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Sample records for distributed neural system

  1. 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.

  2. 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.

  3. 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

  4. 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.

  5. Distributed Energy Neural Network Integration System: Year One Final Report

    Energy Technology Data Exchange (ETDEWEB)

    Regan, T.; Sinnock, H.; Davis, A.

    2003-06-01

    This report describes the work of Orion Engineering Corp. to develop a DER household controller module and demonstrate the ability of a group of these controllers to operate through an intelligent, neighborhood controller. The controllers will provide a smart, technologically advanced, simple, efficient, and economic solution for aggregating a community of small distributed generators into a larger single, virtual generator capable of selling power or other services to a utility, independent system operator (ISO), or other entity in a coordinated manner.

  6. Fuzzy-Neural Controller in Service Requests Distribution Broker for SOA-Based Systems

    Science.gov (United States)

    Fras, Mariusz; Zatwarnicka, Anna; Zatwarnicki, Krzysztof

    The evolution of software architectures led to the rising importance of the Service Oriented Architecture (SOA) concept. This architecture paradigm support building flexible distributed service systems. In the paper the architecture of service request distribution broker designed for use in SOA-based systems is proposed. The broker is built with idea of fuzzy control. The functional and non-functional request requirements in conjunction with monitoring of execution and communication links are used to distribute requests. Decisions are made with use of fuzzy-neural network.

  7. Reliability Worth Analysis of Distribution Systems Using Cascade Correlation Neural Networks

    DEFF Research Database (Denmark)

    Heidari, Alireza; Agelidis, Vassilios; Pou, Josep

    2018-01-01

    Reliability worth analysis is of great importance in the area of distribution network planning and operation. The reliability worth's precision can be affected greatly by the customer interruption cost model used. The choice of the cost models can change system and load point reliability indices....... In this study, a cascade correlation neural network is adopted to further develop two cost models comprising a probabilistic distribution model and an average or aggregate model. A contingency-based analytical technique is adopted to conduct the reliability worth analysis. Furthermore, the possible effects...

  8. Neural network for optimal capacitor placement and its impact on power quality in electric distribution systems

    International Nuclear Information System (INIS)

    Mohamed, A.A.E.S.

    2013-01-01

    Capacitors are widely installed in distribution systems for reactive power compensation to achieve power and energy loss reduction, voltage regulation and system capacity release. The extent of these benefits depends greatly on how the capacitors are placed on the system. The problem of how to place capacitors on the system such that these benefits are achieved and maximized against the cost associated with the capacitor placement is termed the general capacitor placement problem. The capacitor placement problem has been formulated as the maximization of the savings resulted from reduction in both peak power and energy losses considering capacitor installation cost and maintaining the buses voltage within acceptable limits. After an appropriate analysis, the optimization problem was formulated in a quadratic form. For solving capacitor placement a new combinatorial heuristic and quadratic programming technique has been presented and applied in the MATLAB software. The proposed strategy was applied on two different radial distribution feeders. The results have been compared with previous works. The comparison showed the validity and the effectiveness of this strategy. Secondly, two artificial intelligence techniques for predicting the capacitor switching state in radial distribution feeders have been investigated; one is based on basis Radial Basis Neural Network (RBNN) and the other is based on Adaptive Neuro-Fuzzy Inference System (ANFIS). The ANFIS technique gives better results with a minimum total error compared to RBNN. The learning duration of ANFIS was very short than the neural network case. It implied that ANFIS reaches to the target faster than neural network. Thirdly, an artificial intelligence (RBNN) approach for estimation of transient overvoltage during capacitor switching has been studied. The artificial intelligence approach estimated the transient overvoltages with a minimum error in a short computational time. Finally, a capacitor switching

  9. 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.

  10. Consensus-based distributed cooperative learning from closed-loop neural control systems.

    Science.gov (United States)

    Chen, Weisheng; Hua, Shaoyong; Zhang, Huaguang

    2015-02-01

    In this paper, the neural tracking problem is addressed for a group of uncertain nonlinear systems where the system structures are identical but the reference signals are different. This paper focuses on studying the learning capability of neural networks (NNs) during the control process. First, we propose a novel control scheme called distributed cooperative learning (DCL) control scheme, by establishing the communication topology among adaptive laws of NN weights to share their learned knowledge online. It is further proved that if the communication topology is undirected and connected, all estimated weights of NNs can converge to small neighborhoods around their optimal values over a domain consisting of the union of all state orbits. Second, as a corollary it is shown that the conclusion on the deterministic learning still holds in the decentralized adaptive neural control scheme where, however, the estimated weights of NNs just converge to small neighborhoods of the optimal values along their own state orbits. Thus, the learned controllers obtained by DCL scheme have the better generalization capability than ones obtained by decentralized learning method. A simulation example is provided to verify the effectiveness and advantages of the control schemes proposed in this paper.

  11. Toward a distributed free-floating wireless implantable neural recording system.

    Science.gov (United States)

    Pyungwoo Yeon; Xingyuan Tong; Byunghun Lee; Mirbozorgi, Abdollah; Ash, Bruce; Eckhardt, Helmut; Ghovanloo, Maysam

    2016-08-01

    To understand the complex correlations between neural networks across different regions in the brain and their functions at high spatiotemporal resolution, a tool is needed for obtaining long-term single unit activity (SUA) across the entire brain area. The concept and preliminary design of a distributed free-floating wireless implantable neural recording (FF-WINeR) system are presented, which can enabling SUA acquisition by dispersedly implanting tens to hundreds of untethered 1 mm3 neural recording probes, floating with the brain and operating wirelessly across the cortical surface. For powering FF-WINeR probes, a 3-coil link with an intermediate high-Q resonator provides a minimum S21 of -22.22 dB (in the body medium) and -21.23 dB (in air) at 2.8 cm coil separation, which translates to 0.76%/759 μW and 0.6%/604 μW of power transfer efficiency (PTE) / power delivered to a 9 kΩ load (PDL), in body and air, respectively. A mock-up FF-WINeR is implemented to explore microassembly method of the 1×1 mm2 micromachined silicon die with a bonding wire-wound coil and a tungsten micro-wire electrode. Circuit design methods to fit the active circuitry in only 0.96 mm2 of die area in a 130 nm standard CMOS process, and satisfy the strict power and performance requirements (in simulations) are discussed.

  12. 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.

  13. 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.

  14. 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.

  15. 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...

  16. pH prediction by artificial neural networks for the drinking water of the distribution system of Hyderabad city

    International Nuclear Information System (INIS)

    Memon, N.A.; Unar, M.A.; Ansari, A.K.

    2012-01-01

    In this research, feed forward ANN (Artificial Neural Network) model is developed and validated for predicting the pH at 10 different locations of the distribution system of drinking water of Hyderabad city. The developed model is MLP (Multilayer Perceptron) with back propagation algorithm. The data for the training and testing of the model are collected through an experimental analysis on weekly basis in a routine examination for maintaining the quality of drinking water in the city. 17 parameters are taken into consideration including pH. These all parameters are taken as input variables for the model and then pH is predicted for 03 phases;raw water of river Indus,treated water in the treatment plants and then treated water in the distribution system of drinking water. The training and testing results of this model reveal that MLP neural networks are exceedingly extrapolative for predicting the pH of river water, untreated and treated water at all locations of the distribution system of drinking water of Hyderabad city. The optimum input and output weights are generated with minimum MSE (Mean Square Error) < 5%. Experimental, predicted and tested values of pH are plotted and the effectiveness of the model is determined by calculating the coefficient of correlation (R2=0.999) of trained and tested results. (author)

  17. 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.

  18. Estimation of stress distribution in ferromagnetic tensile specimens using low cost eddy current stress measurement system and BP neural network.

    Directory of Open Access Journals (Sweden)

    Jianwei Li

    Full Text Available Estimation of the stress distribution in ferromagnetic components is very important for evaluating the working status of mechanical equipment and implementing preventive maintenance. Eddy current testing technology is a promising method in this field because of its advantages of safety, no need of coupling agent, etc. In order to reduce the cost of eddy current stress measurement system, and obtain the stress distribution in ferromagnetic materials without scanning, a low cost eddy current stress measurement system based on Archimedes spiral planar coil was established, and a method based on BP neural network to obtain the stress distribution using the stress of several discrete test points was proposed. To verify the performance of the developed test system and the validity of the proposed method, experiment was implemented using structural steel (Q235 specimens. Standard curves of sensors at each test point were achieved, the calibrated data were used to establish the BP neural network model for approximating the stress variation on the specimen surface, and the stress distribution curve of the specimen was obtained by interpolating with the established model. The results show that there is a good linear relationship between the change of signal modulus and the stress in most elastic range of the specimen, and the established system can detect the change in stress with a theoretical average sensitivity of -0.4228 mV/MPa. The obtained stress distribution curve is well consonant with the theoretical analysis result. At last, possible causes and improving methods of problems appeared in the results were discussed. This research has important significance for reducing the cost of eddy current stress measurement system, and advancing the engineering application of eddy current stress testing.

  19. Estimation of stress distribution in ferromagnetic tensile specimens using low cost eddy current stress measurement system and BP neural network.

    Science.gov (United States)

    Li, Jianwei; Zhang, Weimin; Zeng, Weiqin; Chen, Guolong; Qiu, Zhongchao; Cao, Xinyuan; Gao, Xuanyi

    2017-01-01

    Estimation of the stress distribution in ferromagnetic components is very important for evaluating the working status of mechanical equipment and implementing preventive maintenance. Eddy current testing technology is a promising method in this field because of its advantages of safety, no need of coupling agent, etc. In order to reduce the cost of eddy current stress measurement system, and obtain the stress distribution in ferromagnetic materials without scanning, a low cost eddy current stress measurement system based on Archimedes spiral planar coil was established, and a method based on BP neural network to obtain the stress distribution using the stress of several discrete test points was proposed. To verify the performance of the developed test system and the validity of the proposed method, experiment was implemented using structural steel (Q235) specimens. Standard curves of sensors at each test point were achieved, the calibrated data were used to establish the BP neural network model for approximating the stress variation on the specimen surface, and the stress distribution curve of the specimen was obtained by interpolating with the established model. The results show that there is a good linear relationship between the change of signal modulus and the stress in most elastic range of the specimen, and the established system can detect the change in stress with a theoretical average sensitivity of -0.4228 mV/MPa. The obtained stress distribution curve is well consonant with the theoretical analysis result. At last, possible causes and improving methods of problems appeared in the results were discussed. This research has important significance for reducing the cost of eddy current stress measurement system, and advancing the engineering application of eddy current stress testing.

  20. Distributed neural network control for adaptive synchronization of uncertain dynamical multiagent systems.

    Science.gov (United States)

    Peng, Zhouhua; Wang, Dan; Zhang, Hongwei; Sun, Gang

    2014-08-01

    This paper addresses the leader-follower synchronization problem of uncertain dynamical multiagent systems with nonlinear dynamics. Distributed adaptive synchronization controllers are proposed based on the state information of neighboring agents. The control design is developed for both undirected and directed communication topologies without requiring the accurate model of each agent. This result is further extended to the output feedback case where a neighborhood observer is proposed based on relative output information of neighboring agents. Then, distributed observer-based synchronization controllers are derived and a parameter-dependent Riccati inequality is employed to prove the stability. This design has a favorable decouple property between the observer and the controller designs for nonlinear multiagent systems. For both cases, the developed controllers guarantee that the state of each agent synchronizes to that of the leader with bounded residual errors. Two illustrative examples validate the efficacy of the proposed methods.

  1. 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.

  2. The neural representation of personally familiar and unfamiliar faces in the distributed system for face perception.

    Science.gov (United States)

    Visconti di Oleggio Castello, Matteo; Halchenko, Yaroslav O; Guntupalli, J Swaroop; Gors, Jason D; Gobbini, M Ida

    2017-09-25

    Personally familiar faces are processed more robustly and efficiently than unfamiliar faces. The human face processing system comprises a core system that analyzes the visual appearance of faces and an extended system for the retrieval of person-knowledge and other nonvisual information. We applied multivariate pattern analysis to fMRI data to investigate aspects of familiarity that are shared by all familiar identities and information that distinguishes specific face identities from each other. Both identity-independent familiarity information and face identity could be decoded in an overlapping set of areas in the core and extended systems. Representational similarity analysis revealed a clear distinction between the two systems and a subdivision of the core system into ventral, dorsal and anterior components. This study provides evidence that activity in the extended system carries information about both individual identities and personal familiarity, while clarifying and extending the organization of the core system for face perception.

  3. Artificial Neural Network for Short-Term Load Forecasting in Distribution Systems

    Directory of Open Access Journals (Sweden)

    Luis Hernández

    2014-03-01

    Full Text Available The new paradigms and latest developments in the Electrical Grid are based on the introduction of distributed intelligence at several stages of its physical layer, giving birth to concepts such as Smart Grids, Virtual Power Plants, microgrids, Smart Buildings and Smart Environments. Distributed Generation (DG is a philosophy in which energy is no longer produced exclusively in huge centralized plants, but also in smaller premises which take advantage of local conditions in order to minimize transmission losses and optimize production and consumption. This represents a new opportunity for renewable energy, because small elements such as solar panels and wind turbines are expected to be scattered along the grid, feeding local installations or selling energy to the grid depending on their local generation/consumption conditions. The introduction of these highly dynamic elements will lead to a substantial change in the curves of demanded energy. The aim of this paper is to apply Short-Term Load Forecasting (STLF in microgrid environments with curves and similar behaviours, using two different data sets: the first one packing electricity consumption information during four years and six months in a microgrid along with calendar data, while the second one will be just four months of the previous parameters along with the solar radiation from the site. For the first set of data different STLF models will be discussed, studying the effect of each variable, in order to identify the best one. That model will be employed with the second set of data, in order to make a comparison with a new model that takes into account the solar radiation, since the photovoltaic installations of the microgrid will cause the power demand to fluctuate depending on the solar radiation.

  4. The distributed neural system for top-down letter processing: an fMRI study

    Science.gov (United States)

    Liu, Jiangang; Feng, Lu; Li, Ling; Tian, Jie

    2011-03-01

    This fMRI study used Psychophysiological interaction (PPI) to investigate top-down letter processing with an illusory letter detection task. After an initial training that became increasingly difficult, participant was instructed to detect a letter from pure noise images where there was actually no letter. Such experimental paradigm allowed for isolating top-down components of letter processing and minimizing the influence of bottom-up perceptual input. A distributed cortical network of top-down letter processing was identified by analyzing the functional connectivity patterns of letter-preferential area (LA) within the left fusiform gyrus. Such network extends from the visual cortex to high level cognitive cortexes, including the left middle frontal gyrus, left medial frontal gyrus, left superior parietal gyrus, bilateral precuneus, and left inferior occipital gyrus. These findings suggest that top-down letter processing contains not only regions for processing of letter phonology and appearance, but also those involved in internal information generation and maintenance, and attention and memory processing.

  5. Use of Savitzky–Golay Filter for Performances Improvement of SHM Systems Based on Neural Networks and Distributed PZT Sensors

    Directory of Open Access Journals (Sweden)

    Mario A. de Oliveira

    2018-01-01

    Full Text Available A considerable amount of research has focused on monitoring structural damage using Structural Health Monitoring (SHM technologies, which has had recent advances. However, it is important to note the challenges and unresolved problems that disqualify currently developed monitoring systems. One of the frontline SHM technologies, the Electromechanical Impedance (EMI technique, has shown its potential to overcome remaining problems and challenges. Unfortunately, the recently developed neural network algorithms have not shown significant improvements in the accuracy of rate and the required processing time. In order to fill this gap in advanced neural networks used with EMI techniques, this paper proposes an enhanced and reliable strategy for improving the structural damage detection via: (1 Savitzky–Golay (SG filter, using both first and second derivatives; (2 Probabilistic Neural Network (PNN; and, (3 Simplified Fuzzy ARTMAP Network (SFAN. Those three methods were employed to analyze the EMI data experimentally obtained from an aluminum plate containing three attached PZT (Lead Zirconate Titanate patches. In this present study, the damage scenarios were simulated by attaching a small metallic nut at three different positions in the aluminum plate. We found that the proposed method achieves a hit rate of more than 83%, which is significantly higher than current state-of-the-art approaches. Furthermore, this approach results in an improvement of 93% when considering the best case scenario.

  6. Use of Savitzky-Golay Filter for Performances Improvement of SHM Systems Based on Neural Networks and Distributed PZT Sensors.

    Science.gov (United States)

    de Oliveira, Mario A; Araujo, Nelcileno V S; da Silva, Rodolfo N; da Silva, Tony I; Epaarachchi, Jayantha

    2018-01-08

    A considerable amount of research has focused on monitoring structural damage using Structural Health Monitoring (SHM) technologies, which has had recent advances. However, it is important to note the challenges and unresolved problems that disqualify currently developed monitoring systems. One of the frontline SHM technologies, the Electromechanical Impedance (EMI) technique, has shown its potential to overcome remaining problems and challenges. Unfortunately, the recently developed neural network algorithms have not shown significant improvements in the accuracy of rate and the required processing time. In order to fill this gap in advanced neural networks used with EMI techniques, this paper proposes an enhanced and reliable strategy for improving the structural damage detection via: (1) Savitzky-Golay (SG) filter, using both first and second derivatives; (2) Probabilistic Neural Network (PNN); and, (3) Simplified Fuzzy ARTMAP Network (SFAN). Those three methods were employed to analyze the EMI data experimentally obtained from an aluminum plate containing three attached PZT (Lead Zirconate Titanate) patches. In this present study, the damage scenarios were simulated by attaching a small metallic nut at three different positions in the aluminum plate. We found that the proposed method achieves a hit rate of more than 83%, which is significantly higher than current state-of-the-art approaches. Furthermore, this approach results in an improvement of 93% when considering the best case scenario.

  7. Use of Savitzky–Golay Filter for Performances Improvement of SHM Systems Based on Neural Networks and Distributed PZT Sensors

    Science.gov (United States)

    Araujo, Nelcileno V. S.; da Silva, Rodolfo N.; da Silva, Tony I.; Epaarachchi, Jayantha

    2018-01-01

    A considerable amount of research has focused on monitoring structural damage using Structural Health Monitoring (SHM) technologies, which has had recent advances. However, it is important to note the challenges and unresolved problems that disqualify currently developed monitoring systems. One of the frontline SHM technologies, the Electromechanical Impedance (EMI) technique, has shown its potential to overcome remaining problems and challenges. Unfortunately, the recently developed neural network algorithms have not shown significant improvements in the accuracy of rate and the required processing time. In order to fill this gap in advanced neural networks used with EMI techniques, this paper proposes an enhanced and reliable strategy for improving the structural damage detection via: (1) Savitzky–Golay (SG) filter, using both first and second derivatives; (2) Probabilistic Neural Network (PNN); and, (3) Simplified Fuzzy ARTMAP Network (SFAN). Those three methods were employed to analyze the EMI data experimentally obtained from an aluminum plate containing three attached PZT (Lead Zirconate Titanate) patches. In this present study, the damage scenarios were simulated by attaching a small metallic nut at three different positions in the aluminum plate. We found that the proposed method achieves a hit rate of more than 83%, which is significantly higher than current state-of-the-art approaches. Furthermore, this approach results in an improvement of 93% when considering the best case scenario. PMID:29316693

  8. Voltage Estimation in Active Distribution Grids Using Neural Networks

    DEFF Research Database (Denmark)

    Pertl, Michael; Heussen, Kai; Gehrke, Oliver

    2016-01-01

    the observability of distribution systems has to be improved. To increase the situational awareness of the power system operator data driven methods can be employed. These methods benefit from newly available data sources such as smart meters. This paper presents a voltage estimation method based on neural networks...

  9. Distributed systems

    CERN Document Server

    Van Steen, Maarten

    2017-01-01

    For this third edition of "Distributed Systems," the material has been thoroughly revised and extended, integrating principles and paradigms into nine chapters: 1. Introduction 2. Architectures 3. Processes 4. Communication 5. Naming 6. Coordination 7. Replication 8. Fault tolerance 9. Security A separation has been made between basic material and more specific subjects. The latter have been organized into boxed sections, which may be skipped on first reading. To assist in understanding the more algorithmic parts, example programs in Python have been included. The examples in the book leave out many details for readability, but the complete code is available through the book's Website, hosted at www.distributed-systems.net.

  10. 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

  11. 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.

  12. 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

  13. Distributed Adaptive Neural Network Output Tracking of Leader-Following High-Order Stochastic Nonlinear Multiagent Systems With Unknown Dead-Zone Input.

    Science.gov (United States)

    Hua, Changchun; Zhang, Liuliu; Guan, Xinping

    2017-01-01

    This paper studies the problem of distributed output tracking consensus control for a class of high-order stochastic nonlinear multiagent systems with unknown nonlinear dead-zone under a directed graph topology. The adaptive neural networks are used to approximate the unknown nonlinear functions and a new inequality is used to deal with the completely unknown dead-zone input. Then, we design the controllers based on backstepping method and the dynamic surface control technique. It is strictly proved that the resulting closed-loop system is stable in probability in the sense of semiglobally uniform ultimate boundedness and the tracking errors between the leader and the followers approach to a small residual set based on Lyapunov stability theory. Finally, two simulation examples are presented to show the effectiveness and the advantages of the proposed techniques.

  14. Global Robust Stability of Switched Interval Neural Networks with Discrete and Distributed Time-Varying Delays of Neural Type

    Directory of Open Access Journals (Sweden)

    Huaiqin Wu

    2012-01-01

    Full Text Available By combing the theories of the switched systems and the interval neural networks, the mathematics model of the switched interval neural networks with discrete and distributed time-varying delays of neural type is presented. A set of the interval parameter uncertainty neural networks with discrete and distributed time-varying delays of neural type are used as the individual subsystem, and an arbitrary switching rule is assumed to coordinate the switching between these networks. By applying the augmented Lyapunov-Krasovskii functional approach and linear matrix inequality (LMI techniques, a delay-dependent criterion is achieved to ensure to such switched interval neural networks to be globally asymptotically robustly stable in terms of LMIs. The unknown gain matrix is determined by solving this delay-dependent LMIs. Finally, an illustrative example is given to demonstrate the validity of the theoretical results.

  15. 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...

  16. Self-Tuning Fully-Connected PID Neural Network System for Distributed Temperature Sensing and Control of Instrument with Multi-Modules.

    Science.gov (United States)

    Zhang, Zhen; Ma, Cheng; Zhu, Rong

    2016-10-14

    High integration of multi-functional instruments raises a critical issue in temperature control that is challenging due to its spatial-temporal complexity. This paper presents a multi-input multi-output (MIMO) self-tuning temperature sensing and control system for efficiently modulating the temperature environment within a multi-module instrument. The smart system ensures that the internal temperature of the instrument converges to a target without the need of a system model, thus making the control robust. The system consists of a fully-connected proportional-integral-derivative (PID) neural network (FCPIDNN) and an on-line self-tuning module. The experimental results show that the presented system can effectively control the internal temperature under various mission scenarios, in particular, it is able to self-reconfigure upon actuator failure. The system provides a new scheme for a complex and time-variant MIMO control system which can be widely applied for the distributed measurement and control of the environment in instruments, integration electronics, and house constructions.

  17. Self-Tuning Fully-Connected PID Neural Network System for Distributed Temperature Sensing and Control of Instrument with Multi-Modules

    Directory of Open Access Journals (Sweden)

    Zhen Zhang

    2016-10-01

    Full Text Available High integration of multi-functional instruments raises a critical issue in temperature control that is challenging due to its spatial–temporal complexity. This paper presents a multi-input multi-output (MIMO self-tuning temperature sensing and control system for efficiently modulating the temperature environment within a multi-module instrument. The smart system ensures that the internal temperature of the instrument converges to a target without the need of a system model, thus making the control robust. The system consists of a fully-connected proportional–integral–derivative (PID neural network (FCPIDNN and an on-line self-tuning module. The experimental results show that the presented system can effectively control the internal temperature under various mission scenarios, in particular, it is able to self-reconfigure upon actuator failure. The system provides a new scheme for a complex and time-variant MIMO control system which can be widely applied for the distributed measurement and control of the environment in instruments, integration electronics, and house constructions.

  18. 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.

  19. 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.

  20. 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.

  1. Toward multi-area distributed network of implanted neural interrogators

    Science.gov (United States)

    Powell, Marc P.; Hou, Xiaoxiao; Galligan, Craig; Ashe, Jeffrey; Borton, David A.

    2017-08-01

    As we aim to improve our understanding of the brain, it is critical that researchers have simultaneous multi-area, large-scale access to the brain. Information processing in the brain occurs through close and distant coupling of functional sub-domains, as opposed to within isolated single neurons. However, commercially available neural interfaces capable of sensing electrophysiology of single neurons, currently allow access to only a small, mm3 volume of cortical cells, are not scalable to recording from orders of magnitude more neurons, and leverage bulky, skull mounted hardware and cabling sensitive to relative movements of the skull and brain. In this work, we propose a system capable of recording from many individual distributed neural interrogator nodes, untethered from any external electronics. Using an array of epidural inductive coils to wirelessly power the implanted electronics, the system is intended to be agnostic to the surgical placement of any individual node. Here, we demonstrate the ability to transmit nearly 15mW of power with greater than 50% power transfer efficiency, benchtop testing of individual subcircuit system components showing successful digitization of neural signals, and wireless transmission currently supporting a data rate of 3.84Mbps. We leverage a software defined radio based RF receiver to demodulate the data which can be stored in memory for later retrieval. Finally, we introduce a packaging technology capable of isolating active electronics from the surrounding tissue while providing capability for electrical feed-through assemblies for external neural interfacing. We expect, based on the presented preliminary findings, that the system can be integrated into a platform technology for the study of the intricate interactions between cortical domains.

  2. Estimating Conditional Distributions by Neural Networks

    DEFF Research Database (Denmark)

    Kulczycki, P.; Schiøler, Henrik

    1998-01-01

    Neural Networks for estimating conditionaldistributions and their associated quantiles are investigated in this paper. A basic network structure is developed on the basis of kernel estimation theory, and consistency property is considered from a mild set of assumptions. A number of applications...

  3. 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.

  4. Neural network determination of parton distributions: the nonsinglet case

    International Nuclear Information System (INIS)

    Del Debbio, Luigi; Forte, Stefano; Latorre, Jose I.; Piccione, Andrea; Rojo, Joan

    2007-01-01

    We provide a determination of the isotriplet quark distribution from available deep-inelastic data using neural networks. We give a general introduction to the neural network approach to parton distributions, which provides a solution to the problem of constructing a faithful and unbiased probability distribution of parton densities based on available experimental information. We discuss in detail the techniques which are necessary in order to construct a Monte Carlo representation of the data, to construct and evolve neural parton distributions, and to train them in such a way that the correct statistical features of the data are reproduced. We present the results of the application of this method to the determination of the nonsinglet quark distribution up to next-to-next-to-leading order, and compare them with those obtained using other approaches

  5. 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…

  6. 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.

  7. Global asymptotic stability analysis of bidirectional associative memory neural networks with distributed delays and impulse

    International Nuclear Information System (INIS)

    Huang Zaitang; Luo Xiaoshu; Yang Qigui

    2007-01-01

    Many systems existing in physics, chemistry, biology, engineering and information science can be characterized by impulsive dynamics caused by abrupt jumps at certain instants during the process. These complex dynamical behaviors can be model by impulsive differential system or impulsive neural networks. This paper formulates and studies a new model of impulsive bidirectional associative memory (BAM) networks with finite distributed delays. Several fundamental issues, such as global asymptotic stability and existence and uniqueness of such BAM neural networks with impulse and distributed delays, are established

  8. Determination of size distribution using neural networks

    NARCIS (Netherlands)

    Stevens, JH; Nijhuis, JAG; Spaanenburg, L; Mohammadian, M

    1999-01-01

    In this paper we present a novel approach to the estimation of size distributions of grains in water from images. External conditions such as the concentrations of grains in water cannot be controlled. This poses problems for local image analysis which tries to identify and measure single grains.

  9. Drinking Water Distribution Systems

    Science.gov (United States)

    Learn about an overview of drinking water distribution systems, the factors that degrade water quality in the distribution system, assessments of risk, future research about these risks, and how to reduce cross-connection control risk.

  10. 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)

  11. Periodic bidirectional associative memory neural networks with distributed delays

    Science.gov (United States)

    Chen, Anping; Huang, Lihong; Liu, Zhigang; Cao, Jinde

    2006-05-01

    Some sufficient conditions are obtained for the existence and global exponential stability of a periodic solution to the general bidirectional associative memory (BAM) neural networks with distributed delays by using the continuation theorem of Mawhin's coincidence degree theory and the Lyapunov functional method and the Young's inequality technique. These results are helpful for designing a globally exponentially stable and periodic oscillatory BAM neural network, and the conditions can be easily verified and be applied in practice. An example is also given to illustrate our results.

  12. Stability analysis of impulsive fuzzy cellular neural networks with distributed delays and reaction-diffusion terms

    International Nuclear Information System (INIS)

    Li Zuoan; Li Kelin

    2009-01-01

    In this paper, we investigate a class of impulsive fuzzy cellular neural networks with distributed delays and reaction-diffusion terms. By employing the delay differential inequality with impulsive initial conditions and M-matrix theory, we find some sufficient conditions ensuring the existence, uniqueness and global exponential stability of equilibrium point for impulsive fuzzy cellular neural networks with distributed delays and reaction-diffusion terms. In particular, the estimate of the exponential converging index is also provided, which depends on the system parameters. An example is given to show the effectiveness of the results obtained here.

  13. 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...

  14. 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.

  15. 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.

  16. Smart Distribution Systems

    Directory of Open Access Journals (Sweden)

    Yazhou Jiang

    2016-04-01

    Full Text Available The increasing importance of system reliability and resilience is changing the way distribution systems are planned and operated. To achieve a distribution system self-healing against power outages, emerging technologies and devices, such as remote-controlled switches (RCSs and smart meters, are being deployed. The higher level of automation is transforming traditional distribution systems into the smart distribution systems (SDSs of the future. The availability of data and remote control capability in SDSs provides distribution operators with an opportunity to optimize system operation and control. In this paper, the development of SDSs and resulting benefits of enhanced system capabilities are discussed. A comprehensive survey is conducted on the state-of-the-art applications of RCSs and smart meters in SDSs. Specifically, a new method, called Temporal Causal Diagram (TCD, is used to incorporate outage notifications from smart meters for enhanced outage management. To fully utilize the fast operation of RCSs, the spanning tree search algorithm is used to develop service restoration strategies. Optimal placement of RCSs and the resulting enhancement of system reliability are discussed. Distribution system resilience with respect to extreme events is presented. Test cases are used to demonstrate the benefit of SDSs. Active management of distributed generators (DGs is introduced. Future research in a smart distribution environment is proposed.

  17. Electric distribution systems

    CERN Document Server

    Sallam, A A

    2010-01-01

    "Electricity distribution is the penultimate stage in the delivery of electricity to end users. The only book that deals with the key topics of interest to distribution system engineers, Electric Distribution Systems presents a comprehensive treatment of the subject with an emphasis on both the practical and academic points of view. Reviewing traditional and cutting-edge topics, the text is useful to practicing engineers working with utility companies and industry, undergraduate graduate and students, and faculty members who wish to increase their skills in distribution system automation and monitoring."--

  18. 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.

  19. Distributed Operating Systems

    NARCIS (Netherlands)

    Mullender, Sape J.

    1987-01-01

    In the past five years, distributed operating systems research has gone through a consolidation phase. On a large number of design issues there is now considerable consensus between different research groups. In this paper, an overview of recent research in distributed systems is given. In turn, the

  20. Calculations of dose distributions using a neural network model

    International Nuclear Information System (INIS)

    Mathieu, R; Martin, E; Gschwind, R; Makovicka, L; Contassot-Vivier, S; Bahi, J

    2005-01-01

    The main goal of external beam radiotherapy is the treatment of tumours, while sparing, as much as possible, surrounding healthy tissues. In order to master and optimize the dose distribution within the patient, dosimetric planning has to be carried out. Thus, for determining the most accurate dose distribution during treatment planning, a compromise must be found between the precision and the speed of calculation. Current techniques, using analytic methods, models and databases, are rapid but lack precision. Enhanced precision can be achieved by using calculation codes based, for example, on Monte Carlo methods. However, in spite of all efforts to optimize speed (methods and computer improvements), Monte Carlo based methods remain painfully slow. A newer way to handle all of these problems is to use a new approach in dosimetric calculation by employing neural networks. Neural networks (Wu and Zhu 2000 Phys. Med. Biol. 45 913-22) provide the advantages of those various approaches while avoiding their main inconveniences, i.e., time-consumption calculations. This permits us to obtain quick and accurate results during clinical treatment planning. Currently, results obtained for a single depth-dose calculation using a Monte Carlo based code (such as BEAM (Rogers et al 2003 NRCC Report PIRS-0509(A) rev G)) require hours of computing. By contrast, the practical use of neural networks (Mathieu et al 2003 Proceedings Journees Scientifiques Francophones, SFRP) provides almost instant results and quite low errors (less than 2%) for a two-dimensional dosimetric map

  1. Pervasive Electricity Distribution System

    Directory of Open Access Journals (Sweden)

    Muhammad Usman Tahir

    2017-06-01

    Full Text Available Now a days a country cannot become economically strong until and unless it has enough electrical power to fulfil industrial and domestic needs. Electrical power being the pillar of any country’s economy, needs to be used in an efficient way. The same step is taken here by proposing a new system for energy distribution from substation to consumer houses, also it monitors the consumer consumption and record data. Unlike traditional manual Electrical systems, pervasive electricity distribution system (PEDS introduces a fresh perspective to monitor the feeder line status at distribution and consumer level. In this system an effort is taken to address the issues of electricity theft, manual billing, online monitoring of electrical distribution system and automatic control of electrical distribution points. The project is designed using microcontroller and different sensors, its GUI is designed in Labview software.

