neural Network (GRNN model and Multilayer Perceptron Neural Network (MPNN model for Host based Intrusion Detection System using log files that are generated by a single personal computer. The simulation results show correctly classified percentage of normal and abnormal (intrusion class using confusion matrix. On the basis of results and discussion, we found that the Host based Intrusion Systems Model (HISM significantly improved the detection accuracy while retaining minimum false alarm rate.

  3. The AutoAssociative Neural Network in signal analysis: II. Application to on-line monitoring of a simulated BWR component

    International Nuclear Information System (INIS)

    Marseguerra, M.; Zoia, A.

    2005-01-01

    In this paper, Robust AutoAssociative Neural Networks (RAANN) are applied to a series of signals produced by the Halden simulator of the 1200MWe BWR Forsmark-3 plant in Sweden. The applications concern: - correction of drifts and gross errors in sensors, for diagnostic and control purposes, - cluster analysis, to individuate a failed component and the intensity of the failure, - forecasting system signals, for safety or economic purposes, - reconstruction of unmeasured signals (virtual sensors). In the attainment of the above results, the geometric interpretation of the mapping performed by the network, propounded in Part I of this work, has provided a reasoned choice of the most critical free parameter, i.e., the number f of nodes of the bottleneck layer, thus allowing a deep understanding of the network functioning and also avoiding the traditional and troubling procedure of selection by trial-and-error. The theoretical basis of this analysis, discussed in details in the companion paper, is founded on the idea of dimension and in particular of fractal dimension, which has been used as a numerical estimator of f

  4. Probabilistic structural analysis methods for select space propulsion system components

    Science.gov (United States)

    Millwater, H. R.; Cruse, T. A.

    1989-01-01

    The Probabilistic Structural Analysis Methods (PSAM) project developed at the Southwest Research Institute integrates state-of-the-art structural analysis techniques with probability theory for the design and analysis of complex large-scale engineering structures. An advanced efficient software system (NESSUS) capable of performing complex probabilistic analysis has been developed. NESSUS contains a number of software components to perform probabilistic analysis of structures. These components include: an expert system, a probabilistic finite element code, a probabilistic boundary element code and a fast probability integrator. The NESSUS software system is shown. An expert system is included to capture and utilize PSAM knowledge and experience. NESSUS/EXPERT is an interactive menu-driven expert system that provides information to assist in the use of the probabilistic finite element code NESSUS/FEM and the fast probability integrator (FPI). The expert system menu structure is summarized. The NESSUS system contains a state-of-the-art nonlinear probabilistic finite element code, NESSUS/FEM, to determine the structural response and sensitivities. A broad range of analysis capabilities and an extensive element library is present.

  5. SWANN: The Snow Water Artificial Neural Network Modelling System

    Science.gov (United States)

    Broxton, P. D.; van Leeuwen, W.; Biederman, J. A.

    2017-12-01

    Snowmelt from mountain forests is important for water supply and ecosystem health. Along Arizona's Mogollon Rim, snowmelt contributes to rivers and streams that provide a significant water supply for hydro-electric power generation, agriculture, and human consumption in central Arizona. In this project, we are building a snow monitoring system for the Salt River Project (SRP), which supplies water and power to millions of customers in the Phoenix metropolitan area. We are using process-based hydrological models and artificial neural networks (ANNs) to generate information about both snow water equivalent (SWE) and snow cover. The snow-cover data is generated with ANNs that are applied to Landsat and MODIS satellite reflectance data. The SWE data is generated using a combination of gridded SWE estimates generated by process-based snow models and ANNs that account for variations in topography, forest cover, and solar radiation. The models are trained and evaluated with snow data from SNOTEL stations as well as from aerial LiDAR and field data that we collected this past winter in northern Arizona, as well as with similar data from other sites in the Southwest US. These snow data are produced in near-real time, and we have built a prototype decision support tool to deliver them to SRP. This tool is designed to provide daily-to annual operational monitoring of spatial and temporal changes in SWE and snow cover conditions over the entire Salt River Watershed (covering 17,000 km2), and features advanced web mapping capabilities and watershed analytics displayed as graphical data.

  6. A Sliding Mode Control-based on a RBF Neural Network for Deburring Industry Robotic Systems

    OpenAIRE

    Tao, Yong; Zheng, Jiaqi; Lin, Yuanchang

    2016-01-01

    A sliding mode control method based on radial basis function (RBF) neural network is proposed for the deburring of industry robotic systems. First, a dynamic model for deburring the robot system is established. Then, a conventional SMC scheme is introduced for the joint position tracking of robot manipulators. The RBF neural network based sliding mode control (RBFNN-SMC) has the ability to learn uncertain control actions. In the RBFNN-SMC scheme, the adaptive tuning algorithms for network par...

  7. Neural networks for combined control of capacitor banks and voltage regulators in distribution systems

    Energy Technology Data Exchange (ETDEWEB)

    Gu, Z.; Rizy, D.T.

    1996-02-01

    A neural network for controlling shunt capacitor banks and feeder voltage regulators in electric distribution systems is presented. The objective of the neural controller is to minimize total I{sup 2}R losses and maintain all bus voltages within standard limits. The performance of the neural network for different input selections and training data is discussed and compared. Two different input selections are tried, one using the previous control states of the capacitors and regulator along with measured line flows and voltage which is equivalent to having feedback and the other with measured line flows and voltage without previous control settings. The results indicate that the neural net controller with feedback can outperform the one without. Also, proper selection of a training data set that adequately covers the operating space of the distribution system is important for achieving satisfactory performance with the neural controller. The neural controller is tested on a radially configured distribution system with 30 buses, 5 switchable capacitor banks an d one nine tap line regulator to demonstrate the performance characteristics associated with these principles. Monte Carlo simulations show that a carefully designed and relatively compact neural network with a small but carefully developed training set can perform quite well under slight and extreme variation of loading conditions.

  8. Minimally-Invasive Neural Interface for Distributed Wireless Electrocorticogram Recording Systems

    Directory of Open Access Journals (Sweden)

    Sun-Il Chang

    2018-01-01

    Full Text Available This paper presents a minimally-invasive neural interface for distributed wireless electrocorticogram (ECoG recording systems. The proposed interface equips all necessary components for ECoG recording, such as the high performance front-end integrated circuits, a fabricated flexible microelectrode array, and wireless communication inside a miniaturized custom-made platform. The multiple units of the interface systems can be deployed to cover a broad range of the target brain region and transmit signals via a built-in intra-skin communication (ISCOM module. The core integrated circuit (IC consists of 16-channel, low-power push-pull double-gated preamplifiers, in-channel successive approximation register analog-to-digital converters (SAR ADC with a single-clocked bootstrapping switch and a time-delayed control unit, an ISCOM module for wireless data transfer through the skin instead of a power-hungry RF wireless transmitter, and a monolithic voltage/current reference generator to support the aforementioned analog and mixed-signal circuit blocks. The IC was fabricated using 250 nm CMOS processes in an area of 3.2 × 0.9 mm2 and achieved the low-power operation of 2.5 µW per channel. Input-referred noise was measured as 5.62 µVrms for 10 Hz to 10 kHz and ENOB of 7.21 at 31.25 kS/s. The implemented system successfully recorded multi-channel neural activities in vivo from a primate and demonstrated modular expandability using the ISCOM with power consumption of 160 µW.

  9. Minimally-Invasive Neural Interface for Distributed Wireless Electrocorticogram Recording Systems.

    Science.gov (United States)

    Chang, Sun-Il; Park, Sung-Yun; Yoon, Euisik

    2018-01-17

    This paper presents a minimally-invasive neural interface for distributed wireless electrocorticogram (ECoG) recording systems. The proposed interface equips all necessary components for ECoG recording, such as the high performance front-end integrated circuits, a fabricated flexible microelectrode array, and wireless communication inside a miniaturized custom-made platform. The multiple units of the interface systems can be deployed to cover a broad range of the target brain region and transmit signals via a built-in intra-skin communication (ISCOM) module. The core integrated circuit (IC) consists of 16-channel, low-power push-pull double-gated preamplifiers, in-channel successive approximation register analog-to-digital converters (SAR ADC) with a single-clocked bootstrapping switch and a time-delayed control unit, an ISCOM module for wireless data transfer through the skin instead of a power-hungry RF wireless transmitter, and a monolithic voltage/current reference generator to support the aforementioned analog and mixed-signal circuit blocks. The IC was fabricated using 250 nm CMOS processes in an area of 3.2 × 0.9 mm² and achieved the low-power operation of 2.5 µW per channel. Input-referred noise was measured as 5.62 µV rms for 10 Hz to 10 kHz and ENOB of 7.21 at 31.25 kS/s. The implemented system successfully recorded multi-channel neural activities in vivo from a primate and demonstrated modular expandability using the ISCOM with power consumption of 160 µW.

  10. Robotic system construction with mechatronic components inverted pendulum: humanoid robot

    Science.gov (United States)

    Sandru, Lucian Alexandru; Crainic, Marius Florin; Savu, Diana; Moldovan, Cristian; Dolga, Valer; Preitl, Stefan

    2017-03-01

    Mechatronics is a new methodology used to achieve an optimal design of an electromechanical product. This methodology is collection of practices, procedures and rules used by those who work in particular branch of knowledge or discipline. Education in mechatronics at the Polytechnic University Timisoara is organized on three levels: bachelor, master and PhD studies. These activities refer and to design the mechatronics systems. In this context the design, implementation and experimental study of a family of mechatronic demonstrator occupy an important place. In this paper, a variant for a mechatronic demonstrator based on the combination of the electrical and mechanical components is proposed. The demonstrator, named humanoid robot, is equivalent with an inverted pendulum. Is presented the analyze of components for associated functions of the humanoid robot. This type of development the mechatronic systems by the combination of hardware and software, offers the opportunity to build the optimal solutions.

  11. MODELING OF SYSTEM COMPONENTS OF EDUCATIONAL PROGRAMS IN HIGH SCHOOL

    Directory of Open Access Journals (Sweden)

    E. K. Samerkhanova

    2016-01-01

    Full Text Available Based on the principles of System Studies, describes the components of the educational programs of the control system. Educational Program Management is a set of substantive, procedural, resource, subject-activity, efficiently and evaluation components, which ensures the integrity of integration processes at all levels of education. Ensuring stability and development in the management of educational programs is achieved by identifying and securing social norms, the status of the educational institution program managers to ensure the achievement of modern quality of education.Content Management provides the relevant educational content in accordance with the requirements of the educational and professional standards; process control ensures the efficient organization of rational distribution process flows; Resource Management provides optimal distribution of personnel, information and methodological, material and technical equipment of the educational program; contingent management provides subject-activity interaction of participants of the educational process; quality control ensures the quality of educational services.

  12. Automated implementation of rule-based expert systems with neural networks for time-critical applications

    Science.gov (United States)

    Ramamoorthy, P. A.; Huang, Song; Govind, Girish

    1991-01-01

    In fault diagnosis, control and real-time monitoring, both timing and accuracy are critical for operators or machines to reach proper solutions or appropriate actions. Expert systems are becoming more popular in the manufacturing community for dealing with such problems. In recent years, neural networks have revived and their applications have spread to many areas of science and engineering. A method of using neural networks to implement rule-based expert systems for time-critical applications is discussed here. This method can convert a given rule-based system into a neural network with fixed weights and thresholds. The rules governing the translation are presented along with some examples. We also present the results of automated machine implementation of such networks from the given rule-base. This significantly simplifies the translation process to neural network expert systems from conventional rule-based systems. Results comparing the performance of the proposed approach based on neural networks vs. the classical approach are given. The possibility of very large scale integration (VLSI) realization of such neural network expert systems is also discussed.

  13. Secure wireless embedded systems via component-based design

    DEFF Research Database (Denmark)

    Hjorth, T.; Torbensen, R.

    2010-01-01

    This paper introduces the method secure-by-design as a way of constructing wireless embedded systems using component-based modeling frameworks. This facilitates design of secure applications through verified, reusable software. Following this method we propose a security framework with a secure c......, with full support for confidentiality, authentication, and integrity using keypairs. The approach has been demonstrated in a multi-platform home automation prototype that can remotely unlock a door using a PDA over the Internet....

  14. Component and system simulation models for High Flux Isotope Reactor

    International Nuclear Information System (INIS)

    Sozer, A.

    1989-08-01

    Component models for the High Flux Isotope Reactor (HFIR) have been developed. The models are HFIR core, heat exchangers, pressurizer pumps, circulation pumps, letdown valves, primary head tank, generic transport delay (pipes), system pressure, loop pressure-flow balance, and decay heat. The models were written in FORTRAN and can be run on different computers, including IBM PCs, as they do not use any specific simulation languages such as ACSL or CSMP. 14 refs., 13 figs

  15. Baking system for vacuum components in INDUS-2

    International Nuclear Information System (INIS)

    Bhange, Nilesh J.; Bhatnagar, Prateek; Shukla, S.K.

    2005-01-01

    Optimized bake-out procedures are very important for the overall reliability of vacuum system. In this process the UHV components like Sputter ion pump (SIP), Titanium sublimation pump (TSP) are subjected to temperature rise for sufficiently long period of time. Baking is necessary for obtaining low out gassing rates. In order to provide controlled baking of UHV (Ultra High Vacuum) components for INDUS-II an intelligent ON/OFF control system was needed. For that purpose distributed control system was suitable. For fulfillment of this need modular baking system was developed. This system contains Temperature controller unit (TCU), Pressure Monitoring Unit (PMU), and Temperature control and pressure Monitoring Interface Software (TCPMIS). Each TCU is an eight channel temperature controlling unit. PMU is eight channel pressure monitoring unit to which analog data from gauges like Penning, BA Gauge controllers is given. TCPMIS is a user interface software developed for, controlling up to 5 TCU's. In this way 40 channel temperature control, data logging of 40 channel temperature and logging of eight channel pressures was realized. The present paper describes details about computer controlled baking system. (author)

  16. Effect of component aging on PWR control rod drive systems

    International Nuclear Information System (INIS)

    Grove, E.; Gunther, W.; Sullivan, K.

    1992-01-01

    An aging assessment of PWR control rod drive (CRD) systems has been completed as part of the US NRC Nuclear Plant Aging Research (NPAR) Program. The design, construction, maintenance, and operation of the Babcock ampersand Wilcox (B ampersand W), Combustion Engineering (CE), and Westinghouse (W) systems were evaluated to determine the potential for degradation as each system ages. Operating experience data were evaluated to identify the predominant failure modes, causes, and effects. This, coupled with an assessment of the materials of construction and operating environment, demonstrate that each design is subject to degradation, which if left unchecked, could affect its safety function as the plant ages. An industry survey, conducted with the assistance of EPRI and NUMARC, identified current CRD system maintenance and inspection practices. The results of this survey indicate that some plants have performed system modifications, replaced components, or augmented existing preventive maintenance practices in response to system aging. The survey results also supported the operating experience data, which concluded that the timely replacement of degraded components, prior to failure, was not always possible using existing condition monitoring techniques. The recommendations presented in this study also include a discussion of more advanced monitoring techniques, which provide trendable results capable of detecting aging

  17. Seismic Design of ITER Component Cooling Water System-1 Piping

    Science.gov (United States)

    Singh, Aditya P.; Jadhav, Mahesh; Sharma, Lalit K.; Gupta, Dinesh K.; Patel, Nirav; Ranjan, Rakesh; Gohil, Guman; Patel, Hiren; Dangi, Jinendra; Kumar, Mohit; Kumar, A. G. A.

    2017-04-01

    The successful performance of ITER machine very much depends upon the effective removal of heat from the in-vessel components and other auxiliary systems during Tokamak operation. This objective will be accomplished by the design of an effective Cooling Water System (CWS). The optimized piping layout design is an important element in CWS design and is one of the major design challenges owing to the factors of large thermal expansion and seismic accelerations; considering safety, accessibility and maintainability aspects. An important sub-system of ITER CWS, Component Cooling Water System-1 (CCWS-1) has very large diameter of pipes up to DN1600 with many intersections to fulfill the process flow requirements of clients for heat removal. Pipe intersection is the weakest link in the layout due to high stress intensification factor. CCWS-1 piping up to secondary confinement isolation valves as well as in-between these isolation valves need to survive a Seismic Level-2 (SL-2) earthquake during the Tokamak operation period to ensure structural stability of the system in the Safe Shutdown Earthquake (SSE) event. This paper presents the design, qualification and optimization of layout of ITER CCWS-1 loop to withstand SSE event combined with sustained and thermal loads as per the load combinations defined by ITER and allowable limits as per ASME B31.3, This paper also highlights the Modal and Response Spectrum Analyses done to find out the natural frequency and system behavior during the seismic event.

