WorldWideScience

Sample records for integrated neural systems

  1. Integrated Neural Flight and Propulsion Control System

    Science.gov (United States)

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

    2001-01-01

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

  2. 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.

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

    DEFF Research Database (Denmark)

    Lehmann, Torsten

    1998-01-01

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

  4. 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.

  5. Soft computing integrating evolutionary, neural, and fuzzy systems

    CERN Document Server

    Tettamanzi, Andrea

    2001-01-01

    Soft computing encompasses various computational methodologies, which, unlike conventional algorithms, are tolerant of imprecision, uncertainty, and partial truth. Soft computing technologies offer adaptability as a characteristic feature and thus permit the tracking of a problem through a changing environment. Besides some recent developments in areas like rough sets and probabilistic networks, fuzzy logic, evolutionary algorithms, and artificial neural networks are core ingredients of soft computing, which are all bio-inspired and can easily be combined synergetically. This book presents a well-balanced integration of fuzzy logic, evolutionary computing, and neural information processing. The three constituents are introduced to the reader systematically and brought together in differentiated combinations step by step. The text was developed from courses given by the authors and offers numerous illustrations as

  6. Integrated Markov-neural reliability computation method: A case for multiple automated guided vehicle system

    International Nuclear Information System (INIS)

    Fazlollahtabar, Hamed; Saidi-Mehrabad, Mohammad; Balakrishnan, Jaydeep

    2015-01-01

    This paper proposes an integrated Markovian and back propagation neural network approaches to compute reliability of a system. While states of failure occurrences are significant elements for accurate reliability computation, Markovian based reliability assessment method is designed. Due to drawbacks shown by Markovian model for steady state reliability computations and neural network for initial training pattern, integration being called Markov-neural is developed and evaluated. To show efficiency of the proposed approach comparative analyses are performed. Also, for managerial implication purpose an application case for multiple automated guided vehicles (AGVs) in manufacturing networks is conducted. - Highlights: • Integrated Markovian and back propagation neural network approach to compute reliability. • Markovian based reliability assessment method. • Managerial implication is shown in an application case for multiple automated guided vehicles (AGVs) in manufacturing networks

  7. 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.

  8. 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.

  9. 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

  10. Optimization and modeling of a photovoltaic solar integrated system by neural networks

    International Nuclear Information System (INIS)

    Ashhab, Moh'd Sami S.

    2008-01-01

    A photovoltaic solar integrated system is modeled with artificial neural networks (ANN's). Data relevant to the system performance was collected on April, 4th 1993 and every 15 min during the day. This input-output data is used to train the ANN. The ANN approximates the data well and therefore can be relied on in predicting the system performance, namely, system efficiencies. The solar system consists of a solar trainer which contains a photovoltaic panel, a DC centrifugal pump, flat plate collectors, storage tank, a flowmeter for measuring the water mass flow rate, pipes, pyranometer for measuring the solar intensity, thermocouples for measuring various system temperatures and wind speed meter. The complex method constrained optimization is applied to the solar system ANN model to find the operating conditions of the system that will produce the maximum system efficiencies. This information will be very hard to obtain by just looking at the available historical input-output data

  11. Optimization and modeling of a photovoltaic solar integrated system by neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Ashhab, Moh' d Sami S. [Department of Mechanical Engineering, The Hashemite University, Zarqa 13115 (Jordan)

    2008-11-15

    A photovoltaic solar integrated system is modeled with artificial neural networks (ANN's). Data relevant to the system performance was collected on April, 4th 1993 and every 15 min during the day. This input-output data is used to train the ANN. The ANN approximates the data well and therefore can be relied on in predicting the system performance, namely, system efficiencies. The solar system consists of a solar trainer which contains a photovoltaic panel, a DC centrifugal pump, flat plate collectors, storage tank, a flowmeter for measuring the water mass flow rate, pipes, pyranometer for measuring the solar intensity, thermocouples for measuring various system temperatures and wind speed meter. The complex method constrained optimization is applied to the solar system ANN model to find the operating conditions of the system that will produce the maximum system efficiencies. This information will be very hard to obtain by just looking at the available historical input-output data. (author)

  12. An intelligent sales forecasting system through integration of artificial neural networks and fuzzy neural networks with fuzzy weight elimination.

    Science.gov (United States)

    Kuo, R J; Wu, P; Wang, C P

    2002-09-01

    Sales forecasting plays a very prominent role in business strategy. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average (ARMA). However, sales forecasting is very complicated owing to influence by internal and external environments. Recently, artificial neural networks (ANNs) have also been applied in sales forecasting since their promising performances in the areas of control and pattern recognition. However, further improvement is still necessary since unique circumstances, e.g. promotion, cause a sudden change in the sales pattern. Thus, this study utilizes a proposed fuzzy neural network (FNN), which is able to eliminate the unimportant weights, for the sake of learning fuzzy IF-THEN rules obtained from the marketing experts with respect to promotion. The result from FNN is further integrated with the time series data through an ANN. Both the simulated and real-world problem results show that FNN with weight elimination can have lower training error compared with the regular FNN. Besides, real-world problem results also indicate that the proposed estimation system outperforms the conventional statistical method and single ANN in accuracy.

  13. Integrated control of the cooling system and surface openings using the artificial neural networks

    International Nuclear Information System (INIS)

    Moon, Jin Woo

    2015-01-01

    This study aimed at suggesting an indoor temperature control method that can provide a comfortable thermal environment through the integrated control of the cooling system and the surface openings. Four control logic were developed, employing different application levels of rules and artificial neural network models. Rule-based control methods represented the conventional approach while ANN-based methods were applied for the predictive and adaptive controls. Comparative performance tests for the conventional- and ANN-based methods were numerically conducted for the double-skin-facade building, using the MATLAB (Matrix Laboratory) and TRNSYS (Transient Systems Simulation) software, after proving the validity by comparing the simulation and field measurement results. Analysis revealed that the ANN-based controls of the cooling system and surface openings improved the indoor temperature conditions with increased comfortable temperature periods and decreased standard deviation of the indoor temperature from the center of the comfortable range. In addition, the proposed ANN-based logic effectively reduced the number of operating condition changes of the cooling system and surface openings, which can prevent system failure. The ANN-based logic, however, did not show superiority in energy efficiency over the conventional logic. Instead, they have increased the amount of heat removal by the cooling system. From the analysis, it can be concluded that the ANN-based temperature control logic was able to keep the indoor temperature more comfortably and stably within the comfortable range due to its predictive and adaptive features. - Highlights: • Integrated rule-based and artificial neural network based logics were developed. • A cooling device and surface openings were controlled in an integrated manner. • Computer simulation method was employed for comparative performance tests. • ANN-based logics showed the advanced features of thermal environment. • Rule

  14. FPGA IMPLEMENTATION OF ADAPTIVE INTEGRATED SPIKING NEURAL NETWORK FOR EFFICIENT IMAGE RECOGNITION SYSTEM

    Directory of Open Access Journals (Sweden)

    T. Pasupathi

    2014-05-01

    Full Text Available Image recognition is a technology which can be used in various applications such as medical image recognition systems, security, defense video tracking, and factory automation. In this paper we present a novel pipelined architecture of an adaptive integrated Artificial Neural Network for image recognition. In our proposed work we have combined the feature of spiking neuron concept with ANN to achieve the efficient architecture for image recognition. The set of training images are trained by ANN and target output has been identified. Real time videos are captured and then converted into frames for testing purpose and the image were recognized. The machine can operate at up to 40 frames/sec using images acquired from the camera. The system has been implemented on XC3S400 SPARTAN-3 Field Programmable Gate Arrays.

  15. A Neural Systems-Based Neurobiology and Neuropsychiatry Course: Integrating Biology, Psychodynamics, and Psychology in the Psychiatric Curriculum

    Science.gov (United States)

    Lacy, Timothy; Hughes, John D.

    2006-01-01

    Objective: Psychotherapy and biological psychiatry remain divided in psychiatry residency curricula. Behavioral neurobiology and neuropsychiatry provide a systems-level framework that allows teachers to integrate biology, psychodynamics, and psychology. Method: The authors detail the underlying assumptions and outline of a neural systems-based…

  16. A Fully Integrated Wireless Compressed Sensing Neural Signal Acquisition System for Chronic Recording and Brain Machine Interface.

    Science.gov (United States)

    Liu, Xilin; Zhang, Milin; Xiong, Tao; Richardson, Andrew G; Lucas, Timothy H; Chin, Peter S; Etienne-Cummings, Ralph; Tran, Trac D; Van der Spiegel, Jan

    2016-07-18

    Reliable, multi-channel neural recording is critical to the neuroscience research and clinical treatment. However, most hardware development of fully integrated, multi-channel wireless neural recorders to-date, is still in the proof-of-concept stage. To be ready for practical use, the trade-offs between performance, power consumption, device size, robustness, and compatibility need to be carefully taken into account. This paper presents an optimized wireless compressed sensing neural signal recording system. The system takes advantages of both custom integrated circuits and universal compatible wireless solutions. The proposed system includes an implantable wireless system-on-chip (SoC) and an external wireless relay. The SoC integrates 16-channel low-noise neural amplifiers, programmable filters and gain stages, a SAR ADC, a real-time compressed sensing module, and a near field wireless power and data transmission link. The external relay integrates a 32 bit low-power microcontroller with Bluetooth 4.0 wireless module, a programming interface, and an inductive charging unit. The SoC achieves high signal recording quality with minimized power consumption, while reducing the risk of infection from through-skin connectors. The external relay maximizes the compatibility and programmability. The proposed compressed sensing module is highly configurable, featuring a SNDR of 9.78 dB with a compression ratio of 8×. The SoC has been fabricated in a 180 nm standard CMOS technology, occupying 2.1 mm × 0.6 mm silicon area. A pre-implantable system has been assembled to demonstrate the proposed paradigm. The developed system has been successfully used for long-term wireless neural recording in freely behaving rhesus monkey.

  17. Hybrid information privacy system: integration of chaotic neural network and RSA coding

    Science.gov (United States)

    Hsu, Ming-Kai; Willey, Jeff; Lee, Ting N.; Szu, Harold H.

    2005-03-01

    Electronic mails are adopted worldwide; most are easily hacked by hackers. In this paper, we purposed a free, fast and convenient hybrid privacy system to protect email communication. The privacy system is implemented by combining private security RSA algorithm with specific chaos neural network encryption process. The receiver can decrypt received email as long as it can reproduce the specified chaos neural network series, so called spatial-temporal keys. The chaotic typing and initial seed value of chaos neural network series, encrypted by the RSA algorithm, can reproduce spatial-temporal keys. The encrypted chaotic typing and initial seed value are hidden in watermark mixed nonlinearly with message media, wrapped with convolution error correction codes for wireless 3rd generation cellular phones. The message media can be an arbitrary image. The pattern noise has to be considered during transmission and it could affect/change the spatial-temporal keys. Since any change/modification on chaotic typing or initial seed value of chaos neural network series is not acceptable, the RSA codec system must be robust and fault-tolerant via wireless channel. The robust and fault-tolerant properties of chaos neural networks (CNN) were proved by a field theory of Associative Memory by Szu in 1997. The 1-D chaos generating nodes from the logistic map having arbitrarily negative slope a = p/q generating the N-shaped sigmoid was given first by Szu in 1992. In this paper, we simulated the robust and fault-tolerance properties of CNN under additive noise and pattern noise. We also implement a private version of RSA coding and chaos encryption process on messages.

  18. 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...

  19. 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.

  20. Implantable neurotechnologies: a review of integrated circuit neural amplifiers.

    Science.gov (United States)

    Ng, Kian Ann; Greenwald, Elliot; Xu, Yong Ping; Thakor, Nitish V

    2016-01-01

    Neural signal recording is critical in modern day neuroscience research and emerging neural prosthesis programs. Neural recording requires the use of precise, low-noise amplifier systems to acquire and condition the weak neural signals that are transduced through electrode interfaces. Neural amplifiers and amplifier-based systems are available commercially or can be designed in-house and fabricated using integrated circuit (IC) technologies, resulting in very large-scale integration or application-specific integrated circuit solutions. IC-based neural amplifiers are now used to acquire untethered/portable neural recordings, as they meet the requirements of a miniaturized form factor, light weight and low power consumption. Furthermore, such miniaturized and low-power IC neural amplifiers are now being used in emerging implantable neural prosthesis technologies. This review focuses on neural amplifier-based devices and is presented in two interrelated parts. First, neural signal recording is reviewed, and practical challenges are highlighted. Current amplifier designs with increased functionality and performance and without penalties in chip size and power are featured. Second, applications of IC-based neural amplifiers in basic science experiments (e.g., cortical studies using animal models), neural prostheses (e.g., brain/nerve machine interfaces) and treatment of neuronal diseases (e.g., DBS for treatment of epilepsy) are highlighted. The review concludes with future outlooks of this technology and important challenges with regard to neural signal amplification.

  1. Integrating neural network technology and noise analysis

    International Nuclear Information System (INIS)

    Uhrig, R.E.; Oak Ridge National Lab., TN

    1995-01-01

    The integrated use of neural network and noise analysis technologies offers advantages not available by the use of either technology alone. The application of neural network technology to noise analysis offers an opportunity to expand the scope of problems where noise analysis is useful and unique ways in which the integration of these technologies can be used productively. The two-sensor technique, in which the responses of two sensors to an unknown driving source are related, is used to demonstration such integration. The relationship between power spectral densities (PSDs) of accelerometer signals is derived theoretically using noise analysis to demonstrate its uniqueness. This relationship is modeled from experimental data using a neural network when the system is working properly, and the actual PSD of one sensor is compared with the PSD of that sensor predicted by the neural network using the PSD of the other sensor as an input. A significant deviation between the actual and predicted PSDs indicate that system is changing (i.e., failing). Experiments carried out on check values and bearings illustrate the usefulness of the methodology developed. (Author)

  2. Neural Network Training by Integration of Adjoint Systems of Equations Forward in Time

    Science.gov (United States)

    Toomarian, Nikzad (Inventor); Barhen, Jacob (Inventor)

    1999-01-01

    A method and apparatus for supervised neural learning of time dependent trajectories exploits the concepts of adjoint operators to enable computation of the gradient of an objective functional with respect to the various parameters of the network architecture in a highly efficient manner. Specifically. it combines the advantage of dramatic reductions in computational complexity inherent in adjoint methods with the ability to solve two adjoint systems of equations together forward in time. Not only is a large amount of computation and storage saved. but the handling of real-time applications becomes also possible. The invention has been applied it to two examples of representative complexity which have recently been analyzed in the open literature and demonstrated that a circular trajectory can be learned in approximately 200 iterations compared to the 12000 reported in the literature. A figure eight trajectory was achieved in under 500 iterations compared to 20000 previously required. Tbc trajectories computed using our new method are much closer to the target trajectories than was reported in previous studies.

  3. Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence.

    Directory of Open Access Journals (Sweden)

    Yongcheng Li

    Full Text Available Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including 'random' and '4Q' (cultured neurons artificially divided into four interconnected parts neural network. Compared to the random cultures, the '4Q' cultures presented absolutely different activities, and the robot controlled by the '4Q' network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems.

  4. Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence.

    Science.gov (United States)

    Li, Yongcheng; Sun, Rong; Zhang, Bin; Wang, Yuechao; Li, Hongyi

    2015-01-01

    Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including 'random' and '4Q' (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the '4Q' cultures presented absolutely different activities, and the robot controlled by the '4Q' network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems.

  5. Differential neural network configuration during human path integration

    Science.gov (United States)

    Arnold, Aiden E. G. F; Burles, Ford; Bray, Signe; Levy, Richard M.; Iaria, Giuseppe

    2014-01-01

    Path integration is a fundamental skill for navigation in both humans and animals. Despite recent advances in unraveling the neural basis of path integration in animal models, relatively little is known about how path integration operates at a neural level in humans. Previous attempts to characterize the neural mechanisms used by humans to visually path integrate have suggested a central role of the hippocampus in allowing accurate performance, broadly resembling results from animal data. However, in recent years both the central role of the hippocampus and the perspective that animals and humans share similar neural mechanisms for path integration has come into question. The present study uses a data driven analysis to investigate the neural systems engaged during visual path integration in humans, allowing for an unbiased estimate of neural activity across the entire brain. Our results suggest that humans employ common task control, attention and spatial working memory systems across a frontoparietal network during path integration. However, individuals differed in how these systems are configured into functional networks. High performing individuals were found to more broadly express spatial working memory systems in prefrontal cortex, while low performing individuals engaged an allocentric memory system based primarily in the medial occipito-temporal region. These findings suggest that visual path integration in humans over short distances can operate through a spatial working memory system engaging primarily the prefrontal cortex and that the differential configuration of memory systems recruited by task control networks may help explain individual biases in spatial learning strategies. PMID:24808849

  6. Sustained NMDA receptor hypofunction induces compromised neural systems integration and schizophrenia-like alterations in functional brain networks.

    Science.gov (United States)

    Dawson, Neil; Xiao, Xiaolin; McDonald, Martin; Higham, Desmond J; Morris, Brian J; Pratt, Judith A

    2014-02-01

    Compromised functional integration between cerebral subsystems and dysfunctional brain network organization may underlie the neurocognitive deficits seen in psychiatric disorders. Applying topological measures from network science to brain imaging data allows the quantification of complex brain network connectivity. While this approach has recently been used to further elucidate the nature of brain dysfunction in schizophrenia, the value of applying this approach in preclinical models of psychiatric disease has not been recognized. For the first time, we apply both established and recently derived algorithms from network science (graph theory) to functional brain imaging data from rats treated subchronically with the N-methyl-D-aspartic acid (NMDA) receptor antagonist phencyclidine (PCP). We show that subchronic PCP treatment induces alterations in the global properties of functional brain networks akin to those reported in schizophrenia. Furthermore, we show that subchronic PCP treatment induces compromised functional integration between distributed neural systems, including between the prefrontal cortex and hippocampus, that have established roles in cognition through, in part, the promotion of thalamic dysconnectivity. We also show that subchronic PCP treatment promotes the functional disintegration of discrete cerebral subsystems and also alters the connectivity of neurotransmitter systems strongly implicated in schizophrenia. Therefore, we propose that sustained NMDA receptor hypofunction contributes to the pathophysiology of dysfunctional brain network organization in schizophrenia.

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

    Directory of Open Access Journals (Sweden)

    Yongcheng Li

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

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

    Science.gov (United States)

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

    2016-01-01

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

  9. Integration of Adaptive Neuro-Fuzzy Inference System, Neural Networks and Geostatistical Methods for Fracture Density Modeling

    Directory of Open Access Journals (Sweden)

    Ja’fari A.

    2014-01-01

    Full Text Available Image logs provide useful information for fracture study in naturally fractured reservoir. Fracture dip, azimuth, aperture and fracture density can be obtained from image logs and have great importance in naturally fractured reservoir characterization. Imaging all fractured parts of hydrocarbon reservoirs and interpreting the results is expensive and time consuming. In this study, an improved method to make a quantitative correlation between fracture densities obtained from image logs and conventional well log data by integration of different artificial intelligence systems was proposed. The proposed method combines the results of Adaptive Neuro-Fuzzy Inference System (ANFIS and Neural Networks (NN algorithms for overall estimation of fracture density from conventional well log data. A simple averaging method was used to obtain a better result by combining results of ANFIS and NN. The algorithm applied on other wells of the field to obtain fracture density. In order to model the fracture density in the reservoir, we used variography and sequential simulation algorithms like Sequential Indicator Simulation (SIS and Truncated Gaussian Simulation (TGS. The overall algorithm applied to Asmari reservoir one of the SW Iranian oil fields. Histogram analysis applied to control the quality of the obtained models. Results of this study show that for higher number of fracture facies the TGS algorithm works better than SIS but in small number of fracture facies both algorithms provide approximately same results.

  10. Application of Hierarchical Dissociated Neural Network in Closed-Loop Hybrid System Integrating Biological and Mechanical Intelligence

    Science.gov (United States)

    Zhang, Bin; Wang, Yuechao; Li, Hongyi

    2015-01-01

    Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including ‘random’ and ‘4Q’ (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the ‘4Q’ cultures presented absolutely different activities, and the robot controlled by the ‘4Q’ network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems. PMID:25992579

  11. Integration and long distance axonal regeneration in the central nervous system from transplanted primitive neural stem cells.

    Science.gov (United States)

    Zhao, Jiagang; Sun, Woong; Cho, Hyo Min; Ouyang, Hong; Li, Wenlin; Lin, Ying; Do, Jiun; Zhang, Liangfang; Ding, Sheng; Liu, Yizhi; Lu, Paul; Zhang, Kang

    2013-01-04

    Spinal cord injury (SCI) results in devastating motor and sensory deficits secondary to disrupted neuronal circuits and poor regenerative potential. Efforts to promote regeneration through cell extrinsic and intrinsic manipulations have met with limited success. Stem cells represent an as yet unrealized therapy in SCI. Recently, we identified novel culture methods to induce and maintain primitive neural stem cells (pNSCs) from human embryonic stem cells. We tested whether transplanted human pNSCs can integrate into the CNS of the developing chick neural tube and injured adult rat spinal cord. Following injection of pNSCs into the developing chick CNS, pNSCs integrated into the dorsal aspects of the neural tube, forming cell clusters that spontaneously differentiated into neurons. Furthermore, following transplantation of pNSCs into the lesioned rat spinal cord, grafted pNSCs survived, differentiated into neurons, and extended long distance axons through the scar tissue at the graft-host interface and into the host spinal cord to form terminal-like structures near host spinal neurons. Together, these findings suggest that pNSCs derived from human embryonic stem cells differentiate into neuronal cell types with the potential to extend axons that associate with circuits of the CNS and, more importantly, provide new insights into CNS integration and axonal regeneration, offering hope for repair in SCI.

  12. Assessment of giant panda habitat based on integration of expert system and neural network

    NARCIS (Netherlands)

    Liu, X.; Skidmore, A.K.; Bronsveld, M.C.

    2006-01-01

    To conserve giant panda effectively, it is important to understand the spatial pattern and temporal change of its habitat. Mapping is an effective approach for wildlife habitat evaluation and monitoring. The application of recently developed artificial intelligence tools, including expert systems

  13. Neural nets for the plausibility check of measured values in the integrated measurement and information system for the surveillance of environmental radioactivity (IMIS)

    International Nuclear Information System (INIS)

    Haase, G.

    2003-01-01

    Neural nets to the plausibility check of measured values in the ''integrated measurement and information system for the surveillance of environmental radioactivity, IMIS'' is a research project supported by the Federal Minister for the Environment, Nature Conservation and Nuclear Safety. A goal of this project was the automatic recognition of implausible measured values in the data base ORACLE, which measured values from surveillance of environmental radioactivity of most diverse environmental media contained. The conversion of this project [ 1 ] was realized by institut of logic, complexity and deduction systems of the university Karlsruhe under the direction of Professor Dr. Menzel, Dr. Martin Riedmueller and Martin Lauer. (orig.)

  14. 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

  15. Systems integration.

    Science.gov (United States)

    Siemieniuch, C E; Sinclair, M A

    2006-01-01

    The paper presents a view of systems integration, from an ergonomics/human factors perspective, emphasising the process of systems integration as is carried out by humans. The first section discusses some of the fundamental issues in systems integration, such as the significance of systems boundaries, systems lifecycle and systems entropy, issues arising from complexity, the implications of systems immortality, and so on. The next section outlines various generic processes for executing systems integration, to act as guides for practitioners. These address both the design of the system to be integrated and the preparation of the wider system in which the integration will occur. Then the next section outlines some of the human-specific issues that would need to be addressed in such processes; for example, indeterminacy and incompleteness, the prediction of human reliability, workload issues, extended situation awareness, and knowledge lifecycle management. For all of these, suggestions and further readings are proposed. Finally, the conclusions section reiterates in condensed form the major issues arising from the above.

  16. 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

  17. 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...

  18. An analog integrated front-end amplifier for neural applications

    OpenAIRE

    Zhou, Zhijun; Warr, Paul

    2017-01-01

    The front-end amplifier forms the critical element for signal detection and pre-processing within neural monitoring systems. It determines not only the fidelity of the biosignal, but also impacts power consumption and detector size. In this paper, a combined feedback loop-controlled approach is proposed to neutralize for the input leakage currents generated by low noise amplifiers when in integrated circuit form, alongside signal leakage into the input bias network. Significantly, this loop t...

  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. Systems integration (automation system). System integration (automation system)

    Energy Technology Data Exchange (ETDEWEB)

    Fujii, K; Komori, T; Fukuma, Y; Oikawa, M [Nippon Steal Corp., Tokyo (Japan)

    1991-09-26

    This paper introduces business activities on an automation systems integration (SI) started by a company in July,1988, and describes the SI concepts. The business activities include, with the CIM (unified production carried out on computers) and AMENITY (living environment) as the mainstays, a single responsibility construction ranging from consultation on structuring optimal systems for processing and assembling industries and intelligent buildings to system design, installation and after-sales services. With an SI standing on users {prime} position taken most importantly, the business starts from a planning and consultation under close coordination. On the conceptual basis of structuring optimal systems using the ompany {prime}s affluent know-hows and tools and adapting and applying with multi-vendors, open networks, centralized and distributed systems, the business is promoted with the accumulated technologies capable of realizing artificial intelligence and neural networks in its background, and supported with highly valuable business results in the past. 10 figs., 1 tab.

  2. Adaptive Moving Object Tracking Integrating Neural Networks And Intelligent Processing

    Science.gov (United States)

    Lee, James S. J.; Nguyen, Dziem D.; Lin, C.

    1989-03-01

    A real-time adaptive scheme is introduced to detect and track moving objects under noisy, dynamic conditions including moving sensors. This approach integrates the adaptiveness and incremental learning characteristics of neural networks with intelligent reasoning and process control. Spatiotemporal filtering is used to detect and analyze motion, exploiting the speed and accuracy of multiresolution processing. A neural network algorithm constitutes the basic computational structure for classification. A recognition and learning controller guides the on-line training of the network, and invokes pattern recognition to determine processing parameters dynamically and to verify detection results. A tracking controller acts as the central control unit, so that tracking goals direct the over-all system. Performance is benchmarked against the Widrow-Hoff algorithm, for target detection scenarios presented in diverse FLIR image sequences. Efficient algorithm design ensures that this recognition and control scheme, implemented in software and commercially available image processing hardware, meets the real-time requirements of tracking applications.

  3. FUZZY NEURAL NETWORK FOR OBJECT IDENTIFICATION ON INTEGRATED CIRCUIT LAYOUTS

    Directory of Open Access Journals (Sweden)

    A. A. Doudkin

    2015-01-01

    Full Text Available Fuzzy neural network model based on neocognitron is proposed to identify layout objects on images of topological layers of integrated circuits. Testing of the model on images of real chip layouts was showed a highеr degree of identification of the proposed neural network in comparison to base neocognitron.

  4. Commentary. Integrative Modeling and the Role of Neural Constraints

    Czech Academy of Sciences Publication Activity Database

    Bantegnie, Brice

    2017-01-01

    Roč. 8, SEP 5 (2017), s. 1-2, č. článku 1531. ISSN 1664-1078 Institutional support: RVO:67985955 Keywords : mechanistic explanation * functional analysis * mechanistic integration * reverse inference * neural plasticity * neural networks Subject RIV: AA - Philosophy ; Religion Impact factor: 2.323, year: 2016

  5. 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.

  6. 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.

  7. Neural Networks Integrated Circuit for Biomimetics MEMS Microrobot

    Directory of Open Access Journals (Sweden)

    Ken Saito

    2014-06-01

    Full Text Available In this paper, we will propose the neural networks integrated circuit (NNIC which is the driving waveform generator of the 4.0, 2.7, 2.5 mm, width, length, height in size biomimetics microelectromechanical systems (MEMS microrobot. The microrobot was made from silicon wafer fabricated by micro fabrication technology. The mechanical system of the robot was equipped with small size rotary type actuators, link mechanisms and six legs to realize the ant-like switching behavior. The NNIC generates the driving waveform using synchronization phenomena such as biological neural networks. The driving waveform can operate the actuators of the MEMS microrobot directly. Therefore, the NNIC bare chip realizes the robot control without using any software programs or A/D converters. The microrobot performed forward and backward locomotion, and also changes direction by inputting an external single trigger pulse. The locomotion speed of the microrobot was 26.4 mm/min when the step width was 0.88 mm. The power consumption of the system was 250 mWh when the room temperature was 298 K.

  8. 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…

  9. 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.

  10. Integration of active devices on smart polymers for neural interfaces

    Science.gov (United States)

    Avendano-Bolivar, Adrian Emmanuel

    The increasing ability to ever more precisely identify and measure neural interactions and other phenomena in the central and peripheral nervous systems is revolutionizing our understanding of the human body and brain. To facilitate further understanding, more sophisticated neural devices, perhaps using microelectronics processing, must be fabricated. Materials often used in these neural interfaces, while compatible with these fabrication processes, are not optimized for long-term use in the body and are often orders of magnitude stiffer than the tissue with which they interact. Using the smart polymer substrates described in this work, suitability for processing as well as chronic implantation is demonstrated. We explore how to integrate reliable circuitry onto these flexible, biocompatible substrates that can withstand the aggressive environment of the body. To increase the capabilities of these devices beyond individual channel sensing and stimulation, active electronics must also be included onto our systems. In order to add this functionality to these substrates and explore the limits of these devices, we developed a process to fabricate single organic thin film transistors with mobilities up to 0.4 cm2/Vs and threshold voltages close to 0V. A process for fabricating organic light emitting diodes on flexible substrates is also addressed. We have set a foundation and demonstrated initial feasibility for integrating multiple transistors onto thin-film flexible devices to create new applications, such as matrix addressable functionalized electrodes and organic light emitting diodes. A brief description on how to integrate waveguides for their use in optogenetics is addressed. We have built understanding about device constraints on mechanical, electrical and in vivo reliability and how various conditions affect the electronics' lifetime. We use a bi-layer gate dielectric using an inorganic material such as HfO 2 combined with organic Parylene-c. A study of

  11. Flexible neural interfaces with integrated stiffening shank

    Energy Technology Data Exchange (ETDEWEB)

    Tooker, Angela C.; Felix, Sarah H.; Pannu, Satinderpall S.; Shah, Kedar G.; Sheth, Heeral; Tolosa, Vanessa

    2017-10-17

    A neural interface includes a first dielectric material having at least one first opening for a first electrical conducting material, a first electrical conducting material in the first opening, and at least one first interconnection trace electrical conducting material connected to the first electrical conducting material. A stiffening shank material is located adjacent the first dielectric material, the first electrical conducting material, and the first interconnection trace electrical conducting material.

  12. Flexible neural interfaces with integrated stiffening shank

    Science.gov (United States)

    Tooker, Angela C.; Felix, Sarah H.; Pannu, Satinderpall S.; Shah, Kedar G.; Sheth, Heeral; Tolosa, Vanessa

    2016-07-26

    A neural interface includes a first dielectric material having at least one first opening for a first electrical conducting material, a first electrical conducting material in the first opening, and at least one first interconnection trace electrical conducting material connected to the first electrical conducting material. A stiffening shank material is located adjacent the first dielectric material, the first electrical conducting material, and the first interconnection trace electrical conducting material.

  13. 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.

  14. Optimal multiple-information integration inherent in a ring neural network

    International Nuclear Information System (INIS)

    Takiyama, Ken

    2017-01-01

    Although several behavioral experiments have suggested that our neural system integrates multiple sources of information based on the certainty of each type of information in the manner of maximum-likelihood estimation, it is unclear how the maximum-likelihood estimation is implemented in our neural system. Here, I investigate the relationship between maximum-likelihood estimation and a widely used ring-type neural network model that is used as a model of visual, motor, or prefrontal cortices. Without any approximation or ansatz, I analytically demonstrate that the equilibrium of an order parameter in the neural network model exactly corresponds to the maximum-likelihood estimation when the strength of the symmetrical recurrent synaptic connectivity within a neural population is appropriately stronger than that of asymmetrical connectivity, that of local and external inputs, and that of symmetrical or asymmetrical connectivity between different neural populations. In this case, strengths of local and external inputs or those of symmetrical connectivity between different neural populations exactly correspond to the input certainty in maximum-likelihood estimation. Thus, my analysis suggests appropriately strong symmetrical recurrent connectivity as a possible candidate for implementing the maximum-likelihood estimation within our neural system. (paper)

  15. 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)

  16. Molecular annotation of integrative feeding neural circuits.

    Science.gov (United States)

    Pérez, Cristian A; Stanley, Sarah A; Wysocki, Robert W; Havranova, Jana; Ahrens-Nicklas, Rebecca; Onyimba, Frances; Friedman, Jeffrey M

    2011-02-02

    The identity of higher-order neurons and circuits playing an associative role to control feeding is unknown. We injected pseudorabies virus, a retrograde tracer, into masseter muscle, salivary gland, and tongue of BAC-transgenic mice expressing GFP in specific neural populations and identified several CNS regions that project multisynaptically to the periphery. MCH and orexin neurons were identified in the lateral hypothalamus, and Nurr1 and Cnr1 in the amygdala and insular/rhinal cortices. Cholera toxin β tracing showed that insular Nurr1(+) and Cnr1(+) neurons project to the amygdala or lateral hypothalamus, respectively. Finally, we show that cortical Cnr1(+) neurons show increased Cnr1 mRNA and c-Fos expression after fasting, consistent with a possible role for Cnr1(+) neurons in feeding. Overall, these studies define a general approach for identifying specific molecular markers for neurons in complex neural circuits. These markers now provide a means for functional studies of specific neuronal populations in feeding or other complex behaviors. Copyright © 2011 Elsevier Inc. All rights reserved.

  17. Ultra low-power integrated circuit design for wireless neural interfaces

    CERN Document Server

    Holleman, Jeremy; Otis, Brian

    2014-01-01

    Presenting results from real prototype systems, this volume provides an overview of ultra low-power integrated circuits and systems for neural signal processing and wireless communication. Topics include analog, radio, and signal processing theory and design for ultra low-power circuits.

  18. Tactical Systems Integration Laboratory

    Data.gov (United States)

    Federal Laboratory Consortium — The Tactical Systems Integration Laboratory is used to design and integrate computer hardware and software and related electronic subsystems for tactical vehicles....

  19. Power Systems Integration Laboratory | Energy Systems Integration Facility

    Science.gov (United States)

    | NREL Power Systems Integration Laboratory Power Systems Integration Laboratory Research in the Energy System Integration Facility's Power Systems Integration Laboratory focuses on the microgrid applications. Photo of engineers testing an inverter in the Power Systems Integration Laboratory

  20. A model of interval timing by neural integration.

    Science.gov (United States)

    Simen, Patrick; Balci, Fuat; de Souza, Laura; Cohen, Jonathan D; Holmes, Philip

    2011-06-22

    We show that simple assumptions about neural processing lead to a model of interval timing as a temporal integration process, in which a noisy firing-rate representation of time rises linearly on average toward a response threshold over the course of an interval. Our assumptions include: that neural spike trains are approximately independent Poisson processes, that correlations among them can be largely cancelled by balancing excitation and inhibition, that neural populations can act as integrators, and that the objective of timed behavior is maximal accuracy and minimal variance. The model accounts for a variety of physiological and behavioral findings in rodents, monkeys, and humans, including ramping firing rates between the onset of reward-predicting cues and the receipt of delayed rewards, and universally scale-invariant response time distributions in interval timing tasks. It furthermore makes specific, well-supported predictions about the skewness of these distributions, a feature of timing data that is usually ignored. The model also incorporates a rapid (potentially one-shot) duration-learning procedure. Human behavioral data support the learning rule's predictions regarding learning speed in sequences of timed responses. These results suggest that simple, integration-based models should play as prominent a role in interval timing theory as they do in theories of perceptual decision making, and that a common neural mechanism may underlie both types of behavior.

  1. 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...

  2. 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.

  3. An integrative neural model of social perception, action observation, and theory of mind

    Science.gov (United States)

    Yang, Daniel Y.-J.; Rosenblau, Gabriela; Keifer, Cara; Pelphrey, Kevin A.

    2016-01-01

    In the field of social neuroscience, major branches of research have been instrumental in describing independent components of typical and aberrant social information processing, but the field as a whole lacks a comprehensive model that integrates different branches. We review existing research related to the neural basis of three key neural systems underlying social information processing: social perception, action observation, and theory of mind. We propose an integrative model that unites these three processes and highlights the posterior superior temporal sulcus (pSTS), which plays a central role in all three systems. Furthermore, we integrate these neural systems with the dual system account of implicit and explicit social information processing. Large-scale meta-analyses based on Neurosynth confirmed that the pSTS is at the intersection of the three neural systems. Resting-state functional connectivity analysis with 1000 subjects confirmed that the pSTS is connected to all other regions in these systems. The findings presented in this review are specifically relevant for psychiatric research especially disorders characterized by social deficits such as autism spectrum disorder. PMID:25660957

  4. An integrative neural model of social perception, action observation, and theory of mind.

    Science.gov (United States)

    Yang, Daniel Y-J; Rosenblau, Gabriela; Keifer, Cara; Pelphrey, Kevin A

    2015-04-01

    In the field of social neuroscience, major branches of research have been instrumental in describing independent components of typical and aberrant social information processing, but the field as a whole lacks a comprehensive model that integrates different branches. We review existing research related to the neural basis of three key neural systems underlying social information processing: social perception, action observation, and theory of mind. We propose an integrative model that unites these three processes and highlights the posterior superior temporal sulcus (pSTS), which plays a central role in all three systems. Furthermore, we integrate these neural systems with the dual system account of implicit and explicit social information processing. Large-scale meta-analyses based on Neurosynth confirmed that the pSTS is at the intersection of the three neural systems. Resting-state functional connectivity analysis with 1000 subjects confirmed that the pSTS is connected to all other regions in these systems. The findings presented in this review are specifically relevant for psychiatric research especially disorders characterized by social deficits such as autism spectrum disorder. Copyright © 2015 Elsevier Ltd. All rights reserved.

