WorldWideScience

Sample records for neural systems conference

  1. International Conference on Artificial Neural Networks (ICANN)

    CERN Document Server

    Mladenov, Valeri; Kasabov, Nikola; Artificial Neural Networks : Methods and Applications in Bio-/Neuroinformatics

    2015-01-01

    The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gestalt laws, embodied theory of language understanding, saccadic gaze shifts and memory formation, and new training algorithms for Deep Boltzmann Machines, as well as dynamic neural networks and kernel machines. It also reports on new approaches to reinforcement learning, optimal control of discrete time-delay systems, new al...

  2. 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.

  3. 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...

  4. 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.

  5. 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

  6. Dynamical Systems Conference

    CERN Document Server

    Gils, S; Hoveijn, I; Takens, F; Nonlinear Dynamical Systems and Chaos

    1996-01-01

    Symmetries in dynamical systems, "KAM theory and other perturbation theories", "Infinite dimensional systems", "Time series analysis" and "Numerical continuation and bifurcation analysis" were the main topics of the December 1995 Dynamical Systems Conference held in Groningen in honour of Johann Bernoulli. They now form the core of this work which seeks to present the state of the art in various branches of the theory of dynamical systems. A number of articles have a survey character whereas others deal with recent results in current research. It contains interesting material for all members of the dynamical systems community, ranging from geometric and analytic aspects from a mathematical point of view to applications in various sciences.

  7. 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.

  8. Sixth International Conference on Complex Systems

    CERN Document Server

    Minai, Ali; Bar-Yam, Yaneer; Unifying Themes in Complex Systems

    2008-01-01

    The International Conference on Complex Systems (ICCS) creates a unique atmosphere for scientists of all fields, engineers, physicians, executives, and a host of other professionals to explore the common themes and applications of complex systems science. In June 2006, 500 participants convened in Boston for the sixth ICCS, exploring an array of topics, including networks, systems biology, evolution and ecology, nonlinear dynamics and pattern formation, as well as neural, psychological, psycho-social, socio-economic, and global systems. This volume selects 77 papers from over 300 presented at the conference. With this new volume, Unifying Themes in Complex Systems continues to build common ground between the wide-ranging domains of complex systems science.

  9. Operation quality assessment model for video conference system

    Science.gov (United States)

    Du, Bangshi; Qi, Feng; Shao, Sujie; Wang, Ying; Li, Weijian

    2018-01-01

    Video conference system has become an important support platform for smart grid operation and management, its operation quality is gradually concerning grid enterprise. First, the evaluation indicator system covering network, business and operation maintenance aspects was established on basis of video conference system's operation statistics. Then, the operation quality assessment model combining genetic algorithm with regularized BP neural network was proposed, which outputs operation quality level of the system within a time period and provides company manager with some optimization advice. The simulation results show that the proposed evaluation model offers the advantages of fast convergence and high prediction accuracy in contrast with regularized BP neural network, and its generalization ability is superior to LM-BP neural network and Bayesian BP neural network.

  10. EDITORIAL: Special issue containing contributions from the 39th Neural Interfaces Conference Special issue containing contributions from the 39th Neural Interfaces Conference

    Science.gov (United States)

    Weiland, James D.

    2011-07-01

    Implantable neural interfaces provide substantial benefits to individuals with neurological disorders. That was the unequivocal message delivered by speaker after speaker from the podium of the 39th Neural Interfaces Conference (NIC2010) held in Long Beach, California, in June 2010. Giving benefit to patients is the most important measure for any biomedical technology, and myriad presentations at NIC2010 made clear that implantable neurostimulation technology has achieved this goal. Cochlear implants allow deaf people to communicate through speech. Deep brain stimulators give back mobility and dexterity necessary for so many daily tasks that are often taken for granted. Chronic pain can be alleviated through spinal cord stimulation. Motor prosthesis systems have been demonstrated in humans, through both reanimation of paralyzed limbs and neural control of robotic arms. Earlier this year, a retinal prosthesis was approved for sale in Europe, providing some hope for the blind. In sum, current clinical implants have been tremendously beneficial for today's patients and experimental systems that will be translated to the clinic promise to expand the number of people helped through bioelectronic therapies. Yet there are significant opportunities for improvement. For sensory prostheses, patients report an artificial sensation, clearly different from the natural sensation they remember. Neuromodulation systems, such as deep brain stimulation and pain stimulators, often have side effects that are tolerated as long as the side effects are less impactful than the disease. The papers published in the special issue from NIC2010 reflect the maturing and expanding field of neural interfaces. Our field has moved past proof-of-principle demonstrations and is now focusing on proving the longevity required for clinical implementation of new devices, extending existing approaches to new diseases and improving current devices for better outcomes. Closed-loop neuromodulation is a

  11. 2015 Chinese Intelligent Systems Conference

    CERN Document Server

    Du, Junping; Li, Hongbo; Zhang, Weicun; CISC’15

    2016-01-01

    This book presents selected research papers from the 2015 Chinese Intelligent Systems Conference (CISC’15), held in Yangzhou, China. The topics covered include multi-agent systems, evolutionary computation, artificial intelligence, complex systems, computation intelligence and soft computing, intelligent control, advanced control technology, robotics and applications, intelligent information processing, iterative learning control, and machine learning. Engineers and researchers from academia, industry and the government can gain valuable insights into solutions combining ideas from multiple disciplines in the field of intelligent systems.