  18. Fundamentals of computational intelligence neural networks, fuzzy systems, and evolutionary computation

    CERN Document Server

    Keller, James M; Fogel, David B

    2016-01-01

    This book covers the three fundamental topics that form the basis of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation. Discusses single-layer and multilayer neural networks, radial-basi function networks, and recurrent neural networks Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzz...

  19. Diagnosis of mechanical pumping system using neural networks and system parameters analysis

    International Nuclear Information System (INIS)

    Tsai, Tai Ming; Wang, Wei Hui

    2009-01-01

    Normally, a mechanical pumping system is equipped to monitor some of the important input and output signals which are set to the prescribed values. This paper addressed dealing with these signals to establish the database of input- output relation by using a number of neural network models through learning algorithms. These signals encompass normal and abnormal running conditions. The abnormal running conditions were artificially generated. Meanwhile, for the purpose of setting up an on-line diagnosis network, the learning speed and accuracy of three kinds of networks, viz., the backpropagation (BPN), radial basis function (RBF) and adaptive linear (ADALINE) neural networks have been compared and assessed. The assessment criteria of the networks are compared with the correlation result matrix in terms of the neuron vectors. Both BPN and RBF are judged by the maximum vector based on the post-regression analysis, and the ADALINE is judged by the minimum vector based on the least mean square error analysis. By ignoring the neural network training time, it has been shown that if the mechanical diagnosis system is tackled off-line, the RBF method is suggested. However, for on-line diagnosis, the BPN method is recommended

  20. Diagnosis of mechanical pumping system using neural networks and system parameters analysis

    Energy Technology Data Exchange (ETDEWEB)

    Tsai, Tai Ming; Wang, Wei Hui [National Taiwan Ocean University, Keelung (China)

    2009-01-15

    Normally, a mechanical pumping system is equipped to monitor some of the important input and output signals which are set to the prescribed values. This paper addressed dealing with these signals to establish the database of input- output relation by using a number of neural network models through learning algorithms. These signals encompass normal and abnormal running conditions. The abnormal running conditions were artificially generated. Meanwhile, for the purpose of setting up an on-line diagnosis network, the learning speed and accuracy of three kinds of networks, viz., the backpropagation (BPN), radial basis function (RBF) and adaptive linear (ADALINE) neural networks have been compared and assessed. The assessment criteria of the networks are compared with the correlation result matrix in terms of the neuron vectors. Both BPN and RBF are judged by the maximum vector based on the post-regression analysis, and the ADALINE is judged by the minimum vector based on the least mean square error analysis. By ignoring the neural network training time, it has been shown that if the mechanical diagnosis system is tackled off-line, the RBF method is suggested. However, for on-line diagnosis, the BPN method is recommended

  1. Computer simulation system of neural PID control on nuclear reactor

    International Nuclear Information System (INIS)

    Chen Yuzhong; Yang Kaijun; Shen Yongping

    2001-01-01

    Neural network proportional integral differential (PID) controller on nuclear reactor is designed, and the control process is simulated by computer. The simulation result show that neutral network PID controller can automatically adjust its parameter to ideal state, and good control result can be gotten in reactor control process

  2. An adaptive neuro fuzzy model for estimating the reliability of component-based software systems

    Directory of Open Access Journals (Sweden)

    Kirti Tyagi

    2014-01-01

    Full Text Available Although many algorithms and techniques have been developed for estimating the reliability of component-based software systems (CBSSs, much more research is needed. Accurate estimation of the reliability of a CBSS is difficult because it depends on two factors: component reliability and glue code reliability. Moreover, reliability is a real-world phenomenon with many associated real-time problems. Soft computing techniques can help to solve problems whose solutions are uncertain or unpredictable. A number of soft computing approaches for estimating CBSS reliability have been proposed. These techniques learn from the past and capture existing patterns in data. The two basic elements of soft computing are neural networks and fuzzy logic. In this paper, we propose a model for estimating CBSS reliability, known as an adaptive neuro fuzzy inference system (ANFIS, that is based on these two basic elements of soft computing, and we compare its performance with that of a plain FIS (fuzzy inference system for different data sets.

  3. Large aperture components for solid state laser fusion systems

    International Nuclear Information System (INIS)

    Simmons, W.W.

    1978-01-01

    Solid state lasers for fusion experiments must reliably deliver maximum power to small (approximately .5 mm) targets from stand-off focal distances of 1 m or more. This requirement places stringent limits upon the optical quality, resistance to damage, and overall performance of the several major components--amplifiers, Faraday isolators, spatial filters--in each amplifier train. Component development centers about achieving (1) highest functional material figure of merit, (2) best optical quality, and (3) maximum resistance to optical damage. Specific examples of the performance of large aperture components will be presented within the context of the Argus and Shiva laser systems, which are presently operational at Lawrence Livermore Laboratory. Shiva comprises twenty amplifiers, each of 20 cm output clear aperture. Terawatt beams from these amplifiers are focused through two opposed, nested clusters of f/6 lenses onto such targets. Design requirements upon the larger aperture Nova laser components, up to 35 cm in clear aperture, will also be discussed; these pose a significant challenge to the optical industry

  4. Global existence and blow-up phenomena for two-component Degasperis-Procesi system and two-component b-family system

    OpenAIRE

    Liu, Jingjing; Yin, Zhaoyang

    2014-01-01

    This paper is concerned with global existence and blow-up phenomena for two-component Degasperis-Procesi system and two-component b-family system. The strategy relies on our observation on new conservative quantities of these systems. Several new global existence results and a new blowup result of strong solutions to the two-component Degasperis- Procesi system and the two-component b-family system are presented by using these new conservative quantities.

  5. The farming system component of European agricultural landscapes

    DEFF Research Database (Denmark)

    Andersen, Erling

    2017-01-01

    Agricultural landscapes are the outcome of combined natural and human factors over time. This paper explores the scope of perceiving the agricultural landscapes of the European Union (EU) as distinct patterns of farming systems and landscape elements in homogeneous biophysical and administrative...... landscapes evolve from the praxis of the farmers and takes into account the scale, intensity and specialisation of the agricultural production. From farming system design point of view, the approach can be used to integrate the landscape in the design process. From a policy point of view, the approach offers...... endowments. The focus is on the farming systems component of the agricultural landscapes by applying a typology to the sample farms of the Farm Accountancy Data Network and scaling up the results to the landscape level for the territory of the EU. The farming system approach emphasises that agricultural...

  6. Intelligent neural network and fuzzy logic control of industrial and power systems

    Science.gov (United States)

    Kuljaca, Ognjen

    The main role played by neural network and fuzzy logic intelligent control algorithms today is to identify and compensate unknown nonlinear system dynamics. There are a number of methods developed, but often the stability analysis of neural network and fuzzy control systems was not provided. This work will meet those problems for the several algorithms. Some more complicated control algorithms included backstepping and adaptive critics will be designed. Nonlinear fuzzy control with nonadaptive fuzzy controllers is also analyzed. An experimental method for determining describing function of SISO fuzzy controller is given. The adaptive neural network tracking controller for an autonomous underwater vehicle is analyzed. A novel stability proof is provided. The implementation of the backstepping neural network controller for the coupled motor drives is described. Analysis and synthesis of adaptive critic neural network control is also provided in the work. Novel tuning laws for the system with action generating neural network and adaptive fuzzy critic are given. Stability proofs are derived for all those control methods. It is shown how these control algorithms and approaches can be used in practical engineering control. Stability proofs are given. Adaptive fuzzy logic control is analyzed. Simulation study is conducted to analyze the behavior of the adaptive fuzzy system on the different environment changes. A novel stability proof for adaptive fuzzy logic systems is given. Also, adaptive elastic fuzzy logic control architecture is described and analyzed. A novel membership function is used for elastic fuzzy logic system. The stability proof is proffered. Adaptive elastic fuzzy logic control is compared with the adaptive nonelastic fuzzy logic control. The work described in this dissertation serves as foundation on which analysis of particular representative industrial systems will be conducted. Also, it gives a good starting point for analysis of learning abilities of

  7. Compact holographic optical neural network system for real-time pattern recognition

    Science.gov (United States)

    Lu, Taiwei; Mintzer, David T.; Kostrzewski, Andrew A.; Lin, Freddie S.

    1996-08-01

    One of the important characteristics of artificial neural networks is their capability for massive interconnection and parallel processing. Recently, specialized electronic neural network processors and VLSI neural chips have been introduced in the commercial market. The number of parallel channels they can handle is limited because of the limited parallel interconnections that can be implemented with 1D electronic wires. High-resolution pattern recognition problems can require a large number of neurons for parallel processing of an image. This paper describes a holographic optical neural network (HONN) that is based on high- resolution volume holographic materials and is capable of performing massive 3D parallel interconnection of tens of thousands of neurons. A HONN with more than 16,000 neurons packaged in an attache case has been developed. Rotation- shift-scale-invariant pattern recognition operations have been demonstrated with this system. System parameters such as the signal-to-noise ratio, dynamic range, and processing speed are discussed.

  8. System-Level Design of a 64-Channel Low Power Neural Spike Recording Sensor.

    Science.gov (United States)

    Delgado-Restituto, Manuel; Rodriguez-Perez, Alberto; Darie, Angela; Soto-Sanchez, Cristina; Fernandez-Jover, Eduardo; Rodriguez-Vazquez, Angel

    2017-04-01

    This paper reports an integrated 64-channel neural spike recording sensor, together with all the circuitry to process and configure the channels, process the neural data, transmit via a wireless link the information and receive the required instructions. Neural signals are acquired, filtered, digitized and compressed in the channels. Additionally, each channel implements an auto-calibration algorithm which individually configures the transfer characteristics of the recording site. The system has two transmission modes; in one case the information captured by the channels is sent as uncompressed raw data; in the other, feature vectors extracted from the detected neural spikes are released. Data streams coming from the channels are serialized by the embedded digital processor. Experimental results, including in vivo measurements, show that the power consumption of the complete system is lower than 330 μW.

  9. Inductive differentiation of two neural lineages reconstituted in a microculture system from Xenopus early gastrula cells.

    Science.gov (United States)

    Mitani, S; Okamoto, H

    1991-05-01

    Neural induction of ectoderm cells has been reconstituted and examined in a microculture system derived from dissociated early gastrula cells of Xenopus laevis. We have used monoclonal antibodies as specific markers to monitor cellular differentiation from three distinct ectoderm lineages in culture (N1 for CNS neurons from neural tube, Me1 for melanophores from neural crest and E3 for skin epidermal cells from epidermal lineages). CNS neurons and melanophores differentiate when deep layer cells of the ventral ectoderm (VE, prospective epidermis region; 150 cells/culture) and an appropriate region of the marginal zone (MZ, prospective mesoderm region; 5-150 cells/culture) are co-cultured, but not in cultures of either cell type on their own; VE cells cultured alone yield epidermal cells as we have previously reported. The extent of inductive neural differentiation in the co-culture system strongly depends on the origin and number of MZ cells initially added to culture wells. The potency to induce CNS neurons is highest for dorsal MZ cells and sharply decreases as more ventrally located cells are used. The same dorsoventral distribution of potency is seen in the ability of MZ cells to inhibit epidermal differentiation. In contrast, the ability of MZ cells to induce melanophores shows the reverse polarity, ventral to dorsal. These data indicate that separate developmental mechanisms are used for the induction of neural tube and neural crest lineages. Co-differentiation of CNS neurons or melanophores with epidermal cells can be obtained in a single well of co-cultures of VE cells (150) and a wide range of numbers of MZ cells (5 to 100). Further, reproducible differentiation of both neural lineages requires intimate association between cells from the two gastrula regions; virtually no differentiation is obtained when cells from the VE and MZ are separated in a culture well. These results indicate that the inducing signals from MZ cells for both neural tube and neural

  10. Zero-range approximation for two-component boson systems

    International Nuclear Information System (INIS)

    Sogo, T.; Fedorov, D.V.; Jensen, A.S.

    2005-01-01

    The hyperspherical adiabatic expansion method is combined with the zero-range approximation to derive angular Faddeev-like equations for two-component boson systems. The angular eigenvalues are solutions to a transcendental equation obtained as a vanishing determinant of a 3 x 3 matrix. The eigenfunctions are linear combinations of Jacobi functions of argument proportional to the distance between pairs of particles. We investigate numerically the influence of two-body correlations on the eigenvalue spectrum, the eigenfunctions and the effective hyperradial potential. Correlations decrease or increase the distance between pairs for effectively attractive or repulsive interactions, respectively. New structures appear for non-identical components. Fingerprints can be found in the nodal structure of the density distributions of the condensates. (author)

  11. Flexible Multibody Systems Models Using Composite Materials Components

    International Nuclear Information System (INIS)

    Neto, Maria Augusta; Ambr'osio, Jorge A. C.; Leal, Rog'erio Pereira

    2004-01-01

    The use of a multibody methodology to describe the large motion of complex systems that experience structural deformations enables to represent the complete system motion, the relative kinematics between the components involved, the deformation of the structural members and the inertia coupling between the large rigid body motion and the system elastodynamics. In this work, the flexible multibody dynamics formulations of complex models are extended to include elastic components made of composite materials, which may be laminated and anisotropic. The deformation of any structural member must be elastic and linear, when described in a coordinate frame fixed to one or more material points of its domain, regardless of the complexity of its geometry. To achieve the proposed flexible multibody formulation, a finite element model for each flexible body is used. For the beam composite material elements, the sections properties are found using an asymptotic procedure that involves a two-dimensional finite element analysis of their cross-section. The equations of motion of the flexible multibody system are solved using an augmented Lagrangian formulation and the accelerations and velocities are integrated in time using a multi-step multi-order integration algorithm based on the Gear method

  12. Using Graph Components Derived from an Associative Concept Dictionary to Predict fMRI Neural Activation Patterns that Represent the Meaning of Nouns.

    Directory of Open Access Journals (Sweden)

    Hiroyuki Akama

    Full Text Available In this study, we introduce an original distance definition for graphs, called the Markov-inverse-F measure (MiF. This measure enables the integration of classical graph theory indices with new knowledge pertaining to structural feature extraction from semantic networks. MiF improves the conventional Jaccard and/or Simpson indices, and reconciles both the geodesic information (random walk and co-occurrence adjustment (degree balance and distribution. We measure the effectiveness of graph-based coefficients through the application of linguistic graph information for a neural activity recorded during conceptual processing in the human brain. Specifically, the MiF distance is computed between each of the nouns used in a previous neural experiment and each of the in-between words in a subgraph derived from the Edinburgh Word Association Thesaurus of English. From the MiF-based information matrix, a machine learning model can accurately obtain a scalar parameter that specifies the degree to which each voxel in (the MRI image of the brain is activated by each word or each principal component of the intermediate semantic features. Furthermore, correlating the voxel information with the MiF-based principal components, a new computational neurolinguistics model with a network connectivity paradigm is created. This allows two dimensions of context space to be incorporated with both semantic and neural distributional representations.

  13. Integration Test of the High Voltage Hall Accelerator System Components

    Science.gov (United States)

    Kamhawi, Hani; Haag, Thomas; Huang, Wensheng; Pinero, Luis; Peterson, Todd; Dankanich, John

    2013-01-01

    NASA Glenn Research Center is developing a 4 kilowatt-class Hall propulsion system for implementation in NASA science missions. NASA science mission performance analysis was completed using the latest high voltage Hall accelerator (HiVHAc) and Aerojet-Rocketdyne's state-of-the-art BPT-4000 Hall thruster performance curves. Mission analysis results indicated that the HiVHAc thruster out performs the BPT-4000 thruster for all but one of the missions studied. Tests of the HiVHAc system major components were performed. Performance evaluation of the HiVHAc thruster at NASA Glenn's vacuum facility 5 indicated that thruster performance was lower than performance levels attained during tests in vacuum facility 12 due to the lower background pressures attained during vacuum facility 5 tests when compared to vacuum facility 12. Voltage-Current characterization of the HiVHAc thruster in vacuum facility 5 showed that the HiVHAc thruster can operate stably for a wide range of anode flow rates for discharge voltages between 250 and 600 volts. A Colorado Power Electronics enhanced brassboard power processing unit was tested in vacuum for 1,500 hours and the unit demonstrated discharge module efficiency of 96.3% at 3.9 kilowatts and 650 volts. Stand-alone open and closed loop tests of a VACCO TRL 6 xenon flow control module were also performed. An integrated test of the HiVHAc thruster, brassboard power processing unit, and xenon flow control module was performed and confirmed that integrated operation of the HiVHAc system major components. Future plans include continuing the maturation of the HiVHAc system major components and the performance of a single-string integration test.