  5. 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.

  6. Accelerated DNA Methylation Age: Associations with PTSD and Neural Integrity

    Science.gov (United States)

    Wolf, Erika J.; Logue, Mark W.; Hayes, Jasmeet P.; Sadeh, Naomi; Schichman, Steven A.; Stone, Annjanette; Salat, David H.; Milberg, William; McGlinchey, Regina; Miller, Mark W.

    2015-01-01

    Background Accumulating evidence suggests that post traumatic stress disorder (PTSD) may accelerate cellular aging and lead to premature morbidity and neurocognitive decline. Methods This study evaluated associations between PTSD and DNA methylation (DNAm) age using recently developed algorithms of cellular age by Horvath (2013) and Hannum et al. (2013). These estimates reflect accelerated aging when they exceed chronological age. We also examined if accelerated cellular age manifested in degraded neural integrity, indexed via diffusion tensor imaging. Results Among 281 male and female veterans of the conflicts in Iraq and Afghanistan, DNAm age was strongly related to chronological age (rs ~.88). Lifetime PTSD severity was associated with Hannum DNAm age estimates residualized for chronological age (β = .13, p= .032). Advanced DNAm age was associated with reduced integrity in the genu of the corpus callosum (β = −.17, p= .009) and indirectly linked to poorer working memory performance via this region (indirect β = − .05, p= .029). Horvath DNAm age estimates were not associated with PTSD or neural integrity. Conclusions Results provide novel support for PTSD-related accelerated aging in DNAm and extend the evidence base of known DNAm age correlates to the domains of neural integrity and cognition. PMID:26447678

  7. Integrated security system definition

    International Nuclear Information System (INIS)

    Campbell, G.K.; Hall, J.R. II

    1985-01-01

    The objectives of an integrated security system are to detect intruders and unauthorized activities with a high degree of reliability and the to deter and delay them until effective response/engagement can be accomplished. Definition of an effective integrated security system requires proper application of a system engineering methodology. This paper summarizes a methodology and describes its application to the problem of integrated security system definition. This process includes requirements identification and analysis, allocation of identified system requirements to the subsystem level and provides a basis for identification of synergistic subsystem elements and for synthesis into an integrated system. The paper discusses how this is accomplished, emphasizing at each step how system integration and subsystem synergism is considered. The paper concludes with the product of the process: implementation of an integrated security system

  8. 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.

  9. Neural Correlates of Impaired Reward-Effort Integration in Remitted Bulimia Nervosa.

    Science.gov (United States)

    Mueller, Stefanie Verena; Morishima, Yosuke; Schwab, Simon; Wiest, Roland; Federspiel, Andrea; Hasler, Gregor

    2018-03-01

    The integration of reward magnitudes and effort costs is required for an effective behavioral guidance. This reward-effort integration was reported to be dependent on dopaminergic neurotransmission. As bulimia nervosa has been associated with a dysregulated dopamine system and catecholamine depletion led to reward-processing deficits in remitted bulimia nervosa, the purpose of this study was to identify the role of catecholamine dysfunction and its relation to behavioral and neural reward-effort integration in bulimia nervosa. To investigate the interaction between catecholamine functioning and behavioral, and neural responses directly, 17 remitted bulimic (rBN) and 21 healthy individuals (HC) received alpha-methyl-paratyrosine (AMPT) over 24 h to achieve catecholamine depletion in a randomized, crossover study design. We used functional magnetic resonance imaging (fMRI) and the monetary incentive delay (MID) task to assess reward-effort integration in relation to catecholaminergic neurotransmission at the behavioral and neural level. AMPT reduced the ability to integrate rewards and efforts effectively in HC participants. In contrast, in rBN participants, the reduced reward-effort integration was associated with illness duration in the sham condition and unrelated to catecholamine depletion. Regarding neural activation, AMPT decreased the reward anticipation-related neural activation in the anteroventral striatum. This decrease was associated with the AMPT-induced reduction of monetary earning in HC in contrast to rBN participants. Our findings contributed to the theory of a desensitized dopaminergic system in bulimia nervosa. A disrupted processing of reward magnitudes and effort costs might increase the probability of maintenance of bulimic symptoms.

  10. Securing Digital Images Integrity using Artificial Neural Networks

    Science.gov (United States)

    Hajji, Tarik; Itahriouan, Zakaria; Ouazzani Jamil, Mohammed

    2018-05-01

    Digital image signature is a technique used to protect the image integrity. The application of this technique can serve several areas of imaging applied to smart cities. The objective of this work is to propose two methods to protect digital image integrity. We present a description of two approaches using artificial neural networks (ANN) to digitally sign an image. The first one is “Direct Signature without learning” and the second is “Direct Signature with learning”. This paper presents the theory of proposed approaches and an experimental study to test their effectiveness.

  11. Searching for integrable systems

    International Nuclear Information System (INIS)

    Cary, J.R.

    1984-01-01

    Lack of integrability leads to undesirable consequences in a number of physical systems. The lack of integrability of the magnetic field leads to enhanced particle transport in stellarators and tokamaks with tearing-mode turbulence. Limitations of the luminosity of colliding beams may be due to the onset of stochasticity. Enhanced radial transport in mirror machines caused by the lack of integrability and/or the presence of resonances may be a significant problem in future devices. To improve such systems one needs a systematic method for finding integrable systems. Of course, it is easy to find integrable systems if no restrictions are imposed; textbooks are full of such examples. The problem is to find integrable systems given a set of constraints. An example of this type of problem is that of finding integrable vacuum magnetic fields with rotational transform. The solution to this problem is relevant to the magnetic-confinement program

  12. 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.

  13. Integrating Neural Circuits Controlling Female Sexual Behavior.

    Science.gov (United States)

    Micevych, Paul E; Meisel, Robert L

    2017-01-01

    The hypothalamus is most often associated with innate behaviors such as is hunger, thirst and sex. While the expression of these behaviors important for survival of the individual or the species is nested within the hypothalamus, the desire (i.e., motivation) for them is centered within the mesolimbic reward circuitry. In this review, we will use female sexual behavior as a model to examine the interaction of these circuits. We will examine the evidence for a hypothalamic circuit that regulates consummatory aspects of reproductive behavior, i.e., lordosis behavior, a measure of sexual receptivity that involves estradiol membrane-initiated signaling in the arcuate nucleus (ARH), activating β-endorphin projections to the medial preoptic nucleus (MPN), which in turn modulate ventromedial hypothalamic nucleus (VMH) activity-the common output from the hypothalamus. Estradiol modulates not only a series of neuropeptides, transmitters and receptors but induces dendritic spines that are for estrogenic induction of lordosis behavior. Simultaneously, in the nucleus accumbens of the mesolimbic system, the mating experience produces long term changes in dopamine signaling and structure. Sexual experience sensitizes the response of nucleus accumbens neurons to dopamine signaling through the induction of a long lasting early immediate gene. While estrogen alone increases spines in the ARH, sexual experience increases dendritic spine density in the nucleus accumbens. These two circuits appear to converge onto the medial preoptic area where there is a reciprocal influence of motivational circuits on consummatory behavior and vice versa . While it has not been formally demonstrated in the human, such circuitry is generally highly conserved and thus, understanding the anatomy, neurochemistry and physiology can provide useful insight into the motivation for sexual behavior and other innate behaviors in humans.

  14. Integrating Neural Circuits Controlling Female Sexual Behavior

    Directory of Open Access Journals (Sweden)

    Paul E. Micevych

    2017-06-01

    Full Text Available The hypothalamus is most often associated with innate behaviors such as is hunger, thirst and sex. While the expression of these behaviors important for survival of the individual or the species is nested within the hypothalamus, the desire (i.e., motivation for them is centered within the mesolimbic reward circuitry. In this review, we will use female sexual behavior as a model to examine the interaction of these circuits. We will examine the evidence for a hypothalamic circuit that regulates consummatory aspects of reproductive behavior, i.e., lordosis behavior, a measure of sexual receptivity that involves estradiol membrane-initiated signaling in the arcuate nucleus (ARH, activating β-endorphin projections to the medial preoptic nucleus (MPN, which in turn modulate ventromedial hypothalamic nucleus (VMH activity—the common output from the hypothalamus. Estradiol modulates not only a series of neuropeptides, transmitters and receptors but induces dendritic spines that are for estrogenic induction of lordosis behavior. Simultaneously, in the nucleus accumbens of the mesolimbic system, the mating experience produces long term changes in dopamine signaling and structure. Sexual experience sensitizes the response of nucleus accumbens neurons to dopamine signaling through the induction of a long lasting early immediate gene. While estrogen alone increases spines in the ARH, sexual experience increases dendritic spine density in the nucleus accumbens. These two circuits appear to converge onto the medial preoptic area where there is a reciprocal influence of motivational circuits on consummatory behavior and vice versa. While it has not been formally demonstrated in the human, such circuitry is generally highly conserved and thus, understanding the anatomy, neurochemistry and physiology can provide useful insight into the motivation for sexual behavior and other innate behaviors in humans.

  15. Artificial Neural Network Based State Estimators Integrated into Kalmtool

    DEFF Research Database (Denmark)

    Bayramoglu, Enis; Ravn, Ole; Poulsen, Niels Kjølstad

    2012-01-01

    In this paper we present a toolbox enabling easy evaluation and comparison of dierent ltering algorithms. The toolbox is called Kalmtool and is a set of MATLAB tools for state estimation of nonlinear systems. The toolbox now contains functions for Articial Neural Network Based State Estimation as...

  16. Neural Network Control for the Probe Landing Based on Proportional Integral Observer

    Directory of Open Access Journals (Sweden)

    Yuanchun Li

    2015-01-01

    Full Text Available For the probe descending and landing safely, a neural network control method based on proportional integral observer (PIO is proposed. First, the dynamics equation of the probe under the landing site coordinate system is deduced and the nominal trajectory meeting the constraints in advance on three axes is preplanned. Then the PIO designed by using LMI technique is employed in the control law to compensate the effect of the disturbance. At last, the neural network control algorithm is used to guarantee the double zero control of the probe and ensure the probe can land safely. An illustrative design example is employed to demonstrate the effectiveness of the proposed control approach.

  17. Avionics systems integration technology

    Science.gov (United States)

    Stech, George; Williams, James R.

    1988-01-01

    A very dramatic and continuing explosion in digital electronics technology has been taking place in the last decade. The prudent and timely application of this technology will provide Army aviation the capability to prevail against a numerically superior enemy threat. The Army and NASA have exploited this technology explosion in the development and application of avionics systems integration technology for new and future aviation systems. A few selected Army avionics integration technology base efforts are discussed. Also discussed is the Avionics Integration Research Laboratory (AIRLAB) that NASA has established at Langley for research into the integration and validation of avionics systems, and evaluation of advanced technology in a total systems context.

  18. Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control

    Science.gov (United States)

    Hageman, Jacob J.; Smith, Mark S.; Stachowiak, Susan

    2003-01-01

    An indirect adaptive system has been constructed for robust control of an aircraft with uncertain aerodynamic characteristics. This system consists of a multilayer perceptron pre-trained neural network, online stability and control derivative identification, a dynamic cell structure online learning neural network, and a model following control system based on the stochastic optimal feedforward and feedback technique. The pre-trained neural network and model following control system have been flight-tested, but the online parameter identification and online learning neural network are new additions used for in-flight adaptation of the control system model. A description of the modification and integration of these two stand-alone software packages into the complete system in preparation for initial flight tests is presented. Open-loop results using both simulation and flight data, as well as closed-loop performance of the complete system in a nonlinear, six-degree-of-freedom, flight validated simulation, are analyzed. Results show that this online learning system, in contrast to the nonlearning system, has the ability to adapt to changes in aerodynamic characteristics in a real-time, closed-loop, piloted simulation, resulting in improved flying qualities.

  19. Wireless Neural Recording With Single Low-Power Integrated Circuit

    Science.gov (United States)

    Harrison, Reid R.; Kier, Ryan J.; Chestek, Cynthia A.; Gilja, Vikash; Nuyujukian, Paul; Ryu, Stephen; Greger, Bradley; Solzbacher, Florian; Shenoy, Krishna V.

    2010-01-01

    We present benchtop and in vivo experimental results from an integrated circuit designed for wireless implantable neural recording applications. The chip, which was fabricated in a commercially available 0.6-μm 2P3M BiCMOS process, contains 100 amplifiers, a 10-bit analog-to-digital converter (ADC), 100 threshold-based spike detectors, and a 902–928 MHz frequency-shift-keying (FSK) transmitter. Neural signals from a selected amplifier are sampled by the ADC at 15.7 kSps and telemetered over the FSK wireless data link. Power, clock, and command signals are sent to the chip wirelessly over a 2.765-MHz inductive (coil-to-coil) link. The chip is capable of operating with only two off-chip components: a power/command receiving coil and a 100-nF capacitor. PMID:19497825

  20. Wireless neural recording with single low-power integrated circuit.

    Science.gov (United States)

    Harrison, Reid R; Kier, Ryan J; Chestek, Cynthia A; Gilja, Vikash; Nuyujukian, Paul; Ryu, Stephen; Greger, Bradley; Solzbacher, Florian; Shenoy, Krishna V

    2009-08-01

    We present benchtop and in vivo experimental results from an integrated circuit designed for wireless implantable neural recording applications. The chip, which was fabricated in a commercially available 0.6- mum 2P3M BiCMOS process, contains 100 amplifiers, a 10-bit analog-to-digital converter (ADC), 100 threshold-based spike detectors, and a 902-928 MHz frequency-shift-keying (FSK) transmitter. Neural signals from a selected amplifier are sampled by the ADC at 15.7 kSps and telemetered over the FSK wireless data link. Power, clock, and command signals are sent to the chip wirelessly over a 2.765-MHz inductive (coil-to-coil) link. The chip is capable of operating with only two off-chip components: a power/command receiving coil and a 100-nF capacitor.

  1. 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.

  2. Integrated Reporting Information System -

    Data.gov (United States)

    Department of Transportation — The Integrated Reporting Information System (IRIS) is a flexible and scalable web-based system that supports post operational analysis and evaluation of the National...

  3. 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

  4. 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

  5. Functional Stem Cell Integration into Neural Networks Assessed by Organotypic Slice Cultures.

    Science.gov (United States)

    Forsberg, David; Thonabulsombat, Charoensri; Jäderstad, Johan; Jäderstad, Linda Maria; Olivius, Petri; Herlenius, Eric

    2017-08-14

    Re-formation or preservation of functional, electrically active neural networks has been proffered as one of the goals of stem cell-mediated neural therapeutics. A primary issue for a cell therapy approach is the formation of functional contacts between the implanted cells and the host tissue. Therefore, it is of fundamental interest to establish protocols that allow us to delineate a detailed time course of grafted stem cell survival, migration, differentiation, integration, and functional interaction with the host. One option for in vitro studies is to examine the integration of exogenous stem cells into an existing active neural network in ex vivo organotypic cultures. Organotypic cultures leave the structural integrity essentially intact while still allowing the microenvironment to be carefully controlled. This allows detailed studies over time of cellular responses and cell-cell interactions, which are not readily performed in vivo. This unit describes procedures for using organotypic slice cultures as ex vivo model systems for studying neural stem cell and embryonic stem cell engraftment and communication with CNS host tissue. © 2017 by John Wiley & Sons, Inc. Copyright © 2017 John Wiley & Sons, Inc.

  6. From behavior to neural dynamics: An integrated theory of attention

    Science.gov (United States)

    Buschman, Timothy J.; Kastner, Sabine

    2015-01-01

    The brain has a limited capacity and therefore needs mechanisms to selectively enhance the information most relevant to one’s current behavior. We refer to these mechanisms as ‘attention’. Attention acts by increasing the strength of selected neural representations and preferentially routing them through the brain’s large-scale network. This is a critical component of cognition and therefore has been a central topic in cognitive neuroscience. Here we review a diverse literature that has studied attention at the level of behavior, networks, circuits and neurons. We then integrate these disparate results into a unified theory of attention. PMID:26447577

  7. An integrated artificial neural networks approach for predicting global radiation

    International Nuclear Information System (INIS)

    Azadeh, A.; Maghsoudi, A.; Sohrabkhani, S.

    2009-01-01

    This article presents an integrated artificial neural network (ANN) approach for predicting solar global radiation by climatological variables. The integrated ANN trains and tests data with multi layer perceptron (MLP) approach which has the lowest mean absolute percentage error (MAPE). The proposed approach is particularly useful for locations where no available measurement equipment. Also, it considers all related climatological and meteorological parameters as input variables. To show the applicability and superiority of the integrated ANN approach, monthly data were collected for 6 years (1995-2000) in six nominal cities in Iran. Separate model for each city is considered and the quantity of solar global radiation in each city is calculated. Furthermore an integrated ANN model has been introduced for prediction of solar global radiation. The acquired results of the integrated model have shown high accuracy of about 94%. The results of the integrated model have been compared with traditional angstrom's model to show its considerable accuracy. Therefore, the proposed approach can be used as an efficient tool for prediction of solar radiation in the remote and rural locations with no direct measurement equipment.

  8. 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.

  9. 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.

  10. Function integrated track system

    OpenAIRE

    Hohnecker, Eberhard

    2010-01-01

    The paper discusses a function integrated track system that focuses on the reduction of acoustic emissions from railway lines. It is shown that the combination of an embedded rail system (ERS), a sound absorbing track surface, and an integrated mini sound barrier has significant acoustic advantages compared to a standard ballast superstructure. The acoustic advantages of an embedded rail system are particularly pronounced in the case of railway bridges. Finally, it is shown that a...

  11. Integrated management systems

    CERN Document Server

    Bugdol, Marek

    2015-01-01

    Examining the challenges of integrated management, this book explores the importance and potential benefits of using an integrated approach as a cross-functional concept of management. It covers not only standardized management systems (e.g. International Organization for Standardization), but also models of self-assessment, as well as different types of integration. Furthermore, it demonstrates how processes and systems can be integrated, and how management efficiency can be increased. The major part of this book focuses on management concepts which use integration as a key tool of management processes (e.g. the systematic approach, supply chain management, virtual and network organizations, processes management and total quality management). Case studies, illustrations, and tables are also provided to exemplify and illuminate the content, as well as examples of successful and failed integrations. Providing a particularly useful resource to managers and specialists involved in the improvement of organization...

  12. Integration of reusable systems

    CERN Document Server

    Rubin, Stuart

    2014-01-01

    Software reuse and integration has been described as the process of creating software systems from existing software rather than building software systems from scratch. Whereas reuse solely deals with the artifacts creation, integration focuses on how reusable artifacts interact with the already existing parts of the specified transformation. Currently, most reuse research focuses on creating and integrating adaptable components at development or at compile time. However, with the emergence of ubiquitous computing, reuse technologies that can support adaptation and reconfiguration of architectures and components at runtime are in demand. This edited book includes 15 high quality research papers written by experts in information reuse and integration to cover the most recent advances in the field. These papers are extended versions of the best papers which were presented at IEEE International Conference on Information Reuse and Integration and IEEE International Workshop on Formal Methods Integration, which wa...

  13. Energy Systems Integration Facility Videos | Energy Systems Integration

    Science.gov (United States)

    Facility | NREL Energy Systems Integration Facility Videos Energy Systems Integration Facility Integration Facility NREL + SolarCity: Maximizing Solar Power on Electrical Grids Redefining What's Possible for Renewable Energy: Grid Integration Robot-Powered Reliability Testing at NREL's ESIF Microgrid

  14. Energy Systems Integration Laboratory | Energy Systems Integration Facility

    Science.gov (United States)

    | NREL Integration Laboratory Energy Systems Integration Laboratory Research in the Energy Systems Integration Laboratory is advancing engineering knowledge and market deployment of hydrogen technologies. Applications include microgrids, energy storage for renewables integration, and home- and station

  15. 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...

  16. Integrated inventory information system

    Digital Repository Service at National Institute of Oceanography (India)

    Sarupria, J.S.; Kunte, P.D.

    The nature of oceanographic data and the management of inventory level information are described in Integrated Inventory Information System (IIIS). It is shown how a ROSCOPO (report on observations/samples collected during oceanographic programme...

  17. Systems Integration Fact Sheet

    Energy Technology Data Exchange (ETDEWEB)

    None

    2016-06-01

    This fact sheet is an overview of the Systems Integration subprogram at the U.S. Department of Energy SunShot Initiative. The Systems Integration subprogram enables the widespread deployment of safe, reliable, and cost-effective solar energy technologies by addressing the associated technical and non-technical challenges. These include timely and cost-effective interconnection procedures, optimal system planning, accurate prediction of solar resources, monitoring and control of solar power, maintaining grid reliability and stability, and many more. To address the challenges associated with interconnecting and integrating hundreds of gigawatts of solar power onto the electricity grid, the Systems Integration program funds research, development, and demonstration projects in four broad, interrelated focus areas: grid performance and reliability, dispatchability, power electronics, and communications.

  18. INTEGRATION OF FRACTAL AND NEURAL NETWORK TECHNOLOGIES IN PEDAGOGICAL MONITORING AND ASSESSMENT OF KNOWLEDGE OF TRAINEES

    Directory of Open Access Journals (Sweden)

    Svetlana N Dvoryatkina

    2017-12-01

    Full Text Available The possibility of statement and solution of the problem of searching of theoretical justification and development of efficient didactic mechanisms of the organization of process of pedagogical monitoring and assessment of level of knowledge of trainees can be based on convergence of the leading psychological and pedagogical, mathematical, and informational technologies with accounting of the modern achievements in science. In the article, the pedagogical expediency of realization of opportunities of means of informational technologies in monitoring and assessment of the composite mathematical knowledge, in the management of cognitive activity of students is proved. The ability to integrate fractal methods and neural network technologies in perfecting of a system of pedagogical monitoring of mathematical knowledge of trainees as a part of the automated training systems (ATS is investigated and realized in practice. It is proved that fractal methods increase the accuracy and depth of estimation of the level of proficiency of students and also complexes of intellectual operations of the integrative qualities allowing to master and apply cross-disciplinary knowledge and abilities in professional activity. Neural network technologies solve a problem of realization of the personal focused tutoring from positions of optimum individualization of mathematical education and self-realization of the person. The technology of projection of integrative system of pedagogical monitoring of knowledge of students includes the following stages: establishment of the required tutoring parameters; definition and preparation of input data for realization of integration of fractal and neural network technologies; development of the diagnostic module as a part of the block of an artificial intelligence of ATS, filling of the databases structured by system; start of system for obtaining the forecast. In development of the integrative automated system of pedagogical

  19. 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.

  20. Attention Modulates the Neural Processes Underlying Multisensory Integration of Emotion

    Directory of Open Access Journals (Sweden)

    Hao Tam Ho

    2011-10-01

    Full Text Available Integrating emotional information from multiple sensory modalities is generally assumed to be a pre-attentive process (de Gelder et al., 1999. This assumption, however, presupposes that the integrative process occurs independent of attention. Using event-potentials (ERP the present study investigated whether the neural processes underlying the integration of dynamic facial expression and emotional prosody is indeed unaffected by attentional manipulations. To this end, participants were presented with congruent and incongruent face-voice combinations (eg, an angry face combined with a neutral voice and performed different two-choice tasks in four consecutive blocks. Three of the tasks directed the participants' attention to emotion expressions in the face, the voice or both. The fourth task required participants to attend to the synchronicity between voice and lip movements. The results show divergent modulations of early ERP components by the different attentional manipulations. For example, when attention was directed to the face (or the voice, incongruent stimuli elicited a reduced N1 as compared to congruent stimuli. This effect was absent, when attention was diverted away from the emotionality in both face and voice suggesting that the detection of emotional incongruence already requires attention. Based on these findings, we question whether multisensory integration of emotion occurs indeed pre-attentively.

  1. 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.

  2. 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.

  3. 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.

  4. Control system integration

    CERN Document Server

    Shea, T J

    2008-01-01

    This lecture begins with a definition of an accelerator control system, and then reviews the control system architectures that have been deployed at the larger accelerator facilities. This discussion naturally leads to identification of the major subsystems and their interfaces. We shall explore general strategies for integrating intelligent devices and signal processing subsystems based on gate arrays and programmable DSPs. The following topics will also be covered: physical packaging; timing and synchronization; local and global communication technologies; interfacing to machine protection systems; remote debugging; configuration management and source code control; and integration of commercial software tools. Several practical realizations will be presented.

  5. Two multichannel integrated circuits for neural recording and signal processing.

    Science.gov (United States)

    Obeid, Iyad; Morizio, James C; Moxon, Karen A; Nicolelis, Miguel A L; Wolf, Patrick D

    2003-02-01

    We have developed, manufactured, and tested two analog CMOS integrated circuit "neurochips" for recording from arrays of densely packed neural electrodes. Device A is a 16-channel buffer consisting of parallel noninverting amplifiers with a gain of 2 V/V. Device B is a 16-channel two-stage analog signal processor with differential amplification and high-pass filtering. It features selectable gains of 250 and 500 V/V as well as reference channel selection. The resulting amplifiers on Device A had a mean gain of 1.99 V/V with an equivalent input noise of 10 microV(rms). Those on Device B had mean gains of 53.4 and 47.4 dB with a high-pass filter pole at 211 Hz and an equivalent input noise of 4.4 microV(rms). Both devices were tested in vivo with electrode arrays implanted in the somatosensory cortex.

  6. Three dimensional system integration

    CERN Document Server

    Papanikolaou, Antonis; Radojcic, Riko

    2010-01-01

    Three-dimensional (3D) integrated circuit (IC) stacking is the next big step in electronic system integration. It enables packing more functionality, as well as integration of heterogeneous materials, devices, and signals, in the same space (volume). This results in consumer electronics (e.g., mobile, handheld devices) which can run more powerful applications, such as full-length movies and 3D games, with longer battery life. This technology is so promising that it is expected to be a mainstream technology a few years from now, less than 10-15 years from its original conception. To achieve thi

  7. The systems integration modeling system

    International Nuclear Information System (INIS)

    Danker, W.J.; Williams, J.R.

    1990-01-01

    This paper discusses the systems integration modeling system (SIMS), an analysis tool for the detailed evaluation of the structure and related performance of the Federal Waste Management System (FWMS) and its interface with waste generators. It's use for evaluations in support of system-level decisions as to FWMS configurations, the allocation, sizing, balancing and integration of functions among elements, and the establishment of system-preferred waste selection and sequencing methods and other operating strategies is presented. SIMS includes major analysis submodels which quantify the detailed characteristics of individual waste items, loaded casks and waste packages, simulate the detailed logistics of handling and processing discrete waste items and packages, and perform detailed cost evaluations

  8. What Is Energy Systems Integration? | Energy Systems Integration Facility |

    Science.gov (United States)

    NREL What Is Energy Systems Integration? What Is Energy Systems Integration? Energy systems integration (ESI) is an approach to solving big energy challenges that explores ways for energy systems to Research Community NREL is a founding member of the International Institute for Energy Systems Integration

  9. ADDRESS SYSTEM INTEGRATION BUSINESS

    Directory of Open Access Journals (Sweden)

    Lionel Manuel Carbonell-Zamora

    2016-01-01

    Full Text Available The Integrated Strategic Direction constitutes a superior stage of Direction that expresses the coordinated system of external and internal relations with full participation in order to reach the vision of the organization. It can be insured by the use of the Strategic Direction model for the integration of the Company Direction System. This model has been applied in several companies. Recently, it was applied in the Inspection State Unit of MICONS in Santiago de Cuba through the investigation thesis for master degree developed during 18 months which objective was to validate its effectiveness in a budgeted unit, obtaining positive results when the levels of integration in the direction system increased in their external and internal relations expressed in a 37 % and 15 % respectively, which impacted the increment of the efficiency and effectiveness of all processes of the organization. 

  10. Integral consideration of integrated management systems

    International Nuclear Information System (INIS)

    Frauenknecht, Stefan; Schmitz, Hans

    2010-01-01

    Aim of the project for the NPPs Kruemmel and Brunsbuettel (Vattenfall) is the integral view of the business process as basis for the implementation and operation of management systems in the domains quality, safety and environment. The authors describe the integral view of the business processes in the frame of integrated management systems with the focus nuclear safety, lessons learned in the past, the concept of a process-based controlling system and experiences from the practical realization.

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

    Science.gov (United States)

    Gao, Rong

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

  12. Which coordinate system for modelling path integration?

    Science.gov (United States)

    Vickerstaff, Robert J; Cheung, Allen

    2010-03-21

    Path integration is a navigation strategy widely observed in nature where an animal maintains a running estimate, called the home vector, of its location during an excursion. Evidence suggests it is both ancient and ubiquitous in nature, and has been studied for over a century. In that time, canonical and neural network models have flourished, based on a wide range of assumptions, justifications and supporting data. Despite the importance of the phenomenon, consensus and unifying principles appear lacking. A fundamental issue is the neural representation of space needed for biological path integration. This paper presents a scheme to classify path integration systems on the basis of the way the home vector records and updates the spatial relationship between the animal and its home location. Four extended classes of coordinate systems are used to unify and review both canonical and neural network models of path integration, from the arthropod and mammalian literature. This scheme demonstrates analytical equivalence between models which may otherwise appear unrelated, and distinguishes between models which may superficially appear similar. A thorough analysis is carried out of the equational forms of important facets of path integration including updating, steering, searching and systematic errors, using each of the four coordinate systems. The type of available directional cue, namely allothetic or idiothetic, is also considered. It is shown that on balance, the class of home vectors which includes the geocentric Cartesian coordinate system, appears to be the most robust for biological systems. A key conclusion is that deducing computational structure from behavioural data alone will be difficult or impossible, at least in the absence of an analysis of random errors. Consequently it is likely that further theoretical insights into path integration will require an in-depth study of the effect of noise on the four classes of home vectors. Copyright 2009 Elsevier Ltd

  13. 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)

  14. Discrete systems and integrability

    CERN Document Server

    Hietarinta, J; Nijhoff, F W

    2016-01-01

    This first introductory text to discrete integrable systems introduces key notions of integrability from the vantage point of discrete systems, also making connections with the continuous theory where relevant. While treating the material at an elementary level, the book also highlights many recent developments. Topics include: Darboux and Bäcklund transformations; difference equations and special functions; multidimensional consistency of integrable lattice equations; associated linear problems (Lax pairs); connections with Padé approximants and convergence algorithms; singularities and geometry; Hirota's bilinear formalism for lattices; intriguing properties of discrete Painlevé equations; and the novel theory of Lagrangian multiforms. The book builds the material in an organic way, emphasizing interconnections between the various approaches, while the exposition is mostly done through explicit computations on key examples. Written by respected experts in the field, the numerous exercises and the thoroug...

  15. 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.

  16. Integrated management systems

    DEFF Research Database (Denmark)

    Jørgensen, Tine Herreborg; Remmen, Arne; Mellado, M. Dolores

    2006-01-01

    Different approaches to integration of management systems (ISO 9001, ISO 14001, OHSAS 18001 and SA 8000) with various levels of ambition have emerged. The tendency of increased compatibility between these standards has paved the road for discussions of, how to understand the different aspects of ...

  17. Integrable and superintegrable systems

    CERN Document Server

    1990-01-01

    Some of the most active practitioners in the field of integrable systems have been asked to describe what they think of as the problems and results which seem to be most interesting and important now and are likely to influence future directions. The papers in this collection, representing their authors' responses, offer a broad panorama of the subject as it enters the 1990's.

  18. 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.

  19. 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.

  20. A biologically inspired neural model for visual and proprioceptive integration including sensory training.

    Science.gov (United States)

    Saidi, Maryam; Towhidkhah, Farzad; Gharibzadeh, Shahriar; Lari, Abdolaziz Azizi

    2013-12-01

    Humans perceive the surrounding world by integration of information through different sensory modalities. Earlier models of multisensory integration rely mainly on traditional Bayesian and causal Bayesian inferences for single causal (source) and two causal (for two senses such as visual and auditory systems), respectively. In this paper a new recurrent neural model is presented for integration of visual and proprioceptive information. This model is based on population coding which is able to mimic multisensory integration of neural centers in the human brain. The simulation results agree with those achieved by casual Bayesian inference. The model can also simulate the sensory training process of visual and proprioceptive information in human. Training process in multisensory integration is a point with less attention in the literature before. The effect of proprioceptive training on multisensory perception was investigated through a set of experiments in our previous study. The current study, evaluates the effect of both modalities, i.e., visual and proprioceptive training and compares them with each other through a set of new experiments. In these experiments, the subject was asked to move his/her hand in a circle and estimate its position. The experiments were performed on eight subjects with proprioception training and eight subjects with visual training. Results of the experiments show three important points: (1) visual learning rate is significantly more than that of proprioception; (2) means of visual and proprioceptive errors are decreased by training but statistical analysis shows that this decrement is significant for proprioceptive error and non-significant for visual error, and (3) visual errors in training phase even in the beginning of it, is much less than errors of the main test stage because in the main test, the subject has to focus on two senses. The results of the experiments in this paper is in agreement with the results of the neural model

  1. Activity in part of the neural correlates of consciousness reflects integration.

    Science.gov (United States)

    Eriksson, Johan

    2017-10-01

    Integration is commonly viewed as a key process for generating conscious experiences. Accordingly, there should be increased activity within the neural correlates of consciousness when demands on integration increase. We used fMRI and "informational masking" to isolate the neural correlates of consciousness and measured how the associated brain activity changed as a function of required integration. Integration was manipulated by comparing the experience of hearing simple reoccurring tones to hearing harmonic tone triplets. The neural correlates of auditory consciousness included superior temporal gyrus, lateral and medial frontal regions, cerebellum, and also parietal cortex. Critically, only activity in left parietal cortex increased significantly as a function of increasing demands on integration. We conclude that integration can explain part of the neural activity associated with the generation conscious experiences, but that much of associated brain activity apparently reflects other processes. Copyright © 2017 Elsevier Inc. All rights reserved.

  2. Integration of neural networks with fuzzy reasoning for measuring operational parameters in a nuclear reactor

    International Nuclear Information System (INIS)

    Ikonomopoulos, A.; Tsoukalas, L.H.

    1993-01-01

    A novel approach is described for measuring variables with operational significance in a complex system such as a nuclear reactor. The methodology is based on the integration of artificial neural networks with fuzzy reasoning. Neural networks are used to map dynamic time series to a set of user-defined linguistic labels called fuzzy values. The process takes place in a manner analogous to that of measurement. Hence, the entire procedure is referred to as virtual measurement and its software implementation as a virtual measuring device. An optimization algorithm based on information criteria and fuzzy algebra augments the process and assists in the identification of different states of the monitored parameter. The proposed technique is applied for monitoring parameters such as performance, valve position, transient type, and reactivity. The results obtained from the application of the neural network-fuzzy reasoning integration in a high power research reactor clearly demonstrate the excellent tolerance of the virtual measuring device to faulty signals as well as its ability to accommodate noisy inputs

  3. Integrated material accountancy system

    International Nuclear Information System (INIS)

    Calabozo, M.; Buiza, A.

    1991-01-01

    In this paper we present the system that we are actually using for Nuclear Material Accounting and Manufacturing Management in our UO 2 Fuel Fabrication Plant located at Juzbado, Salamanca, Spain. The system is based mainly on a real time data base which gather data for all the operations performed in our factory from UO 2 powder reception to fuel assemblies shipment to the customers. The accountancy is just an important part of the whole integrated system covering all the aspects related to manufacturing: planning, traceability, Q.C. analysis, production control and accounting data

  4. 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.

  5. 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

  6. Neural Network Based Model of an Industrial Oil-Fired Boiler System ...

    African Journals Online (AJOL)

    A two-layer feed-forward neural network with Hyperbolic tangent sigmoid ... The neural network model when subjected to test, using the validation input data; ... Proportional Integral Derivative (PID) Controller is used to control the neural ...

  7. 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.

  8. Systems Integration | Photovoltaic Research | NREL

    Science.gov (United States)

    Integration Systems Integration The National Center for Photovoltaics (NCPV) at NREL provides grid integration support, system-level testing, and systems analysis for the Department of Energy's solar distributed grid integration projects supported by the SunShot Initiative. These projects address technical

  9. 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....

  10. 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

  11. Energy Systems Integration Facility News | Energy Systems Integration

    Science.gov (United States)

    Facility | NREL Energy Systems Integration Facility News Energy Systems Integration Facility Energy Dataset A massive amount of wind data was recently made accessible online, greatly expanding the Energy's National Renewable Energy Laboratory (NREL) has completed technology validation testing for Go

  12. Integrated management system

    International Nuclear Information System (INIS)

    Florescu, N.