  2. Distributed Recurrent Neural Forward Models with Neural Control for Complex Locomotion in Walking Robots

    DEFF Research Database (Denmark)

    Dasgupta, Sakyasingha; Goldschmidt, Dennis; Wörgötter, Florentin

    2015-01-01

    here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of (1) central pattern generator based control for generating basic rhythmic patterns and coordinated......Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental...... conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain...

  3. Cooling water distribution system

    Science.gov (United States)

    Orr, Richard

    1994-01-01

    A passive containment cooling system for a nuclear reactor containment vessel. Disclosed is a cooling water distribution system for introducing cooling water by gravity uniformly over the outer surface of a steel containment vessel using an interconnected series of radial guide elements, a plurality of circumferential collector elements and collector boxes to collect and feed the cooling water into distribution channels extending along the curved surface of the steel containment vessel. The cooling water is uniformly distributed over the curved surface by a plurality of weirs in the distribution channels.

  4. A distributed current stimulator ASIC for high density neural stimulation.

    Science.gov (United States)

    Jeong Hoan Park; Chaebin Kim; Seung-Hee Ahn; Tae Mok Gwon; Joonsoo Jeong; Sang Beom Jun; Sung June Kim

    2016-08-01

    This paper presents a novel distributed neural stimulator scheme. Instead of a single stimulator ASIC in the package, multiple ASICs are embedded at each electrode site for stimulation with a high density electrode array. This distributed architecture enables the simplification of wiring between electrodes and stimulator ASIC that otherwise could become too complex as the number of electrode increases. The individual ASIC chip is designed to have a shared data bus that independently controls multiple stimulating channels. Therefore, the number of metal lines is determined by the distributed ASICs, not by the channel number. The function of current steering is also implemented within each ASIC in order to increase the effective number of channels via pseudo channel stimulation. Therefore, the chip area can be used more efficiently. The designed chip was fabricated with area of 0.3 mm2 using 0.18 μm BCDMOS process, and the bench-top test was also conducted to validate chip performance.

  5. Advanced Distribution Management System

    OpenAIRE

    Avazov, Artur; Sobinova, Lubov Anatolievna

    2016-01-01

    This article describes the advisability of using advanced distribution management systems in the electricity distribution networks area and considers premises of implementing ADMS within the Smart Grid era. Also, it gives the big picture of ADMS and discusses the ADMS advantages and functionalities.

  6. Advanced Distribution Management System

    Science.gov (United States)

    Avazov, Artur R.; Sobinova, Liubov A.

    2016-02-01

    This article describes the advisability of using advanced distribution management systems in the electricity distribution networks area and considers premises of implementing ADMS within the Smart Grid era. Also, it gives the big picture of ADMS and discusses the ADMS advantages and functionalities.

  7. Advanced Distribution Management System

    Directory of Open Access Journals (Sweden)

    Avazov Artur R.

    2016-01-01

    Full Text Available This article describes the advisability of using advanced distribution management systems in the electricity distribution networks area and considers premises of implementing ADMS within the Smart Grid era. Also, it gives the big picture of ADMS and discusses the ADMS advantages and functionalities.

  8. 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.

  9. A distribution management system

    Energy Technology Data Exchange (ETDEWEB)

    Verho, P.; Jaerventausta, P.; Kaerenlampi, M.; Paulasaari, H. [Tampere Univ. of Technology (Finland); Partanen, J. [Lappeenranta Univ. of Technology (Finland)

    1996-12-31

    The development of new distribution automation applications is considerably wide nowadays. One of the most interesting areas is the development of a distribution management system (DMS) as an expansion of the traditional SCADA system. At the power transmission level such a system is called an energy management system (EMS). The idea of these expansions is to provide supporting tools for control center operators in system analysis and operation planning. The needed data for new applications is mainly available in some existing systems. Thus the computer systems of utilities must be integrated. The main data source for the new applications in the control center are the AM/FM/GIS (i.e. the network database system), the SCADA, and the customer information system (CIS). The new functions can be embedded in some existing computer system. This means a strong dependency on the vendor of the existing system. An alternative strategy is to develop an independent system which is integrated with other computer systems using well-defined interfaces. The latter approach makes it possible to use the new applications in various computer environments, having only a weak dependency on the vendors of the other systems. In the research project this alternative is preferred and used in developing an independent distribution management system

  10. A distribution management system

    Energy Technology Data Exchange (ETDEWEB)

    Verho, P; Jaerventausta, P; Kaerenlampi, M; Paulasaari, H [Tampere Univ. of Technology (Finland); Partanen, J [Lappeenranta Univ. of Technology (Finland)

    1997-12-31

    The development of new distribution automation applications is considerably wide nowadays. One of the most interesting areas is the development of a distribution management system (DMS) as an expansion of the traditional SCADA system. At the power transmission level such a system is called an energy management system (EMS). The idea of these expansions is to provide supporting tools for control center operators in system analysis and operation planning. The needed data for new applications is mainly available in some existing systems. Thus the computer systems of utilities must be integrated. The main data source for the new applications in the control center are the AM/FM/GIS (i.e. the network database system), the SCADA, and the customer information system (CIS). The new functions can be embedded in some existing computer system. This means a strong dependency on the vendor of the existing system. An alternative strategy is to develop an independent system which is integrated with other computer systems using well-defined interfaces. The latter approach makes it possible to use the new applications in various computer environments, having only a weak dependency on the vendors of the other systems. In the research project this alternative is preferred and used in developing an independent distribution management system

  11. 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.

  12. A distribution management system

    Energy Technology Data Exchange (ETDEWEB)

    Jaerventausta, P; Verho, P; Kaerenlampi, M; Pitkaenen, M [Tampere Univ. of Technology (Finland); Partanen, J [Lappeenranta Univ. of Technology (Finland)

    1998-08-01

    The development of new distribution automation applications is considerably wide nowadays. One of the most interesting areas is the development of a distribution management system (DMS) as an expansion to the traditional SCADA system. At the power transmission level such a system is called an energy management system (EMS). The idea of these expansions is to provide supporting tools for control center operators in system analysis and operation planning. Nowadays the SCADA is the main computer system (and often the only) in the control center. However, the information displayed by the SCADA is often inadequate, and several tasks cannot be solved by a conventional SCADA system. A need for new computer applications in control center arises from the insufficiency of the SCADA and some other trends. The latter means that the overall importance of the distribution networks is increasing. The slowing down of load-growth has often made network reinforcements unprofitable. Thus the existing network must be operated more efficiently. At the same time larger distribution areas are for economical reasons being monitored at one control center and the size of the operation staff is decreasing. The quality of supply requirements are also becoming stricter. The needed data for new applications is mainly available in some existing systems. Thus the computer systems of utilities must be integrated. The main data source for the new applications in the control center are the AM/FM/GIS (i.e. the network database system), the SCADA, and the customer information system (CIS). The new functions can be embedded in some existing computer system. This means a strong dependency on the vendor of the existing system. An alternative strategy is to develop an independent system which is integrated with other computer systems using well-defined interfaces. The latter approach makes it possible to use the new applications in various computer environments, having only a weak dependency on the

  13. Research on 3D power distribution of PWR reactor core based on RBF neural network

    International Nuclear Information System (INIS)

    Xia Hong; Li Bin; Liu Jianxin

    2014-01-01

    Real-time monitor for 3D power distribution is critical to nuclear safety and high efficiency of NPP's operation as well as the control system optimization. A method was proposed to set up a real-time monitor system for 3D power distribution by using of ex-core neutron detecting system and RBF neural network for improving the instantaneity of the monitoring results and reducing the fitting error of the 3D power distribution. A series of experiments were operated on a 300 MW PWR simulation system. The results demonstrate that the new monitor system works very well under condition of certain burnup range during the fuel cycle and reconstructs the real-time 3D distribution of reactor core power. The accuracy of the model is improved effectively with the help of several methods. (authors)

  14. Distributed processor systems

    International Nuclear Information System (INIS)

    Zacharov, B.

    1976-01-01

    In recent years, there has been a growing tendency in high-energy physics and in other fields to solve computational problems by distributing tasks among the resources of inter-coupled processing devices and associated system elements. This trend has gained further momentum more recently with the increased availability of low-cost processors and with the development of the means of data distribution. In two lectures, the broad question of distributed computing systems is examined and the historical development of such systems reviewed. An attempt is made to examine the reasons for the existence of these systems and to discern the main trends for the future. The components of distributed systems are discussed in some detail and particular emphasis is placed on the importance of standards and conventions in certain key system components. The ideas and principles of distributed systems are discussed in general terms, but these are illustrated by a number of concrete examples drawn from the context of the high-energy physics environment. (Auth.)

  15. Distributed Treatment Systems.

    Science.gov (United States)

    Zgonc, David; Plante, Luke

    2017-10-01

    This section presents a review of the literature published in 2016 on topics relating to distributed treatment systems. This review is divided into the following sections with multiple subsections under each: constituent removal; treatment technologies; and planning and treatment system management.

  16. 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.

  17. Distributed Computerized Catalog System

    Science.gov (United States)

    Borgen, Richard L.; Wagner, David A.

    1995-01-01

    DarkStar Distributed Catalog System describes arbitrary data objects in unified manner, providing end users with versatile, yet simple search mechanism for locating and identifying objects. Provides built-in generic and dynamic graphical user interfaces. Design of system avoids some of problems of standard DBMS, and system provides more flexibility than do conventional relational data bases, or object-oriented data bases. Data-collection lattice partly hierarchical representation of relationships among collections, subcollections, and data objects.

  18. 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

  19. 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

  20. [Distribution of neural memory, loading factor, its regulation and optimization].

    Science.gov (United States)

    Radchenko, A N

    1999-01-01

    Recording and retrieving functions of the neural memory are simulated as a control of local conformational processes in neural synaptic fields. The localization of conformational changes is related to the afferent temporal-spatial pulse pattern flow, the microstructure of connections and a plurality of temporal delays in synaptic fields and afferent pathways. The loci of conformations are described by sets of afferent addresses named address domains. Being superimposed on each other, address domains form a multilayer covering of the address space of the neuron or the ensemble. The superposition factor determines the dissemination of the conformational process, and the fuzzing of memory, and its accuracy and reliability. The engram is formed as detects in the packing of the address space and hence can be retrieved in inverse form. The accuracy of the retrieved information depends on the threshold level of conformational transitions, the distribution of conformational changes in synaptic fields of the neuronal population, and the memory loading factor. The latter is represented in the model by a slow potential. It reflects total conformational changes and displaces the membrane potential to monostable conformational regimes, by governing the exit from the recording regime, the potentiation of the neurone, and the readiness to reproduction. A relative amplitude of the slow potential and the coefficient of postconformational modification of ionic conductivity, which provides maximum reliability, accuracy, and capacity of memory, are calculated.

  1. Supervised learning of probability distributions by neural networks

    Science.gov (United States)

    Baum, Eric B.; Wilczek, Frank

    1988-01-01

    Supervised learning algorithms for feedforward neural networks are investigated analytically. The back-propagation algorithm described by Werbos (1974), Parker (1985), and Rumelhart et al. (1986) is generalized by redefining the values of the input and output neurons as probabilities. The synaptic weights are then varied to follow gradients in the logarithm of likelihood rather than in the error. This modification is shown to provide a more rigorous theoretical basis for the algorithm and to permit more accurate predictions. A typical application involving a medical-diagnosis expert system is discussed.

  2. 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.

  3. Optimizing electrical distribution systems

    International Nuclear Information System (INIS)

    Scott, W.G.

    1990-01-01

    Electrical utility distribution systems are in the middle of an unprecedented technological revolution in planning, design, maintenance and operation. The prime movers of the revolution are the major economic shifts that affect decision making. The major economic influence on the revolution is the cost of losses (technical and nontechnical). The vehicle of the revolution is the computer, which enables decision makers to examine alternatives in greater depth and detail than their predecessors could. The more important elements of the technological revolution are: system planning, computers, load forecasting, analytical systems (primary systems, transformers and secondary systems), system losses and coming technology. The paper is directed towards the rather unique problems encountered by engineers of utilities in developing countries - problems that are being solved through high technology, such as the recent World Bank-financed engineering computer system for Sri Lanka. This system includes a DEC computer, digitizer, plotter and engineering software to model the distribution system via a digitizer, analyse the system and plot single-line diagrams. (author). 1 ref., 4 tabs., 6 figs

  4. 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.

  5. Distributed Learning, Recognition, and Prediction by ART and ARTMAP Neural Networks.

    Science.gov (United States)

    Carpenter, Gail A.

    1997-11-01

    A class of adaptive resonance theory (ART) models for learning, recognition, and prediction with arbitrarily distributed code representations is introduced. Distributed ART neural networks combine the stable fast learning capabilities of winner-take-all ART systems with the noise tolerance and code compression capabilities of multilayer perceptrons. With a winner-take-all code, the unsupervised model dART reduces to fuzzy ART and the supervised model dARTMAP reduces to fuzzy ARTMAP. With a distributed code, these networks automatically apportion learned changes according to the degree of activation of each coding node, which permits fast as well as slow learning without catastrophic forgetting. Distributed ART models replace the traditional neural network path weight with a dynamic weight equal to the rectified difference between coding node activation and an adaptive threshold. Thresholds increase monotonically during learning according to a principle of atrophy due to disuse. However, monotonic change at the synaptic level manifests itself as bidirectional change at the dynamic level, where the result of adaptation resembles long-term potentiation (LTP) for single-pulse or low frequency test inputs but can resemble long-term depression (LTD) for higher frequency test inputs. This paradoxical behavior is traced to dual computational properties of phasic and tonic coding signal components. A parallel distributed match-reset-search process also helps stabilize memory. Without the match-reset-search system, dART becomes a type of distributed competitive learning network.

  6. Distributed Systems 3/e

    NARCIS (Netherlands)

    Tanenbaum, A.S.; van Steen, M.R.

    2016-01-01

    For this third edition of "Distributed Systems," the material has been thoroughly revised and extended, integrating principles and paradigms into nine chapters: 1. Introduction 2. Architectures 3. Processes 4. Communication 5. Naming 6. Coordination 7. Replication 8. Fault tolerance 9. Security A

  7. New results on global exponential dissipativity analysis of memristive inertial neural networks with distributed time-varying delays.

    Science.gov (United States)

    Zhang, Guodong; Zeng, Zhigang; Hu, Junhao

    2018-01-01

    This paper is concerned with the global exponential dissipativity of memristive inertial neural networks with discrete and distributed time-varying delays. By constructing appropriate Lyapunov-Krasovskii functionals, some new sufficient conditions ensuring global exponential dissipativity of memristive inertial neural networks are derived. Moreover, the globally exponential attractive sets and positive invariant sets are also presented here. In addition, the new proposed results here complement and extend the earlier publications on conventional or memristive neural network dynamical systems. Finally, numerical simulations are given to illustrate the effectiveness of obtained results. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. 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...

  9. Distributed Optimization System

    Science.gov (United States)

    Hurtado, John E.; Dohrmann, Clark R.; Robinett, III, Rush D.

    2004-11-30

    A search system and method for controlling multiple agents to optimize an objective using distributed sensing and cooperative control. The search agent can be one or more physical agents, such as a robot, and can be software agents for searching cyberspace. The objective can be: chemical sources, temperature sources, radiation sources, light sources, evaders, trespassers, explosive sources, time dependent sources, time independent sources, function surfaces, maximization points, minimization points, and optimal control of a system such as a communication system, an economy, a crane, and a multi-processor computer.

  10. Distributed Data Management and Distributed File Systems

    CERN Document Server

    Girone, Maria

    2015-01-01

    The LHC program has been successful in part due to the globally distributed computing resources used for collecting, serving, processing, and analyzing the large LHC datasets. The introduction of distributed computing early in the LHC program spawned the development of new technologies and techniques to synchronize information and data between physically separated computing centers. Two of the most challenges services are the distributed file systems and the distributed data management systems. In this paper I will discuss how we have evolved from local site services to more globally independent services in the areas of distributed file systems and data management and how these capabilities may continue to evolve into the future. I will address the design choices, the motivations, and the future evolution of the computing systems used for High Energy Physics.

  11. 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.

  12. Distributed System Design Checklist

    Science.gov (United States)

    Hall, Brendan; Driscoll, Kevin

    2014-01-01

    This report describes a design checklist targeted to fault-tolerant distributed electronic systems. Many of the questions and discussions in this checklist may be generally applicable to the development of any safety-critical system. However, the primary focus of this report covers the issues relating to distributed electronic system design. The questions that comprise this design checklist were created with the intent to stimulate system designers' thought processes in a way that hopefully helps them to establish a broader perspective from which they can assess the system's dependability and fault-tolerance mechanisms. While best effort was expended to make this checklist as comprehensive as possible, it is not (and cannot be) complete. Instead, we expect that this list of questions and the associated rationale for the questions will continue to evolve as lessons are learned and further knowledge is established. In this regard, it is our intent to post the questions of this checklist on a suitable public web-forum, such as the NASA DASHLink AFCS repository. From there, we hope that it can be updated, extended, and maintained after our initial research has been completed.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. Electrical distribution system management

    International Nuclear Information System (INIS)

    Hajos, L.; Mortarulo, M.; Chang, K.; Sparks, T.

    1990-01-01

    This paper reports that maintenance of electrical system data is essential to the operation, maintenance, and modification of a nuclear station. Load and equipment changes affect equipment sizing, available short-circuit currents and protection coordination. System parameters must be maintained in a controlled manner to enable evaluation of proposed modifications and provide adequate verification and traceability. For this purpose, Public Service Electric and Gas Company has implemented a Verified and Validated Electric Distribution System Management (EDSM) program at the Hope Creek and Salem Nuclear Power Stations. EDSM program integrates computerized configuration management of electrical systems with calculational software the Technical Standard procedures. The software platform is PC-based. The Database Manager and Calculational programs have been linked together through a user friendly menu system. The database management nodule enable s assembly and maintenance of databases for individual loads, buses, and branches within the electrical systems with system access and approval controlled through electronic security incorporated within the database manger. Reports drawn from the database serve as the as-built and/or as-designed record of the system configurations. This module also creates input data files of network parameters in a format readable by the calculational modules. Calculations modules provide load flow, voltage drop, motor starting, and short-circuit analyses, as well as dynamic analyses of bus transfers

  19. Planning Systems for Distributed Operations

    Science.gov (United States)

    Maxwell, Theresa G.

    2002-01-01

    This viewgraph representation presents an overview of the mission planning process involving distributed operations (such as the International Space Station (ISS)) and the computer hardware and software systems needed to support such an effort. Topics considered include: evolution of distributed planning systems, ISS distributed planning, the Payload Planning System (PPS), future developments in distributed planning systems, Request Oriented Scheduling Engine (ROSE) and Next Generation distributed planning systems.

  20. Managing Distributed Knowledge Systems

    DEFF Research Database (Denmark)

    Sørensen, Brian Vejrum; Gelbuda, Modestas

    2005-01-01

    . This paper contributes to the research on organizations as distributed knowledge systems by addressing two weaknesses of the social practice literature. Firstly, it downplays the importance of formal structure and organizational design and intervention efforts by key organizational members. Secondly, it does......The article argues that the growth of de novo knowledge-based organization depends on managing and coordinating increasingly growing and, therefore, distributed knowledge. Moreover, the growth in knowledge is often accompanied by an increasing organizational complexity, which is a result...... of integrating new people, building new units and adding activities to the existing organization. It is argued that knowledge is not a stable capacity that belongs to any actor alone, but that it is rather an ongoing social accomplishment, which is created and recreated as actors engage in mutual activities...

  1. Distributed road assessment system

    Science.gov (United States)

    Beer, N. Reginald; Paglieroni, David W

    2014-03-25

    A system that detects damage on or below the surface of a paved structure or pavement is provided. A distributed road assessment system includes road assessment pods and a road assessment server. Each road assessment pod includes a ground-penetrating radar antenna array and a detection system that detects road damage from the return signals as the vehicle on which the pod is mounted travels down a road. Each road assessment pod transmits to the road assessment server occurrence information describing each occurrence of road damage that is newly detected on a current scan of a road. The road assessment server maintains a road damage database of occurrence information describing the previously detected occurrences of road damage. After the road assessment server receives occurrence information for newly detected occurrences of road damage for a portion of a road, the road assessment server determines which newly detected occurrences correspond to which previously detected occurrences of road damage.

  2. Use of artificial neural network in estimating channel power distribution of a 220 MWe PHWR

    International Nuclear Information System (INIS)

    Dubey, B.P.; Chandra, A.K.; Govindarajan, G.; Jagannathan, V.; Kataria, S.K.

    1998-01-01

    Knowledge of the distribution of power in all the 306 channels of a Pressurised Heavy Water Reactor (PHWR) as a result of the movement of one or more of the four regulating rods is important from the operation and maintenance point view of the reactor. Conventional computer codes available for this purpose take several minutes to calculate the channel power distribution on PC-AT/486. An Artificial Neural network (ANN), based on the RPROP algorithm has been developed and employed in predicting channel power distribution of a 220 MWe Indian PHWR as a result of a perturbation caused by the movement of one or more of the four regulating rods of the reactor. The ANN based system produces the result of an analysis much faster than that produced by a conventional computer code usually employed for this application. The ANN based system is rugged, accurate and fast, and therefore, has potential to be used in real-time applications. (author)

  3. Communication Facilities for Distributed Systems

    Directory of Open Access Journals (Sweden)

    V. Barladeanu

    1997-01-01

    Full Text Available The design of physical networks and communication protocols in Distributed Systems can have a direct impact on system efficiency and reliability. This paper tries to identify efficient mechanisms and paradigms for communication in distributed systems.

  4. Optimizing queries in distributed systems

    Directory of Open Access Journals (Sweden)

    Ion LUNGU

    2006-01-01

    Full Text Available This research presents the main elements of query optimizations in distributed systems. First, data architecture according with system level architecture in a distributed environment is presented. Then the architecture of a distributed database management system (DDBMS is described on conceptual level followed by the presentation of the distributed query execution steps on these information systems. The research ends with presentation of some aspects of distributed database query optimization and strategies used for that.

  5. Quality monitored distributed voting system

    Science.gov (United States)

    Skogmo, David

    1997-01-01

    A quality monitoring system can detect certain system faults and fraud attempts in a distributed voting system. The system uses decoy voters to cast predetermined check ballots. Absent check ballots can indicate system faults. Altered check ballots can indicate attempts at counterfeiting votes. The system can also cast check ballots at predetermined times to provide another check on the distributed voting system.

  6. 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.

  7. Short term and medium term power distribution load forecasting by neural networks

    International Nuclear Information System (INIS)

    Yalcinoz, T.; Eminoglu, U.

    2005-01-01

    Load forecasting is an important subject for power distribution systems and has been studied from different points of view. In general, load forecasts should be performed over a broad spectrum of time intervals, which could be classified into short term, medium term and long term forecasts. Several research groups have proposed various techniques for either short term load forecasting or medium term load forecasting or long term load forecasting. This paper presents a neural network (NN) model for short term peak load forecasting, short term total load forecasting and medium term monthly load forecasting in power distribution systems. The NN is used to learn the relationships among past, current and future temperatures and loads. The neural network was trained to recognize the peak load of the day, total load of the day and monthly electricity consumption. The suitability of the proposed approach is illustrated through an application to real load shapes from the Turkish Electricity Distribution Corporation (TEDAS) in Nigde. The data represents the daily and monthly electricity consumption in Nigde, Turkey

  8. Cellular Neural Network-Based Methods for Distributed Network Intrusion Detection

    Directory of Open Access Journals (Sweden)

    Kang Xie

    2015-01-01

    Full Text Available According to the problems of current distributed architecture intrusion detection systems (DIDS, a new online distributed intrusion detection model based on cellular neural network (CNN was proposed, in which discrete-time CNN (DTCNN was used as weak classifier in each local node and state-controlled CNN (SCCNN was used as global detection method, respectively. We further proposed a new method for design template parameters of SCCNN via solving Linear Matrix Inequality. Experimental results based on KDD CUP 99 dataset show its feasibility and effectiveness. Emerging evidence has indicated that this new approach is affordable to parallelism and analog very large scale integration (VLSI implementation which allows the distributed intrusion detection to be performed better.

  9. A note on exponential convergence of neural networks with unbounded distributed delays

    Energy Technology Data Exchange (ETDEWEB)

    Chu Tianguang [Intelligent Control Laboratory, Center for Systems and Control, Department of Mechanics and Engineering Science, Peking University, Beijing 100871 (China)]. E-mail: chutg@pku.edu.cn; Yang Haifeng [Intelligent Control Laboratory, Center for Systems and Control, Department of Mechanics and Engineering Science, Peking University, Beijing 100871 (China)

    2007-12-15

    This note examines issues concerning global exponential convergence of neural networks with unbounded distributed delays. Sufficient conditions are derived by exploiting exponentially fading memory property of delay kernel functions. The method is based on comparison principle of delay differential equations and does not need the construction of any Lyapunov functionals. It is simple yet effective in deriving less conservative exponential convergence conditions and more detailed componentwise decay estimates. The results of this note and [Chu T. An exponential convergence estimate for analog neural networks with delay. Phys Lett A 2001;283:113-8] suggest a class of neural networks whose globally exponentially convergent dynamics is completely insensitive to a wide range of time delays from arbitrary bounded discrete type to certain unbounded distributed type. This is of practical interest in designing fast and reliable neural circuits. Finally, an open question is raised on the nature of delay kernels for attaining exponential convergence in an unbounded distributed delayed neural network.

  10. A note on exponential convergence of neural networks with unbounded distributed delays

    International Nuclear Information System (INIS)

    Chu Tianguang; Yang Haifeng

    2007-01-01

    This note examines issues concerning global exponential convergence of neural networks with unbounded distributed delays. Sufficient conditions are derived by exploiting exponentially fading memory property of delay kernel functions. The method is based on comparison principle of delay differential equations and does not need the construction of any Lyapunov functionals. It is simple yet effective in deriving less conservative exponential convergence conditions and more detailed componentwise decay estimates. The results of this note and [Chu T. An exponential convergence estimate for analog neural networks with delay. Phys Lett A 2001;283:113-8] suggest a class of neural networks whose globally exponentially convergent dynamics is completely insensitive to a wide range of time delays from arbitrary bounded discrete type to certain unbounded distributed type. This is of practical interest in designing fast and reliable neural circuits. Finally, an open question is raised on the nature of delay kernels for attaining exponential convergence in an unbounded distributed delayed neural network

  11. Process evaluation distributed system

    Science.gov (United States)

    Moffatt, Christopher L. (Inventor)

    2006-01-01

    The distributed system includes a database server, an administration module, a process evaluation module, and a data display module. The administration module is in communication with the database server for providing observation criteria information to the database server. The process evaluation module is in communication with the database server for obtaining the observation criteria information from the database server and collecting process data based on the observation criteria information. The process evaluation module utilizes a personal digital assistant (PDA). A data display module in communication with the database server, including a website for viewing collected process data in a desired metrics form, the data display module also for providing desired editing and modification of the collected process data. The connectivity established by the database server to the administration module, the process evaluation module, and the data display module, minimizes the requirement for manual input of the collected process data.

  12. Distributed security in closed distributed systems

    DEFF Research Database (Denmark)

    Hernandez, Alejandro Mario

    properties. This is also restricted to distributed systems in which the set of locations is known a priori. All this follows techniques borrowed from both the model checking and the static analysis communities. In the end, we reach a step towards solving the problem of enforcing security in distributed...... systems. We achieve the goal of showing how this can be done, though we restrict ourselves to closed systems and with a limited set of enforceable security policies. In this setting, our approach proves to be efficient. Finally, we achieve all this by bringing together several fields of Computer Science......The goal of the present thesis is to discuss, argue and conclude about ways to provide security to the information travelling around computer systems consisting of several known locations. When developing software systems, security of the information managed by these plays an important role...

  13. 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.

  14. 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

  15. Using a Simple Neural Network to Delineate Some Principles of Distributed Economic Choice.

    Science.gov (United States)

    Balasubramani, Pragathi P; Moreno-Bote, Rubén; Hayden, Benjamin Y

    2018-01-01

    The brain uses a mixture of distributed and modular organization to perform computations and generate appropriate actions. While the principles under which the brain might perform computations using modular systems have been more amenable to modeling, the principles by which the brain might make choices using distributed principles have not been explored. Our goal in this perspective is to delineate some of those distributed principles using a neural network method and use its results as a lens through which to reconsider some previously published neurophysiological data. To allow for direct comparison with our own data, we trained the neural network to perform binary risky choices. We find that value correlates are ubiquitous and are always accompanied by non-value information, including spatial information (i.e., no pure value signals). Evaluation, comparison, and selection were not distinct processes; indeed, value signals even in the earliest stages contributed directly, albeit weakly, to action selection. There was no place, other than at the level of action selection, at which dimensions were fully integrated. No units were specialized for specific offers; rather, all units encoded the values of both offers in an anti-correlated format, thus contributing to comparison. Individual network layers corresponded to stages in a continuous rotation from input to output space rather than to functionally distinct modules. While our network is likely to not be a direct reflection of brain processes, we propose that these principles should serve as hypotheses to be tested and evaluated for future studies.

  16. Using a Simple Neural Network to Delineate Some Principles of Distributed Economic Choice

    Directory of Open Access Journals (Sweden)

    Pragathi P. Balasubramani

    2018-03-01

    Full Text Available The brain uses a mixture of distributed and modular organization to perform computations and generate appropriate actions. While the principles under which the brain might perform computations using modular systems have been more amenable to modeling, the principles by which the brain might make choices using distributed principles have not been explored. Our goal in this perspective is to delineate some of those distributed principles using a neural network method and use its results as a lens through which to reconsider some previously published neurophysiological data. To allow for direct comparison with our own data, we trained the neural network to perform binary risky choices. We find that value correlates are ubiquitous and are always accompanied by non-value information, including spatial information (i.e., no pure value signals. Evaluation, comparison, and selection were not distinct processes; indeed, value signals even in the earliest stages contributed directly, albeit weakly, to action selection. There was no place, other than at the level of action selection, at which dimensions were fully integrated. No units were specialized for specific offers; rather, all units encoded the values of both offers in an anti-correlated format, thus contributing to comparison. Individual network layers corresponded to stages in a continuous rotation from input to output space rather than to functionally distinct modules. While our network is likely to not be a direct reflection of brain processes, we propose that these principles should serve as hypotheses to be tested and evaluated for future studies.

  17. Distribution System Pricing with Distributed Energy Resources

    Energy Technology Data Exchange (ETDEWEB)

    Hledik, Ryan [The Brattle Group, Cambridge, MA (United States); Lazar, Jim [The Regulatory Assistance Project, Montpelier, VT (United States); Schwartz, Lisa [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

    2017-08-16

    Technological changes in the electric utility industry bring tremendous opportunities and significant challenges. Customers are installing clean sources of on-site generation such as rooftop solar photovoltaic (PV) systems. At the same time, smart appliances and control systems that can communicate with the grid are entering the retail market. Among the opportunities these changes create are a cleaner and more diverse power system, the ability to improve system reliability and system resilience, and the potential for lower total costs. Challenges include integrating these new resources in a way that maintains system reliability, provides an equitable sharing of system costs, and avoids unbalanced impacts on different groups of customers, including those who install distributed energy resources (DERs) and low-income households who may be the least able to afford the transition.

  18. Trade-offs in the distribution of neural networks in a wireless sensor network

    NARCIS (Netherlands)

    Holenderski, M.J.; Lukkien, J.J.; Tham, C.K.

    2005-01-01

    This article investigates the tradeoff between communication and memory usage in different methods of distributing neural networks in a Wireless Sensor Network. A structural approach is presented, categorized in two dimensions: horizontal and vertical decomposition. Horizontal decomposition turns

  19. Development of neural network simulating power distribution of a BWR fuel bundle

    International Nuclear Information System (INIS)

    Tanabe, A.; Yamamoto, T.; Shinfuku, K.; Nakamae, T.