  14. Adaptive neural network/expert system that learns fault diagnosis for different structures

    Science.gov (United States)

    Simon, Solomon H.

    1992-08-01

    Corporations need better real-time monitoring and control systems to improve productivity by watching quality and increasing production flexibility. The innovative technology to achieve this goal is evolving in the form artificial intelligence and neural networks applied to sensor processing, fusion, and interpretation. By using these advanced Al techniques, we can leverage existing systems and add value to conventional techniques. Neural networks and knowledge-based expert systems can be combined into intelligent sensor systems which provide real-time monitoring, control, evaluation, and fault diagnosis for production systems. Neural network-based intelligent sensor systems are more reliable because they can provide continuous, non-destructive monitoring and inspection. Use of neural networks can result in sensor fusion and the ability to model highly, non-linear systems. Improved models can provide a foundation for more accurate performance parameters and predictions. We discuss a research software/hardware prototype which integrates neural networks, expert systems, and sensor technologies and which can adapt across a variety of structures to perform fault diagnosis. The flexibility and adaptability of the prototype in learning two structures is presented. Potential applications are discussed.

  15. Sensor Failure Detection of FASSIP System using Principal Component Analysis

    Science.gov (United States)

    Sudarno; Juarsa, Mulya; Santosa, Kussigit; Deswandri; Sunaryo, Geni Rina

    2018-02-01

    In the nuclear reactor accident of Fukushima Daiichi in Japan, the damages of core and pressure vessel were caused by the failure of its active cooling system (diesel generator was inundated by tsunami). Thus researches on passive cooling system for Nuclear Power Plant are performed to improve the safety aspects of nuclear reactors. The FASSIP system (Passive System Simulation Facility) is an installation used to study the characteristics of passive cooling systems at nuclear power plants. The accuracy of sensor measurement of FASSIP system is essential, because as the basis for determining the characteristics of a passive cooling system. In this research, a sensor failure detection method for FASSIP system is developed, so the indication of sensor failures can be detected early. The method used is Principal Component Analysis (PCA) to reduce the dimension of the sensor, with the Squarred Prediction Error (SPE) and statistic Hotteling criteria for detecting sensor failure indication. The results shows that PCA method is capable to detect the occurrence of a failure at any sensor.

  16. A test system for electronics components of the PANDA MVD

    Energy Technology Data Exchange (ETDEWEB)

    Lai, Alessandra; Stockmanns, Tobias; Ritman, James [Forschungszentrum Juelich (Germany); Collaboration: PANDA-Collaboration

    2016-07-01

    The PANDA experiment is one of the main devices at the upcoming Facility for Antiproton and Ion Research(FAIR), under construction in Darmstadt, Germany. This fixed target experiment will study the transition region between perturbative and non-perturbative QCD in the energy regime of the charmonium. The innermost sub-detector system of the target spectrometer of the PANDA experiment is the Micro Vertex Detector(MVD). Two types of silicon detectors will be used: pixel detectors and double-sided strip detectors. Two front-end chips are required: the Torino Pixel ASIC(ToPix) and the PANDA Strip ASIC(PASTA). Both are designed to transmit data at a rate of several hundred Megabits per second and are capable of handling the expected hit rate in hot spots of the detector. One key component in the development of new front-end electronics is a test system capable to handle these high rates. It should be flexible enough to test different kinds of front-end electronics and it should be easy to adapt to new prototypes. Therefore, an FPGA-based system is the ideal candidate. For this test system suitable firmware and a software framework are needed. Such a system is under development at the Forschungszentrum Juelich. The main component of the Juelich Digital Readout System(JDRS) is a Virtex 6 FPGA on a development board from Xilinx. In this talk, the mentioned read-out system are introduced, and lab tests with the front-end electronics of the MVD are presented.

  17. Developing and using expert systems and neural networks in medicine: a review on benefits and challenges.

    Science.gov (United States)

    Sheikhtaheri, Abbas; Sadoughi, Farahnaz; Hashemi Dehaghi, Zahra

    2014-09-01

    Complicacy of clinical decisions justifies utilization of information systems such as artificial intelligence (e.g. expert systems and neural networks) to achieve better decisions, however, application of these systems in the medical domain faces some challenges. We aimed at to review the applications of these systems in the medical domain and discuss about such challenges. Following a brief introduction of expert systems and neural networks by representing few examples, the challenges of these systems in the medical domain are discussed. We found that the applications of expert systems and artificial neural networks have been increased in the medical domain. These systems have shown many advantages such as utilization of experts' knowledge, gaining rare knowledge, more time for assessment of the decision, more consistent decisions, and shorter decision-making process. In spite of all these advantages, there are challenges ahead of developing and using such systems including maintenance, required experts, inputting patients' data into the system, problems for knowledge acquisition, problems in modeling medical knowledge, evaluation and validation of system performance, wrong recommendations and responsibility, limited domains of such systems and necessity of integrating such systems into the routine work flows. We concluded that expert systems and neural networks can be successfully used in medicine; however, there are many concerns and questions to be answered through future studies and discussions.

  18. Detecting and mitigating aging in component cooling water systems

    International Nuclear Information System (INIS)

    Lofaro, R.J.

    1991-01-01

    The time-dependent effects of aging on component cooling water (CCW) systems in nuclear power plants has been studied and documented as part of a research program sponsored by the US Nuclear Regulatory Commission. It was found that age related degradation leads to failures in the CCW system which can result in an increase in system unavailability, if not properly detected and mitigated. To identify effective methods of managing this degradation, information on inspection, monitoring, and maintenance practices currently available was obtained from various operating plants and reviewed. The findings were correlated with the most common aging mechanisms and failure modes and a compilation of aging detection and mitigation practices was formulated. This paper discusses the results of this work

  19. Detecting and mitigating aging in component cooling water systems

    International Nuclear Information System (INIS)

    Lofaro, R.J.; Aggarwal, S.

    1992-01-01

    The time-dependent effects of aging on component cooling water (CCW) systems in nuclear power plants has been studied and documented as part of a research program sponsored by the US Nuclear Regulatory Commission. It was found that age related degradation leads to failures in the CCW system which can result in an increase in system unavailability, if not properly detected and mitigated. To identify effective methods of managing this degradation, information on inspection, monitoring, and maintenance practices currently available was obtained from various operating plants and reviewed. The findings were correlated with the most common aging mechanisms and failure modes, and a compilation of aging detection and mitigation practices was formulated. This paper discusses the results of this work

  20. Soft computing integrating evolutionary, neural, and fuzzy systems

    CERN Document Server

    Tettamanzi, Andrea

    2001-01-01

    Soft computing encompasses various computational methodologies, which, unlike conventional algorithms, are tolerant of imprecision, uncertainty, and partial truth. Soft computing technologies offer adaptability as a characteristic feature and thus permit the tracking of a problem through a changing environment. Besides some recent developments in areas like rough sets and probabilistic networks, fuzzy logic, evolutionary algorithms, and artificial neural networks are core ingredients of soft computing, which are all bio-inspired and can easily be combined synergetically. This book presents a well-balanced integration of fuzzy logic, evolutionary computing, and neural information processing. The three constituents are introduced to the reader systematically and brought together in differentiated combinations step by step. The text was developed from courses given by the authors and offers numerous illustrations as

  1. Identification and adaptive neural network control of a DC motor system with dead-zone characteristics.

    Science.gov (United States)

    Peng, Jinzhu; Dubay, Rickey

    2011-10-01

    In this paper, an adaptive control approach based on the neural networks is presented to control a DC motor system with dead-zone characteristics (DZC), where two neural networks are proposed to formulate the traditional identification and control approaches. First, a Wiener-type neural network (WNN) is proposed to identify the motor DZC, which formulates the Wiener model with a linear dynamic block in cascade with a nonlinear static gain. Second, a feedforward neural network is proposed to formulate the traditional PID controller, termed as PID-type neural network (PIDNN), which is then used to control and compensate for the DZC. In this way, the DC motor system with DZC is identified by the WNN identifier, which provides model information to the PIDNN controller in order to make it adaptive. Back-propagation algorithms are used to train both neural networks. Also, stability and convergence analysis are conducted using the Lyapunov theorem. Finally, experiments on the DC motor system demonstrated accurate identification and good compensation for dead-zone with improved control performance over the conventional PID control. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

  2. Psychological Processing in Chronic Pain: A Neural Systems Approach

    OpenAIRE

    Simons, Laura; Elman, Igor; Borsook, David

    2013-01-01

    Our understanding of chronic pain involves complex brain circuits that include sensory, emotional, cognitive and interoceptive processing. The feed-forward interactions between physical (e.g., trauma) and emotional pain and the consequences of altered psychological status on the expression of pain have made the evaluation and treatment of chronic pain a challenge in the clinic. By understanding the neural circuits involved in psychological processes, a mechanistic approach to the implementati...

  3. Application of fuzzy-MOORA method: Ranking of components for reliability estimation of component-based software systems

    Directory of Open Access Journals (Sweden)

    Zeeshan Ali Siddiqui

    2016-01-01

    Full Text Available Component-based software system (CBSS development technique is an emerging discipline that promises to take software development into a new era. As hardware systems are presently being constructed from kits of parts, software systems may also be assembled from components. It is more reliable to reuse software than to create. It is the glue code and individual components reliability that contribute to the reliability of the overall system. Every component contributes to overall system reliability according to the number of times it is being used, some components are of critical usage, known as usage frequency of component. The usage frequency decides the weight of each component. According to their weights, each component contributes to the overall reliability of the system. Therefore, ranking of components may be obtained by analyzing their reliability impacts on overall application. In this paper, we propose the application of fuzzy multi-objective optimization on the basis of ratio analysis, Fuzzy-MOORA. The method helps us find the best suitable alternative, software component, from a set of available feasible alternatives named software components. It is an accurate and easy to understand tool for solving multi-criteria decision making problems that have imprecise and vague evaluation data. By the use of ratio analysis, the proposed method determines the most suitable alternative among all possible alternatives, and dimensionless measurement will realize the job of ranking of components for estimating CBSS reliability in a non-subjective way. Finally, three case studies are shown to illustrate the use of the proposed technique.

  4. An Efficient Connected Component Labeling Architecture for Embedded Systems

    Directory of Open Access Journals (Sweden)

    Fanny Spagnolo

    2018-03-01

    Full Text Available Connected component analysis is one of the most fundamental steps used in several image processing systems. This technique allows for distinguishing and detecting different objects in images by assigning a unique label to all pixels that refer to the same object. Most of the previous published algorithms have been designed for implementation by software. However, due to the large number of memory accesses and compare, lookup, and control operations when executed on a general-purpose processor, they do not satisfy the speed performance required by the next generation high performance computer vision systems. In this paper, we present the design of a new Connected Component Labeling hardware architecture suitable for high performance heterogeneous image processing of embedded designs. When implemented on a Zynq All Programmable-System on Chip (AP-SOC 7045 chip, the proposed design allows a throughput rate higher of 220 Mpixels/s to be reached using less than 18,000 LUTs and 5000 FFs, dissipating about 620 μJ.

  5. Aging of systems: theoretical investigations on system and components time behaviour

    International Nuclear Information System (INIS)

    Eid, M.; Coudray, R.

    1995-01-01

    Being a direct indicator of aging, the systems time-dependent failure rates need to be evaluated using qualified methodologies and starting from basic components time-dependent failure data. Basic component time-dependent failure data are not often available. Components failure data used in the paper are issued from some theoretical considerations rather than from field statistical observations. Four academic cases are presented and their results are discussed. Evaluations result in, very often, systems time-dependent failure rates that require understanding and careful interpretation. Kinetic trends of systems and of components may sometimes be different. (authors). 4 figs., 3 tabs., 3 refs., 1 appendix

  6. Optical Components for Reconfigurable Photonic Networks and Mobile Systems

    DEFF Research Database (Denmark)

    Vegas Olmos, Juan José; Rodríguez Páez, Juan Sebastián; Rommel, Simon

    communication needs to move to the millimeter-wavelength or sub-terahertz range. Radio-over-Fiber (RoF) technologies have evolved from a blue sky academic topic in the 90s to a main driver within the current quest for the 5th generation mobile systems (5G). A twist in RoF technologies is that it has found along...... processing, high speed modulators and photodiodes and optical switching technologies. A deep overview on the state-of-the-art and current efforts towards optical components enabling photonic reconfigurability will be given during the presentation....

  7. Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems

    Science.gov (United States)

    Broccard, Frédéric D.; Joshi, Siddharth; Wang, Jun; Cauwenberghs, Gert

    2017-08-01

    Objective. Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks. Mixed analog/digital neuromorphic VLSI systems are compact, consume little power and operate in real time independently of the size and complexity of the model. Approach. This article highlights the current efforts to interface neuromorphic systems with neural systems at multiple levels of biological organization, from the synaptic to the system level, and discusses the prospects for future biohybrid systems with neuromorphic circuits of greater complexity. Main results. Single silicon neurons have been interfaced successfully with invertebrate and vertebrate neural networks. This approach allowed the investigation of neural properties that are inaccessible with traditional techniques while providing a realistic biological context not achievable with traditional numerical modeling methods. At the network level, populations of neurons are envisioned to communicate bidirectionally with neuromorphic processors of hundreds or thousands of silicon neurons. Recent work on brain-machine interfaces suggests that this is feasible with current neuromorphic technology. Significance. Biohybrid interfaces between biological neurons and VLSI neuromorphic systems of varying complexity have started to emerge in the literature. Primarily intended as a

  8. Neural Network based Control of SG based Standalone Generating System with Energy Storage for Power Quality Enhancement

    Science.gov (United States)

    Nayar, Priya; Singh, Bhim; Mishra, Sukumar

    2017-08-01

    An artificial intelligence based control algorithm is used in solving power quality problems of a diesel engine driven synchronous generator with automatic voltage regulator and governor based standalone system. A voltage source converter integrated with a battery energy storage system is employed to mitigate the power quality problems. An adaptive neural network based signed regressor control algorithm is used for the estimation of the fundamental component of load currents for control of a standalone system with load leveling as an integral feature. The developed model of the system performs accurately under varying load conditions and provides good dynamic response to the step changes in loads. The real time performance is achieved using MATLAB along with simulink/simpower system toolboxes and results adhere to an IEEE-519 standard for power quality enhancement.

  9. Effect of component design in retrieved bipolar hip hemiarthroplasty systems.

    Science.gov (United States)

    Hess, Matthew D; Baker, Erin A; Salisbury, Meagan R; Kaplan, Lige M; Greene, Ryan T; Greene, Perry W

    2013-09-01

    Primary articulation of bipolar hemiarthroplasty systems is at the femoral head-liner interface. The purpose of this study was to compare observed damage modes on 36 retrieved bipolar systems with implant, demographic, intraoperative, and radiographic data to elucidate the effects of component design, specifically locking mechanism, on clinical performance. Retrieved bipolar hip hemiarthroplasty systems of 3 different design types were obtained, disassembled, and evaluated macro- and microscopically for varying modes of wear, including abrasion, burnishing, embedding, scratching, and pitting. Clinical record review and radiographic analysis were performed by a senior orthopedic surgery resident. Average bipolar hip hemiarthroplasty system term of service was 46 months (range, 0.27-187 months). All devices contained wear debris captured within the articulating space between the femoral head and liner. In 31% of patients without infection, lucency was observed on immediate prerevision radiographs. The system with a leaf locking mechanism showed significantly increased radiographically observed osteolysis (P=.03) compared with a system with a stopper ring locking mechanism. In addition, implant design and observed damage modes, including pitting and third-body particle embedding, were significantly associated with radiographically observed osteolysis. Copyright 2013, SLACK Incorporated.

  10. Integrated Markov-neural reliability computation method: A case for multiple automated guided vehicle system

    International Nuclear Information System (INIS)

    Fazlollahtabar, Hamed; Saidi-Mehrabad, Mohammad; Balakrishnan, Jaydeep

    2015-01-01

    This paper proposes an integrated Markovian and back propagation neural network approaches to compute reliability of a system. While states of failure occurrences are significant elements for accurate reliability computation, Markovian based reliability assessment method is designed. Due to drawbacks shown by Markovian model for steady state reliability computations and neural network for initial training pattern, integration being called Markov-neural is developed and evaluated. To show efficiency of the proposed approach comparative analyses are performed. Also, for managerial implication purpose an application case for multiple automated guided vehicles (AGVs) in manufacturing networks is conducted. - Highlights: • Integrated Markovian and back propagation neural network approach to compute reliability. • Markovian based reliability assessment method. • Managerial implication is shown in an application case for multiple automated guided vehicles (AGVs) in manufacturing networks

  11. Fault detection and classification in electrical power transmission system using artificial neural network.

    Science.gov (United States)

    Jamil, Majid; Sharma, Sanjeev Kumar; Singh, Rajveer

    2015-01-01

    This paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB(®) environment.