    2003-01-01

    A management system is developed in order to reflect the needs of the business and to ensure that the objectives of the organization will be achieved. The process model and each individual process within the system then needs to identify the drives or requirements from external customers and stakeholders, regulations, and standards such as ISO and 50-C-Q. The processes are then developed to address these drivers. Developing the process in this way makes it fully integrated and capable of incorporating any new requirements. The International Standard (ISO 9000:2000) promotes the adoption of a process approach when developing, implementing and improving the effectiveness of a quality management system to enhance customer satisfaction by meeting customer requirements. The IAEA Code recognizes that the entire work is a process which can be planned, assessed and improved. For an organization to function effectively, numerous linked activities have to be identified and managed. By definition a process is an activity that using resources and taking into account all the constraints imposed executes the necessary operations which transform the inputs in outcomes. Running a system of processes within an organization, identification of the interaction between the processes and their management can be referred to as a 'process approach'. The advantage of such an approach is the ensuring of the ongoing control over the linkage between the individual processes composing the system as well as over their combination and interaction. Developing a management system implies: identification of the process which delivers Critical Success Factor (CSFs) of the business; identifying the support processes enabling the CSFs to be accomplished; identifying the processes that deliver the business fundamentals. An integrated management system should include all activities not only those related to Quality, Health and Safety. When developing an IMS it is necessary to identify all of the drivers

  13. A Novel Fractional-Order PID Controller for Integrated Pressurized Water Reactor Based on Wavelet Kernel Neural Network Algorithm

    Directory of Open Access Journals (Sweden)

    Yu-xin Zhao

    2014-01-01

    Full Text Available This paper presents a novel wavelet kernel neural network (WKNN with wavelet kernel function. It is applicable in online learning with adaptive parameters and is applied on parameters tuning of fractional-order PID (FOPID controller, which could handle time delay problem of the complex control system. Combining the wavelet function and the kernel function, the wavelet kernel function is adopted and validated the availability for neural network. Compared to the conservative wavelet neural network, the most innovative character of the WKNN is its rapid convergence and high precision in parameters updating process. Furthermore, the integrated pressurized water reactor (IPWR system is established by RELAP5, and a novel control strategy combining WKNN and fuzzy logic rule is proposed for shortening controlling time and utilizing the experiential knowledge sufficiently. Finally, experiment results verify that the control strategy and controller proposed have the practicability and reliability in actual complicated system.

  14. Energy Systems Integration News | Energy Systems Integration Facility |

    Science.gov (United States)

    the Energy Systems Integration Facility as part of NREL's work with SolarCity and the Hawaiian Electric Companies. Photo by Amy Glickson, NREL Welcome to Energy Systems Integration News, NREL's monthly date on the latest energy systems integration (ESI) developments at NREL and worldwide. Have an item

  15. NET system integration

    International Nuclear Information System (INIS)

    Farfaletti-Casali, F.; Mitchell, N.; Salpietro, E.; Buzzi, U.; Gritzmann, P.

    1985-01-01

    The NET system integration procedure is the process by which the requirements of the various Tokamak machine design areas are brought together to form a compatible machine layout. Each design area produces requirements which generally allow components to be built at minimum cost and operate with minimum technical risk, and the final machine assembly should be achieved with minimum departure from these optimum designs. This is carried out in NET by allowing flexibility in the maintenance and access methods to the machine internal components which must be regularly replaced by remote handling, in segmentation of these internal components and in the number of toroidal field coils

  16. 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

  17. TCR industrial system integration strategy

    CERN Document Server

    Bartolomé, R; Sollander, P; Martini, R; Vercoutter, B; Trebulle, M

    1999-01-01

    New turnkey data acquisition systems purchased from industry are being integrated into CERN's Technical Data Server. The short time available for system integration and the large amount of data per system require a standard and modular design. Four different integration layers have been defined in order to easily 'plug in' industrial systems. The first layer allows the integration of the equipment at the digital I/O port or fieldbus (Profibus-DP) level. A second layer permits the integration of PLCs (Siemens S5, S7 and Telemecanique); a third layer integrates equipment drivers. The fourth layer integrates turnkey mimic diagrams in the TCR operator console. The second and third layers use two new event-driven protocols based on TCP/IP. Using this structure, new systems are integrated in the data transmission chain, the layer at which they are integrated depending only on their integration capabilities.

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

    KAUST Repository

    Bressloff, Paul C.

    2010-01-01

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

  19. Radon integral measurement system

    International Nuclear Information System (INIS)

    Garcia H, J.M.

    1994-01-01

    The Radon Integral Measurement System (SMIR) is a device designed specially to detect, to count and to store the data of the acquisition of alpha particles emitted by Radon-222 coming from the underground. The system includes a detection chamber, a radiation detector, a digital system with bateries backup and an auxiliary photovoltaic cell. A personal computer fixes the mode in which the system works, transmitting the commands to the system by the serial port. The heart of the system is a microprocesor working with interrupts by hardware. Every external device to the microprocessor sends his own interrupt request and the microprocessor handles the interrupts with a defined priority. The system uses a real time clock, compatible with the microprocessor, to take care of the real timing and date of the acquisition. A non volatile RAM is used to store data of two bytes every 15 minutes along 41 days as a maximum. After the setting up to the system by the computer, it can operate in stand alone way for up 41 days in the working place without the lose of any data. If the memory is full the next data will be written in the first locations of the memory. The memory is divided in pages corresponding every one of this to a different day of the acquisition. The counting time for every acquisition can be programmed by the user from 15 minutes to 65535 minutes but it is recommended to use a small time not to reach the limit of 65535 counts in every acquisition period. We can take information of the system without affecting the acquisition process in the field by using a lap top computer, then the information can be stored in a file. There is a program in the computer that can show the information in a table of values or in a bar graph. (Author)

  20. 3D silicon neural probe with integrated optical fibers for optogenetic modulation.

    Science.gov (United States)

    Kim, Eric G R; Tu, Hongen; Luo, Hao; Liu, Bin; Bao, Shaowen; Zhang, Jinsheng; Xu, Yong

    2015-07-21

    Optogenetics is a powerful modality for neural modulation that can be useful for a wide array of biomedical studies. Penetrating microelectrode arrays provide a means of recording neural signals with high spatial resolution. It is highly desirable to integrate optics with neural probes to allow for functional study of neural tissue by optogenetics. In this paper, we report the development of a novel 3D neural probe coupled simply and robustly to optical fibers using a hollow parylene tube structure. The device shanks are hollow tubes with rigid silicon tips, allowing the insertion and encasement of optical fibers within the shanks. The position of the fiber tip can be precisely controlled relative to the electrodes on the shank by inherent design features. Preliminary in vivo rat studies indicate that these devices are capable of optogenetic modulation simultaneously with 3D neural signal recording.

  1. 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.

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

    Science.gov (United States)

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

    2017-04-01

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

  3. Neural precursor cells in the ischemic brain - integration, cellular crosstalk and consequences for stroke recovery

    Directory of Open Access Journals (Sweden)

    Dirk M. Hermann

    2014-09-01

    Full Text Available After an ischemic stroke, neural precursor cells (NPCs proliferate within major germinal niches of the brain. Endogenous NPCs subsequently migrate towards the ischemic lesion where they promote tissue remodelling and neural repair. Unfortunately, this restorative process is generally insufficient and thus unable to support a full recovery of lost neurological functions. Supported by solid experimental and preclinical data, the transplantation of exogenous NPCs has emerged as a potential tool for stroke treatment. Transplanted NPCs are thought to act mainly via trophic and immune modulatory effects, thereby complementing the restorative responses initially executed by the endogenous NPC population. Recent studies have attempted to elucidate how the therapeutic properties of transplanted NPCs vary depending on the route of transplantation. Systemic NPC delivery leads to potent immune modulatory actions, which prevent secondary neuronal degeneration, reduces glial scar formation, diminishes oxidative stress and stabilizes blood-brain barrier integrity. On the contrary, local stem cell delivery, allows for the accumulation of large numbers of transplanted NPCs in the brain, thus achieving high levels of locally available tissue trophic factors, which may better induce a strong endogenous NPC proliferative response.Herein we describe the diverse capabilities of exogenous (systemically vs locally transplanted NPCs in enhancing the endogenous neurogenic response after stroke, and how the route of transplantation may affect migration, survival, bystander effects and integration of the cellular graft. It is the authors’ claim that understanding these aspects will be of pivotal importance in discerning how transplanted NPCs exert their therapeutic effects in stroke.

  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. 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

  6. Structural reliability calculation method based on the dual neural network and direct integration method.

    Science.gov (United States)

    Li, Haibin; He, Yun; Nie, Xiaobo

    2018-01-01

    Structural reliability analysis under uncertainty is paid wide attention by engineers and scholars due to reflecting the structural characteristics and the bearing actual situation. The direct integration method, started from the definition of reliability theory, is easy to be understood, but there are still mathematics difficulties in the calculation of multiple integrals. Therefore, a dual neural network method is proposed for calculating multiple integrals in this paper. Dual neural network consists of two neural networks. The neural network A is used to learn the integrand function, and the neural network B is used to simulate the original function. According to the derivative relationships between the network output and the network input, the neural network B is derived from the neural network A. On this basis, the performance function of normalization is employed in the proposed method to overcome the difficulty of multiple integrations and to improve the accuracy for reliability calculations. The comparisons between the proposed method and Monte Carlo simulation method, Hasofer-Lind method, the mean value first-order second moment method have demonstrated that the proposed method is an efficient and accurate reliability method for structural reliability problems.

  7. 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.

  8. 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.

  9. Neural initialization of audiovisual integration in prereaders at varying risk for developmental dyslexia.

    Science.gov (United States)

    I Karipidis, Iliana; Pleisch, Georgette; Röthlisberger, Martina; Hofstetter, Christoph; Dornbierer, Dario; Stämpfli, Philipp; Brem, Silvia

    2017-02-01

    Learning letter-speech sound correspondences is a major step in reading acquisition and is severely impaired in children with dyslexia. Up to now, it remains largely unknown how quickly neural networks adopt specific functions during audiovisual integration of linguistic information when prereading children learn letter-speech sound correspondences. Here, we simulated the process of learning letter-speech sound correspondences in 20 prereading children (6.13-7.17 years) at varying risk for dyslexia by training artificial letter-speech sound correspondences within a single experimental session. Subsequently, we acquired simultaneously event-related potentials (ERP) and functional magnetic resonance imaging (fMRI) scans during implicit audiovisual presentation of trained and untrained pairs. Audiovisual integration of trained pairs correlated with individual learning rates in right superior temporal, left inferior temporal, and bilateral parietal areas and with phonological awareness in left temporal areas. In correspondence, a differential left-lateralized parietooccipitotemporal ERP at 400 ms for trained pairs correlated with learning achievement and familial risk. Finally, a late (650 ms) posterior negativity indicating audiovisual congruency of trained pairs was associated with increased fMRI activation in the left occipital cortex. Taken together, a short (audiovisual integration in neural systems that are responsible for processing linguistic information in proficient readers. To conclude, the ability to learn grapheme-phoneme correspondences, the familial history of reading disability, and phonological awareness of prereading children account for the degree of audiovisual integration in a distributed brain network. Such findings on emerging linguistic audiovisual integration could allow for distinguishing between children with typical and atypical reading development. Hum Brain Mapp 38:1038-1055, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals

  10. Choosing the Right Systems Integration

    Directory of Open Access Journals (Sweden)

    Péči Matúš

    2014-12-01

    Full Text Available The paper examines systems integration and its main levels at higher levels of control. At present, the systems integration is one of the main aspects participating in the consolidation processes and financial flows of a company. Systems Integration is a complicated emotionconsuming process and it is often a problem to choose the right approach and level of integration. The research focused on four levels of integration, while each of them is characterized by specific conditions. At each level, there is a summary of recommendations and practical experience. The paper also discusses systems integration between the information and MES levels. The main part includes user-level integration where we describe an example of such integration. Finally, we list recommendations and also possible predictions of the systems integration as one of the important factors in the future.

  11. 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

  12. Neural Integration of Information Specifying Human Structure from Form, Motion, and Depth

    Science.gov (United States)

    Jackson, Stuart; Blake, Randolph

    2010-01-01

    Recent computational models of biological motion perception operate on ambiguous two-dimensional representations of the body (e.g., snapshots, posture templates) and contain no explicit means for disambiguating the three-dimensional orientation of a perceived human figure. Are there neural mechanisms in the visual system that represent a moving human figure’s orientation in three dimensions? To isolate and characterize the neural mechanisms mediating perception of biological motion, we used an adaptation paradigm together with bistable point-light (PL) animations whose perceived direction of heading fluctuates over time. After exposure to a PL walker with a particular stereoscopically defined heading direction, observers experienced a consistent aftereffect: a bistable PL walker, which could be perceived in the adapted orientation or reversed in depth, was perceived predominantly reversed in depth. A phase-scrambled adaptor produced no aftereffect, yet when adapting and test walkers differed in size or appeared on opposite sides of fixation aftereffects did occur. Thus, this heading direction aftereffect cannot be explained by local, disparity-specific motion adaptation, and the properties of scale and position invariance imply higher-level origins of neural adaptation. Nor is disparity essential for producing adaptation: when suspended on top of a stereoscopically defined, rotating globe, a context-disambiguated “globetrotter” was sufficient to bias the bistable walker’s direction, as were full-body adaptors. In sum, these results imply that the neural signals supporting biomotion perception integrate information on the form, motion, and three-dimensional depth orientation of the moving human figure. Models of biomotion perception should incorporate mechanisms to disambiguate depth ambiguities in two-dimensional body representations. PMID:20089892

  13. 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.

  14. Energy Systems Integration News - October 2016 | Energy Systems Integration

    Science.gov (United States)

    Facility | NREL October 2016 Energy Systems Integration News A monthly recap of the latest energy systems integration (ESI) developments at NREL and around the world. Subscribe Archives October Integration Facility's main control room. OMNETRIC Group Demonstrates a Distributed Control Hierarchy for

  15. The modulation of neural gain facilitates a transition between functional segregation and integration in the brain.

    Science.gov (United States)

    Shine, James M; Aburn, Matthew J; Breakspear, Michael; Poldrack, Russell A

    2018-01-29

    Cognitive function relies on a dynamic, context-sensitive balance between functional integration and segregation in the brain. Previous work has proposed that this balance is mediated by global fluctuations in neural gain by projections from ascending neuromodulatory nuclei. To test this hypothesis in silico, we studied the effects of neural gain on network dynamics in a model of large-scale neuronal dynamics. We found that increases in neural gain directed the network through an abrupt dynamical transition, leading to an integrated network topology that was maximal in frontoparietal 'rich club' regions. This gain-mediated transition was also associated with increased topological complexity, as well as increased variability in time-resolved topological structure, further highlighting the potential computational benefits of the gain-mediated network transition. These results support the hypothesis that neural gain modulation has the computational capacity to mediate the balance between integration and segregation in the brain. © 2018, Shine et al.

  16. System integration for radiation records

    International Nuclear Information System (INIS)

    Lawson, B.J.; Farrell, L.; Meacham, C.; Tapio, J.

    1994-01-01

    System integration is the process where through networking and/or software development, necessary business information is available in a common computing environment. System integration is becoming an important objective for many businesses. System integration can improve productivity and efficiency, reduce redundant stored information and errors, and improve availability of information. This paper will discuss the information flow in a radiation health environment, and how system integration can help. Information handled includes external dosimetry and internal dosimetry. The paper will focus on an ORACLE based system integration software product

  17. Digital integrated protection system

    International Nuclear Information System (INIS)

    Savornin, M.; Furet, M.

    1978-01-01

    As a result of technological progress it is now possible to achieve more elaborate protection functions able to follow more closely the phenomena to be supervised. For this reason the CEA, Framatome and Merlin/Gerin/CERCI have undertaken in commonn to develop a Digital Integrated Protection System (D.I.P.S.). This system is designed with the following aims: to improve the safety of the station, . to improve its availability, . to facilitate installation, . to facilitate tests and maintenance. The main characteristics adopted are: . possibilities of obtaining more elaborate monitoring and protection algorithm treatments, . order 4 redundancy of transducers, associated instruments and signal processing, . possibility of inhibiting part of the protection system, . standardisation of equipment, physical and electrical separation of redundant units, . use of multiplexed connections, . automation of tests. Four flow charts are presented: - DIPS with four APUP (Acquisition and Processing Unit for Protection) - APUP - LSU (Logic Safeguard Unit), number LSU corresponding to number fluidic safeguard circuits, - structure of a function unit, - main functions of the APUP [fr

  18. Application of Integrated Neural Network Method to Fault Diagnosis of Nuclear Steam Generator

    International Nuclear Information System (INIS)

    Zhou Gang; Yang Li

    2009-01-01

    A new fault diagnosis method based on integrated neural networks for nuclear steam generator (SG) was proposed in view of the shortcoming of the conventional fault monitoring and diagnosis method. In the method, two neural networks (ANNs) were employed for the fault diagnosis of steam generator. A neural network, which was used for predicting the values of steam generator operation parameters, was taken as the dynamics model of steam generator. The principle of fault monitoring method using the neural network model is to detect the deviations between process signals measured from an operating steam generator and corresponding output signals from the neural network model of steam generator. When the deviation exceeds the limit set in advance, the abnormal event is thought to occur. The other neural network as a fault classifier conducts the fault classification of steam generator. So, the fault types of steam generator are given by the fault classifier. The clear information on steam generator faults was obtained by fusing the monitoring and diagnosis results of two neural networks. The simulation results indicate that employing integrated neural networks can improve the capacity of fault monitoring and diagnosis for the steam generator. (authors)

  19. Advanced Integrated Traction System

    Energy Technology Data Exchange (ETDEWEB)

    Greg Smith; Charles Gough

    2011-08-31

    The United States Department of Energy elaborates the compelling need for a commercialized competitively priced electric traction drive system to proliferate the acceptance of HEVs, PHEVs, and FCVs in the market. The desired end result is a technically and commercially verified integrated ETS (Electric Traction System) product design that can be manufactured and distributed through a broad network of competitive suppliers to all auto manufacturers. The objectives of this FCVT program are to develop advanced technologies for an integrated ETS capable of 55kW peak power for 18 seconds and 30kW of continuous power. Additionally, to accommodate a variety of automotive platforms the ETS design should be scalable to 120kW peak power for 18 seconds and 65kW of continuous power. The ETS (exclusive of the DC/DC Converter) is to cost no more than $660 (55kW at $12/kW) to produce in quantities of 100,000 units per year, should have a total weight less than 46kg, and have a volume less than 16 liters. The cost target for the optional Bi-Directional DC/DC Converter is $375. The goal is to achieve these targets with the use of engine coolant at a nominal temperature of 105C. The system efficiency should exceed 90% at 20% of rated torque over 10% to 100% of maximum speed. The nominal operating system voltage is to be 325V, with consideration for higher voltages. This project investigated a wide range of technologies, including ETS topologies, components, and interconnects. Each technology and its validity for automotive use were verified and then these technologies were integrated into a high temperature ETS design that would support a wide variety of applications (fuel cell, hybrids, electrics, and plug-ins). This ETS met all the DOE 2010 objectives of cost, weight, volume and efficiency, and the specific power and power density 2015 objectives. Additionally a bi-directional converter was developed that provides charging and electric power take-off which is the first step

  20. Neural network integration during the perception of in-group and out-group members.

    Science.gov (United States)

    Greven, Inez M; Ramsey, Richard

    2017-11-01

    Group biases guide social interactions by promoting in-group favouritism, but the neural mechanisms underpinning group biases remain unclear. While neuroscience research has shown that distributed brain circuits are associated with seeing in-group and out-group members as "us" and "them", it is less clear how these networks exchange signals. This fMRI study uses functional connectivity analyses to investigate the contribution of functional integration to group bias modulation of person perception. Participants were assigned to an arbitrary group and during scanning they observed bodies of in-group or out-group members that cued the recall of positive or negative social knowledge. The results showed that functional coupling between perceptual and cognitive neural networks is tuned to particular combinations of group membership and social knowledge valence. Specifically, coupling between body perception and theory-of-mind networks is biased towards seeing a person that had previously been paired with information consistent with group bias (positive for in-group and negative for out-group). This demonstrates how brain regions associated with visual analysis of others and belief reasoning exchange and integrate signals when evaluating in-group and out-group members. The results update models of person perception by showing how and when interplay occurs between perceptual and extended systems when developing a representation of another person. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  1. 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

  2. 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.

  3. Integrative radiation systems biology

    International Nuclear Information System (INIS)

    Unger, Kristian

    2014-01-01

    Maximisation of the ratio of normal tissue preservation and tumour cell reduction is the main concept of radiotherapy alone or combined with chemo-, immuno- or biologically targeted therapy. The foremost parameter influencing this ratio is radiation sensitivity and its modulation towards a more efficient killing of tumour cells and a better preservation of normal tissue at the same time is the overall aim of modern therapy schemas. Nevertheless, this requires a deep understanding of the molecular mechanisms of radiation sensitivity in order to identify its key players as potential therapeutic targets. Moreover, the success of conventional approaches that tried to statistically associate altered radiation sensitivity with any molecular phenotype such as gene expression proofed to be somewhat limited since the number of clinically used targets is rather sparse. However, currently a paradigm shift is taking place from pure frequentistic association analysis to the rather holistic systems biology approach that seeks to mathematically model the system to be investigated and to allow the prediction of an altered phenotype as the function of one single or a signature of biomarkers. Integrative systems biology also considers the data from different molecular levels such as the genome, transcriptome or proteome in order to partially or fully comprehend the causal chain of molecular mechanisms. An example for the application of this concept currently carried out at the Clinical Cooperation Group “Personalized Radiotherapy in Head and Neck Cancer” of the Helmholtz-Zentrum München and the LMU Munich is described. This review article strives for providing a compact overview on the state of the art of systems biology, its actual challenges, potential applications, chances and limitations in radiation oncology research working towards improved personalised therapy concepts using this relatively new methodology

  4. Early warning of illegal development for protected areas by integrating cellular automata with neural networks.

    Science.gov (United States)

    Li, Xia; Lao, Chunhua; Liu, Yilun; Liu, Xiaoping; Chen, Yimin; Li, Shaoying; Ai, Bing; He, Zijian

    2013-11-30

    Ecological security has become a major issue under fast urbanization in China. As the first two cities in this country, Shenzhen and Dongguan issued the ordinance of Eco-designated Line of Control (ELC) to "wire" ecologically important areas for strict protection in 2005 and 2009 respectively. Early warning systems (EWS) are a useful tool for assisting the implementation ELC. In this study, a multi-model approach is proposed for the early warning of illegal development by integrating cellular automata (CA) and artificial neural networks (ANN). The objective is to prevent the ecological risks or catastrophe caused by such development at an early stage. The integrated model is calibrated by using the empirical information from both remote sensing and handheld GPS (global positioning systems). The MAR indicator which is the ratio of missing alarms to all the warnings is proposed for better assessment of the model performance. It is found that the fast urban development has caused significant threats to natural-area protection in the study area. The integration of CA, ANN and GPS provides a powerful tool for describing and predicting illegal development which is in highly non-linear and fragmented forms. The comparison shows that this multi-model approach has much better performances than the single-model approach for the early warning. Compared with the single models of CA and ANN, this integrated multi-model can improve the value of MAR by 65.48% and 5.17% respectively. Copyright © 2013 Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

    Simon, Solomon H.

    1992-08-01

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

  6. INTEGRATING ARTIFICIAL NEURAL NETWORKS FOR DEVELOPING TELEMEDICINE SOLUTION

    Directory of Open Access Journals (Sweden)

    Mihaela GHEORGHE

    2015-06-01

    Full Text Available Artificial intelligence is assuming an increasing important role in the telemedicine field, especially neural networks with their ability to achieve meaning from large sets of data characterized by lacking exactness and accuracy. These can be used for assisting physicians or other clinical staff in the process of taking decisions under uncertainty. Thus, machine learning methods which are specific to this technology are offering an approach for prediction based on pattern classification. This paper aims to present the importance of neural networks in detecting trends and extracting patterns which can be used within telemedicine domains, particularly for taking medical diagnosis decisions.

  7. 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.

  8. Developing and using expert systems and neural networks in medicine: a review on benefits and challenges.

    Science.gov (United States)

    Sheikhtaheri, Abbas; Sadoughi, Farahnaz; Hashemi Dehaghi, Zahra

    2014-09-01

    Complicacy of clinical decisions justifies utilization of information systems such as artificial intelligence (e.g. expert systems and neural networks) to achieve better decisions, however, application of these systems in the medical domain faces some challenges. We aimed at to review the applications of these systems in the medical domain and discuss about such challenges. Following a brief introduction of expert systems and neural networks by representing few examples, the challenges of these systems in the medical domain are discussed. We found that the applications of expert systems and artificial neural networks have been increased in the medical domain. These systems have shown many advantages such as utilization of experts' knowledge, gaining rare knowledge, more time for assessment of the decision, more consistent decisions, and shorter decision-making process. In spite of all these advantages, there are challenges ahead of developing and using such systems including maintenance, required experts, inputting patients' data into the system, problems for knowledge acquisition, problems in modeling medical knowledge, evaluation and validation of system performance, wrong recommendations and responsibility, limited domains of such systems and necessity of integrating such systems into the routine work flows. We concluded that expert systems and neural networks can be successfully used in medicine; however, there are many concerns and questions to be answered through future studies and discussions.

  9. Common Systems Integration Lab (CSIL)

    Data.gov (United States)

    Federal Laboratory Consortium — The Common Systems Integration Lab (CSIL)supports the PMA-209 Air Combat Electronics Program Office. CSIL also supports development, test, integration and life cycle...

  10. Human-Systems Integration Processes

    Data.gov (United States)

    National Aeronautics and Space Administration — The goal of this project is to baseline a Human-Systems Integration Processes (HSIP) document as a companion to the NASA-STD-3001 and Human Integration Design...

  11. 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

  12. 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.

  13. Effects of sleep deprivation on neural functioning: an integrative review

    NARCIS (Netherlands)

    Boonstra, T.W.; Stins, J.F.; Daffertshofer, A.; Beek, P.J.

    2007-01-01

    Sleep deprivation has a broad variety of effects on human performance and neural functioning that manifest themselves at different levels of description. On a macroscopic level, sleep deprivation mainly affects executive functions, especially in novel tasks. Macroscopic and mesoscopic effects of

  14. Simulation of sensory integration dysfunction in autism with dynamic neural fields model

    NARCIS (Netherlands)

    Chonnaparamutt, W.; Barakova, E.I.; Rutkowski, L.; Taseusiewicz, R.

    2008-01-01

    This paper applies dynamic neural fields model [1,23,7] to multimodal interaction of sensory cues obtained from a mobile robot, and shows the impact of different temporal aspects of the integration to the precision of movements. We speculate that temporally uncoordinated sensory integration might be

  15. Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Petr Maca

    2016-01-01

    Full Text Available The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI and the standardized precipitation evaporation index (SPEI and were derived for the period of 1948–2002 on two US catchments. The meteorological and hydrological data were obtained from MOPEX experiment. The training of both neural network models was made by the adaptive version of differential evolution, JADE. The comparison of models was based on six model performance measures. The results of drought indices forecast, explained by the values of four model performance indices, show that the integrated neural network model was superior to the feedforward multilayer perceptron with one hidden layer of neurons.

  16. Integrated system checkout report

    International Nuclear Information System (INIS)

    1991-01-01

    The planning and preparation phase of the Integrated Systems Checkout Program (ISCP) was conducted from October 1989 to July 1991. A copy of the ISCP, DOE-WIPP 90--002, is included in this report as an appendix. The final phase of the Checkout was conducted from July 10, 1991, to July 23, 1991. This phase exercised all the procedures and equipment required to receive, emplace, and retrieve contact handled transuranium (CH TRU) waste filled dry bins. In addition, abnormal events were introduced to simulate various equipment failures, loose surface radioactive contamination events, and personnel injury. This report provides a detailed summary of each days activities during this period. Qualification of personnel to safely conduct the tasks identified in the procedures and the abnormal events were verified by observers familiar with the Bin-Scale CH TRU Waste Test requirements. These observers were members of the staffs of Westinghouse WID Engineering, QA, Training, Health Physics, Safety, and SNL. Observers representing a number of DOE departments, the state of new Mexico, and the Defense Nuclear Facilities Safety Board observed those Checkout activities conducted during the period from July 17, 1991, to July 23, 1991. Observer comments described in this report are those obtained from the staff member observers. 1 figs., 1 tab

  17. 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

  18. 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.

  19. 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

  20. Comparing the neural basis of monetary reward and cognitive feedback during information-integration category learning.

    Science.gov (United States)

    Daniel, Reka; Pollmann, Stefan

    2010-01-06

    The dopaminergic system is known to play a central role in reward-based learning (Schultz, 2006), yet it was also observed to be involved when only cognitive feedback is given (Aron et al., 2004). Within the domain of information-integration category learning, in which information from several stimulus dimensions has to be integrated predecisionally (Ashby and Maddox, 2005), the importance of contingent feedback is well established (Maddox et al., 2003). We examined the common neural correlates of reward anticipation and prediction error in this task. Sixteen subjects performed two parallel information-integration tasks within a single event-related functional magnetic resonance imaging session but received a monetary reward only for one of them. Similar functional areas including basal ganglia structures were activated in both task versions. In contrast, a single structure, the nucleus accumbens, showed higher activation during monetary reward anticipation compared with the anticipation of cognitive feedback in information-integration learning. Additionally, this activation was predicted by measures of intrinsic motivation in the cognitive feedback task and by measures of extrinsic motivation in the rewarded task. Our results indicate that, although all other structures implicated in category learning are not significantly affected by altering the type of reward, the nucleus accumbens responds to the positive incentive properties of an expected reward depending on the specific type of the reward.

  1. 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.

  2. Advantages of comparative studies in songbirds to understand the neural basis of sensorimotor integration.

    Science.gov (United States)

    Murphy, Karagh; James, Logan S; Sakata, Jon T; Prather, Jonathan F

    2017-08-01

    Sensorimotor integration is the process through which the nervous system creates a link between motor commands and associated sensory feedback. This process allows for the acquisition and refinement of many behaviors, including learned communication behaviors such as speech and birdsong. Consequently, it is important to understand fundamental mechanisms of sensorimotor integration, and comparative analyses of this process can provide vital insight. Songbirds offer a powerful comparative model system to study how the nervous system links motor and sensory information for learning and control. This is because the acquisition, maintenance, and control of birdsong critically depend on sensory feedback. Furthermore, there is an incredible diversity of song organizations across songbird species, ranging from songs with simple, stereotyped sequences to songs with complex sequencing of vocal gestures, as well as a wide diversity of song repertoire sizes. Despite this diversity, the neural circuitry for song learning, control, and maintenance remains highly similar across species. Here, we highlight the utility of songbirds for the analysis of sensorimotor integration and the insights about mechanisms of sensorimotor integration gained by comparing different songbird species. Key conclusions from this comparative analysis are that variation in song sequence complexity seems to covary with the strength of feedback signals in sensorimotor circuits and that sensorimotor circuits contain distinct representations of elements in the vocal repertoire, possibly enabling evolutionary variation in repertoire sizes. We conclude our review by highlighting important areas of research that could benefit from increased comparative focus, with particular emphasis on the integration of new technologies. Copyright © 2017 the American Physiological Society.

  3. Integration of Neural Networks and Cellular Automata for Urban Planning

    Institute of Scientific and Technical Information of China (English)

    Anthony Gar-on Yeh; LI Xia

    2004-01-01

    This paper presents a new type of cellular automata (CA) model for the simulation of alternative land development using neural networks for urban planning. CA models can be regarded as a planning tool because they can generate alternative urban growth. Alternative development patterns can be formed by using different sets of parameter values in CA simulation. A critical issue is how to define parameter values for realistic and idealized simulation. This paper demonstrates that neural networks can simplify CA models but generate more plausible results. The simulation is based on a simple three-layer network with an output neuron to generate conversion probability. No transition rules are required for the simulation. Parameter values are automatically obtained from the training of network by using satellite remote sensing data. Original training data can be assessed and modified according to planning objectives. Alternative urban patterns can be easily formulated by using the modified training data sets rather than changing the model.

  4. Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems

    Science.gov (United States)

    Broccard, Frédéric D.; Joshi, Siddharth; Wang, Jun; Cauwenberghs, Gert

    2017-08-01

    Objective. Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks. Mixed analog/digital neuromorphic VLSI systems are compact, consume little power and operate in real time independently of the size and complexity of the model. Approach. This article highlights the current efforts to interface neuromorphic systems with neural systems at multiple levels of biological organization, from the synaptic to the system level, and discusses the prospects for future biohybrid systems with neuromorphic circuits of greater complexity. Main results. Single silicon neurons have been interfaced successfully with invertebrate and vertebrate neural networks. This approach allowed the investigation of neural properties that are inaccessible with traditional techniques while providing a realistic biological context not achievable with traditional numerical modeling methods. At the network level, populations of neurons are envisioned to communicate bidirectionally with neuromorphic processors of hundreds or thousands of silicon neurons. Recent work on brain-machine interfaces suggests that this is feasible with current neuromorphic technology. Significance. Biohybrid interfaces between biological neurons and VLSI neuromorphic systems of varying complexity have started to emerge in the literature. Primarily intended as a

  5. Experimental integrated photovoltaic systems

    International Nuclear Information System (INIS)

    Pop-Jordanov, Jordan; Markovska, Natasha; Dimitrov, D.; Kocev, K.; Dimitrovski, D.

    2000-01-01

    Recently, the interest in building-integrated photovoltaic installations has started to increase within governmental and municipality authorities, as well as some industrial companies. To serve a national public-awareness program of solar electricity promotion and education, the indigenous solar energy potential, optimization of possible PV installation, and three test cases of building-integrated grid-connected experimental facilities have been studied. The results showed the feasibility and performance of the proposed concepts. (Original)

  6. 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

  7. Neural substrates of reliability-weighted visual-tactile multisensory integration

    Directory of Open Access Journals (Sweden)

    Michael S Beauchamp

    2010-06-01

    Full Text Available As sensory systems deteriorate in aging or disease, the brain must relearn the appropriate weights to assign each modality during multisensory integration. Using blood-oxygen level dependent functional magnetic resonance imaging (BOLD fMRI of human subjects, we tested a model for the neural mechanisms of sensory weighting, termed “weighted connections”. This model holds that the connection weights between early and late areas vary depending on the reliability of the modality, independent of the level of early sensory cortex activity. When subjects detected viewed and felt touches to the hand, a network of brain areas was active, including visual areas in lateral occipital cortex, somatosensory areas in inferior parietal lobe, and multisensory areas in the intraparietal sulcus (IPS. In agreement with the weighted connection model, the connection weight measured with structural equation modeling between somatosensory cortex and IPS increased for somatosensory-reliable stimuli, and the connection weight between visual cortex and IPS increased for visual-reliable stimuli. This double dissociation of connection strengths was similar to the pattern of behavioral responses during incongruent multisensory stimulation, suggesting that weighted connections may be a neural mechanism for behavioral reliability weighting.for behavioral reliability weighting.

  8. 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

  9. Integrated systems innovations and applications

    CERN Document Server

    2015-01-01

    This book presents the results of discussions and presentation from the latest ISDT event (2014) which was dedicated to the 94th birthday anniversary of Prof. Lotfi A. Zade, father of Fuzzy logic. The book consists of three main chapters, namely: Chapter 1: Integrated Systems Design Chapter 2: Knowledge, Competence and Business Process Management Chapter 3: Integrated Systems Technologies Each article presents novel and scientific research results with respect to the target goal of improving our common understanding of KT integration.

  10. 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...

  11. 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.)

  12. 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.

  13. 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.

  14. Integrated RIS-PACS system

    International Nuclear Information System (INIS)

    Nishihara, Eitaro; Kura, Hiroyuki; Fukushima, Yuki

    1994-01-01

    We have developed an integrated RIS-PACS (radiology information system-picture archiving and communication system) system which supports examination, interpretation, and management in the diagnostic imaging department. The system was introduced in the Toshiba Hospital in May 1993, concurrently with the renewal of the hospital facilities. The integrated RIS-PACS system consists of a radiology information management system, and an image management system. The system supports wet (immediate) reading and chronological comparative reading using viewing workstation, enables routine operations to be performed in the diagnostic imaging department without film transportation, and contributes to the improvement of management efficiency in the department. (author)

  15. Duality for discrete integrable systems

    International Nuclear Information System (INIS)

    Quispel, G R W; Capel, H W; Roberts, J A G

    2005-01-01

    A new class of discrete dynamical systems is introduced via a duality relation for discrete dynamical systems with a number of explicitly known integrals. The dual equation can be defined via the difference of an arbitrary linear combination of integrals and its upshifted version. We give an example of an integrable mapping with two parameters and four integrals leading to a (four-dimensional) dual mapping with four parameters and two integrals. We also consider a more general class of higher-dimensional mappings arising via a travelling-wave reduction from the (integrable) MKdV partial-difference equation. By differencing the trace of the monodromy matrix we obtain a class of novel dual mappings which is shown to be integrable as level-set-dependent versions of the original ones

  16. 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.