    1992-01-01

    A neural network model is developed to simulate the precise nuclear physics analysis program code for quick scoping survey calculations. The relation between enrichment and local power distribution of BWR fuel bundles was learned using two layers neural network (ENET). A new model is to introduce burnable neutron absorber (Gadolinia), added to several fuel rods to decrease initial reactivity of fresh bundle. The 2nd stages three layers neural network (GNET) is added on the 1st stage network ENET. GNET studies the local distribution difference caused by Gadolinia. Using this method, it becomes possible to survey of the gradients of sigmoid functions and back propagation constants with reasonable time. Using 99 learning patterns of zero burnup, good error convergence curve is obtained after many trials. This neural network model is able to simulate no learned cases fairly as well as the learned cases. Computer time of this neural network model is about 100 times faster than a precise analysis model. (author)

  20. Global exponential stability of mixed discrete and distributively delayed cellular neural network

    International Nuclear Information System (INIS)

    Yao Hong-Xing; Zhou Jia-Yan

    2011-01-01

    This paper concernes analysis for the global exponential stability of a class of recurrent neural networks with mixed discrete and distributed delays. It first proves the existence and uniqueness of the balance point, then by employing the Lyapunov—Krasovskii functional and Young inequality, it gives the sufficient condition of global exponential stability of cellular neural network with mixed discrete and distributed delays, in addition, the example is provided to illustrate the applicability of the result. (general)

  1. Existence and stability of periodic solution in impulsive Hopfield neural networks with finite distributed delays

    International Nuclear Information System (INIS)

    Yang Xiaofan; Liao Xiaofeng; Evans, David J.; Tang Yuanyan

    2005-01-01

    In this Letter, we introduce a class of Hopfield neural networks with periodic impulses and finite distributed delays. We then derive a sufficient condition for the existence and global exponential stability of a unique periodic solution of the networks, which assumes neither the differentiability nor the monotonicity of the activation functions. Our condition extends and generalizes a known condition for the global exponential periodicity of continuous Hopfield neural networks with finite distributed delays

  2. Validation of a Robust Neural Real-Time Voltage Estimator for Active Distribution Grids on Field Data

    DEFF Research Database (Denmark)

    Pertl, Michael; Douglass, Philip James; Heussen, Kai

    2018-01-01

    network approach for voltage estimation in active distribution grids by means of measured data from two feeders of a real low voltage distribution grid. The approach enables a real-time voltage estimation at locations in the distribution grid, where otherwise only non-real-time measurements are available......The installation of measurements in distribution grids enables the development of data driven methods for the power system. However, these methods have to be validated in order to understand the limitations and capabilities for their use. This paper presents a systematic validation of a neural...

  3. 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....

  4. 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

  5. RBAC Administration in Distributed Systems

    NARCIS (Netherlands)

    Dekker, M.A.C.; Crampton, J.; Etalle, Sandro

    2007-01-01

    Despite a large body of literature on the administration of RBAC policies in centralized systems, the problem of the administration of a distributed system has hardly been addressed. We present a formal system for modelling a distributed RBAC system and its administration. We define two basic

  6. Development of neural network for analysis of local power distributions in BWR fuel bundles

    International Nuclear Information System (INIS)

    Tanabe, Akira; Yamamoto, Toru; Shinfuku, Kimihiro; Nakamae, Takuji.

    1993-01-01

    A neural network model has been developed to learn the local power distributions in a BWR fuel bundle. A two layers neural network with total 128 elements is used for this model. The neural network learns 33 cases of local power peaking factors of fuel rods with given enrichment distribution as the teacher signals, which were calculated by a fuel bundle nuclear analysis code based on precise physical models. This neural network model studied well the teacher signals within 1 % error. It is also able to calculate the local power distributions within several % error for the different enrichment distributions from the teacher signals when the average enrichment is close to 2 %. This neural network is simple and the computing speed of this model is 300 times faster than that of the precise nuclear analysis code. This model was applied to survey the enrichment distribution to meet a target local power distribution in a fuel bundle, and the enrichment distribution with flat power shape are obtained within short computing time. (author)

  7. Measurement of void fraction distribution in two-phase flow by impedance CT with neural network

    International Nuclear Information System (INIS)

    Hayashi, Hideaki; Sumida, Isao; Sakai, Sinji; Wakai, Kazunori

    1996-01-01

    This paper describes a new method for measurement of void distribution using impedance CT with a hierarchical neural network. The present method consists of four processes. First, output electric currents are calculated by simulation of various distributions of void fraction. The relationship between distribution of void fraction and electric current is called 'teaching data'. Second, the neural network learns the teaching data by the back propagation method. Third, output electric currents are measured about actual two-phase flow. Finally, distribution of void fraction is calculated by the taught neural network using the measured electric currents. In this paper, measurement and learning parameters are adjusted, experimental results obtained using the impedance CT method are compared with data obtained by the impedance probe method. The results show that our method is effective for measurement of void fraction distribution. (author)

  8. 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

  9. Distribution system modeling and analysis

    CERN Document Server

    Kersting, William H

    2001-01-01

    For decades, distribution engineers did not have the sophisticated tools developed for analyzing transmission systems-often they had only their instincts. Things have changed, and we now have computer programs that allow engineers to simulate, analyze, and optimize distribution systems. Powerful as these programs are, however, without a real understanding of the operating characteristics of a distribution system, engineers using the programs can easily make serious errors in their designs and operating procedures. Distribution System Modeling and Analysis helps prevent those errors. It gives readers a basic understanding of the modeling and operating characteristics of the major components of a distribution system. One by one, the author develops and analyzes each component as a stand-alone element, then puts them all together to analyze a distribution system comprising the various shunt and series devices for power-flow and short-circuit studies. He includes the derivation of all models and includes many num...

  10. 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.

  11. Distribution network fault section identification and fault location using artificial neural network

    DEFF Research Database (Denmark)

    Dashtdar, Masoud; Dashti, Rahman; Shaker, Hamid Reza

    2018-01-01

    In this paper, a method for fault location in power distribution network is presented. The proposed method uses artificial neural network. In order to train the neural network, a series of specific characteristic are extracted from the recorded fault signals in relay. These characteristics...... components of the sequences as well as three-phase signals could be obtained using statistics to extract the hidden features inside them and present them separately to train the neural network. Also, since the obtained inputs for the training of the neural network strongly depend on the fault angle, fault...... resistance, and fault location, the training data should be selected such that these differences are properly presented so that the neural network does not face any issues for identification. Therefore, selecting the signal processing function, data spectrum and subsequently, statistical parameters...

  12. 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

  13. Distributed systems status and control

    Science.gov (United States)

    Kreidler, David; Vickers, David

    1990-01-01

    Concepts are investigated for an automated status and control system for a distributed processing environment. System characteristics, data requirements for health assessment, data acquisition methods, system diagnosis methods and control methods were investigated in an attempt to determine the high-level requirements for a system which can be used to assess the health of a distributed processing system and implement control procedures to maintain an accepted level of health for the system. A potential concept for automated status and control includes the use of expert system techniques to assess the health of the system, detect and diagnose faults, and initiate or recommend actions to correct the faults. Therefore, this research included the investigation of methods by which expert systems were developed for real-time environments and distributed systems. The focus is on the features required by real-time expert systems and the tools available to develop real-time expert systems.

  14. Artificial neural networks for spatial distribution of fuel assemblies in reload of PWR reactors

    International Nuclear Information System (INIS)

    Oliveira, Edyene; Castro, Victor F.; Velásquez, Carlos E.; Pereira, Claubia

    2017-01-01

    An artificial neural network methodology is being developed in order to find an optimum spatial distribution of the fuel assemblies in a nuclear reactor core during reload. The main bounding parameter of the modelling was the neutron multiplication factor, k ef f . The characteristics of the network are defined by the nuclear parameters: cycle, burnup, enrichment, fuel type, and average power peak of each element. These parameters were obtained by the ORNL nuclear code package SCALE6.0. As for the artificial neural network, the ANN Feedforward Multi L ayer P erceptron with various layers and neurons were constructed. Three algorithms were used and tested: LM (Levenberg-Marquardt), SCG (Scaled Conjugate Gradient) and BayR (Bayesian Regularization). Artificial neural network have implemented using MATLAB 2015a version. As preliminary results, the spatial distribution of the fuel assemblies in the core using a neural network was slightly better than the standard core. (author)

  15. 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.

  16. Approximation model of three-dimensional power distribution in boiling water reactor using neural networks

    International Nuclear Information System (INIS)

    Kobayashi, Yoko; Aiyoshi, Eitaro

    2001-01-01

    Fast and accurate prediction of three-dimensional (3D) power distribution is essential in a boiling water reactor (BWR). The prediction method of 3D power distribution in BWR is developed using the neural network. Application of the neural network starts with selecting the learning algorithm. In the proposed method, we use the learning algorithms based on a class of Quasi-Newton optimization techniques called Self-Scaling Variable Metric (SSVM) methods. Prediction studies were done for a core of actual BWR plant with octant symmetry. Compared to classical Quasi-Newton methods, it is shown that the SSVM method reduces the number of iterations in the learning mode. The results of prediction demonstrate that the neural network can predict 3D power distribution of BWR reasonably well. The proposed method will be very useful for BWR loading pattern optimization problems where 3D power distribution for a huge number of loading patterns (LPs) must be performed. (author)

  17. Water Treatment Technology - Distribution Systems.

    Science.gov (United States)

    Ross-Harrington, Melinda; Kincaid, G. David

    One of twelve water treatment technology units, this student manual on distribution systems provides instructional materials for six competencies. (The twelve units are designed for a continuing education training course for public water supply operators.) The competencies focus on the following areas: types of pipe for distribution systems, types…

  18. 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

  19. 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.

  20. 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

  1. 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.

  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. Global exponential stability of bidirectional associative memory neural networks with distributed delays

    Science.gov (United States)

    Song, Qiankun; Cao, Jinde

    2007-05-01

    A bidirectional associative memory neural network model with distributed delays is considered. By constructing a new Lyapunov functional, employing the homeomorphism theory, M-matrix theory and the inequality (a[greater-or-equal, slanted]0,bk[greater-or-equal, slanted]0,qk>0 with , and r>1), a sufficient condition is obtained to ensure the existence, uniqueness and global exponential stability of the equilibrium point for the model. Moreover, the exponential converging velocity index is estimated, which depends on the delay kernel functions and the system parameters. The results generalize and improve the earlier publications, and remove the usual assumption that the activation functions are bounded . Two numerical examples are given to show the effectiveness of the obtained results.

  4. 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.

  5. 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

  6. 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.

  7. Axial power distribution calculation using a neural network in the nuclear reactor core

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Y. H.; Cha, K. H.; Lee, S. H. [Korea Electric Power Research Institute, Taejon (Korea, Republic of)

    1997-12-31

    This paper is concerned with an algorithm based on neural networks to calculate the axial power distribution using excore detector signals in the nuclear reactor core. The fundamental basis of the algorithm is that the detector response can be fairly accurately estimated using computational codes. In other words, the training set, which represents relationship between detector signals and axial power distributions, for the neural network can be obtained through calculations instead of measurements. Application of the new method to the Yonggwang nuclear power plant unit 3 (YGN-3) shows that it is superior to the current algorithm in place. 7 refs., 4 figs. (Author)

  8. Axial power distribution calculation using a neural network in the nuclear reactor core

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Y H; Cha, K H; Lee, S H [Korea Electric Power Research Institute, Taejon (Korea, Republic of)

    1998-12-31

    This paper is concerned with an algorithm based on neural networks to calculate the axial power distribution using excore detector signals in the nuclear reactor core. The fundamental basis of the algorithm is that the detector response can be fairly accurately estimated using computational codes. In other words, the training set, which represents relationship between detector signals and axial power distributions, for the neural network can be obtained through calculations instead of measurements. Application of the new method to the Yonggwang nuclear power plant unit 3 (YGN-3) shows that it is superior to the current algorithm in place. 7 refs., 4 figs. (Author)

  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. 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

  11. 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.

  12. Determination of size distribution of bubbles in a bubbly column two phase flows by ultrasound and neural networks

    International Nuclear Information System (INIS)

    Baroni, Douglas B.; Lamy, Carlos A.; Bittencourt, Marcelo S.Q.; Pereira, Claudio M.N.A.; Cunha Filho, Jurandyr S.; Motta, Mauricio S.

    2011-01-01

    The development of advanced nuclear reactor conceptions depends largely on the amount of available data to the designer. Non invasive ultrasonic techniques can contribute to the evaluation of gas-liquid two-phase regimes in the nuclear thermo-hydraulic circuits. A key-point for success of those techniques is the interpretation of the ultrasonic signal. In this work, a methodology based in artificial neural networks (ANN) is proposed to predict size distribution of bubbles in a bubbly flow. To accomplish that, an air feed system control was used to obtain specific bubbly flows in an experimental system utilizing a Plexiglas vertical bubbly column. Four different size distribution of bubbles were generated. The bubbles were photographed and measured. To evaluate the different size distribution of bubbles it was used the ultrasonic reflected echo on the opposite wall of the column. Then, an ANN has been developed for predicting size distribution of bubbles by using the frequency spectra of the ultrasonic signal as input. A trained artificial neural network using ultrasonic signal in the frequency domain can evaluate with a good precision the size distribution of bubbles generated in this system. (author)

  13. Determination of size distribution of bubbles in a bubbly column two phase flows by ultrasound and neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Baroni, Douglas B.; Lamy, Carlos A.; Bittencourt, Marcelo S.Q.; Pereira, Claudio M.N.A., E-mail: douglasbaroni@ien.gov.b, E-mail: lamy@ien.gov.b, E-mail: bittenc@ien.gov.b, E-mail: cmnap@ien.gov.b [Instituto de Engenharia Nuclear (IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil); Cunha Filho, Jurandyr S. [Escola Tecnica Estadual Visconde de Maua (ETEVM/RJ), Rio de Janeiro, RJ (Brazil); Motta, Mauricio S., E-mail: mmotta@cefet-rj.b [Centro Federal de Educacao Tecnologica Celso Suckow da Fonseca (CEFET/RJ), Rio de Janeiro, RJ (Brazil)

    2011-07-01

    The development of advanced nuclear reactor conceptions depends largely on the amount of available data to the designer. Non invasive ultrasonic techniques can contribute to the evaluation of gas-liquid two-phase regimes in the nuclear thermo-hydraulic circuits. A key-point for success of those techniques is the interpretation of the ultrasonic signal. In this work, a methodology based in artificial neural networks (ANN) is proposed to predict size distribution of bubbles in a bubbly flow. To accomplish that, an air feed system control was used to obtain specific bubbly flows in an experimental system utilizing a Plexiglas vertical bubbly column. Four different size distribution of bubbles were generated. The bubbles were photographed and measured. To evaluate the different size distribution of bubbles it was used the ultrasonic reflected echo on the opposite wall of the column. Then, an ANN has been developed for predicting size distribution of bubbles by using the frequency spectra of the ultrasonic signal as input. A trained artificial neural network using ultrasonic signal in the frequency domain can evaluate with a good precision the size distribution of bubbles generated in this system. (author)

  14. 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.

  15. Variable synaptic strengths controls the firing rate distribution in feedforward neural networks.

    Science.gov (United States)

    Ly, Cheng; Marsat, Gary

    2018-02-01

    Heterogeneity of firing rate statistics is known to have severe consequences on neural coding. Recent experimental recordings in weakly electric fish indicate that the distribution-width of superficial pyramidal cell firing rates (trial- and time-averaged) in the electrosensory lateral line lobe (ELL) depends on the stimulus, and also that network inputs can mediate changes in the firing rate distribution across the population. We previously developed theoretical methods to understand how two attributes (synaptic and intrinsic heterogeneity) interact and alter the firing rate distribution in a population of integrate-and-fire neurons with random recurrent coupling. Inspired by our experimental data, we extend these theoretical results to a delayed feedforward spiking network that qualitatively capture the changes of firing rate heterogeneity observed in in-vivo recordings. We demonstrate how heterogeneous neural attributes alter firing rate heterogeneity, accounting for the effect with various sensory stimuli. The model predicts how the strength of the effective network connectivity is related to intrinsic heterogeneity in such delayed feedforward networks: the strength of the feedforward input is positively correlated with excitability (threshold value for spiking) when firing rate heterogeneity is low and is negatively correlated with excitability with high firing rate heterogeneity. We also show how our theory can be used to predict effective neural architecture. We demonstrate that neural attributes do not interact in a simple manner but rather in a complex stimulus-dependent fashion to control neural heterogeneity and discuss how it can ultimately shape population codes.

  16. The THUDSOS Distributed Operating System

    Institute of Scientific and Technical Information of China (English)

    廖先Zhi; 刘旭峰; 等

    1991-01-01

    The THUDSOS is a distributed operating system modeled as an abstract machine which provides decentralized control,transparency,availability,and reliability,as welol as a good degree of autonomy at each node,that makes our distributed system usable.Our operating system supports transparent access to data through network wide filesystem.The simultaneous access to any device is discussed for the case when the peripherals are treated as files.This operating system allows spawning of parallel application programs to solve problems in the fields,such as numerical analysis and artificial intelligence.

  17. Energy efficient distributed computing systems

    CERN Document Server

    Lee, Young-Choon

    2012-01-01

    The energy consumption issue in distributed computing systems raises various monetary, environmental and system performance concerns. Electricity consumption in the US doubled from 2000 to 2005.  From a financial and environmental standpoint, reducing the consumption of electricity is important, yet these reforms must not lead to performance degradation of the computing systems.  These contradicting constraints create a suite of complex problems that need to be resolved in order to lead to 'greener' distributed computing systems.  This book brings together a group of outsta

  18. Coordination control of distributed systems

    CERN Document Server

    Villa, Tiziano

    2015-01-01

    This book describes how control of distributed systems can be advanced by an integration of control, communication, and computation. The global control objectives are met by judicious combinations of local and nonlocal observations taking advantage of various forms of communication exchanges between distributed controllers. Control architectures are considered according to  increasing degrees of cooperation of local controllers:  fully distributed or decentralized control,  control with communication between controllers,  coordination control, and multilevel control.  The book covers also topics bridging computer science, communication, and control, like communication for control of networks, average consensus for distributed systems, and modeling and verification of discrete and of hybrid systems. Examples and case studies are introduced in the first part of the text and developed throughout the book. They include: control of underwater vehicles, automated-guided vehicles on a container terminal, contro...

  19. 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

  20. Research and design of distributed intelligence fault diagnosis system in nuclear power plant

    International Nuclear Information System (INIS)

    Liu Yongkuo; Xie Chunli; Cheng Shouyu; Xia Hong

    2011-01-01

    In order to further reduce the misoperation after the faults occurring of nuclear power plant, according to the function distribution of nuclear power equipment and the distributed control features of digital instrument control system, a nuclear power plant distributed condition monitoring and fault diagnosis system was researched and designed. Based on decomposition-integrated diagnostic thinking, a fuzzy neural network and RBF neural network was presented to do the distributed local diagnosis and multi-source information fusion technology for the global integrated diagnosis. Simulation results show that the developed distributed status monitoring and fault diagnosis system can diagnose more typical accidents of PWR to provide effective diagnosis and operation information. (authors)

  1. 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

  2. Folic acid supplementation influences the distribution of neural tube defect subtypes : A registry-based study

    NARCIS (Netherlands)

    Bergman, J. E. H.; Otten, E.; Verheij, J. B. G. M.; de Walle, H. E. K.

    Periconceptional folic acid (FA) reduces neural tube defect (NTD) risk, but seems to have a varying effect per NTD subtype. We aimed to study the effect of FA supplementation on NTD subtype distribution using data from EUROCAT Northern Netherlands. We included all birth types with non-syndromal NTDs

  3. Global asymptotic stability of Cohen-Grossberg neural network with continuously distributed delays

    International Nuclear Information System (INIS)

    Wan Li; Sun Jianhua

    2005-01-01

    The convergence dynamical behaviors of Cohen-Grossberg neural network with continuously distributed delays are discussed. By using Brouwer's fixed point theorem, matrix theory and analysis techniques such as Gronwall inequality, some new sufficient conditions guaranteeing the existence, uniqueness of an equilibrium point and its global asymptotic stability are obtained. An example is given to illustrate the theoretical results

  4. Global exponential stability of cellular neural networks with continuously distributed delays and impulses

    International Nuclear Information System (INIS)

    Wang Yixuan; Xiong Wanmin; Zhou Qiyuan; Xiao Bing; Yu Yuehua

    2006-01-01

    In this Letter cellular neural networks with continuously distributed delays and impulses are considered. Sufficient conditions for the existence and global exponential stability of a unique equilibrium point are established by using the fixed point theorem and differential inequality techniques. The results of this Letter are new and they complement previously known results

  5. RBAC administration in distributed systems

    NARCIS (Netherlands)

    Dekker, M.A.C.; Crampton, J.; Etalle, Sandro; Li, N.

    Large and distributed access control systems are increasingly common, for example in health care. In such settings, access control policies may become very complex, thus complicating correct and efficient adminstration of the access control system. Despite being one of the most widely used access

  6. 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...

  7. 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.)

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. Systems Measures of Water Distribution System Resilience

    Energy Technology Data Exchange (ETDEWEB)

    Klise, Katherine A. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Murray, Regan [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Walker, La Tonya Nicole [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2015-01-01

    Resilience is a concept that is being used increasingly to refer to the capacity of infrastructure systems to be prepared for and able to respond effectively and rapidly to hazardous events. In Section 2 of this report, drinking water hazards, resilience literature, and available resilience tools are presented. Broader definitions, attributes and methods for measuring resilience are presented in Section 3. In Section 4, quantitative systems performance measures for water distribution systems are presented. Finally, in Section 5, the performance measures and their relevance to measuring the resilience of water systems to hazards is discussed along with needed improvements to water distribution system modeling tools.

  13. THE FRANCHISE SYSTEM OF DISTRIBUTION.

    Science.gov (United States)

    The working relationships between franchise companies and their franchised dealers are analyzed. The benefits derived from the use of a franchisesise...system of distribution for both the franchisor and franchisee are determined. The principal problems encountered by the parties to the franchise ...agreement are isolated, and this method of distribution is evaluated from the standpoint of both the franchise company and franchised dealers and to assess its impact on the marketing economy of the nation.

  14. 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

  15. Stability results for stochastic delayed recurrent neural networks with discrete and distributed delays

    Science.gov (United States)

    Chen, Guiling; Li, Dingshi; Shi, Lin; van Gaans, Onno; Verduyn Lunel, Sjoerd

    2018-03-01

    We present new conditions for asymptotic stability and exponential stability of a class of stochastic recurrent neural networks with discrete and distributed time varying delays. Our approach is based on the method using fixed point theory, which do not resort to any Liapunov function or Liapunov functional. Our results neither require the boundedness, monotonicity and differentiability of the activation functions nor differentiability of the time varying delays. In particular, a class of neural networks without stochastic perturbations is also considered. Examples are given to illustrate our main results.

  16. Global Exponential Stability of Periodic Oscillation for Nonautonomous BAM Neural Networks with Distributed Delay

    Directory of Open Access Journals (Sweden)

    Hongli Liu

    2009-01-01

    Full Text Available We derive a new criterion for checking the global stability of periodic oscillation of bidirectional associative memory (BAM neural networks with periodic coefficients and distributed delay, and find that the criterion relies on the Lipschitz constants of the signal transmission functions, weights of the neural network, and delay kernels. The proposed model transforms the original interacting network into matrix analysis problem which is easy to check, thereby significantly reducing the computational complexity and making analysis of periodic oscillation for even large-scale networks.

  17. Maintaining consistency in distributed systems

    Science.gov (United States)

    Birman, Kenneth P.

    1991-01-01

    In systems designed as assemblies of independently developed components, concurrent access to data or data structures normally arises within individual programs, and is controlled using mutual exclusion constructs, such as semaphores and monitors. Where data is persistent and/or sets of operation are related to one another, transactions or linearizability may be more appropriate. Systems that incorporate cooperative styles of distributed execution often replicate or distribute data within groups of components. In these cases, group oriented consistency properties must be maintained, and tools based on the virtual synchrony execution model greatly simplify the task confronting an application developer. All three styles of distributed computing are likely to be seen in future systems - often, within the same application. This leads us to propose an integrated approach that permits applications that use virtual synchrony with concurrent objects that respect a linearizability constraint, and vice versa. Transactional subsystems are treated as a special case of linearizability.

  18. Distribution network strengthens sales systems

    International Nuclear Information System (INIS)

    Janoska, J.

    2003-01-01

    Liberalisation of the electricity market pushes Slovak distribution companies to upgrade their sale technologies. The first one to invest into a complex electronic sales system will be Stredoslovenska energetika, a.s., Zilina. The system worth 200 million Sk (4,83 million Euro) will be supplied by Polish software company Winuel. The company should also supply a software that would allow forecasting and planning of sales. The system should be fully operational by 2006. TREND has not managed to obtain information regarding plans Zapadoslovenska energetika - the largest and most active distribution company - might have in this area. In eastern Slovakia distribution company Vychodoslovenska energetika, a.s., Kosice has also started addressing this issue. (Author)

  19. Distributed computing methodology for training neural networks in an image-guided diagnostic application.

    Science.gov (United States)

    Plagianakos, V P; Magoulas, G D; Vrahatis, M N

    2006-03-01

    Distributed computing is a process through which a set of computers connected by a network is used collectively to solve a single problem. In this paper, we propose a distributed computing methodology for training neural networks for the detection of lesions in colonoscopy. Our approach is based on partitioning the training set across multiple processors using a parallel virtual machine. In this way, interconnected computers of varied architectures can be used for the distributed evaluation of the error function and gradient values, and, thus, training neural networks utilizing various learning methods. The proposed methodology has large granularity and low synchronization, and has been implemented and tested. Our results indicate that the parallel virtual machine implementation of the training algorithms developed leads to considerable speedup, especially when large network architectures and training sets are used.

  20. Predicting dihedral angle probability distributions for protein coil residues from primary sequence using neural networks

    DEFF Research Database (Denmark)

    Helles, Glennie; Fonseca, Rasmus

    2009-01-01

    residue in the input-window. The trained neural network shows a significant improvement (4-68%) in predicting the most probable bin (covering a 30°×30° area of the dihedral angle space) for all amino acids in the data set compared to first order statistics. An accuracy comparable to that of secondary...... seem to have a significant influence on the dihedral angles adopted by the individual amino acids in coil segments. In this work we attempt to predict a probability distribution of these dihedral angles based on the flanking residues. While attempts to predict dihedral angles of coil segments have been...... done previously, none have, to our knowledge, presented comparable results for the probability distribution of dihedral angles. Results: In this paper we develop an artificial neural network that uses an input-window of amino acids to predict a dihedral angle probability distribution for the middle...

  1. Compressed sensing for distributed systems

    CERN Document Server

    Coluccia, Giulio; Magli, Enrico

    2015-01-01

    This book presents a survey of the state-of-the art in the exciting and timely topic of compressed sensing for distributed systems. It has to be noted that, while compressed sensing has been studied for some time now, its distributed applications are relatively new. Remarkably, such applications are ideally suited to exploit all the benefits that compressed sensing can provide. The objective of this book is to provide the reader with a comprehensive survey of this topic, from the basic concepts to different classes of centralized and distributed reconstruction algorithms, as well as a comparison of these techniques. This book collects different contributions on these aspects. It presents the underlying theory in a complete and unified way for the first time, presenting various signal models and their use cases. It contains a theoretical part collecting latest results in rate-distortion analysis of distributed compressed sensing, as well as practical implementations of algorithms obtaining performance close to...

  2. 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.

  3. Enhanced distributed energy resource system

    Science.gov (United States)

    Atcitty, Stanley [Albuquerque, NM; Clark, Nancy H [Corrales, NM; Boyes, John D [Albuquerque, NM; Ranade, Satishkumar J [Las Cruces, NM

    2007-07-03

    A power transmission system including a direct current power source electrically connected to a conversion device for converting direct current into alternating current, a conversion device connected to a power distribution system through a junction, an energy storage device capable of producing direct current connected to a converter, where the converter, such as an insulated gate bipolar transistor, converts direct current from an energy storage device into alternating current and supplies the current to the junction and subsequently to the power distribution system. A microprocessor controller, connected to a sampling and feedback module and the converter, determines when the current load is higher than a set threshold value, requiring triggering of the converter to supply supplemental current to the power transmission system.

  4. Distributed Supervisory Protection Interlock System

    International Nuclear Information System (INIS)

    Walz, H.V.; Agostini, R.C.; Barker, L.; Cherkassky, R.; Constant, T.; Matheson, R.

    1989-03-01

    The Distributed Supervisory Protection Interlock System, DSPI, is under development at the Stanford Linear Accelerator Center for requirements in the areas of personnel protection, beam containment and equipment protection interlocks. The DSPI system, distributed over the application site, consists of segments with microprocessor-based controller and I/O modules, local area networks for communication, and a global supervisor computer. Segments are implemented with commercially available controller and I/O modules arranged in local interlock clusters, and associated software. Segments provide local interlock data acquisition, processing and control. Local area networks provide the communication backbone between segments and a global supervisor processor. The supervisor processor monitors the overall system, reports detail status and provides human interfaces. Details of an R and D test system, which will implement the requirements for personnel protection of 4 typical linear accelerator sectors, will be described. 4 refs., 2 figs

  5. Global stability of stochastic high-order neural networks with discrete and distributed delays

    International Nuclear Information System (INIS)

    Wang Zidong; Fang Jianan; Liu Xiaohui

    2008-01-01

    High-order neural networks can be considered as an expansion of Hopfield neural networks, and have stronger approximation property, faster convergence rate, greater storage capacity, and higher fault tolerance than lower-order neural networks. In this paper, the global asymptotic stability analysis problem is considered for a class of stochastic high-order neural networks with discrete and distributed time-delays. Based on an Lyapunov-Krasovskii functional and the stochastic stability analysis theory, several sufficient conditions are derived, which guarantee the global asymptotic convergence of the equilibrium point in the mean square. It is shown that the stochastic high-order delayed neural networks under consideration are globally asymptotically stable in the mean square if two linear matrix inequalities (LMIs) are feasible, where the feasibility of LMIs can be readily checked by the Matlab LMI toolbox. It is also shown that the main results in this paper cover some recently published works. A numerical example is given to demonstrate the usefulness of the proposed global stability criteria

  6. Distributed Systems: The Hard Problems

    CERN Multimedia

    CERN. Geneva

    2015-01-01

    **Nicholas Bellerophon** works as a client services engineer at Basho Technologies, helping customers setup and run distributed systems at scale in the wild. He has also worked in massively multiplayer games, and recently completed a live scalable simulation engine. He is an avid TED-watcher with interests in many areas of the arts, science, and engineering, including of course high-energy physics.

  7. Distribution system analysis and automation

    CERN Document Server

    Gers, Juan

    2013-01-01

    A comprehensive guide to techniques that allow engineers to simulate, analyse and optimise power distribution systems which combined with automation, underpin the emerging concept of the "smart grid". This book is supported by theoretical concepts with real-world applications and MATLAB exercises.

  8. Turboelectric Distributed Propulsion System Modelling

    OpenAIRE

    Liu, Chengyuan

    2013-01-01

    The Blended-Wing-Body is a conceptual aircraft design with rear-mounted, over wing engines. Turboelectric distributed propulsion system with boundary layer ingestion has been considered for this aircraft. It uses electricity to transmit power from the core turbine to the fans, therefore dramatically increases bypass ratio to reduce fuel consumption and noise. This dissertation presents methods on designing the TeDP system, evaluating effects of boundary layer ingestion, modelling engine perfo...

  9. A Distributed User Information System

    Science.gov (United States)

    1990-03-01

    NOE08 Department of Computer Science NOVO 8 1990 University of Maryland S College Park, MD 20742 D Abstract Current user information database technology ...Transactions on Computer Systems, May 1988. [So189] K. Sollins. A plan for internet directory services. Technical report, DDN Network Information Center...2424 A Distributed User Information System DTiC Steven D. Miller, Scott Carson, and Leo Mark DELECTE Institute for Advanced Computer Studies and

  10. 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.

  11. 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.

  12. 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.

  13. The ATLAS distributed analysis system

    International Nuclear Information System (INIS)

    Legger, F

    2014-01-01

    In the LHC operations era, analysis of the multi-petabyte ATLAS data sample by globally distributed physicists is a challenging task. To attain the required scale the ATLAS Computing Model was designed around the concept of Grid computing, realized in the Worldwide LHC Computing Grid (WLCG), the largest distributed computational resource existing in the sciences. The ATLAS experiment currently stores over 140 PB of data and runs about 140,000 concurrent jobs continuously at WLCG sites. During the first run of the LHC, the ATLAS Distributed Analysis (DA) service has operated stably and scaled as planned. More than 1600 users submitted jobs in 2012, with 2 million or more analysis jobs per week, peaking at about a million jobs per day. The system dynamically distributes popular data to expedite processing and maximally utilize resources. The reliability of the DA service is high and steadily improving; Grid sites are continually validated against a set of standard tests, and a dedicated team of expert shifters provides user support and communicates user problems to the sites. Both the user support techniques and the direct feedback of users have been effective in improving the success rate and user experience when utilizing the distributed computing environment. In this contribution a description of the main components, activities and achievements of ATLAS distributed analysis is given. Several future improvements being undertaken will be described.