  12. Adaptive Control of Nonlinear Discrete-Time Systems by Using OS-ELM Neural Networks

    Directory of Open Access Journals (Sweden)

    Xiao-Li Li

    2014-01-01

    Full Text Available As a kind of novel feedforward neural network with single hidden layer, ELM (extreme learning machine neural networks are studied for the identification and control of nonlinear dynamic systems. The property of simple structure and fast convergence of ELM can be shown clearly. In this paper, we are interested in adaptive control of nonlinear dynamic plants by using OS-ELM (online sequential extreme learning machine neural networks. Based on data scope division, the problem that training process of ELM neural network is sensitive to the initial training data is also solved. According to the output range of the controlled plant, the data corresponding to this range will be used to initialize ELM. Furthermore, due to the drawback of conventional adaptive control, when the OS-ELM neural network is used for adaptive control of the system with jumping parameters, the topological structure of the neural network can be adjusted dynamically by using multiple model switching strategy, and an MMAC (multiple model adaptive control will be used to improve the control performance. Simulation results are included to complement the theoretical results.

  13. A Comparative Study of Neural Networks and Fuzzy Systems in Modeling of a Nonlinear Dynamic System

    Directory of Open Access Journals (Sweden)

    Metin Demirtas

    2011-07-01

    Full Text Available The aim of this paper is to compare the neural networks and fuzzy modeling approaches on a nonlinear system. We have taken Permanent Magnet Brushless Direct Current (PMBDC motor data and have generated models using both approaches. The predictive performance of both methods was compared on the data set for model configurations. The paper describes the results of these tests and discusses the effects of changing model parameters on predictive and practical performance. Modeling sensitivity was used to compare for two methods.

  14. Major Components of the National TRU Waste System Optimization Project

    International Nuclear Information System (INIS)

    Moody, D.C.; Bennington, B.; Sharif, F.

    2002-01-01

    The National Transuranic (TRU) Program (NTP) is being optimized to allow for disposing of the legacy TRU waste at least 10 years earlier than originally planned. This acceleration will save the nation an estimated $713. The Department of Energy's (DOE'S) Carlsbad Field Office (CBFO) has initiated the National TRU Waste System Optimization Project to propose, and upon approvaI, implement activities that produce significant cost saving by improving efficiency, thereby accelerating the rate of TRU waste disposal without compromising safety. In its role as NTP agent of change, the National TRU Waste System Optimization Project (the Project) (1) interacts closely with all NTP activities. Three of the major components of the Project are the Central Characterization Project (CCP), the Central Confirmation Facility (CCF), and the MobiIe/Modular Deployment Program.

  15. System and method for manufacture of airfoil components

    Science.gov (United States)

    Moors, Thomas Michael

    2016-11-29

    Embodiments of the present disclosure relate generally to systems and methods for manufacturing an airfoil component. The system can include: a geometrical mold; an elongated flexible sleeve having a closed-off interior and positioned within the geometrical mold, wherein the elongated flexible sleeve is further positioned to have a desired geometry; an infusing channel in fluid communication with the closed-off interior of the elongated flexible sleeve and configured to communicate a resinous material thereto; a vacuum channel in fluid communication with the closed-off interior of the elongated flexible sleeve and configured to vacuum seal the closed-off interior of the elongated flexible sleeve; and a glass fiber layer positioned within the closed-off interior of the elongated flexible sleeve.

  16. Development of expert system for structural design of FBR components

    International Nuclear Information System (INIS)

    Ueda, Hiroyoshi; Uno, Masayoshi; Ogawa, Hiroshi; Shimakawa, Takashi; Yoshimura, Shinobu; Yagawa, Genki.

    1995-01-01

    The characteristics of structural design processes for nuclear components can be summarized as follows : (1) Many engineers belonging to different fields are working in parallel, exchanging a huge amount of data and information. (2) A final solution is determined after a number of iterative design processes. (3) Solutions have to be examined many times based on sophisticated design codes. (4) Sophisticated calculation methods such as the finite element method are frequently utilized, and experts' knowledge on such analyses plays important roles in the design process. Taking these issues into consideration, a new expert system for structural design is developed in the present study. Here, the object-oriented data flow mechanism and the blackboard model are utilized to systematize structural design processes in a computer. An automated finite element calculation module is implemented, and experts' knowledge is stored in knowledge base. In addition, a new algorithm is employed to automatically draw the design window, which is defined as an area of permissible solutions in a design parameter space. The developed system is successfully applied to obtain the design windows of four components selected from the demonstration FBR structures. (author)

  17. Artificial frame filling using adaptive neural fuzzy inference system for particle image velocimetry dataset

    Science.gov (United States)

    Akdemir, Bayram; Doǧan, Sercan; Aksoy, Muharrem H.; Canli, Eyüp; Özgören, Muammer

    2015-03-01

    Liquid behaviors are very important for many areas especially for Mechanical Engineering. Fast camera is a way to observe and search the liquid behaviors. Camera traces the dust or colored markers travelling in the liquid and takes many pictures in a second as possible as. Every image has large data structure due to resolution. For fast liquid velocity, there is not easy to evaluate or make a fluent frame after the taken images. Artificial intelligence has much popularity in science to solve the nonlinear problems. Adaptive neural fuzzy inference system is a common artificial intelligence in literature. Any particle velocity in a liquid has two dimension speed and its derivatives. Adaptive Neural Fuzzy Inference System has been used to create an artificial frame between previous and post frames as offline. Adaptive neural fuzzy inference system uses velocities and vorticities to create a crossing point vector between previous and post points. In this study, Adaptive Neural Fuzzy Inference System has been used to fill virtual frames among the real frames in order to improve image continuity. So this evaluation makes the images much understandable at chaotic or vorticity points. After executed adaptive neural fuzzy inference system, the image dataset increase two times and has a sequence as virtual and real, respectively. The obtained success is evaluated using R2 testing and mean squared error. R2 testing has a statistical importance about similarity and 0.82, 0.81, 0.85 and 0.8 were obtained for velocities and derivatives, respectively.

  18. Coupling Strength and System Size Induce Firing Activity of Globally Coupled Neural Network

    International Nuclear Information System (INIS)

    Wei Duqu; Luo Xiaoshu; Zou Yanli

    2008-01-01

    We investigate how firing activity of globally coupled neural network depends on the coupling strength C and system size N. Network elements are described by space-clamped FitzHugh-Nagumo (SCFHN) neurons with the values of parameters at which no firing activity occurs. It is found that for a given appropriate coupling strength, there is an intermediate range of system size where the firing activity of globally coupled SCFHN neural network is induced and enhanced. On the other hand, for a given intermediate system size level, there exists an optimal value of coupling strength such that the intensity of firing activity reaches its maximum. These phenomena imply that the coupling strength and system size play a vital role in firing activity of neural network

  19. Fuzzy wavelet plus a quantum neural network as a design base for power system stability enhancement.

    Science.gov (United States)

    Ganjefar, Soheil; Tofighi, Morteza; Karami, Hamidreza

    2015-11-01

    In this study, we introduce an indirect adaptive fuzzy wavelet neural controller (IAFWNC) as a power system stabilizer to damp inter-area modes of oscillations in a multi-machine power system. Quantum computing is an efficient method for improving the computational efficiency of neural networks, so we developed an identifier based on a quantum neural network (QNN) to train the IAFWNC in the proposed scheme. All of the controller parameters are tuned online based on the Lyapunov stability theory to guarantee the closed-loop stability. A two-machine, two-area power system equipped with a static synchronous series compensator as a series flexible ac transmission system was used to demonstrate the effectiveness of the proposed controller. The simulation and experimental results demonstrated that the proposed IAFWNC scheme can achieve favorable control performance. Copyright © 2015 Elsevier Ltd. All rights reserved.

  20. Coding of level of ambiguity within neural systems mediating choice.

    Science.gov (United States)

    Lopez-Paniagua, Dan; Seger, Carol A

    2013-01-01

    Data from previous neuroimaging studies exploring neural activity associated with uncertainty suggest varying levels of activation associated with changing degrees of uncertainty in neural regions that mediate choice behavior. The present study used a novel task that parametrically controlled the amount of information hidden from the subject; levels of uncertainty ranged from full ambiguity (no information about probability of winning) through multiple levels of partial ambiguity, to a condition of risk only (zero ambiguity with full knowledge of the probability of winning). A parametric analysis compared a linear model in which weighting increased as a function of level of ambiguity, and an inverted-U quadratic models in which partial ambiguity conditions were weighted most heavily. Overall we found that risk and all levels of ambiguity recruited a common "fronto-parietal-striatal" network including regions within the dorsolateral prefrontal cortex, intraparietal sulcus, and dorsal striatum. Activation was greatest across these regions and additional anterior and superior prefrontal regions for the quadratic function which most heavily weighs trials with partial ambiguity. These results suggest that the neural regions involved in decision processes do not merely track the absolute degree ambiguity or type of uncertainty (risk vs. ambiguity). Instead, recruitment of prefrontal regions may result from greater degree of difficulty in conditions of partial ambiguity: when information regarding reward probabilities important for decision making is hidden or not easily obtained the subject must engage in a search for tractable information. Additionally, this study identified regions of activity related to the valuation of potential gains associated with stimuli or options (including the orbitofrontal and medial prefrontal cortices and dorsal striatum) and related to winning (including orbitofrontal cortex and ventral striatum).

  1. Child Maltreatment and Neural Systems Underlying Emotion Regulation.

    Science.gov (United States)

    McLaughlin, Katie A; Peverill, Matthew; Gold, Andrea L; Alves, Sonia; Sheridan, Margaret A

    2015-09-01

    The strong associations between child maltreatment and psychopathology have generated interest in identifying neurodevelopmental processes that are disrupted following maltreatment. Previous research has focused largely on neural response to negative facial emotion. We determined whether child maltreatment was associated with neural responses during passive viewing of negative and positive emotional stimuli and effortful attempts to regulate emotional responses. A total of 42 adolescents aged 13 to 19 years, half with exposure to physical and/or sexual abuse, participated. Blood oxygen level-dependent (BOLD) response was measured during passive viewing of negative and positive emotional stimuli and attempts to modulate emotional responses using cognitive reappraisal. Maltreated adolescents exhibited heightened response in multiple nodes of the salience network, including amygdala, putamen, and anterior insula, to negative relative to neutral stimuli. During attempts to decrease responses to negative stimuli relative to passive viewing, maltreatment was associated with greater recruitment of superior frontal gyrus, dorsal anterior cingulate cortex, and frontal pole; adolescents with and without maltreatment down-regulated amygdala response to a similar degree. No associations were observed between maltreatment and neural response to positive emotional stimuli during passive viewing or effortful regulation. Child maltreatment heightens the salience of negative emotional stimuli. Although maltreated adolescents modulate amygdala responses to negative cues to a degree similar to that of non-maltreated youths, they use regions involved in effortful control to a greater degree to do so, potentially because greater effort is required to modulate heightened amygdala responses. These findings are promising, given the centrality of cognitive restructuring in trauma-focused treatments for children. Copyright © 2015 American Academy of Child and Adolescent Psychiatry

  2. Artificial neural networks contribution to the operational security of embedded systems. Artificial neural networks contribution to fault tolerance of on-board functions in space environment

    International Nuclear Information System (INIS)

    Vintenat, Lionel

    1999-01-01

    A good quality often attributed to artificial neural networks is fault tolerance. In general presentation works, this property is almost always introduced as 'natural', i.e. being obtained without any specific precaution during learning. Besides, space environment is known to be aggressive towards on-board hardware, inducing various abnormal operations. Particularly, digital components suffer from upset phenomenon, i.e. misplaced switches of memory flip-flops. These two observations lead to the question: would neural chips constitute an interesting and robust solution to implement some board functions of spacecrafts? First, the various aspects of the problem are detailed: artificial neural networks and their fault tolerance, neural chips, space environment and resulting failures. Further to this presentation, a particular technique to carry out neural chips is selected because of its simplicity, and especially because it requires few memory flip-flops: random pulse streams. An original method for star recognition inside a field-of-view is then proposed for the board function 'attitude computation'. This method relies on a winner-takes-all competition network, and on a Kohonen self-organized map. An hardware implementation of those two neural models is then proposed using random pulse streams. Thanks to this realization, on one hand difficulties related to that particular implementation technique can be highlighted, and on the other hand a first evaluation of its practical fault tolerance can be carried out. (author) [fr

  3. Sign Language Recognition System using Neural Network for Digital Hardware Implementation

    International Nuclear Information System (INIS)

    Vargas, Lorena P; Barba, Leiner; Torres, C O; Mattos, L

    2011-01-01

    This work presents an image pattern recognition system using neural network for the identification of sign language to deaf people. The system has several stored image that show the specific symbol in this kind of language, which is employed to teach a multilayer neural network using a back propagation algorithm. Initially, the images are processed to adapt them and to improve the performance of discriminating of the network, including in this process of filtering, reduction and elimination noise algorithms as well as edge detection. The system is evaluated using the signs without including movement in their representation.

  4. On-line identification of hybrid systems using an adaptive growing and pruning RBF neural network

    DEFF Research Database (Denmark)

    Alizadeh, Tohid

    2008-01-01

    This paper introduces an adaptive growing and pruning radial basis function (GAP-RBF) neural network for on-line identification of hybrid systems. The main idea is to identify a global nonlinear model that can predict the continuous outputs of hybrid systems. In the proposed approach, GAP......-RBF neural network uses a modified unscented kalman filter (UKF) with forgetting factor scheme as the required on-line learning algorithm. The effectiveness of the resulting identification approach is tested and evaluated on a simulated benchmark hybrid system....

  5. An intelligent sales forecasting system through integration of artificial neural networks and fuzzy neural networks with fuzzy weight elimination.

    Science.gov (United States)

    Kuo, R J; Wu, P; Wang, C P

    2002-09-01

    Sales forecasting plays a very prominent role in business strategy. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average (ARMA). However, sales forecasting is very complicated owing to influence by internal and external environments. Recently, artificial neural networks (ANNs) have also been applied in sales forecasting since their promising performances in the areas of control and pattern recognition. However, further improvement is still necessary since unique circumstances, e.g. promotion, cause a sudden change in the sales pattern. Thus, this study utilizes a proposed fuzzy neural network (FNN), which is able to eliminate the unimportant weights, for the sake of learning fuzzy IF-THEN rules obtained from the marketing experts with respect to promotion. The result from FNN is further integrated with the time series data through an ANN. Both the simulated and real-world problem results show that FNN with weight elimination can have lower training error compared with the regular FNN. Besides, real-world problem results also indicate that the proposed estimation system outperforms the conventional statistical method and single ANN in accuracy.

  6. Computer software program for monitoring the availability of systems and components of electric power generating systems

    International Nuclear Information System (INIS)

    Petersen, T.A.; Hilsmeier, T.A.; Kapinus, D.M.

    1994-01-01

    As availabilities of electric power generating stations systems and components become more and more important from a financial, personnel safety, and regulatory requirements standpoint, it is evident that a comprehensive, yet simple and user-friendly program for system and component tracking and monitoring is needed to assist in effectively managing the large volume of systems and components with their large numbers of associated maintenance/availability records. A user-friendly computer software program for system and component availability monitoring has been developed that calculates, displays and monitors selected component and system availabilities. This is a Windows trademark based (Graphical User Interface) program that utilizes a system flow diagram for the data input screen which also provides a visual representation of availability values and limits for the individual components and associated systems. This program can be customized to the user's plant-specific system and component selections and configurations. As will be discussed herein, this software program is well suited for availability monitoring and ultimately providing valuable information for improving plant performance and reducing operating costs

  7. Economic Optimization of Component Sizing for Residential Battery Storage Systems

    Directory of Open Access Journals (Sweden)

    Holger C. Hesse

    2017-06-01

    Full Text Available Battery energy storage systems (BESS coupled with rooftop-mounted residential photovoltaic (PV generation, designated as PV-BESS, draw increasing attention and market penetration as more and more such systems become available. The manifold BESS deployed to date rely on a variety of different battery technologies, show a great variation of battery size, and power electronics dimensioning. However, given today’s high investment costs of BESS, a well-matched design and adequate sizing of the storage systems are prerequisites to allow profitability for the end-user. The economic viability of a PV-BESS depends also on the battery operation, storage technology, and aging of the system. In this paper, a general method for comprehensive PV-BESS techno-economic analysis and optimization is presented and applied to the state-of-art PV-BESS to determine its optimal parameters. Using a linear optimization method, a cost-optimal sizing of the battery and power electronics is derived based on solar energy availability and local demand. At the same time, the power flow optimization reveals the best storage operation patterns considering a trade-off between energy purchase, feed-in remuneration, and battery aging. Using up to date technology-specific aging information and the investment cost of battery and inverter systems, three mature battery chemistries are compared; a lead-acid (PbA system and two lithium-ion systems, one with lithium-iron-phosphate (LFP and another with lithium-nickel-manganese-cobalt (NMC cathode. The results show that different storage technology and component sizing provide the best economic performances, depending on the scenario of load demand and PV generation.