  17. Neural associative memories for the integration of language, vision and action in an autonomous agent.

    Science.gov (United States)

    Markert, H; Kaufmann, U; Kara Kayikci, Z; Palm, G

    2009-03-01

    Language understanding is a long-standing problem in computer science. However, the human brain is capable of processing complex languages with seemingly no difficulties. This paper shows a model for language understanding using biologically plausible neural networks composed of associative memories. The model is able to deal with ambiguities on the single word and grammatical level. The language system is embedded into a robot in order to demonstrate the correct semantical understanding of the input sentences by letting the robot perform corresponding actions. For that purpose, a simple neural action planning system has been combined with neural networks for visual object recognition and visual attention control mechanisms.

  18. Smart systems integration and simulation

    CERN Document Server

    Poncino, Massimo; Pravadelli, Graziano

    2016-01-01

    This book-presents new methods and tools for the integration and simulation of smart devices. The design approach described in this book explicitly accounts for integration of Smart Systems components and subsystems as a specific constraint. It includes methodologies and EDA tools to enable multi-disciplinary and multi-scale modeling and design, simulation of multi-domain systems, subsystems and components at all levels of abstraction, system integration and exploration for optimization of functional and non-functional metrics. By covering theoretical and practical aspects of smart device design, this book targets people who are working and studying on hardware/software modelling, component integration and simulation under different positions (system integrators, designers, developers, researchers, teachers, students etc.). In particular, it is a good introduction to people who have interest in managing heterogeneous components in an efficient and effective way on different domains and different abstraction l...

  19. Identification of complex systems by artificial neural networks. Applications to mechanical frictions

    International Nuclear Information System (INIS)

    Dominguez, Manuel

    1998-01-01

    In the frame of complex systems modelization, we describe in this report the contribution of neural networks to mechanical friction modelization. This thesis is divided in three parts, each one corresponding to every stage of the realized work. The first part takes stock of the properties of neural networks by replacing them in the statistic frame of learning theory (particularly: non-linear and non-parametric regression models) and by showing the existing links with other more 'classic' techniques from automatics. We show then how identification models can be integrated in the neural networks description as a larger nonlinear model class. A methodology of neural networks use have been developed. We focused on validation techniques using correlation functions for non-linear systems, and on the use of regularization methods. The second part deals with the problematic of friction in mechanical systems. Particularly, we present the main current identified physical phenomena, which are integrated in advanced friction modelization. Characterization of these phenomena allows us to state a priori knowledge to be used in the identification stage. We expose some of the most well-known friction models: Dahl's model, Reset Integrator and Canuda's dynamical model, which are then used in simulation studies. The last part links the former one by illustrating a real-world application: an electric jack from SFIM-Industries, used in the Very Large Telescope (VLT) control scheme. This part begins with physical system presentation. The results are compared with more 'classic' methods. We finish using neural networks compensation scheme in closed-loop control. (author) [fr

  20. A Neural Networks Based Operation Guidance System for Procedure Presentation and Validation

    International Nuclear Information System (INIS)

    Seung, Kun Mo; Lee, Seung Jun; Seong, Poong Hyun

    2006-01-01

    In this paper, a neural network based operator support system is proposed to reduce operator's errors in abnormal situations in nuclear power plants (NPPs). There are many complicated situations, in which regular and suitable operations should be done by operators accordingly. In order to regulate and validate operators' operations, it is necessary to develop an operator support system which includes computer based procedures with the functions for operation validation. Many computerized procedures systems (CPS) have been recently developed. Focusing on the human machine interface (HMI) design and procedures' computerization, most of CPSs used various methodologies to enhance system's convenience, reliability and accessibility. Other than only showing procedures, the proposed system integrates a simple CPS and an operation validation system (OVS) by using artificial neural network (ANN) for operational permission and quantitative evaluation

  1. Integrated control systems

    International Nuclear Information System (INIS)

    Smith, D.J.

    1991-01-01

    This paper reports that instrument manufacturers must develop standard network interfaces to pull together interrelated systems such as automatic start-up, optimization programs, and online diagnostic systems. In the past individual control system manufacturers have developed their own data highways with proprietary hardware and software designs. In the future, electric utilities will require that future systems, irrespective of manufacturer, should be able to communicate with each other. Until now the manufactures of control systems have not agreed on the standard high-speed data highway system. Currently, the Electric Power Research Institute (EPRI), in conjunction with several electric utilities and equipment manufactures, is working on developing a standard protocol for communicating between various manufacturers' control systems. According to N. Michael of Sargent and Lundy, future control room designs will require that more of the control and display functions be accessible from the control room through CRTs. There will be less emphasis on traditional hard-wired control panels

  2. An integrated CANDU system

    International Nuclear Information System (INIS)

    Donnelly, J.

    1982-09-01

    Twenty years of experience have shown that the early choices of heavy water as moderator and natural uranium as fuel imposed a discipline on CANDU design that has led to outstanding performance. The integrated structure of the industry in Canada, incorporating development, design, supply, manufacturing, and operation functions, has reinforced this performance and has provided a basis on which to continue development in the future. These same fundamental characteristics of the CANDU program open up propsects for further improvements in economy and resource utilization through increased reactor size and the development of the thorium fuel cycle

  3. Two new discrete integrable systems

    International Nuclear Information System (INIS)

    Chen Xiao-Hong; Zhang Hong-Qing

    2013-01-01

    In this paper, we focus on the construction of new (1+1)-dimensional discrete integrable systems according to a subalgebra of loop algebra à 1 . By designing two new (1+1)-dimensional discrete spectral problems, two new discrete integrable systems are obtained, namely, a 2-field lattice hierarchy and a 3-field lattice hierarchy. When deriving the two new discrete integrable systems, we find the generalized relativistic Toda lattice hierarchy and the generalized modified Toda lattice hierarchy. Moreover, we also obtain the Hamiltonian structures of the two lattice hierarchies by means of the discrete trace identity

  4. Secure integrated circuits and systems

    CERN Document Server

    Verbauwhede, Ingrid MR

    2010-01-01

    On any advanced integrated circuit or 'system-on-chip' there is a need for security. In many applications the actual implementation has become the weakest link in security rather than the algorithms or protocols. The purpose of the book is to give the integrated circuits and systems designer an insight into the basics of security and cryptography from the implementation point of view. As a designer of integrated circuits and systems it is important to know both the state-of-the-art attacks as well as the countermeasures. Optimizing for security is different from optimizations for speed, area,

  5. 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.

  6. Integrated Eye Tracking and Neural Monitoring for Enhanced Assessment of Mild TBI

    Science.gov (United States)

    2017-06-01

    working memory load effects after mild traumatic brain injury. Neuroimage, 2001. 14(5): p. 1004-12. 2. Chen, J.K., et al., Functional abnormalities in...report. 10 Supporting Data None. Integrated Eye Tracking and Neural Monitoring for Enhanced Assessment of Mild TBI Psychological Health

  7. Neural basis of limb ownership in individuals with body integrity identity disorder

    NARCIS (Netherlands)

    van Dijk, Milenna T.; van Wingen, Guido A.; van Lammeren, Anouk; Blom, Rianne M.; de Kwaasteniet, Bart P.; Scholte, H. Steven; Denys, Damiaan

    2013-01-01

    Our body feels like it is ours. However, individuals with body integrity identity disorder (BIID) lack this feeling of ownership for distinct limbs and desire amputation of perfectly healthy body parts. This extremely rare condition provides us with an opportunity to study the neural basis

  8. Integrated low noise low power interface for neural bio-potentials recording and conditioning

    Science.gov (United States)

    Bottino, Emanuele; Martinoia, Sergio; Valle, Maurizio

    2005-06-01

    The recent progress in both neurobiology and microelectronics suggests the creation of new, powerful tools to investigate the basic mechanisms of brain functionality. In particular, a lot of efforts are spent by scientific community to define new frameworks devoted to the analysis of in-vitro cultured neurons. One possible approach is recording their spiking activity to monitor the coordinated cellular behaviour and get insights about neural plasticity. Due to the nature of neurons action-potentials, when considering the design of an integrated microelectronic-based recording system, a number of problems arise. First, one would desire to have a high number of recording sites (i.e. several hundreds): this poses constraints on silicon area and power consumption. In this regard, our aim is to integrate-through on-chip post-processing techniques-hundreds of bio-compatible microsensors together with CMOS standard-process low-power (i.e. some tenths of uW per channel) conditioning electronics. Each recording channel is provided with sampling electronics to insure synchronous recording so that, for example, cross-correlation between signals coming from different sites can be performed. Extra-cellular potentials are in the range of [50-150] uV, so a comparison in terms of noise-efficiency was carried out among different architectures and very low-noise pre-amplification electronics (i.e. less than 5 uVrms) was designed. As spikes measurements are made with respect to the voltage of a reference electrode, we opted for an AC-coupled differential-input preamplifier provided with band-pass filtering capability. To achieve this, we implemented large time-constant (up to seconds) integrated components in the preamp feedback path. Thus, we got rid also of random slow-drifting DC-offsets and common mode signals. The paper will present our achievements in the design and implementation of a fully integrated bio-abio interface to record neural spiking activity. In particular

  9. The role of neural networks in nuclear power plant safety systems

    International Nuclear Information System (INIS)

    Boger, Z.

    1993-01-01

    Neural networks (NN) techniques have been applied in recent years to many systems by researchers in the nuclear power industry, mainly for modeling and sensor validation. Recent results are reviewed, including new directions in applications to control systems, safety analysis, and ''virtual'' instruments. As new fast learning algorithms become available, large systems may be learned effectively, even with few training examples. The nuclear industry hesitates to include NN in safety related systems, but it seems that the obstacles could be overcome with the demonstration of successful applications, even from other industries. Coupling of full-scale reactor simulators, as fault database generators, with neural networks learning should be explored. The integration of Expert System technology with NN should improve the Validation and Verification tasks, and also help overcome psychological barriers. It may prove that the potential of NN to help operators, compared with the existing and proposed alternatives, outweigh the risks. (author). 58 refs, 2 figs

  10. 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.

  11. 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.

  12. Integrated Risk Information System (IRIS)

    Data.gov (United States)

    U.S. Environmental Protection Agency — EPA?s Integrated Risk Information System (IRIS) is a compilation of electronic reports on specific substances found in the environment and their potential to cause...

  13. PC driven integrated vacuum system

    International Nuclear Information System (INIS)

    Curuia, M.; Culcer, M.; Brandea, I.; Anghel, M.

    2001-01-01

    The paper presents a integrated vacuum system which was designed and manufactured in our institute. The main parts of this system are the power supply unit for turbo-melecular pumps and the vacuummeter. Both parts of the system are driven by means of a personal computer using a serial communication, according to the RS 232 hardware standard.(author)

  14. An Integrated Knowledge Management System

    Directory of Open Access Journals (Sweden)

    Vasile Mazilescu

    2014-11-01

    Full Text Available The aim of this paper is to present a Knowledge Management System based on Fuzzy Logic (FLKMS, a real-time expert system to meet the challenges of the dynamic environment. The main feature of our integrated shell FLKMS is that it models and integrates the temporal relationships between the dynamic of the evolution of an economic process with some fuzzy inferential methods, using a knowledge model for control, embedded within the expert system’s operational knowledge base.

  15. The neural circuits of innate fear: detection, integration, action, and memorization

    Science.gov (United States)

    Silva, Bianca A.; Gross, Cornelius T.

    2016-01-01

    How fear is represented in the brain has generated a lot of research attention, not only because fear increases the chances for survival when appropriately expressed but also because it can lead to anxiety and stress-related disorders when inadequately processed. In this review, we summarize recent progress in the understanding of the neural circuits processing innate fear in rodents. We propose that these circuits are contained within three main functional units in the brain: a detection unit, responsible for gathering sensory information signaling the presence of a threat; an integration unit, responsible for incorporating the various sensory information and recruiting downstream effectors; and an output unit, in charge of initiating appropriate bodily and behavioral responses to the threatful stimulus. In parallel, the experience of innate fear also instructs a learning process leading to the memorization of the fearful event. Interestingly, while the detection, integration, and output units processing acute fear responses to different threats tend to be harbored in distinct brain circuits, memory encoding of these threats seems to rely on a shared learning system. PMID:27634145

  16. Neural systems analysis of decision making during goal-directed navigation.

    Science.gov (United States)

    Penner, Marsha R; Mizumori, Sheri J Y

    2012-01-01

    The ability to make adaptive decisions during goal-directed navigation is a fundamental and highly evolved behavior that requires continual coordination of perceptions, learning and memory processes, and the planning of behaviors. Here, a neurobiological account for such coordination is provided by integrating current literatures on spatial context analysis and decision-making. This integration includes discussions of our current understanding of the role of the hippocampal system in experience-dependent navigation, how hippocampal information comes to impact midbrain and striatal decision making systems, and finally the role of the striatum in the implementation of behaviors based on recent decisions. These discussions extend across cellular to neural systems levels of analysis. Not only are key findings described, but also fundamental organizing principles within and across neural systems, as well as between neural systems functions and behavior, are emphasized. It is suggested that studying decision making during goal-directed navigation is a powerful model for studying interactive brain systems and their mediation of complex behaviors. Copyright © 2011. Published by Elsevier Ltd.

  17. [Ocular surface system integrity].

    Science.gov (United States)

    Safonova, T N; Pateyuk, L S

    2015-01-01

    The interplay of different structures belonging to either the anterior segment of the eye or its accessory visual apparatus, which all share common embryological, anatomical, functional, and physiological features, is discussed. Explanation of such terms, as ocular surface, lacrimal functional unit, and ocular surface system, is provided.

  18. Integrated turbine bypass system

    Energy Technology Data Exchange (ETDEWEB)

    Johnson, L.H.; Dickenson, R.J.; Parry, W.T.; Retzlaff, K.M.

    1982-07-01

    Turbine steam-flow bypasses have been used for years in various sizes and applications. Because of differing system requirements, their use has been more predominant in Europe than in the United States. Recently, some utilities and consulting engineers have been re-evaluating their need for various types of bypass operation in fossil-fuelled power plants.

  19. Integrated system reliability analysis

    DEFF Research Database (Denmark)

    Gintautas, Tomas; Sørensen, John Dalsgaard

    Specific targets: 1) The report shall describe the state of the art of reliability and risk-based assessment of wind turbine components. 2) Development of methodology for reliability and risk-based assessment of the wind turbine at system level. 3) Describe quantitative and qualitative measures...

  20. Dynamic stability analysis of fractional order leaky integrator echo state neural networks

    Science.gov (United States)

    Pahnehkolaei, Seyed Mehdi Abedi; Alfi, Alireza; Tenreiro Machado, J. A.

    2017-06-01

    The Leaky integrator echo state neural network (Leaky-ESN) is an improved model of the recurrent neural network (RNN) and adopts an interconnected recurrent grid of processing neurons. This paper presents a new proof for the convergence of a Lyapunov candidate function to zero when time tends to infinity by means of the Caputo fractional derivative with order lying in the range (0, 1). The stability of Fractional-Order Leaky-ESN (FO Leaky-ESN) is then analyzed, and the existence, uniqueness and stability of the equilibrium point are provided. A numerical example demonstrates the feasibility of the proposed method.

  1. Intelligent Integrated System Health Management

    Science.gov (United States)

    Figueroa, Fernando

    2012-01-01

    Intelligent Integrated System Health Management (ISHM) is the management of data, information, and knowledge (DIaK) with the purposeful objective of determining the health of a system (Management: storage, distribution, sharing, maintenance, processing, reasoning, and presentation). Presentation discusses: (1) ISHM Capability Development. (1a) ISHM Knowledge Model. (1b) Standards for ISHM Implementation. (1c) ISHM Domain Models (ISHM-DM's). (1d) Intelligent Sensors and Components. (2) ISHM in Systems Design, Engineering, and Integration. (3) Intelligent Control for ISHM-Enabled Systems

  2. 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 ...

  3. 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.

  4. Integrated Project Management System description

    International Nuclear Information System (INIS)

    1987-03-01

    The Uranium Mill Tailings Remedial Action (UMTRA) Project is a Department of Energy (DOE) designated Major System Acquisition (MSA). To execute and manage the Project mission successfully and to comply with the MSA requirements, the UMTRA Project Office (''Project Office'') has implemented and operates an Integrated Project Management System (IPMS). The Project Office is assisted by the Technical Assistance Contractor's (TAC) Project Integration and Control (PIC) Group in system operation. Each participant, in turn, provides critical input to system operation and reporting requirements. The IPMS provides a uniform structured approach for integrating the work of Project participants. It serves as a tool for planning and control, workload management, performance measurement, and specialized reporting within a standardized format. This system description presents the guidance for its operation. Appendices 1 and 2 contain definitions of commonly used terms and abbreviations and acronyms, respectively. 17 figs., 5 tabs

  5. An Integrated Circuit for Simultaneous Extracellular Electrophysiology Recording and Optogenetic Neural Manipulation.

    Science.gov (United States)

    Chen, Chang Hao; McCullagh, Elizabeth A; Pun, Sio Hang; Mak, Peng Un; Vai, Mang I; Mak, Pui In; Klug, Achim; Lei, Tim C

    2017-03-01

    The ability to record and to control action potential firing in neuronal circuits is critical to understand how the brain functions. The objective of this study is to develop a monolithic integrated circuit (IC) to record action potentials and simultaneously control action potential firing using optogenetics. A low-noise and high input impedance (or low input capacitance) neural recording amplifier is combined with a high current laser/light-emitting diode (LED) driver in a single IC. The low input capacitance of the amplifier (9.7 pF) was achieved by adding a dedicated unity gain stage optimized for high impedance metal electrodes. The input referred noise of the amplifier is [Formula: see text], which is lower than the estimated thermal noise of the metal electrode. Thus, the action potentials originating from a single neuron can be recorded with a signal-to-noise ratio of at least 6.6. The LED/laser current driver delivers a maximum current of 330 mA, which is adequate for optogenetic control. The functionality of the IC was tested with an anesthetized Mongolian gerbil and auditory stimulated action potentials were recorded from the inferior colliculus. Spontaneous firings of fifth (trigeminal) nerve fibers were also inhibited using the optogenetic protein Halorhodopsin. Moreover, a noise model of the system was derived to guide the design. A single IC to measure and control action potentials using optogenetic proteins is realized so that more complicated behavioral neuroscience research and the translational neural disorder treatments become possible in the future.

  6. Radiation oncology systems integration

    International Nuclear Information System (INIS)

    Ragan, D.P.

    1991-01-01

    ROLE7 is intended as a complementary addition to the HL7 Standard and not as an alternative standard. Attempt should be made to mould data elements which are specific to radiation therapy with existing HL7 elements. This can be accomplished by introducing additional values to some element's table-of-options. Those elements which might be specific to radiation therapy could from new segments to be added to the Ancillary Data Reporting set. In order to accomplish ROLE7, consensus groups need be formed to identify the various functions related to radiation oncology that might motivate information exchange. For each of these functions, the specific data elements and their format must be identified. HL7 is organized with a number of applications which communicate asynchronously. Implementation of ROLE7 would allow uniform access to information across vendors and functions. It would provide improved flexibility in system selection. It would allow a more flexible and affordable upgrade path as systems in radiation oncology improve. (author). 5 refs

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

    Science.gov (United States)

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

    2016-09-01

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

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

    Science.gov (United States)

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

    2017-08-01

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

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

    International Nuclear Information System (INIS)

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

    2008-01-01

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

  10. DKIST facility management system integration

    Science.gov (United States)

    White, Charles R.; Phelps, LeEllen

    2016-07-01

    The Daniel K. Inouye Solar Telescope (DKIST) Observatory is under construction at Haleakalā, Maui, Hawai'i. When complete, the DKIST will be the largest solar telescope in the world. The Facility Management System (FMS) is a subsystem of the high-level Facility Control System (FCS) and directly controls the Facility Thermal System (FTS). The FMS receives operational mode information from the FCS while making process data available to the FCS and includes hardware and software to integrate and control all aspects of the FTS including the Carousel Cooling System, the Telescope Chamber Environmental Control Systems, and the Temperature Monitoring System. In addition it will integrate the Power Energy Management System and several service systems such as heating, ventilation, and air conditioning (HVAC), the Domestic Water Distribution System, and the Vacuum System. All of these subsystems must operate in coordination to provide the best possible observing conditions and overall building management. Further, the FMS must actively react to varying weather conditions and observational requirements. The physical impact of the facility must not interfere with neighboring installations while operating in a very environmentally and culturally sensitive area. The FMS system will be comprised of five Programmable Automation Controllers (PACs). We present a pre-build overview of the functional plan to integrate all of the FMS subsystems.

  11. 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.

  12. 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.

  13. 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

  14. Crossmodal integration enhances neural representation of task-relevant features in audiovisual face perception.

    Science.gov (United States)

    Li, Yuanqing; Long, Jinyi; Huang, Biao; Yu, Tianyou; Wu, Wei; Liu, Yongjian; Liang, Changhong; Sun, Pei

    2015-02-01

    Previous studies have shown that audiovisual integration improves identification performance and enhances neural activity in heteromodal brain areas, for example, the posterior superior temporal sulcus/middle temporal gyrus (pSTS/MTG). Furthermore, it has also been demonstrated that attention plays an important role in crossmodal integration. In this study, we considered crossmodal integration in audiovisual facial perception and explored its effect on the neural representation of features. The audiovisual stimuli in the experiment consisted of facial movie clips that could be classified into 2 gender categories (male vs. female) or 2 emotion categories (crying vs. laughing). The visual/auditory-only stimuli were created from these movie clips by removing the auditory/visual contents. The subjects needed to make a judgment about the gender/emotion category for each movie clip in the audiovisual, visual-only, or auditory-only stimulus condition as functional magnetic resonance imaging (fMRI) signals were recorded. The neural representation of the gender/emotion feature was assessed using the decoding accuracy and the brain pattern-related reproducibility indices, obtained by a multivariate pattern analysis method from the fMRI data. In comparison to the visual-only and auditory-only stimulus conditions, we found that audiovisual integration enhanced the neural representation of task-relevant features and that feature-selective attention might play a role of modulation in the audiovisual integration. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  15. Neural Circuit to Integrate Opposing Motions in the Visual Field.

    Science.gov (United States)

    Mauss, Alex S; Pankova, Katarina; Arenz, Alexander; Nern, Aljoscha; Rubin, Gerald M; Borst, Alexander

    2015-07-16

    When navigating in their environment, animals use visual motion cues as feedback signals that are elicited by their own motion. Such signals are provided by wide-field neurons sampling motion directions at multiple image points as the animal maneuvers. Each one of these neurons responds selectively to a specific optic flow-field representing the spatial distribution of motion vectors on the retina. Here, we describe the discovery of a group of local, inhibitory interneurons in the fruit fly Drosophila key for filtering these cues. Using anatomy, molecular characterization, activity manipulation, and physiological recordings, we demonstrate that these interneurons convey direction-selective inhibition to wide-field neurons with opposite preferred direction and provide evidence for how their connectivity enables the computation required for integrating opposing motions. Our results indicate that, rather than sharpening directional selectivity per se, these circuit elements reduce noise by eliminating non-specific responses to complex visual information. Copyright © 2015 Elsevier Inc. All rights reserved.

  16. Neural multigrid for gauge theories and other disordered systems

    International Nuclear Information System (INIS)

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

    1992-09-01

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

  17. Neural computing thermal comfort index for HVAC systems

    International Nuclear Information System (INIS)

    Atthajariyakul, S.; Leephakpreeda, T.

    2005-01-01

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

  18. Energy Systems Integration News | Energy Systems Integration Facility |

    Science.gov (United States)

    , utilities can operate more efficiently and profitably. That can increase the use of renewable energy sources challenge to utility companies, grid operators, and other stakeholders involved in wind energy integration recording is available from the July 16 webinar "Smart Grid Research at NREL's Energy Systems

  19. 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.

  20. Cost reduction through system integration

    International Nuclear Information System (INIS)

    Helsing, P.

    1994-01-01

    In resent years cost reduction has been a key issue in the petroleum industry. Several findings are not economically attractive at the current cost level, and for this and other reasons some of the major oil companies require the suppliers to have implemented a cost reduction programme to prequalify for projects. The present paper addresses cost reduction through system design and integration in both product development and working methods. This is to be obtained by the combination of contracts by reducing unnecessary coordination and allow re-use of proven interface designs, improve subsystem integration by ''top down'' system design, and improve communication and exchange of experience. 3 figs

  1. Efficient Embedded Decoding of Neural Network Language Models in a Machine Translation System.

    Science.gov (United States)

    Zamora-Martinez, Francisco; Castro-Bleda, Maria Jose

    2018-02-22

    Neural Network Language Models (NNLMs) are a successful approach to Natural Language Processing tasks, such as Machine Translation. We introduce in this work a Statistical Machine Translation (SMT) system which fully integrates NNLMs in the decoding stage, breaking the traditional approach based on [Formula: see text]-best list rescoring. The neural net models (both language models (LMs) and translation models) are fully coupled in the decoding stage, allowing to more strongly influence the translation quality. Computational issues were solved by using a novel idea based on memorization and smoothing of the softmax constants to avoid their computation, which introduces a trade-off between LM quality and computational cost. These ideas were studied in a machine translation task with different combinations of neural networks used both as translation models and as target LMs, comparing phrase-based and [Formula: see text]-gram-based systems, showing that the integrated approach seems more promising for [Formula: see text]-gram-based systems, even with nonfull-quality NNLMs.

  2. 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.

  3. A neurally inspired musical instrument classification system based upon the sound onset.

    Science.gov (United States)

    Newton, Michael J; Smith, Leslie S

    2012-06-01

    Physiological evidence suggests that sound onset detection in the auditory system may be performed by specialized neurons as early as the cochlear nucleus. Psychoacoustic evidence shows that the sound onset can be important for the recognition of musical sounds. Here the sound onset is used in isolation to form tone descriptors for a musical instrument classification task. The task involves 2085 isolated musical tones from the McGill dataset across five instrument categories. A neurally inspired tone descriptor is created using a model of the auditory system's response to sound onset. A gammatone filterbank and spiking onset detectors, built from dynamic synapses and leaky integrate-and-fire neurons, create parallel spike trains that emphasize the sound onset. These are coded as a descriptor called the onset fingerprint. Classification uses a time-domain neural network, the echo state network. Reference strategies, based upon mel-frequency cepstral coefficients, evaluated either over the whole tone or only during the sound onset, provide context to the method. Classification success rates for the neurally-inspired method are around 75%. The cepstral methods perform between 73% and 76%. Further testing with tones from the Iowa MIS collection shows that the neurally inspired method is considerably more robust when tested with data from an unrelated dataset.

  4. Multiloop integral system test (MIST)

    International Nuclear Information System (INIS)

    Gloudemans, J.R.

    1989-07-01

    The multiloop integral system test (MIST) was part of a multiphase program started in 1983 to address small-break loss-of-coolant accidents (SBLOCAs) specific to Babcock and Wilcox-designed plants. MIST was sponsored by the US Nuclear Regulatory Commission, the Babcock and Wilcox Owners Group, the Electric Power Research Institute, and Babcock and Wilcox. The unique features of the Babcock and Wilcox design, specifically the hot leg U-bends and steam generators, prevented the use of existing integral system data or existing integral system facilities to address the thermal-hydraulic SBLOCA questions. MIST and two other supporting facilities were specifically designed and constructed for this program, and an existing facility -- the once-through integral system (OTIS) -- was also used. Data from MIST and the other facilities will be used to benchmark the adequacy of system codes, such as RELAP5 and TRAC, for predicting abnormal plant transients. The individual tests are described in detail in Volumes 2 through 8 and Volume 11, and are summarized in Volume 1. Inter-group comparisons are addressed in this document, Volume 9. These comparisons are grouped as follows: mapping versus SBLOCA transients, SBLOCA, pump effects, and the effects of noncondensible gases. Appendix A provides an index and description of the microfiched plots for each test, which are enclosed with the corresponding Volumes 2 through 8. 147 figs., 5 tabs

  5. 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

  6. Neural Basis of Limb Ownership in Individuals with Body Integrity Identity Disorder

    OpenAIRE

    van Dijk, Milenna T.; van Wingen, Guido A.; van Lammeren, Anouk; Blom, Rianne M.; de Kwaasteniet, Bart P.; Scholte, H. Steven; Denys, Damiaan

    2013-01-01

    Our body feels like it is ours. However, individuals with body integrity identity disorder (BIID) lack this feeling of ownership for distinct limbs and desire amputation of perfectly healthy body parts. This extremely rare condition provides us with an opportunity to study the neural basis underlying the feeling of limb ownership, since these individuals have a feeling of disownership for a limb in the absence of apparent brain damage. Here we directly compared brain activation between limbs ...

  7. 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.

  8. 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.

  9. A wireless integrated circuit for 100-channel charge-balanced neural stimulation.

    Science.gov (United States)

    Thurgood, B K; Warren, D J; Ledbetter, N M; Clark, G A; Harrison, R R

    2009-12-01

    The authors present the design of an integrated circuit for wireless neural stimulation, along with benchtop and in - vivo experimental results. The chip has the ability to drive 100 individual stimulation electrodes with constant-current pulses of varying amplitude, duration, interphasic delay, and repetition rate. The stimulation is performed by using a biphasic (cathodic and anodic) current source, injecting and retracting charge from the nervous system. Wireless communication and power are delivered over a 2.765-MHz inductive link. Only three off-chip components are needed to operate the stimulator: a 10-nF capacitor to aid in power-supply regulation, a small capacitor (power and command reception. The chip was fabricated in a commercially available 0.6- mum 2P3M BiCMOS process. The chip was able to activate motor fibers to produce muscle twitches via a Utah Slanted Electrode Array implanted in cat sciatic nerve, and to activate sensory fibers to recruit evoked potentials in somatosensory cortex.

  10. Implementation of integrated management system

    International Nuclear Information System (INIS)

    Gaspar Junior, Joao Carlos A.; Fonseca, Victor Zidan da

    2007-01-01

    In present day exist quality assurance system, environment, occupational health and safety such as ISO9001, ISO14001 and OHSAS18001 and others standards will can create. These standards can be implemented and certified they guarantee one record system, quality assurance, documents control, operational control, responsibility definition, training, preparing and serve to emergency, monitoring, internal audit, corrective action, continual improvement, prevent of pollution, write procedure, reduce costs, impact assessment, risk assessment , standard, decree, legal requirements of municipal, state, federal and local scope. These procedure and systems when isolate applied cause many management systems and bureaucracy. Integration Management System reduce to bureaucracy, excess of documents, documents storage and conflict documents and easy to others standards implementation in future. The Integrated Management System (IMS) will be implemented in 2007. INB created a management group for implementation, this group decides planing, works, policy and advertisement. Legal requirements were surveyed, internal audits, pre-audits and audits were realized. INB is partially in accordance with ISO14001, OSHAS18001 standards. But very soon, it will be totally in accordance with this norms. Many studies and works were contracted to deal with legal requirements. This work have intention of show implementation process of ISO14001, OHSAS18001 and Integrated Management System on INB. (author)

  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. A model for integrating elementary neural functions into delayed-response behavior.

    Directory of Open Access Journals (Sweden)

    Thomas Gisiger

    2006-04-01

    Full Text Available It is well established that various cortical regions can implement a wide array of neural processes, yet the mechanisms which integrate these processes into behavior-producing, brain-scale activity remain elusive. We propose that an important role in this respect might be played by executive structures controlling the traffic of information between the cortical regions involved. To illustrate this hypothesis, we present a neural network model comprising a set of interconnected structures harboring stimulus-related activity (visual representation, working memory, and planning, and a group of executive units with task-related activity patterns that manage the information flowing between them. The resulting dynamics allows the network to perform the dual task of either retaining an image during a delay (delayed-matching to sample task, or recalling from this image another one that has been associated with it during training (delayed-pair association task. The model reproduces behavioral and electrophysiological data gathered on the inferior temporal and prefrontal cortices of primates performing these same tasks. It also makes predictions on how neural activity coding for the recall of the image associated with the sample emerges and becomes prospective during the training phase. The network dynamics proves to be very stable against perturbations, and it exhibits signs of scale-invariant organization and cooperativity. The present network represents a possible neural implementation for active, top-down, prospective memory retrieval in primates. The model suggests that brain activity leading to performance of cognitive tasks might be organized in modular fashion, simple neural functions becoming integrated into more complex behavior by executive structures harbored in prefrontal cortex and/or basal ganglia.

  15. A model for integrating elementary neural functions into delayed-response behavior.

    Science.gov (United States)

    Gisiger, Thomas; Kerszberg, Michel

    2006-04-01

    It is well established that various cortical regions can implement a wide array of neural processes, yet the mechanisms which integrate these processes into behavior-producing, brain-scale activity remain elusive. We propose that an important role in this respect might be played by executive structures controlling the traffic of information between the cortical regions involved. To illustrate this hypothesis, we present a neural network model comprising a set of interconnected structures harboring stimulus-related activity (visual representation, working memory, and planning), and a group of executive units with task-related activity patterns that manage the information flowing between them. The resulting dynamics allows the network to perform the dual task of either retaining an image during a delay (delayed-matching to sample task), or recalling from this image another one that has been associated with it during training (delayed-pair association task). The model reproduces behavioral and electrophysiological data gathered on the inferior temporal and prefrontal cortices of primates performing these same tasks. It also makes predictions on how neural activity coding for the recall of the image associated with the sample emerges and becomes prospective during the training phase. The network dynamics proves to be very stable against perturbations, and it exhibits signs of scale-invariant organization and cooperativity. The present network represents a possible neural implementation for active, top-down, prospective memory retrieval in primates. The model suggests that brain activity leading to performance of cognitive tasks might be organized in modular fashion, simple neural functions becoming integrated into more complex behavior by executive structures harbored in prefrontal cortex and/or basal ganglia.

  16. Integrated Systems Health Management for Intelligent Systems

    Science.gov (United States)

    Figueroa, Fernando; Melcher, Kevin

    2011-01-01

    The implementation of an integrated system health management (ISHM) capability is fundamentally linked to the management of data, information, and knowledge (DIaK) with the purposeful objective of determining the health of a system. It is akin to having a team of experts who are all individually and collectively observing and analyzing a complex system, and communicating effectively with each other in order to arrive at an accurate and reliable assessment of its health. In this paper, concepts, procedures, and approaches are presented as a foundation for implementing an intelligent systems ]relevant ISHM capability. The capability stresses integration of DIaK from all elements of a system. Both ground-based (remote) and on-board ISHM capabilities are compared and contrasted. The information presented is the result of many years of research, development, and maturation of technologies, and of prototype implementations in operational systems.

  17. Integrated logistic support analysis system

    International Nuclear Information System (INIS)

    Carnicero Iniguez, E.J.; Garcia de la Sen, R.

    1993-01-01

    Integrating logic support into a system results in a large volume of information having to be managed which can only be achieved with the help of computer applications. Both past experience and growing needs in such tasks have led Emperesarios Agrupados to undertake an ambitious development project which is described in this paper. (author)

  18. Semiclassical geometry of integrable systems

    Science.gov (United States)

    Reshetikhin, Nicolai

    2018-04-01

    The main result of this paper is a formula for the scalar product of semiclassical eigenvectors of two integrable systems on the same symplectic manifold. An important application of this formula is the Ponzano–Regge type of asymptotic of Racah–Wigner coefficients. Dedicated to the memory of P P Kulish.

  19. Jacobi fields of completely integrable Hamiltonian systems

    International Nuclear Information System (INIS)

    Giachetta, G.; Mangiarotti, L.; Sardanashvily, G.

    2003-01-01

    We show that Jacobi fields of a completely integrable Hamiltonian system of m degrees of freedom make up an extended completely integrable system of 2m degrees of freedom, where m additional first integrals characterize a relative motion

  20. Waste assay measurement integration system user interface

    International Nuclear Information System (INIS)

    Mousseau, K.C.; Hempstead, A.R.; Becker, G.K.

    1995-01-01

    The Waste Assay Measurement Integration System (WAMIS) is being developed to improve confidence in and lower the uncertainty of waste characterization data. There are two major components to the WAMIS: a data access and visualization component and a data interpretation component. The intent of the access and visualization software is to provide simultaneous access to all data sources that describe the contents of any particular container of waste. The visualization software also allows the user to display data at any level from raw to reduced output. Depending on user type, the software displays a menuing hierarchy, related to level of access, that allows the user to observe only those data sources s/he has been authorized to view. Access levels include system administrator, physicist, QA representative, shift operations supervisor, and data entry. Data sources are displayed in separate windows and presently include (1) real-time radiography video, (2) gamma spectra, (3) passive and active neutron, (4) radionuclide mass estimates, (5) total alpha activity (Ci), (6) container attributes, (7) thermal power (w), and (8) mass ratio estimates for americium, plutonium, and uranium isotopes. The data interpretation component is in the early phases of design, but will include artificial intelligence, expert system, and neural network techniques. The system is being developed on a Pentium PC using Microsoft Visual C++. Future generations of WAMIS will be UNIX based and will incorporate more generically radiographic/tomographic, gamma spectroscopic/tomographics, neutron, and prompt gamma measurements

  1. 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.