  14. The ATLAS distributed analysis system

    Science.gov (United States)

    Legger, F.; Atlas Collaboration

    2014-06-01

    In the LHC operations era, analysis of the multi-petabyte ATLAS data sample by globally distributed physicists is a challenging task. To attain the required scale the ATLAS Computing Model was designed around the concept of Grid computing, realized in the Worldwide LHC Computing Grid (WLCG), the largest distributed computational resource existing in the sciences. The ATLAS experiment currently stores over 140 PB of data and runs about 140,000 concurrent jobs continuously at WLCG sites. During the first run of the LHC, the ATLAS Distributed Analysis (DA) service has operated stably and scaled as planned. More than 1600 users submitted jobs in 2012, with 2 million or more analysis jobs per week, peaking at about a million jobs per day. The system dynamically distributes popular data to expedite processing and maximally utilize resources. The reliability of the DA service is high and steadily improving; Grid sites are continually validated against a set of standard tests, and a dedicated team of expert shifters provides user support and communicates user problems to the sites. Both the user support techniques and the direct feedback of users have been effective in improving the success rate and user experience when utilizing the distributed computing environment. In this contribution a description of the main components, activities and achievements of ATLAS distributed analysis is given. Several future improvements being undertaken will be described.

  15. Artificial neural networks for spatial distribution of fuel assemblies in reload of PWR reactors

    Energy Technology Data Exchange (ETDEWEB)

    Oliveira, Edyene; Castro, Victor F.; Velásquez, Carlos E.; Pereira, Claubia, E-mail: claubia@nuclear.ufmg.br [Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG (Brazil). Programa de Pós-Graduação em Ciências e Técnicas Nucleares

    2017-07-01

    An artificial neural network methodology is being developed in order to find an optimum spatial distribution of the fuel assemblies in a nuclear reactor core during reload. The main bounding parameter of the modelling was the neutron multiplication factor, k{sub ef{sub f}}. The characteristics of the network are defined by the nuclear parameters: cycle, burnup, enrichment, fuel type, and average power peak of each element. These parameters were obtained by the ORNL nuclear code package SCALE6.0. As for the artificial neural network, the ANN Feedforward Multi{sub L}ayer{sub P}erceptron with various layers and neurons were constructed. Three algorithms were used and tested: LM (Levenberg-Marquardt), SCG (Scaled Conjugate Gradient) and BayR (Bayesian Regularization). Artificial neural network have implemented using MATLAB 2015a version. As preliminary results, the spatial distribution of the fuel assemblies in the core using a neural network was slightly better than the standard core. (author)

  16. Population-wide distributions of neural activity during perceptual decision-making

    Science.gov (United States)

    Machens, Christian

    2018-01-01

    Cortical activity involves large populations of neurons, even when it is limited to functionally coherent areas. Electrophysiological recordings, on the other hand, involve comparatively small neural ensembles, even when modern-day techniques are used. Here we review results which have started to fill the gap between these two scales of inquiry, by shedding light on the statistical distributions of activity in large populations of cells. We put our main focus on data recorded in awake animals that perform simple decision-making tasks and consider statistical distributions of activity throughout cortex, across sensory, associative, and motor areas. We transversally review the complexity of these distributions, from distributions of firing rates and metrics of spike-train structure, through distributions of tuning to stimuli or actions and of choice signals, and finally the dynamical evolution of neural population activity and the distributions of (pairwise) neural interactions. This approach reveals shared patterns of statistical organization across cortex, including: (i) long-tailed distributions of activity, where quasi-silence seems to be the rule for a majority of neurons; that are barely distinguishable between spontaneous and active states; (ii) distributions of tuning parameters for sensory (and motor) variables, which show an extensive extrapolation and fragmentation of their representations in the periphery; and (iii) population-wide dynamics that reveal rotations of internal representations over time, whose traces can be found both in stimulus-driven and internally generated activity. We discuss how these insights are leading us away from the notion of discrete classes of cells, and are acting as powerful constraints on theories and models of cortical organization and population coding. PMID:23123501

  17. Distributed Recurrent Neural Forward Models with Synaptic Adaptation and CPG-based control for Complex Behaviors of Walking Robots

    Directory of Open Access Journals (Sweden)

    Sakyasingha eDasgupta

    2015-09-01

    Full Text Available Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures with the underlying neural mechanisms. The neural mechanisms consist of 1 central pattern generator based control for generating basic rhythmic patterns and coordinated movements, 2 distributed (at each leg recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and 3 searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex locomotive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps as well as climbing over high obstacles. Furthermore we demonstrate that the newly developed recurrent network based approach to sensorimotor prediction outperforms the previous state of the art adaptive neuron

  18. Distributed optimization system and method

    Science.gov (United States)

    Hurtado, John E.; Dohrmann, Clark R.; Robinett, III, Rush D.

    2003-06-10

    A search system and method for controlling multiple agents to optimize an objective using distributed sensing and cooperative control. The search agent can be one or more physical agents, such as a robot, and can be software agents for searching cyberspace. The objective can be: chemical sources, temperature sources, radiation sources, light sources, evaders, trespassers, explosive sources, time dependent sources, time independent sources, function surfaces, maximization points, minimization points, and optimal control of a system such as a communication system, an economy, a crane, and a multi-processor computer.

  19. World-wide distribution automation systems

    International Nuclear Information System (INIS)

    Devaney, T.M.

    1994-01-01

    A worldwide power distribution automation system is outlined. Distribution automation is defined and the status of utility automation is discussed. Other topics discussed include a distribution management system, substation feeder, and customer functions, potential benefits, automation costs, planning and engineering considerations, automation trends, databases, system operation, computer modeling of system, and distribution management systems

  20. Existence and global exponential stability of periodic solution to BAM neural networks with periodic coefficients and continuously distributed delays

    International Nuclear Information System (INIS)

    Zhou Tiejun; Chen Anping; Zhou Yuyuan

    2005-01-01

    By using the continuation theorem of coincidence degree theory and Liapunov function, we obtain some sufficient criteria to ensure the existence and global exponential stability of periodic solution to the bidirectional associative memory (BAM) neural networks with periodic coefficients and continuously distributed delays. These results improve and generalize the works of papers [J. Cao, L. Wang, Phys. Rev. E 61 (2000) 1825] and [Z. Liu, A. Chen, J. Cao, L. Huang, IEEE Trans. Circuits Systems I 50 (2003) 1162]. An example is given to illustrate that the criteria are feasible

  1. Existence and global exponential stability of periodic solution to BAM neural networks with periodic coefficients and continuously distributed delays

    Science.gov (United States)

    Zhou, distributed delays [rapid communication] T.; Chen, A.; Zhou, Y.

    2005-08-01

    By using the continuation theorem of coincidence degree theory and Liapunov function, we obtain some sufficient criteria to ensure the existence and global exponential stability of periodic solution to the bidirectional associative memory (BAM) neural networks with periodic coefficients and continuously distributed delays. These results improve and generalize the works of papers [J. Cao, L. Wang, Phys. Rev. E 61 (2000) 1825] and [Z. Liu, A. Chen, J. Cao, L. Huang, IEEE Trans. Circuits Systems I 50 (2003) 1162]. An example is given to illustrate that the criteria are feasible.

  2. 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....

  3. 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 ...

  4. 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.

  5. Distributed Persistent Identifiers System Design

    Directory of Open Access Journals (Sweden)

    Pavel Golodoniuc

    2017-06-01

    Full Text Available The need to identify both digital and physical objects is ubiquitous in our society. Past and present persistent identifier (PID systems, of which there is a great variety in terms of technical and social implementation, have evolved with the advent of the Internet, which has allowed for globally unique and globally resolvable identifiers. PID systems have, by in large, catered for identifier uniqueness, integrity, and persistence, regardless of the identifier’s application domain. Trustworthiness of these systems has been measured by the criteria first defined by Bütikofer (2009 and further elaborated by Golodoniuc 'et al'. (2016 and Car 'et al'. (2017. Since many PID systems have been largely conceived and developed by a single organisation they faced challenges for widespread adoption and, most importantly, the ability to survive change of technology. We believe that a cause of PID systems that were once successful fading away is the centralisation of support infrastructure – both organisational and computing and data storage systems. In this paper, we propose a PID system design that implements the pillars of a trustworthy system – ensuring identifiers’ independence of any particular technology or organisation, implementation of core PID system functions, separation from data delivery, and enabling the system to adapt for future change. We propose decentralisation at all levels — persistent identifiers and information objects registration, resolution, and data delivery — using Distributed Hash Tables and traditional peer-to-peer networks with information replication and caching mechanisms, thus eliminating the need for a central PID data store. This will increase overall system fault tolerance thus ensuring its trustworthiness. We also discuss important aspects of the distributed system’s governance, such as the notion of the authoritative source and data integrity

  6. An analysis of global robust stability of uncertain cellular neural networks with discrete and distributed delays

    International Nuclear Information System (INIS)

    Park, Ju H.

    2007-01-01

    This paper considers the robust stability analysis of cellular neural networks with discrete and distributed delays. Based on the Lyapunov stability theory and linear matrix inequality (LMI) technique, a novel stability criterion guaranteeing the global robust convergence of the equilibrium point is derived. The criterion can be solved easily by various convex optimization algorithms. An example is given to illustrate the usefulness of our results

  7. On global stability criterion for neural networks with discrete and distributed delays

    International Nuclear Information System (INIS)

    Park, Ju H.

    2006-01-01

    Based on the Lyapunov functional stability analysis for differential equations and the linear matrix inequality (LMI) optimization approach, a new delay-dependent criterion for neural networks with discrete and distributed delays is derived to guarantee global asymptotic stability. The criterion is expressed in terms of LMIs, which can be solved easily by various convex optimization algorithms. Some numerical examples are given to show the effectiveness of proposed method

  8. Distributed hierarchical radiation monitoring system

    International Nuclear Information System (INIS)

    Barak, D.

    1985-01-01

    A solution to the problem of monitoring the radiation levels in and around a nuclear facility is presented in this paper. This is a private case of a large scale general purpose data acqisition system with high reliability, availability and short maintenance time. The physical layout of the detectors in the plant, and the strict control demands dictated a distributed and hierarchical system. The system is comprised of three levels, each level contains modules. Level one contains the Control modules which collects data from groups of detectors and executes emergency local control tasks. In level two are the Group controllers which concentrate data from the Control modules, and enable local display and communication. The system computer is in level three, enabling the plant operator to receive information from the detectors and execute control tasks. The described system was built and is operating successfully for about two years. (author)

  9. 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.

  10. 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.

  11. 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)

  12. Delay-dependent exponential stability for neural networks with discrete and distributed time-varying delays

    International Nuclear Information System (INIS)

    Zhu Xunlin; Wang Youyi

    2009-01-01

    This Letter studies the exponential stability for a class of neural networks (NNs) with both discrete and distributed time-varying delays. Under weaker assumptions on the activation functions, by defining a more general type of Lyapunov functionals and developing a new convex combination technique, new less conservative and less complex stability criteria are established to guarantee the global exponential stability of the discussed NNs. The obtained conditions are dependent on both discrete and distributed delays, are expressed in terms of linear matrix inequalities (LMIs), and contain fewer decision variables. Numerical examples are given to illustrate the effectiveness and the less conservatism of the proposed conditions.

  13. Stability analysis of Markovian jumping stochastic Cohen—Grossberg neural networks with discrete and distributed time varying delays

    International Nuclear Information System (INIS)

    Ali, M. Syed

    2014-01-01

    In this paper, the global asymptotic stability problem of Markovian jumping stochastic Cohen—Grossberg neural networks with discrete and distributed time-varying delays (MJSCGNNs) is considered. A novel LMI-based stability criterion is obtained by constructing a new Lyapunov functional to guarantee the asymptotic stability of MJSCGNNs. Our results can be easily verified and they are also less restrictive than previously known criteria and can be applied to Cohen—Grossberg neural networks, recurrent neural networks, and cellular neural networks. Finally, the proposed stability conditions are demonstrated with numerical examples

  14. The ATLAS Distributed Analysis System

    CERN Document Server

    Legger, F; The ATLAS collaboration; Pacheco Pages, A; Stradling, A

    2013-01-01

    In the LHC operations era, analysis of the multi-petabyte ATLAS data sample by globally distributed physicists is a challenging task. To attain the required scale the ATLAS Computing Model was designed around the concept of grid computing, realized in the Worldwide LHC Computing Grid (WLCG), the largest distributed computational resource existing in the sciences. The ATLAS experiment currently stores over 140 PB of data and runs about 140,000 concurrent jobs continuously at WLCG sites. During the first run of the LHC, the ATLAS Distributed Analysis (DA) service has operated stably and scaled as planned. More than 1600 users submitted jobs in 2012, with 2 million or more analysis jobs per week, peaking at about a million jobs per day. The system dynamically distributes popular data to expedite processing and maximally utilize resources. The reliability of the DA service is high but steadily improving; grid sites are continually validated against a set of standard tests, and a dedicated team of expert shifters ...

  15. The ATLAS Distributed Analysis System

    CERN Document Server

    Legger, F; The ATLAS collaboration

    2014-01-01

    In the LHC operations era, analysis of the multi-petabyte ATLAS data sample by globally distributed physicists is a challenging task. To attain the required scale the ATLAS Computing Model was designed around the concept of grid computing, realized in the Worldwide LHC Computing Grid (WLCG), the largest distributed computational resource existing in the sciences. The ATLAS experiment currently stores over 140 PB of data and runs about 140,000 concurrent jobs continuously at WLCG sites. During the first run of the LHC, the ATLAS Distributed Analysis (DA) service has operated stably and scaled as planned. More than 1600 users submitted jobs in 2012, with 2 million or more analysis jobs per week, peaking at about a million jobs per day. The system dynamically distributes popular data to expedite processing and maximally utilize resources. The reliability of the DA service is high but steadily improving; grid sites are continually validated against a set of standard tests, and a dedicated team of expert shifters ...

  16. 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.

  17. 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

  18. Distributed optimal coordination for distributed energy resources in power systems

    DEFF Research Database (Denmark)

    Wu, Di; Yang, Tao; Stoorvogel, A.

    2017-01-01

    Driven by smart grid technologies, distributed energy resources (DERs) have been rapidly developing in recent years for improving reliability and efficiency of distribution systems. Emerging DERs require effective and efficient coordination in order to reap their potential benefits. In this paper......, we consider an optimal DER coordination problem over multiple time periods subject to constraints at both system and device levels. Fully distributed algorithms are proposed to dynamically and automatically coordinate distributed generators with multiple/single storages. With the proposed algorithms...

  19. 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)

  20. Small Aircraft Data Distribution System

    Science.gov (United States)

    Chazanoff, Seth L.; Dinardo, Steven J.

    2012-01-01

    The CARVE Small Aircraft Data Distribution System acquires the aircraft location and attitude data that is required by the various programs running on a distributed network. This system distributes the data it acquires to the data acquisition programs for inclusion in their data files. It uses UDP (User Datagram Protocol) to broadcast data over a LAN (Local Area Network) to any programs that might have a use for the data. The program is easily adaptable to acquire additional data and log that data to disk. The current version also drives displays using precision pitch and roll information to aid the pilot in maintaining a level-level attitude for radar/radiometer mapping beyond the degree available by flying visually or using a standard gyro-driven attitude indicator. The software is designed to acquire an array of data to help the mission manager make real-time decisions as to the effectiveness of the flight. This data is displayed for the mission manager and broadcast to the other experiments on the aircraft for inclusion in their data files. The program also drives real-time precision pitch and roll displays for the pilot and copilot to aid them in maintaining the desired attitude, when required, during data acquisition on mapping lines.

  1. 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.)

  2. 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

  3. 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.

  4. Adaptive Overcurrent Protection for Microgrids in Extensive Distribution Systems

    DEFF Research Database (Denmark)

    Lin, Hengwei; Guerrero, Josep M.; Jia, Chenxi

    2016-01-01

    Microgrid is regarded as a new form to integrate the increasing penetration of distributed generation units (DGs) in the extensive distribution systems. This paper proposes an adaptive overcurrent protection strategy for a microgrid network. The protection coordination of the overcurrent relays...... measurements in another neural network model. Reconfigurations can be performed to modify the settings of the on-field relays to enhance the reliable operation for the different operational situations. The test results show that the adaptive overcurrent protection scheme with the assistance of estimation model...... can modify the protective settings for the new operation state accurately and intelligently....

  5. 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.

  6. Video distribution system cost model

    Science.gov (United States)

    Gershkoff, I.; Haspert, J. K.; Morgenstern, B.

    1980-01-01

    A cost model that can be used to systematically identify the costs of procuring and operating satellite linked communications systems is described. The user defines a network configuration by specifying the location of each participating site, the interconnection requirements, and the transmission paths available for the uplink (studio to satellite), downlink (satellite to audience), and voice talkback (between audience and studio) segments of the network. The model uses this information to calculate the least expensive signal distribution path for each participating site. Cost estimates are broken downy by capital, installation, lease, operations and maintenance. The design of the model permits flexibility in specifying network and cost structure.

  7. 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

  8. 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.

  9. 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.

  10. 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.

  11. 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

  12. 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.

  13. 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.

  14. Simple Algorithms for Distributed Leader Election in Anonymous Synchronous Rings and Complete Networks Inspired by Neural Development in Fruit Flies.

    Science.gov (United States)

    Xu, Lei; Jeavons, Peter

    2015-11-01

    Leader election in anonymous rings and complete networks is a very practical problem in distributed computing. Previous algorithms for this problem are generally designed for a classical message passing model where complex messages are exchanged. However, the need to send and receive complex messages makes such algorithms less practical for some real applications. We present some simple synchronous algorithms for distributed leader election in anonymous rings and complete networks that are inspired by the development of the neural system of the fruit fly. Our leader election algorithms all assume that only one-bit messages are broadcast by nodes in the network and processors are only able to distinguish between silence and the arrival of one or more messages. These restrictions allow implementations to use a simpler message-passing architecture. Even with these harsh restrictions our algorithms are shown to achieve good time and message complexity both analytically and experimentally.

  15. Distribution system protection with communication technologies

    DEFF Research Database (Denmark)

    Wei, Mu; Chen, Zhe

    2010-01-01

    Due to the communication technologies’ involvement in the distribution power system, the time-critical protection function may be implemented more accurately, therefore distribution power systems’ stability, reliability and security could be improved. This paper presents an active distribution...

  16. 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.

  17. 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

  18. Loss Allocation in a Distribution System with Distributed Generation Units

    DEFF Research Database (Denmark)

    Lund, Torsten; Nielsen, Arne Hejde; Sørensen, Poul Ejnar

    2007-01-01

    In Denmark, a large part of the electricity is produced by wind turbines and combined heat and power plants (CHPs). Most of them are connected to the network through distribution systems. This paper presents a new algorithm for allocation of the losses in a distribution system with distributed...... generation. The algorithm is based on a reduced impedance matrix of the network and current injections from loads and production units. With the algorithm, the effect of the covariance between production and consumption can be evaluated. To verify the theoretical results, a model of the distribution system...

  19. 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

  20. Neural network-based distributed attitude coordination control for spacecraft formation flying with input saturation.

    Science.gov (United States)

    Zou, An-Min; Kumar, Krishna Dev

    2012-07-01

    This brief considers the attitude coordination control problem for spacecraft formation flying when only a subset of the group members has access to the common reference attitude. A quaternion-based distributed attitude coordination control scheme is proposed with consideration of the input saturation and with the aid of the sliding-mode observer, separation principle theorem, Chebyshev neural networks, smooth projection algorithm, and robust control technique. Using graph theory and a Lyapunov-based approach, it is shown that the distributed controller can guarantee the attitude of all spacecraft to converge to a common time-varying reference attitude when the reference attitude is available only to a portion of the group of spacecraft. Numerical simulations are presented to demonstrate the performance of the proposed distributed controller.

  1. The brain as a distributed intelligent processing system: an EEG study.

    Science.gov (United States)

    da Rocha, Armando Freitas; Rocha, Fábio Theoto; Massad, Eduardo

    2011-03-15

    Various neuroimaging studies, both structural and functional, have provided support for the proposal that a distributed brain network is likely to be the neural basis of intelligence. The theory of Distributed Intelligent Processing Systems (DIPS), first developed in the field of Artificial Intelligence, was proposed to adequately model distributed neural intelligent processing. In addition, the neural efficiency hypothesis suggests that individuals with higher intelligence display more focused cortical activation during cognitive performance, resulting in lower total brain activation when compared with individuals who have lower intelligence. This may be understood as a property of the DIPS. In our study, a new EEG brain mapping technique, based on the neural efficiency hypothesis and the notion of the brain as a Distributed Intelligence Processing System, was used to investigate the correlations between IQ evaluated with WAIS (Wechsler Adult Intelligence Scale) and WISC (Wechsler Intelligence Scale for Children), and the brain activity associated with visual and verbal processing, in order to test the validity of a distributed neural basis for intelligence. The present results support these claims and the neural efficiency hypothesis.

  2. The brain as a distributed intelligent processing system: an EEG study.

    Directory of Open Access Journals (Sweden)

    Armando Freitas da Rocha

    Full Text Available BACKGROUND: Various neuroimaging studies, both structural and functional, have provided support for the proposal that a distributed brain network is likely to be the neural basis of intelligence. The theory of Distributed Intelligent Processing Systems (DIPS, first developed in the field of Artificial Intelligence, was proposed to adequately model distributed neural intelligent processing. In addition, the neural efficiency hypothesis suggests that individuals with higher intelligence display more focused cortical activation during cognitive performance, resulting in lower total brain activation when compared with individuals who have lower intelligence. This may be understood as a property of the DIPS. METHODOLOGY AND PRINCIPAL FINDINGS: In our study, a new EEG brain mapping technique, based on the neural efficiency hypothesis and the notion of the brain as a Distributed Intelligence Processing System, was used to investigate the correlations between IQ evaluated with WAIS (Wechsler Adult Intelligence Scale and WISC (Wechsler Intelligence Scale for Children, and the brain activity associated with visual and verbal processing, in order to test the validity of a distributed neural basis for intelligence. CONCLUSION: The present results support these claims and the neural efficiency hypothesis.

  3. The Brain as a Distributed Intelligent Processing System: An EEG Study

    Science.gov (United States)

    da Rocha, Armando Freitas; Rocha, Fábio Theoto; Massad, Eduardo

    2011-01-01

    Background Various neuroimaging studies, both structural and functional, have provided support for the proposal that a distributed brain network is likely to be the neural basis of intelligence. The theory of Distributed Intelligent Processing Systems (DIPS), first developed in the field of Artificial Intelligence, was proposed to adequately model distributed neural intelligent processing. In addition, the neural efficiency hypothesis suggests that individuals with higher intelligence display more focused cortical activation during cognitive performance, resulting in lower total brain activation when compared with individuals who have lower intelligence. This may be understood as a property of the DIPS. Methodology and Principal Findings In our study, a new EEG brain mapping technique, based on the neural efficiency hypothesis and the notion of the brain as a Distributed Intelligence Processing System, was used to investigate the correlations between IQ evaluated with WAIS (Whechsler Adult Intelligence Scale) and WISC (Wechsler Intelligence Scale for Children), and the brain activity associated with visual and verbal processing, in order to test the validity of a distributed neural basis for intelligence. Conclusion The present results support these claims and the neural efficiency hypothesis. PMID:21423657

  4. 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.

  5. Quantum distribution function of nonequilibrium system

    International Nuclear Information System (INIS)

    Sogo, Kiyoshi; Fujimoto, Yasushi.

    1990-03-01

    A path integral representation is derived for the Wigner distribution function of a nonequilibrium system coupled with heat bath. Under appropriate conditions, the Wigner distribution function approaches an equilibrium distribution, which manifests shifting and broadening of spectral lines due to the interaction with heat bath. It is shown that the equilibrium distribution becomes the quantum canonical distribution in the vanishing coupling constant limit. (author)

  6. 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.

  7. The Planetary Data System Distributed Inventory System

    Science.gov (United States)

    Hughes, J. Steven; McMahon, Susan K.

    1996-01-01

    The advent of the World Wide Web (Web) and the ability to easily put data repositories on-line has resulted in a proliferation of digital libraries. The heterogeneity of the underlying systems, the autonomy of the individual sites, and distributed nature of the technology has made both interoperability across the sites and the search for resources within a site major research topics. This article will describe a system that addresses both issues using standard Web protocols and meta-data labels to implement an inventory of on-line resources across a group of sites. The success of this system is strongly dependent on the existence of and adherence to a standards architecture that guides the management of meta-data within participating sites.

  8. Convergence dynamics of hybrid bidirectional associative memory neural networks with distributed delays

    International Nuclear Information System (INIS)

    Liao Xiaofeng; Wong, K.-W.; Yang Shizhong

    2003-01-01

    In this Letter, the characteristics of the convergence dynamics of hybrid bidirectional associative memory neural networks with distributed transmission delays are studied. Without assuming the symmetry of synaptic connection weights and the monotonicity and differentiability of activation functions, the Lyapunov functionals are constructed and the generalized Halanay-type inequalities are employed to derive the delay-independent sufficient conditions under which the networks converge exponentially to the equilibria associated with temporally uniform external inputs. Some examples are given to illustrate the correctness of our results

  9. Global exponential stability analysis on impulsive BAM neural networks with distributed delays

    Science.gov (United States)

    Li, Yao-Tang; Yang, Chang-Bo

    2006-12-01

    Using M-matrix and topological degree tool, sufficient conditions are obtained for the existence, uniqueness and global exponential stability of the equilibrium point of bidirectional associative memory (BAM) neural networks with distributed delays and subjected to impulsive state displacements at fixed instants of time by constructing a suitable Lyapunov functional. The results remove the usual assumptions that the boundedness, monotonicity, and differentiability of the activation functions. It is shown that in some cases, the stability criteria can be easily checked. Finally, an illustrative example is given to show the effectiveness of the presented criteria.

  10. Energy Management of Smart Distribution Systems

    Science.gov (United States)

    Ansari, Bananeh

    Electric power distribution systems interface the end-users of electricity with the power grid. Traditional distribution systems are operated in a centralized fashion with the distribution system owner or operator being the only decision maker. The management and control architecture of distribution systems needs to gradually transform to accommodate the emerging smart grid technologies, distributed energy resources, and active electricity end-users or prosumers. The content of this document concerns with developing multi-task multi-objective energy management schemes for: 1) commercial/large residential prosumers, and 2) distribution system operator of a smart distribution system. The first part of this document describes a method of distributed energy management of multiple commercial/ large residential prosumers. These prosumers not only consume electricity, but also generate electricity using their roof-top solar photovoltaics systems. When photovoltaics generation is larger than local consumption, excess electricity will be fed into the distribution system, creating a voltage rise along the feeder. Distribution system operator cannot tolerate a significant voltage rise. ES can help the prosumers manage their electricity exchanges with the distribution system such that minimal voltage fluctuation occurs. The proposed distributed energy management scheme sizes and schedules each prosumer's ES to reduce the electricity bill and mitigate voltage rise along the feeder. The second part of this document focuses on emergency energy management and resilience assessment of a distribution system. The developed emergency energy management system uses available resources and redundancy to restore the distribution system's functionality fully or partially. The success of the restoration maneuver depends on how resilient the distribution system is. Engineering resilience terminology is used to evaluate the resilience of distribution system. The proposed emergency energy

  11. 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.

  12. A Neural Network Controller for Variable-Speed Variable-Pitch Wind Energy Conversion Systems Using Generalized Minimum Entropy Criterion

    Directory of Open Access Journals (Sweden)

    Mifeng Ren

    2014-01-01

    Full Text Available This paper considers the neural network controller design problem for variable pitch wind energy conversion systems (WECS with non-Gaussian wind speed disturbances in the stochastic distribution control framework. The approach here is used to directly model the unknown control law based on a fixed neural network (the number of layers and nodes in a neural network is fixed without the need to construct a separate model for the WECS. In order to characterize the randomness of the WECS, a generalized minimum entropy criterion is established to train connection weights of the neural network. For the train purpose, both kernel density estimation method and sliding window technique are adopted to estimate the PDF of tracking error and entropies. Due to the unknown process dynamics, the gradient of the objective function in a gradient-descent-type algorithm is estimated using an incremental perturbation method. The proposed approach is illustrated on a simulated WECS with non-Gaussian wind speed.

  13. 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.

  14. 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

  15. 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.

  16. 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

  17. 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...

  18. Natural gas demand forecast system based on the application of artificial neural networks

    International Nuclear Information System (INIS)

    Sanfeliu, J.M.; Doumanian, J.E.

    1997-01-01

    Gas Natural BAN, as a distribution gas company since 1993 in the north and west area of Buenos Aires Argentina, with 1,000,000 customers, had to develop a gas demand forecast system which should comply with the following basic requirements: Be able to do reliable forecasts with short historical information (2 years); Distinguish demands in areas of different characteristics, i.e. mainly residential, mainly industrial; Self-learning capability. To accomplish above goals, Gas Natural BAN chose in view of its own necessities, an artificial intelligence application (neural networks). 'SANDRA', the gas demand forecast system for gas distribution used by Gas Natural BAN, has the following features: Daily gas demand forecast, Hourly gas demand forecast and Breakdown of both forecast for each of the 3 basic zones in which the distribution area of Gas Natural BAN is divided. (au)

  19. The Impact of Connecting Distributed Generation to the Distribution System

    Directory of Open Access Journals (Sweden)

    E. V. Mgaya

    2007-01-01

    Full Text Available This paper deals with the general problem of utilizing of renewable energy sources to generate electric energy. Recent advances in renewable energy power generation technologies, e.g., wind and photovoltaic (PV technologies, have led to increased interest in the application of these generation devices as distributed generation (DG units. This paper presents the results of an investigation into possible improvements in the system voltage profile and reduction of system losses when adding wind power DG (wind-DG to a distribution system. Simulation results are given for a case study, and these show that properly sized wind DGs, placed at carefully selected sites near key distribution substations, could be very effective in improving the distribution system voltage profile and reducing power losses, and hence could  improve the effective capacity of the system

  20. Using a Neural Network Approach to Find Unusual Butterfly Pitch Angle Distribution Shapes

    Science.gov (United States)

    Medeiros, C.; Sibeck, D. G.; Souza, V. M. C. E. S.; Vieira, L.; Alves, L. R.; Da Silva, L. A.; Kanekal, S. G.; Baker, D. N.

    2017-12-01

    A special kind of neural network referred to as a Self-Organizing Map (SOM) was previously adopted to identify, in pitch angle-resolved relativistic electron flux data provided by the REPT instrument onboard the Van Allen Probes, three major types of electron pitch angle distributions (PADs), namely 90o-peaked, butterfly and flattop (Souza et al., 2016), following the classification scheme employed by Gannon et al. (2007). Previous studies show that butterfly distribution can be found in more than one shape. They usually exhibit an intense decrease near 90° pitch angles compared to the peaks usually around 30° and 150°. Sometimes unusual butterfly PAD shapes with peaks near 45° and 135° pitch angles can be observed. These could be correlated with different physical processes that govern the production and loss of energetic particles in the Van Allen radiation belt. A neural network approach allows the distinction of different kinds of butterfly PADs which were not analyzed in detail by Souza et al. (2016). This study uses SOM methodology to find these unusual butterfly PAD shape during the interval between January 1, 2014 and October 1, 2015, during which Van Allen Probes orbit covered all MLT. The spatial and temporal occurrence of these events were investigated as well as their solar wind and magnetospheric drivers.

  1. A Self-Organizing Incremental Neural Network based on local distribution learning.

    Science.gov (United States)

    Xing, Youlu; Shi, Xiaofeng; Shen, Furao; Zhou, Ke; Zhao, Jinxi

    2016-12-01

    In this paper, we propose an unsupervised incremental learning neural network based on local distribution learning, which is called Local Distribution Self-Organizing Incremental Neural Network (LD-SOINN). The LD-SOINN combines the advantages of incremental learning and matrix learning. It can automatically discover suitable nodes to fit the learning data in an incremental way without a priori knowledge such as the structure of the network. The nodes of the network store rich local information regarding the learning data. The adaptive vigilance parameter guarantees that LD-SOINN is able to add new nodes for new knowledge automatically and the number of nodes will not grow unlimitedly. While the learning process continues, nodes that are close to each other and have similar principal components are merged to obtain a concise local representation, which we call a relaxation data representation. A denoising process based on density is designed to reduce the influence of noise. Experiments show that the LD-SOINN performs well on both artificial and real-word data. Copyright © 2016 Elsevier Ltd. All rights reserved.

  2. Control and operation of distributed generation in distribution systems

    DEFF Research Database (Denmark)

    Mahat, Pukar; Chen, Zhe; Bak-Jensen, Birgitte

    2011-01-01

    Many distribution systems nowadays have significant penetration of distributed generation (DG)and thus, islanding operation of these distribution systems is becoming a viable option for economical and technical reasons. The DG should operate optimally during both grid-connected and island...... algorithm, which uses average rate of change off requency (Af5) and real power shift RPS), in the islanded mode. RPS will increase or decrease the power set point of the generator with increasing or decreasing system frequency, respectively. Simulation results show that the proposed method can operate...