  12. neural control system

    International Nuclear Information System (INIS)

    Elshazly, A.A.E.

    2002-01-01

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

  13. XXIII International Conference on Nonlinear Dynamics of Electronic Systems

    CERN Document Server

    Stoop, Ruedi; Stramaglia, Sebastiano

    2017-01-01

    This book collects contributions to the XXIII international conference “Nonlinear dynamics of electronic systems”. Topics range from non-linearity in electronic circuits to synchronisation effects in complex networks to biological systems, neural dynamics and the complex organisation of the brain. Resting on a solid mathematical basis, these investigations address highly interdisciplinary problems in physics, engineering, biology and biochemistry.

  14. 37th National Systems Conference

    CERN Document Server

    Yadav, Sandeep; Adhikari, Bibhas; Seshadri, Harinipriya; Fulwani, Deepak

    2015-01-01

    The book is a collection of peer-reviewed scientific papers submitted by active researchers in the 37th National System Conference (NSC 2013). NSC is an annual event of the Systems Society of India (SSI), primarily oriented to strengthen the systems movement and its applications for the welfare of humanity. A galaxy of academicians, professionals, scientists, statesman and researchers from different parts of the country and abroad are invited to attend the conference. The book presents research articles in the areas of system’s modelling, complex network modelling, cyber security, sustainable systems design, health care systems, socio-economic systems, and clean and green technologies. The book can be used as a tool for further research.

  15. 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

  16. 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...

  17. European Conference on Complex Systems

    CERN Document Server

    Pellegrini, Francesco; Caldarelli, Guido; Merelli, Emanuela

    2016-01-01

    This work contains a stringent selection of extended contributions presented at the meeting of 2014 and its satellite meetings, reflecting scope, diversity and richness of research areas in the field, both fundamental and applied. The ECCS meeting, held under the patronage of the Complex Systems Society, is an annual event that has become the leading European conference devoted to complexity science. It offers cutting edge research and unique opportunities to study novel scientific approaches in a multitude of application areas. ECCS'14, its eleventh occurrence, took place in Lucca, Italy. It gathered some 650 scholars representing a wide range of topics relating to complex systems research, with emphasis on interdisciplinary approaches. The editors are among the best specialists in the area. The book is of great interest to scientists, researchers and graduate students in complexity, complex systems and networks.

  18. Photovoltaic conference on system services

    International Nuclear Information System (INIS)

    Burges, Karsten; Freier, Karin; Vincent, Jeremy; Montigny, Marie; Engel, Bernd; Konstanciak, Wilhelm; Makdessi, Georges; Acres, Adrien; Schlaaff, Torsten; Defaix, Christophe

    2015-01-01

    The French-German office for Renewable energies (OFAEnR) organised a photovoltaic conference on system services and photovoltaic facilities. In the framework of this French-German exchange of experience, about 100 participants have analysed and discussed the regulatory, technical and economical context of system services, their evolution and implementation in the framework of an accelerated development of photovoltaic conversion in both countries. This document brings together the available presentations (slides) made during this event: 1 - Technical Introduction to system services: principles, actors and perspectives (Karsten Burges); 2 - Legal guidelines of EEG (Renewable energy Sources Act) and the System Stability Ordinance as well as future measures for PV grid integration (Karin Freier); 3 - evolution of ancillary services regulation; opening the possibility for new market players to participate in maintaining the system stability (Jeremy Vincent, Marie Montigny); 4 - Paradigm shift for ancillary services: PV as a new stakeholder (Bernd Engel); 5 - Challenges of RES integration (Wilhelm Konstanciak 6 - System services supplied by PV inverters, solutions for frequency and active/reactive power control at the injection point (Georges Makdessi); 7 - Grid disturbance abatement and voltage stability control by monitoring local scale PV production (Adrien Acres); 8 - Flexibly Adaptable Power Plant Controller - The Answer to Various Grid Requirements (Torsten Schlaaff); 9 - ENR-pool project: What kind of business model for ancillary services by PV power plants? (Christophe Defaix)