  8. Investigation on Prefabricated Building System Skilled Component Installers

    Directory of Open Access Journals (Sweden)

    Nurul Rezuana Buyung

    2013-09-01

    Full Text Available In the face of an increasingly challenging era of globalization, skills and new equipments which includes prefabricated building components, known as Industrialized Building System (IBS has been introduced towards achievement of sustainable construction. IBS is a construction system in which the components are manufactured in a factory, on or off site, positioned and assembled into complete structures with minimal additional site work. IBS requires high construction precision and needs a higher skill level of workers. Compared to the conventional construction method, the skill level of IBS workers is more demanding. Although there are a lot benefits in implementing IBS, the construction industry still not rapidly implementing IBS. The IBS method still considered new and even though there are a lot of benefits it still faces barriers. In an IBS construction, the role of the contractor is shifted from a builder to an assembler on the site. Therefore, this requires the contractor to be prepared technologically with IBS knowledge and skills. It is generally perceived that the number of skilled IBS installers in Malaysia is still low even though the system has been implemented for a long time. This research is carried out to find out whether the existing number of IBS installer is sufficient. Primary data was collected by carrying out interviews with the contractors at the IBS construction site in Penang Island in order to get the contractor’s feedback regarding this issue. Meanwhile, the secondary data was collected from government agencies to get the number of existing IBS installer and the number of IBS projects done in government projects. The results from this study indicated that not all categories of skill workers are in shortage. However, the number of precast concrete installer is in a critical shortage.

  9. Component-Level Electronic-Assembly Repair (CLEAR) System Architecture

    Science.gov (United States)

    Oeftering, Richard C.; Bradish, Martin A.; Juergens, Jeffrey R.; Lewis, Michael J.; Vrnak, Daniel R.

    2011-01-01

    This document captures the system architecture for a Component-Level Electronic-Assembly Repair (CLEAR) capability needed for electronics maintenance and repair of the Constellation Program (CxP). CLEAR is intended to improve flight system supportability and reduce the mass of spares required to maintain the electronics of human rated spacecraft on long duration missions. By necessity it allows the crew to make repairs that would otherwise be performed by Earth based repair depots. Because of practical knowledge and skill limitations of small spaceflight crews they must be augmented by Earth based support crews and automated repair equipment. This system architecture covers the complete system from ground-user to flight hardware and flight crew and defines an Earth segment and a Space segment. The Earth Segment involves database management, operational planning, and remote equipment programming and validation processes. The Space Segment involves the automated diagnostic, test and repair equipment required for a complete repair process. This document defines three major subsystems including, tele-operations that links the flight hardware to ground support, highly reconfigurable diagnostics and test instruments, and a CLEAR Repair Apparatus that automates the physical repair process.

  10. Distributed neural system for emotional intelligence revealed by lesion mapping.

    Science.gov (United States)

    Barbey, Aron K; Colom, Roberto; Grafman, Jordan

    2014-03-01

    Cognitive neuroscience has made considerable progress in understanding the neural architecture of human intelligence, identifying a broadly distributed network of frontal and parietal regions that support goal-directed, intelligent behavior. However, the contributions of this network to social and emotional aspects of intellectual function remain to be well characterized. Here we investigated the neural basis of emotional intelligence in 152 patients with focal brain injuries using voxel-based lesion-symptom mapping. Latent variable modeling was applied to obtain measures of emotional intelligence, general intelligence and personality from the Mayer, Salovey, Caruso Emotional Intelligence Test (MSCEIT), the Wechsler Adult Intelligence Scale and the Neuroticism-Extroversion-Openness Inventory, respectively. Regression analyses revealed that latent scores for measures of general intelligence and personality reliably predicted latent scores for emotional intelligence. Lesion mapping results further indicated that these convergent processes depend on a shared network of frontal, temporal and parietal brain regions. The results support an integrative framework for understanding the architecture of executive, social and emotional processes and make specific recommendations for the interpretation and application of the MSCEIT to the study of emotional intelligence in health and disease.

  11. Distributed neural system for emotional intelligence revealed by lesion mapping

    Science.gov (United States)

    Colom, Roberto; Grafman, Jordan

    2014-01-01

    Cognitive neuroscience has made considerable progress in understanding the neural architecture of human intelligence, identifying a broadly distributed network of frontal and parietal regions that support goal-directed, intelligent behavior. However, the contributions of this network to social and emotional aspects of intellectual function remain to be well characterized. Here we investigated the neural basis of emotional intelligence in 152 patients with focal brain injuries using voxel-based lesion-symptom mapping. Latent variable modeling was applied to obtain measures of emotional intelligence, general intelligence and personality from the Mayer, Salovey, Caruso Emotional Intelligence Test (MSCEIT), the Wechsler Adult Intelligence Scale and the Neuroticism-Extroversion-Openness Inventory, respectively. Regression analyses revealed that latent scores for measures of general intelligence and personality reliably predicted latent scores for emotional intelligence. Lesion mapping results further indicated that these convergent processes depend on a shared network of frontal, temporal and parietal brain regions. The results support an integrative framework for understanding the architecture of executive, social and emotional processes and make specific recommendations for the interpretation and application of the MSCEIT to the study of emotional intelligence in health and disease. PMID:23171618

  12. A clinical decision support system using multilayer perceptron neural network to assess well being in diabetes.

    Science.gov (United States)

    Narasingarao, M R; Manda, R; Sridhar, G R; Madhu, K; Rao, A A

    2009-02-01

    Diabetes mellitus is an increasingly common life-style disorder whose management outcomes are measured in symptomatic, biochemical as well as psychological areas. Well being as an outcome of treatment is being increasingly recognized as a crucial component of treatment. There is little published literature on psychosocial outcomes and the factors influencing them. Therefore we have developed a neural network system which is trained to predict the well being in diabetes, using data generated in real life. We developed a Multi Layer Perceptron Neural Network model, which had been trained by back propagation algorithm. Data was used from a cohort of 241 individuals with diabetes. We used age, gender, weight, fasting plasma glucose as a set of inputs and predicted measures of well-being (depression, anxiety, energy and positive well-being). It was observed that female patients report significantly higher levels of depression than their male counter parts. Some slight high or no significant differences are observed between males and female patients with regard to the number of persons with whom they share their anxieties and fears regarding diabetes. There is not much difference has been observed in energy levels of both males and females. Also, Males have higher pwb value when compared with the female counterparts. Also, this may be due to women tend to react more emotionally to disease and hence experience more difficulty in coping with it. The present sample of women being predominantly house wives may be worrying more about their health and its problems. Also, it is observed that, gender differences are significant with regard to total general well-being. With five inputs (age, sex, weight, fasting plasma glucose, bias), four outputs are four (depression, anxiety, energy and positive well-being) the momentum rate was 0.9, the learning rate 0.7, using a sample of 50. the maximum individual error is 0.001 when the number of iterations were 500, number of hidden layers

  13. Reliability Prediction Of System And Component Of Process System Of RSG-GAS Reactor

    International Nuclear Information System (INIS)

    Sitorus Pane, Jupiter

    2001-01-01

    The older the reactor the higher the probability of the system and components suffer from loss of function or degradation. This phenomenon occurred because of wear, corrosion, and fatigue. Study on component reliability was generally performed deterministically and statistically. This paper would describe an analysis of using statistical method, i.e. regression Cox, in order to predict the reliability of the components and their environmental influence's factors. The result showed that the dynamics, non safety related, and mechanic components have higher risk of failure, whereas static, safety related, and electric have lower risk of failures. The relative risk value for variable of components dynamics, quality, dummy 1 and dummy 2 are of 1.54, 1.59, 1.50, and 0.83 compare to other components type with each variable. Component with the higher risk have lower reliability than lower one

  14. Modular Engine Noise Component Prediction System (MCP) Program Users' Guide

    Science.gov (United States)

    Golub, Robert A. (Technical Monitor); Herkes, William H.; Reed, David H.

    2004-01-01

    This is a user's manual for Modular Engine Noise Component Prediction System (MCP). This computer code allows the user to predict turbofan engine noise estimates. The program is based on an empirical procedure that has evolved over many years at The Boeing Company. The data used to develop the procedure include both full-scale engine data and small-scale model data, and include testing done by Boeing, by the engine manufacturers, and by NASA. In order to generate a noise estimate, the user specifies the appropriate engine properties (including both geometry and performance parameters), the microphone locations, the atmospheric conditions, and certain data processing options. The version of the program described here allows the user to predict three components: inlet-radiated fan noise, aft-radiated fan noise, and jet noise. MCP predicts one-third octave band noise levels over the frequency range of 50 to 10,000 Hertz. It also calculates overall sound pressure levels and certain subjective noise metrics (e.g., perceived noise levels).

  15. System, structure, and component evaluation for life-cycle management

    International Nuclear Information System (INIS)

    Hanley, N.E.; Banerjee, A.K.; Woods, P.B.; Perrin, J.S.; Marian, F.A.

    1992-01-01

    In recent years, many nuclear organizations and utilities have studied the possibility of extending the service life of nuclear power plants beyond the original license period. From these studies, recommendations have resulted for maintaining the option of future decisions concerning operating license renewal. Several of the recommendations are considered beneficial to the management and operation of nuclear plants in meeting many of their near-term goals. In 1986, Public Service Electric and Gas (PSE and G) concluded that a full-scale nuclear plant license renewal program for their Salem 1 and 2 and Hope Creek nuclear stations was not cost-effective at that time. Rather, it would be better served if the nuclear plant life extension (PLEX) option were maintained for future consideration. To help plan for the life extension option, a strategic 5-yr life cycle management (LCM) program was begun. In support of the LCM program, evaluations for the following Salem structures and components were performed: (1) intake structures, (2) reactor vessel support, (3) containment liner, and (4) containment structure (below grade). This paper discusses the systems, structures, and components (SSC) evaluation methodology and, as an example, discusses the evaluation performed for reactor vessel support

  16. Color Image Encryption Algorithm Based on TD-ERCS System and Wavelet Neural Network

    Directory of Open Access Journals (Sweden)

    Kun Zhang

    2015-01-01

    Full Text Available In order to solve the security problem of transmission image across public networks, a new image encryption algorithm based on TD-ERCS system and wavelet neural network is proposed in this paper. According to the permutation process and the binary XOR operation from the chaotic series by producing TD-ERCS system and wavelet neural network, it can achieve image encryption. This encryption algorithm is a reversible algorithm, and it can achieve original image in the rule inverse process of encryption algorithm. Finally, through computer simulation, the experiment results show that the new chaotic encryption algorithm based on TD-ERCS system and wavelet neural network is valid and has higher security.

  17. Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition

    CERN Document Server

    Melin, Patricia

    2012-01-01

    This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural ne...

  18. Neural network modeling of nonlinear systems based on Volterra series extension of a linear model

    Science.gov (United States)

    Soloway, Donald I.; Bialasiewicz, Jan T.

    1992-01-01

    A Volterra series approach was applied to the identification of nonlinear systems which are described by a neural network model. A procedure is outlined by which a mathematical model can be developed from experimental data obtained from the network structure. Applications of the results to the control of robotic systems are discussed.

  19. Absolute stability of nonlinear systems with time delays and applications to neural networks

    Directory of Open Access Journals (Sweden)

    Xinzhi Liu

    2001-01-01

    Full Text Available In this paper, absolute stability of nonlinear systems with time delays is investigated. Sufficient conditions on absolute stability are derived by using the comparison principle and differential inequalities. These conditions are simple and easy to check. In addition, exponential stability conditions for some special cases of nonlinear delay systems are discussed. Applications of those results to cellular neural networks are presented.

  20. An Improved Recurrent Neural Network for Complex-Valued Systems of Linear Equation and Its Application to Robotic Motion Tracking.

    Science.gov (United States)

    Ding, Lei; Xiao, Lin; Liao, Bolin; Lu, Rongbo; Peng, Hua

    2017-01-01

    To obtain the online solution of complex-valued systems of linear equation in complex domain with higher precision and higher convergence rate, a new neural network based on Zhang neural network (ZNN) is investigated in this paper. First, this new neural network for complex-valued systems of linear equation in complex domain is proposed and theoretically proved to be convergent within finite time. Then, the illustrative results show that the new neural network model has the higher precision and the higher convergence rate, as compared with the gradient neural network (GNN) model and the ZNN model. Finally, the application for controlling the robot using the proposed method for the complex-valued systems of linear equation is realized, and the simulation results verify the effectiveness and superiorness of the new neural network for the complex-valued systems of linear equation.

  1. Calorimetric Measuring Systems for Characterizing High Frequency Power Losses in Power Electronic Components and Systems

    DEFF Research Database (Denmark)

    Blaabjerg, Frede; Pedersen, John Kim; Ritchie, Andrew Ewen

    2002-01-01

    High frequency power losses in power electronic components and systems are very difficult to measure. The same applies to the efficiency of high-efficiency systems and components. An important method to measure losses with high accuracy is the calorimetric measuring systems. This paper describes...... to calibrate such systems are proposed and different applications of the system are given. Two practical examples end the description of the research. It is concluded that such systems have a relative long time-constant but they are accurate and useful for precise power loss measurement....

  2. Aging techniques and qualified life for safety system components

    International Nuclear Information System (INIS)

    Weaver, W.W.

    1980-01-01

    Presently, the qualified life objective for Class IE safety system components in nuclear power plants is somewhat of a subjective engineering judgment. When the desired qualified life is ascertained, there are other choices that must be made (which may be influenced by the desired qualified life) such as selecting the aging procedure to use in the qualification process. Adding complexity to the situation is the fact that there are some limitations in aging techniques at the present time. This article presents (1) a discussion of the limitations in aging procedures, (2) the general philosophy of qualification, and (3) a proposed method for specifying a desired qualified life, which uses a probabilistic approach. The probabilistic approach proposed in item 3 can be applied to natural aging programs and eventually to accelerated aging once the present technical difficulties are overcome

  3. Critical mm-wave components for synthetic automatic test systems

    CERN Document Server

    Hrobak, Michael

    2015-01-01

    Michael Hrobak studied hybrid integrated front end modules for high frequency measurement equipment and especially for synthetic automatic test systems. Recent developments of innovative, critical millimeter-wave components like frequency multipliers, directional couplers, filters, triple balanced mixers and power detectors are illustrated by the author separately and in combination.  Contents Synthetic Instruments Resistive Diode Frequency Multipliers Planar Directional Couplers and Filters Triple Balanced Mixers Zero Bias Schottky Power Detectors Integrated Front End Assemblies  Target Groups Scientists and students in the field of electrical engineering with main emphasis on high frequency technology Engineers and Practitioners dealing with the development of micro- and millimeter-wave measurement instruments  About the Author Dr. Michael Hrobak is with the Microwave Department of the Ferdinand-Braun-Institut (FBH), Berlin, Germany, where he is involved in the development and measurement of monolithic i...

  4. Phosphatase activity tunes two-component system sensor detection threshold.

    Science.gov (United States)

    Landry, Brian P; Palanki, Rohan; Dyulgyarov, Nikola; Hartsough, Lucas A; Tabor, Jeffrey J

    2018-04-12

    Two-component systems (TCSs) are the largest family of multi-step signal transduction pathways in biology, and a major source of sensors for biotechnology. However, the input concentrations to which biosensors respond are often mismatched with application requirements. Here, we utilize a mathematical model to show that TCS detection thresholds increase with the phosphatase activity of the sensor histidine kinase. We experimentally validate this result in engineered Bacillus subtilis nitrate and E. coli aspartate TCS sensors by tuning their detection threshold up to two orders of magnitude. We go on to apply our TCS tuning method to recently described tetrathionate and thiosulfate sensors by mutating a widely conserved residue previously shown to impact phosphatase activity. Finally, we apply TCS tuning to engineer B. subtilis to sense and report a wide range of fertilizer concentrations in soil. This work will enable the engineering of tailor-made biosensors for diverse synthetic biology applications.