  3. 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.

  4. 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.

  5. 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.

  6. Improved Shape Parameter Estimation in Pareto Distributed Clutter with Neural Networks

    Directory of Open Access Journals (Sweden)

    José Raúl Machado-Fernández

    2016-12-01

    Full Text Available The main problem faced by naval radars is the elimination of the clutter input which is a distortion signal appearing mixed with target reflections. Recently, the Pareto distribution has been related to sea clutter measurements suggesting that it may provide a better fit than other traditional distributions. The authors propose a new method for estimating the Pareto shape parameter based on artificial neural networks. The solution achieves a precise estimation of the parameter, having a low computational cost, and outperforming the classic method which uses Maximum Likelihood Estimates (MLE. The presented scheme contributes to the development of the NATE detector for Pareto clutter, which uses the knowledge of clutter statistics for improving the stability of the detection, among other applications.

  7. Distributed representations of action sequences in anterior cingulate cortex: A recurrent neural network approach.

    Science.gov (United States)

    Shahnazian, Danesh; Holroyd, Clay B

    2018-02-01

    Anterior cingulate cortex (ACC) has been the subject of intense debate over the past 2 decades, but its specific computational function remains controversial. Here we present a simple computational model of ACC that incorporates distributed representations across a network of interconnected processing units. Based on the proposal that ACC is concerned with the execution of extended, goal-directed action sequences, we trained a recurrent neural network to predict each successive step of several sequences associated with multiple tasks. In keeping with neurophysiological observations from nonhuman animals, the network yields distributed patterns of activity across ACC neurons that track the progression of each sequence, and in keeping with human neuroimaging data, the network produces discrepancy signals when any step of the sequence deviates from the predicted step. These simulations illustrate a novel approach for investigating ACC function.

  8. 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.

  9. 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.

  10. Islanding Operation of Distribution System with Distributed Generations

    DEFF Research Database (Denmark)

    Mahat, Pukar; Chen, Zhe; Bak-Jensen, Birgitte

    2010-01-01

    The growing interest in distributed generations (DGs) due to environmental concern and various other reasons have resulted in significant penetration of DGs in many distribution system worldwide. DGs come with many benefits. One of the benefits is improved reliability by supplying load during power...

  11. 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

  12. 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

  13. Low jitter RF distribution system

    Science.gov (United States)

    Wilcox, Russell; Doolittle, Lawrence; Huang, Gang

    2012-09-18

    A timing signal distribution system includes an optical frequency stabilized laser signal amplitude modulated at an rf frequency. A transmitter box transmits a first portion of the laser signal and receive a modified optical signal, and outputs a second portion of the laser signal and a portion of the modified optical signal. A first optical fiber carries the first laser signal portion and the modified optical signal, and a second optical fiber carries the second portion of the laser signal and the returned modified optical signal. A receiver box receives the first laser signal portion, shifts the frequency of the first laser signal portion outputs the modified optical signal, and outputs an electrical signal on the basis of the laser signal. A detector at the end of the second optical fiber outputs a signal based on the modified optical signal. An optical delay sensing circuit outputs a data signal based on the detected modified optical signal. An rf phase detect and correct signal circuit outputs a signal corresponding to a phase stabilized rf signal based on the data signal and the frequency received from the receiver box.

  14. 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.

  15. 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

  16. 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

  17. RELIABILITY ANALYSIS OF POWER DISTRIBUTION SYSTEMS

    Directory of Open Access Journals (Sweden)

    Popescu V.S.

    2012-04-01

    Full Text Available Power distribution systems are basic parts of power systems and reliability of these systems at present is a key issue for power engineering development and requires special attention. Operation of distribution systems is accompanied by a number of factors that produce random data a large number of unplanned interruptions. Research has shown that the predominant factors that have a significant influence on the reliability of distribution systems are: weather conditions (39.7%, defects in equipment(25% and unknown random factors (20.1%. In the article is studied the influence of random behavior and are presented estimations of reliability of predominantly rural electrical distribution systems.

  18. Review on Islanding Operation of Distribution System with Distributed Generation

    DEFF Research Database (Denmark)

    Mahat, Pukar; Chen, Zhe; Bak-Jensen, Birgitte

    2011-01-01

    The growing environmental concern and various benefits of distributed generation (DG) have resulted in significant penetration of DG in many distribution systems worldwide. One of the major expected benefits of DG is the improvement in the reliability of power supply by supplying load during power...... outage by operating in an island mode. However, there are many challenges to overcome before islanding operation of a distribution system with DG can become a viable solution in future. This paper reviews some of the major challenges with islanding operation and explores some possible solutions...

  19. 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.

  20. 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

  1. 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.

  2. 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

  3. VAR control in distribution systems by using artificial intelligence techniques

    Energy Technology Data Exchange (ETDEWEB)

    Golkar, M.A. [Curtin Univ. of Technology, Sarawak (Malaysia). School of Engineering and Science

    2005-07-01

    This paper reviewed artificial intelligence techniques used in VAR control systems. Reactive power controls in distribution systems were also reviewed. While artificial intelligence methods are widely used in power control systems, the techniques require extensive human knowledge bases and experiences in order to operate correctly. Expert systems use knowledge and interface procedures to solve problems that often require human expertise. Expert systems often cause knowledge bottlenecks as they are unable to learn or adopt to new situations. While neural networks possess learning ability, they are computationally expensive. However, test results in recent neural network studies have demonstrated that they work well in a variety of loading conditions. Fuzzy logic techniques are used to accurately represent the operational constraints of power systems. Fuzzy logic has an advantage over other artificial intelligence techniques as it is able to remedy uncertainties in data. Evolutionary computing algorithms use probabilistic transition rules which can search complicated data to determine optimal constraints and parameters. Over 95 per cent of all papers published on power systems use genetic algorithms. It was concluded that hybrid systems using various artificial intelligence techniques are now being used by researchers. 69 refs.

  4. 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.

  5. Distributed radiation protection console system

    International Nuclear Information System (INIS)

    Chhokra, R.S.; Deshpande, V.K.; Mishra, H.; Rajeev, K.P.; Thakur, Bipla B.; Munj, Niket

    2004-01-01

    Radiation exposure control is one of the most important aspects in any nuclear facility . It encompasses continuous monitoring of the various areas of the facility to detect any increase in the radiation level and/or the air activity level beyond preset limits and alarm the O and M personnel working in these areas. Detection and measurement of radiation level and the air activity level is carried out by a number of monitors installed in the areas. These monitors include Area Gamma Monitors, Continuous Air Monitors, Pu-In-Air Monitors, Criticality Monitors etc. Traditionally, these measurements are displayed and recorded on a Central Radiation Protection Console(CRPC), which is located in the central control room of the facility. This methodology suffers from the shortcoming that any worker required to enter a work area will have to inquire about the radiation status of the area either from the CRPC or will get to know the same directly from the installed only after entering the area. This shortcoming can lead to avoidable delays in attending to the work or to unwanted exposure. The authors have designed and developed a system called Distributed Radiation Protection Console (DRPC) to overcome this shortcoming. A DRPC is a console which is located outside the entrance of a given area and displays the radiation status of the area. It presents to health physicist and the plant operators a graphic over-view of the radiation and air activity levels in the particular area of the plant. It also provides audio visual annunciation of the alarm status. Each radioactive area in a nuclear facility will have its own DRPC, which will receive as its inputs the analog and digital signals from radiation monitoring instruments installed in the area and would not only show those readings on its video graphic screen but will also provide warning messages and instructions to the personnel entering the active areas. The various DRPCs can be integrated into a Local Area Network, where the

  6. Fabrication and Microassembly of a mm-Sized Floating Probe for a Distributed Wireless Neural Interface

    Directory of Open Access Journals (Sweden)

    Pyungwoo Yeon

    2016-09-01

    Full Text Available A new class of wireless neural interfaces is under development in the form of tens to hundreds of mm-sized untethered implants, distributed across the target brain region(s. Unlike traditional interfaces that are tethered to a centralized control unit and suffer from micromotions that may damage the surrounding neural tissue, the new free-floating wireless implantable neural recording (FF-WINeR probes will be stand-alone, directly communicating with an external interrogator. Towards development of the FF-WINeR, in this paper we describe the micromachining, microassembly, and hermetic packaging of 1-mm3 passive probes, each of which consists of a thinned micromachined silicon die with a centered Ø(diameter 130 μm through-hole, an Ø81 μm sharpened tungsten electrode, a 7-turn gold wire-wound coil wrapped around the die, two 0201 surface mount capacitors on the die, and parylene-C/Polydimethylsiloxane (PDMS coating. The fabricated passive probe is tested under a 3-coil inductive link to evaluate power transfer efficiency (PTE and power delivered to a load (PDL for feasibility assessment. The minimum PTE/PDL at 137 MHz were 0.76%/240 μW and 0.6%/191 μW in the air and lamb head medium, respectively, with coil separation of 2.8 cm and 9 kΩ receiver (Rx loading. Six hermetically sealed probes went through wireless hermeticity testing, using a 2-coil inductive link under accelerated lifetime testing condition of 85 °C, 1 atm, and 100%RH. The mean-time-to-failure (MTTF of the probes at 37 °C is extrapolated to be 28.7 years, which is over their lifetime.

  7. 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

  8. Control and Operation of Islanded Distribution System

    DEFF Research Database (Denmark)

    Mahat, Pukar

    deviation and real power shift. When a distribution system, with all its generators operating at maximum power, is islanded, the frequency will go down if the total load is more than the total generation. An under-frequency load shedding procedure for islanded distribution systems with DG unit(s) based...... states. Short circuit power also changes when some of the generators in the distribution system are disconnected. This may result in elongation of fault clearing time and hence disconnection of equipments (including generators) in the distribution system or unnecessary operation of protective devices...... operational challenges. But, on the other hand, it has also opened up some opportunities. One opportunity/challenge is an islanded operation of a distribution system with DG unit(s). Islanding is a situation in which a distribution system becomes electrically isolated from the remainder of the power system...

  9. DIAMONDS: Engineering Distributed Object Systems

    National Research Council Canada - National Science Library

    Cheng, Evan

    1997-01-01

    This report describes DIAMONDS, a research project at Syracuse University, that is dedicated to producing both a methodology and corresponding tools to assist in the development of heterogeneous distributed software...

  10. 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

  11. Water sample-collection and distribution system

    Science.gov (United States)

    Brooks, R. R.

    1978-01-01

    Collection and distribution system samples water from six designated stations, filtered if desired, and delivers it to various analytical sensors. System may be controlled by Water Monitoring Data Acquisition System or operated manually.

  12. Water balance estimation in high Alpine terrain by combining distributed modeling and a neural network approach (Berchtesgaden Alps, Germany

    Directory of Open Access Journals (Sweden)

    G. Kraller

    2012-07-01

    Full Text Available The water balance in high Alpine regions is often characterized by significant variation of meteorological variables in space and time, a complex hydrogeological situation and steep gradients. The system is even more complex when the rock composition is dominated by soluble limestone, because unknown underground flow conditions and flow directions lead to unknown storage quantities. Reliable distributed modeling cannot be implemented by traditional approaches due to unknown storage processes at local and catchment scale. We present an artificial neural network extension of a distributed hydrological model (WaSiM-ETH that allows to account for subsurface water transfer in a karstic environment. The extension was developed for the Alpine catchment of the river "Berchtesgadener Ache" (Berchtesgaden Alps, Germany, which is characterized by extreme topography and calcareous rocks. The model assumes porous conditions and does not account for karstic environments, resulting in systematic mismatch of modeled and measured runoff in discharge curves at the outlet points of neighboring high alpine subbasins. Various precipitation interpolation methods did not allow to explain systematic mismatches, and unknown subsurface hydrological processes were concluded as the underlying reason. We introduce a new method that allows to describe the unknown subsurface boundary fluxes, and account for them in the hydrological model. This is achieved by an artificial neural network approach (ANN, where four input variables are taken to calculate the unknown subsurface storage conditions. This was first developed for the high Alpine subbasin Königsseer Ache to improve the monthly water balance. We explicitly derive the algebraic transfer function of an artificial neural net to calculate the missing boundary fluxes. The result of the ANN is then implemented in the groundwater module of the hydrological model as boundary flux, and considered during the consecutive model

  13. The ATLAS distributed analysis system

    OpenAIRE

    Legger, F.

    2014-01-01

    In the LHC operations era, analysis of the multi-petabyte ATLAS data sample by globally distributed physicists is a challenging task. To attain the required scale the ATLAS Computing Model was designed around the concept of grid computing, realized in the Worldwide LHC Computing Grid (WLCG), the largest distributed computational resource existing in the sciences. The ATLAS experiment currently stores over 140 PB of data and runs about 140,000 concurrent jobs continuously at WLCG sites. During...

  14. High-frequency oscillations in distributed neural networks reveal the dynamics of human decision making

    Directory of Open Access Journals (Sweden)

    Adrian G Guggisberg

    2008-03-01

    Full Text Available We examine the relative timing of numerous brain regions involved in human decisions that are based on external criteria, learned information, personal preferences, or unconstrained internal considerations. Using magnetoencephalography (MEG and advanced signal analysis techniques, we were able to non-invasively reconstruct oscillations of distributed neural networks in the high-gamma frequency band (60–150 Hz. The time course of the observed neural activity suggested that two-alternative forced choice tasks are processed in four overlapping stages: processing of sensory input, option evaluation, intention formation, and action execution. Visual areas are activated fi rst, and show recurring activations throughout the entire decision process. The temporo-occipital junction and the intraparietal sulcus are active during evaluation of external values of the options, 250–500 ms after stimulus presentation. Simultaneously, personal preference is mediated by cortical midline structures. Subsequently, the posterior parietal and superior occipital cortices appear to encode intention, with different subregions being responsible for different types of choice. The cerebellum and inferior parietal cortex are recruited for internal generation of decisions and actions, when all options have the same value. Action execution was accompanied by activation peaks in the contralateral motor cortex. These results suggest that high-gamma oscillations as recorded by MEG allow a reliable reconstruction of decision processes with excellent spatiotemporal resolution.

  15. Short-term Music Training Enhances Complex, Distributed Neural Communication during Music and Linguistic Tasks.

    Science.gov (United States)

    Carpentier, Sarah M; Moreno, Sylvain; McIntosh, Anthony R

    2016-10-01

    Musical training is frequently associated with benefits to linguistic abilities, and recent focus has been placed on possible benefits of bilingualism to lifelong executive functions; however, the neural mechanisms for such effects are unclear. The aim of this study was to gain better understanding of the whole-brain functional effects of music and second-language training that could support such previously observed cognitive transfer effects. We conducted a 28-day longitudinal study of monolingual English-speaking 4- to 6-year-old children randomly selected to receive daily music or French language training, excluding weekends. Children completed passive EEG music note and French vowel auditory oddball detection tasks before and after training. Brain signal complexity was measured on source waveforms at multiple temporal scales as an index of neural information processing and network communication load. Comparing pretraining with posttraining, musical training was associated with increased EEG complexity at coarse temporal scales during the music and French vowel tasks in widely distributed cortical regions. Conversely, very minimal decreases in complexity at fine scales and trends toward coarse-scale increases were displayed after French training during the tasks. Spectral analysis failed to distinguish between training types and found overall theta (3.5-7.5 Hz) power increases after all training forms, with spatially fewer decreases in power at higher frequencies (>10 Hz). These findings demonstrate that musical training increased diversity of brain network states to support domain-specific music skill acquisition and music-to-language transfer effects.

  16. Multiobjecitve Sampling Design for Calibration of Water Distribution Network Model Using Genetic Algorithm and Neural Network

    Directory of Open Access Journals (Sweden)

    Kourosh Behzadian

    2008-03-01

    Full Text Available In this paper, a novel multiobjective optimization model is presented for selecting optimal locations in the water distribution network (WDN with the aim of installing pressure loggers. The pressure data collected at optimal locations will be used later on in the calibration of the proposed WDN model. Objective functions consist of maximization of calibrated model prediction accuracy and minimization of the total cost for sampling design. In order to decrease the model run time, an optimization model has been developed using multiobjective genetic algorithm and adaptive neural network (MOGA-ANN. Neural networks (NNs are initially trained after a number of initial GA generations and periodically retrained and updated after generation of a specified number of full model-analyzed solutions. Trained NNs are replaced with the fitness evaluation of some chromosomes within the GA progress. Using cache prevents objective function evaluation of repetitive chromosomes within GA. Optimal solutions are obtained through pareto-optimal front with respect to the two objective functions. Results show that jointing NNs in MOGA for approximating portions of chromosomes’ fitness in each generation leads to considerable savings in model run time and can be promising for reducing run-time in optimization models with significant computational effort.

  17. Research in Distributed Real-Time Systems

    Science.gov (United States)

    Mukkamala, R.

    1997-01-01

    This document summarizes the progress we have made on our study of issues concerning the schedulability of real-time systems. Our study has produced several results in the scalability issues of distributed real-time systems. In particular, we have used our techniques to resolve schedulability issues in distributed systems with end-to-end requirements. During the next year (1997-98), we propose to extend the current work to address the modeling and workload characterization issues in distributed real-time systems. In particular, we propose to investigate the effect of different workload models and component models on the design and the subsequent performance of distributed real-time systems.

  18. 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.

  19. 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.

  20. Artificial neural network application for predicting soil distribution coefficient of nickel

    International Nuclear Information System (INIS)

    Falamaki, Amin

    2013-01-01

    The distribution (or partition) coefficient (K d ) is an applicable parameter for modeling contaminant and radionuclide transport as well as risk analysis. Selection of this parameter may cause significant error in predicting the impacts of contaminant migration or site-remediation options. In this regards, various models were presented to predict K d values for different contaminants specially heavy metals and radionuclides. In this study, artificial neural network (ANN) is used to present simplified model for predicting K d of nickel. The main objective is to develop a more accurate model with a minimal number of parameters, which can be determined experimentally or select by review of different studies. In addition, the effects of training as well as the type of the network are considered. The K d values of Ni is strongly dependent on pH of the soil and mathematical relationships were presented between pH and K d of nickel recently. In this study, the same database of these presented models was used to verify that neural network may be more useful tools for predicting of K d . Two different types of ANN, multilayer perceptron and redial basis function, were used to investigate the effect of the network geometry on the results. In addition, each network was trained by 80 and 90% of the data and tested for 20 and 10% of the rest data. Then the results of the networks compared with the results of the mathematical models. Although the networks trained by 80 and 90% of the data the results show that all the networks predict with higher accuracy relative to mathematical models which were derived by 100% of data. More training of a network increases the accuracy of the network. Multilayer perceptron network used in this study predicts better than redial basis function network. - Highlights: ► Simplified models for predicting K d of nickel presented using artificial neural networks. ► Multilayer perceptron and redial basis function used to predict K d of nickel in

  1. Global exponential stability and periodicity of reaction-diffusion recurrent neural networks with distributed delays and Dirichlet boundary conditions

    International Nuclear Information System (INIS)

    Lu Junguo; Lu Linji

    2009-01-01

    In this paper, global exponential stability and periodicity of a class of reaction-diffusion recurrent neural networks with distributed delays and Dirichlet boundary conditions are studied by constructing suitable Lyapunov functionals and utilizing some inequality techniques. We first prove global exponential convergence to 0 of the difference between any two solutions of the original neural networks, the existence and uniqueness of equilibrium is the direct results of this procedure. This approach is different from the usually used one where the existence, uniqueness of equilibrium and stability are proved in two separate steps. Secondly, we prove periodicity. Sufficient conditions ensuring the existence, uniqueness, and global exponential stability of the equilibrium and periodic solution are given. These conditions are easy to verify and our results play an important role in the design and application of globally exponentially stable neural circuits and periodic oscillatory neural circuits.

  2. 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

  3. Comparison between sparsely distributed memory and Hopfield-type neural network models

    Science.gov (United States)

    Keeler, James D.

    1986-01-01

    The Sparsely Distributed Memory (SDM) model (Kanerva, 1984) is compared to Hopfield-type neural-network models. A mathematical framework for comparing the two is developed, and the capacity of each model is investigated. The capacity of the SDM can be increased independently of the dimension of the stored vectors, whereas the Hopfield capacity is limited to a fraction of this dimension. However, the total number of stored bits per matrix element is the same in the two models, as well as for extended models with higher order interactions. The models are also compared in their ability to store sequences of patterns. The SDM is extended to include time delays so that contextual information can be used to cover sequences. Finally, it is shown how a generalization of the SDM allows storage of correlated input pattern vectors.

  4. Global robust stability of neural networks with multiple discrete delays and distributed delays

    International Nuclear Information System (INIS)

    Gao Ming; Cui Baotong

    2009-01-01

    The problem of global robust stability is investigated for a class of uncertain neural networks with both multiple discrete time-varying delays and distributed time-varying delays. The uncertainties are assumed to be of norm-bounded form and the activation functions are supposed to be bounded and globally Lipschitz continuous. Based on the Lyapunov stability theory and linear matrix inequality technique, some robust stability conditions guaranteeing the global robust convergence of the equilibrium point are derived. The proposed LMI-based criteria are computationally efficient as they can be easily checked by using recently developed algorithms in solving LMIs. Two examples are given to show the effectiveness of the proposed results.

  5. On Distributed Port-Hamiltonian Process Systems

    NARCIS (Netherlands)

    Lopezlena, Ricardo; Scherpen, Jacquelien M.A.

    2004-01-01

    In this paper we use the term distributed port-Hamiltonian Process Systems (DPHPS) to refer to the result of merging the theory of distributed Port-Hamiltonian systems (DPHS) with the theory of process systems (PS). Such concept is useful for combining the systematic interconnection of PHS with the

  6. Distributed Cognition and Distributed Morality: Agency, Artifacts and Systems.

    Science.gov (United States)

    Heersmink, Richard

    2017-04-01

    There are various philosophical approaches and theories describing the intimate relation people have to artifacts. In this paper, I explore the relation between two such theories, namely distributed cognition and distributed morality theory. I point out a number of similarities and differences in these views regarding the ontological status they attribute to artifacts and the larger systems they are part of. Having evaluated and compared these views, I continue by focussing on the way cognitive artifacts are used in moral practice. I specifically conceptualise how such artifacts (a) scaffold and extend moral reasoning and decision-making processes, (b) have a certain moral status which is contingent on their cognitive status, and (c) whether responsibility can be attributed to distributed systems. This paper is primarily written for those interested in the intersection of cognitive and moral theory as it relates to artifacts, but also for those independently interested in philosophical debates in extended and distributed cognition and ethics of (cognitive) technology.

  7. Applying Distributed Object Technology to Distributed Embedded Control Systems

    DEFF Research Database (Denmark)

    Jørgensen, Bo Nørregaard; Dalgaard, Lars

    2012-01-01

    In this paper, we describe our Java RMI inspired Object Request Broker architecture MicroRMI for use with networked embedded devices. MicroRMI relieves the software developer from the tedious and error-prone job of writing communication protocols for interacting with such embedded devices. MicroR...... in developing control systems for distributed embedded platforms possessing severe resource restrictions.......RMI supports easy integration of high-level application specific control logic with low-level device specific control logic. Our experience from applying MicroRMI in the context of a distributed robotics control application, clearly demonstrates that it is feasible to use distributed object technology...

  8. Thermal Distribution System | Energy Systems Integration Facility | NREL

    Science.gov (United States)

    Thermal Distribution System Thermal Distribution System The Energy Systems Integration Facility's . Photo of the roof of the Energy Systems Integration Facility. The thermal distribution bus allows low as 10% of its full load level). The 60-ton chiller cools water with continuous thermal control

  9. Distributed systems for protecting nuclear power stations

    International Nuclear Information System (INIS)

    Jover, P.

    1980-05-01

    The advantages of distributed control systems for the control of nuclear power stations are obviously of great interest. Some years ago, EPRI, (Electric Power Research Institute) showed that multiplexing the signals is technically feasible, that it enables the availability specifications to be met and costs to be reduced. Since then, many distributed control systems have been proposed by the manufacturers. This note offers some comments on the application of the distribution concept to protection systems -what should be distributed- and ends with a brief description of a protection system based on microprocessors for the pressurized power stations now being built in France [fr

  10. Distributed computer systems theory and practice

    CERN Document Server

    Zedan, H S M

    2014-01-01

    Distributed Computer Systems: Theory and Practice is a collection of papers dealing with the design and implementation of operating systems, including distributed systems, such as the amoeba system, argus, Andrew, and grapevine. One paper discusses the concepts and notations for concurrent programming, particularly language notation used in computer programming, synchronization methods, and also compares three classes of languages. Another paper explains load balancing or load redistribution to improve system performance, namely, static balancing and adaptive load balancing. For program effici

  11. Online Scheduling in Distributed Message Converter Systems

    NARCIS (Netherlands)

    Risse, Thomas; Wombacher, Andreas; Surridge, Mike; Taylor, Steve; Aberer, Karl

    The optimal distribution of jobs among hosts in distributed environments is an important factor to achieve high performance. The optimal strategy depends on the application. In this paper we present a new online scheduling strategy for distributed EDI converter system. The strategy is based on the

  12. 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.

  13. 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.

  14. 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)

  15. 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....

  16. Prediction of Groundwater Arsenic Contamination using Geographic Information System and Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Md. Moqbul Hossain

    2013-01-01

    Full Text Available Ground water arsenic contamination is a well known health and environmental problem in Bangladesh. Sources of this heavy metal are known to be geogenic, however, the processes of its release into groundwater are poorly understood phenomena. In quest of mitigation of the problem it is necessary to predict probable contamination before it causes any damage to human health. Hence our research has been carried out to find the factor relations of arsenic contamination and develop an arsenic contamination prediction model. Researchers have generally agreed that the elevated concentration of arsenic is affected by several factors such as soil reaction (pH, organic matter content, geology, iron content, etc. However, the variability of concentration within short lateral and vertical intervals, and the inter-relationships of variables among themselves, make the statistical analyses highly non-linear and difficult to converge with a meaningful relationship. Artificial Neural Networks (ANN comes in handy for such a black box type problem. This research uses Back propagation Neural Networks (BPNN to train and validate the data derived from Geographic Information System (GIS spatial distribution grids. The neural network architecture with (6-20-1 pattern was able to predict the arsenic concentration with reasonable accuracy.

  17. 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

  18. 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.

  19. 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

  20. Protection of Distribution Systems with Distributed Energy Resources

    DEFF Research Database (Denmark)

    Bak-Jensen, Birgitte; Browne, Matthew; Calone, Roberto

    of 17 months of work of the Joint Working Group B5/C6.26/CIRED “Protection of Distribution Systems with Distributed Energy Resources”. The working group used the CIGRE report TB421 “The impact of Renewable Energy Sources and Distributed Generation on Substation Protection and Automation”, published...... by WG B5.34 as the entry document for the work on this report. In doing so, the group aligned the content and the scope of this report, the network structures considered, possible islanding, standardized communication and adaptive protection, interface protection, connection schemes and protection...... are listed (chapter 3). The first main part of the report starts with a summary of the backgrounds on DER and current practices in protection at the distribution level (chapter 4). This chapter contains an analysis of CIGRE TB421, protection relevant characteristics of DER, a review of current practices...

  1. Strategy Guideline. Compact Air Distribution Systems

    Energy Technology Data Exchange (ETDEWEB)

    Burdick, Arlan [IBACOS, Inc., Pittsburgh, PA (United States)

    2013-06-01

    This guideline discusses the benefits and challenges of using a compact air distribution system to handle the reduced loads and reduced air volume needed to condition the space within an energy efficient home. The decision criteria for a compact air distribution system must be determined early in the whole-house design process, considering both supply and return air design. However, careful installation of a compact air distribution system can result in lower material costs from smaller equipment, shorter duct runs, and fewer outlets; increased installation efficiencies, including ease of fitting the system into conditioned space; lower loads on a better balanced HVAC system, and overall improved energy efficiency of the home.

  2. Internet and redefining tourism distribution system

    Directory of Open Access Journals (Sweden)

    Đelić Tanja

    2004-01-01

    Full Text Available Since the introduction, computerized systems that manage reservation systems, rapidly became inevitable distribution channel for all service companies in tourist industry. GDS in reality using communication network connects service offer, selling personnel and air companies.

  3. 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.

  4. DC Distribution Systems and Microgrids

    DEFF Research Database (Denmark)

    Dragicevic, Tomislav; Anvari-Moghaddam, Amjad; Quintero, Juan Carlos Vasquez

    2017-01-01

    summarized. Due to its attractive characteristics in terms of compliance with modern generation, storage and electronic load technologies, high reliability and current carrying capacity, as well as simple control, DC systems are already an indispensable part of power systems. Moreover, the existing......A qualitative overview of different hardware topologies and control systems for DC MGs has been presented in this chapter. Some challenges and design considerations of DC protections systems have also been discussed. Finally, applications of DC MGs in emerging smart grid applications have been...... challenges such as protection issues will be effectively resolved in the near future due to fast progress of semiconductor technology which is a key enabler cheap and reliable future DC solid-state protection systems. Therefore, it is the view of the author that more and more DC systems will appear...

  5. Economic Models and Algorithms for Distributed Systems

    CERN Document Server

    Neumann, Dirk; Altmann, Jorn; Rana, Omer F

    2009-01-01

    Distributed computing models for sharing resources such as Grids, Peer-to-Peer systems, or voluntary computing are becoming increasingly popular. This book intends to discover fresh avenues of research and amendments to existing technologies, aiming at the successful deployment of commercial distributed systems

  6. 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.

  7. Formal Specification of Distributed Information Systems

    NARCIS (Netherlands)

    Vis, J.; Brinksma, Hendrik; de By, R.A.; de By, R.A.

    The design of distributed information systems tends to be complex and therefore error-prone. However, in the field of monolithic, i.e. non-distributed, information systems much has already been achieved, and by now, the principles of their design seem to be fairly well-understood. The past decade

  8. RF phase distribution systems at the SLC

    International Nuclear Information System (INIS)

    Jobe, R.K.; Schwarz, H.D.

    1989-04-01

    Modern large linear accelerators require RF distribution systems with minimal phase drifts and errors. Through the use of existing RF coaxial waveguides, and additional installation of phase reference cables and monitoring equipment, stable RF distribution for the SLC has been achieved. This paper discusses the design and performance of SLAC systems, and some design considerations for future colliders. 6 refs., 4 figs

  9. Silver disinfection in water distribution systems

    Science.gov (United States)

    Silvestry Rodriguez, Nadia

    Silver was evaluated as disinfectant to maintain water quality in water distribution system. It was used to inhibit growth of two opportunistic bacteria in planktonik form and in biofilm formation in Robbins devices with stainless steel and PVC surfaces. The results of this work show that silver is a potential secondary disinfectant to be used in water distribution systems.

  10. Rapid Prototyping of Formally Modelled Distributed Systems

    OpenAIRE

    Buchs, Didier; Buffo, Mathieu; Titsworth, Frances M.

    1999-01-01

    This paper presents various kinds of prototypes, used in the prototyping of formally modelled distributed systems. It presents the notions of prototyping techniques and prototype evolution, and shows how to relate them to the software life-cycle. It is illustrated through the use of the formal modelling language for distributed systems CO-OPN/2.

  11. Programming a Distributed System Using Shared Objects

    NARCIS (Netherlands)

    Tanenbaum, A.S.; Bal, H.E.; Kaashoek, M.F.

    1993-01-01

    Building the hardware for a high-performance distributed computer system is a lot easier than building its software. The authors describe a model for programming distributed systems based on abstract data types that can be replicated on all machines that need them. Read operations are done locally,

  12. BIOFILMS IN DRINKING WATER DISTRIBUTION SYSTEMS

    Science.gov (United States)

    Virtually anywhere a surface comes into contact with the water in a distribution system, one can find biofilms. Biofilms are formed in distribution system pipelines when microbial cells attach to pipe surfaces and multiply to form a film or slime layer on the pipe. Probably withi...

  13. Distributed Synchronization Control of Multiagent Systems With Unknown Nonlinearities.

    Science.gov (United States)

    Su, Shize; Lin, Zongli; Garcia, Alfredo

    2016-01-01

    This paper revisits the distributed adaptive control problem for synchronization of multiagent systems where the dynamics of the agents are nonlinear, nonidentical, unknown, and subject to external disturbances. Two communication topologies, represented, respectively, by a fixed strongly-connected directed graph and by a switching connected undirected graph, are considered. Under both of these communication topologies, we use distributed neural networks to approximate the uncertain dynamics. Decentralized adaptive control protocols are then constructed to solve the cooperative tracker problem, the problem of synchronization of all follower agents to a leader agent. In particular, we show that, under the proposed decentralized control protocols, the synchronization errors are ultimately bounded, and their ultimate bounds can be reduced arbitrarily by choosing the control parameter appropriately. Simulation study verifies the effectiveness of our proposed protocols.

  14. Grid-connected distributed solar power systems

    Science.gov (United States)

    Moyle, R.; Chernoff, H.; Schweizer, T.