  19. Sequentially firing neurons confer flexible timing in neural pattern generators

    International Nuclear Information System (INIS)

    Urban, Alexander; Ermentrout, Bard

    2011-01-01

    Neuronal networks exhibit a variety of complex spatiotemporal patterns that include sequential activity, synchrony, and wavelike dynamics. Inhibition is the primary means through which such patterns are implemented. This behavior is dependent on both the intrinsic dynamics of the individual neurons as well as the connectivity patterns. Many neural circuits consist of networks of smaller subcircuits (motifs) that are coupled together to form the larger system. In this paper, we consider a particularly simple motif, comprising purely inhibitory interactions, which generates sequential periodic dynamics. We first describe the dynamics of the single motif both for general balanced coupling (all cells receive the same number and strength of inputs) and then for a specific class of balanced networks: circulant systems. We couple these motifs together to form larger networks. We use the theory of weak coupling to derive phase models which, themselves, have a certain structure and symmetry. We show that this structure endows the coupled system with the ability to produce arbitrary timing relationships between symmetrically coupled motifs and that the phase relationships are robust over a wide range of frequencies. The theory is applicable to many other systems in biology and physics.

  20. 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.

  1. 1st International Conference on Cognitive Systems and Information Processing

    CERN Document Server

    Hu, Dewen; Liu, Huaping

    2014-01-01

    "Foundations and Practical Applications of Cognitive Systems and Information Processing" presents selected papers from the First International Conference on Cognitive Systems and Information Processing, held in Beijing, China on December 15-17, 2012 (CSIP2012). The aim of this conference is to bring together experts from different fields of expertise to discuss the state-of-the-art in artificial cognitive systems and advanced information processing, and to present new findings and perspectives on future development. This book introduces multidisciplinary perspectives on the subject areas of Cognitive Systems and Information Processing, including cognitive sciences and technology, autonomous vehicles, cognitive psychology, cognitive metrics, information fusion, image/video understanding, brain-computer interfaces, visual cognitive processing, neural computation, bioinformatics, etc. The book will be beneficial for both researchers and practitioners in the fields of Cognitive Science, Computer Science and Cogni...

  2. SIAM conference on applications of dynamical systems

    Energy Technology Data Exchange (ETDEWEB)

    1992-01-01

    A conference (Oct.15--19, 1992, Snowbird, Utah; sponsored by SIAM (Society for Industrial and Applied Mathematics) Activity Group on Dynamical Systems) was held that highlighted recent developments in applied dynamical systems. The main lectures and minisymposia covered theory about chaotic motion, applications in high energy physics and heart fibrillations, turbulent motion, Henon map and attractor, integrable problems in classical physics, pattern formation in chemical reactions, etc. The conference fostered an exchange between mathematicians working on theoretical issues of modern dynamical systems and applied scientists. This two-part document contains abstracts, conference program, and an author index.

  3. International Conference on Systems Science 2016

    CERN Document Server

    Tomczak, Jakub

    2017-01-01

    This book gathers the carefully reviewed proceedings of the 19th International Conference on Systems Science, presenting recent research findings in the areas of Artificial Intelligence, Machine Learning, Communication/Networking and Information Technology, Control Theory, Decision Support, Image Processing and Computer Vision, Optimization Techniques, Pattern Recognition, Robotics, Service Science, Web-based Services, Uncertain Systems and Transportation Systems. The International Conference on Systems Science was held in Wroclaw, Poland from September 7 to 9, 2016, and addressed a range of topics, including systems theory, control theory, machine learning, artificial intelligence, signal processing, communication and information technologies, transportation systems, multi-robotic systems and uncertain systems, as well as their applications. The aim of the conference is to provide a platform for communication between young and established researchers and practitioners, fostering future joint research in syst...

  4. 5th International Conference on Complex Systems

    CERN Document Server

    Braha, Dan; Bar-Yam, Yaneer

    2011-01-01

    The International Conference on Complex Systems (ICCS) creates a unique atmosphere for scientists of all fields, engineers, physicians, executives, and a host of other professionals to explore common themes and applications of complex system science. With this new volume, Unifying Themes in Complex Systems continues to build common ground between the wide-ranging domains of complex system science.

  5. 7th International Conference on Complex Systems

    CERN Document Server

    Braha, Dan; Bar-Yam, Yaneer

    2012-01-01

    The International Conference on Complex Systems (ICCS) creates a unique atmosphere for scientists of all fields, engineers, physicians, executives, and a host of other professionals to explore common themes and applications of complex system science. With this new volume, Unifying Themes in Complex Systems continues to build common ground between the wide-ranging domains of complex system science.

  6. 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.