  5. Efficient decoding with steady-state Kalman filter in neural interface systems.

    Science.gov (United States)

    Malik, Wasim Q; Truccolo, Wilson; Brown, Emery N; Hochberg, Leigh R

    2011-02-01

    The Kalman filter is commonly used in neural interface systems to decode neural activity and estimate the desired movement kinematics. We analyze a low-complexity Kalman filter implementation in which the filter gain is approximated by its steady-state form, computed offline before real-time decoding commences. We evaluate its performance using human motor cortical spike train data obtained from an intracortical recording array as part of an ongoing pilot clinical trial. We demonstrate that the standard Kalman filter gain converges to within 95% of the steady-state filter gain in 1.5±0.5 s (mean ±s.d.). The difference in the intended movement velocity decoded by the two filters vanishes within 5 s, with a correlation coefficient of 0.99 between the two decoded velocities over the session length. We also find that the steady-state Kalman filter reduces the computational load (algorithm execution time) for decoding the firing rates of 25±3 single units by a factor of 7.0±0.9. We expect that the gain in computational efficiency will be much higher in systems with larger neural ensembles. The steady-state filter can thus provide substantial runtime efficiency at little cost in terms of estimation accuracy. This far more efficient neural decoding approach will facilitate the practical implementation of future large-dimensional, multisignal neural interface systems.

  6. Noise of screen-film systems: origins and components

    Energy Technology Data Exchange (ETDEWEB)

    Kuhn, H

    1983-01-01

    When using the more sensitive rare-earth intensifying screens in radiography applying the screen-film system, one has to cope with an increase in quantum noise. Measurement of Wiener spectra will help to determine the noise of the film. With an appropriate apparatus, the noise spectra of screen-film systems of different sensitivity have been ascertained and compared with theoretical assessments. The integral noise made up of the components film noise, screen noise and quantum noise have been thoroughly analysed. Adequate choice of radiographic conditions (such as modification of film exposure time via the screen, change of tube voltage) will affect the number of absorbed X-ray quanta in the luminous substance and thus the quantum noise which, as was found out, largely contributes to the integral noise together with another factor, graininess of the film. The study shows that although quantum noise has to be cut back, this should not be done at any price, and due regard must be paid to other factors influencing the image quality of the system, such as contrast and MTF.

  7. Solid Propulsion Systems, Subsystems, and Components Service Life Extension

    Science.gov (United States)

    Hundley, Nedra H.; Jones, Connor

    2011-01-01

    The service life extension of solid propulsion systems, subsystems, and components will be discussed based on the service life extension of the Space Transportation System Reusable Solid Rocket Motor (RSRM) and Booster Separation Motors (BSM). The RSRM is certified for an age life of five years. In the aftermath of the Columbia accident there were a number of motors that were approaching the end of their five year service life certification. The RSRM Project initiated an assessment to determine if the service life of these motors could be extended. With the advent of the Constellation Program, a flight test was proposed that would utilize one of the RSRMs which had been returned from the launch site due to the expiration of its five year service life certification and twelve surplus Chemical Systems Division BSMs which had exceeded their eight year service life. The RSRM age life tracking philosophy which establishes when the clock starts for age life tracking will be described. The role of the following activities in service life extension will be discussed: subscale testing, accelerated aging, dissecting full scale aged hardware, static testing full scale aged motors, data mining industry data, and using the fleet leader approach. The service life certification and extension of the BSMs will also be presented.

  8. Modeling and validation of existing VAV system components

    Energy Technology Data Exchange (ETDEWEB)

    Nassif, N.; Kajl, S.; Sabourin, R. [Ecole de Technologie Superieure, Montreal, PQ (Canada)

    2004-07-01

    The optimization of supervisory control strategies and local-loop controllers can improve the performance of HVAC (heating, ventilating, air-conditioning) systems. In this study, the component model of the fan, the damper and the cooling coil were developed and validated against monitored data of an existing variable air volume (VAV) system installed at Montreal's Ecole de Technologie Superieure. The measured variables that influence energy use in individual HVAC models included: (1) outdoor and return air temperature and relative humidity, (2) supply air and water temperatures, (3) zone airflow rates, (4) supply duct, outlet fan, mixing plenum static pressures, (5) fan speed, and (6) minimum and principal damper and cooling and heating coil valve positions. The additional variables that were considered, but not measured were: (1) fan and outdoor airflow rate, (2) inlet and outlet cooling coil relative humidity, and (3) liquid flow rate through the heating or cooling coils. The paper demonstrates the challenges of the validation process when monitored data of existing VAV systems are used. 7 refs., 11 figs.

  9. Neural networks

    International Nuclear Information System (INIS)

    Denby, Bruce; Lindsey, Clark; Lyons, Louis

    1992-01-01

    The 1980s saw a tremendous renewal of interest in 'neural' information processing systems, or 'artificial neural networks', among computer scientists and computational biologists studying cognition. Since then, the growth of interest in neural networks in high energy physics, fueled by the need for new information processing technologies for the next generation of high energy proton colliders, can only be described as explosive

  10. Artificial neural systems for interpretation and inversion of seismic data

    Science.gov (United States)

    Calderon-Macias, Carlos

    The goal of this work is to investigate the feasibility of using neural network (NN) models for solving geophysical exploration problems. First, a feedforward neural network (FNN) is used to solve inverse problems. The operational characteristics of a FNN are primarily controlled by a set of weights and a nonlinear function that performs a mapping between two sets of data. In a process known as training, the FNN weights are iteratively adjusted to perform the mapping. After training, the computed weights encode important features of the data that enable one pattern to be distinguished from another. Synthetic data computed from an ensemble of earth models and the corresponding models provide the training data. Two training methods are studied: the backpropagation method which is a gradient scheme, and a global optimization method called very fast simulated annealing (VFSA). A trained network is then used to predict models from new data (e.g., data from a new location) in a one-step procedure. The application of this method to the problems of obtaining formation resistivities and layer thicknesses from resistivity sounding data and 1D velocity models from seismic data shows that trained FNNs produce reasonably accurate earth models when observed data are input to the FNNs. In a second application, a FNN is used for automating the NMO correction process of seismic reflection data. The task of the FNN is to map CMP data at control locations along a seismic line into subsurface velocities. The network is trained while the velocity analyses are performed at the control locations. Once trained, the computed weights are used as an operator that acts on the remaining CMP data as a velocity interpolator, resulting in a fast method for NMO correction. The second part of this dissertation describes the application of a Hopfield neural network (HNN) to the problems of deconvolution and multiple attenuation. In these applications, the unknown parameters (reflection coefficients

  11. Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Wang, L [School of Aeronautics and Astronautics, Tongji University, Shanghai (China); Zhang, Y Y [Chinese-German School of Postgraduate Studies, Tongji University (China); Ding, L [Chinese-German School of Postgraduate Studies, Tongji University (China)

    2006-10-15

    The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module.

  12. Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks

    Science.gov (United States)

    Wang, L.; Zhang, Y. Y.; Ding, L.

    2006-10-01

    The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module.

  13. Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks

    International Nuclear Information System (INIS)

    Wang, L; Zhang, Y Y; Ding, L

    2006-01-01

    The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module

  14. ISC feedforward control of gasoline engine. Adaptive system using neural network; Jidoshayo gasoline engine no ISC feedforward seigyo. Neural network wo mochiita tekioka

    Energy Technology Data Exchange (ETDEWEB)

    Kinugawa, N; Morita, S; Takiyama, T [Osaka City University, Osaka (Japan)

    1997-10-01

    For fuel economy and a good driver`s feeling, it is necessary for idle-speed to keep at a constant low speed. But keeping low speed has danger of engine stall when the engine torque is disturbed by the alternator, and so on. In this paper, adaptive feedforward idle-speed control system against electrical loads was investigated. This system was based on the reversed tansfer functions of the object system, and a neural network was used to adapt this system for aging. Then, this neural network was also used for creating feedforward table map. Good experimental results were obtained. 2 refs., 11 figs.

  15. Indoor location system based on discriminant-adaptive neural network in IEEE 802.11 environments.

    Science.gov (United States)

    Fang, Shih-Hau; Lin, Tsung-Nan

    2008-11-01

    This brief paper presents a novel localization algorithm, named discriminant-adaptive neural network (DANN), which takes the received signal strength (RSS) from the access points (APs) as inputs to infer the client position in the wireless local area network (LAN) environment. We extract the useful information into discriminative components (DCs) for network learning. The nonlinear relationship between RSS and the position is then accurately constructed by incrementally inserting the DCs and recursively updating the weightings in the network until no further improvement is required. Our localization system is developed in a real-world wireless LAN WLAN environment, where the realistic RSS measurement is collected. We implement the traditional approaches on the same test bed, including weighted kappa-nearest neighbor (WKNN), maximum likelihood (ML), and multilayer perceptron (MLP), and compare the results. The experimental results indicate that the proposed algorithm is much higher in accuracy compared with other examined techniques. The improvement can be attributed to that only the useful information is efficiently extracted for positioning while the redundant information is regarded as noise and discarded. Finally, the analysis shows that our network intelligently accomplishes learning while the inserted DCs provide sufficient information.

  16. Evolving networks and the development of neural systems

    International Nuclear Information System (INIS)

    Johnson, Samuel; Marro, J; Torres, Joaquín J

    2010-01-01

    It is now generally assumed that the heterogeneity of most networks in nature probably arises via preferential attachment of some sort. However, the origin of various other topological features, such as degree–degree correlations and related characteristics, is often not clear, and they may arise from specific functional conditions. We show how it is possible to analyse a very general scenario in which nodes can gain or lose edges according to any (e.g., nonlinear) function of local and/or global degree information. Applying our method to two rather different examples of brain development—synaptic pruning in humans and the neural network of the worm C. Elegans—we find that simple biologically motivated assumptions lead to very good agreement with experimental data. In particular, many nontrivial topological features of the worm's brain arise naturally at a critical point

  17. Neural Networks for Self-tuning Control Systems

    Directory of Open Access Journals (Sweden)

    A. Noriega Ponce

    2004-01-01

    Full Text Available In this paper, we presented a self-tuning control algorithm based on a three layers perceptron type neural network. The proposed algorithm is advantageous in the sense that practically a previous training of the net is not required and some changes in the set-point are generally enough to adjust the learning coefficient. Optionally, it is possible to introduce a self-tuning mechanism of the learning coefficient although by the moment it is not possible to give final conclusions about this possibility. The proposed algorithm has the special feature that the regulation error instead of the net output error is retropropagated for the weighting coefficients modifications. 

  18. System Identification Using Multilayer Differential Neural Networks: A New Result

    Directory of Open Access Journals (Sweden)

    J. Humberto Pérez-Cruz

    2012-01-01

    Full Text Available In previous works, a learning law with a dead zone function was developed for multilayer differential neural networks. This scheme requires strictly a priori knowledge of an upper bound for the unmodeled dynamics. In this paper, the learning law is modified in such a way that this condition is relaxed. By this modification, the tuning process is simpler and the dead-zone function is not required anymore. On the basis of this modification and by using a Lyapunov-like analysis, a stronger result is here demonstrated: the exponential convergence of the identification error to a bounded zone. Besides, a value for upper bound of such zone is provided. The workability of this approach is tested by a simulation example.

  19. Artificial neural network application for space station power system fault diagnosis

    Science.gov (United States)

    Momoh, James A.; Oliver, Walter E.; Dias, Lakshman G.

    1995-01-01

    This study presents a methodology for fault diagnosis using a Two-Stage Artificial Neural Network Clustering Algorithm. Previously, SPICE models of a 5-bus DC power distribution system with assumed constant output power during contingencies from the DDCU were used to evaluate the ANN's fault diagnosis capabilities. This on-going study uses EMTP models of the components (distribution lines, SPDU, TPDU, loads) and power sources (DDCU) of Space Station Alpha's electrical Power Distribution System as a basis for the ANN fault diagnostic tool. The results from the two studies are contrasted. In the event of a major fault, ground controllers need the ability to identify the type of fault, isolate the fault to the orbital replaceable unit level and provide the necessary information for the power management expert system to optimally determine a degraded-mode load schedule. To accomplish these goals, the electrical power distribution system's architecture can be subdivided into three major classes: DC-DC converter to loads, DC Switching Unit (DCSU) to Main bus Switching Unit (MBSU), and Power Sources to DCSU. Each class which has its own electrical characteristics and operations, requires a unique fault analysis philosophy. This study identifies these philosophies as Riddles 1, 2 and 3 respectively. The results of the on-going study addresses Riddle-1. It is concluded in this study that the combination of the EMTP models of the DDCU, distribution cables and electrical loads yields a more accurate model of the behavior and in addition yielded more accurate fault diagnosis using ANN versus the results obtained with the SPICE models.

  20. Study on algorithm of process neural network for soft sensing in sewage disposal system

    Science.gov (United States)

    Liu, Zaiwen; Xue, Hong; Wang, Xiaoyi; Yang, Bin; Lu, Siying

    2006-11-01

    A new method of soft sensing based on process neural network (PNN) for sewage disposal system is represented in the paper. PNN is an extension of traditional neural network, in which the inputs and outputs are time-variation. An aggregation operator is introduced to process neuron, and it makes the neuron network has the ability to deal with the information of space-time two dimensions at the same time, so the data processing enginery of biological neuron is imitated better than traditional neuron. Process neural network with the structure of three layers in which hidden layer is process neuron and input and output are common neurons for soft sensing is discussed. The intelligent soft sensing based on PNN may be used to fulfill measurement of the effluent BOD (Biochemical Oxygen Demand) from sewage disposal system, and a good training result of soft sensing was obtained by the method.

  1. A Wavelet Neural Network Optimal Control Model for Traffic-Flow Prediction in Intelligent Transport Systems

    Science.gov (United States)

    Huang, Darong; Bai, Xing-Rong

    Based on wavelet transform and neural network theory, a traffic-flow prediction model, which was used in optimal control of Intelligent Traffic system, is constructed. First of all, we have extracted the scale coefficient and wavelet coefficient from the online measured raw data of traffic flow via wavelet transform; Secondly, an Artificial Neural Network model of Traffic-flow Prediction was constructed and trained using the coefficient sequences as inputs and raw data as outputs; Simultaneous, we have designed the running principium of the optimal control system of traffic-flow Forecasting model, the network topological structure and the data transmitted model; Finally, a simulated example has shown that the technique is effectively and exactly. The theoretical results indicated that the wavelet neural network prediction model and algorithms have a broad prospect for practical application.

  2. Neural Stem Cells: Implications for the Conventional Radiotherapy of Central Nervous System Malignancies

    International Nuclear Information System (INIS)

    Barani, Igor J.; Benedict, Stanley H.; Lin, Peck-Sun

    2007-01-01

    Advances in basic neuroscience related to neural stem cells and their malignant counterparts are challenging traditional models of central nervous system tumorigenesis and intrinsic brain repair. Neurogenesis persists into adulthood predominantly in two neurogenic centers: subventricular zone and subgranular zone. Subventricular zone is situated adjacent to lateral ventricles and subgranular zone is confined to the dentate gyrus of the hippocampus. Neural stem cells not only self-renew and differentiate along multiple lineages in these regions, but also contribute to intrinsic brain plasticity and repair. Ionizing radiation can depopulate these exquisitely sensitive regions directly or impair in situ neurogenesis by indirect, dose-dependent and inflammation-mediated mechanisms, even at doses <2 Gy. This review discusses the fundamental neural stem cell concepts within the framework of cumulative clinical experience with the treatment of central nervous system malignancies using conventional radiotherapy

  3. Towards an Irritable Bowel Syndrome Control System Based on Artificial Neural Networks

    Science.gov (United States)

    Podolski, Ina; Rettberg, Achim

    To solve health problems with medical applications that use complex algorithms is a trend nowadays. It could also be a chance to help patients with critical problems caused from nerve irritations to overcome them and provide a better living situation. In this paper a system for monitoring and controlling the nerves from the intestine is described on a theoretical basis. The presented system could be applied to the irritable bowel syndrome. For control a neural network is used. The advantages for using a neural network for the control of irritable bowel syndrome are the adaptation and learning. These two aspects are important because the syndrome behavior varies from patient to patient and have also concerning the time a lot of variations with respect to each patient. The developed neural network is implemented and can be simulated. Therefore, it can be shown how the network monitor and control the nerves for individual input parameters.