    This paper discusses some important, though often ignored, technical and economic issues of distributed solar power systems: protection of the utility system and nonsolar customers requires suitable interfaced equipment. Purchase criteria must mirror reality; most analyses use life-cycle costing with low discount rates - most buyers use short payback periods. Distributing, installing, and marketing small, distributed solar systems is more costly than most analyses estimate. Results show that certain local conditions and uncommon purchase considerations can combine to make small, distributed solar power attractive, but lower interconnect costs (per kW), lower marketing and product distribution costs, and more favorable purchase criteria make large, centralized solar energy more attractive. Specifically, the value of dispersed solar systems to investors and utilities can be higher than $2000/kw. However, typical residential owners place a value of well under $1000 on the installed system.

  15. Neural network and parton two fireball model for pseudo-rapidity distribution in proton-proton collision

    International Nuclear Information System (INIS)

    El-Bakry, M.Y.

    2000-01-01

    Pseudo-Rapidity distribution of created pions from proton-proton (p-p) interaction has been studied in the framework of artificial neural network (ANN) and the parton two fireball model (PTFM). The predicted distributions from the ANN based model and the parton two fireball model is compared with the corresponding experimental results. The ANN model has proved better matching for experimental data specially at high energies where the conventional two fireball model representation deteriorates

  16. A Neural Network Approach for Identifying Particle Pitch Angle Distributions in Van Allen Probes Data

    Science.gov (United States)

    Souza, V. M.; Vieira, L. E. A.; Medeiros, C.; Da Silva, L. A.; Alves, L. R.; Koga, D.; Sibeck, D. G.; Walsh, B. M.; Kanekal, S. G.; Jauer, P. R.; hide

    2016-01-01

    Analysis of particle pitch angle distributions (PADs) has been used as a means to comprehend a multitude of different physical mechanisms that lead to flux variations in the Van Allen belts and also to particle precipitation into the upper atmosphere. In this work we developed a neural network-based data clustering methodology that automatically identifies distinct PAD types in an unsupervised way using particle flux data. One can promptly identify and locate three well-known PAD types in both time and radial distance, namely, 90deg peaked, butterfly, and flattop distributions. In order to illustrate the applicability of our methodology, we used relativistic electron flux data from the whole month of November 2014, acquired from the Relativistic Electron-Proton Telescope instrument on board the Van Allen Probes, but it is emphasized that our approach can also be used with multiplatform spacecraft data. Our PAD classification results are in reasonably good agreement with those obtained by standard statistical fitting algorithms. The proposed methodology has a potential use for Van Allen belt's monitoring.

  17. 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.

  18. Reliability assessment of distribution power systems including distributed generations

    International Nuclear Information System (INIS)

    Megdiche, M.

    2004-12-01

    Nowadays, power systems have reached a good level of reliability. Nevertheless, considering the modifications induced by the connections of small independent producers to distribution networks, there's a need to assess the reliability of these new systems. Distribution networks present several functional characteristics, highlighted by the qualitative study of the failures, as dispersed loads at several places, variable topology and some electrotechnical phenomena which must be taken into account to model the events that can occur. The adopted reliability calculations method is Monte Carlo simulations, the probabilistic method most powerful and most flexible to model complex operating of the distribution system. We devoted a first part on the case of a 20 kV feeder to which a cogeneration unit is connected. The method was applied to a software of stochastic Petri nets simulations. Then a second part related to the study of a low voltage power system supplied by dispersed generations. Here, the complexity of the events required to code the method in an environment of programming allowing the use of power system calculations (load flow, short-circuit, load shedding, management of units powers) in order to analyse the system state for each new event. (author)

  19. 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.

  20. 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.

  1. Large scale network-centric distributed systems

    CERN Document Server

    Sarbazi-Azad, Hamid

    2014-01-01

    A highly accessible reference offering a broad range of topics and insights on large scale network-centric distributed systems Evolving from the fields of high-performance computing and networking, large scale network-centric distributed systems continues to grow as one of the most important topics in computing and communication and many interdisciplinary areas. Dealing with both wired and wireless networks, this book focuses on the design and performance issues of such systems. Large Scale Network-Centric Distributed Systems provides in-depth coverage ranging from ground-level hardware issu

  2. 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.

  3. Delay-distribution-dependent H∞ state estimation for delayed neural networks with (x,v)-dependent noises and fading channels.

    Science.gov (United States)

    Sheng, Li; Wang, Zidong; Tian, Engang; Alsaadi, Fuad E

    2016-12-01

    This paper deals with the H ∞ state estimation problem for a class of discrete-time neural networks with stochastic delays subject to state- and disturbance-dependent noises (also called (x,v)-dependent noises) and fading channels. The time-varying stochastic delay takes values on certain intervals with known probability distributions. The system measurement is transmitted through fading channels described by the Rice fading model. The aim of the addressed problem is to design a state estimator such that the estimation performance is guaranteed in the mean-square sense against admissible stochastic time-delays, stochastic noises as well as stochastic fading signals. By employing the stochastic analysis approach combined with the Kronecker product, several delay-distribution-dependent conditions are derived to ensure that the error dynamics of the neuron states is stochastically stable with prescribed H ∞ performance. Finally, a numerical example is provided to illustrate the effectiveness of the obtained results. Copyright © 2016 Elsevier Ltd. All rights reserved.

  4. Differences Between Distributed and Parallel Systems

    Energy Technology Data Exchange (ETDEWEB)

    Brightwell, R.; Maccabe, A.B.; Rissen, R.

    1998-10-01

    Distributed systems have been studied for twenty years and are now coming into wider use as fast networks and powerful workstations become more readily available. In many respects a massively parallel computer resembles a network of workstations and it is tempting to port a distributed operating system to such a machine. However, there are significant differences between these two environments and a parallel operating system is needed to get the best performance out of a massively parallel system. This report characterizes the differences between distributed systems, networks of workstations, and massively parallel systems and analyzes the impact of these differences on operating system design. In the second part of the report, we introduce Puma, an operating system specifically developed for massively parallel systems. We describe Puma portals, the basic building blocks for message passing paradigms implemented on top of Puma, and show how the differences observed in the first part of the report have influenced the design and implementation of Puma.

  5. A Neural Network Aero Design System for Advanced Turbo-Engines

    Science.gov (United States)

    Sanz, Jose M.

    1999-01-01

    An inverse design method calculates the blade shape that produces a prescribed input pressure distribution. By controlling this input pressure distribution the aerodynamic design objectives can easily be met. Because of the intrinsic relationship between pressure distribution and airfoil physical properties, a neural network can be trained to choose the optimal pressure distribution that would meet a set of physical requirements. The neural network technique works well not only as an interpolating device but also as an extrapolating device to achieve blade designs from a given database. Two validating test cases are discussed.

  6. Asymptotic work distributions in driven bistable systems

    International Nuclear Information System (INIS)

    Nickelsen, D; Engel, A

    2012-01-01

    The asymptotic tails of the probability distributions of thermodynamic quantities convey important information about the physics of nanoscopic systems driven out of equilibrium. We apply a recently proposed method to analytically determine the asymptotics of work distributions in Langevin systems to an one-dimensional model of single-molecule force spectroscopy. The results are in excellent agreement with numerical simulations, even in the centre of the distributions. We compare our findings with a recent proposal for an universal form of the asymptotics of work distributions in single-molecule experiments.

  7. Centralised and distributed electricity systems

    International Nuclear Information System (INIS)

    Bouffard, Francois; Kirschen, Daniel S.

    2008-01-01

    Because of their high level of integration, centralised energy supply systems are vulnerable to disturbances in the supply chain. In the case of electricity especially, this supply paradigm is losing some of its appeal. Apart from vulnerability, a number of further aggravating factors are reducing its attractiveness. They include the depletion of fossil fuels and their climate change impact, the insecurities affecting energy transportation infrastructure, and the desire of investors to minimise risks through the deployment of smaller-scale, modular generation and transmission systems. Small-scale decentralised systems, where energy production and consumption are usually tightly coupled, are emerging as a viable alternative. They are less dependent upon centralised energy supply, and can sometimes use more than one energy source. They are less sensitive to the uncertain availability of remote primary energy and transportation networks. In addition, the close connection between energy generation and use makes decentralised systems cleaner because they are most often based on renewable energies or on high-efficiency fossil fuel-based technologies such as combined heat and power (CHP). Fully decentralised energy supply is not currently possible or even truly desirable. The secure and clean energy systems of the future will be those flexible enough to allow for a spectrum of hybrid modes of operation and investment, combining the best attributes of both paradigms. A large part of this flexibility will come from the networks that make it possible to combine these two types of infrastructures and obtain the benefits of both approaches

  8. Light distribution system comprising spectral conversion means

    DEFF Research Database (Denmark)

    2012-01-01

    , longer wavelength,a spectral conversion characteristics of the spectral conversion fibre being essentially determined by the spectral absorption and emission properties of the photoluminescent agent, the amount of photo- luminescent agent,and the distribution of the photoluminescent agent in the spectral......System (200, 300) for the distribution of white light, having a supply side (201, 301, 401) and a delivery side (202, 302, 402), the system being configured for guiding light with a multitude of visible wavelengths in a propagation direction P from the supply side to the distribution side...... of providing a light distribution system and a method of correcting the spectral transmission characteristics of a light distribution system are disclosed....

  9. Evaluation of two typical distributed energy systems

    Science.gov (United States)

    Han, Miaomiao; Tan, Xiu

    2018-03-01

    According to the two-natural gas distributed energy system driven by gas engine driven and gas turbine, in this paper, the first and second laws of thermodynamics are used to measure the distributed energy system from the two parties of “quantity” and “quality”. The calculation results show that the internal combustion engine driven distributed energy station has a higher energy efficiency, but the energy efficiency is low; the gas turbine driven distributed energy station energy efficiency is high, but the primary energy utilization rate is relatively low. When configuring the system, we should determine the applicable natural gas distributed energy system technology plan and unit configuration plan according to the actual load factors of the project and the actual factors such as the location, background and environmental requirements of the project. “quality” measure, the utilization of waste heat energy efficiency index is proposed.

  10. PFS: a distributed and customizable file system

    NARCIS (Netherlands)

    Bosch, H.G.P.; Mullender, Sape J.

    1996-01-01

    In this paper we present our ongoing work on the Pegasus File System (PFS), a distributed and customizable file system that can be used for off-line file system experiments and on-line file system storage. PFS is best described as an object-oriented component library from which either a true file

  11. 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)

  12. Archtecture of distributed real-time systems

    OpenAIRE

    Wing Leung, Cheuk

    2013-01-01

    CRAFTERS (Constraint and Application Driven Framework for Tailoring Embedded Real-time System) project aims to address the problem of uncertainty and heterogeneity in a distributed system by providing seamless, portable connectivity and middleware. This thesis contributes to the project by investigating the techniques that can be used in a distributed real-time embedded system. The conclusion is that, there is a list of specifications to be meet in order to provide a transparent and real-time...

  13. 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.

  14. 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....

  15. Evaluation of the maximum-likelihood adaptive neural system (MLANS) applications to noncooperative IFF

    Science.gov (United States)

    Chernick, Julian A.; Perlovsky, Leonid I.; Tye, David M.

    1994-06-01

    This paper describes applications of maximum likelihood adaptive neural system (MLANS) to the characterization of clutter in IR images and to the identification of targets. The characterization of image clutter is needed to improve target detection and to enhance the ability to compare performance of different algorithms using diverse imagery data. Enhanced unambiguous IFF is important for fratricide reduction while automatic cueing and targeting is becoming an ever increasing part of operations. We utilized MLANS which is a parametric neural network that combines optimal statistical techniques with a model-based approach. This paper shows that MLANS outperforms classical classifiers, the quadratic classifier and the nearest neighbor classifier, because on the one hand it is not limited to the usual Gaussian distribution assumption and can adapt in real time to the image clutter distribution; on the other hand MLANS learns from fewer samples and is more robust than the nearest neighbor classifiers. Future research will address uncooperative IFF using fused IR and MMW data.

  16. An efficient approach for electric load forecasting using distributed ART (adaptive resonance theory) and HS-ARTMAP (Hyper-spherical ARTMAP network) neural network

    International Nuclear Information System (INIS)

    Cai, Yuan; Wang, Jian-zhou; Tang, Yun; Yang, Yu-chen

    2011-01-01

    This paper presents a neural network based on adaptive resonance theory, named distributed ART (adaptive resonance theory) and HS-ARTMAP (Hyper-spherical ARTMAP network), applied to the electric load forecasting problem. The distributed ART combines the stable fast learning capabilities of winner-take-all ART systems with the noise tolerance and code compression capabilities of multi-layer perceptions. The HS-ARTMAP, a hybrid of an RBF (Radial Basis Function)-network-like module which uses hyper-sphere basis function substitute the Gaussian basis function and an ART-like module, performs incremental learning capabilities in function approximation problem. The HS-ARTMAP only receives the compressed distributed coding processed by distributed ART to deal with the proliferation problem which ARTMAP (adaptive resonance theory map) architecture often encounters and still performs well in electric load forecasting. To demonstrate the performance of the methodology, data from New South Wales and Victoria in Australia are illustrated. Results show that the developed method is much better than the traditional BP and single HS-ARTMAP neural network. -- Research highlights: → The processing of the presented network is based on compressed distributed data. It's an innovation among the adaptive resonance theory architecture. → The presented network decreases the proliferation the Fuzzy ARTMAP architectures usually encounter. → The network on-line forecasts electrical load accurately, stably. → Both one-period and multi-period load forecasting are executed using data of different cities.

  17. 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.

  18. Support system for ATLAS distributed computing operations

    CERN Document Server

    Kishimoto, Tomoe; The ATLAS collaboration

    2018-01-01

    The ATLAS distributed computing system has allowed the experiment to successfully meet the challenges of LHC Run 2. In order for distributed computing to operate smoothly and efficiently, several support teams are organized in the ATLAS experiment. The ADCoS (ATLAS Distributed Computing Operation Shifts) is a dedicated group of shifters who follow and report failing jobs, failing data transfers between sites, degradation of ATLAS central computing services, and more. The DAST (Distributed Analysis Support Team) provides user support to resolve issues related to running distributed analysis on the grid. The CRC (Computing Run Coordinator) maintains a global view of the day-to-day operations. In this presentation, the status and operational experience of the support system for ATLAS distributed computing in LHC Run 2 will be reported. This report also includes operations experience from the grid site point of view, and an analysis of the errors that create the biggest waste of wallclock time. The report of oper...

  19. A DISTRIBUTED SMART HOME ARTIFICIAL INTELLIGENCE SYSTEM

    DEFF Research Database (Denmark)

    Lynggaard, Per

    2013-01-01

    A majority of the research performed today explore artificial intelligence in smart homes by using a centralized approach where a smart home server performs the necessary calculations. This approach has some disadvantages that can be overcome by shifting focus to a distributed approach where...... the artificial intelligence system is implemented as distributed as agents running parts of the artificial intelligence system. This paper presents a distributed smart home architecture that distributes artificial intelligence in smart homes and discusses the pros and cons of such a concept. The presented...... distributed model is a layered model. Each layer offers a different complexity level of the embedded distributed artificial intelligence. At the lowest layer smart objects exists, they are small cheap embedded microcontroller based smart devices that are powered by batteries. The next layer contains a more...

  20. Parallel and Distributed System Simulation

    Science.gov (United States)

    Dongarra, Jack

    1998-01-01

    This exploratory study initiated our research into the software infrastructure necessary to support the modeling and simulation techniques that are most appropriate for the Information Power Grid. Such computational power grids will use high-performance networking to connect hardware, software, instruments, databases, and people into a seamless web that supports a new generation of computation-rich problem solving environments for scientists and engineers. In this context we looked at evaluating the NetSolve software environment for network computing that leverages the potential of such systems while addressing their complexities. NetSolve's main purpose is to enable the creation of complex applications that harness the immense power of the grid, yet are simple to use and easy to deploy. NetSolve uses a modular, client-agent-server architecture to create a system that is very easy to use. Moreover, it is designed to be highly composable in that it readily permits new resources to be added by anyone willing to do so. In these respects NetSolve is to the Grid what the World Wide Web is to the Internet. But like the Web, the design that makes these wonderful features possible can also impose significant limitations on the performance and robustness of a NetSolve system. This project explored the design innovations that push the performance and robustness of the NetSolve paradigm as far as possible without sacrificing the Web-like ease of use and composability that make it so powerful.

  1. Integrating photovoltaics into utility distribution systems

    International Nuclear Information System (INIS)

    Zaininger, H.W.; Barnes, P.R.

    1995-01-01

    Electric utility distribution system impacts associated with the integration of distributed photovoltaic (PV) energy sources vary from site to site and utility to utility. The objective of this paper is to examine several utility- and site-specific conditions which may affect economic viability of distributed PV applications to utility systems. Assessment methodology compatible with technical and economic assessment techniques employed by utility engineers and planners is employed to determine PV benefits for seven different utility systems. The seven case studies are performed using utility system characteristics and assumptions obtained from appropriate utility personnel. The resulting site-specific distributed PV benefits increase nonsite-specific generation system benefits available to central station PV plants as much as 46%, for one utility located in the Southwest

  2. Equilibrium distribution function in collisionless systems

    International Nuclear Information System (INIS)

    Pergamenshchik, V.M.

    1988-01-01

    Collisionless systems of a large number of N particles interacting by Coulomb forces are widely spread in cosmic and laboratory plasma. A statistical theory of equilibrium state of collisionless Coulomb systems which evolution obeys Vlasov equation is proposed. The developed formalism permits a sequential consideration of such distributed in one-particle six-dimensional phase space of a system and to obtain a simple result: equilibrium distribution function has the form of Fermi-Dirac distribution and doesn't depend on initial state factors

  3. Advanced smartgrids for distribution system operators

    CERN Document Server

    Boillot, Marc

    2014-01-01

    The dynamic of the Energy Transition is engaged in many region of the World. This is a real challenge for electric systems and a paradigm shift for existing distribution networks. With the help of "advanced" smart technologies, the Distribution System Operators will have a central role to integrate massively renewable generation, electric vehicle and demand response programs. Many projects are on-going to develop and assess advanced smart grids solutions, with already some lessons learnt. In the end, the Smart Grid is a mean for Distribution System Operators to ensure the quality and the secu

  4. LOAD FORECASTING FOR POWER SYSTEM PLANNING AND OPERATION USING ARTIFICIAL NEURAL NETWORK AT AL BATINAH REGION OMAN

    Directory of Open Access Journals (Sweden)

    HUSSEIN A. ABDULQADER

    2012-08-01

    Full Text Available Load forecasting is essential part for the power system planning and operation. In this paper the modeling and design of artificial neural network for load forecasting is carried out in a particular region of Oman. Neural network approach helps to reduce the problem associated with conventional method and has the advantage of learning directly from the historical data. The neural network here uses data such as past load; weather information like humidity and temperatures. Once the neural network is trained for the past set of data it can give a prediction of future load. This reduces the capital investment reducing the equipments to be installed. The actual data are taken from the Mazoon Electrical Company, Oman. The data of load for the year 2007, 2008 and 2009 are collected for a particular region called Al Batinah in Oman and trained using neural networks to forecast the future. The main objective is to forecast the amount of electricity needed for better load distribution in the areas of this region in Oman. The load forecasting is done for the year 2010 and is validated for the accuracy.

  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

    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...

  6. 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...

  7. Science network resources: Distributed systems

    Science.gov (United States)

    Cline, Neal

    1991-01-01

    The Master Directory, which is overview information about whole data sets, is outlined. The data system environment is depicted. The question is explored of what is a prototype international directory including purpose and features. Advantages of on-line directories are listed. Interconnected directory assumptions are given. A description of given of DIF (Directory Interchange Format), which is an exchange file for directory information, along with information content of DIF and directories. The directory population status is given in a percentage viewgraph. The present and future directory interconnections status at GSFC is also listed.

  8. PROWAY - a standard for distributed control systems

    International Nuclear Information System (INIS)

    Gellie, R.W.

    1980-01-01

    The availability of cheap and powerful microcomputer and data communications equipment has led to a major revision of instrumentation and control systems. Intelligent devices can now be used and distributed about the control system in a systematic and economic manner. These sub-units are linked by a communications system to provide a total system capable of meeting the required plant objectives. PROWAY, an international standard process data highway for interconnecting processing units in distributed industrial process control systems, is currently being developed. This paper describes the salient features and current status of the PROWAY effort. (auth)

  9. Water distribution systems design optimisation using metaheuristics ...

    African Journals Online (AJOL)

    The topic of multi-objective water distribution systems (WDS) design optimisation using metaheuristics is investigated, comparing numerous modern metaheuristics, including several multi-objective evolutionary algorithms, an estimation of distribution algorithm and a recent hyperheuristic named AMALGAM (an evolutionary ...

  10. Programming Languages for Distributed Computing Systems

    NARCIS (Netherlands)

    Bal, H.E.; Steiner, J.G.; Tanenbaum, A.S.

    1989-01-01

    When distributed systems first appeared, they were programmed in traditional sequential languages, usually with the addition of a few library procedures for sending and receiving messages. As distributed applications became more commonplace and more sophisticated, this ad hoc approach became less

  11. Distributed Administrative Management Information System (DAMIS).

    Science.gov (United States)

    Juckiewicz, Robert; Kroculick, Joseph

    Columbia University's major program to distribute its central administrative data processing to its various schools and departments is described. The Distributed Administrative Management Information System (DAMIS) will link every department and school within the university via micrcomputers, terminals, and/or minicomputers to the central…

  12. DIstributed VIRtual System (DIVIRS) Project

    Science.gov (United States)

    Schorr, Herbert; Neuman, B. Clifford; Gaines, Stockton R.; Mizell, David

    1996-01-01

    The development of Prospero moved from the University of Washington to ISI and several new versions of the software were released from ISI during the contract period. Changes in the first release from ISI included bug fixes and extensions to support the needs of specific users. Among these changes was a new option to directory queries that allows attributes to be returned for all files in a directory together with the directory listing. This change greatly improves the performance of their server and reduces the number of packets sent across their trans-pacific connection to the rest of the internet. Several new access method were added to the Prospero file method. The Prospero Data Access Protocol was designed, to support secure retrieval of data from systems running Prospero.

  13. Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network

    Science.gov (United States)

    Ben Ali, Jaouher; Chebel-Morello, Brigitte; Saidi, Lotfi; Malinowski, Simon; Fnaiech, Farhat

    2015-05-01

    Accurate remaining useful life (RUL) prediction of critical assets is an important challenge in condition based maintenance to improve reliability and decrease machine's breakdown and maintenance's cost. Bearing is one of the most important components in industries which need to be monitored and the user should predict its RUL. The challenge of this study is to propose an original feature able to evaluate the health state of bearings and to estimate their RUL by Prognostics and Health Management (PHM) techniques. In this paper, the proposed method is based on the data-driven prognostic approach. The combination of Simplified Fuzzy Adaptive Resonance Theory Map (SFAM) neural network and Weibull distribution (WD) is explored. WD is used just in the training phase to fit measurement and to avoid areas of fluctuation in the time domain. SFAM training process is based on fitted measurements at present and previous inspection time points as input. However, the SFAM testing process is based on real measurements at present and previous inspections. Thanks to the fuzzy learning process, SFAM has an important ability and a good performance to learn nonlinear time series. As output, seven classes are defined; healthy bearing and six states for bearing degradation. In order to find the optimal RUL prediction, a smoothing phase is proposed in this paper. Experimental results show that the proposed method can reliably predict the RUL of rolling element bearings (REBs) based on vibration signals. The proposed prediction approach can be applied to prognostic other various mechanical assets.

  14. Fault tolerance of artificial neural networks with applications in critical systems

    Science.gov (United States)

    Protzel, Peter W.; Palumbo, Daniel L.; Arras, Michael K.

    1992-01-01

    This paper investigates the fault tolerance characteristics of time continuous recurrent artificial neural networks (ANN) that can be used to solve optimization problems. The principle of operations and performance of these networks are first illustrated by using well-known model problems like the traveling salesman problem and the assignment problem. The ANNs are then subjected to 13 simultaneous 'stuck at 1' or 'stuck at 0' faults for network sizes of up to 900 'neurons'. The effects of these faults is demonstrated and the cause for the observed fault tolerance is discussed. An application is presented in which a network performs a critical task for a real-time distributed processing system by generating new task allocations during the reconfiguration of the system. The performance degradation of the ANN under the presence of faults is investigated by large-scale simulations, and the potential benefits of delegating a critical task to a fault tolerant network are discussed.

  15. Novel delay-distribution-dependent stability analysis for continuous-time recurrent neural networks with stochastic delay

    International Nuclear Information System (INIS)

    Wang Shen-Quan; Feng Jian; Zhao Qing

    2012-01-01

    In this paper, the problem of delay-distribution-dependent stability is investigated for continuous-time recurrent neural networks (CRNNs) with stochastic delay. Different from the common assumptions on time delays, it is assumed that the probability distribution of the delay taking values in some intervals is known a priori. By making full use of the information concerning the probability distribution of the delay and by using a tighter bounding technique (the reciprocally convex combination method), less conservative asymptotic mean-square stable sufficient conditions are derived in terms of linear matrix inequalities (LMIs). Two numerical examples show that our results are better than the existing ones. (general)

  16. 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.

  17. AC distribution system for TFTR pulsed loads

    International Nuclear Information System (INIS)

    Carroll, R.F.; Ramakrishnan, S.; Lemmon, G.N.; Moo, W.I.

    1977-01-01

    This paper outlines the AC distribution system associated with the Tokamak Fusion Test Reactor and discusses the significant areas related to design, protection, and equipment selection, particularly where there is a departure from normal utility and industrial applications

  18. A distributed computer system for digitising machines

    International Nuclear Information System (INIS)

    Bairstow, R.; Barlow, J.; Waters, M.; Watson, J.

    1977-07-01

    This paper describes a Distributed Computing System, based on micro computers, for the monitoring and control of digitising tables used by the Rutherford Laboratory Bubble Chamber Research Group in the measurement of bubble chamber photographs. (author)

  19. Man-systems distributed system for Space Station Freedom

    Science.gov (United States)

    Lewis, J. L.

    1990-01-01

    Viewgraphs on man-systems distributed system for Space Station Freedom are presented. Topics addressed include: description of man-systems (definition, requirements, scope, subsystems, and topologies); implementation (approach, tools); man-systems interfaces (system to element and system to system); prime/supporting development relationship; selected accomplishments; and technical challenges.

  20. Supervisory Control and Diagnostics System Distributed Operating System

    International Nuclear Information System (INIS)

    McGoldrick, P.R.

    1979-01-01

    This paper contains a description of the Supervisory Control and Diagnostics System (SCDS) Distributed Operating System. The SCDS consists of nine 32-bit minicomputers with shared memory. The system's main purpose is to control a large Mirror Fusion Test Facility

  1. Anti-correlations in the degree distribution increase stimulus detection performance in noisy spiking neural networks.

    Science.gov (United States)

    Martens, Marijn B; Houweling, Arthur R; E Tiesinga, Paul H

    2017-02-01

    Neuronal circuits in the rodent barrel cortex are characterized by stable low firing rates. However, recent experiments show that short spike trains elicited by electrical stimulation in single neurons can induce behavioral responses. Hence, the underlying neural networks provide stability against internal fluctuations in the firing rate, while simultaneously making the circuits sensitive to small external perturbations. Here we studied whether stability and sensitivity are affected by the connectivity structure in recurrently connected spiking networks. We found that anti-correlation between the number of afferent (in-degree) and efferent (out-degree) synaptic connections of neurons increases stability against pathological bursting, relative to networks where the degrees were either positively correlated or uncorrelated. In the stable network state, stimulation of a few cells could lead to a detectable change in the firing rate. To quantify the ability of networks to detect the stimulation, we used a receiver operating characteristic (ROC) analysis. For a given level of background noise, networks with anti-correlated degrees displayed the lowest false positive rates, and consequently had the highest stimulus detection performance. We propose that anti-correlation in the degree distribution may be a computational strategy employed by sensory cortices to increase the detectability of external stimuli. We show that networks with anti-correlated degrees can in principle be formed by applying learning rules comprised of a combination of spike-timing dependent plasticity, homeostatic plasticity and pruning to networks with uncorrelated degrees. To test our prediction we suggest a novel experimental method to estimate correlations in the degree distribution.

  2. Distribution of language-related Cntnap2 protein in neural circuits critical for vocal learning.

    Science.gov (United States)

    Condro, Michael C; White, Stephanie A

    2014-01-01

    Variants of the contactin associated protein-like 2 (Cntnap2) gene are risk factors for language-related disorders including autism spectrum disorder, specific language impairment, and stuttering. Songbirds are useful models for study of human speech disorders due to their shared capacity for vocal learning, which relies on similar cortico-basal ganglia circuitry and genetic factors. Here we investigate Cntnap2 protein expression in the brain of the zebra finch, a songbird species in which males, but not females, learn their courtship songs. We hypothesize that Cntnap2 has overlapping functions in vocal learning species, and expect to find protein expression in song-related areas of the zebra finch brain. We further expect that the distribution of this membrane-bound protein may not completely mirror its mRNA distribution due to the distinct subcellular localization of the two molecular species. We find that Cntnap2 protein is enriched in several song control regions relative to surrounding tissues, particularly within the adult male, but not female, robust nucleus of the arcopallium (RA), a cortical song control region analogous to human layer 5 primary motor cortex. The onset of this sexually dimorphic expression coincides with the onset of sensorimotor learning in developing males. Enrichment in male RA appears due to expression in projection neurons within the nucleus, as well as to additional expression in nerve terminals of cortical projections to RA from the lateral magnocellular nucleus of the nidopallium. Cntnap2 protein expression in zebra finch brain supports the hypothesis that this molecule affects neural connectivity critical for vocal learning across taxonomic classes. Copyright © 2013 Wiley Periodicals, Inc.

  3. 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.

  4. Distributed context-aware systems

    CERN Document Server

    Ferreira, Paulo

    2014-01-01

    Context-aware systems aim to deliver a rich user experience by taking into?account the current user context (location, time, activity, etc.), possibly?captured without his intervention. For example, cell phones are now able to?continuously update a user's location while, at the same time, users execute?an increasing amount of activities online, where their actions may be easily?captured (e.g. login in a web application) without user consent. In the last decade, this topic has seen numerous developments that demonstrate its relevance and usefulness. The?trend was accelerated with the widespread?availability of powerful mobile devices (e.g. smartphones) that include a myriad of?sensors which enable applications to capture the user context. However, there are several challenges that must be addressed; we focus on scalability?(large number of context aware messages) and privacy (personal data that may be propagated).?This book is organized in five chapters starting with an introduction to?the theme raising the mo...

  5. 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.

  6. Distributed operating system for NASA ground stations

    Science.gov (United States)

    Doyle, John F.

    1987-01-01

    NASA ground stations are characterized by ever changing support requirements, so application software is developed and modified on a continuing basis. A distributed operating system was designed to optimize the generation and maintenance of those applications. Unusual features include automatic program generation from detailed design graphs, on-line software modification in the testing phase, and the incorporation of a relational database within a real-time, distributed system.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. Sex differences of gray matter morphology in cortico-limbic-striatal neural system in major depressive disorder.

    Science.gov (United States)

    Kong, Lingtao; Chen, Kaiyuan; Womer, Fay; Jiang, Wenyan; Luo, Xingguang; Driesen, Naomi; Liu, Jie; Blumberg, Hilary; Tang, Yanqing; Xu, Ke; Wang, Fei

    2013-06-01

    Sex differences are observed in both epidemiological and clinical aspects of major depressive disorder (MDD). The cortico-limbic-striatal neural system, including the prefrontal cortex, amygdala, hippocampus, and striatum, have shown sexually dimorphic morphological features and have been implicated in the dysfunctional regulation of mood and emotion in MDD. In this study, we utilized a whole-brain, voxel-based approach to examine sex differences in the regional distribution of gray matter (GM) morphological abnormalities in medication-naïve participants with MDD. Participants included 29 medication-naïve individuals with MDD (16 females and 13 males) and 33 healthy controls (HC) (17 females and 16 males). Gray matter morphology of the cortico-limbic-striatal neural system was examined using voxel-based morphometry analyzes of high-resolution structural magnetic resonance imaging scans. The main effect of diagnosis and interaction effect of diagnosis by sex on GM morphology were statistically significant (p sex-related patterns of abnormalities within the cortico-limbic-strial neural system, such as predominant prefrontal-limbic abnormalities in MDD females vs. predominant prefrontal-striatal abnormalities in MDD males, suggest differences in neural circuitry that may mediate sex differences in the clinical presentation of MDD and potential targets for sex-differentiated treatment of the disorder. Copyright © 2013 Elsevier Ltd. All rights reserved.

  13. Distributed computer control system for reactor optimization

    International Nuclear Information System (INIS)

    Williams, A.H.