  7. Computational Intelligence in Information Systems Conference

    CERN Document Server

    Au, Thien-Wan; Omar, Saiful

    2017-01-01

    This book constitutes the Proceedings of the Computational Intelligence in Information Systems conference (CIIS 2016), held in Brunei, November 18–20, 2016. The CIIS conference provides a platform for researchers to exchange the latest ideas and to present new research advances in general areas related to computational intelligence and its applications. The 26 revised full papers presented in this book have been carefully selected from 62 submissions. They cover a wide range of topics and application areas in computational intelligence and informatics.

  8. Third International Conference on Complex Systems

    CERN Document Server

    Minai, Ali A; Unifying Themes in Complex Systems

    2006-01-01

    In recent years, scientists have applied the principles of complex systems science to increasingly diverse fields. The results have been nothing short of remarkable: their novel approaches have provided answers to long-standing questions in biology, ecology, physics, engineering, computer science, economics, psychology and sociology. The Third International Conference on Complex Systems attracted over 400 researchers from around the world. The conference aimed to encourage cross-fertilization between the many disciplines represented and to deepen our understanding of the properties common to all complex systems. This volume contains over 35 papers selected from those presented at the conference on topics including: self-organization in biology, ecological systems, language, economic modeling, ecological systems, artificial life, robotics, and complexity and art. ALI MINAI is an Affiliate of the New England Complex Systems Institute and an Associate Professor in the Department of Electrical and Computer Engine...

  9. International Conference on Systems Science 2013

    CERN Document Server

    Grzech, Adam; Swiątek, Paweł; Tomczak, Jakub

    2014-01-01

    The International Conference on Systems Science 2013 (ICSS 2013) was the 18th event of the series of international scientific conferences for researchers and practitioners in the fields of systems science and systems engineering. The conference took place in Wroclaw, Poland during September 10-12, 2013 and was organized  by Wroclaw University of Technology and co-organized by: Committee of Automatics and Robotics of Polish Academy of Sciences, Committee of Computer Science of Polish Academy of Sciences and Polish Section of IEEE. The papers included in the proceedings cover the following topics: Control Theory, Databases and Data Mining, Image and Signal Processing, Machine Learning, Modeling and Simulation, Operational Research, Service Science, Time series and System Identification. The accepted and presented papers highlight new trends and challenges in systems science and systems engineering.

  10. European Conference on Complex Systems 2012

    CERN Document Server

    Kirkilionis, Markus; Nicolis, Gregoire

    2013-01-01

    The European Conference on Complex Systems, held under the patronage of the Complex Systems Society, is an annual event that has become the leading European conference devoted to complexity science. ECCS'12, its ninth edition, took place in Brussels, during the first week of September 2012. It gathered about 650 scholars representing a wide range of topics relating to complex systems research, with emphasis on interdisciplinary approaches. More specifically, the following tracks were covered:  1. Foundations of Complex Systems 2. Complexity, Information and Computation 3. Prediction, Policy and Planning, Environment 4. Biological Complexity 5. Interacting Populations, Collective Behavior 6. Social Systems, Economics and Finance This book contains a selection of the contributions presented at the conference and its satellite meetings. Its contents reflect the extent, diversity and richness of research areas in the field, both fundamental and applied.  

  11. Learning in Artificial Neural Systems

    Science.gov (United States)

    Matheus, Christopher J.; Hohensee, William E.

    1987-01-01

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

  12. 20th Annual Systems Engineering Conference, Thursday, Volume 4

    Science.gov (United States)

    2017-10-26

    20th Annual Systems Engineering Conference October 23-26, 2017 | Waterford at Springfield | Springfield, VA NDIA.org/systemsengineering...Conference Program SYSTEMS ENGINEERING CONFERENCE 2 Welcome to the NDIA Systems Engineering Conference On behalf of the National Defense Industrial...Association’s Systems Engineering Division, I would like to extend a very warm welcome to the 20th Annual Systems Engineering Conference. Yes, the 20th Annual

  13. Fourth International Conference on Complex Systems

    CERN Document Server

    Minai, Ali A; Unifying Themes in Complex Systems IV

    2008-01-01

    In June of 2002, over 500 professors, students and researchers met in Boston, Massachusetts for the Fourth International Conference on Complex Systems. The attendees represented a remarkably diverse collection of fields: biology, ecology, physics, engineering, computer science, economics, psychology and sociology, The goal of the conference was to encourage cross-fertilization between the many disciplines represented and to deepen understanding of the properties common to all complex systems. This volume contains 43 papers selected from the more than 200 presented at the conference. Topics include: cellular automata, neurology, evolution, computer science, network dynamics, and urban planning. About NECSI: For over 10 years, The New England Complex Systems Institute (NECSI) has been instrumental in the development of complex systems science and its applications. NECSI conducts research, education, knowledge dissemination, and community development around the world for the promotion of the study of complex sys...