  2. 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

  3. ITER lower port systems integration

    Energy Technology Data Exchange (ETDEWEB)

    Levesy, B., E-mail: bruno.levesy@iter.org [ITER Organization, CS 90 046, 13067 St Paul Lez Durance Cedex (France); Baker, D.; Boussier, B.; Bryan, S.; Cordier, J.J.; Dremel, M.; Dell' Orco, G.; Daly, E.; Doshi, B.; Jeannoutot, T.; Friconneau, J.P.; Gliss, C.; Le Barbier, R.; Lachevre, F.; Loughlin, M.; Martin, A.; Martins, J.P.; Maruyama, S.; Palmer, J.; Reichle, R. [ITER Organization, CS 90 046, 13067 St Paul Lez Durance Cedex (France)

    2011-10-15

    The lower port systems are installed inside the vacuum vessel lower ports and in the adjacent port cells. The vacuum vessel ports and penetrations are allocated as follow: -4 ports dedicated to remote handling of the divertor cassettes, contain diagnostics racks and divertor cooling pipes. -5 ports connecting the main vessel to the torus cryopumps, contain divertor cooling pipes, pellet and gas injection pipes and vertical stabilization coil feeders. -3 penetrations connecting torus cryopumps are connected to the vacuum vessel by branch pipes. -Specific penetrations for divertor cooling lines, in-vessel viewing and glow discharge systems. The general layout of the port systems has been revised recently to improve the cryopump (8 t weight, 1.8 m diameter and 2.5 m long) maintenance scheme with remote handling tools and integrate the in-vessel vertical stabilization coil feeders. The port allocation, the pumping ports design, and interfaces in-between ports and cryostat and in-between cryopumps and cryostat have been up-dated. The integration inside the 18 port cells (11 m x 4 m each) has been reviewed to avoid clashes in between systems and to fix the openings in the port cell concrete walls. The new layout integrates safety and neutron-shielding requirements as well as remote handling and maintenance compatibility for the different systems. The paper presents the up-dated integration of the lower port systems inside the ports and the port cells. Interfaces of the port systems with the vacuum vessel, the cryostat and the port cells are described.

  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. Stress affects the neural ensemble for integrating new information and prior knowledge.

    Science.gov (United States)

    Vogel, Susanne; Kluen, Lisa Marieke; Fernández, Guillén; Schwabe, Lars

    2018-06-01

    Prior knowledge, represented as a schema, facilitates memory encoding. This schema-related learning is assumed to rely on the medial prefrontal cortex (mPFC) that rapidly integrates new information into the schema, whereas schema-incongruent or novel information is encoded by the hippocampus. Stress is a powerful modulator of prefrontal and hippocampal functioning and first studies suggest a stress-induced deficit of schema-related learning. However, the underlying neural mechanism is currently unknown. To investigate the neural basis of a stress-induced schema-related learning impairment, participants first acquired a schema. One day later, they underwent a stress induction or a control procedure before learning schema-related and novel information in the MRI scanner. In line with previous studies, learning schema-related compared to novel information activated the mPFC, angular gyrus, and precuneus. Stress, however, affected the neural ensemble activated during learning. Whereas the control group distinguished between sets of brain regions for related and novel information, stressed individuals engaged the hippocampus even when a relevant schema was present. Additionally, stressed participants displayed aberrant functional connectivity between brain regions involved in schema processing when encoding novel information. The failure to segregate functional connectivity patterns depending on the presence of prior knowledge was linked to impaired performance after stress. Our results show that stress affects the neural ensemble underlying the efficient use of schemas during learning. These findings may have relevant implications for clinical and educational settings. Copyright © 2018 Elsevier Inc. All rights reserved.

  6. Analysis of integrated energy systems

    International Nuclear Information System (INIS)

    Matsuhashi, Takaharu; Kaya, Yoichi; Komiyama, Hiroshi; Hayashi, Taketo; Yasukawa, Shigeru.

    1988-01-01

    World attention is now attracted to the concept of Novel Horizontally Integrated Energy System (NHIES). In NHIES, all fossil fuels are fist converted into CO and H 2 . Potential environmental contaminants such as sulfur are removed during this process. CO turbines are mainly used to generate electric power. Combustion is performed in pure oxygen produced through air separation, making it possible to completely prevent the formation of thermal NOx. Thus, NHIES would release very little amount of such substances that would contribute to acid rain. In this system, the intermediate energy sources of CO, H 2 and O 2 are integrated horizontally. They are combined appropriately to produce a specific form of final energy source. The integration of intermediate energy sources can provide a wide variety of final energy sources, allowing any type of fossil fuel to serve as an alternative to other types of fossil fuel. Another feature of NHIES is the positive use of nuclear fuel to reduce the formation of CO 2 . Studies are under way in Japan to develop a new concept of integrated energy system. These studies are especially aimed at decreased overall efficiency and introduction of new liquid fuels that are high in conversion efficiency. Considerations are made on the final form of energy source, robust control, acid fallout, and CO 2 reduction. (Nogami, K.)

  7. Neural Population Dynamics during Reaching Are Better Explained by a Dynamical System than Representational Tuning.

    Science.gov (United States)

    Michaels, Jonathan A; Dann, Benjamin; Scherberger, Hansjörg

    2016-11-01

    Recent models of movement generation in motor cortex have sought to explain neural activity not as a function of movement parameters, known as representational models, but as a dynamical system acting at the level of the population. Despite evidence supporting this framework, the evaluation of representational models and their integration with dynamical systems is incomplete in the literature. Using a representational velocity-tuning based simulation of center-out reaching, we show that incorporating variable latency offsets between neural activity and kinematics is sufficient to generate rotational dynamics at the level of neural populations, a phenomenon observed in motor cortex. However, we developed a covariance-matched permutation test (CMPT) that reassigns neural data between task conditions independently for each neuron while maintaining overall neuron-to-neuron relationships, revealing that rotations based on the representational model did not uniquely depend on the underlying condition structure. In contrast, rotations based on either a dynamical model or motor cortex data depend on this relationship, providing evidence that the dynamical model more readily explains motor cortex activity. Importantly, implementing a recurrent neural network we demonstrate that both representational tuning properties and rotational dynamics emerge, providing evidence that a dynamical system can reproduce previous findings of representational tuning. Finally, using motor cortex data in combination with the CMPT, we show that results based on small numbers of neurons or conditions should be interpreted cautiously, potentially informing future experimental design. Together, our findings reinforce the view that representational models lack the explanatory power to describe complex aspects of single neuron and population level activity.

  8. Integrated Visualisation and Description of Complex Systems

    National Research Council Canada - National Science Library

    Goodburn, D

    1999-01-01

    ... on system topographies and feature overlays. System information from the domain's information space is filtered and integrated into a Composite Systems Model that provides a basis for consistency and integration between all system views...

  9. Robustness of a Neural Network Model for Power Peak Factor Estimation in Protection Systems

    International Nuclear Information System (INIS)

    Souza, Rose Mary G.P.; Moreira, Joao M.L.

    2006-01-01

    This work presents results of robustness verification of artificial neural network correlations that improve the real time prediction of the power peak factor for reactor protection systems. The input variables considered in the correlation are those available in the reactor protection systems, namely, the axial power differences obtained from measured ex-core detectors, and the position of control rods. The correlations, based on radial basis function (RBF) and multilayer perceptron (MLP) neural networks, estimate the power peak factor, without faulty signals, with average errors between 0.13%, 0.19% and 0.15%, and maximum relative error of 2.35%. The robustness verification was performed for three different neural network correlations. The results show that they are robust against signal degradation, producing results with faulty signals with a maximum error of 6.90%. The average error associated to faulty signals for the MLP network is about half of that of the RBF network, and the maximum error is about 1% smaller. These results demonstrate that MLP neural network correlation is more robust than the RBF neural network correlation. The results also show that the input variables present redundant information. The axial power difference signals compensate the faulty signal for the position of a given control rod, and improves the results by about 10%. The results show that the errors in the power peak factor estimation by these neural network correlations, even in faulty conditions, are smaller than the current PWR schemes which may have uncertainties as high as 8%. Considering the maximum relative error of 2.35%, these neural network correlations would allow decreasing the power peak factor safety margin by about 5%. Such a reduction could be used for operating the reactor with a higher power level or with more flexibility. The neural network correlation has to meet requirements of high integrity software that performs safety grade actions. It is shown that the

  10. Integration of hydro-climatic data and land use in neural networks for ...

    African Journals Online (AJOL)

    BEEMG

    Technology. Full Length Research ... Operating Company and Airport Development and Meteorology ... These variants of models are ... neurons. During this process the parameters of neural models are ... and the system searches through successive iterations to obtain .... quality. They also thank the various teachers who.

  11. 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.

  12. Radioactive waste integrated management system

    Energy Technology Data Exchange (ETDEWEB)

    Song, D Y; Choi, S S; Han, B S [Atomic Creative Technology, Taejon (Korea, Republic of)

    2003-10-01

    In this paper, we present an integrated management system for radioactive waste, which can keep watch on the whole transporting process of each drum from nuclear power plant temporary storage house to radioactive waste storage house remotely. Our approach use RFID(Radio Frequency Identification) system, which can recognize the data information without touch, GSP system, which can calculate the current position precisely using the accurate time and distance measured from satellites, and the spread spectrum technology CDMA, which is widely used in the area of mobile communication.

  13. Radioactive waste integrated management system

    International Nuclear Information System (INIS)

    Song, D. Y.; Choi, S. S.; Han, B. S.

    2003-01-01

    In this paper, we present an integrated management system for radioactive waste, which can keep watch on the whole transporting process of each drum from nuclear power plant temporary storage house to radioactive waste storage house remotely. Our approach use RFID(Radio Frequency Identification) system, which can recognize the data information without touch, GSP system, which can calculate the current position precisely using the accurate time and distance measured from satellites, and the spread spectrum technology CDMA, which is widely used in the area of mobile communication

  14. 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.

  15. 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.

  16. 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

  17. DO DYNAMIC NEURAL NETWORKS STAND A BETTER CHANCE IN FRACTIONALLY INTEGRATED PROCESS FORECASTING?

    Directory of Open Access Journals (Sweden)

    Majid Delavari

    2013-04-01

    Full Text Available The main purpose of the present study was to investigate the capabilities of two generations of models such as those based on dynamic neural network (e.g., Nonlinear Neural network Auto Regressive or NNAR model and a regressive (Auto Regressive Fractionally Integrated Moving Average model which is based on Fractional Integration Approach in forecasting daily data related to the return index of Tehran Stock Exchange (TSE. In order to compare these models under similar conditions, Mean Square Error (MSE and also Root Mean Square Error (RMSE were selected as criteria for the models’ simulated out-of-sample forecasting performance. Besides, fractal markets hypothesis was examined and according to the findings, fractal structure was confirmed to exist in the time series under investigation. Another finding of the study was that dynamic artificial neural network model had the best performance in out-of-sample forecasting based on the criteria introduced for calculating forecasting error in comparison with the ARFIMA model.

  18. An integral term adaptive neural control of fed-batch fermentation biotechnological process; Control neuronal adaptable con termino integral para un proceso biotecnologico de fermentacion por lote alimentado

    Energy Technology Data Exchange (ETDEWEB)

    Baruch, Ieroham; Hernandez, Luis Alberto; Barrera Cortes, Josefina [Centro de Investigacion y de Estudios Avanzados, Instituto Politecnico Nacional, Mexico D.F. (Mexico)

    2005-07-15

    A nonlinear mathematical model of aerobic biotechnological process of a fed-batch fermentation system is derived using ordinary differential equations. A neurocontrol is applied using Recurrent Trainable Neural Network (RTNN) plus integral term; the first network performs an approximation of the plant's output; the second network generates the control signal so that the biomass concentration could be regulated by the nutrient influent flow rate into the bioreactor. [Spanish] Un modelo matematico no lineal de un proceso biotecnologico aerobio de un sistema de fermentacion por lote alimentado es presentado mediante ecuaciones diferenciales ordinarias. Es propuesto un control utilizando dos redes neuronales recurrentes entrenables (RNRE) con la adicion de un termino integral; la primera red representa un aproximador de la salida de la planta y la segunda genera la senal de control tal que la concentracion de la biomasa pueda ser regulada mediante la alimentacion de un flujo con nutrientes al biorreactor.

  19. 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.

  20. 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.

  1. 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.

  2. 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

  3. Glassy carbon MEMS for novel origami-styled 3D integrated intracortical and epicortical neural probes

    Science.gov (United States)

    Goshi, Noah; Castagnola, Elisa; Vomero, Maria; Gueli, Calogero; Cea, Claudia; Zucchini, Elena; Bjanes, David; Maggiolini, Emma; Moritz, Chet; Kassegne, Sam; Ricci, Davide; Fadiga, Luciano

    2018-06-01

    We report on a novel technology for microfabricating 3D origami-styled micro electro-mechanical systems (MEMS) structures with glassy carbon (GC) features and a supporting polymer substrate. GC MEMS devices that open to form 3D microstructures are microfabricated from GC patterns that are made through pyrolysis of polymer precursors on high-temperature resisting substrates like silicon or quartz and then transferring the patterned devices to a flexible substrate like polyimide followed by deposition of an insulation layer. The devices on flexible substrate are then folded into 3D form in an origami-fashion. These 3D MEMS devices have tunable mechanical properties that are achieved by selectively varying the thickness of the polymeric substrate and insulation layers at any desired location. This technology opens new possibilities by enabling microfabrication of a variety of 3D GC MEMS structures suited to applications ranging from biochemical sensing to implantable microelectrode arrays. As a demonstration of the technology, a neural signal recording microelectrode array platform that integrates both surface (cortical) and depth (intracortical) GC microelectrodes onto a single flexible thin-film device is introduced. When the device is unfurled, a pre-shaped shank of polyimide automatically comes off the substrate and forms the penetrating part of the device in a 3D fashion. With the advantage of being highly reproducible and batch-fabricated, the device introduced here allows for simultaneous recording of electrophysiological signals from both the brain surface (electrocorticography—ECoG) and depth (single neuron). Our device, therefore, has the potential to elucidate the roles of underlying neurons on the different components of µECoG signals. For in vivo validation of the design capabilities, the recording sites are coated with a poly(3,4-ethylenedioxythiophene)—polystyrene sulfonate—carbon nanotube composite, to improve the electrical conductivity of the

  4. Self-Organizing Neural Integration of Pose-Motion Features for Human Action Recognition

    Directory of Open Access Journals (Sweden)

    German Ignacio Parisi

    2015-06-01

    Full Text Available The visual recognition of complex, articulated human movements is fundamental for a wide range of artificial systems oriented towards human-robot communication, action classification, and action-driven perception. These challenging tasks may generally involve the processing of a huge amount of visual information and learning-based mechanisms for generalizing a set of training actions and classifying new samples. To operate in natural environments, a crucial property is the efficient and robust recognition of actions, also under noisy conditions caused by, for instance, systematic sensor errors and temporarily occluded persons. Studies of the mammalian visual system and its outperforming ability to process biological motion information suggest separate neural pathways for the distinct processing of pose and motion features at multiple levels and the subsequent integration of these visual cues for action perception. We present a neurobiologically-motivated approach to achieve noise-tolerant action recognition in real time. Our model consists of self-organizing Growing When Required (GWR networks that obtain progressively generalized representations of sensory inputs and learn inherent spatiotemporal dependencies. During the training, the GWR networks dynamically change their topological structure to better match the input space. We first extract pose and motion features from video sequences and then cluster actions in terms of prototypical pose-motion trajectories. Multi-cue trajectories from matching action frames are subsequently combined to provide action dynamics in the joint feature space. Reported experiments show that our approach outperforms previous results on a dataset of full-body actions captured with a depth sensor, and ranks among the best 21 results for a public benchmark of domestic daily actions.

  5. Simulation electromagnetic scattering on bodies through integral equation and neural networks methods

    Science.gov (United States)

    Lvovich, I. Ya; Preobrazhenskiy, A. P.; Choporov, O. N.

    2018-05-01

    The paper deals with the issue of electromagnetic scattering on a perfectly conducting diffractive body of a complex shape. Performance calculation of the body scattering is carried out through the integral equation method. Fredholm equation of the second time was used for calculating electric current density. While solving the integral equation through the moments method, the authors have properly described the core singularity. The authors determined piecewise constant functions as basic functions. The chosen equation was solved through the moments method. Within the Kirchhoff integral approach it is possible to define the scattered electromagnetic field, in some way related to obtained electrical currents. The observation angles sector belongs to the area of the front hemisphere of the diffractive body. To improve characteristics of the diffractive body, the authors used a neural network. All the neurons contained a logsigmoid activation function and weighted sums as discriminant functions. The paper presents the matrix of weighting factors of the connectionist model, as well as the results of the optimized dimensions of the diffractive body. The paper also presents some basic steps in calculation technique of the diffractive bodies, based on the combination of integral equation and neural networks methods.

  6. Integrable systems, geometry, and topology

    CERN Document Server

    Terng, Chuu-Lian

    2006-01-01

    The articles in this volume are based on lectures from a program on integrable systems and differential geometry held at Taiwan's National Center for Theoretical Sciences. As is well-known, for many soliton equations, the solutions have interpretations as differential geometric objects, and thereby techniques of soliton equations have been successfully applied to the study of geometric problems. The article by Burstall gives a beautiful exposition on isothermic surfaces and their relations to integrable systems, and the two articles by Guest give an introduction to quantum cohomology, carry out explicit computations of the quantum cohomology of flag manifolds and Hirzebruch surfaces, and give a survey of Givental's quantum differential equations. The article by Heintze, Liu, and Olmos is on the theory of isoparametric submanifolds in an arbitrary Riemannian manifold, which is related to the n-wave equation when the ambient manifold is Euclidean. Mukai-Hidano and Ohnita present a survey on the moduli space of ...

  7. CMOS On-Chip Optoelectronic Neural Interface Device with Integrated Light Source for Optogenetics

    International Nuclear Information System (INIS)

    Sawadsaringkarn, Y; Kimura, H; Maezawa, Y; Nakajima, A; Kobayashi, T; Sasagawa, K; Noda, T; Tokuda, T; Ohta, J

    2012-01-01

    A novel optoelectronic neural interface device is proposed for target applications in optogenetics for neural science. The device consists of a light emitting diode (LED) array implemented on a CMOS image sensor for on-chip local light stimulation. In this study, we designed a suitable CMOS image sensor equipped with on-chip electrodes to drive the LEDs, and developed a device structure and packaging process for LED integration. The prototype device produced an illumination intensity of approximately 1 mW with a driving current of 2.0 mA, which is expected to be sufficient to activate channelrhodopsin (ChR2). We also demonstrated the functions of light stimulation and on-chip imaging using a brain slice from a mouse as a target sample.

  8. Integrated photovoltaic (PV) monitoring system

    Science.gov (United States)

    Mahinder Singh, Balbir Singh; Husain, NurSyahidah; Mohamed, Norani Muti

    2012-09-01

    The main aim of this research work is to design an accurate and reliable monitoring system to be integrated with solar electricity generating system. The performance monitoring system is required to ensure that the PVEGS is operating at an optimum level. The PV monitoring system is able to measure all the important parameters that determine an optimum performance. The measured values are recorded continuously, as the data acquisition system is connected to a computer, and data is stored at fixed intervals. The data can be locally used and can also be transmitted via internet. The data that appears directly on the local monitoring system is displayed via graphical user interface that was created by using Visual basic and Apache software was used for data transmission The accuracy and reliability of the developed monitoring system was tested against the data that captured simultaneously by using a standard power quality analyzer device. The high correlation which is 97% values indicates the level of accuracy of the monitoring system. The aim of leveraging on a system for continuous monitoring system is achieved, both locally, and can be viewed simultaneously at a remote system.

  9. Integrated system for seismic evaluations

    International Nuclear Information System (INIS)

    Xu, J.; Philippacopoulos, A.J.; Miller, C.A.; Costantino, C.J.; Graves, H.

    1989-01-01

    This paper describes the various features of the seismic module of the CARES system (computer analysis for rapid evaluation of structures). This system was developed to perform rapid evaluations of structural behavior and capability of nuclear power plant facilities. The CARES is structural in a modular format. Each module performs a specific type of analysis i.e., static or dynamic, linear or nonlinear, etc. This paper describes the features of the seismic module in particular. The development of the seismic modules of the CARES system is based on an approach which incorporates major aspects of seismic analysis currently employed by the industry into an integrated system that allows for carrying out interactively computations of structural response to seismic motions. The code operates on a PC computer system and has multi-graphics capabilities

  10. INTEGRATION OF ENVIRONMENTAL MANAGEMENT SYSTEM

    OpenAIRE

    Tomescu Ada Mirela

    2012-01-01

    The relevance of management as significant factor of business activity can be established on various management systems. These will help to obtain, organise, administrate, evaluate and control particulars: information, quality, environmental protection, health and safety, various resources (time, human, finance, inventory etc). The complexity of nowadays days development, forced us to think ‘integrated’. Sustainable development principles require that environment management policies and p...

  11. Crossmodal deficit in dyslexic children: practice affects the neural timing of letter-speech sound integration

    Directory of Open Access Journals (Sweden)

    Gojko eŽarić

    2015-06-01

    Full Text Available A failure to build solid letter-speech sound associations may contribute to reading impairments in developmental dyslexia. Whether this reduced neural integration of letters and speech sounds changes over time within individual children and how this relates to behavioral gains in reading skills remains unknown. In this research, we examined changes in event-related potential (ERP measures of letter-speech sound integration over a 6-month period during which 9-year-old dyslexic readers (n=17 followed a training in letter-speech sound coupling next to their regular reading curriculum. We presented the Dutch spoken vowels /a/ and /o/ as standard and deviant stimuli in one auditory and two audiovisual oddball conditions. In one audiovisual condition (AV0, the letter ‘a’ was presented simultaneously with the vowels, while in the other (AV200 it was preceding vowel onset for 200 ms. Prior to the training (T1, dyslexic readers showed the expected pattern of typical auditory mismatch responses, together with the absence of letter-speech sound effects in a late negativity (LN window. After the training (T2, our results showed earlier (and enhanced crossmodal effects in the LN window. Most interestingly, earlier LN latency at T2 was significantly related to higher behavioral accuracy in letter-speech sound coupling. On a more general level, the timing of the earlier mismatch negativity (MMN in the simultaneous condition (AV0 measured at T1, significantly related to reading fluency at both T1 and T2 as well as with reading gains. Our findings suggest that the reduced neural integration of letters and speech sounds in dyslexic children may show moderate improvement with reading instruction and training and that behavioral improvements relate especially to individual differences in the timing of this neural integration.

  12. Integrated nonthermal treatment system study

    Energy Technology Data Exchange (ETDEWEB)

    Biagi, C.; Bahar, D.; Teheranian, B.; Vetromile, J. [Morrison Knudsen Corp. (United States); Quapp, W.J. [Nuclear Metals (United States); Bechtold, T.; Brown, B.; Schwinkendorf, W. [Lockheed Martin Idaho Technologies Co., Idaho Falls, ID (United States); Swartz, G. [Swartz and Associates (United States)

    1997-01-01

    This report presents the results of a study of nonthermal treatment technologies. The study consisted of a systematic assessment of five nonthermal treatment alternatives. The treatment alternatives consist of widely varying technologies for safely destroying the hazardous organic components, reducing the volume, and preparing for final disposal of the contact-handled mixed low-level waste (MLLW) currently stored in the US Department of Energy complex. The alternatives considered were innovative nonthermal treatments for organic liquids and sludges, process residue, soil and debris. Vacuum desorption or various washing approaches are considered for treatment of soil, residue and debris. Organic destruction methods include mediated electrochemical oxidation, catalytic wet oxidation, and acid digestion. Other methods studied included stabilization technologies and mercury separation of treatment residues. This study is a companion to the integrated thermal treatment study which examined 19 alternatives for thermal treatment of MLLW waste. The quantities and physical and chemical compositions of the input waste are based on the inventory database developed by the US Department of Energy. The Integrated Nonthermal Treatment Systems (INTS) systems were evaluated using the same waste input (2,927 pounds per hour) as the Integrated Thermal Treatment Systems (ITTS). 48 refs., 68 figs., 37 tabs.

  13. Integrated nonthermal treatment system study

    International Nuclear Information System (INIS)

    Biagi, C.; Bahar, D.; Teheranian, B.; Vetromile, J.; Quapp, W.J.; Bechtold, T.; Brown, B.; Schwinkendorf, W.; Swartz, G.

    1997-01-01

    This report presents the results of a study of nonthermal treatment technologies. The study consisted of a systematic assessment of five nonthermal treatment alternatives. The treatment alternatives consist of widely varying technologies for safely destroying the hazardous organic components, reducing the volume, and preparing for final disposal of the contact-handled mixed low-level waste (MLLW) currently stored in the US Department of Energy complex. The alternatives considered were innovative nonthermal treatments for organic liquids and sludges, process residue, soil and debris. Vacuum desorption or various washing approaches are considered for treatment of soil, residue and debris. Organic destruction methods include mediated electrochemical oxidation, catalytic wet oxidation, and acid digestion. Other methods studied included stabilization technologies and mercury separation of treatment residues. This study is a companion to the integrated thermal treatment study which examined 19 alternatives for thermal treatment of MLLW waste. The quantities and physical and chemical compositions of the input waste are based on the inventory database developed by the US Department of Energy. The Integrated Nonthermal Treatment Systems (INTS) systems were evaluated using the same waste input (2,927 pounds per hour) as the Integrated Thermal Treatment Systems (ITTS). 48 refs., 68 figs., 37 tabs

  14. Integrating probabilistic models of perception and interactive neural networks: a historical and tutorial review.

    Science.gov (United States)

    McClelland, James L

    2013-01-01

    This article seeks to establish a rapprochement between explicitly Bayesian models of contextual effects in perception and neural network models of such effects, particularly the connectionist interactive activation (IA) model of perception. The article is in part an historical review and in part a tutorial, reviewing the probabilistic Bayesian approach to understanding perception and how it may be shaped by context, and also reviewing ideas about how such probabilistic computations may be carried out in neural networks, focusing on the role of context in interactive neural networks, in which both bottom-up and top-down signals affect the interpretation of sensory inputs. It is pointed out that connectionist units that use the logistic or softmax activation functions can exactly compute Bayesian posterior probabilities when the bias terms and connection weights affecting such units are set to the logarithms of appropriate probabilistic quantities. Bayesian concepts such the prior, likelihood, (joint and marginal) posterior, probability matching and maximizing, and calculating vs. sampling from the posterior are all reviewed and linked to neural network computations. Probabilistic and neural network models are explicitly linked to the concept of a probabilistic generative model that describes the relationship between the underlying target of perception (e.g., the word intended by a speaker or other source of sensory stimuli) and the sensory input that reaches the perceiver for use in inferring the underlying target. It is shown how a new version of the IA model called the multinomial interactive activation (MIA) model can sample correctly from the joint posterior of a proposed generative model for perception of letters in words, indicating that interactive processing is fully consistent with principled probabilistic computation. Ways in which these computations might be realized in real neural systems are also considered.

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

    Energy Technology Data Exchange (ETDEWEB)

    Ondrej Linda; Milos Manic; Timothy R. McJunkin

    2011-08-01

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

  16. 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...

  17. Integrable systems and loop coproducts

    International Nuclear Information System (INIS)

    Musso, Fabio

    2010-01-01

    We present a generalization of a framework for the construction of classical integrable systems that we call loop coproduct formulation (Musso 2010 J. Phys. A: Math. Theor. 43 434026). In this paper, the loop coproduct formulation includes systems of Gelfand-Tsetlin type, the linear r-matrix formulation, the Sklyanin algebras, the reflection algebras, the coalgebra symmetry approach and some of its generalizations as particular cases, showing that all these apparently different approaches have a common algebraic origin. On the other hand, all these subcases do not exhaust the domain of applicability of this new technique, so that new possible directions of investigation do naturally emerge in this framework.

  18. 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.

  19. Artificial Neural Network-Based Clutter Reduction Systems for Ship Size Estimation in Maritime Radars

    Directory of Open Access Journals (Sweden)

    M. P. Jarabo-Amores

    2010-01-01

    Full Text Available The existence of clutter in maritime radars deteriorates the estimation of some physical parameters of the objects detected over the sea surface. For that reason, maritime radars should incorporate efficient clutter reduction techniques. Due to the intrinsic nonlinear dynamic of sea clutter, nonlinear signal processing is needed, what can be achieved by artificial neural networks (ANNs. In this paper, an estimation of the ship size using an ANN-based clutter reduction system followed by a fixed threshold is proposed. High clutter reduction rates are achieved using 1-dimensional (horizontal or vertical integration modes, although inaccurate ship width estimations are achieved. These estimations are improved using a 2-dimensional (rhombus integration mode. The proposed system is compared with a CA-CFAR system, denoting a great performance improvement and a great robustness against changes in sea clutter conditions and ship parameters, independently of the direction of movement of the ocean waves and ships.

  20. A scalable neural chip with synaptic electronics using CMOS integrated memristors

    International Nuclear Information System (INIS)

    Cruz-Albrecht, Jose M; Derosier, Timothy; Srinivasa, Narayan

    2013-01-01

    The design and simulation of a scalable neural chip with synaptic electronics using nanoscale memristors fully integrated with complementary metal–oxide–semiconductor (CMOS) is presented. The circuit consists of integrate-and-fire neurons and synapses with spike-timing dependent plasticity (STDP). The synaptic conductance values can be stored in memristors with eight levels, and the topology of connections between neurons is reconfigurable. The circuit has been designed using a 90 nm CMOS process with via connections to on-chip post-processed memristor arrays. The design has about 16 million CMOS transistors and 73 728 integrated memristors. We provide circuit level simulations of the entire chip performing neuronal and synaptic computations that result in biologically realistic functional behavior. (paper)

  1. Integrated therapy safety management system.

    Science.gov (United States)

    Podtschaske, Beatrice; Fuchs, Daniela; Friesdorf, Wolfgang

    2013-09-01

    The aim is to demonstrate the benefit of the medico-ergonomic approach for the redesign of clinical work systems. Based on the six layer model, a concept for an 'integrated therapy safety management' is drafted. This concept could serve as a basis to improve resilience. The concept is developed through a concept-based approach. The state of the art of safety and complexity research in human factors and ergonomics forms the basis. The findings are synthesized to a concept for 'integrated therapy safety management'. The concept is applied by way of example for the 'medication process' to demonstrate its practical implementation. The 'integrated therapy safety management' is drafted in accordance with the six layer model. This model supports a detailed description of specific work tasks, the corresponding responsibilities and related workflows at different layers by using the concept of 'bridge managers'. 'Bridge managers' anticipate potential errors and monitor the controlled system continuously. If disruptions or disturbances occur, they respond with corrective actions which ensure that no harm results and they initiate preventive measures for future procedures. The concept demonstrates that in a complex work system, the human factor is the key element and final authority to cope with the residual complexity. The expertise of the 'bridge managers' and the recursive hierarchical structure results in highly adaptive clinical work systems and increases their resilience. The medico-ergonomic approach is a highly promising way of coping with two complexities. It offers a systematic framework for comprehensive analyses of clinical work systems and promotes interdisciplinary collaboration. © 2013 The Authors. British Journal of Clinical Pharmacology © 2013 The British Pharmacological Society.

  2. Integrated therapy safety management system

    Science.gov (United States)

    Podtschaske, Beatrice; Fuchs, Daniela; Friesdorf, Wolfgang

    2013-01-01

    Aims The aim is to demonstrate the benefit of the medico-ergonomic approach for the redesign of clinical work systems. Based on the six layer model, a concept for an ‘integrated therapy safety management’ is drafted. This concept could serve as a basis to improve resilience. Methods The concept is developed through a concept-based approach. The state of the art of safety and complexity research in human factors and ergonomics forms the basis. The findings are synthesized to a concept for ‘integrated therapy safety management’. The concept is applied by way of example for the ‘medication process’ to demonstrate its practical implementation. Results The ‘integrated therapy safety management’ is drafted in accordance with the six layer model. This model supports a detailed description of specific work tasks, the corresponding responsibilities and related workflows at different layers by using the concept of ‘bridge managers’. ‘Bridge managers’ anticipate potential errors and monitor the controlled system continuously. If disruptions or disturbances occur, they respond with corrective actions which ensure that no harm results and they initiate preventive measures for future procedures. The concept demonstrates that in a complex work system, the human factor is the key element and final authority to cope with the residual complexity. The expertise of the ‘bridge managers’ and the recursive hierarchical structure results in highly adaptive clinical work systems and increases their resilience. Conclusions The medico-ergonomic approach is a highly promising way of coping with two complexities. It offers a systematic framework for comprehensive analyses of clinical work systems and promotes interdisciplinary collaboration. PMID:24007448

  3. 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.

  4. 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.

  5. Neural network based expert system for fault diagnosis of particle accelerators

    International Nuclear Information System (INIS)

    Dewidar, M.M.

    1997-01-01

    Particle accelerators are generators that produce beams of charged particles, acquiring different energies, depending on the accelerator type. The MGC-20 cyclotron is a cyclic particle accelerator used for accelerating protons, deuterons, alpha particles, and helium-3 to different energies. Its applications include isotope production, nuclear reaction, and mass spectroscopy studies. It is a complicated machine, it consists of five main parts, the ion source, the deflector, the beam transport system, the concentric and harmonic coils, and the radio frequency system. The diagnosis of this device is a very complex task. it depends on the conditions of 27 indicators of the control panel of the device. The accurate diagnosis can lead to a high system reliability and save maintenance costs. so an expert system for the cyclotron fault diagnosis is necessary to be built. In this thesis , a hybrid expert system was developed for the fault diagnosis of the MGC-20 cyclotron. Two intelligent techniques, multilayer feed forward back propagation neural network and the rule based expert system, are integrated as a pre-processor loosely coupled model to build the proposed hybrid expert system. The architecture of the developed hybrid expert system consists of two levels. The first level is two feed forward back propagation neural networks, used for isolating the faulty part of the cyclotron. The second level is the rule based expert system, used for troubleshooting the faults inside the isolated faulty part. 4-6 tabs., 4-5 figs., 36 refs

  6. 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

  7. 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

  8. Information Security and Integrity Systems

    Science.gov (United States)

    1990-01-01

    Viewgraphs from the Information Security and Integrity Systems seminar held at the University of Houston-Clear Lake on May 15-16, 1990 are presented. A tutorial on computer security is presented. The goals of this tutorial are the following: to review security requirements imposed by government and by common sense; to examine risk analysis methods to help keep sight of forest while in trees; to discuss the current hot topic of viruses (which will stay hot); to examine network security, now and in the next year to 30 years; to give a brief overview of encryption; to review protection methods in operating systems; to review database security problems; to review the Trusted Computer System Evaluation Criteria (Orange Book); to comment on formal verification methods; to consider new approaches (like intrusion detection and biometrics); to review the old, low tech, and still good solutions; and to give pointers to the literature and to where to get help. Other topics covered include security in software applications and development; risk management; trust: formal methods and associated techniques; secure distributed operating system and verification; trusted Ada; a conceptual model for supporting a B3+ dynamic multilevel security and integrity in the Ada runtime environment; and information intelligence sciences.

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

    Science.gov (United States)

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

    2017-12-01

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

  10. Distributed neural system for emotional intelligence revealed by lesion mapping.

    Science.gov (United States)

    Barbey, Aron K; Colom, Roberto; Grafman, Jordan

    2014-03-01

    Cognitive neuroscience has made considerable progress in understanding the neural architecture of human intelligence, identifying a broadly distributed network of frontal and parietal regions that support goal-directed, intelligent behavior. However, the contributions of this network to social and emotional aspects of intellectual function remain to be well characterized. Here we investigated the neural basis of emotional intelligence in 152 patients with focal brain injuries using voxel-based lesion-symptom mapping. Latent variable modeling was applied to obtain measures of emotional intelligence, general intelligence and personality from the Mayer, Salovey, Caruso Emotional Intelligence Test (MSCEIT), the Wechsler Adult Intelligence Scale and the Neuroticism-Extroversion-Openness Inventory, respectively. Regression analyses revealed that latent scores for measures of general intelligence and personality reliably predicted latent scores for emotional intelligence. Lesion mapping results further indicated that these convergent processes depend on a shared network of frontal, temporal and parietal brain regions. The results support an integrative framework for understanding the architecture of executive, social and emotional processes and make specific recommendations for the interpretation and application of the MSCEIT to the study of emotional intelligence in health and disease.

  11. Distributed neural system for emotional intelligence revealed by lesion mapping

    Science.gov (United States)

    Colom, Roberto; Grafman, Jordan

    2014-01-01

    Cognitive neuroscience has made considerable progress in understanding the neural architecture of human intelligence, identifying a broadly distributed network of frontal and parietal regions that support goal-directed, intelligent behavior. However, the contributions of this network to social and emotional aspects of intellectual function remain to be well characterized. Here we investigated the neural basis of emotional intelligence in 152 patients with focal brain injuries using voxel-based lesion-symptom mapping. Latent variable modeling was applied to obtain measures of emotional intelligence, general intelligence and personality from the Mayer, Salovey, Caruso Emotional Intelligence Test (MSCEIT), the Wechsler Adult Intelligence Scale and the Neuroticism-Extroversion-Openness Inventory, respectively. Regression analyses revealed that latent scores for measures of general intelligence and personality reliably predicted latent scores for emotional intelligence. Lesion mapping results further indicated that these convergent processes depend on a shared network of frontal, temporal and parietal brain regions. The results support an integrative framework for understanding the architecture of executive, social and emotional processes and make specific recommendations for the interpretation and application of the MSCEIT to the study of emotional intelligence in health and disease. PMID:23171618

  12. Agents in an Integrated System Architecture

    DEFF Research Database (Denmark)

    Hartvig, Susanne C; Andersen, Tom

    1997-01-01

    This paper presents research findings from development of an expert system and its integration into an integrated environment. Expert systems has proven hard to integrate because of their interactive nature. A prototype environment was developed using new integration technologies, and research...... findings concerning the use of OLE technology to integrate stand alone applications are discussed. The prototype shows clear advantages of using OLE technology when developing integrated environments....