    1983-01-01

    At the Oldbury power station a prototype distributed computer control system has been installed. This system is designed to support research and development into improved reactor temperature control methods. This work will lead to the development and demonstration of new optimal control systems for improvement of plant efficiency and increase of generated output. The system can collect plant data from special test instrumentation connected to dedicated scanners and from the station's existing data processing system. The system can also, via distributed microprocessor-based interface units, make adjustments to the desired reactor channel gas exit temperatures. The existing control equipment will then adjust the height of control rods to maintain operation at these temperatures. The design of the distributed system is based on extensive experience with distributed systems for direct digital control, operator display and plant monitoring. The paper describes various aspects of this system, with particular emphasis on: (1) the hierarchal system structure; (2) the modular construction of the system to facilitate installation, commissioning and testing, and to reduce maintenance to module replacement; (3) the integration of the system into the station's existing data processing system; (4) distributed microprocessor-based interfaces to the reactor controls, with extensive security facilities implemented by hardware and software; (5) data transfer using point-to-point and bussed data links; (6) man-machine communication based on VDUs with computer input push-buttons and touch-sensitive screens; and (7) the use of a software system supporting a high-level engineer-orientated programming language, at all levels in the system, together with comprehensive data link management

  14. Strategy Guideline: Compact Air Distribution Systems

    Energy Technology Data Exchange (ETDEWEB)

    Burdick, A.

    2013-06-01

    This Strategy Guideline discusses the benefits and challenges of using a compact air distribution system to handle the reduced loads and reduced air volume needed to condition the space within an energy efficient home. Traditional systems sized by 'rule of thumb' (i.e., 1 ton of cooling per 400 ft2 of floor space) that 'wash' the exterior walls with conditioned air from floor registers cannot provide appropriate air mixing and moisture removal in low-load homes. A compact air distribution system locates the HVAC equipment centrally with shorter ducts run to interior walls, and ceiling supply outlets throw the air toward the exterior walls along the ceiling plane; alternatively, high sidewall supply outlets throw the air toward the exterior walls. Potential drawbacks include resistance from installing contractors or code officials who are unfamiliar with compact air distribution systems, as well as a lack of availability of low-cost high sidewall or ceiling supply outlets to meet the low air volumes with good throw characteristics. The decision criteria for a compact air distribution system must be determined early in the whole-house design process, considering both supply and return air design. However, careful installation of a compact air distribution system can result in lower material costs from smaller equipment, shorter duct runs, and fewer outlets; increased installation efficiencies, including ease of fitting the system into conditioned space; lower loads on a better balanced HVAC system, and overall improved energy efficiency of the home.

  15. 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

  16. 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

  17. Distributed redundancy and robustness in complex systems

    KAUST Repository

    Randles, Martin

    2011-03-01

    The uptake and increasing prevalence of Web 2.0 applications, promoting new large-scale and complex systems such as Cloud computing and the emerging Internet of Services/Things, requires tools and techniques to analyse and model methods to ensure the robustness of these new systems. This paper reports on assessing and improving complex system resilience using distributed redundancy, termed degeneracy in biological systems, to endow large-scale complicated computer systems with the same robustness that emerges in complex biological and natural systems. However, in order to promote an evolutionary approach, through emergent self-organisation, it is necessary to specify the systems in an \\'open-ended\\' manner where not all states of the system are prescribed at design-time. In particular an observer system is used to select robust topologies, within system components, based on a measurement of the first non-zero Eigen value in the Laplacian spectrum of the components\\' network graphs; also known as the algebraic connectivity. It is shown, through experimentation on a simulation, that increasing the average algebraic connectivity across the components, in a network, leads to an increase in the variety of individual components termed distributed redundancy; the capacity for structurally distinct components to perform an identical function in a particular context. The results are applied to a specific application where active clustering of like services is used to aid load balancing in a highly distributed network. Using the described procedure is shown to improve performance and distribute redundancy. © 2010 Elsevier Inc.

  18. Support for User Interfaces for Distributed Systems

    Science.gov (United States)

    Eychaner, Glenn; Niessner, Albert

    2005-01-01

    An extensible Java(TradeMark) software framework supports the construction and operation of graphical user interfaces (GUIs) for distributed computing systems typified by ground control systems that send commands to, and receive telemetric data from, spacecraft. Heretofore, such GUIs have been custom built for each new system at considerable expense. In contrast, the present framework affords generic capabilities that can be shared by different distributed systems. Dynamic class loading, reflection, and other run-time capabilities of the Java language and JavaBeans component architecture enable the creation of a GUI for each new distributed computing system with a minimum of custom effort. By use of this framework, GUI components in control panels and menus can send commands to a particular distributed system with a minimum of system-specific code. The framework receives, decodes, processes, and displays telemetry data; custom telemetry data handling can be added for a particular system. The framework supports saving and later restoration of users configurations of control panels and telemetry displays with a minimum of effort in writing system-specific code. GUIs constructed within this framework can be deployed in any operating system with a Java run-time environment, without recompilation or code changes.

  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. Comparison between Different Air Distribution Systems

    DEFF Research Database (Denmark)

    Nielsen, Peter V.

    The aim of an air conditioning system is to remove excess heat in a room and replace room air with fresh air to obtain a high air quality. It is not sufficient to remove heat and contaminated air, it is also necessary to distribute and control the air movement in the room to create thermal comfort...... in the occupied zone. Most air distribution systems are based on mixing ventilation with ceiling or wall-mounted diffusers or on displacement ventilation with wall-mounted low velocity diffusers. New principles for room air distribution were introduced during the last decades, as the textile terminals mounted...... in the ceiling and radial diffusers with swirling flow also mounted in the ceiling. This paper addresses five air distribution systems in all, namely mixing ventilation from a wallmounted terminal, mixing ventilation from a ceiling-mounted diffuser, mixing ventilation from a ceiling-mounted diffuser...

  1. Bluetooth-based distributed measurement system

    International Nuclear Information System (INIS)

    Tang Baoping; Chen Zhuo; Wei Yuguo; Qin Xiaofeng

    2007-01-01

    A novel distributed wireless measurement system, which is consisted of a base station, wireless intelligent sensors and relay nodes etc, is established by combining of Bluetooth-based wireless transmission, virtual instrument, intelligent sensor, and network. The intelligent sensors mounted on the equipments to be measured acquire various parameters and the Bluetooth relay nodes get the acquired data modulated and sent to the base station, where data analysis and processing are done so that the operational condition of the equipment can be evaluated. The establishment of the distributed measurement system is discussed with a measurement flow chart for the distributed measurement system based on Bluetooth technology, and the advantages and disadvantages of the system are analyzed at the end of the paper and the measurement system has successfully been used in Daqing oilfield, China for measurement of parameters, such as temperature, flow rate and oil pressure at an electromotor-pump unit

  2. Bluetooth-based distributed measurement system

    Science.gov (United States)

    Tang, Baoping; Chen, Zhuo; Wei, Yuguo; Qin, Xiaofeng

    2007-07-01

    A novel distributed wireless measurement system, which is consisted of a base station, wireless intelligent sensors and relay nodes etc, is established by combining of Bluetooth-based wireless transmission, virtual instrument, intelligent sensor, and network. The intelligent sensors mounted on the equipments to be measured acquire various parameters and the Bluetooth relay nodes get the acquired data modulated and sent to the base station, where data analysis and processing are done so that the operational condition of the equipment can be evaluated. The establishment of the distributed measurement system is discussed with a measurement flow chart for the distributed measurement system based on Bluetooth technology, and the advantages and disadvantages of the system are analyzed at the end of the paper and the measurement system has successfully been used in Daqing oilfield, China for measurement of parameters, such as temperature, flow rate and oil pressure at an electromotor-pump unit.

  3. Bluetooth-based distributed measurement system

    Energy Technology Data Exchange (ETDEWEB)

    Tang Baoping; Chen Zhuo; Wei Yuguo; Qin Xiaofeng [Department of Mechatronics, College of Mechanical Engineering, Chongqing University, Chongqing, 400030 (China)

    2007-07-15

    A novel distributed wireless measurement system, which is consisted of a base station, wireless intelligent sensors and relay nodes etc, is established by combining of Bluetooth-based wireless transmission, virtual instrument, intelligent sensor, and network. The intelligent sensors mounted on the equipments to be measured acquire various parameters and the Bluetooth relay nodes get the acquired data modulated and sent to the base station, where data analysis and processing are done so that the operational condition of the equipment can be evaluated. The establishment of the distributed measurement system is discussed with a measurement flow chart for the distributed measurement system based on Bluetooth technology, and the advantages and disadvantages of the system are analyzed at the end of the paper and the measurement system has successfully been used in Daqing oilfield, China for measurement of parameters, such as temperature, flow rate and oil pressure at an electromotor-pump unit.

  4. Integrating CLIPS applications into heterogeneous distributed systems

    Science.gov (United States)

    Adler, Richard M.

    1991-01-01

    SOCIAL is an advanced, object-oriented development tool for integrating intelligent and conventional applications across heterogeneous hardware and software platforms. SOCIAL defines a family of 'wrapper' objects called agents, which incorporate predefined capabilities for distributed communication and control. Developers embed applications within agents and establish interactions between distributed agents via non-intrusive message-based interfaces. This paper describes a predefined SOCIAL agent that is specialized for integrating C Language Integrated Production System (CLIPS)-based applications. The agent's high-level Application Programming Interface supports bidirectional flow of data, knowledge, and commands to other agents, enabling CLIPS applications to initiate interactions autonomously, and respond to requests and results from heterogeneous remote systems. The design and operation of CLIPS agents are illustrated with two distributed applications that integrate CLIPS-based expert systems with other intelligent systems for isolating and mapping problems in the Space Shuttle Launch Processing System at the NASA Kennedy Space Center.

  5. 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.

  6. Distributed Access View Integrated Database (DAVID) system

    Science.gov (United States)

    Jacobs, Barry E.

    1991-01-01

    The Distributed Access View Integrated Database (DAVID) System, which was adopted by the Astrophysics Division for their Astrophysics Data System, is a solution to the system heterogeneity problem. The heterogeneous components of the Astrophysics problem is outlined. The Library and Library Consortium levels of the DAVID approach are described. The 'books' and 'kits' level is discussed. The Universal Object Typer Management System level is described. The relation of the DAVID project with the Small Business Innovative Research (SBIR) program is explained.

  7. 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.

  8. 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.

  9. 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

  10. 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)

  11. 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

  12. 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)

  13. Symmetries of a generic utricular projection: neural connectivity and the distribution of utricular information.

    Science.gov (United States)

    Chartrand, Thomas; McCollum, Gin; Hanes, Douglas A; Boyle, Richard D

    2016-02-01

    Sensory contribution to perception and action depends on both sensory receptors and the organization of pathways (or projections) reaching the central nervous system. Unlike the semicircular canals that are divided into three discrete sensitivity directions, the utricle has a relatively complicated anatomical structure, including sensitivity directions over essentially 360° of a curved, two-dimensional disk. The utricle is not flat, and we do not assume it to be. Directional sensitivity of individual utricular afferents decreases in a cosine-like fashion from peak excitation for movement in one direction to a null or near null response for a movement in an orthogonal direction. Directional sensitivity varies slowly between neighboring cells except within the striolar region that separates the medial from the lateral zone, where the directional selectivity abruptly reverses along the reversal line. Utricular primary afferent pathways reach the vestibular nuclei and cerebellum and, in many cases, converge on target cells with semicircular canal primary afferents and afference from other sources. Mathematically, some canal pathways are known to be characterized by symmetry groups related to physical space. These groups structure rotational information and movement. They divide the target neural center into distinct populations according to the innervation patterns they receive. Like canal pathways, utricular pathways combine symmetries from the utricle with those from target neural centers. This study presents a generic set of transformations drawn from the known structure of the utricle and therefore likely to be found in utricular pathways, but not exhaustive of utricular pathway symmetries. This generic set of transformations forms a 32-element group that is a semi-direct product of two simple abelian groups. Subgroups of the group include order-four elements corresponding to discrete rotations. Evaluation of subgroups allows us to functionally identify the

  14. Distributed Optimal Consensus Control for Multiagent Systems With Input Delay.

    Science.gov (United States)

    Zhang, Huaipin; Yue, Dong; Zhao, Wei; Hu, Songlin; Dou, Chunxia; Huaipin Zhang; Dong Yue; Wei Zhao; Songlin Hu; Chunxia Dou; Hu, Songlin; Zhang, Huaipin; Dou, Chunxia; Yue, Dong; Zhao, Wei

    2018-06-01

    This paper addresses the problem of distributed optimal consensus control for a continuous-time heterogeneous linear multiagent system subject to time varying input delays. First, by discretization and model transformation, the continuous-time input-delayed system is converted into a discrete-time delay-free system. Two delicate performance index functions are defined for these two systems. It is shown that the performance index functions are equivalent and the optimal consensus control problem of the input-delayed system can be cast into that of the delay-free system. Second, by virtue of the Hamilton-Jacobi-Bellman (HJB) equations, an optimal control policy for each agent is designed based on the delay-free system and a novel value iteration algorithm is proposed to learn the solutions to the HJB equations online. The proposed adaptive dynamic programming algorithm is implemented on the basis of a critic-action neural network (NN) structure. Third, it is proved that local consensus errors of the two systems and weight estimation errors of the critic-action NNs are uniformly ultimately bounded while the approximated control policies converge to their target values. Finally, two simulation examples are presented to illustrate the effectiveness of the developed method.

  15. Mechanical Design Of Prototype Exoskeleton Robotic System For Human Leg Movements And Implementation Of Gait Data With Neural Network

    Directory of Open Access Journals (Sweden)

    Evren Meltem Toygar

    2012-06-01

    Full Text Available Target of this study is designing a exoskeleton system for single lower extremity disabled person and controlling this exoskeleton system with neural network. Exoskeleton system is modeled by using SolidWorks. At the same time, gait data is acquired on human body and sole is divided four parts after that reaction forces are gauged during the walking. Distributions of strain and deformation are obtained by using experimental gait data. The walking is designed using the obtained data and walking data is derived for control stage. Power requirements of actuators are defined.

  16. 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...

  17. 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

  18. 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

  19. Development of automated system based on neural network algorithm for detecting defects on molds installed on casting machines

    Science.gov (United States)

    Bazhin, V. Yu; Danilov, I. V.; Petrov, P. A.

    2018-05-01

    During the casting of light alloys and ligatures based on aluminum and magnesium, problems of the qualitative distribution of the metal and its crystallization in the mold arise. To monitor the defects of molds on the casting conveyor, a camera with a resolution of 780 x 580 pixels and a shooting rate of 75 frames per second was selected. Images of molds from casting machines were used as input data for neural network algorithm. On the preparation of a digital database and its analytical evaluation stage, the architecture of the convolutional neural network was chosen for the algorithm. The information flow from the local controller is transferred to the OPC server and then to the SCADA system of foundry. After the training, accuracy of neural network defect recognition was about 95.1% on a validation split. After the training, weight coefficients of the neural network were used on testing split and algorithm had identical accuracy with validation images. The proposed technical solutions make it possible to increase the efficiency of the automated process control system in the foundry by expanding the digital database.

  20. Distributed simulation of large computer systems

    International Nuclear Information System (INIS)

    Marzolla, M.

    2001-01-01

    Sequential simulation of large complex physical systems is often regarded as a computationally expensive task. In order to speed-up complex discrete-event simulations, the paradigm of Parallel and Distributed Discrete Event Simulation (PDES) has been introduced since the late 70s. The authors analyze the applicability of PDES to the modeling and analysis of large computer system; such systems are increasingly common in the area of High Energy and Nuclear Physics, because many modern experiments make use of large 'compute farms'. Some feasibility tests have been performed on a prototype distributed simulator

  1. Distributed Database Management Systems A Practical Approach

    CERN Document Server

    Rahimi, Saeed K

    2010-01-01

    This book addresses issues related to managing data across a distributed database system. It is unique because it covers traditional database theory and current research, explaining the difficulties in providing a unified user interface and global data dictionary. The book gives implementers guidance on hiding discrepancies across systems and creating the illusion of a single repository for users. It also includes three sample frameworksâ€"implemented using J2SE with JMS, J2EE, and Microsoft .Netâ€"that readers can use to learn how to implement a distributed database management system. IT and

  2. Distributed expert systems for nuclear reactor control

    International Nuclear Information System (INIS)

    Otaduy, P.J.

    1992-01-01

    A network of distributed expert systems is the heart of a prototype supervisory control architecture developed at the Oak Ridge National Laboratory (ORNL) for an advanced multimodular reactor. Eight expert systems encode knowledge on signal acquisition, diagnostics, safeguards, and control strategies in a hybrid rule-based, multiprocessing and object-oriented distributed computing environment. An interactive simulation of a power block consisting of three reactors and one turbine provides a realistic, testbed for performance analysis of the integrated control system in real-time. Implementation details and representative reactor transients are discussed

  3. A Case Study on Distributed Antenna Systems

    DEFF Research Database (Denmark)

    Sørensen, Troels Bundgaard

    2007-01-01

    Passive distributed antenna systems (DASs) consisting of distributed feeder lines or single point antennas are now often installed in large office buildings where they provide efficient coverage throughout the building. More sophisticated DASs with intelligent reuse and the ability to adapt...... is described in terms of algorithms for power allocation and access port assignment, as well as algorithms for (dynamic) channel assignment. After an outline of simulation assumptions, system capacity comparisons are given between the adaptive DAS and a system with fixed channel and access port assignment...

  4. The evolution of a distributed operating system

    NARCIS (Netherlands)

    van Renesse, Robbert; Tanenbaum, Andrew S.; Mullender, Sape J.; Schröder-Preikschat, Wolfgang; Zimmer, Wolfgang

    AMOEBA is a research project to build a true distributed operating system using the object model. Under the COST11-ter MANDIS project this work was extended to cover wide-area networks. Besides describing the system, this paper discusses the successive versions in the implementation of its model,

  5. Distilled Water Distribution Systems. Laboratory Design Notes.

    Science.gov (United States)

    Sell, J.C.

    Factors concerning water distribution systems, including an evaluation of materials and a recommendation of materials best suited for service in typical facilities are discussed. Several installations are discussed in an effort to bring out typical features in selected applications. The following system types are included--(1) industrial…

  6. Force-independent distribution of correlated neural inputs to hand muscles during three-digit grasping.

    Science.gov (United States)

    Poston, Brach; Danna-Dos Santos, Alessander; Jesunathadas, Mark; Hamm, Thomas M; Santello, Marco

    2010-08-01

    The ability to modulate digit forces during grasping relies on the coordination of multiple hand muscles. Because many muscles innervate each digit, the CNS can potentially choose from a large number of muscle coordination patterns to generate a given digit force. Studies of single-digit force production tasks have revealed that the electromyographic (EMG) activity scales uniformly across all muscles as a function of digit force. However, the extent to which this finding applies to the coordination of forces across multiple digits is unknown. We addressed this question by asking subjects (n = 8) to exert isometric forces using a three-digit grip (thumb, index, and middle fingers) that allowed for the quantification of hand muscle coordination within and across digits as a function of grasp force (5, 20, 40, 60, and 80% maximal voluntary force). We recorded EMG from 12 muscles (6 extrinsic and 6 intrinsic) of the three digits. Hand muscle coordination patterns were quantified in the amplitude and frequency domains (EMG-EMG coherence). EMG amplitude scaled uniformly across all hand muscles as a function of grasp force (muscle x force interaction: P = 0.997; cosines of angle between muscle activation pattern vector pairs: 0.897-0.997). Similarly, EMG-EMG coherence was not significantly affected by force (P = 0.324). However, coherence was stronger across extrinsic than that across intrinsic muscle pairs (P = 0.0039). These findings indicate that the distribution of neural drive to multiple hand muscles is force independent and may reflect the anatomical properties or functional roles of hand muscle groups.

  7. Distributed neural signatures of natural audiovisual speech and music in the human auditory cortex.

    Science.gov (United States)

    Salmi, Juha; Koistinen, Olli-Pekka; Glerean, Enrico; Jylänki, Pasi; Vehtari, Aki; Jääskeläinen, Iiro P; Mäkelä, Sasu; Nummenmaa, Lauri; Nummi-Kuisma, Katarina; Nummi, Ilari; Sams, Mikko

    2017-08-15

    During a conversation or when listening to music, auditory and visual information are combined automatically into audiovisual objects. However, it is still poorly understood how specific type of visual information shapes neural processing of sounds in lifelike stimulus environments. Here we applied multi-voxel pattern analysis to investigate how naturally matching visual input modulates supratemporal cortex activity during processing of naturalistic acoustic speech, singing and instrumental music. Bayesian logistic regression classifiers with sparsity-promoting priors were trained to predict whether the stimulus was audiovisual or auditory, and whether it contained piano playing, speech, or singing. The predictive performances of the classifiers were tested by leaving one participant at a time for testing and training the model using the remaining 15 participants. The signature patterns associated with unimodal auditory stimuli encompassed distributed locations mostly in the middle and superior temporal gyrus (STG/MTG). A pattern regression analysis, based on a continuous acoustic model, revealed that activity in some of these MTG and STG areas were associated with acoustic features present in speech and music stimuli. Concurrent visual stimulus modulated activity in bilateral MTG (speech), lateral aspect of right anterior STG (singing), and bilateral parietal opercular cortex (piano). Our results suggest that specific supratemporal brain areas are involved in processing complex natural speech, singing, and piano playing, and other brain areas located in anterior (facial speech) and posterior (music-related hand actions) supratemporal cortex are influenced by related visual information. Those anterior and posterior supratemporal areas have been linked to stimulus identification and sensory-motor integration, respectively. Copyright © 2017 Elsevier Inc. All rights reserved.

  8. PFS: a distributed and customizable file system

    OpenAIRE

    Bosch, H.G.P.; Mullender, Sape J.

    1996-01-01

    In this paper we present our ongoing work on the Pegasus File System (PFS), a distributed and customizable file system that can be used for off-line file system experiments and on-line file system storage. PFS is best described as an object-oriented component library from which either a true file system or a file-system simulator can be constructed. Each of the components in the library is easily replaced by another implementation to accommodate a wide range of applications.

  9. Distributed Monitoring System Based on ICINGA

    CERN Multimedia

    Haen, C; Neufeld, N

    2011-01-01

    The LHCb online system relies on a large and heterogeneous I.T. infrastructure : it comprises more than 2000 servers and embedded systems and more than 200 network devices. While for the control and monitoring of detectors, PLCs, and readout boards an industry standard SCADA system PVSSII has been put in production, we use a low level monitoring system to monitor the control infrastructure itself. While our previous system was based on a single central NAGIOS server, our current system uses a distributed ICINGA infrastructure.

  10. Synchronization in Quantum Key Distribution Systems

    Directory of Open Access Journals (Sweden)

    Anton Pljonkin

    2017-10-01

    Full Text Available In the description of quantum key distribution systems, much attention is paid to the operation of quantum cryptography protocols. The main problem is the insufficient study of the synchronization process of quantum key distribution systems. This paper contains a general description of quantum cryptography principles. A two-line fiber-optic quantum key distribution system with phase coding of photon states in transceiver and coding station synchronization mode was examined. A quantum key distribution system was built on the basis of the scheme with automatic compensation of polarization mode distortions. Single-photon avalanche diodes were used as optical radiation detecting devices. It was estimated how the parameters used in quantum key distribution systems of optical detectors affect the detection of the time frame with attenuated optical pulse in synchronization mode with respect to its probabilistic and time-domain characteristics. A design method was given for the process that detects the time frame that includes an optical pulse during synchronization. This paper describes the main quantum communication channel attack methods by removing a portion of optical emission. This paper describes the developed synchronization algorithm that takes into account the time required to restore the photodetector’s operation state after the photon has been registered during synchronization. The computer simulation results of the developed synchronization algorithm were analyzed. The efficiency of the developed algorithm with respect to synchronization process protection from unauthorized gathering of optical emission is demonstrated herein.

  11. An Artificial Neural Network for Analyzing Overall Uniformity in Outdoor Lighting Systems

    Directory of Open Access Journals (Sweden)

    Antonio del Corte-Valiente

    2017-02-01

    Full Text Available Street lighting installations are an essential service for modern life due to their capability of creating a welcoming feeling at nighttime. Nevertheless, several studies have highlighted that it is possible to improve the quality of the light significantly improving the uniformity of the illuminance. The main difficulty arises when trying to improve some of the installation’s characteristics based only on statistical analysis of the light distribution. This paper presents a new algorithm that is able to obtain the overall illuminance uniformity in order to improve this sort of installations. To develop this algorithm it was necessary to perform a detailed study of all the elements which are part of street lighting installations. Because classification is one of the most important tasks in the application areas of artificial neural networks, we compared the performances of six types of training algorithms in a feed forward neural network for analyzing the overall uniformity in outdoor lighting systems. We found that the best algorithm that minimizes the error is “Levenberg-Marquardt back-propagation”, which approximates the desired output of the training pattern. By means of this kind of algorithm, it is possible to help to lighting professionals optimize the quality of street lighting installations.

  12. Making the Public Distribution System Work

    Directory of Open Access Journals (Sweden)

    Debarshi Das

    2014-02-01

    Full Text Available Drawing on empirical observations of operation of public distribution system in different states of India, the paper constructs a preliminary game theoretic model. It argues that an effective public distribution must be as universal as possible, delivery mechanism of fair price shops should be re- formed, they should be make them commercially viable and that special attention should be paid to PDS at times of high food inflation.

  13. Electricity distribution management Smart Grid system model

    Directory of Open Access Journals (Sweden)

    Wiesław Nowak

    2012-06-01

    Full Text Available This paper presents issues concerning the implementation of Smart Grid solutions in a real distribution network. The main components possible to quick implementation were presented. Realization of these ideas should bring tangible benefi ts to both customers and distribution system operators. Moreover the paper shows selected research results which examine proposed solutions in area of improving supply reliability and reducing energy losses in analysed network.

  14. 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)

  15. 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)

  16. Control of distributed systems : tutorial and overview

    Czech Academy of Sciences Publication Activity Database

    van Schuppen, J. H.; Boutin, O.; Kempker, P.L.; Komenda, Jan; Masopust, Tomáš; Pambakian, N.; Ran, A.C.M.

    2011-01-01

    Roč. 17, 5-6 (2011), s. 579-602 ISSN 0947-3580 R&D Projects: GA ČR(CZ) GAP103/11/0517; GA ČR(CZ) GPP202/11/P028 Institutional research plan: CEZ:AV0Z10190503 Keywords : distributed system * coordination control * hierarchical control * distributed control * distributed control with communication Subject RIV: BA - General Mathematics Impact factor: 0.817, year: 2011 http://ejc.revuesonline.com/article.jsp?articleId=16873

  17. Existence and Globally Asymptotic Stability of Equilibrium Solution for Fractional-Order Hybrid BAM Neural Networks with Distributed Delays and Impulses

    Directory of Open Access Journals (Sweden)

    Hai Zhang

    2017-01-01

    Full Text Available This paper investigates the existence and globally asymptotic stability of equilibrium solution for Riemann-Liouville fractional-order hybrid BAM neural networks with distributed delays and impulses. The factors of such network systems including the distributed delays, impulsive effects, and two different fractional-order derivatives between the U-layer and V-layer are taken into account synchronously. Based on the contraction mapping principle, the sufficient conditions are derived to ensure the existence and uniqueness of the equilibrium solution for such network systems. By constructing a novel Lyapunov functional composed of fractional integral and definite integral terms, the globally asymptotic stability criteria of the equilibrium solution are obtained, which are dependent on the order of fractional derivative and network parameters. The advantage of our constructed method is that one may directly calculate integer-order derivative of the Lyapunov functional. A numerical example is also presented to show the validity and feasibility of the theoretical results.

  18. Design of a distributed control system

    Energy Technology Data Exchange (ETDEWEB)

    Bilous, O [Commissariat a l' Energie Atomique, Saclay (France).Centre d' Etudes Nucleaires

    1959-07-01

    A digital computer is used to evaluate various pressure control systems for a gaseous diffusion cascade. This is an example of a distributed feedback control system. The paper gives a brief discussion of similar cases of distributed or stage wise control systems, which may occur in multiple temperature control of chemical processes. (author) [French] Une calculatrice digitale est utilisee pour evaluer divers systemes de controle de pression pour une cascade de diffusion gazeuse. C'est un exemple de systeme de controle a reaction distribue. Le rapport presente une breve discussion de cas semblables de systemes de controle distribues ou en etage, qui peuvent se presenter dans de nombreux controles de temperature de reactions chimiques. (auteur)

  19. EGSNRC distributed systems on commercial network

    International Nuclear Information System (INIS)

    McCormack, J.M.

    2001-01-01

    Full text: EGSnrc is a Monte Carlo based simulation program for determining radiation dose distribution within a body. Computational times are large as each individual photon path must be calculated and every energy absorption event stored. This means that EGSnrc lends itself to distributed processing, as each photon is independent of the next, and code is included within the package to enable this. EGSnrc is currently only supported on Unix based computer systems, whilst the department has ∼45 Pentium II and III class workstations all operating under Windows NT within a Novell network. This investigation demonstrates the capability of a windows based system to perform distributed computation of EGSnrc. All Unix scripts were modified to work as one single Windows NT batch file. The source code was then compiled using the gcc C compiler (a Windows NT version of the Unix compiler) without modification of the underlying source code. A small Visual Basic program was used as a trigger to start the simulation as a Windows NT service, with Novell Z.E.N. Works to distribute the trigger code to each system. When a trigger was received, the computer began a simulation as a low priority task in such a way that the user did not see anything on the screen, and so the simulation did not slow down the general running of the computer. The results were then transferred to the network, and collated on a central computer. As an unattended system, a calculation can start within 15 minutes of any desired time, calculate the desired results, and return the results for collation. This demonstrated effectively a distributed Windows NT TM EGSnrc system. Simulations must be chosen carefully to ensure that each photon can be considered independent, as photon histories do not get distributed. Each system that was used for EGSnrc was required to be capable of running the full EGSnrc simulation on its own EGSnrc stored the entire result array locally, so a large, high-resolution body required

  20. Neutral particle beam distributed data acquisition system

    International Nuclear Information System (INIS)

    Daly, R.T.; Kraimer, M.R.; Novick, A.H.

    1987-01-01

    A distributed data acquisition system has been designed to support experiments at the Argonne Neutral Particle Beam Accelerator. The system uses a host VAXstation II/GPX computer acting as an experimenter's station linked via Ethernet with multiple MicroVAX IIs and rtVAXs dedicated to acquiring data and controlling hardware at remote sites. This paper describes the hardware design of the system, the applications support software on the host and target computers, and the real-time performance

  1. MACHINE LEARNING FOR THE SELF-ORGANIZATION OF DISTRIBUTED SYSTEMS IN ECONOMIC APPLICATIONS

    OpenAIRE

    Jerzy Balicki; Waldemar Korłub

    2017-01-01

    In this paper, an application of machine learning to the problem of self-organization of distributed systems has been discussed with regard to economic applications, with particular emphasis on supervised neural network learning to predict stock investments and some ratings of companies. In addition, genetic programming can play an important role in the preparation and testing of several financial information systems. For this reason, machine learning applications have been discussed because ...

  2. Performance of Using Cascade Forward Back Propagation Neural Networks for Estimating Rain Parameters with Rain Drop Size Distribution

    Directory of Open Access Journals (Sweden)

    Siddi Tengeleng

    2014-06-01

    Full Text Available The aim of our study is to estimate the parameters M (water content, R (rain rate and Z (radar reflectivity with raindrop size distribution by using the neural network method. Our investigations have been conducted in five African localities: Abidjan (Côte d’Ivoire, Boyele (Congo-Brazzaville, Debuncha (Cameroon, Dakar (Senegal and Niamey (Niger. For the first time, we have predicted the values of the various parameters in each locality after using neural models (LANN which have been developed with locally obtained disdrometer data. We have shown that each LANN can be used under other latitudes to get satisfactory results. Secondly, we have also constructed a model, using as train-data, a combination of data issued from all five localities. With this last model called PANN, we could obtain satisfactory estimates forall localities. Lastly, we have distinguished between stratiform and convective rain while building the neural networks. In fact, using simulation data from stratiform rain situations, we have obtained smaller root mean square errors (RMSE between neural values and disdrometer values than using data issued from convective situations.

  3. 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.

  4. A Distributed Intelligent System for Emergency Convoy

    Directory of Open Access Journals (Sweden)

    Mohammed Benalla

    2016-09-01

    Full Text Available The general problem that guides this research is the ability to design a distributed intelligent system for guiding the emergency convoys; a solution that will be based on a group of agents and on the analysis of traffic in order to generate collective functional response. It fits into the broader issue of Distributed Artificial System (DAI, which is to operate a cooperatively computer agent into multi-agents system (MAS. This article describes conceptually two fundamental questions of emergency convoys. The first question is dedicated to find a response to the traffic situation (i.e. fluid way, while the second is devoted to the convoy orientation; while putting the point on the distributed and cooperative resolution for the general problem.