  14. Third International Conference on Complex Systems

    CERN Document Server

    Minai, Ali A; Unifying Themes in Complex Systems

    2006-01-01

    In recent years, scientists have applied the principles of complex systems science to increasingly diverse fields. The results have been nothing short of remarkable: their novel approaches have provided answers to long-standing questions in biology, ecology, physics, engineering, computer science, economics, psychology and sociology. The Third International Conference on Complex Systems attracted over 400 researchers from around the world. The conference aimed to encourage cross-fertilization between the many disciplines represented and to deepen our understanding of the properties common to all complex systems. This volume contains selected transcripts from presentations given at the conference. Speakers include: Chris Adami, Kenneth Arrow, Michel Baranger, Dan Braha, Timothy Buchman, Michael Caramanis, Kathleen Carley, Greg Chaitin, David Clark, Jack Cohen, Jim Collins, George Cowan, Clay Easterly, Steven Eppinger, Irving Epstein, Dan Frey, Ary Goldberger, Helen Harte, Leroy Hood, Don Ingber, Atlee Jackson,...

  15. International Conference on Intelligent Unmanned Systems (ICIUS)

    CERN Document Server

    Kartidjo, Muljowidodo; Yoon, Kwang-Joon; Budiyono, Agus; Autonomous Control Systems and Vehicles : Intelligent Unmanned Systems

    2013-01-01

    The International Conference on Intelligent Unmanned Systems 2011 was organized by the International Society of Intelligent Unmanned Systems and locally by the Center for Bio-Micro Robotics Research at Chiba University, Japan. The event was the 7th conference continuing from previous conferences held in Seoul, Korea (2005, 2006), Bali, Indonesia (2007), Nanjing, China (2008), Jeju, Korea (2009), and Bali, Indonesia (2010). ICIUS 2011 focused on both theory and application, primarily covering the topics of robotics, autonomous vehicles, intelligent unmanned technologies, and biomimetics. We invited seven keynote speakers who dealt with related state-of-the-art technologies including unmanned aerial vehicles (UAVs) and micro air vehicles (MAVs), flapping wings (FWs), unmanned ground vehicles (UGVs), underwater vehicles (UVs), bio-inspired robotics, advanced control, and intelligent systems, among others. This book is a collection of excellent papers that were updated after presentation at ICIUS2011. All papers ...

  16. 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…

  17. 23rd International Conference on Systems Engineering

    CERN Document Server

    Zydek, Dawid; Chmaj, Grzegorz

    2015-01-01

    This collection of proceedings from the International Conference on Systems Engineering, Las Vegas, 2014 is orientated toward systems engineering, including topics like aerospace, power systems, industrial automation and robotics, systems theory, control theory, artificial intelligence, signal processing, decision support, pattern recognition and machine learning, information and communication technologies, image processing, and computer vision as well as its applications. The volume’s main focus is on models, algorithms, and software tools that facilitate efficient and convenient utilization of modern achievements in systems engineering.

  18. 76 FR 57746 - Conference on the International Conference on Harmonisation Q10 Pharmaceutical Quality System: A...

    Science.gov (United States)

    2011-09-16

    ...] Conference on the International Conference on Harmonisation Q10 Pharmaceutical Quality System: A Practical Approach to Effective Life- Cycle Implementation of Systems and Processes for Pharmaceutical Manufacturing... ``Pharmaceutical Quality System (ICH Q10) Conference: A Practical Approach to Effective Life- Cycle Implementation...

  19. 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.

  20. Pathways, Networks and Systems Medicine Conferences

    Energy Technology Data Exchange (ETDEWEB)

    Nadeau, Joseph H. [Pacific Northwest Research Institute

    2013-11-25

    The 6th Pathways, Networks and Systems Medicine Conference was held at the Minoa Palace Conference Center, Chania, Crete, Greece (16-21 June 2008). The Organizing Committee was composed of Joe Nadeau (CWRU, Cleveland), Rudi Balling (German Research Centre, Brauschweig), David Galas (Institute for Systems Biology, Seattle), Lee Hood (Institute for Systems Biology, Seattle), Diane Isonaka (Seattle), Fotis Kafatos (Imperial College, London), John Lambris (Univ. Pennsylvania, Philadelphia),Harris Lewin (Univ. of Indiana, Urbana-Champaign), Edison Liu (Genome Institute of Singapore, Singapore), and Shankar Subramaniam (Univ. California, San Diego). A total of 101 individuals from 21 countries participated in the conference: USA (48), Canada (5), France (5), Austria (4), Germany (3), Italy (3), UK (3), Greece (2), New Zealand (2), Singapore (2), Argentina (1), Australia (1), Cuba (1), Denmark (1), Japan (1), Mexico (1), Netherlands (1), Spain (1), Sweden (1), Switzerland (1). With respect to speakers, 29 were established faculty members and 13 were graduate students or postdoctoral fellows. With respect to gender representation, among speakers, 13 were female and 28 were male, and among all participants 43 were female and 58 were male. Program these included the following topics: Cancer Pathways and Networks (Day 1), Metabolic Disease Networks (Day 2), Day 3 ? Organs, Pathways and Stem Cells (Day 3), and Day 4 ? Inflammation, Immunity, Microbes and the Environment (Day 4). Proceedings of the Conference were not published.