  13. Human neural progenitors derived from integration-free iPSCs for SCI therapy

    Directory of Open Access Journals (Sweden)

    Ying Liu

    2017-03-01

    Full Text Available As a potentially unlimited autologous cell source, patient induced pluripotent stem cells (iPSCs provide great capability for tissue regeneration, particularly in spinal cord injury (SCI. However, despite significant progress made in translation of iPSC-derived neural progenitor cells (NPCs to clinical settings, a few hurdles remain. Among them, non-invasive approach to obtain source cells in a timely manner, safer integration-free delivery of reprogramming factors, and purification of NPCs before transplantation are top priorities to overcome. In this study, we developed a safe and cost-effective pipeline to generate clinically relevant NPCs. We first isolated cells from patients' urine and reprogrammed them into iPSCs by non-integrating Sendai viral vectors, and carried out experiments on neural differentiation. NPCs were purified by A2B5, an antibody specifically recognizing a glycoganglioside on the cell surface of neural lineage cells, via fluorescence activated cell sorting. Upon further in vitro induction, NPCs were able to give rise to neurons, oligodendrocytes and astrocytes. To test the functionality of the A2B5+ NPCs, we grafted them into the contused mouse thoracic spinal cord. Eight weeks after transplantation, the grafted cells survived, integrated into the injured spinal cord, and differentiated into neurons and glia. Our specific focus on cell source, reprogramming, differentiation and purification method purposely addresses timing and safety issues of transplantation to SCI models. It is our belief that this work takes one step closer on using human iPSC derivatives to SCI clinical settings.

  14. Design of a Closed-Loop, Bidirectional Brain Machine Interface System With Energy Efficient Neural Feature Extraction and PID Control.

    Science.gov (United States)

    Liu, Xilin; Zhang, Milin; Richardson, Andrew G; Lucas, Timothy H; Van der Spiegel, Jan

    2017-08-01

    This paper presents a bidirectional brain machine interface (BMI) microsystem designed for closed-loop neuroscience research, especially experiments in freely behaving animals. The system-on-chip (SoC) consists of 16-channel neural recording front-ends, neural feature extraction units, 16-channel programmable neural stimulator back-ends, in-channel programmable closed-loop controllers, global analog-digital converters (ADC), and peripheral circuits. The proposed neural feature extraction units includes 1) an ultra low-power neural energy extraction unit enabling a 64-step natural logarithmic domain frequency tuning, and 2) a current-mode action potential (AP) detection unit with time-amplitude window discriminator. A programmable proportional-integral-derivative (PID) controller has been integrated in each channel enabling a various of closed-loop operations. The implemented ADCs include a 10-bit voltage-mode successive approximation register (SAR) ADC for the digitization of the neural feature outputs and/or local field potential (LFP) outputs, and an 8-bit current-mode SAR ADC for the digitization of the action potential outputs. The multi-mode stimulator can be programmed to perform monopolar or bipolar, symmetrical or asymmetrical charge balanced stimulation with a maximum current of 4 mA in an arbitrary channel configuration. The chip has been fabricated in 0.18 μ m CMOS technology, occupying a silicon area of 3.7 mm 2 . The chip dissipates 56 μW/ch on average. General purpose low-power microcontroller with Bluetooth module are integrated in the system to provide wireless link and SoC configuration. Methods, circuit techniques and system topology proposed in this work can be used in a wide range of relevant neurophysiology research, especially closed-loop BMI experiments.

  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. Integrated risk information system (IRIS)

    Energy Technology Data Exchange (ETDEWEB)

    Tuxen, L. [Environmental Protection Agency, Washington, DC (United States)

    1990-12-31

    The Integrated Risk Information System (IRIS) is an electronic information system developed by the US Environmental Protection Agency (EPA) containing information related to health risk assessment. IRIS is the Agency`s primary vehicle for communication of chronic health hazard information that represents Agency consensus following comprehensive review by intra-Agency work groups. The original purpose for developing IRIS was to provide guidance to EPA personnel in making risk management decisions. This original purpose for developing IRIS was to guidance to EPA personnel in making risk management decisions. This role has expanded and evolved with wider access and use of the system. IRIS contains chemical-specific information in summary format for approximately 500 chemicals. IRIS is available to the general public on the National Library of Medicine`s Toxicology Data Network (TOXNET) and on diskettes through the National Technical Information Service (NTIS).

  18. The 128-channel fully differential digital integrated neural recording and stimulation interface.

    Science.gov (United States)

    Shahrokhi, Farzaneh; Abdelhalim, Karim; Serletis, Demitre; Carlen, Peter L; Genov, Roman

    2010-06-01

    We present a fully differential 128-channel integrated neural interface. It consists of an array of 8 X 16 low-power low-noise signal-recording and generation circuits for electrical neural activity monitoring and stimulation, respectively. The recording channel has two stages of signal amplification and conditioning with and a fully differential 8-b column-parallel successive approximation (SAR) analog-to-digital converter (ADC). The total measured power consumption of each recording channel, including the SAR ADC, is 15.5 ¿W. The measured input-referred noise is 6.08 ¿ Vrms over a 5-kHz bandwidth, resulting in a noise efficiency factor of 5.6. The stimulation channel performs monophasic or biphasic voltage-mode stimulation, with a maximum stimulation current of 5 mA and a quiescent power dissipation of 51.5 ¿W. The design is implemented in 0.35-¿m complementary metal-oxide semiconductor technology with the channel pitch of 200 ¿m for a total die size of 3.4 mm × 2.5 mm and a total power consumption of 9.33 mW. The neural interface was validated in in vitro recording of a low-Mg(2+)/high-K(+) epileptic seizure model in an intact hippocampus of a mouse.

  19. Confidentiality and integrity in crowdsourcing systems

    CERN Document Server

    Ranj Bar, Amin

    2014-01-01

    Confidentiality and Integrity in Crowdsourcing Systems focuses on identity, privacy, and security related issues in crowdsourcing systems and in particular the confidentiality and integrity of online data created via crowdsourcing. This book begins with an introduction to crowdsourcing and then covers the privacy and security challenges of Confidentiality. The book examines integrity in these systems and the management and control of crowdsourcing systems.

  20. 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.

  1. 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

  2. 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.

  3. 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

  4. Integrated multisensor perimeter detection systems

    Science.gov (United States)

    Kent, P. J.; Fretwell, P.; Barrett, D. J.; Faulkner, D. A.

    2007-10-01

    The report describes the results of a multi-year programme of research aimed at the development of an integrated multi-sensor perimeter detection system capable of being deployed at an operational site. The research was driven by end user requirements in protective security, particularly in threat detection and assessment, where effective capability was either not available or prohibitively expensive. Novel video analytics have been designed to provide robust detection of pedestrians in clutter while new radar detection and tracking algorithms provide wide area day/night surveillance. A modular integrated architecture based on commercially available components has been developed. A graphical user interface allows intuitive interaction and visualisation with the sensors. The fusion of video, radar and other sensor data provides the basis of a threat detection capability for real life conditions. The system was designed to be modular and extendable in order to accommodate future and legacy surveillance sensors. The current sensor mix includes stereoscopic video cameras, mmWave ground movement radar, CCTV and a commercially available perimeter detection cable. The paper outlines the development of the system and describes the lessons learnt after deployment in a pilot trial.

  5. Integrability of some generalized Lotka - Volterra systems

    Energy Technology Data Exchange (ETDEWEB)

    Bountis, T.C.; Bier, M.; Hijmans, J.

    1983-08-08

    Several integrable systems of nonlinear ordinary differential equations of the Lotka-Volterra type are identified by the Painleve property and completely integrated. One such integrable case of N first order ode's is found, with N - 2 free parameters and N arbitrary. The concept of integrability of a general dynamical system, not necessarily derived from a hamiltonian, is also discussed.

  6. 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.

  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. Integrated system for seismic evaluations

    International Nuclear Information System (INIS)

    Xu, J.; Philippacopoulos, A.J.; Miller, C.A.; Costantino, C.J.; Graves, H.

    1989-01-01

    This paper describes the various features of the Seismic Module of the CARES system (Computer Analysis for Rapid Evaluation of Structures). This system was developed by Brookhaven National Laboratory (BNL) for the US Nuclear Regulatory Commission to perform rapid evaluations of structural behavior and capability of nuclear power plant facilities. The CARES is structured in a modular format. Each module performs a specific type of analysis i.e., static or dynamic, linear or nonlinear, etc. This paper describes the features of the Seismic Module in particular. The development of the Seismic Module of the CARES system is based on an approach which incorporates all major aspects of seismic analysis currently employed by the industry into an integrated system that allows for carrying out interactively computations of structural response to seismic motions. The code operates on a PC computer system and has multi-graphics capabilities. It has been designed with user friendly features and it allows for interactive manipulation of various analysis phases during the seismic design process. The capabilities of the seismic module include (a) generation of artificial time histories compatible with given design ground response spectra, (b) development of Power Spectral Density (PSD) functions associated with the seismic input, (c) deconvolution analysis using vertically propagating shear waves through a given soil profile, and (d) development of in-structure response spectra or corresponding PSD's. It should be pointed out that these types of analyses can also be performed individually by using available computer codes such as FLUSH, SAP, etc. The uniqueness of the CARES, however, lies on its ability to perform all required phases of the seismic analysis in an integrated manner. 5 refs., 6 figs

  9. Recurrent fuzzy neural network by using feedback error learning approaches for LFC in interconnected power system

    International Nuclear Information System (INIS)

    Sabahi, Kamel; Teshnehlab, Mohammad; Shoorhedeli, Mahdi Aliyari

    2009-01-01

    In this study, a new adaptive controller based on modified feedback error learning (FEL) approaches is proposed for load frequency control (LFC) problem. The FEL strategy consists of intelligent and conventional controllers in feedforward and feedback paths, respectively. In this strategy, a conventional feedback controller (CFC), i.e. proportional, integral and derivative (PID) controller, is essential to guarantee global asymptotic stability of the overall system; and an intelligent feedforward controller (INFC) is adopted to learn the inverse of the controlled system. Therefore, when the INFC learns the inverse of controlled system, the tracking of reference signal is done properly. Generally, the CFC is designed at nominal operating conditions of the system and, therefore, fails to provide the best control performance as well as global stability over a wide range of changes in the operating conditions of the system. So, in this study a supervised controller (SC), a lookup table based controller, is addressed for tuning of the CFC. During abrupt changes of the power system parameters, the SC adjusts the PID parameters according to these operating conditions. Moreover, for improving the performance of overall system, a recurrent fuzzy neural network (RFNN) is adopted in INFC instead of the conventional neural network, which was used in past studies. The proposed FEL controller has been compared with the conventional feedback error learning controller (CFEL) and the PID controller through some performance indices

  10. Optimum operating conditions for a water purification process integrated to a heat transformer with energy recycling using neural network inverse

    Energy Technology Data Exchange (ETDEWEB)

    Hernandez, J.A.; Siqueiros, J.; Juarez-Romero, D. [Centro de Investigacion en Ingenieria y Ciencias Aplicadas, Universidad Autonoma del Estado de Morelos (UAEM), Av. Universidad No. 1001, Col. Chamilpa, Cuernavaca, Morelos C.P. 62209 (Mexico); Bassam, A. [Posgrado en Ingenieria y Ciencias Aplicadas, Universidad Autonoma del Estado de Morelos (UAEM), Av. Universidad No. 1001, Col. Chamilpa, Cuernavaca, Morelos C.P. 62209 (Mexico)

    2009-04-15

    Artificial neural network inverse (ANNi) is applied to calculate the optimal operating conditions on the coefficient of performance (COP) for a water purification process integrated to an absorption heat transformer with energy recycling. An artificial neural network (ANN) model is developed to predict the COP which was increased with energy recycling. This ANN model takes into account the input and output temperatures for each one of the four components (absorber, generator, evaporator, and condenser), as well as two pressures and LiBr + H{sub 2}O concentrations. For the network, a feedforward with one hidden layer, a Levenberg-Marquardt learning algorithm, a hyperbolic tangent sigmoid transfer function and a linear transfer function were used. The best fitting training data set was obtained with three neurons in the hidden layer. On the validation data set, simulations and experimental data test were in good agreement (R > 0.99). This ANN model can be used to predict the COP when the input variables (operating conditions) are well known. However, to control the COP in the system, we developed a strategy to estimate the optimal input variables when a COP is required from ANNi. An optimization method (the Nelder-Mead simplex method) is used to fit the unknown input variable resulted from the ANNi. This methodology can be applied to control on-line the performance of the system. (author)

  11. Geometric transitions and integrable systems

    International Nuclear Information System (INIS)

    Diaconescu, D.-E.; Dijkgraaf, R.; Donagi, R.; Hofman, C.; Pantev, T.

    2006-01-01

    We consider B-model large N duality for a new class of noncompact Calabi-Yau spaces modeled on the neighborhood of a ruled surface in a Calabi-Yau threefold. The closed string side of the transition is governed at genus zero by an A 1 Hitchin integrable system on a genus g Riemann surface Σ. The open string side is described by a holomorphic Chern-Simons theory which reduces to a generalized matrix model in which the eigenvalues lie on the compact Riemann surface Σ. We show that the large N planar limit of the generalized matrix model is governed by the same A 1 Hitchin system therefore proving genus zero large N duality for this class of transitions

  12. Integrated roof wind energy system

    Directory of Open Access Journals (Sweden)

    Moonen S.P.G.

    2012-10-01

    Full Text Available Wind is an attractive renewable source of energy. Recent innovations in research and design have reduced to a few alternatives with limited impact on residential construction. Cost effective solutions have been found at larger scale, but storage and delivery of energy to the actual location it is used, remain a critical issue. The Integrated Roof Wind Energy System is designed to overcome the current issues of urban and larger scale renewable energy system. The system is built up by an axial array of skewed shaped funnels that make use of the Venturi Effect to accelerate the wind flow. This inventive use of shape and geometry leads to a converging air capturing inlet to create high wind mass flow and velocity toward a vertical-axis wind turbine in the top of the roof for generation of a relatively high amount of energy. The methods used in this overview of studies include an array of tools from analytical modelling, PIV wind tunnel testing, and CFD simulation studies. The results define the main design parameters for an efficient system, and show the potential for the generation of high amounts of renewable energy with a novel and effective system suited for the built environment.

  13. Integrated piping structural analysis system

    International Nuclear Information System (INIS)

    Motoi, Toshio; Yamadera, Masao; Horino, Satoshi; Idehata, Takamasa

    1979-01-01

    Structural analysis of the piping system for nuclear power plants has become larger in scale and in quantity. In addition, higher quality analysis is regarded as of major importance nowadays from the point of view of nuclear plant safety. In order to fulfill to the above requirements, an integrated piping structural analysis system (ISAP-II) has been developed. Basic philosophy of this system is as follows: 1. To apply the date base system. All information is concentrated. 2. To minimize the manual process in analysis, evaluation and documentation. Especially to apply the graphic system as much as possible. On the basis of the above philosophy four subsystems were made. 1. Data control subsystem. 2. Analysis subsystem. 3. Plotting subsystem. 4. Report subsystem. Function of the data control subsystem is to control all information of the data base. Piping structural analysis can be performed by using the analysis subsystem. Isometric piping drawing and mode shape, etc. can be plotted by using the plotting subsystem. Total analysis report can be made without the manual process through the reporting subsystem. (author)

  14. 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.

  15. 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.

  16. Listening to another sense: somatosensory integration in the auditory system.

    Science.gov (United States)

    Wu, Calvin; Stefanescu, Roxana A; Martel, David T; Shore, Susan E

    2015-07-01

    Conventionally, sensory systems are viewed as separate entities, each with its own physiological process serving a different purpose. However, many functions require integrative inputs from multiple sensory systems and sensory intersection and convergence occur throughout the central nervous system. The neural processes for hearing perception undergo significant modulation by the two other major sensory systems, vision and somatosensation. This synthesis occurs at every level of the ascending auditory pathway: the cochlear nucleus, inferior colliculus, medial geniculate body and the auditory cortex. In this review, we explore the process of multisensory integration from (1) anatomical (inputs and connections), (2) physiological (cellular responses), (3) functional and (4) pathological aspects. We focus on the convergence between auditory and somatosensory inputs in each ascending auditory station. This review highlights the intricacy of sensory processing and offers a multisensory perspective regarding the understanding of sensory disorders.

  17. A web-based system for neural network based classification in temporomandibular joint osteoarthritis.

    Science.gov (United States)

    de Dumast, Priscille; Mirabel, Clément; Cevidanes, Lucia; Ruellas, Antonio; Yatabe, Marilia; Ioshida, Marcos; Ribera, Nina Tubau; Michoud, Loic; Gomes, Liliane; Huang, Chao; Zhu, Hongtu; Muniz, Luciana; Shoukri, Brandon; Paniagua, Beatriz; Styner, Martin; Pieper, Steve; Budin, Francois; Vimort, Jean-Baptiste; Pascal, Laura; Prieto, Juan Carlos

    2018-07-01

    The purpose of this study is to describe the methodological innovations of a web-based system for storage, integration and computation of biomedical data, using a training imaging dataset to remotely compute a deep neural network classifier of temporomandibular joint osteoarthritis (TMJOA). This study imaging dataset consisted of three-dimensional (3D) surface meshes of mandibular condyles constructed from cone beam computed tomography (CBCT) scans. The training dataset consisted of 259 condyles, 105 from control subjects and 154 from patients with diagnosis of TMJ OA. For the image analysis classification, 34 right and left condyles from 17 patients (39.9 ± 11.7 years), who experienced signs and symptoms of the disease for less than 5 years, were included as the testing dataset. For the integrative statistical model of clinical, biological and imaging markers, the sample consisted of the same 17 test OA subjects and 17 age and sex matched control subjects (39.4 ± 15.4 years), who did not show any sign or symptom of OA. For these 34 subjects, a standardized clinical questionnaire, blood and saliva samples were also collected. The technological methodologies in this study include a deep neural network classifier of 3D condylar morphology (ShapeVariationAnalyzer, SVA), and a flexible web-based system for data storage, computation and integration (DSCI) of high dimensional imaging, clinical, and biological data. The DSCI system trained and tested the neural network, indicating 5 stages of structural degenerative changes in condylar morphology in the TMJ with 91% close agreement between the clinician consensus and the SVA classifier. The DSCI remotely ran with a novel application of a statistical analysis, the Multivariate Functional Shape Data Analysis, that computed high dimensional correlations between shape 3D coordinates, clinical pain levels and levels of biological markers, and then graphically displayed the computation results. The findings of this

  18. 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

  19. A recurrent neural model for proto-object based contour integration and figure-ground segregation.

    Science.gov (United States)

    Hu, Brian; Niebur, Ernst

    2017-12-01

    Visual processing of objects makes use of both feedforward and feedback streams of information. However, the nature of feedback signals is largely unknown, as is the identity of the neuronal populations in lower visual areas that receive them. Here, we develop a recurrent neural model to address these questions in the context of contour integration and figure-ground segregation. A key feature of our model is the use of grouping neurons whose activity represents tentative objects ("proto-objects") based on the integration of local feature information. Grouping neurons receive input from an organized set of local feature neurons, and project modulatory feedback to those same neurons. Additionally, inhibition at both the local feature level and the object representation level biases the interpretation of the visual scene in agreement with principles from Gestalt psychology. Our model explains several sets of neurophysiological results (Zhou et al. Journal of Neuroscience, 20(17), 6594-6611 2000; Qiu et al. Nature Neuroscience, 10(11), 1492-1499 2007; Chen et al. Neuron, 82(3), 682-694 2014), and makes testable predictions about the influence of neuronal feedback and attentional selection on neural responses across different visual areas. Our model also provides a framework for understanding how object-based attention is able to select both objects and the features associated with them.

  20. Integrated minicomputer alpha analysis system

    International Nuclear Information System (INIS)

    Vasilik, D.G.; Coy, D.E.; Seamons, M.; Henderson, R.W.; Romero, L.L.; Thomson, D.A.

    1978-01-01

    Approximately 1,000 stack and occupation air samples from plutonium and uranium facilities at LASL are analyzed daily. The concentrations of radio-nuclides in air are determined by measuring absolute alpha activities of particulates collected on air sample filter media. The Integrated Minicomputer Pulse system (IMPULSE) is an interface between many detectors of extremely simple design and a Digital Equipment Corporation (DEC) PDP-11/04 minicomputer. The detectors are photomultiplier tubes faced with zinc sulfide (ZnS). The average detector background is approximately 0.07 cpm. The IMPULSE system includes two mainframes, each of which can hold up to 64 detectors. The current hardware configuration includes 64 detectors in one mainframe and 40 detectors in the other. Each mainframe contains a minicomputer with 28K words of Random Access Memory. One minicomputer controls the detectors in both mainframes. A second computer was added for fail-safe redundancy and to support other laboratory computer requirements. The main minicomputer includes a dual floppy disk system and a dual DEC 'RK05' disk system for mass storage. The RK05 facilitates report generation and trend analysis. The IMPULSE hardware provides for passage of data from the detectors to the computer, and for passage of status and control information from the computer to the detector stations

  1. PC driven integrated vacuum system

    International Nuclear Information System (INIS)

    Curuia, Marian; Culcer, Mihai; Brandea, Iulian; Anghel, Mihai

    2001-01-01

    The monitoring of industrial plants by virtual instrumentation represents the most modern trend in the domain of electronic equipment. The integrated vacuum system presented here has several facilities, including the automated data storing of measurement results on hard disk and providing warning messages for operators when the measured parameters are lower or higher upper than the fixed values. The system can also work stand-alone, receiving the commands from the keyboards placed on his front panel but, when it is included in a automation complex system, a remote control from PC is necessary . Both parts of the system, power supply unit for turbo-molecular pump and the vacuum gage, are controlled by an 80C31 microcontroller. Because this microcontroller has a built-in circuitry for a serial communication, we established a serial communication between the PC and the power supply unit for turbo-molecular pump and the vacuum gage, according to the RS-232 hardware standard. As software, after careful evaluation of several options, we chose to develop a hybrid software packing using two different software development tools: LabVIEW, and assembly language. We chose LabVIEW because it is dedicated to data acquisition and communications, containing libraries for data collection, analysis, display and storage. (authors)

  2. Model-based Engineering for the Integration of Manufacturing Systems with Advanced Analytics

    OpenAIRE

    Lechevalier , David; Narayanan , Anantha; Rachuri , Sudarsan; Foufou , Sebti; Lee , Y Tina

    2016-01-01

    Part 3: Interoperability and Systems Integration; International audience; To employ data analytics effectively and efficiently on manufacturing systems, engineers and data scientists need to collaborate closely to bring their domain knowledge together. In this paper, we introduce a domain-specific modeling approach to integrate a manufacturing system model with advanced analytics, in particular neural networks, to model predictions. Our approach combines a set of meta-models and transformatio...

  3. INTEGRATION OF ENVIRONMENTAL MANAGEMENT SYSTEM

    Directory of Open Access Journals (Sweden)

    Tomescu Ada Mirela

    2012-07-01

    Full Text Available The relevance of management as significant factor of business activity can be established on various management systems. These will help to obtain, organise, administrate, evaluate and control particulars: information, quality, environmental protection, health and safety, various resources (time, human, finance, inventory etc. The complexity of nowadays days development, forced us to think ‘integrated’. Sustainable development principles require that environment management policies and practices are not good in themselves but also integrate with all other environmental objectives, and with social and economic development objectives. The principles of sustainable development involve that environment management policies and practices. These are not sound in them-self but also integrate with all other environmental objectives, and with social and economic development objectives. Those objectives were realized, and followed by development of strategies to effects the objective of sustainable development. Environmental management should embrace recent change in the area of environmental protection, and suit the recently regulations of the field -entire legal and economic, as well as perform management systems to meet the requirements of the contemporary model for economic development. These changes are trailed by abandon the conventional approach of environmental protection and it is replaced by sustainable development (SD. The keys and the aims of Cleaner Productions (CP are presented being implemented in various companies as a non-formalised environmental management system (EMS. This concept is suggested here as a proper model for practice where possible environmental harmful technologies are used -e.g. Rosia Montana. Showing the features and the power of CP this paper is a signal oriented to involve the awareness of policy-makers and top management of diverse Romanian companies. Many companies in European countries are developing

  4. 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.

  5. 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.

  6. 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)

  7. 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....

  8. Integrated control system for electron beam processes

    Science.gov (United States)

    Koleva, L.; Koleva, E.; Batchkova, I.; Mladenov, G.

    2018-03-01

    The ISO/IEC 62264 standard is widely used for integration of the business systems of a manufacturer with the corresponding manufacturing control systems based on hierarchical equipment models, functional data and manufacturing operations activity models. In order to achieve the integration of control systems, formal object communication models must be developed, together with manufacturing operations activity models, which coordinate the integration between different levels of control. In this article, the development of integrated control system for electron beam welding process is presented as part of a fully integrated control system of an electron beam plant, including also other additional processes: surface modification, electron beam evaporation, selective melting and electron beam diagnostics.

  9. Integrated delivery systems. Evolving oligopolies.

    Science.gov (United States)

    Malone, T A

    1998-01-01

    The proliferation of Integrated Delivery Systems (IDSs) in regional health care markets has resulted in the movement of these markets from a monopolistic competitive model of behavior to an oligopoly. An oligopoly is synonymous with competition among the few, as a small number of firms supply a dominant share of an industry's total output. The basic characteristics of a market with competition among the few are: (1) A mutual interdependence among the actions and behaviors of competing firms; (2) competition tends to rely on the differentiation of products; (3) significant barriers to entering the market exist; (4) the demand curve for services may be kinked; and (5) firms can benefit from economies of scale. An understanding of these characteristics is essential to the survival of IDSs as regional managed care markets mature.

  10. Integral measurement system for radon

    International Nuclear Information System (INIS)

    Garcia H, J.M.; Pena E, R.

    1996-01-01

    The Integral measurement system for Radon is an equipment to detect, counting and storage data of alpha particles produced by Radon 222 which is emanated through the terrestrial peel surface. This equipment was designed in the Special Designs Department of the National Institute of Nuclear Research. It supplies information about the behavior at long time (41 days) on each type of alpha radiation that is present into the environment as well as into the terrestrial peel. The program is formed by an User program, where it is possible to determine the operation parameters of a portable probe that contains, a semiconductor detector, a microprocessor as a control central unit, a real time clock and calendar to determine the occurred events chronology, a non-volatile memory device for storage the acquired data and an interface to establish the serial communications with other personal computers. (Author)

  11. Integrated solar energy system optimization

    Science.gov (United States)

    Young, S. K.

    1982-11-01

    The computer program SYSOPT, intended as a tool for optimizing the subsystem sizing, performance, and economics of integrated wind and solar energy systems, is presented. The modular structure of the methodology additionally allows simulations when the solar subsystems are combined with conventional technologies, e.g., a utility grid. Hourly energy/mass flow balances are computed for interconnection points, yielding optimized sizing and time-dependent operation of various subsystems. The program requires meteorological data, such as insolation, diurnal and seasonal variations, and wind speed at the hub height of a wind turbine, all of which can be taken from simulations like the TRNSYS program. Examples are provided for optimization of a solar-powered (wind turbine and parabolic trough-Rankine generator) desalinization plant, and a design analysis for a solar powered greenhouse.

  12. 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.

  13. Energy Systems Integration: Demonstrating Distributed Resource Communications

    Energy Technology Data Exchange (ETDEWEB)

    2017-01-01

    Overview fact sheet about the Electric Power Research Institute (EPRI) and Schneider Electric Integrated Network Testbed for Energy Grid Research and Technology Experimentation (INTEGRATE) project at the Energy Systems Integration Facility. INTEGRATE is part of the U.S. Department of Energy's Grid Modernization Initiative.

  14. DOE systems approach and integration

    International Nuclear Information System (INIS)

    Logan, J.A.

    1987-01-01

    Six sites are now disposing of most of DOE's currently generated waste. These are: Hanford, Idaho National Engineering Laboratory, Los Alamos National Laboratory, the Nevada Test Site, Oak Ridge, and Savannah River. Within DOE, experience with disposal of solid LLW has been that arid site disposal facilities (first four) appear to have performed well, while humid site disposal facilities (last two) have experienced some waste migration. DOE's intent, of course, is to operate all its waste disposal facilities so that public health and safety are not adversely affected. To ensure that this continues to be the case, activities are underway to increase use of waste form stabilization and engineered barriers where appropriate. For the sake of overall economy, these measures are to be applied as and where needed, through use of the systems approach. In this paper the author discusses two topics: the system approach, which DOE has decided to use for resolving waste disposal problems in the management of DOE's low-level wastes; and DOE's intended integration of activities underway at the waste management systems throughout DOE facilities. 2 figures

  15. Advanced integrated solvent extraction systems

    Energy Technology Data Exchange (ETDEWEB)

    Horwitz, E.P.; Dietz, M.L.; Leonard, R.A. [Argonne National Lab., IL (United States)

    1997-10-01

    Advanced integrated solvent extraction systems are a series of novel solvent extraction (SX) processes that will remove and recover all of the major radioisotopes from acidic-dissolved sludge or other acidic high-level wastes. The major focus of this effort during the last 2 years has been the development of a combined cesium-strontium extraction/recovery process, the Combined CSEX-SREX Process. The Combined CSEX-SREX Process relies on a mixture of a strontium-selective macrocyclic polyether and a novel cesium-selective extractant based on dibenzo 18-crown-6. The process offers several potential advantages over possible alternatives in a chemical processing scheme for high-level waste treatment. First, if the process is applied as the first step in chemical pretreatment, the radiation level for all subsequent processing steps (e.g., transuranic extraction/recovery, or TRUEX) will be significantly reduced. Thus, less costly shielding would be required. The second advantage of the Combined CSEX-SREX Process is that the recovered Cs-Sr fraction is non-transuranic, and therefore will decay to low-level waste after only a few hundred years. Finally, combining individual processes into a single process will reduce the amount of equipment required to pretreat the waste and therefore reduce the size and cost of the waste processing facility. In an ongoing collaboration with Lockheed Martin Idaho Technology Company (LMITCO), the authors have successfully tested various segments of the Advanced Integrated Solvent Extraction Systems. Eichrom Industries, Inc. (Darien, IL) synthesizes and markets the Sr extractant and can supply the Cs extractant on a limited basis. Plans are under way to perform a test of the Combined CSEX-SREX Process with real waste at LMITCO in the near future.

  16. 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

  17. 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.

  18. NASA UAS Integration into the NAS Project: Human Systems Integration

    Science.gov (United States)

    Shively, Jay

    2016-01-01

    This presentation provides an overview of the work the Human Systems Integration (HSI) sub-project has done on detect and avoid (DAA) displays while working on the UAS (Unmanned Aircraft System) Integration into the NAS project. The most recent simulation on DAA interoperability with Traffic Collision Avoidance System (TCAS) is discussed in the most detail. The relationship of the work to the larger UAS community and next steps are also detailed.

  19. Geometry and dynamics of integrable systems

    CERN Document Server

    Matveev, Vladimir

    2016-01-01

    Based on lectures given at an advanced course on integrable systems at the Centre de Recerca Matemàtica in Barcelona, these lecture notes address three major aspects of integrable systems: obstructions to integrability from differential Galois theory; the description of singularities of integrable systems on the basis of their relation to bi-Hamiltonian systems; and the generalization of integrable systems to the non-Hamiltonian settings. All three sections were written by top experts in their respective fields. Native to actual problem-solving challenges in mechanics, the topic of integrable systems is currently at the crossroads of several disciplines in pure and applied mathematics, and also has important interactions with physics. The study of integrable systems also actively employs methods from differential geometry. Moreover, it is extremely important in symplectic geometry and Hamiltonian dynamics, and has strong correlations with mathematical physics, Lie theory and algebraic geometry (including mir...

  20. Integrated Compliance Information System (ICIS)

    Data.gov (United States)

    U.S. Environmental Protection Agency — The purpose of ICIS is to meet evolving Enforcement and Compliance business needs for EPA and State users by integrating information into a single integrated data...

  1. 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

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

    Directory of Open Access Journals (Sweden)

    Francesco Mannella

    2016-10-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Bowman, B. [Lawrence Livermore National Lab., CA (United States)

    1994-11-15

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

  5. Integrating phosphoproteomics in systems biology

    Directory of Open Access Journals (Sweden)

    Yu Liu

    2014-07-01

    Full Text Available Phosphorylation of serine, threonine and tyrosine plays significant roles in cellular signal transduction and in modifying multiple protein functions. Phosphoproteins are coordinated and regulated by a network of kinases, phosphatases and phospho-binding proteins, which modify the phosphorylation states, recognize unique phosphopeptides, or target proteins for degradation. Detailed and complete information on the structure and dynamics of these networks is required to better understand fundamental mechanisms of cellular processes and diseases. High-throughput technologies have been developed to investigate phosphoproteomes in model organisms and human diseases. Among them, mass spectrometry (MS-based technologies are the major platforms and have been widely applied, which has led to explosive growth of phosphoproteomic data in recent years. New bioinformatics tools are needed to analyze and make sense of these data. Moreover, most research has focused on individual phosphoproteins and kinases. To gain a more complete knowledge of cellular processes, systems biology approaches, including pathways and networks modeling, have to be applied to integrate all components of the phosphorylation machinery, including kinases, phosphatases, their substrates, and phospho-binding proteins. This review presents the latest developments of bioinformatics methods and attempts to apply systems biology to analyze phosphoproteomics data generated by MS-based technologies. Challenges and future directions in this field will be also discussed.

  6. 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.

  7. Impulsive synchronization of Markovian jumping randomly coupled neural networks with partly unknown transition probabilities via multiple integral approach.

    Science.gov (United States)

    Chandrasekar, A; Rakkiyappan, R; Cao, Jinde

    2015-10-01

    This paper studies the impulsive synchronization of Markovian jumping randomly coupled neural networks with partly unknown transition probabilities via multiple integral approach. The array of neural networks are coupled in a random fashion which is governed by Bernoulli random variable. The aim of this paper is to obtain the synchronization criteria, which is suitable for both exactly known and partly unknown transition probabilities such that the coupled neural network is synchronized with mixed time-delay. The considered impulsive effects can be synchronized at partly unknown transition probabilities. Besides, a multiple integral approach is also proposed to strengthen the Markovian jumping randomly coupled neural networks with partly unknown transition probabilities. By making use of Kronecker product and some useful integral inequalities, a novel Lyapunov-Krasovskii functional was designed for handling the coupled neural network with mixed delay and then impulsive synchronization criteria are solvable in a set of linear matrix inequalities. Finally, numerical examples are presented to illustrate the effectiveness and advantages of the theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

  8. INTEGRATED HSEQ MANAGEMENT SYSTEMS: DEVELOPMENTS AND TRENDS

    OpenAIRE

    Osmo Kauppila; Janne Härkönen; Seppo Väyrynen

    2015-01-01

    The integration of health and safety, environmental and quality (HSEQ) management systems has become a current topic in the 21st century, as the need for systems thinking has grown along with the number of management system standards. This study aims to map current developments and trends in integrated HSEQ management. Three viewpoints are taken: the current state of the main HSEQ management standards, research literature on integrated management systems (IMS), and a case study of an industry...

  9. Explicit integration of some integrable systems of classical mechanics

    OpenAIRE

    Basak Gancheva, Inna

    2011-01-01

    The main objective of the thesis is the analytical and geometrical study of several integrable finite-dimentional dynamical systems of classical mechanics, which are closely related, namely: - the classical generalization of the Euler top: the Zhukovski-Volterra (ZV) system describing the free motion of a gyrostat, i.e., a rigid body carrying a symmetric rotator whose axis is fixed in the body; - the Steklov-Lyapunov integrable case of the Kirchhoff equations describing the motio...

  10. Neural basis of the time window for subjective motor-auditory integration

    Directory of Open Access Journals (Sweden)

    Koichi eToida

    2016-01-01

    Full Text Available Temporal contiguity between an action and corresponding auditory feedback is crucial to the perception of self-generated sound. However, the neural mechanisms underlying motor–auditory temporal integration are unclear. Here, we conducted four experiments with an oddball paradigm to examine the specific event-related potentials (ERPs elicited by delayed auditory feedback for a self-generated action. The first experiment confirmed that a pitch-deviant auditory stimulus elicits mismatch negativity (MMN and P300, both when it is generated passively and by the participant’s action. In our second and third experiments, we investigated the ERP components elicited by delayed auditory feedback of for a self-generated action. We found that delayed auditory feedback elicited an enhancement of P2 (enhanced-P2 and a N300 component, which were apparently different from the MMN and P300 components observed in the first experiment. We further investigated the sensitivity of the enhanced-P2 and N300 to delay length in our fourth experiment. Strikingly, the amplitude of the N300 increased as a function of the delay length. Additionally, the N300 amplitude was significantly correlated with the conscious detection of the delay (the 50% detection point was around 200 ms, and hence reduction in the feeling of authorship of the sound (the sense of agency. In contrast, the enhanced-P2 was most prominent in short-delay (≤ 200 ms conditions and diminished in long-delay conditions. Our results suggest that different neural mechanisms are employed for the processing of temporally-deviant and pitch-deviant auditory feedback. Additionally, the temporal window for subjective motor–auditory integration is likely about 200 ms, as indicated by these auditory ERP components.