  5. The ATLAS Distributed Data Management System & Databases

    CERN Document Server

    Garonne, V; The ATLAS collaboration; Barisits, M; Beermann, T; Vigne, R; Serfon, C

    2013-01-01

    The ATLAS Distributed Data Management (DDM) System is responsible for the global management of petabytes of high energy physics data. The current system, DQ2, has a critical dependency on Relational Database Management Systems (RDBMS), like Oracle. RDBMS are well-suited to enforcing data integrity in online transaction processing applications, however, concerns have been raised about the scalability of its data warehouse-like workload. In particular, analysis of archived data or aggregation of transactional data for summary purposes is problematic. Therefore, we have evaluated new approaches to handle vast amounts of data. We have investigated a class of database technologies commonly referred to as NoSQL databases. This includes distributed filesystems, like HDFS, that support parallel execution of computational tasks on distributed data, as well as schema-less approaches via key-value stores, like HBase. In this talk we will describe our use cases in ATLAS, share our experiences with various databases used ...

  6. Ship Detection in Gaofen-3 SAR Images Based on Sea Clutter Distribution Analysis and Deep Convolutional Neural Network.

    Science.gov (United States)

    An, Quanzhi; Pan, Zongxu; You, Hongjian

    2018-01-24

    Target detection is one of the important applications in the field of remote sensing. The Gaofen-3 (GF-3) Synthetic Aperture Radar (SAR) satellite launched by China is a powerful tool for maritime monitoring. This work aims at detecting ships in GF-3 SAR images using a new land masking strategy, the appropriate model for sea clutter and a neural network as the discrimination scheme. Firstly, the fully convolutional network (FCN) is applied to separate the sea from the land. Then, by analyzing the sea clutter distribution in GF-3 SAR images, we choose the probability distribution model of Constant False Alarm Rate (CFAR) detector from K-distribution, Gamma distribution and Rayleigh distribution based on a tradeoff between the sea clutter modeling accuracy and the computational complexity. Furthermore, in order to better implement CFAR detection, we also use truncated statistic (TS) as a preprocessing scheme and iterative censoring scheme (ICS) for boosting the performance of detector. Finally, we employ a neural network to re-examine the results as the discrimination stage. Experiment results on three GF-3 SAR images verify the effectiveness and efficiency of this approach.

  7. 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...

  8. 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...

  9. 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.

  10. Cardea: Dynamic Access Control in Distributed Systems

    Science.gov (United States)

    Lepro, Rebekah

    2004-01-01

    Modern authorization systems span domains of administration, rely on many different authentication sources, and manage complex attributes as part of the authorization process. This . paper presents Cardea, a distributed system that facilitates dynamic access control, as a valuable piece of an inter-operable authorization framework. First, the authorization model employed in Cardea and its functionality goals are examined. Next, critical features of the system architecture and its handling of the authorization process are then examined. Then the S A M L and XACML standards, as incorporated into the system, are analyzed. Finally, the future directions of this project are outlined and connection points with general components of an authorization system are highlighted.

  11. Concurrency control in distributed database systems

    CERN Document Server

    Cellary, W; Gelenbe, E

    1989-01-01

    Distributed Database Systems (DDBS) may be defined as integrated database systems composed of autonomous local databases, geographically distributed and interconnected by a computer network.The purpose of this monograph is to present DDBS concurrency control algorithms and their related performance issues. The most recent results have been taken into consideration. A detailed analysis and selection of these results has been made so as to include those which will promote applications and progress in the field. The application of the methods and algorithms presented is not limited to DDBSs but a

  12. Distributed formation tracking using local coordinate systems

    DEFF Research Database (Denmark)

    Yang, Qingkai; Cao, Ming; Garcia de Marina, Hector

    2018-01-01

    This paper studies the formation tracking problem for multi-agent systems, for which a distributed estimator–controller scheme is designed relying only on the agents’ local coordinate systems such that the centroid of the controlled formation tracks a given trajectory. By introducing a gradient...... descent term into the estimator, the explicit knowledge of the bound of the agents’ speed is not necessary in contrast to existing works, and each agent is able to compute the centroid of the whole formation in finite time. Then, based on the centroid estimation, a distributed control algorithm...

  13. Converters for Distributed Power Generation Systems

    DEFF Research Database (Denmark)

    Blaabjerg, Frede; Yang, Yongheng

    2015-01-01

    Power electronics technology has become the enabling technology for the integration of distributed power generation systems (DPGS) such as offshore wind turbine power systems and commercial photovoltaic power plants. Depending on the applications, a vast array of DPGS-based power converter...... topologies has been developed and more are coming into the market in order to achieve an efficient and reliable power conversion from the renewables. In addition, stringent demands from both the distribution system operators and the consumers have been imposed on the renewable-based DPGS. This article...... presents an overview of the power converters for the DPGS, mainly based on wind turbine systems and photovoltaic systems, covering a wide range of applications. Moreover, the modulation schemes and interfacing power filters for the power converters are also exemplified. Finally, the general control...

  14. DC Home Appliances for DC Distribution System

    Directory of Open Access Journals (Sweden)

    MUHAMMAD KAMRAN

    2017-10-01

    Full Text Available This paper strengthens the idea of DC distribution system for DC microgrid consisting of a building of 50 apartments. Since the war of currents AC system has been dominant because of the paucity of research in the protection of the DC system. Now with the advance research in power electronics material and components, generation of electricity is inherently DC as by solar PV, fuel cell and thermoelectric generator that eliminates the rectification process. Transformers are replaced by the power electronics buck-boost converters. DC circuit breakers have solved the protection problems for both DC transmission and distribution system. In this paper 308V DC microgrid is proposed and home appliances (DC internal are modified to operate on 48V DC from DC distribution line. Instead of using universal and induction motors in rotary appliances, BLDC (Brushless DC motors are proposed that are highly efficient with minimum electro-mechanical and no commutation losses. Proposed DC system reduces the power conversion stages, hence diminishes the associated power losses and standby losses that boost the overall system efficiency. So in view of all this a conventional AC system can be replaced by a DC system that has many advantages by cost as well as by performance

  15. Adaptive intelligent power systems: Active distribution networks

    International Nuclear Information System (INIS)

    McDonald, Jim

    2008-01-01

    Electricity networks are extensive and well established. They form a key part of the infrastructure that supports industrialised society. These networks are moving from a period of stability to a time of potentially major transition, driven by a need for old equipment to be replaced, by government policy commitments to cleaner and renewable sources of electricity generation, and by change in the power industry. This paper looks at moves towards active distribution networks. The novel transmission and distribution systems of the future will challenge today's system designs. They will cope with variable voltages and frequencies, and will offer more flexible, sustainable options. Intelligent power networks will need innovation in several key areas of information technology. Active control of flexible, large-scale electrical power systems is required. Protection and control systems will have to react to faults and unusual transient behaviour and ensure recovery after such events. Real-time network simulation and performance analysis will be needed to provide decision support for system operators, and the inputs to energy and distribution management systems. Advanced sensors and measurement will be used to achieve higher degrees of network automation and better system control, while pervasive communications will allow networks to be reconfigured by intelligent systems

  16. 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.

  17. 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.

  18. 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...

  19. 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.

  20. Electric distribution systems and embedded generation capacity

    International Nuclear Information System (INIS)

    Calderaro, V.; Galdi, V.; Piccolo, A.; Siano, P.

    2006-01-01

    The main policy issues of European States are sustainable energy supply promotion and liberalization of energy markets, which introduced market competition in electricity production and created support mechanisms to encourage renewable electricity production and consumption. As a result of liberalization, any generator, including small-scale and renewable energy based units, can sell electricity on the free market. In order to meet future sustainability targets, connection of a higher number of Distributed Generation (DG) units to the electrical power system is expected, requiring changes in the design and operation of distribution electricity systems, as well as changes in electricity network regulation. In order to assist distribution system operators in planning and managing DG connections and in maximizing DG penetration and renewable sources exploitation, this paper proposed a reconfiguration methodology based on a Genetic Algorithm (GA), that was tested on a 70-bus system with DG units. The simulation results confirmed that the methodology represents a suitable tool for distribution system operators when dealing with DG capacity expansion and power loss issues, providing information regarding the potential penetration network-wide and allowing maximum exploitation of renewable generation. 35 refs., 4 tabs., 6 figs

  1. Application of algorithms and artificial-intelligence approach for locating multiple harmonics in distribution systems

    Energy Technology Data Exchange (ETDEWEB)

    Hong, Y.-Y.; Chen, Y.-C. [Chung Yuan University (China). Dept. of Electrical Engineering

    1999-05-01

    A new method is proposed for locating multiple harmonic sources in distribution systems. The proposed method first determines the proper locations for metering measurement using fuzzy clustering. Next, an artificial neural network based on the back-propagation approach is used to identify the most likely location for multiple harmonic sources. A set of systematic algorithmic steps is developed until all harmonic locations are identified. The simulation results for an 18-busbar system show that the proposed method is very efficient in locating the multiple harmonics in a distribution system. (author)

  2. In-Line Acoustic Device Inspection of Leakage in Water Distribution Pipes Based on Wavelet and Neural Network

    Directory of Open Access Journals (Sweden)

    Dileep Kumar

    2017-01-01

    Full Text Available Traditionally permanent acoustic sensors leak detection techniques have been proven to be very effective in water distribution pipes. However, these methods need long distance deployment and proper position of sensors and cannot be implemented on underground pipelines. An inline-inspection acoustic device is developed which consists of acoustic sensors. The device will travel by the flow of water through the pipes which record all noise events and detect small leaks. However, it records all the noise events regarding background noises, but the time domain noisy acoustic signal cannot manifest complete features such as the leak flow rate which does not distinguish the leak signal and environmental disturbance. This paper presents an algorithm structure with the modularity of wavelet and neural network, which combines the capability of wavelet transform analyzing leakage signals and classification capability of artificial neural networks. This study validates that the time domain is not evident to the complete features regarding noisy leak signals and significance of selection of mother wavelet to extract the noise event features in water distribution pipes. The simulation consequences have shown that an appropriate mother wavelet has been selected and localized to extract the features of the signal with leak noise and background noise, and by neural network implementation, the method improves the classification performance of extracted features.

  3. Intelligent Control and Operation of Distribution System

    DEFF Research Database (Denmark)

    Bhattarai, Bishnu Prasad

    methodology to ensure efficient control and operation of the future distribution networks. The major scientific challenge is thus to develop control models and strategies to coordinate responses from widely distributed controllable loads and local generations. Detailed models of key Smart Grid (SG) elements...... in this direction but also benefit distribution system operators in the planning and development of the distribution network. The major contributions of this work are described in the following four stages: In the first stage, an intelligent Demand Response (DR) control architecture is developed for coordinating...... the key SG actors, namely consumers, network operators, aggregators, and electricity market entities. A key intent of the architecture is to facilitate market participation of residential consumers and prosumers. A Hierarchical Control Architecture (HCA) having primary, secondary, and tertiary control...

  4. 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.

  5. Wireless distributed functional electrical stimulation system

    Directory of Open Access Journals (Sweden)

    Jovičić Nenad S

    2012-08-01

    Full Text Available Abstract Background The control of movement in humans is hierarchical and distributed and uses feedback. An assistive system could be best integrated into the therapy of a human with a central nervous system lesion if the system is controlled in a similar manner. Here, we present a novel wireless architecture and routing protocol for a distributed functional electrical stimulation system that enables control of movement. Methods The new system comprises a set of miniature battery-powered devices with stimulating and sensing functionality mounted on the body of the subject. The devices communicate wirelessly with one coordinator device, which is connected to a host computer. The control algorithm runs on the computer in open- or closed-loop form. A prototype of the system was designed using commercial, off-the-shelf components. The propagation characteristics of electromagnetic waves and the distributed nature of the system were considered during the development of a two-hop routing protocol, which was implemented in the prototype’s software. Results The outcomes of this research include a novel system architecture and routing protocol and a functional prototype based on commercial, off-the-shelf components. A proof-of-concept study was performed on a hemiplegic subject with paresis of the right arm. The subject was tasked with generating a fully functional palmar grasp (closing of the fingers. One node was used to provide this movement, while a second node controlled the activation of extensor muscles to eliminate undesired wrist flexion. The system was tested with the open- and closed-loop control algorithms. Conclusions The system fulfilled technical and application requirements. The novel communication protocol enabled reliable real-time use of the system in both closed- and open-loop forms. The testing on a patient showed that the multi-node system could operate effectively to generate functional movement.

  6. Wireless distributed functional electrical stimulation system.

    Science.gov (United States)

    Jovičić, Nenad S; Saranovac, Lazar V; Popović, Dejan B

    2012-08-09

    The control of movement in humans is hierarchical and distributed and uses feedback. An assistive system could be best integrated into the therapy of a human with a central nervous system lesion if the system is controlled in a similar manner. Here, we present a novel wireless architecture and routing protocol for a distributed functional electrical stimulation system that enables control of movement. The new system comprises a set of miniature battery-powered devices with stimulating and sensing functionality mounted on the body of the subject. The devices communicate wirelessly with one coordinator device, which is connected to a host computer. The control algorithm runs on the computer in open- or closed-loop form. A prototype of the system was designed using commercial, off-the-shelf components. The propagation characteristics of electromagnetic waves and the distributed nature of the system were considered during the development of a two-hop routing protocol, which was implemented in the prototype's software. The outcomes of this research include a novel system architecture and routing protocol and a functional prototype based on commercial, off-the-shelf components. A proof-of-concept study was performed on a hemiplegic subject with paresis of the right arm. The subject was tasked with generating a fully functional palmar grasp (closing of the fingers). One node was used to provide this movement, while a second node controlled the activation of extensor muscles to eliminate undesired wrist flexion. The system was tested with the open- and closed-loop control algorithms. The system fulfilled technical and application requirements. The novel communication protocol enabled reliable real-time use of the system in both closed- and open-loop forms. The testing on a patient showed that the multi-node system could operate effectively to generate functional movement.

  7. 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...

  8. Deceit: A flexible distributed file system

    Science.gov (United States)

    Siegel, Alex; Birman, Kenneth; Marzullo, Keith

    1989-01-01

    Deceit, a distributed file system (DFS) being developed at Cornell, focuses on flexible file semantics in relation to efficiency, scalability, and reliability. Deceit servers are interchangeable and collectively provide the illusion of a single, large server machine to any clients of the Deceit service. Non-volatile replicas of each file are stored on a subset of the file servers. The user is able to set parameters on a file to achieve different levels of availability, performance, and one-copy serializability. Deceit also supports a file version control mechanism. In contrast with many recent DFS efforts, Deceit can behave like a plain Sun Network File System (NFS) server and can be used by any NFS client without modifying any client software. The current Deceit prototype uses the ISIS Distributed Programming Environment for all communication and process group management, an approach that reduces system complexity and increases system robustness.

  9. Recurrent neural network approach to quantum signal: coherent state restoration for continuous-variable quantum key distribution

    Science.gov (United States)

    Lu, Weizhao; Huang, Chunhui; Hou, Kun; Shi, Liting; Zhao, Huihui; Li, Zhengmei; Qiu, Jianfeng

    2018-05-01

    In continuous-variable quantum key distribution (CV-QKD), weak signal carrying information transmits from Alice to Bob; during this process it is easily influenced by unknown noise which reduces signal-to-noise ratio, and strongly impacts reliability and stability of the communication. Recurrent quantum neural network (RQNN) is an artificial neural network model which can perform stochastic filtering without any prior knowledge of the signal and noise. In this paper, a modified RQNN algorithm with expectation maximization algorithm is proposed to process the signal in CV-QKD, which follows the basic rule of quantum mechanics. After RQNN, noise power decreases about 15 dBm, coherent signal recognition rate of RQNN is 96%, quantum bit error rate (QBER) drops to 4%, which is 6.9% lower than original QBER, and channel capacity is notably enlarged.

  10. Distributed control system for the FMIT

    International Nuclear Information System (INIS)

    Johnson, J.A.; Machen, D.R.; Suyama, R.M.

    1979-01-01

    The control system for the Fusion Materials Irradiation Test (FMIT) Facility will provide the primary data acquisition, control, and interface components that integrate all of the individual FMIT systems into a functional facility. The control system consists of a distributed computer network, control consoles and instrumentation subsystems. The FMIT Facility will be started, operated and secured from a Central Control Room. All FMIT systems and experimental functions will be monitored from the Central Control Room. The data acquisition and control signals will be handled by a data communications network, which connects dual computers in the Central Control Room to the microcomputers in CAMAC crates near the various subsystems of the facility

  11. Comparison between wavelet and wavelet packet transform features for classification of faults in distribution system

    Science.gov (United States)

    Arvind, Pratul

    2012-11-01

    The ability to identify and classify all ten types of faults in a distribution system is an important task for protection engineers. Unlike transmission system, distribution systems have a complex configuration and are subjected to frequent faults. In the present work, an algorithm has been developed for identifying all ten types of faults in a distribution system by collecting current samples at the substation end. The samples are subjected to wavelet packet transform and artificial neural network in order to yield better classification results. A comparison of results between wavelet transform and wavelet packet transform is also presented thereby justifying the feature extracted from wavelet packet transform yields promising results. It should also be noted that current samples are collected after simulating a 25kv distribution system in PSCAD software.

  12. Left-forbidding cooperating distributed grammar systems

    Czech Academy of Sciences Publication Activity Database

    Goldefus, F.; Masopust, Tomáš; Meduna, A.

    2010-01-01

    Roč. 411, 40-42 (2010), s. 3661-3667 ISSN 0304-3975 Institutional research plan: CEZ:AV0Z10190503 Keywords : cooperating distributed grammar system * cooperating derivation mode * left-forbidding grammar * generative power * descriptional complexity Subject RIV: BA - General Mathematics Impact factor: 0.838, year: 2010 http://www.sciencedirect.com/science/article/pii/S0304397510003440

  13. Modelling Dynamic Forgetting in Distributed Information Systems

    NARCIS (Netherlands)

    N.F. Höning (Nicolas); M.C. Schut

    2010-01-01

    htmlabstractWe describe and model a new aspect in the design of distributed information systems. We build upon a previously described problem on the microlevel, which asks how quickly agents should discount (forget) their experience: If they cherish their memories, they can build their reports on

  14. Workflow management in large distributed systems

    International Nuclear Information System (INIS)

    Legrand, I; Newman, H; Voicu, R; Dobre, C; Grigoras, C

    2011-01-01

    The MonALISA (Monitoring Agents using a Large Integrated Services Architecture) framework provides a distributed service system capable of controlling and optimizing large-scale, data-intensive applications. An essential part of managing large-scale, distributed data-processing facilities is a monitoring system for computing facilities, storage, networks, and the very large number of applications running on these systems in near realtime. All this monitoring information gathered for all the subsystems is essential for developing the required higher-level services—the components that provide decision support and some degree of automated decisions—and for maintaining and optimizing workflow in large-scale distributed systems. These management and global optimization functions are performed by higher-level agent-based services. We present several applications of MonALISA's higher-level services including optimized dynamic routing, control, data-transfer scheduling, distributed job scheduling, dynamic allocation of storage resource to running jobs and automated management of remote services among a large set of grid facilities.

  15. Wind Power in Electrical Distribution Systems

    DEFF Research Database (Denmark)

    Chen, Zhe

    2013-01-01

    Recent years, wind power is experiencing a rapid growth, large number of wind turbines/wind farms have been installed and connected to power systems. In addition to the large centralised wind farms connected to transmission grids, many distributed wind turbines and wind farms are operated as dist...

  16. Gradient remediability in linear distributed parabolic systems ...

    African Journals Online (AJOL)

    The aim of this paper is the introduction of a new concept that concerned the analysis of a large class of distributed parabolic systems. It is the general concept of gradient remediability. More precisely, we study with respect to the gradient observation, the existence of an input operator (gradient efficient actuators) ensuring ...

  17. Boundary feedback stabilization of distributed parameter systems

    DEFF Research Database (Denmark)

    Pedersen, Michael

    1988-01-01

    The author introduces the method of pseudo-differential stabilization. He notes that the theory of pseudo-differential boundary operators is a fruitful approach to problems arising in control and stabilization theory of distributed-parameter systems. The basic pseudo-differential calculus can...

  18. Distribution system reliability evaluation using credibility theory

    African Journals Online (AJOL)

    Xufeng Xu, Joydeep Mitra

    have found that credibility theory, which broadens the scope of fuzzy set theory, is an effective tool for representing fuzzy events, and have developed a theoretical .... Based on the status of switches, the distribution system can be divided into multiple SPSS, which are connected with tie switches. For example, SPSS.

  19. Trading Freshness for Performance in Distributed Systems

    Science.gov (United States)

    2014-12-01

    C. Hsieh, Deborah A. Wallach, Mike Burrows , Tushar Chandra, Andrew Fikes, and Robert E. Gruber. BigTable: A distributed storage system for structured... Larry Brilliant. Detecting influenza epidemics using search engine query data. Nature, pages 1012–1014, February 2009. 3.2 Goetz Graefe. Write

  20. Workflow management in large distributed systems

    Science.gov (United States)

    Legrand, I.; Newman, H.; Voicu, R.; Dobre, C.; Grigoras, C.

    2011-12-01

    The MonALISA (Monitoring Agents using a Large Integrated Services Architecture) framework provides a distributed service system capable of controlling and optimizing large-scale, data-intensive applications. An essential part of managing large-scale, distributed data-processing facilities is a monitoring system for computing facilities, storage, networks, and the very large number of applications running on these systems in near realtime. All this monitoring information gathered for all the subsystems is essential for developing the required higher-level services—the components that provide decision support and some degree of automated decisions—and for maintaining and optimizing workflow in large-scale distributed systems. These management and global optimization functions are performed by higher-level agent-based services. We present several applications of MonALISA's higher-level services including optimized dynamic routing, control, data-transfer scheduling, distributed job scheduling, dynamic allocation of storage resource to running jobs and automated management of remote services among a large set of grid facilities.

  1. Mansion, A Distributed Multi-Agent System

    NARCIS (Netherlands)

    van t Noordende, G.; Brazier, F.M.; Tanenbaum, A.S.

    2001-01-01

    In this position summary we present work in progress on a worldwide, scalable multi-agent system, based on a paradigm of hyperlinked rooms. The framework offers facilities for managing distribution, security and mobility aspects for both active elements (agents) and passive elements (objects) in the

  2. 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

  3. 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

  4. Programming model for distributed intelligent systems

    Science.gov (United States)

    Sztipanovits, J.; Biegl, C.; Karsai, G.; Bogunovic, N.; Purves, B.; Williams, R.; Christiansen, T.

    1988-01-01

    A programming model and architecture which was developed for the design and implementation of complex, heterogeneous measurement and control systems is described. The Multigraph Architecture integrates artificial intelligence techniques with conventional software technologies, offers a unified framework for distributed and shared memory based parallel computational models and supports multiple programming paradigms. The system can be implemented on different hardware architectures and can be adapted to strongly different applications.

  5. The new bank note distribution system

    OpenAIRE

    Gerrit Bilkes

    1997-01-01

    In this article, the author outlines the recent changes made to the way Canada's bank notes are distributed. The new system allows financial institutions to exchange notes directly with one another at designated points across the country, rather than through Bank of Canada agencies, as was previously the case. The institutions communicate with the Bank of Canada through a computerized inventory-management system. Two Bank of Canada operations centres monitor note quality and supply new notes ...

  6. Planning and Optimization Methods for Active Distribution Systems

    DEFF Research Database (Denmark)

    Abbey, Chad; Baitch, Alex; Bak-Jensen, Birgitte

    distribution planning. Active distribution networks (ADNs) have systems in place to control a combination of distributed energy resources (DERs), defined as generators, loads and storage. With these systems in place, the AND becomes an Active Distribution System (ADS). Distribution system operators (DSOs) have...

  7. 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

  8. Optimization of the kernel functions in a probabilistic neural network analyzing the local pattern distribution.

    Science.gov (United States)

    Galleske, I; Castellanos, J

    2002-05-01

    This article proposes a procedure for the automatic determination of the elements of the covariance matrix of the gaussian kernel function of probabilistic neural networks. Two matrices, a rotation matrix and a matrix of variances, can be calculated by analyzing the local environment of each training pattern. The combination of them will form the covariance matrix of each training pattern. This automation has two advantages: First, it will free the neural network designer from indicating the complete covariance matrix, and second, it will result in a network with better generalization ability than the original model. A variation of the famous two-spiral problem and real-world examples from the UCI Machine Learning Repository will show a classification rate not only better than the original probabilistic neural network but also that this model can outperform other well-known classification techniques.

  9. Reorganizing Neural Network System for Two Spirals and Linear Low-Density Polyethylene Copolymer Problems

    Directory of Open Access Journals (Sweden)

    G. M. Behery

    2009-01-01

    Full Text Available This paper presents an automatic system of neural networks (NNs that has the ability to simulate and predict many of applied problems. The system architectures are automatically reorganized and the experimental process starts again, if the required performance is not reached. This processing is continued until the performance obtained. This system is first applied and tested on the two spiral problem; it shows that excellent generalization performance obtained by classifying all points of the two-spirals correctly. After that, it is applied and tested on the shear stress and the pressure drop problem across the short orifice die as a function of shear rate at different mean pressures for linear low-density polyethylene copolymer (LLDPE at 190∘C. The system shows a better agreement with an experimental data of the two cases: shear stress and pressure drop. The proposed system has been also designed to simulate other distributions not presented in the training set (predicted and matched them effectively.

  10. The "Family Tree" of Air Distribution Systems

    DEFF Research Database (Denmark)

    Nielsen, Peter V.

    2011-01-01

    In this paper all total volume air distribution principles are addressed based on discussions of air flow pattern in a room with heat sources giving a cooling load. The supply and exhaust air openings are considered to have different locations and sizes in the room, and it is possible to show tha...... conditions which are not used for air distribution in general. A number of experiments with different air distribution systems are addressed, and they illustrate the behaviour at the different conditions discussed in the paper....... that all the known types of air distribution systems are interconnected in a “family tree”. The influence of supplied momentum flow versus buoyancy forces is discussed, and geometries for high ventilation effectiveness are indicated as well as geometries for fully mixed flow. The paper will also show......In this paper all total volume air distribution principles are addressed based on discussions of air flow pattern in a room with heat sources giving a cooling load. The supply and exhaust air openings are considered to have different locations and sizes in the room, and it is possible to show...

  11. Design of distributed systems of hydrolithosphere processes management. A synthesis of distributed management systems

    Science.gov (United States)

    Pershin, I. M.; Pervukhin, D. A.; Ilyushin, Y. V.; Afanaseva, O. V.

    2017-10-01

    The paper considers an important problem of designing distributed systems of hydrolithosphere processes management. The control actions on the hydrolithosphere processes under consideration are implemented by a set of extractive wells. The article shows the method of defining the approximation links for description of the dynamic characteristics of hydrolithosphere processes. The structure of distributed regulators, used in the management systems by the considered processes, is presented. The paper analyses the results of the synthesis of the distributed management system and the results of modelling the closed-loop control system by the parameters of the hydrolithosphere process.

  12. Communication Systems and Study Method for Active Distribution Power systems

    DEFF Research Database (Denmark)

    Wei, Mu; Chen, Zhe

    Due to the involvement and evolvement of communication technologies in contemporary power systems, the applications of modern communication technologies in distribution power system are becoming increasingly important. In this paper, the International Organization for Standardization (ISO......) reference seven-layer model of communication systems, and the main communication technologies and protocols on each corresponding layer are introduced. Some newly developed communication techniques, like Ethernet, are discussed with reference to the possible applications in distributed power system....... The suitability of the communication technology to the distribution power system with active renewable energy based generation units is discussed. Subsequently the typical possible communication systems are studied by simulation. In this paper, a novel method of integrating communication system impact into power...

  13. 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

  14. Laser spark distribution and ignition system

    Science.gov (United States)

    Woodruff, Steven [Morgantown, WV; McIntyre, Dustin L [Morgantown, WV

    2008-09-02

    A laser spark distribution and ignition system that reduces the high power optical requirements for use in a laser ignition and distribution system allowing for the use of optical fibers for delivering the low peak energy pumping pulses to a laser amplifier or laser oscillator. An optical distributor distributes and delivers optical pumping energy from an optical pumping source to multiple combustion chambers incorporating laser oscillators or laser amplifiers for inducing a laser spark within a combustion chamber. The optical distributor preferably includes a single rotating mirror or lens which deflects the optical pumping energy from the axis of rotation and into a plurality of distinct optical fibers each connected to a respective laser media or amplifier coupled to an associated combustion chamber. The laser spark generators preferably produce a high peak power laser spark, from a single low power pulse. The laser spark distribution and ignition system has application in natural gas fueled reciprocating engines, turbine combustors, explosives and laser induced breakdown spectroscopy diagnostic sensors.

  15. Distributed systems design using separable communications

    International Nuclear Information System (INIS)

    Capel, A.C.; Yan, G.

    1980-01-01

    One of the promises of distributed systems is the ability to design each process function largely independently of the others, and in many cases locate the resulting hardware in close proximity to the application. The communications architecture for such systems should be approached in the same way, using separable communications facilities to meet individual sets of requirements while at the same time reducing the interactions between functions. Where complete physical separation is not feasible and hardware resource sharing is required, the protocols should be designed emphasizing the logical separation of communication paths. This paper discusses the different types of communications for process control applictions and the parameters which need to be characterized in designing separable communications for distributed systems. (auth)

  16. Overview of the ATLAS distributed computing system

    CERN Document Server

    Elmsheuser, Johannes; The ATLAS collaboration

    2018-01-01

    The CERN ATLAS experiment successfully uses a worldwide computing infrastructure to support the physics program during LHC Run 2. The grid workflow system PanDA routinely manages 250 to 500 thousand concurrently running production and analysis jobs to process simulation and detector data. In total more than 300 PB of data is distributed over more than 150 sites in the WLCG and handled by the ATLAS data management system Rucio. To prepare for the ever growing LHC luminosity in future runs new developments are underway to even more efficiently use opportunistic resources such as HPCs and utilize new technologies. This presentation will review and explain the outline and the performance of the ATLAS distributed computing system and give an outlook to new workflow and data management ideas for the beginning of the LHC Run 3.

  17. Distributed radiation data acquisition system for KAPP

    International Nuclear Information System (INIS)

    Narsaiah, A.; Anantakrishnan, T.S.; Bairi, B.R.

    1992-01-01

    Increased concern for the safety of personnel working in Nuclear Power Stations demands an efficient centralized Radiation Data Acquisition System (RADAS) to monitor different types of radiation at distributed locations of the plant. The system provides a comprehensive picture of radio-activity level distribution in the reactor building to facilitate prompt and correct decision making to take care of any emergency situation. The system is build around an industrial IBM-PC connected to remote intelligent data acquisition units using serial data communication links. To ensure high system availability and ease of maintenance the mechanical moving disk storage has been replaced by solid state memory storage (RAM Disk). Functional CRT displays have been substituted by the assembly of IBM-PC Mother Board with built-in firmware and standard TV Monitor. The computer handles a variety of processing functions which include the conversion to engineering units, checking of alarms, display/printing of plant radiation level status and system diagnostics. Intelligent terminals have been provided with graphic and text formatting capabilities. A hot standby computer connected to analog and digital inputs takes over the system functions on the failure of the host system. Modular software written in the higher level C-language runs under a standard real-time operating system Kernel. This provides for easy modification and expandability at site. Based on the experience of its commissioning at Kakrapar Power Station a new compact version is being designed for a specific application in another class of reactor. (author). 3 refs., 1 fig

  18. A neural network approach to burst detection.

    Science.gov (United States)

    Mounce, S R; Day, A J; Wood, A S; Khan, A; Widdop, P D; Machell, J

    2002-01-01

    This paper describes how hydraulic and water quality data from a distribution network may be used to provide a more efficient leakage management capability for the water industry. The research presented concerns the application of artificial neural networks to the issue of detection and location of leakage in treated water distribution systems. An architecture for an Artificial Neural Network (ANN) based system is outlined. The neural network uses time series data produced by sensors to directly construct an empirical model for predication and classification of leaks. Results are presented using data from an experimental site in Yorkshire Water's Keighley distribution system.

  19. Real time computer system with distributed microprocessors

    International Nuclear Information System (INIS)

    Heger, D.; Steusloff, H.; Syrbe, M.

    1979-01-01

    The usual centralized structure of computer systems, especially of process computer systems, cannot sufficiently use the progress of very large-scale integrated semiconductor technology with respect to increasing the reliability and performance and to decreasing the expenses especially of the external periphery. This and the increasing demands on process control systems has led the authors to generally examine the structure of such systems and to adapt it to the new surroundings. Computer systems with distributed, optical fibre-coupled microprocessors allow a very favourable problem-solving with decentralized controlled buslines and functional redundancy with automatic fault diagnosis and reconfiguration. A fit programming system supports these hardware properties: PEARL for multicomputer systems, dynamic loader, processor and network operating system. The necessary design principles for this are proved mainly theoretically and by value analysis. An optimal overall system of this new generation of process control systems was established, supported by results of 2 PDV projects (modular operating systems, input/output colour screen system as control panel), for the purpose of testing by apllying the system for the control of 28 pit furnaces of a steel work. (orig.) [de

  20. 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.