  4. Global neural dynamic surface tracking control of strict-feedback systems with application to hypersonic flight vehicle.

    Science.gov (United States)

    Xu, Bin; Yang, Chenguang; Pan, Yongping

    2015-10-01

    This paper studies both indirect and direct global neural control of strict-feedback systems in the presence of unknown dynamics, using the dynamic surface control (DSC) technique in a novel manner. A new switching mechanism is designed to combine an adaptive neural controller in the neural approximation domain, together with the robust controller that pulls the transient states back into the neural approximation domain from the outside. In comparison with the conventional control techniques, which could only achieve semiglobally uniformly ultimately bounded stability, the proposed control scheme guarantees all the signals in the closed-loop system are globally uniformly ultimately bounded, such that the conventional constraints on initial conditions of the neural control system can be relaxed. The simulation studies of hypersonic flight vehicle (HFV) are performed to demonstrate the effectiveness of the proposed global neural DSC design.

  5. The condition monitoring system of turbine system components for nuclear power plants

    International Nuclear Information System (INIS)

    Ono, Shigetoshi

    2013-01-01

    The thermal and nuclear power plants have been imposed a stable supply of electricity. To certainly achieve this, we built the plant condition monitoring system based on the heat and mass balance calculation. If there are some performance changes on the turbine system components of their power plants, the heat and mass balance of the turbine system will change. This system has ability to detect the abnormal signs of their components by finding the changes of the heat and mass balance. Moreover we note that this system is built for steam turbine cycle operating with saturated steam conditions. (author)

  6. Biological neural networks as model systems for designing future parallel processing computers

    Science.gov (United States)

    Ross, Muriel D.

    1991-01-01

    One of the more interesting debates of the present day centers on whether human intelligence can be simulated by computer. The author works under the premise that neurons individually are not smart at all. Rather, they are physical units which are impinged upon continuously by other matter that influences the direction of voltage shifts across the units membranes. It is only the action of a great many neurons, billions in the case of the human nervous system, that intelligent behavior emerges. What is required to understand even the simplest neural system is painstaking analysis, bit by bit, of the architecture and the physiological functioning of its various parts. The biological neural network studied, the vestibular utricular and saccular maculas of the inner ear, are among the most simple of the mammalian neural networks to understand and model. While there is still a long way to go to understand even this most simple neural network in sufficient detail for extrapolation to computers and robots, a start was made. Moreover, the insights obtained and the technologies developed help advance the understanding of the more complex neural networks that underlie human intelligence.

  7. Neural mirroring and social interaction: Motor system involvement during action observation relates to early peer cooperation.

    Science.gov (United States)

    Endedijk, H M; Meyer, M; Bekkering, H; Cillessen, A H N; Hunnius, S

    2017-04-01

    Whether we hand over objects to someone, play a team sport, or make music together, social interaction often involves interpersonal action coordination, both during instances of cooperation and entrainment. Neural mirroring is thought to play a crucial role in processing other's actions and is therefore considered important for social interaction. Still, to date, it is unknown whether interindividual differences in neural mirroring play a role in interpersonal coordination during different instances of social interaction. A relation between neural mirroring and interpersonal coordination has particularly relevant implications for early childhood, since successful early interaction with peers is predictive of a more favorable social development. We examined the relation between neural mirroring and children's interpersonal coordination during peer interaction using EEG and longitudinal behavioral data. Results showed that 4-year-old children with higher levels of motor system involvement during action observation (as indicated by lower beta-power) were more successful in early peer cooperation. This is the first evidence for a relation between motor system involvement during action observation and interpersonal coordination during other instances of social interaction. The findings suggest that interindividual differences in neural mirroring are related to interpersonal coordination and thus successful social interaction. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  8. Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

    Science.gov (United States)

    Williams-Hayes, Peggy S.

    2004-01-01

    The NASA F-15 Intelligent Flight Control System project team developed a series of flight control concepts designed to demonstrate neural network-based adaptive controller benefits, with the objective to develop and flight-test control systems using neural network technology to optimize aircraft performance under nominal conditions and stabilize the aircraft under failure conditions. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to baseline aerodynamic derivatives in flight. This open-loop flight test set was performed in preparation for a future phase in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed - pitch frequency sweep and automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. Flight data examination shows that addition of flight-identified aerodynamic derivative increments into the simulation improved aircraft pitch handling qualities.

  9. Estimating the behavior of RC beams strengthened with NSM system using artificial neural networks

    Directory of Open Access Journals (Sweden)

    Seyed Rohollah Hosseini Vaez

    2017-12-01

    Full Text Available In the last decade, conventional materials such as steel and concrete are being replaced by fiber reinforced polymer (FRP materials for the strengthening of concrete structures. Among the strengthening techniques based on Fiber Reinforced Polymer composites, the use of near-surface mounted (NSM FRP rods is emerging as a promising technology for increasing flexural and shear strength of deficient concrete, masonry and timber members. An artificial neural network is an information processing tool that is inspired by the way biological nervous systems (such as the brain process the information. The key element of this tool is the novel structure of the information processing system. In engineering applications, a neural network can be a vector mapper which maps an input vector to an output one. In the present study, a new approach is developed to predict the behavior of strengthened concrete beam using a large number of experimental data by applying artificial neural networks. Having parameters used as input nodes in ANN modeling such as elastic modulus of the FRP reinforcement, the ratio of the steel longitudinal reinforcement, dimensions of the beam section, the ratio of the NSM-FRP reinforcement and characteristics of concrete, the output node was the flexural strength of beams. The idealized neural network was employed to generate empirical charts and equations to be used in design. The aim of this study is to investigate the behavior of strengthened RC beam using artificial neural networks.

  10. Architecture Support for Runtime Integration and Verification of Component-based Systems of Systems

    NARCIS (Netherlands)

    Gonzalez, A.; Piel, E.; Gross, H.G.

    2008-01-01

    Preprint of paper published in: ASE 2008 - 23rd IEEE/ACM International Conference on Automated Software Engineering, 15-19 September 2008; doi:10.1109/ASEW.2008.4686292 Systems-of-Systems (SoS) represent a novel kind of system, for which runtime evolution is a key requirement, as components join and

  11. Stop and Restart Effects on Modern Vehicle Starting System Components

    Energy Technology Data Exchange (ETDEWEB)

    Windover, Paul R. [Argonne National Lab. (ANL), Argonne, IL (United States); Owens, Russell J. [Argonne National Lab. (ANL), Argonne, IL (United States); Levinson, Terry M. [Argonne National Lab. (ANL), Argonne, IL (United States); Laughlin, Michael [Argonne National Lab. (ANL), Argonne, IL (United States); Gaines, Linda [Argonne National Lab. (ANL), Argonne, IL (United States)

    2015-01-01

    Many drivers of personal and commercial vehicles believe that turning the vehicle off and on frequently instead of idling will cause premature wear of the starter system (starter motor and starter battery). As a result, they are concerned that the replacement cost of the starter motor and/or battery due to increased manual engine cycling would be more than the cumulative cost of the fuel saved by not idling unnecessarily. A number of variables play a role in addressing this complex concern, including the number of starting cycles per day, the time between starting cycles, the intended design life of the starting system, the amount of fuel used to restart an engine, and the cumulative cost of the saved fuel. Qualitative and quantitative information from a variety of sources was used to develop a life-cycle economic model to evaluate the cost and quantify the realistic factors that are related to the permissible frequency of starter motor cycles for the average vehicle to economically minimize engine idle time. Annual cost savings can be calculated depending on shutdown duration and the number of shutdown cycles per day. Analysis shows that cost savings are realized by eliminating idling exceeding one minute by shutting down the engine and restarting it. For a typical motorist, the damage to starting system components resulting from additional daily start cycles will be negligible. Overall, it was found that starter life is mostly dependent on the total number of start cycles, while battery life is more dependent on ensuring a full charge between start events.

  12. Real-time camera-based face detection using a modified LAMSTAR neural network system

    Science.gov (United States)

    Girado, Javier I.; Sandin, Daniel J.; DeFanti, Thomas A.; Wolf, Laura K.

    2003-03-01

    This paper describes a cost-effective, real-time (640x480 at 30Hz) upright frontal face detector as part of an ongoing project to develop a video-based, tetherless 3D head position and orientation tracking system. The work is specifically targeted for auto-stereoscopic displays and projection-based virtual reality systems. The proposed face detector is based on a modified LAMSTAR neural network system. At the input stage, after achieving image normalization and equalization, a sub-window analyzes facial features using a neural network. The sub-window is segmented, and each part is fed to a neural network layer consisting of a Kohonen Self-Organizing Map (SOM). The output of the SOM neural networks are interconnected and related by correlation-links, and can hence determine the presence of a face with enough redundancy to provide a high detection rate. To avoid tracking multiple faces simultaneously, the system is initially trained to track only the face centered in a box superimposed on the display. The system is also rotationally and size invariant to a certain degree.

  13. Differences between otolith- and semicircular canal-activated neural circuitry in the vestibular system.

    Science.gov (United States)

    Uchino, Yoshio; Kushiro, Keisuke

    2011-12-01

    In the last two decades, we have focused on establishing a reliable technique for focal stimulation of vestibular receptors to evaluate neural connectivity. Here, we summarize the vestibular-related neuronal circuits for the vestibulo-ocular reflex, vestibulocollic reflex, and vestibulospinal reflex arcs. The focal stimulating technique also uncovered some hidden neural mechanisms. In the otolith system, we identified two hidden neural mechanisms that enhance otolith receptor sensitivity. The first is commissural inhibition, which boosts sensitivity by incorporating inputs from bilateral otolith receptors, the existence of which was in contradiction to the classical understanding of the otolith system but was observed in the utricular system. The second mechanism, cross-striolar inhibition, intensifies the sensitivity of inputs from both sides of receptive cells across the striola in a single otolith sensor. This was an entirely novel finding and is typically observed in the saccular system. We discuss the possible functional meaning of commissural and cross-striolar inhibition. Finally, our focal stimulating technique was applied to elucidate the different constructions of axonal projections from each vestibular receptor to the spinal cord. We also discuss the possible function of the unique neural connectivity observed in each vestibular receptor system. Copyright © 2011 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

  14. Container-code recognition system based on computer vision and deep neural networks

    Science.gov (United States)

    Liu, Yi; Li, Tianjian; Jiang, Li; Liang, Xiaoyao

    2018-04-01

    Automatic container-code recognition system becomes a crucial requirement for ship transportation industry in recent years. In this paper, an automatic container-code recognition system based on computer vision and deep neural networks is proposed. The system consists of two modules, detection module and recognition module. The detection module applies both algorithms based on computer vision and neural networks, and generates a better detection result through combination to avoid the drawbacks of the two methods. The combined detection results are also collected for online training of the neural networks. The recognition module exploits both character segmentation and end-to-end recognition, and outputs the recognition result which passes the verification. When the recognition module generates false recognition, the result will be corrected and collected for online training of the end-to-end recognition sub-module. By combining several algorithms, the system is able to deal with more situations, and the online training mechanism can improve the performance of the neural networks at runtime. The proposed system is able to achieve 93% of overall recognition accuracy.

  15. Development of a component centered fault monitoring and diagnosis knowledge based system for space power system

    Science.gov (United States)

    Lee, S. C.; Lollar, Louis F.

    1988-01-01

    The overall approach currently being taken in the development of AMPERES (Autonomously Managed Power System Extendable Real-time Expert System), a knowledge-based expert system for fault monitoring and diagnosis of space power systems, is discussed. The system architecture, knowledge representation, and fault monitoring and diagnosis strategy are examined. A 'component-centered' approach developed in this project is described. Critical issues requiring further study are identified.

  16. Neural coding in the visual system of Drosophila melanogaster: How do small neural populations support visually guided behaviours?

    Science.gov (United States)

    Dewar, Alex D M; Wystrach, Antoine; Philippides, Andrew; Graham, Paul

    2017-10-01

    All organisms wishing to survive and reproduce must be able to respond adaptively to a complex, changing world. Yet the computational power available is constrained by biology and evolution, favouring mechanisms that are parsimonious yet robust. Here we investigate the information carried in small populations of visually responsive neurons in Drosophila melanogaster. These so-called 'ring neurons', projecting to the ellipsoid body of the central complex, are reported to be necessary for complex visual tasks such as pattern recognition and visual navigation. Recently the receptive fields of these neurons have been mapped, allowing us to investigate how well they can support such behaviours. For instance, in a simulation of classic pattern discrimination experiments, we show that the pattern of output from the ring neurons matches observed fly behaviour. However, performance of the neurons (as with flies) is not perfect and can be easily improved with the addition of extra neurons, suggesting the neurons' receptive fields are not optimised for recognising abstract shapes, a conclusion which casts doubt on cognitive explanations of fly behaviour in pattern recognition assays. Using artificial neural networks, we then assess how easy it is to decode more general information about stimulus shape from the ring neuron population codes. We show that these neurons are well suited for encoding information about size, position and orientation, which are more relevant behavioural parameters for a fly than abstract pattern properties. This leads us to suggest that in order to understand the properties of neural systems, one must consider how perceptual circuits put information at the service of behaviour.

  17. Study on application of adaptive fuzzy control and neural network in the automatic leveling system

    Science.gov (United States)

    Xu, Xiping; Zhao, Zizhao; Lan, Weiyong; Sha, Lei; Qian, Cheng

    2015-04-01

    This paper discusses the adaptive fuzzy control and neural network BP algorithm in large flat automatic leveling control system application. The purpose is to develop a measurement system with a flat quick leveling, Make the installation on the leveling system of measurement with tablet, to be able to achieve a level in precision measurement work quickly, improve the efficiency of the precision measurement. This paper focuses on the automatic leveling system analysis based on fuzzy controller, Use of the method of combining fuzzy controller and BP neural network, using BP algorithm improve the experience rules .Construct an adaptive fuzzy control system. Meanwhile the learning rate of the BP algorithm has also been run-rate adjusted to accelerate convergence. The simulation results show that the proposed control method can effectively improve the leveling precision of automatic leveling system and shorten the time of leveling.

  18. A Neural Networks Based Operation Guidance System for Procedure Presentation and Validation

    International Nuclear Information System (INIS)

    Seung, Kun Mo; Lee, Seung Jun; Seong, Poong Hyun

    2006-01-01

    In this paper, a neural network based operator support system is proposed to reduce operator's errors in abnormal situations in nuclear power plants (NPPs). There are many complicated situations, in which regular and suitable operations should be done by operators accordingly. In order to regulate and validate operators' operations, it is necessary to develop an operator support system which includes computer based procedures with the functions for operation validation. Many computerized procedures systems (CPS) have been recently developed. Focusing on the human machine interface (HMI) design and procedures' computerization, most of CPSs used various methodologies to enhance system's convenience, reliability and accessibility. Other than only showing procedures, the proposed system integrates a simple CPS and an operation validation system (OVS) by using artificial neural network (ANN) for operational permission and quantitative evaluation

  19. Directive Nanophysical Cues for Regenerative Neural Cell Systems

    Science.gov (United States)

    Ayres, Virginia; Tiryaki, Volkan Mujdat; Ahmed, Ijaz; Shreiber, David

    Until recently, implantables such as stents, probes, wafers and scaffolds have been viewed as passive vehicles for the delivery of physical, pharmacological and cellular interventions. Recent research, however, indicates that the physical environments that implantables present supply directive cues in their own right that work in conjunction with biochemical cues and produce a jointly-directed outcome. We will present our research in CNS repairs using advanced scanning probe microscopy, electron microscopies and contact angle measurements to quantitatively describe the nanoscale elasticity, surface roughness, work of adhesion and surface polarity for investigation of scaffold environments. We will also present our research using super-resolution immunocytochemistry and atomic force microscopy to evaluate neural cell morphological responses with associated micro filament, microtubule and intermediate filament expressions, along with results on how and which integrin-family receptors are possibly involved. Finally, we will present our novel application of k-means cluster analysis applied across multiple experimental modalities for quantification of synergistic scaffold properties and cell responses.

  20. The use of neural networks in the D0 data acquisition system

    International Nuclear Information System (INIS)

    Cutts, D.; Hoftun, J.S.; Sornborger, A.; Astur, R.V.; Johnson, C.R.; Zeller, R.T.