  11. 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

  12. 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)

  13. Integrated Project Management System description

    International Nuclear Information System (INIS)

    1994-09-01

    The Integrated Program Management System (IPMS) Description is a ''working'' document that describes the work processes of the Uranium Mill Tailings Remedial Action Project Office (UMTRA) and IPMS Group. This document has undergone many revisions since the UMTRA Project began; this revision not only updates the work processes but more clearly explains the relationships between the Project Office, contractors, and other participants. The work process flow style has been revised to better describe Project work and the relationships of participants. For each work process, more background and guidance on ''why'' and ''what is expected'' is given. For example, a description of activity data sheets has been added in the work organization and the Project performance and reporting processes, as well as additional detail about the federal budget process and funding management and improved flow charts and explanations of cost and schedule management. A chapter has been added describing the Cost Reduction/Productivity Improvement Program. The Change Control Board (CCB) procedures (Appendix A) have been updated. Project critical issues meeting (PCIM) procedures have been added as Appendix B. Budget risk assessment meeting procedures have been added as Appendix C. These appendices are written to act as stand-alone documentation for each process. As the procedures are improved and updated, the documentation can be updated separately

  14. Cotangent Models for Integrable Systems

    Science.gov (United States)

    Kiesenhofer, Anna; Miranda, Eva

    2017-03-01

    We associate cotangent models to a neighbourhood of a Liouville torus in symplectic and Poisson manifolds focusing on b-Poisson/ b-symplectic manifolds. The semilocal equivalence with such models uses the corresponding action-angle theorems in these settings: the theorem of Liouville-Mineur-Arnold for symplectic manifolds and an action-angle theorem for regular Liouville tori in Poisson manifolds (Laurent- Gengoux et al., IntMath Res Notices IMRN 8: 1839-1869, 2011). Our models comprise regular Liouville tori of Poisson manifolds but also consider the Liouville tori on the singular locus of a b-Poisson manifold. For this latter class of Poisson structures we define a twisted cotangent model. The equivalence with this twisted cotangent model is given by an action-angle theorem recently proved by the authors and Scott (Math. Pures Appl. (9) 105(1):66-85, 2016). This viewpoint of cotangent models provides a new machinery to construct examples of integrable systems, which are especially valuable in the b-symplectic case where not many sources of examples are known. At the end of the paper we introduce non-degenerate singularities as lifted cotangent models on b-symplectic manifolds and discuss some generalizations of these models to general Poisson manifolds.

  15. Sandia National Laboratories: Integrated Military Systems

    Science.gov (United States)

    Defense Systems & Assessments About Defense Systems & Assessments Program Areas Accomplishments Audit Sandia's Economic Impact Licensing & Technology Transfer Browse Technology Portfolios ; Culture Work-Life Balance Special Programs Integrated Military Systems (IMS) Capabilities Facilities

  16. 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.

  17. 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....

  18. 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.

  19. Evaluation of mobile systems: an integrative framework.

    NARCIS (Netherlands)

    Högler, T.; Versendaal, J.; Batenburg, R.

    2015-01-01

    This work presents an integrative framework for the evaluation of mobile systems. In comparison to stationary systems, mobile systems have a bundle of specific singularities that should be considered for evaluation. Further analysis of existing approaches clarifies that an integrative approach for

  20. Formal First Integrals of General Dynamical Systems

    Directory of Open Access Journals (Sweden)

    Jia Jiao

    2016-01-01

    Full Text Available The goal of this paper is trying to make a complete study on the integrability for general analytic nonlinear systems by first integrals. We will firstly give an exhaustive discussion on analytic planar systems. Then a class of higher dimensional systems with invariant manifolds will be considered; we will develop several criteria for existence of formal integrals and give some applications to illustrate our results at last.

  1. Symplectic topology of integrable Hamiltonian systems

    International Nuclear Information System (INIS)

    Nguyen Tien Zung.

    1993-08-01

    We study the topology of integrable Hamiltonian systems, giving the main attention to the affine structure of their orbit spaces. In particular, we develop some aspects of Fomenko's theory about topological classification of integrable non-degenerate systems, and consider some relations between such systems and ''pure'' contact and symplectic geometry. We give a notion of integrable surgery and use it to obtain some interesting symplectic structures. (author). Refs, 10 figs

  2. Laser and photonic systems design and integration

    CERN Document Server

    Nof, Shimon Y; Cheng, Gary J

    2014-01-01

    New, significant scientific discoveries in laser and photonic technologies, systems perspectives, and integrated design approaches can improve even further the impact in critical areas of challenge. Yet this knowledge is dispersed across several disciplines and research arenas. Laser and Photonic Systems: Design and Integration brings together a multidisciplinary group of experts to increase understanding of the ways in which systems perspectives may influence laser and photonic innovations and application integration.By bringing together chapters from leading scientists and technologists, ind

  3. 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

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

    DEFF Research Database (Denmark)

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

    2001-01-01

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

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

    DEFF Research Database (Denmark)

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

    2001-01-01

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

  6. Lie Algebras and Integrable Systems

    International Nuclear Information System (INIS)

    Zhang Yufeng; Mei Jianqin

    2012-01-01

    A 3 × 3 matrix Lie algebra is first introduced, its subalgebras and the generated Lie algebras are obtained, respectively. Applications of a few Lie subalgebras give rise to two integrable nonlinear hierarchies of evolution equations from their reductions we obtain the nonlinear Schrödinger equations, the mKdV equations, the Broer-Kaup (BK) equation and its generalized equation, etc. The linear and nonlinear integrable couplings of one integrable hierarchy presented in the paper are worked out by casting a 3 × 3 Lie subalgebra into a 2 × 2 matrix Lie algebra. Finally, we discuss the elliptic variable solutions of a generalized BK equation. (general)

  7. A battery-free multichannel digital neural/EMG telemetry system for flying insects.

    Science.gov (United States)

    Thomas, Stewart J; Harrison, Reid R; Leonardo, Anthony; Reynolds, Matthew S

    2012-10-01

    This paper presents a digital neural/EMG telemetry system small enough and lightweight enough to permit recording from insects in flight. It has a measured flight package mass of only 38 mg. This system includes a single-chip telemetry integrated circuit (IC) employing RF power harvesting for battery-free operation, with communication via modulated backscatter in the UHF (902-928 MHz) band. An on-chip 11-bit ADC digitizes 10 neural channels with a sampling rate of 26.1 kSps and 4 EMG channels at 1.63 kSps, and telemeters this data wirelessly to a base station. The companion base station transceiver includes an RF transmitter of +36 dBm (4 W) output power to wirelessly power the telemetry IC, and a digital receiver with a sensitivity of -70 dBm for 10⁻⁵ BER at 5.0 Mbps to receive the data stream from the telemetry IC. The telemetry chip was fabricated in a commercial 0.35 μ m 4M1P (4 metal, 1 poly) CMOS process. The die measures 2.36 × 1.88 mm, is 250 μm thick, and is wire bonded into a flex circuit assembly measuring 4.6 × 6.8 mm.

  8. Integrated Computer System of Management in Logistics

    Science.gov (United States)

    Chwesiuk, Krzysztof

    2011-06-01

    This paper aims at presenting a concept of an integrated computer system of management in logistics, particularly in supply and distribution chains. Consequently, the paper includes the basic idea of the concept of computer-based management in logistics and components of the system, such as CAM and CIM systems in production processes, and management systems for storage, materials flow, and for managing transport, forwarding and logistics companies. The platform which integrates computer-aided management systems is that of electronic data interchange.

  9. An Integrated Virtual Environment System

    National Research Council Canada - National Science Library

    Hahn, James K; Gritz, Larry; Darken, Rudolph; Geigel, Joseph; Lee, Jong W

    1993-01-01

    .... A joint research at the George Washington University and the Naval Research Laboratory is bringing together issues from these domains to study the factors that contribute to an integrated virtual environment...

  10. Integrated Systems Engineering Framework (ISEF)

    Data.gov (United States)

    Federal Laboratory Consortium — The ISEF is an integrated SE framework built to create and capture knowledge using a decision-centric method, high-quality data visualizations, intuitive navigation...

  11. 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.

  12. Hyperdeterminants as integrable discrete systems

    International Nuclear Information System (INIS)

    Tsarev, Sergey P; Wolf, Thomas

    2009-01-01

    We give the basic definitions and some theoretical results about hyperdeterminants, introduced by A Cayley in 1845. We prove integrability (understood as 4D consistency) of a nonlinear difference equation defined by the 2 x 2 x 2-hyperdeterminant. This result gives rise to the following hypothesis: the difference equations defined by hyperdeterminants of any size are integrable. We show that this hypothesis already fails in the case of the 2 x 2 x 2 x 2-hyperdeterminant.

  13. Hyperdeterminants as integrable discrete systems

    Energy Technology Data Exchange (ETDEWEB)

    Tsarev, Sergey P [Institute of Space and Information Technologies, Siberian Federal University, Svobodnyi Avenue, 79, 660041, Krasnoyarsk (Russian Federation); Wolf, Thomas [Department of Mathematics, Brock University, 500 Glenridge Avenue, St Catharines, Ontario L2S 3A1 (Canada)], E-mail: sptsarev@mail.ru, E-mail: twolf@brocku.ca

    2009-10-30

    We give the basic definitions and some theoretical results about hyperdeterminants, introduced by A Cayley in 1845. We prove integrability (understood as 4D consistency) of a nonlinear difference equation defined by the 2 x 2 x 2-hyperdeterminant. This result gives rise to the following hypothesis: the difference equations defined by hyperdeterminants of any size are integrable. We show that this hypothesis already fails in the case of the 2 x 2 x 2 x 2-hyperdeterminant.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2010-07-01

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

  15. An integrated multichannel neural recording analog front-end ASIC with area-efficient driven right leg circuit.

    Science.gov (United States)

    Tao Tang; Wang Ling Goh; Lei Yao; Jia Hao Cheong; Yuan Gao

    2017-07-01

    This paper describes an integrated multichannel neural recording analog front end (AFE) with a novel area-efficient driven right leg (DRL) circuit to improve the system common mode rejection ratio (CMRR). The proposed AFE consists of an AC-coupled low-noise programmable-gain amplifier, an area-efficient DRL block and a 10-bit SAR ADC. Compared to conventional DRL circuit, the proposed capacitor-less DRL design achieves 90% chip area reduction with enhanced CMRR performance, making it ideal for multichannel biomedical recording applications. The AFE circuit has been designed in a standard 0.18-μm CMOS process. Post-layout simulation results show that the AFE provides two gain settings of 54dB/60dB while consuming 1 μA per channel under a supply voltage of 1 V. The input-referred noise of the AFE integrated from 1 Hz to 10k Hz is only 4 μVrms and the CMRR is 110 dB.

  16. 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.

  17. 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.

  18. 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.

  19. The neural substrate and functional integration of uncertainty in decision making: an information theory approach.

    Science.gov (United States)

    Goñi, Joaquín; Aznárez-Sanado, Maite; Arrondo, Gonzalo; Fernández-Seara, María; Loayza, Francis R; Heukamp, Franz H; Pastor, María A

    2011-03-09

    Decision making can be regarded as the outcome of cognitive processes leading to the selection of a course of action among several alternatives. Borrowing a central measurement from information theory, Shannon entropy, we quantified the uncertainties produced by decisions of participants within an economic decision task under different configurations of reward probability and time. These descriptors were used to obtain blood oxygen level-dependent (BOLD) signal correlates of uncertainty and two clusters codifying the Shannon entropy of task configurations were identified: a large cluster including parts of the right middle cingulate cortex (MCC) and left and right pre-supplementary motor areas (pre-SMA) and a small cluster at the left anterior thalamus. Subsequent functional connectivity analyses using the psycho-physiological interactions model identified areas involved in the functional integration of uncertainty. Results indicate that clusters mostly located at frontal and temporal cortices experienced an increased connectivity with the right MCC and left and right pre-SMA as the uncertainty was higher. Furthermore, pre-SMA was also functionally connected to a rich set of areas, most of them associative areas located at occipital and parietal lobes. This study provides a map of the human brain segregation and integration (i.e., neural substrate and functional connectivity respectively) of the uncertainty associated to an economic decision making paradigm.

  20. Neural basis of limb ownership in individuals with body integrity identity disorder.

    Directory of Open Access Journals (Sweden)

    Milenna T van Dijk

    Full Text Available Our body feels like it is ours. However, individuals with body integrity identity disorder (BIID lack this feeling of ownership for distinct limbs and desire amputation of perfectly healthy body parts. This extremely rare condition provides us with an opportunity to study the neural basis underlying the feeling of limb ownership, since these individuals have a feeling of disownership for a limb in the absence of apparent brain damage. Here we directly compared brain activation between limbs that do and do not feel as part of the body using functional MRI during separate tactile stimulation and motor execution experiments. In comparison to matched controls, individuals with BIID showed heightened responsivity of a large somatosensory network including the parietal cortex and right insula during tactile stimulation, regardless of whether the stimulated leg felt owned or alienated. Importantly, activity in the ventral premotor cortex depended on the feeling of ownership and was reduced during stimulation of the alienated compared to the owned leg. In contrast, no significant differences between groups were observed during the performance of motor actions. These results suggest that altered somatosensory processing in the premotor cortex is associated with the feeling of disownership in BIID, which may be related to altered integration of somatosensory and proprioceptive information.

  1. Neural basis of limb ownership in individuals with body integrity identity disorder.

    Science.gov (United States)

    van Dijk, Milenna T; van Wingen, Guido A; van Lammeren, Anouk; Blom, Rianne M; de Kwaasteniet, Bart P; Scholte, H Steven; Denys, Damiaan

    2013-01-01

    Our body feels like it is ours. However, individuals with body integrity identity disorder (BIID) lack this feeling of ownership for distinct limbs and desire amputation of perfectly healthy body parts. This extremely rare condition provides us with an opportunity to study the neural basis underlying the feeling of limb ownership, since these individuals have a feeling of disownership for a limb in the absence of apparent brain damage. Here we directly compared brain activation between limbs that do and do not feel as part of the body using functional MRI during separate tactile stimulation and motor execution experiments. In comparison to matched controls, individuals with BIID showed heightened responsivity of a large somatosensory network including the parietal cortex and right insula during tactile stimulation, regardless of whether the stimulated leg felt owned or alienated. Importantly, activity in the ventral premotor cortex depended on the feeling of ownership and was reduced during stimulation of the alienated compared to the owned leg. In contrast, no significant differences between groups were observed during the performance of motor actions. These results suggest that altered somatosensory processing in the premotor cortex is associated with the feeling of disownership in BIID, which may be related to altered integration of somatosensory and proprioceptive information.

  2. The neural substrate and functional integration of uncertainty in decision making: an information theory approach.

    Directory of Open Access Journals (Sweden)

    Joaquín Goñi

    Full Text Available Decision making can be regarded as the outcome of cognitive processes leading to the selection of a course of action among several alternatives. Borrowing a central measurement from information theory, Shannon entropy, we quantified the uncertainties produced by decisions of participants within an economic decision task under different configurations of reward probability and time. These descriptors were used to obtain blood oxygen level-dependent (BOLD signal correlates of uncertainty and two clusters codifying the Shannon entropy of task configurations were identified: a large cluster including parts of the right middle cingulate cortex (MCC and left and right pre-supplementary motor areas (pre-SMA and a small cluster at the left anterior thalamus. Subsequent functional connectivity analyses using the psycho-physiological interactions model identified areas involved in the functional integration of uncertainty. Results indicate that clusters mostly located at frontal and temporal cortices experienced an increased connectivity with the right MCC and left and right pre-SMA as the uncertainty was higher. Furthermore, pre-SMA was also functionally connected to a rich set of areas, most of them associative areas located at occipital and parietal lobes. This study provides a map of the human brain segregation and integration (i.e., neural substrate and functional connectivity respectively of the uncertainty associated to an economic decision making paradigm.

  3. Integrated design for space transportation system

    CERN Document Server

    Suresh, B N

    2015-01-01

    The book addresses the overall integrated design aspects of a space transportation system involving several disciplines like propulsion, vehicle structures, aerodynamics, flight mechanics, navigation, guidance and control systems, stage auxiliary systems, thermal systems etc. and discusses the system approach for design, trade off analysis, system life cycle considerations, important aspects in mission management, the risk assessment, etc. There are several books authored to describe the design aspects of various areas, viz., propulsion, aerodynamics, structures, control, etc., but there is no book which presents space transportation system (STS) design in an integrated manner. This book attempts to fill this gap by addressing systems approach for STS design, highlighting the integrated design aspects, interactions between various subsystems and interdependencies. The main focus is towards the complex integrated design to arrive at an optimum, robust and cost effective space transportation system. The orbit...

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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

  10. 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

  11. Adaptive Integration of Nonsmooth Dynamical Systems

    Science.gov (United States)

    2017-10-11

    2017 W911NF-12-R-0012-03: Adaptive Integration of Nonsmooth Dynamical Systems The views, opinions and/or findings contained in this report are those of...Integration of Nonsmooth Dynamical Systems Report Term: 0-Other Email: drum@gwu.edu Distribution Statement: 1-Approved for public release; distribution is...classdrake_1_1systems_1_1_integrator_base.html ; 3) a solver for dynamical systems with arbitrary unilateral and bilateral constraints (the key component of the time stepping systems )- see

  12. INTEGRATIVE AUGMENTATION OF STANDARDIZED MANAGEMENT SYSTEMS

    Directory of Open Access Journals (Sweden)

    Stanislav Karapetrovic

    2008-03-01

    Full Text Available The development, features and integrating abilities of different international standards related to management systems are discussed. A group of such standards that augment the performance of quality management systems in organizations is specifically focused on. The concept, characteristics and an illustrative example of one augmenting standard, namely ISO 10001, are addressed. Integration of standardized augmenting systems, both by themselves and within the overall management system, is examined. It is argued that, in research and practice alike, integrative augmentation represents the future of standardized quality and other management systems.

  13. 76 FR 48159 - Integrated System Power Rates

    Science.gov (United States)

    2011-08-08

    ... DEPARTMENT OF ENERGY Southwestern Power Administration Integrated System Power Rates AGENCY... American Electric Reliability Corporation and to cover increased investments and replacements in..., prepared a Current Power Repayment Study using existing system rates. The Study indicates that Southwestern...

  14. Integrated Procurement Management System, Version II

    Science.gov (United States)

    Collier, L. J.

    1985-01-01

    Integrated Procurement Management System, Version II (IPMS II) is online/ batch system for collecting developing, managing and disseminating procurementrelated data at NASA Johnson Space Center. Portions of IPMS II adaptable to other procurement situations.

  15. I-15 integrated corridor management : system requirements.

    Science.gov (United States)

    2011-07-01

    This document is intended as a listing and discussion of the Requirements for the I-15 Integrated Corridor Management System : (ICMS) Demonstration Project in San Diego. This document describes what the system is to do (the functional requirements), ...

  16. Integrable Hamiltonian systems and spectral theory

    CERN Document Server

    Moser, J

    1981-01-01

    Classical integrable Hamiltonian systems and isospectral deformations ; geodesics on an ellipsoid and the mechanical system of C. Neumann ; the Schrödinger equation for almost periodic potentials ; finite band potentials ; limit cases, Bargmann potentials.

  17. An Integrated Library System: Preliminary Considerations.

    Science.gov (United States)

    Neroda, Edward

    Noting difficulties experienced by small to medium sized colleges in acquiring integrated library computer systems, this position paper outlines issues related to the subject with the intention of increasing familiarity and interest in integrated library systems. The report includes: a brief review of technological advances as they relate to…

  18. Integrated neuron circuit for implementing neuromorphic system with synaptic device

    Science.gov (United States)

    Lee, Jeong-Jun; Park, Jungjin; Kwon, Min-Woo; Hwang, Sungmin; Kim, Hyungjin; Park, Byung-Gook

    2018-02-01

    In this paper, we propose and fabricate Integrate & Fire neuron circuit for implementing neuromorphic system. Overall operation of the circuit is verified by measuring discrete devices and the output characteristics of the circuit. Since the neuron circuit shows asymmetric output characteristic that can drive synaptic device with Spike-Timing-Dependent-Plasticity (STDP) characteristic, the autonomous weight update process is also verified by connecting the synaptic device and the neuron circuit. The timing difference of the pre-neuron and the post-neuron induce autonomous weight change of the synaptic device. Unlike 2-terminal devices, which is frequently used to implement neuromorphic system, proposed scheme of the system enables autonomous weight update and simple configuration by using 4-terminal synapse device and appropriate neuron circuit. Weight update process in the multi-layer neuron-synapse connection ensures implementation of the hardware-based artificial intelligence, based on Spiking-Neural- Network (SNN).

  19. Integrated analysis of miRNA and mRNA expression in childhood medulloblastoma compared with neural stem cells.

    Directory of Open Access Journals (Sweden)

    Laura A Genovesi

    Full Text Available Medulloblastoma (MB is the most common malignant brain tumor in children and a leading cause of cancer-related mortality and morbidity. Several molecular sub-types of MB have been identified, suggesting they may arise from distinct cells of origin. Data from animal models indicate that some MB sub-types arise from multipotent cerebellar neural stem cells (NSCs. Hence, microRNA (miRNA expression profiles of primary MB samples were compared to CD133+ NSCs, aiming to identify deregulated miRNAs involved in MB pathogenesis. Expression profiling of 662 miRNAs in primary MB specimens, MB cell lines, and human CD133+ NSCs and CD133- neural progenitor cells was performed by qRT-PCR. Clustering analysis identified two distinct sub-types of MB primary specimens, reminiscent of sub-types obtained from their mRNA profiles. 21 significantly up-regulated and 12 significantly down-regulated miRNAs were identified in MB primary specimens relative to CD133+ NSCs (p<0.01. The majority of up-regulated miRNAs mapped to chromosomal regions 14q32 and 17q. Integration of the predicted targets of deregulated miRNAs with mRNA expression data from the same specimens revealed enrichment of pathways regulating neuronal migration, nervous system development and cell proliferation. Transient over-expression of a down-regulated miRNA, miR-935, resulted in significant down-regulation of three of the seven predicted miR-935 target genes at the mRNA level in a MB cell line, confirming the validity of this approach. This study represents the first integrated analysis of MB miRNA and mRNA expression profiles and is the first to compare MB miRNA expression profiles to those of CD133+ NSCs. We identified several differentially expressed miRNAs that potentially target networks of genes and signaling pathways that may be involved in the transformation of normal NSCs to brain tumor stem cells. Based on this integrative approach, our data provide an important platform for future

  20. 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.

  1. 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.

  2. Support Systems for Treatment Integrity

    NARCIS (Netherlands)

    Goense, Pauline Brigitta; Boendermaker, Leonieke; van Yperen, Tom

    Objective: This systematic review evaluates the content of effective support provided to practitioners of evidence-based interventions in order to establish and maintain treatment integrity. Method: Four articles covering six outcome studies are included in this review, these studies (1) adequately

  3. 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

  4. 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

  5. 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)

  6. Design of FPGA Based Neural Network Controller for Earth Station Power System

    Directory of Open Access Journals (Sweden)

    Hassen T. Dorrah

    2012-06-01

    Full Text Available Automation of generating hardware description language code from neural networks models can highly decrease time of implementation those networks into a digital devices, thus significant money savings. To implement the neural network into hardware designer, it is required to translate generated model into device structure. VHDL language is used to describe those networks into hardware. VHDL code has been proposed to implement ANNs as well as to present simulation results with floating point arithmetic of the earth station and the satellite power systems using ModelSim PE 6.6 simulator tool. Integration between MATLAB and VHDL is used to save execution time of computation. The results shows that a good agreement between MATLAB and VHDL and a fast/flexible feed forward NN which is capable of dealing with floating point arithmetic operations; minimum number of CLB slices; and good speed of performance. FPGA synthesis results are obtained with view RTL schematic and technology schematic from Xilinix tool. Minimum number of utilized resources is obtained by using Xilinix VERTIX5.

  7. 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...

  8. 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

  9. 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

  10. QUANTITATIVE СHARACTERISTICS OF COMPLEMENTARY INTEGRATED HEALTH CARE SYSTEM AND INTEGRATED MEDICATION MANAGEMENT INFORMATION SYSTEM

    Directory of Open Access Journals (Sweden)

    L. Yu. Babintseva

    2015-05-01

    i mportant elements of state regulation of the pharmaceutical sector health. For the first time creation of two information systems: integrated medication management infor mation system and integrated health care system in an integrated medical infor mation area, operating based on th e principle of complementarity was justified. Global and technological coefficients of these systems’ functioning were introduced.

  11. Vibration control of uncertain multiple launch rocket system using radial basis function neural network

    Science.gov (United States)

    Li, Bo; Rui, Xiaoting

    2018-01-01

    Poor dispersion characteristics of rockets due to the vibration of Multiple Launch Rocket System (MLRS) have always restricted the MLRS development for several decades. Vibration control is a key technique to improve the dispersion characteristics of rockets. For a mechanical system such as MLRS, the major difficulty in designing an appropriate control strategy that can achieve the desired vibration control performance is to guarantee the robustness and stability of the control system under the occurrence of uncertainties and nonlinearities. To approach this problem, a computed torque controller integrated with a radial basis function neural network is proposed to achieve the high-precision vibration control for MLRS. In this paper, the vibration response of a computed torque controlled MLRS is described. The azimuth and elevation mechanisms of the MLRS are driven by permanent magnet synchronous motors and supposed to be rigid. First, the dynamic model of motor-mechanism coupling system is established using Lagrange method and field-oriented control theory. Then, in order to deal with the nonlinearities, a computed torque controller is designed to control the vibration of the MLRS when it is firing a salvo of rockets. Furthermore, to compensate for the lumped uncertainty due to parametric variations and un-modeled dynamics in the design of the computed torque controller, a radial basis function neural network estimator is developed to adapt the uncertainty based on Lyapunov stability theory. Finally, the simulated results demonstrate the effectiveness of the proposed control system and show that the proposed controller is robust with regard to the uncertainty.

  12. Systems integration of business systems. Business system ni kanshite

    Energy Technology Data Exchange (ETDEWEB)

    Furukawa, H [Nippon Steel Corp., Tokyo (Japan)

    1991-09-26

    System integration (SI) is defined as combining hardwares and softwares as the infrastructures with know-hows for their use comprehensively to respond to high-level needs of users. This paper reports the features of an SI being developed by a company (E Company), its concept on the systematized development methodology, and the conceptual models. With the primary policy placed on creative integration standing on customers positions, the SI concept comprises three parts of models for evaluations as seen from the customers, evaluations as seen from the E Company, and the development object systems to link both parts. The third part is further consisted of several lower hierarchies including a customer controlled system hierarchy (this enables customers to control the system through visualization, for example, and includes three logic models (multiple solution selection, optimal solution under restricted conditions, and numerical solution)). 2 refs., 9 figs.

  13. Energy Systems Integration News | Energy Systems Integration Facility |

    Science.gov (United States)

    organization and independent system operator settle energy transactions in its real-time markets at the same time interval it dispatches energy, and settle operating reserves transactions in its real-time markets the electric grid. These control systems will enable real-time coordination between distributed energy

  14. Energy Systems Integration News | Energy Systems Integration Facility |

    Science.gov (United States)

    determine how well a solar photovoltaic (PV) system with battery energy storage can provide backup power to . These analyses will result in a design guide for climate-specific sizing of the system. NREL's Erfan , feasibility, and operational analyses for photovoltaic and concentrating solar power generation projects

  15. Neural Network based Control of SG based Standalone Generating System with Energy Storage for Power Quality Enhancement

    Science.gov (United States)

    Nayar, Priya; Singh, Bhim; Mishra, Sukumar

    2017-08-01

    An artificial intelligence based control algorithm is used in solving power quality problems of a diesel engine driven synchronous generator with automatic voltage regulator and governor based standalone system. A voltage source converter integrated with a battery energy storage system is employed to mitigate the power quality problems. An adaptive neural network based signed regressor control algorithm is used for the estimation of the fundamental component of load currents for control of a standalone system with load leveling as an integral feature. The developed model of the system performs accurately under varying load conditions and provides good dynamic response to the step changes in loads. The real time performance is achieved using MATLAB along with simulink/simpower system toolboxes and results adhere to an IEEE-519 standard for power quality enhancement.

  16. Systems integration processes for space nuclear electric propulsion systems

    International Nuclear Information System (INIS)

    Olsen, C.S.; Rice, J.W.; Stanley, M.L.

    1991-01-01

    The various components and subsystems that comprise a nuclear electric propulsion system should be developed and integrated so that each functions ideally and so that each is properly integrated with the other components and subsystems in the optimum way. This paper discusses how processes similar to those used in the development and intergration of the subsystems that comprise the Multimegawatt Space Nuclear Power System concepts can be and are being efficiently and effectively utilized for these purposes. The processes discussed include the development of functional and operational requirements at the system and subsystem level; the assessment of individual nuclear power supply and thruster concepts and their associated technologies; the conduct of systems integration efforts including the evaluation of the mission benefits for each system; the identification and resolution of concepts development, technology development, and systems integration feasibility issues; subsystem, system, and technology development and integration; and ground and flight subsystem and integrated system testing

  17. 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

  18. SUBSURFACE REPOSITORY INTEGRATED CONTROL SYSTEM DESIGN

    International Nuclear Information System (INIS)

    C.J. Fernado

    1998-01-01

    The purpose of this document is to develop preliminary high-level functional and physical control system architectures for the proposed subsurface repository at Yucca Mountain. This document outlines overall control system concepts that encompass and integrate the many diverse systems being considered for use within the subsurface repository. This document presents integrated design concepts for monitoring and controlling the diverse set of subsurface operations. The subsurface repository design will be composed of a series of diverse systems that will be integrated to accomplish a set of overall functions and objectives. The subsurface repository contains several Instrumentation and Control (I andC) related systems including: waste emplacement systems, ventilation systems, communication systems, radiation monitoring systems, rail transportation systems, ground control monitoring systems, utility monitoring systems (electrical, lighting, water, compressed air, etc.), fire detection and protection systems, retrieval systems, and performance confirmation systems. Each of these systems involve some level of I andC and will typically be integrated over a data communication network. The subsurface I andC systems will also integrate with multiple surface-based site-wide systems such as emergency response, health physics, security and safeguards, communications, utilities and others. The scope and primary objectives of this analysis are to: (1) Identify preliminary system level functions and interface needs (Presented in the functional diagrams in Section 7.2). (2) Examine the overall system complexity and determine how and on what levels these control systems will be controlled and integrated (Presented in Section 7.2). (3) Develop a preliminary subsurface facility-wide design for an overall control system architecture, and depict this design by a series of control system functional block diagrams (Presented in Section 7.2). (4) Develop a series of physical architectures

  19. SUBSURFACE REPOSITORY INTEGRATED CONTROL SYSTEM DESIGN

    Energy Technology Data Exchange (ETDEWEB)

    C.J. Fernado

    1998-09-17

    The purpose of this document is to develop preliminary high-level functional and physical control system architectures for the proposed subsurface repository at Yucca Mountain. This document outlines overall control system concepts that encompass and integrate the many diverse systems being considered for use within the subsurface repository. This document presents integrated design concepts for monitoring and controlling the diverse set of subsurface operations. The subsurface repository design will be composed of a series of diverse systems that will be integrated to accomplish a set of overall functions and objectives. The subsurface repository contains several Instrumentation and Control (I&C) related systems including: waste emplacement systems, ventilation systems, communication systems, radiation monitoring systems, rail transportation systems, ground control monitoring systems, utility monitoring systems (electrical, lighting, water, compressed air, etc.), fire detection and protection systems, retrieval systems, and performance confirmation systems. Each of these systems involve some level of I&C and will typically be integrated over a data communication network. The subsurface I&C systems will also integrate with multiple surface-based site-wide systems such as emergency response, health physics, security and safeguards, communications, utilities and others. The scope and primary objectives of this analysis are to: (1) Identify preliminary system level functions and interface needs (Presented in the functional diagrams in Section 7.2). (2) Examine the overall system complexity and determine how and on what levels these control systems will be controlled and integrated (Presented in Section 7.2). (3) Develop a preliminary subsurface facility-wide design for an overall control system architecture, and depict this design by a series of control system functional block diagrams (Presented in Section 7.2). (4) Develop a series of physical architectures that

  20. 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)

  1. 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)

  2. 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)

  3. Energy Systems Integration News | Energy Systems Integration Facility |

    Science.gov (United States)

    grids. In terms of paper sessions, NREL ESI researcher Santosh Veda chaired a session on energy Kroposki chaired a session on advanced renewable energy power systems. While Veda, Muljadi, and Kroposki

  4. Integrated crop protection as a system approach

    OpenAIRE

    Haan, de, J.J.; Wijnands, F.G.; Sukkel, W.

    2005-01-01

    New farming systems in vegetable production are required as demands for high quality products that do not pollute the environment are rising, and production risks are large and incomes low. The methodology of prototyping new systems is described, especially the themes, parameters and target values connected to integrated crop protection. The role of integrated crop protection in prototyping new systems is discussed. The results of twenty years working with this prototyping methodology are pre...

  5. Integrated System Health Management Development Toolkit

    Science.gov (United States)

    Figueroa, Jorge; Smith, Harvey; Morris, Jon

    2009-01-01

    This software toolkit is designed to model complex systems for the implementation of embedded Integrated System Health Management (ISHM) capability, which focuses on determining the condition (health) of every element in a complex system (detect anomalies, diagnose causes, and predict future anomalies), and to provide data, information, and knowledge (DIaK) to control systems for safe and effective operation.

  6. Mobility Integration of ERP systems

    Directory of Open Access Journals (Sweden)

    Álvaro LOZANO

    2015-03-01

    Full Text Available Nowadays a lot of enterprises work with ERP systems. It usefulness is generally used in office environments and different enterprises which offer this software are developing mobile applications. These mobile applications work with their own system and they don’t usually work in other platforms. Currently any mobile application can communicate with more than one ERP system because each one has its own communications methods. This article presents a system that expect unify the communication between different ERP systems and allows mobile applications to communicate with them in a homogeneous way.

  7. Enhanced biocompatibility of neural probes by integrating microstructures and delivering anti-inflammatory agents via microfluidic channels

    Science.gov (United States)

    Liu, Bin; Kim, Eric; Meggo, Anika; Gandhi, Sachin; Luo, Hao; Kallakuri, Srinivas; Xu, Yong; Zhang, Jinsheng

    2017-04-01

    Objective. Biocompatibility is a major issue for chronic neural implants, involving inflammatory and wound healing responses of neurons and glial cells. To enhance biocompatibility, we developed silicon-parylene hybrid neural probes with open architecture electrodes, microfluidic channels and a reservoir for drug delivery to suppress tissue responses. Approach. We chronically implanted our neural probes in the rat auditory cortex and investigated (1) whether open architecture electrode reduces inflammatory reaction by measuring glial responses; and (2) whether delivery of antibiotic minocycline reduces inflammatory and tissue reaction. Four weeks after implantation, immunostaining for glial fibrillary acid protein (astrocyte marker) and ionizing calcium-binding adaptor molecule 1 (macrophages/microglia cell marker) were conducted to identify immunoreactive astrocyte and microglial cells, and to determine the extent of astrocytes and microglial cell reaction/activation. A comparison was made between using traditional solid-surface electrodes and newly-designed electrodes with open architecture, as well as between deliveries of minocycline and artificial cerebral-spinal fluid diffused through microfluidic channels. Main results. The new probes with integrated micro-structures induced minimal tissue reaction compared to traditional electrodes at 4 weeks after implantation. Microcycline delivered through integrated microfluidic channels reduced tissue response as indicated by decreased microglial reaction around the neural probes implanted. Significance. The new design will help enhance the long-term stability of the implantable devices.

  8. GENERIC Integrators: Structure Preserving Time Integration for Thermodynamic Systems

    Science.gov (United States)

    Öttinger, Hans Christian

    2018-04-01

    Thermodynamically admissible evolution equations for non-equilibrium systems are known to possess a distinct mathematical structure. Within the GENERIC (general equation for the non-equilibrium reversible-irreversible coupling) framework of non-equilibrium thermodynamics, which is based on continuous time evolution, we investigate the possibility of preserving all the structural elements in time-discretized equations. Our approach, which follows Moser's [1] construction of symplectic integrators for Hamiltonian systems, is illustrated for the damped harmonic oscillator. Alternative approaches are sketched.

  9. 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.