  1. 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)

  2. 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.

  3. 3rd annual biomass energy systems conference

    Energy Technology Data Exchange (ETDEWEB)

    1979-10-01

    The main objectives of the 3rd Annual Biomass Energy Systems Conference were (1) to review the latest research findings in the clean fuels from biomass field, (2) to summarize the present engineering and economic status of Biomass Energy Systems, (3) to encourage interaction and information exchange among people working or interested in the field, and (4) to identify and discuss existing problems relating to ongoing research and explore opportunities for future research. Abstracts for each paper presented were edited separately. (DC)

  4. 13th International Conference Intelligent Autonomous Systems

    CERN Document Server

    Michael, Nathan; Berns, Karsten; Yamaguchi, Hiroaki

    2016-01-01

    This book describes the latest research accomplishments, innovations, and visions in the field of robotics as presented at the 13th International Conference on Intelligent Autonomous Systems (IAS), held in Padua in July 2014, by leading researchers, engineers, and practitioners from across the world. The contents amply confirm that robots, machines, and systems are rapidly achieving intelligence and autonomy, mastering more and more capabilities such as mobility and manipulation, sensing and perception, reasoning, and decision making. A wide range of research results and applications are covered, and particular attention is paid to the emerging role of autonomous robots and intelligent systems in industrial production, which reflects their maturity and robustness. The contributions have been selected through a rigorous peer-review process and contain many exciting and visionary ideas that will further galvanize the research community, spurring novel research directions. The series of biennial IAS conferences ...

  5. 7th IEEE International Conference Intelligent Systems

    CERN Document Server

    Atanassov, KT; Doukovska, L; Hadjiski, M; Jotsov, V; Kacprzyk, J; Kasabov, N; Sotirov, S; Szmidt, E; Zadrożny, S; Filev, D; Jabłkowski, J; Kacprzyk, J; Krawczak, M; Popchev, I; Rutkowski, L; Sgurev, V; Sotirova, E; Szynkarczyk, P

    2015-01-01

    This two volume set of books constitutes the proceedings of the 2014  7th IEEE International Conference Intelligent Systems (IS), or IEEE IS’2014 for short, held on September 24‐26, 2014 in Warsaw, Poland. Moreover, it contains some selected papers from the collocated IWIFSGN'2014-Thirteenth International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets.The conference was organized by the Systems Research Institute, Polish Academy of Sciences, Department IV of Engineering Sciences, Polish Academy of Sciences, and Industrial Institute of Automation and Measurements - PIAP.The papers included in the two proceedings volumes have been subject to a thorough review process by three highly qualified peer reviewers.Comments and suggestions from them have considerable helped improve the quality of the papers but also the division of the volumes into parts, and assignment of the papers to the best suited parts.  

  6. 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...

  7. 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.

  8. The 25th European Conference on Information Systems (ECIS 2017

    Directory of Open Access Journals (Sweden)

    Michal Doležel

    2017-12-01

    Full Text Available This short note reports on my experience from The 25th European Conference on Information Systems (ECIS. The conference was titled “Information Systems for a smart, sustainable and inclusive world“, and took place in Guimarães, Portugal in June 2017. I discuss the thematic focus of the conference and its scope. Then I refer about two workshops I attended during the conference.

  9. 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.

  10. International Conference on Soft Computing Systems

    CERN Document Server

    Panigrahi, Bijaya

    2016-01-01

    The book is a collection of high-quality peer-reviewed research papers presented in International Conference on Soft Computing Systems (ICSCS 2015) held at Noorul Islam Centre for Higher Education, Chennai, India. These research papers provide the latest developments in the emerging areas of Soft Computing in Engineering and Technology. The book is organized in two volumes and discusses a wide variety of industrial, engineering and scientific applications of the emerging techniques. It presents invited papers from the inventors/originators of new applications and advanced technologies.

  11. 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.

  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. 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.

  14. 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.

  15. 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

  16. Electronic Conference Tracking and Approval System

    Data.gov (United States)

    US Agency for International Development — eCTAS tracks information relevant to attendance and hosting conferences of USAID's Washington offices and Missions worldwide including cost, numbers attending,...