    1989-01-01

    We discuss the possible application of algorithms derived from neural networks to the D0 experiment. The D0 data acquisition system is based on a large farm of MicroVAXes, each independently performing real-time event filtering. A new generation of multiport memories in each MicroVAX node will enable special function processors to have direct access to event data. We describe an exploratory study of back propagation neural networks, such as might be configured in the nodes, for more efficient event filtering. 9 refs., 3 figs., 1 tab

  1. A Drone Remote Sensing for Virtual Reality Simulation System for Forest Fires: Semantic Neural Network Approach

    Science.gov (United States)

    Narasimha Rao, Gudikandhula; Jagadeeswara Rao, Peddada; Duvvuru, Rajesh

    2016-09-01

    Wild fires have significant impact on atmosphere and lives. The demand of predicting exact fire area in forest may help fire management team by using drone as a robot. These are flexible, inexpensive and elevated-motion remote sensing systems that use drones as platforms are important for substantial data gaps and supplementing the capabilities of manned aircraft and satellite remote sensing systems. In addition, powerful computational tools are essential for predicting certain burned area in the duration of a forest fire. The reason of this study is to built up a smart system based on semantic neural networking for the forecast of burned areas. The usage of virtual reality simulator is used to support the instruction process of fire fighters and all users for saving of surrounded wild lives by using a naive method Semantic Neural Network System (SNNS). Semantics are valuable initially to have a enhanced representation of the burned area prediction and better alteration of simulation situation to the users. In meticulous, consequences obtained with geometric semantic neural networking is extensively superior to other methods. This learning suggests that deeper investigation of neural networking in the field of forest fires prediction could be productive.

  2. Systematic Self-Regulation of the Neural System Essential for Peak Performance and Wellbeing.

    Science.gov (United States)

    Cassel, Russell N.

    1985-01-01

    Balance and harmony within one's neural system is dynamic and changing, and restoring that balance is essential for peak performance. With a minimum amount of training individuals are able to restore this delicate balance and thereby enhance their own wellbeing. Autogenic feedback training has been demonstrated to be an effective means for…

  3. Systemic Injection of Neural Stem/progenitor Cells in Mice With Chronic EAE

    OpenAIRE

    Donegà, Matteo; Giusto, Elena; Cossetti, Chiara; Schaeffer, Julia; Pluchino, Stefano

    2014-01-01

    Neural stem/precursor cells (NPCs) are a promising stem cell source for transplantation approaches aiming at brain repair or restoration in regenerative neurology. This directive has arisen from the extensive evidence that brain repair is achieved after focal or systemic NPC transplantation in several pre clinical models of neurological diseases.

  4. Comparable mechanisms for action and language: Neural systems behind intentions, goals and means

    NARCIS (Netherlands)

    Schie, H.T. van; Toni, I.; Bekkering, H.

    2006-01-01

    In this position paper we explore correspondence between neural systems for language and action starting from recent electrophysiological findings on the roles of posterior and frontal areas in goal-directed grasping actions. The paper compares the perceptual and motor organization for action and

  5. Artificial neural network decision support systems for new product development project selection

    NARCIS (Netherlands)

    Thieme, R.J.; Song, Michael; Calantone, R.J.

    2000-01-01

    The authors extend and develop an artificial neural network decision support system and demonstrate how it can guide managers when they make complex new product development decisions. The authors use data from 612 projects to compare this new method with traditional methods for predicting various

  6. A New Method for Studying the Periodic System Based on a Kohonen Neural Network

    Science.gov (United States)

    Chen, David Zhekai

    2010-01-01

    A new method for studying the periodic system is described based on the combination of a Kohonen neural network and a set of chemical and physical properties. The classification results are directly shown in a two-dimensional map and easy to interpret. This is one of the major advantages of this approach over other methods reported in the…

  7. Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2014-11-01

    Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.

  8. A Low-Cost Maximum Power Point Tracking System Based on Neural Network Inverse Model Controller

    Directory of Open Access Journals (Sweden)

    Carlos Robles Algarín

    2018-01-01

    Full Text Available This work presents the design, modeling, and implementation of a neural network inverse model controller for tracking the maximum power point of a photovoltaic (PV module. A nonlinear autoregressive network with exogenous inputs (NARX was implemented in a serial-parallel architecture. The PV module mathematical modeling was developed, a buck converter was designed to operate in the continuous conduction mode with a switching frequency of 20 KHz, and the dynamic neural controller was designed using the Neural Network Toolbox from Matlab/Simulink (MathWorks, Natick, MA, USA, and it was implemented on an open-hardware Arduino Mega board. To obtain the reference signals for the NARX and determine the 65 W PV module behavior, a system made of a 0.8 W PV cell, a temperature sensor, a voltage sensor and a static neural network, was used. To evaluate performance a comparison with the P&O traditional algorithm was done in terms of response time and oscillations around the operating point. Simulation results demonstrated the superiority of neural controller over the P&O. Implementation results showed that approximately the same power is obtained with both controllers, but the P&O controller presents oscillations between 7 W and 10 W, in contrast to the inverse controller, which had oscillations between 1 W and 2 W.

  9. Pipeline protection with multi component liquid polyurethane coating systems

    Energy Technology Data Exchange (ETDEWEB)

    Kuprion, Rainer; Hornig, Maja [TIB Chemicals Ag, Mannheim (Germany)

    2009-07-01

    Protective coating systems are one of the major defence mechanisms against corrosion for transmission pipelines, pipes within a refinery or petrochemical processing facilities. More and more pipelines are being constructed for the supply and transmission of gas and oil, each year but, in addition many existing pipelines are approaching an age where inspection reveals the necessity to consider complete refurbishment. However, the number of rehabilitation projects each year is still relatively small. Therefore, in the coming years, a rising need and necessity can be expected, for the owners and operating companies to be faced with the option of either replacing the pipeline or refurbishing of the existing pipeline. If the pipeline is known to have external corrosion, then safe and economic operation should be assured. Rehabilitation should be done before it is too late in order to ensure its future integrity and operational life. Rehabilitation of pipelines has been both the economic solution and, more significantly, the ecological solution and in many of those cases the coatings selected for the external protection has been multi component liquids based on 100% solids polyurethanes. (author)

  10. Intercrystalline internal adsorption in systems with limiting solubility of components

    International Nuclear Information System (INIS)

    Krysova, S.K.; Stepanova, V.A.; Mozgovoj, M.V.

    1979-01-01

    The decrease of the excessive energy of the intercrystalline boundary of ion by additions of transitional elements having unlimited solubility in iron has been studied. The data obtained agree with the results of an earlier work based on materials of a less higher initial purity. For the systems studied (Fe-V, Fe-Cr, Fe-Mn, Fe-Ni) the degree of the intercrystalline interval adsorption is independent of either the annealing temperature or the cooling method. This corresponds to the notion of the relationship between the intercrystalline internal adsorption and the cubic solubility of the addition in a given solvent. For pure ion, a weak temperature dependence of the excessive energy of the intercrystalline boundries was found in the lower section of the examined temperature range. The constants of cumulative recrystallization of the alloys studied in α-phase show a stepwise dependence upon the atomic number of the solute component, what indicates the relationship between the cumulative recrystallization and the intercrystalline internal adsorption. A monotonous decrease of the constant of cumulative recrystallization is observed for the same alloys in α-phase, on both sides of iron

  11. Safety design requirements for safety systems and components of JSFR

    International Nuclear Information System (INIS)

    Kubo, Shigenobu; Shimakawa, Yoshio; Yamano, Hidemasa; Kotake, Shoji

    2011-01-01

    Safety design requirements for JSFR were summarized taking the development targets of the FaCT project and design feature of JSFR into account. The related safety principle and requirements for Monju, CRBRP, PRISM, SPX, LWRs, IAEA standards, goals of GIF, basic principle of INPRO etc. were also taken into account so that the safety design requirements can be a next-generation global standard. The development targets for safety and reliability are set based on those of FaCT, namely, ensuring safety and reliability equal to future LWR and related fuel cycle facilities. In order to achieve these targets, the defence-in-depth concept is used as the basic safety design principle. General features of the safety design requirements are 1) Achievement of higher reliability, 2) Achievement of higher inspectability and maintainability, 3) Introduction of passive safety features, 4) Reduction of operator action needs, 5) Design consideration against Beyond Design Basis Events, 6) In-Vessel Retention of degraded core materials, 7) Prevention and mitigation against sodium chemical reactions, and 8) Design against external events. The current specific requirements for each system and component are summarized taking the basic design concept of JSFR into account, which is an advanced loop-type large-output power plant with a mixed-oxide-fuelled core. (author)

  12. Optimization of Thermoelectric Components for Automobile Waste Heat Recovery Systems

    Science.gov (United States)

    Kumar, Sumeet; Heister, Stephen D.; Xu, Xianfan; Salvador, James R.

    2015-10-01

    For a typical spark ignition engine approximately 40% of available thermal energy is lost as hot exhaust gas. To improve fuel economy, researchers are currently evaluating technology which exploits exhaust stream thermal power by use of thermoelectric generators (TEGs) that operate on the basis of the Seebeck effect. A 5% improvement in fuel economy, achieved by use of TEG output power, is a stated objective for light-duty trucks and personal automobiles. System modeling of thermoelectric (TE) components requires solution of coupled thermal and electric fluxes through the n and p-type semiconductor legs, given appropriate thermal boundary conditions at the junctions. Such applications have large thermal gradients along the semiconductor legs, and material properties are highly dependent on spatially varying temperature profiles. In this work, one-dimensional heat flux and temperature variations across thermoelectric legs were solved by using an iterative numerical approach to optimize both TE module and TEG designs. Design traits were investigated by assuming use of skutterudite as a thermoelectric material with potential for automotive applications in which exhaust gas and heat exchanger temperatures typically vary from 100°C to over 600°C. Dependence of leg efficiency, thermal fluxes and electric power generation on leg geometry, fill fractions, electric current, thermal boundary conditions, etc., were studied in detail. Optimum leg geometries were computed for a variety of automotive exhaust conditions.

  13. Engineering applications of fpgas chaotic systems, artificial neural networks, random number generators, and secure communication systems

    CERN Document Server

    Tlelo-Cuautle, Esteban; de la Fraga, Luis Gerardo

    2016-01-01

    This book offers readers a clear guide to implementing engineering applications with FPGAs, from the mathematical description to the hardware synthesis, including discussion of VHDL programming and co-simulation issues. Coverage includes FPGA realizations such as: chaos generators that are described from their mathematical models; artificial neural networks (ANNs) to predict chaotic time series, for which a discussion of different ANN topologies is included, with different learning techniques and activation functions; random number generators (RNGs) that are realized using different chaos generators, and discussions of their maximum Lyapunov exponent values and entropies. Finally, optimized chaotic oscillators are synchronized and realized to implement a secure communication system that processes black and white and grey-scale images. In each application, readers will find VHDL programming guidelines and computer arithmetic issues, along with co-simulation examples with Active-HDL and Simulink. Readers will b...

  14. Neural-net based unstable machine identification using individual energy functions. [Transient disturbances in power systems

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M [Institut Nikola Tesla, Belgrade (Yugoslavia); Sobajic, D J; Pao, Yohhan [Case Western Reserve Univ., Cleveland, OH (United States)

    1991-10-01

    The identification of the mode of instability plays an essential role in generating principal energy boundary hypersurfaces. We present a new method for unstable machine identification based on the use of supervised learning neural-net technology, and the adaptive pattern recognition concept. It is shown that using individual energy functions as pattern features, appropriately trained neural-nets can retrieve the reliable characterization of the transient process including critical clearing time parameter, mode of instability and energy margins. Generalization capabilities of the neural-net processing allow for these assessments to be made independently of load levels. The results obtained from computer simulations are presented using the New England power system, as an example. (author).

  15. Design and Implementation of Behavior Recognition System Based on Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    Yu Bo

    2017-01-01

    Full Text Available We build a set of human behavior recognition system based on the convolution neural network constructed for the specific human behavior in public places. Firstly, video of human behavior data set will be segmented into images, then we process the images by the method of background subtraction to extract moving foreground characters of body. Secondly, the training data sets are trained into the designed convolution neural network, and the depth learning network is constructed by stochastic gradient descent. Finally, the various behaviors of samples are classified and identified with the obtained network model, and the recognition results are compared with the current mainstream methods. The result show that the convolution neural network can study human behavior model automatically and identify human’s behaviors without any manually annotated trainings.

  16. A novel joint-processing adaptive nonlinear equalizer using a modular recurrent neural network for chaotic communication systems.

    Science.gov (United States)

    Zhao, Haiquan; Zeng, Xiangping; Zhang, Jiashu; Liu, Yangguang; Wang, Xiaomin; Li, Tianrui

    2011-01-01

    To eliminate nonlinear channel distortion in chaotic communication systems, a novel joint-processing adaptive nonlinear equalizer based on a pipelined recurrent neural network (JPRNN) is proposed, using a modified real-time recurrent learning (RTRL) algorithm. Furthermore, an adaptive amplitude RTRL algorithm is adopted to overcome the deteriorating effect introduced by the nesting process. Computer simulations illustrate that the proposed equalizer outperforms the pipelined recurrent neural network (PRNN) and recurrent neural network (RNN) equalizers. Copyright © 2010 Elsevier Ltd. All rights reserved.

  17. Decoupled ARX and RBF Neural Network Modeling Using PCA and GA Optimization for Nonlinear Distributed Parameter Systems.

    Science.gov (United States)

    Zhang, Ridong; Tao, Jili; Lu, Renquan; Jin, Qibing

    2018-02-01

    Modeling of distributed parameter systems is difficult because of their nonlinearity and infinite-dimensional characteristics. Based on principal component analysis (PCA), a hybrid modeling strategy that consists of a decoupled linear autoregressive exogenous (ARX) model and a nonlinear radial basis function (RBF) neural network model are proposed. The spatial-temporal output is first divided into a few dominant spatial basis functions and finite-dimensional temporal series by PCA. Then, a decoupled ARX model is designed to model the linear dynamics of the dominant modes of the time series. The nonlinear residual part is subsequently parameterized by RBFs, where genetic algorithm is utilized to optimize their hidden layer structure and the parameters. Finally, the nonlinear spatial-temporal dynamic system is obtained after the time/space reconstruction. Simulation results of a catalytic rod and a heat conduction equation demonstrate the effectiveness of the proposed strategy compared to several other methods.

  18. A Red-Light Running Prevention System Based on Artificial Neural Network and Vehicle Trajectory Data

    Directory of Open Access Journals (Sweden)

    Pengfei Li

    2014-01-01

    Full Text Available The high frequency of red-light running and complex driving behaviors at the yellow onset at intersections cannot be explained solely by the dilemma zone and vehicle kinematics. In this paper, the author presented a red-light running prevention system which was based on artificial neural networks (ANNs to approximate the complex driver behaviors during yellow and all-red clearance and serve as the basis of an innovative red-light running prevention system. The artificial neural network and vehicle trajectory are applied to identify the potential red-light runners. The ANN training time was also acceptable and its predicting accurate rate was over 80%. Lastly, a prototype red-light running prevention system with the trained ANN model was described. This new system can be directly retrofitted into the existing traffic signal systems.

  19. New Solutions to the Firing Squad Synchronization Problems for Neural and Hyperdag P Systems

    Directory of Open Access Journals (Sweden)

    Michael J. Dinneen

    2009-11-01

    Full Text Available We propose two uniform solutions to an open question: the Firing Squad Synchronization Problem (FSSP, for hyperdag and symmetric neural P systems, with anonymous cells. Our solutions take e_c+5 and 6e_c+7 steps, respectively, where e_c is the eccentricity of the commander cell of the dag or digraph underlying these P systems. The first and fast solution is based on a novel proposal, which dynamically extends P systems with mobile channels. The second solution is substantially longer, but is solely based on classical rules and static channels. In contrast to the previous solutions, which work for tree-based P systems, our solutions synchronize to any subset of the underlying digraph; and do not require membrane polarizations or conditional rules, but require states, as typically used in hyperdag and neural P systems.

  20. A red-light running prevention system based on artificial neural network and vehicle trajectory data.

    Science.gov (United States)

    Li, Pengfei; Li, Yan; Guo, Xiucheng

    2014-01-01

    The high frequency of red-light running and complex driving behaviors at the yellow onset at intersections cannot be explained solely by the dilemma zone and vehicle kinematics. In this paper, the author presented a red-light running prevention system which was based on artificial neural networks (ANNs) to approximate the complex driver behaviors during yellow and all-red clearance and serve as the basis of an innovative red-light running prevention system. The artificial neural network and vehicle trajectory are applied to identify the potential red-light runners. The ANN training time was also acceptable and its predicting accurate rate was over 80%. Lastly, a prototype red-light running prevention system with the trained ANN model was described. This new system can be directly retrofitted into the existing traffic signal systems.