  10. An Inductively-Powered Wireless Neural Recording System with a Charge Sampling Analog Front-End.

    Science.gov (United States)

    Lee, Seung Bae; Lee, Byunghun; Kiani, Mehdi; Mahmoudi, Babak; Gross, Robert; Ghovanloo, Maysam

    2016-01-15

    An inductively-powered wireless integrated neural recording system (WINeR-7) is presented for wireless and battery less neural recording from freely-behaving animal subjects inside a wirelessly-powered standard homecage. The WINeR-7 system employs a novel wide-swing dual slope charge sampling (DSCS) analog front-end (AFE) architecture, which performs amplification, filtering, sampling, and analog-to-time conversion (ATC) with minimal interference and small amount of power. The output of the DSCS-AFE produces a pseudo-digital pulse width modulated (PWM) signal. A circular shift register (CSR) time division multiplexes (TDM) the PWM pulses to create a TDM-PWM signal, which is fed into an on-chip 915 MHz transmitter (Tx). The AFE and Tx are supplied at 1.8 V and 4.2 V, respectively, by a power management block, which includes a high efficiency active rectifier and automatic resonance tuning (ART), operating at 13.56 MHz. The 8-ch system-on-a-chip (SoC) was fabricated in a 0.35-μm CMOS process, occupying 5.0 × 2.5 mm 2 and consumed 51.4 mW. For each channel, the sampling rate is 21.48 kHz and the power consumption is 19.3 μW. In vivo experiments were conducted on freely behaving rats in an energized homecage by continuously delivering 51.4 mW to the WINeR-7 system in a closed-loop fashion and recording local field potentials (LFP).

  11. Noise-tolerant inverse analysis models for nondestructive evaluation of transportation infrastructure systems using neural networks

    Science.gov (United States)

    Ceylan, Halil; Gopalakrishnan, Kasthurirangan; Birkan Bayrak, Mustafa; Guclu, Alper

    2013-09-01

    The need to rapidly and cost-effectively evaluate the present condition of pavement infrastructure is a critical issue concerning the deterioration of ageing transportation infrastructure all around the world. Nondestructive testing (NDT) and evaluation methods are well-suited for characterising materials and determining structural integrity of pavement systems. The falling weight deflectometer (FWD) is a NDT equipment used to assess the structural condition of highway and airfield pavement systems and to determine the moduli of pavement layers. This involves static or dynamic inverse analysis (referred to as backcalculation) of FWD deflection profiles in the pavement surface under a simulated truck load. The main objective of this study was to employ biologically inspired computational systems to develop robust pavement layer moduli backcalculation algorithms that can tolerate noise or inaccuracies in the FWD deflection data collected in the field. Artificial neural systems, also known as artificial neural networks (ANNs), are valuable computational intelligence tools that are increasingly being used to solve resource-intensive complex engineering problems. Unlike the linear elastic layered theory commonly used in pavement layer backcalculation, non-linear unbound aggregate base and subgrade soil response models were used in an axisymmetric finite element structural analysis programme to generate synthetic database for training and testing the ANN models. In order to develop more robust networks that can tolerate the noisy or inaccurate pavement deflection patterns in the NDT data, several network architectures were trained with varying levels of noise in them. The trained ANN models were capable of rapidly predicting the pavement layer moduli and critical pavement responses (tensile strains at the bottom of the asphalt concrete layer, compressive strains on top of the subgrade layer and the deviator stresses on top of the subgrade layer), and also pavement

  12. Integrating Artificial Neural Networks into the VIC Model for Rainfall-Runoff Modeling

    Directory of Open Access Journals (Sweden)

    Changqing Meng

    2016-09-01

    Full Text Available A hybrid rainfall-runoff model was developed in this study by integrating the variable infiltration capacity (VIC model with artificial neural networks (ANNs. In the proposed model, the prediction interval of the ANN replaces separate, individual simulation (i.e., single simulation. The spatial heterogeneity of horizontal resolution, subgrid-scale features and their influence on the streamflow can be assessed according to the VIC model. In the routing module, instead of a simple linear superposition of the streamflow generated from each subbasin, ANNs facilitate nonlinear mappings of the streamflow produced from each subbasin into the total streamflow at the basin outlet. A total of three subbasins were delineated and calibrated independently via the VIC model; daily runoff errors were simulated for each subbasin, then corrected by an ANN bias-correction model. The initial streamflow and corrected runoff from the simulation for individual subbasins serve as inputs to the ANN routing model. The feasibility of this proposed method was confirmed according to the performance of its application to a case study on rainfall-runoff prediction in the Jinshajiang River Basin, the headwater area of the Yangtze River.

  13. Integrated approach to model decomposed flow hydrograph using artificial neural network and conceptual techniques

    Science.gov (United States)

    Jain, Ashu; Srinivasulu, Sanaga

    2006-02-01

    This paper presents the findings of a study aimed at decomposing a flow hydrograph into different segments based on physical concepts in a catchment, and modelling different segments using different technique viz. conceptual and artificial neural networks (ANNs). An integrated modelling framework is proposed capable of modelling infiltration, base flow, evapotranspiration, soil moisture accounting, and certain segments of the decomposed flow hydrograph using conceptual techniques and the complex, non-linear, and dynamic rainfall-runoff process using ANN technique. Specifically, five different multi-layer perceptron (MLP) and two self-organizing map (SOM) models have been developed. The rainfall and streamflow data derived from the Kentucky River catchment were employed to test the proposed methodology and develop all the models. The performance of all the models was evaluated using seven different standard statistical measures. The results obtained in this study indicate that (a) the rainfall-runoff relationship in a large catchment consists of at least three or four different mappings corresponding to different dynamics of the underlying physical processes, (b) an integrated approach that models the different segments of the decomposed flow hydrograph using different techniques is better than a single ANN in modelling the complex, dynamic, non-linear, and fragmented rainfall runoff process, (c) a simple model based on the concept of flow recession is better than an ANN to model the falling limb of a flow hydrograph, and (d) decomposing a flow hydrograph into the different segments corresponding to the different dynamics based on the physical concepts is better than using the soft decomposition employed using SOM.

  14. Energy optimization and prediction of complex petrochemical industries using an improved artificial neural network approach integrating data envelopment analysis

    International Nuclear Information System (INIS)

    Han, Yong-Ming; Geng, Zhi-Qiang; Zhu, Qun-Xiong

    2016-01-01

    Graphical abstract: This paper proposed an energy optimization and prediction of complex petrochemical industries based on a DEA-integrated ANN approach (DEA-ANN). The proposed approach utilizes the DEA model with slack variables for sensitivity analysis to determine the effective decision making units (DMUs) and indicate the optimized direction of the ineffective DMUs. Compared with the traditional ANN approach, the DEA-ANN prediction model is effectively verified by executing a linear comparison between all DMUs and the effective DMUs through the standard data source from the UCI (University of California at Irvine) repository. Finally, the proposed model is validated through an application in a complex ethylene production system of China petrochemical industry. Meanwhile, the optimization result and the prediction value are obtained to reduce energy consumption of the ethylene production system, guide ethylene production and improve energy efficiency. - Highlights: • The DEA-integrated ANN approach is proposed. • The DEA-ANN prediction model is effectively verified through the standard data source from the UCI repository. • The energy optimization and prediction framework of complex petrochemical industries based on the proposed method is obtained. • The proposed method is valid and efficient in improvement of energy efficiency in complex petrochemical plants. - Abstract: Since the complex petrochemical data have characteristics of multi-dimension, uncertainty and noise, it is difficult to accurately optimize and predict the energy usage of complex petrochemical systems. Therefore, this paper proposes a data envelopment analysis (DEA) integrated artificial neural network (ANN) approach (DEA-ANN). The proposed approach utilizes the DEA model with slack variables for sensitivity analysis to determine the effective decision making units (DMUs) and indicate the optimized direction of the ineffective DMUs. Compared with the traditional ANN approach, the DEA

  15. 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...

  16. 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...

  17. 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.

  18. 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.

  19. Integrating a Storage Factor into R-NARX Neural Networks for Flood Forecasts

    Science.gov (United States)

    Chou, Po-Kai; Chang, Li-Chiu; Chang, Fi-John; Shih, Ban-Jwu

    2017-04-01

    Because mountainous terrains and steep landforms rapidly accelerate the speed of flood flow in Taiwan island, accurate multi-step-ahead inflow forecasts during typhoon events for providing reliable information benefiting the decision-makings of reservoir pre-storm release and flood-control operation are considered crucial and challenging. Various types of artificial neural networks (ANNs) have been successfully applied in hydrological fields. This study proposes a recurrent configuration of the nonlinear autoregressive with exogenous inputs (NARX) network, called R-NARX, with various effective inputs to forecast the inflows of the Feitsui Reservoir, a pivot reservoir for water supply to Taipei metropolitan in Taiwan, during typhoon periods. The proposed R-NARX is constructed based on the recurrent neural network (RNN), which is commonly used for modelling nonlinear dynamical systems. A large number of hourly rainfall and inflow data sets collected from 95 historical typhoon events in the last thirty years are used to train, validate and test the models. The potential input variables, including rainfall in previous time steps (one to six hours), cumulative rainfall, the storage factor and the storage function, are assessed, and various models are constructed with their reliability and accuracy being tested. We find that the previous (t-2) rainfall and cumulative rainfall are crucial inputs and the storage factor and the storage function would also improve the forecast accuracy of the models. We demonstrate that the R-NARX model not only can accurately forecast the inflows but also effectively catch the peak flow without adopting observed inflow data during the entire typhoon period. Besides, the model with the storage factor is superior to the model with the storage function, where its improvement can reach 24%. This approach can well model the rainfall-runoff process for the entire flood forecasting period without the use of observed inflow data and can provide

  20. 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.

  1. Functional integration of grafted neural stem cell-derived dopaminergic neurons monitored by optogenetics in an in vitro Parkinson model

    DEFF Research Database (Denmark)

    Tønnesen, Jan; Parish, Clare L; Sørensen, Andreas T

    2011-01-01

    Intrastriatal grafts of stem cell-derived dopamine (DA) neurons induce behavioral recovery in animal models of Parkinson's disease (PD), but how they functionally integrate in host neural circuitries is poorly understood. Here, Wnt5a-overexpressing neural stem cells derived from embryonic ventral...... of post-synaptic currents, and functional expression of DA D₂ autoreceptors. These properties resembled those recorded from identical cells in acute slices of intrastriatal grafts in the 6-hydroxy-DA-induced mouse PD model and from DA neurons in intact substantia nigra. Optogenetic activation...... using optogenetics that ectopically grafted stem cell-derived DA neurons become functionally integrated in the DA-denervated striatum. Further optogenetic dissection of the synaptic wiring between grafted and host neurons will be crucial to clarify the cellular and synaptic mechanisms underlying...

  2. The first neural probe integrated with light source (blue laser diode) for optical stimulation and electrical recording.

    Science.gov (United States)

    Park, HyungDal; Shin, Hyun-Joon; Cho, Il-Joo; Yoon, Eui-sung; Suh, Jun-Kyo Francis; Im, Maesoon; Yoon, Euisik; Kim, Yong-Jun; Kim, Jinseok

    2011-01-01

    In this paper, we report a neural probe which can selectively stimulate target neurons optically through Si wet etched mirror surface and record extracellular neural signals in iridium oxide tetrodes. Consequently, the proposed approach provides to improve directional problem and achieve at least 150/m gap distance between stimulation and recording sites by wet etched mirror surface in V-groove. Also, we developed light source, blue laser diode (OSRAM Blue Laser Diode_PL 450), integration through simple jig for one-touch butt-coupling. Furthermore, optical power and impedance of iridium oxide tetrodes were measured as 200 μW on 5 mW from LD and 206.5 k Ω at 1 kHz and we demonstrated insertion test of probe in 0.5% agarose-gel successfully. We have successfully transmitted a light of 450 nm to optical fiber through the integrated LD using by butt-coupling method.

  3. 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...

  4. Identifying the Integrated Neural Networks Involved in Capsaicin-Induced Pain Using fMRI in Awake TRPV1 Knockout and Wild-Type Rats

    Directory of Open Access Journals (Sweden)

    Jason Richard Yee

    2015-02-01

    Full Text Available In the present study, we used functional MRI in awake rats to investigate the pain response that accompanies intradermal injection of capsaicin into the hindpaw. To this end, we used BOLD imaging together with a 3D segmented, annotated rat atlas and computational analysis to identify the integrated neural circuits involved in capsaicin-induced pain. The specificity of the pain response to capsaicin was tested in a transgenic model that contains a biallelic deletion of the gene encoding for the transient receptor potential cation channel subfamily V member 1 (TRPV1. Capsaicin is an exogenous ligand for the TRPV1 receptor, and in wild-type rats, activated the putative pain neural circuit. In addition, capsaicin-treated wild-type rats exhibited activation in brain regions comprising the Papez circuit and habenular system, systems that play important roles in the integration of emotional information, and learning and memory of aversive information, respectively. As expected, capsaicin administration to TRPV1-KO rats failed to elicit the robust BOLD activation pattern observed in wild-type controls. However, the intradermal injection of formalin elicited a significant activation of the putative pain pathway as represented by such areas as the anterior cingulate, somatosensory cortex, parabrachial nucleus, and periaqueductal gray. Notably, comparison of neural responses to capsaicin in wild-type versus knock-out rats uncovered evidence that capsaicin may function in an antinociceptive capacity independent of TRPV1 signaling. Our data suggest that neuroimaging of pain in awake, conscious animals has the potential to inform the neurobiological basis of full and integrated perceptions of pain.

  5. Smart Systems Integration for Autonomous Wireless Communications

    NARCIS (Netherlands)

    Danesh, M.

    2012-01-01

    Integration of sensors and wireless transceivers for system networking aims at emerging applications that are highly integrated, self-powered, and low cost, relying on efficient power management schemes to prolong lifetime, thus eliminating the need for batteries as a limited primary source of

  6. Integrated system for nuclear installation control

    International Nuclear Information System (INIS)

    Mirchev, M.; Boyiklieva, R.; Peneva, A.

    2008-01-01

    A comparison between the international requirements and standards to an integrated management system is presented. The IAEA GS-R-3, BS PASS 99, ISO 9001, ISO 14001, BS OHSAS 18001 and ISO/IEC 27001 are reviewed and compared by the following aspects: definition and integration of processes; safety culture, risk analyses, satisfaction of the concerned parties, actions in case of discrepancy

  7. Partial Automated Alignment and Integration System

    Science.gov (United States)

    Kelley, Gary Wayne (Inventor)

    2014-01-01

    The present invention is a Partial Automated Alignment and Integration System (PAAIS) used to automate the alignment and integration of space vehicle components. A PAAIS includes ground support apparatuses, a track assembly with a plurality of energy-emitting components and an energy-receiving component containing a plurality of energy-receiving surfaces. Communication components and processors allow communication and feedback through PAAIS.

  8. 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.

  9. MosquitoNet: investigating the use of UAV and artificial neural networks for integrated mosquito management

    Science.gov (United States)

    Case, E.; Ren, Y.; Shragai, T.; Erickson, D.

    2017-12-01

    Integrated mosquito control is expensive and resource intensive, and changing climatic factors are predicted to expand the habitable range of disease-carrying mosquitoes into new regions in the United States. Of particular concern in the northeastern United States are aedes albopictus, an aggressive, invasive species of mosquito that can transmit both native and exotic disease. Ae. albopictus prefer to live near human populations and breed in artificial containers with as little as two millimeters of standing water, exponentially increasing the difficulty of source control in suburban and urban areas. However, low-cost unmanned aerial vehicles (UAVs) can be used to photograph large regions at centimeter-resolution, and can image containers of interest in suburban neighborhoods. While proofs-of-concepts have been shown using UAVs to identify naturally occurring bodies of water, they have not been used to identify mosquito habitat in more populated areas. One of the primary challenges is that post-processing high-resolution aerial imagery is still time intensive, often labelled by hand or with programs built for satellite imagery. Artificial neural networks have been highly successful at image recognition tasks; in the past five years, convolutional neural networks (CNN) have surpassed or aided trained humans in identification of skin cancer, agricultural crops, and poverty levels from satellite imagery. MosquitoNet, a dual classifier built from the Single Shot Multibox Detector and VGG16 architectures, was trained on UAV­­­­­ aerial imagery taken during a larval study in Westchester County in southern New York State in July and August 2017. MosquitoNet was designed to assess the habitat risk of suburban properties by automating the identification and counting of containers like tires, toys, garbage bins, flower pots, etc. The SSD-based architecture marked small containers and other habitat indicators while the VGG16-based architecture classified the type of

  10. Synthesis of a novel adaptive wavelet optimized neural cascaded steam blow-off control system for a nuclear power plant

    International Nuclear Information System (INIS)

    Malik, A.H.; Memon, A.A.; Arshad, F.

    2013-01-01

    Blow-Off System Controller (MIMO AWNN-SBOSC) is designed based on real time dynamic parametric plant data of steam blow-off system with conventional Single-Input Multi-Output Proportional plus Integral plus Derivative Controller (SIMO PIDC). The proposed MIMO AWANN-SBOSC is designed using three Multi-Input Single-Output Adaptive Wavelet Neural Network based Steam Blow-Off System Controllers (MISO AWNN-SBOSC). The hidden layer of each MISO AWNN-SBOSC is formulated using Mother Wavelet Transforms (MWT). Using nonlinear dynamic neural data of designed MIMO AWNN-SBOSC, a Multi-Input Multi-Output Adaptive Wavelet Neural Network based Steam Blow-Off System Model (MIMO AWNN-SBOSM) is developed in cascaded mode. MIMO AWNN-SBOSM is designed using two MISO AWNN-SBOSM. All training, testing and validation of MIMO AWNN-SBOSC and MIMO AWNN-SBOSM are carried out in MA TLAB while all simulation experiments are performed in Visual C. The results of the new design is evaluated against conventional controller based measured data and found robust, fast and much better in performance. (author)

  11. Integrated Monitoring System of Production Processes

    Directory of Open Access Journals (Sweden)

    Oborski Przemysław

    2016-12-01

    Full Text Available Integrated monitoring system for discrete manufacturing processes is presented in the paper. The multilayer hardware and software reference model was developed. Original research are an answer for industry needs of the integration of information flow in production process. Reference model corresponds with proposed data model based on multilayer data tree allowing to describe orders, products, processes and save monitoring data. Elaborated models were implemented in the integrated monitoring system demonstrator developed in the project. It was built on the base of multiagent technology to assure high flexibility and openness on applying intelligent algorithms for data processing. Currently on the base of achieved experience an application integrated monitoring system for real production system is developed. In the article the main problems of monitoring integration are presented, including specificity of discrete production, data processing and future application of Cyber-Physical-Systems. Development of manufacturing systems is based more and more on taking an advantage of applying intelligent solutions into machine and production process control and monitoring. Connection of technical systems, machine tools and manufacturing processes monitoring with advanced information processing seems to be one of the most important areas of near future development. It will play important role in efficient operation and competitiveness of the whole production system. It is also important area of applying in the future Cyber-Physical-Systems that can radically improve functionally of monitoring systems and reduce the cost of its implementation.

  12. Recurrent Neural Network Approach Based on the Integral Representation of the Drazin Inverse.

    Science.gov (United States)

    Stanimirović, Predrag S; Živković, Ivan S; Wei, Yimin

    2015-10-01

    In this letter, we present the dynamical equation and corresponding artificial recurrent neural network for computing the Drazin inverse for arbitrary square real matrix, without any restriction on its eigenvalues. Conditions that ensure the stability of the defined recurrent neural network as well as its convergence toward the Drazin inverse are considered. Several illustrative examples present the results of computer simulations.

  13. Defining the neural fulcrum for chronic vagus nerve stimulation: implications for integrated cardiac control.

    Science.gov (United States)

    Ardell, Jeffrey L; Nier, Heath; Hammer, Matthew; Southerland, E Marie; Ardell, Christopher L; Beaumont, Eric; KenKnight, Bruce H; Armour, J Andrew

    2017-11-15

    VNS active phase owing to afferent modulation of parasympathetic central drive. When functional effects of afferent and efferent fibre activation were balanced, a null HRR was evoked (defined as 'neural fulcrum') during which HRR ≈ 0. As intensity increased further, HR was reduced during the active phase of VNS. While qualitatively similar, VNS delivered in the epilepsy configuration resulted in more pronounced HR acceleration and reduced HR deceleration during VNS. At termination, under anaesthesia, transection of the vagi rostral to the stimulation site eliminated the augmenting response to VNS and enhanced the parasympathetic efferent-mediated suppressing effect on electrical and mechanical function of the heart. In conclusion, VNS activates central then peripheral aspects of the cardiac nervous system. VNS control over cardiac function is maintained during chronic therapy. © 2017 The Authors. The Journal of Physiology © 2017 The Physiological Society.

  14. Integrated Control System Engineering Support.

    Science.gov (United States)

    1984-12-01

    Advanced Medium Range Air to Air Missile ASTEC Advanced Speech Technology Experimental Configuration BA Body Axis BCIU Bus Control Interface Unit BMU Bus...support nreeded to tie an ASTEC speech recognition system into the DIGISYN fJcility and support an FIGR experiment designed to investigate the voice...information passed to the PDP computer consisted of integers which represented words or phrases recognized by the ASTEC recognition system. An interface

  15. Development of system integration technology for integral reactor

    Energy Technology Data Exchange (ETDEWEB)

    Chang, Moon Hee; Kang, D. J.; Kim, K. K. and others

    1999-03-01

    The objective of this report is to integrate the conceptual design of an integral reactor, SMART producing thermal energy of 330 MW, which will be utilized to supply energy for seawater desalination and small-scale power generation. This project also aims to develop system integration technology for effective design of the reactor. For the conceptual design of SMART, preliminary design requirements including the top-tier requirements and design bases were evaluated and established. Furthermore, in the view of the application of codes and standards to the SMART design, existing laws, codes and standards were analyzed and evaluated with respect to its applicability. As a part of this evaluation, directions and guidelines were proposed for the development of new codes and standards which shall be applied to the SMART design. Regarding the integration of SMART conceptual designs, major design activities and interfaces between design departments were established and coordinated through the design process. For the effective management of all design schedules, a work performance evaluation system was developed and applied to the design process. As the results of this activity, an integrated output of SMART designs was produced. Two additional scopes performed in this project include the preliminary economic analysis on the SMART utilization for seawater desalination, and the planning of verification tests for technology implemented into SMART and establishing development plan of the computer codes to be used for SMART design in the next phase. The technical cooperation with foreign country and international organization for securing technologies for integral reactor design and its application was coordinated and managed through this project. (author)

  16. Beyond Neural Cubism: Promoting a Multidimensional View of Brain Disorders by Enhancing the Integration of Neurology and Psychiatry in Education

    Science.gov (United States)

    Taylor, Joseph J.; Williams, Nolan R.; George, Mark S.

    2014-01-01

    Cubism was an influential early 20th century art movement characterized by angular, disjointed imagery. The two-dimensional appearance of Cubist figures and objects is created through juxtaposition of angles. The authors posit that the constrained perspectives found in Cubism may also be found in the clinical classification of brain disorders. Neurological disorders are often separated from psychiatric disorders as if they stem from different organ systems. Maintaining two isolated clinical disciplines fractionalizes the brain in the same way that Pablo Picasso fractionalized figures and objects in his Cubist art. This Neural Cubism perpetuates a clinical divide that does not reflect the scope and depth of neuroscience. All brain disorders are complex and multidimensional, with aberrant circuitry and resultant psychopharmacology manifesting as altered behavior, affect, mood or cognition. Trainees should receive a multidimensional education based on modern neuroscience, not a partial education based on clinical precedent. The authors briefly outline the rationale for increasing the integration of neurology and psychiatry and discuss a nested model with which clinical neuroscientists (neurologists and psychiatrists) can approach and treat brain disorders. PMID:25340364

  17. Integrability and boundary conditions of supersymmetric systems

    International Nuclear Information System (INIS)

    Yue Ruihong; Liang Hong

    1996-01-01

    By studying the solutions of the reflection equations, we find out a series of integrable supersymmetric systems with different boundary conditions. The Hamiltonian contains four free parameters which describe the contribution of the boundary terms

  18. Core Flight System (CFS) Integrated Development Environment

    Data.gov (United States)

    National Aeronautics and Space Administration — The purpose of this project is to create an Integrated Development Environment (IDE) for the Core Flight System (CFS) software to reduce the time it takes to...

  19. Multisignal detecting system of pile integrity testing

    Science.gov (United States)

    Liu, Zuting; Luo, Ying; Yu, Shihai

    2002-05-01

    The low strain reflection wave method plays a principal rule in the integrating detection of base piles. However, there are some deficiencies with this method. For example, there is a blind area of detection on top of the tested pile; it is difficult to recognize the defects at deep-seated parts of the pile; there is still the planar of 3D domino effect, etc. It is very difficult to solve these problems only with the single-transducer pile integrity testing system. A new multi-signal piles integrity testing system is proposed in this paper, which is able to impulse and collect signals on multiple points on top of the pile. By using the multiple superposition data processing method, the detecting system can effectively restrain the interference and elevate the precision and SNR of pile integrity testing. The system can also be applied to the evaluation of engineering structure health.

  20. Development of the Integrated Information Technology System

    National Research Council Canada - National Science Library

    2005-01-01

    The Integrated Medical Information Technology System (IMITS) Program is focused on implementation of advanced technology solutions that eliminate inefficiencies, increase utilization and improve quality of care for active duty forces...

  1. SUBSURFACE REPOSITORY INTEGRATED CONTROL SYSTEM DESIGN

    International Nuclear Information System (INIS)

    Randle, D.C.

    2000-01-01

    The primary purpose of this document is to develop a preliminary high-level functional and physical control system architecture for the potential repository at Yucca Mountain. This document outlines an overall control system concept that encompasses and integrates the many diverse process and communication systems being developed for the subsurface repository design. This document presents integrated design concepts for monitoring and controlling the diverse set of subsurface operations. The Subsurface Repository Integrated Control System design will be composed of a series of diverse process systems and communication networks. The subsurface repository design contains many systems related to instrumentation and control (I andC) for both repository development and waste emplacement operations. These systems include waste emplacement, waste retrieval, ventilation, radiological and air monitoring, rail transportation, construction development, utility systems (electrical, lighting, water, compressed air, etc.), fire protection, backfill emplacement, and performance confirmation. Each of these systems involves some level of I andC and will typically be integrated over a data communications network throughout the subsurface facility. The subsurface I andC systems will also interface with multiple surface-based systems such as site operations, rail transportation, security and safeguards, and electrical/piped utilities. In addition to the I andC systems, the subsurface repository design also contains systems related to voice and video communications. The components for each of these systems will be distributed and linked over voice and video communication networks throughout the subsurface facility. The scope and primary objectives of this design analysis are to: (1) Identify preliminary system-level functions and interfaces (Section 6.2). (2) Examine the overall system complexity and determine how and on what levels the engineered process systems will be monitored

  2. 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...

  3. Integration of SPS with utility system networks

    Energy Technology Data Exchange (ETDEWEB)

    Kaupang, B.M.

    1980-06-01

    This paper will discuss the integration of SPS power in electric utility power systems. Specifically treated will be the nature of the power output variations from the spacecraft to the rectenna, the operational characteristics of the rectenna power and the impacts on the electric utility system from utilizing SPS power to serve part of the system load.

  4. How to Evaluate Integrated Library Automation Systems.

    Science.gov (United States)

    Powell, James R.; Slach, June E.

    1985-01-01

    This paper describes methodology used in compiling a list of candidate integrated library automation systems at a corporate technical library. Priorities for automation, identification of candidate systems, the filtering process, information for suppliers, software and hardware considerations, on-site evaluations, and final system selection are…

  5. 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

  6. 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

  7. Formalisms for reuse and systems integration

    CERN Document Server

    Rubin, Stuart

    2015-01-01

    Reuse and integration are defined as synergistic concepts, where reuse addresses how to minimize redundancy in the creation of components; while, integration focuses on component composition. Integration supports reuse and vice versa. These related concepts support the design of software and systems for maximizing performance while minimizing cost. Knowledge, like data, is subject to reuse; and, each can be interpreted as the other. This means that inherent complexity, a measure of the potential utility of a system, is directly proportional to the extent to which it maximizes reuse and integration. Formal methods can provide an appropriate context for the rigorous handling of these synergistic concepts. Furthermore, formal languages allow for non ambiguous model specification; and, formal verification techniques provide support for insuring the validity of reuse and integration mechanisms.   This edited book includes 12 high quality research papers written by experts in formal aspects of reuse and integratio...

  8. Integrability of dynamical systems algebra and analysis

    CERN Document Server

    Zhang, Xiang

    2017-01-01

    This is the first book to systematically state the fundamental theory of integrability and its development of ordinary differential equations with emphasis on the Darboux theory of integrability and local integrability together with their applications. It summarizes the classical results of Darboux integrability and its modern development together with their related Darboux polynomials and their applications in the reduction of Liouville and elementary integrabilty and in the center—focus problem, the weakened Hilbert 16th problem on algebraic limit cycles and the global dynamical analysis of some realistic models in fields such as physics, mechanics and biology. Although it can be used as a textbook for graduate students in dynamical systems, it is intended as supplementary reading for graduate students from mathematics, physics, mechanics and engineering in courses related to the qualitative theory, bifurcation theory and the theory of integrability of dynamical systems.

  9. Coal Integrated Gasification Fuel Cell System Study

    Energy Technology Data Exchange (ETDEWEB)

    Chellappa Balan; Debashis Dey; Sukru-Alper Eker; Max Peter; Pavel Sokolov; Greg Wotzak

    2004-01-31

    This study analyzes the performance and economics of power generation systems based on Solid Oxide Fuel Cell (SOFC) technology and fueled by gasified coal. System concepts that integrate a coal gasifier with a SOFC, a gas turbine, and a steam turbine were developed and analyzed for plant sizes in excess of 200 MW. Two alternative integration configurations were selected with projected system efficiency of over 53% on a HHV basis, or about 10 percentage points higher than that of the state-of-the-art Integrated Gasification Combined Cycle (IGCC) systems. The initial cost of both selected configurations was found to be comparable with the IGCC system costs at approximately $1700/kW. An absorption-based CO2 isolation scheme was developed, and its penalty on the system performance and cost was estimated to be less approximately 2.7% and $370/kW. Technology gaps and required engineering development efforts were identified and evaluated.

  10. NASA Docking System (NDS) Technical Integration Meeting

    Science.gov (United States)

    Lewis, James L.

    2010-01-01

    This slide presentation reviews the NASA Docking System (NDS) as NASA's implementation of the International Docking System Standard (IDSS). The goals of the NDS, is to build on proven technologies previously demonstrated in flight and to advance the state of the art of docking systems by incorporating Low Impact Docking System (LIDS) technology into the NDS. A Hardware Demonstration was included in the meeting, and there was discussion about software, NDS major system interfaces, integration information, schedule, and future upgrades.

  11. Enhanced robust fractional order proportional-plus-integral controller based on neural network for velocity control of permanent magnet synchronous motor.

    Science.gov (United States)

    Zhang, Bitao; Pi, YouGuo

    2013-07-01

    The traditional integer order proportional-integral-differential (IO-PID) controller is sensitive to the parameter variation or/and external load disturbance of permanent magnet synchronous motor (PMSM). And the fractional order proportional-integral-differential (FO-PID) control scheme based on robustness tuning method is proposed to enhance the robustness. But the robustness focuses on the open-loop gain variation of controlled plant. In this paper, an enhanced robust fractional order proportional-plus-integral (ERFOPI) controller based on neural network is proposed. The control law of the ERFOPI controller is acted on a fractional order implement function (FOIF) of tracking error but not tracking error directly, which, according to theory analysis, can enhance the robust performance of system. Tuning rules and approaches, based on phase margin, crossover frequency specification and robustness rejecting gain variation, are introduced to obtain the parameters of ERFOPI controller. And the neural network algorithm is used to adjust the parameter of FOIF. Simulation and experimental results show that the method proposed in this paper not only achieve favorable tracking performance, but also is robust with regard to external load disturbance and parameter variation. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

  12. Integrating the behavioral and neural dynamics of response selection in a dual-task paradigm: a dynamic neural field model of Dux et al. (2009).

    Science.gov (United States)

    Buss, Aaron T; Wifall, Tim; Hazeltine, Eliot; Spencer, John P

    2014-02-01

    People are typically slower when executing two tasks than when only performing a single task. These dual-task costs are initially robust but are reduced with practice. Dux et al. (2009) explored the neural basis of dual-task costs and learning using fMRI. Inferior frontal junction (IFJ) showed a larger hemodynamic response on dual-task trials compared with single-task trial early in learning. As dual-task costs were eliminated, dual-task hemodynamics in IFJ reduced to single-task levels. Dux and colleagues concluded that the reduction of dual-task costs is accomplished through increased efficiency of information processing in IFJ. We present a dynamic field theory of response selection that addresses two questions regarding these results. First, what mechanism leads to the reduction of dual-task costs and associated changes in hemodynamics? We show that a simple Hebbian learning mechanism is able to capture the quantitative details of learning at both the behavioral and neural levels. Second, is efficiency isolated to cognitive control areas such as IFJ, or is it also evident in sensory motor areas? To investigate this, we restrict Hebbian learning to different parts of the neural model. None of the restricted learning models showed the same reductions in dual-task costs as the unrestricted learning model, suggesting that efficiency is distributed across cognitive control and sensory motor processing systems.

  13. Advanced optical manufacturing digital integrated system

    Science.gov (United States)

    Tao, Yizheng; Li, Xinglan; Li, Wei; Tang, Dingyong

    2012-10-01

    It is necessarily to adapt development of advanced optical manufacturing technology with modern science technology development. To solved these problems which low of ration, ratio of finished product, repetition, consistent in big size and high precision in advanced optical component manufacturing. Applied business driven and method of Rational Unified Process, this paper has researched advanced optical manufacturing process flow, requirement of Advanced Optical Manufacturing integrated System, and put forward architecture and key technology of it. Designed Optical component core and Manufacturing process driven of Advanced Optical Manufacturing Digital Integrated System. the result displayed effective well, realized dynamic planning Manufacturing process, information integration improved ratio of production manufactory.

  14. Precision Interval Estimation of the Response Surface by Means of an Integrated Algorithm of Neural Network and Linear Regression

    Science.gov (United States)

    Lo, Ching F.

    1999-01-01

    The integration of Radial Basis Function Networks and Back Propagation Neural Networks with the Multiple Linear Regression has been accomplished to map nonlinear response surfaces over a wide range of independent variables in the process of the Modem Design of Experiments. The integrated method is capable to estimate the precision intervals including confidence and predicted intervals. The power of the innovative method has been demonstrated by applying to a set of wind tunnel test data in construction of response surface and estimation of precision interval.

  15. Integrating Systems into Accounting Instruction.

    Science.gov (United States)

    Heatherington, Ralph

    1980-01-01

    By incorporating a discussion of systems into the beginning accounting class, students will have a more accurate picture of business and the role accounting plays in it. Students should understand the purpose of forms, have a basic knowledge of flowcharting principles and symbols, and know how source documents are created. (CT)

  16. Integrating renewables into energy systems

    International Nuclear Information System (INIS)

    1999-03-01

    An analysis of renewable energy schemes was undertaken via case studies in China, India, Indonesia, Kenya, South Africa, Thailand and Zimbabwe, that provided an insight into the application of best practice for overcoming market, technical and financial barriers to the establishment of the sustainable markets required for the large-scale deployment of renewable energy technologies. The project showed clearly the need to select and target interventions according to the context. Lessons were extracted against a number of themes, as well as against the various technologies analysed and simple guides to the principles of best practice were derived under the following headings:- experience of gaining access to (micro) finance; the technical and non-technical issues raised when small, typically independent, generators seek access to central electricity grid systems; how to best undertake awareness raising and dissemination activities; promoting, building and operating biogas systems; promoting, building and operating solar (photovoltaic) home systems; promoting, building and operating grid connected wind power; promoting, building and operating solar hot water systems; promoting agricultural cogeneration using crop residues. (author)

  17. Integrated Satellite-HAP Systems

    DEFF Research Database (Denmark)

    Cianca, Ernestina; De Sanctis, Mauro; De Luise, Aldo

    2005-01-01

    Thus far, high-altitude platform (HAP)-based systems have been mainly conceived as an alternative to satellites for complementing the terrestrial network. This article aims to show that HAP should no longer be seen as a competitor technology by investors of satellites, but as a key element for an...

  18. Integrated information in discrete dynamical systems: motivation and theoretical framework.

    Directory of Open Access Journals (Sweden)

    David Balduzzi

    2008-06-01

    are optimized to achieve tension between local and global interactions. These basic examples appear to match well against neurobiological evidence concerning the neural substrates of consciousness. More generally, phi appears to be a useful metric to characterize the capacity of any physical system to integrate information.

  19. Integrated information in discrete dynamical systems: motivation and theoretical framework.

    Science.gov (United States)

    Balduzzi, David; Tononi, Giulio

    2008-06-13

    to achieve tension between local and global interactions. These basic examples appear to match well against neurobiological evidence concerning the neural substrates of consciousness. More generally, phi appears to be a useful metric to characterize the capacity of any physical system to integrate information.

  20. Energy Systems Integration: A Convergence of Ideas

    Energy Technology Data Exchange (ETDEWEB)

    Kroposki, B.; Garrett, B.; MacMillan, S.; Rice, B.; Komomua, C.; O' Malley, M.; Zimmerle, D.

    2012-07-01

    Energy systems integration (ESI) enables the effective analysis, design, and control of these interactions and interdependencies along technical, economic, regulatory, and social dimensions. By focusing on the optimization of energy from all systems, across all pathways, and at all scales, we can better understand and make use of the co-benefits that result to increase reliability and performance, reduce cost, and minimize environmental impacts. This white paper discusses systems integration and the research in new control architectures that are optimized at smaller scales but can be aggregated to optimize energy systems at any scale and would allow replicable energy solutions across boundaries of existing and new energy pathways.