  17. 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.

  18. 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.

  19. 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...

  20. Hypoxia Epigenetically Confers Astrocytic Differentiation Potential on Human Pluripotent Cell-Derived Neural Precursor Cells

    Directory of Open Access Journals (Sweden)

    Tetsuro Yasui

    2017-06-01

    Full Text Available Human neural precursor cells (hNPCs derived from pluripotent stem cells display a high propensity for neuronal differentiation, but they require long-term culturing to differentiate efficiently into astrocytes. The mechanisms underlying this biased fate specification of hNPCs remain elusive. Here, we show that hypoxia confers astrocytic differentiation potential on hNPCs through epigenetic gene regulation, and that this was achieved by cooperation between hypoxia-inducible factor 1α and Notch signaling, accompanied by a reduction of DNA methylation level in the promoter region of a typical astrocyte-specific gene, Glial fibrillary acidic protein. Furthermore, we found that this hypoxic culture condition could be applied to rapid generation of astrocytes from Rett syndrome patient-derived hNPCs, and that these astrocytes impaired neuronal development. Thus, our findings shed further light on the molecular mechanisms regulating hNPC differentiation and provide attractive tools for the development of therapeutic strategies for treating astrocyte-mediated neurological disorders.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. Conference on Radiation and its Effects on Components and Systems

    CERN Document Server

    2017-01-01

    The aim of RADECS conferences is to provide an annual European forum for the presentation and discussion of the latest advances in the field of radiation effects on electronic and photonic materials, devices, circuits, sensors, and systems. The scope of the conference encompasses technological processes and design techniques for producing radiation tolerant systems for space, aeronautical or terrestrial applications, as well as relevant methodologies for their characterization and qualification. The conference features a technical program, an Industrial Exhibit, and one day tutorial or ‘short course’ on radiation effects. The technical program includes oral and poster sessions and round tables.

  6. Conference on Manned Systems Design : New Methods and Equipment

    CERN Document Server

    Kraiss, K-F

    1981-01-01

    This volume contains the proceedings of a conference held in Freiburg, West Germany, September 22-25, 1980, entitled "Manned Systems Design, New Methods and Equipment". The conference was sponsored by the Special Programme Panel on Human Factors of the Scientific Affairs Division of NATO, and supported by Panel VIII, AC/243, on "Human and Biomedical Sciences". Their sponsorship and support are gratefully acknowledged. The contributions in the book are grouped according to the main themes of the conference with special emphasis on analytical approaches, measurement of performance, and simulator design and evaluat ion. The design of manned systems covers many and highly diversified areas. Therefore, a conference under the general title of "Manned Systems Design" is rather ambitious in itself. However, scientists and engineers engaged in the design of manned systems very often are confronted with problems that can be solved only by having several disciplines working together. So it was felt that knowledge about ...

  7. 2015 International Conference on Information Technology and Intelligent Transportation Systems

    CERN Document Server

    Jain, Lakhmi; Zhao, Xiangmo

    2017-01-01

    This volume includes the proceedings of the 2015 International Conference on Information Technology and Intelligent Transportation Systems (ITITS 2015) which was held in Xi’an on December 12-13, 2015. The conference provided a platform for all professionals and researchers from industry and academia to present and discuss recent advances in the field of Information Technology and Intelligent Transportation Systems. The presented information technologies are connected to intelligent transportation systems including wireless communication, computational technologies, floating car data/floating cellular data, sensing technologies, and video vehicle detection. The articles focusing on intelligent transport systems vary in the technologies applied, from basic management systems to more application systems including topics such as emergency vehicle notification systems, automatic road enforcement, collision avoidance systems and some cooperative systems. The conference hosted 12 invited speakers and over 200 part...

  8. 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.

  9. Summary of the International Conference on Software and System Processes

    DEFF Research Database (Denmark)

    Kuhrmann, Marco; O'Connor, Rory V.; Perry, Dewayne E.

    2016-01-01

    The International Conference on Software and Systems Process (ICSSP), continuing the success of Software Process Workshop (SPW), the Software Process Modeling and Simulation Workshop (ProSim) and the International Conference on Software Process (ICSP) conference series, has become the established...... premier event in the field of software and systems engineering processes. It provides a leading forum for the exchange of research outcomes and industrial best-practices in process development from software and systems disciplines. ICSSP 2016 was held in Austin, Texas, from 14-15 May 2016, co......-located with the 38th International Conference on Software Engineering (ICSE). The theme of mICSSP 2016 was studying "Process(es) in Action" by recognizing that the AS-Planned and AS-Practiced processes can be quite different in many ways including their ows, their complexity and the evolving needs of stakeholders...

  10. International Conference on Intelligent Systems for Molecular Biology (ISMB)

    Energy Technology Data Exchange (ETDEWEB)

    Goldberg, Debra; Hibbs, Matthew; Kall, Lukas; Komandurglayavilli, Ravikumar; Mahony, Shaun; Marinescu, Voichita; Mayrose, Itay; Minin, Vladimir; Neeman, Yossef; Nimrod, Guy; Novotny, Marian; Opiyo, Stephen; Portugaly, Elon; Sadka, Tali; Sakabe, Noboru; Sarkar, Indra; Schaub, Marc; Shafer, Paul; Shmygelska, Olena; Singer, Gregory; Song, Yun; Soumyaroop, Bhattacharya; Stadler, Michael; Strope, Pooja; Su, Rong; Tabach, Yuval; Tae, Hongseok; Taylor, Todd; Terribilini, Michael; Thomas, Asha; Tran, Nam; Tseng, Tsai-Tien; Vashist, Akshay; Vijaya, Parthiban; Wang, Kai; Wang, Ting; Wei, Lai; Woo, Yong; Wu, Chunlei; Yamanishi, Yoshihiro; Yan, Changhui; Yang, Jack; Yang, Mary; Ye, Ping; Zhang, Miao

    2009-12-29

    The Intelligent Systems for Molecular Biology (ISMB) conference has provided a general forum for disseminating the latest developments in bioinformatics on an annual basis for the past 13 years. ISMB is a multidisciplinary conference that brings together scientists from computer science, molecular biology, mathematics and statistics. The goal of the ISMB meeting is to bring together biologists and computational scientists in a focus on actual biological problems, i.e., not simply theoretical calculations. The combined focus on "intelligent systems" and actual biological data makes ISMB a unique and highly important meeting, and 13 years of experience in holding the conference has resulted in a consistently well organized, well attended, and highly respected annual conference. The ISMB 2005 meeting was held June 25-29, 2005 at the Renaissance Center in Detroit, Michigan. The meeting attracted over 1,730 attendees. The science presented was exceptional, and in the course of the five-day meeting, 56 scientific papers, 710 posters, 47 Oral Abstracts, 76 Software demonstrations, and 14 tutorials were presented. The attendees represented a broad spectrum of backgrounds with 7% from commercial companies, over 28% qualifying for student registration, and 41 countries were represented at the conference, emphasizing its important international aspect. The ISMB conference is especially important because the cultures of computer science and biology are so disparate. ISMB, as a full-scale technical conference with refereed proceedings that have been indexed by both MEDLINE and Current Contents since 1996, bridges this cultural gap.

  11. International Conference on Nano-electronics, Circuits & Communication Systems

    CERN Document Server

    2017-01-01

    This volume comprises select papers from the International Conference on Nano-electronics, Circuits & Communication Systems(NCCS). The conference focused on the frontier issues and their applications in business, academia, industry, and other allied areas. This international conference aimed to bring together scientists, researchers, engineers from academia and industry. The book covers technological developments and current trends in key areas such as VLSI design, IC manufacturing, and applications such as communications, ICT, and hybrid electronics. The contents of this volume will prove useful to researchers, professionals, and students alike.

  12. Mobile-Based Recommendation System in Conferences

    OpenAIRE

    Hernández de la Iglesia, Daniel; Moreno García, María N.; Omatu, Sigeru

    2017-01-01

    Today information is a very important asset for organizations. Obtaining and interpreting information in real time can be a major benefit in decision making. For this reason, we have developed a mobile application that uses WiFi access points to locate users who are attending conferences and exhibits. The application can generate a dataset according to the user’s preferences and on-site location, and provide personalized recommendations accordingly.

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

    Science.gov (United States)

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

    2015-01-01

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

  14. Theory of Neural Information Processing Systems

    International Nuclear Information System (INIS)

    Galla, Tobias

    2006-01-01

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

  15. 2nd International Conference on Advanced Intelligent Systems and Informatics

    CERN Document Server

    Shaalan, Khaled; Gaber, Tarek; Azar, Ahmad; Tolba, M

    2017-01-01

    This book gathers the proceedings of the 2nd International Conference on Advanced Intelligent Systems and Informatics (AISI2016), which took place in Cairo, Egypt during October 24–26, 2016. This international interdisciplinary conference, which highlighted essential research and developments in the field of informatics and intelligent systems, was organized by the Scientific Research Group in Egypt (SRGE) and sponsored by the IEEE Computational Intelligence Society (Egypt chapter) and the IEEE Robotics and Automation Society (Egypt Chapter). The book’s content is divided into four main sections: Intelligent Language Processing, Intelligent Systems, Intelligent Robotics Systems, and Informatics.

  16. 5th International Conference on Complex Systems Design & Management

    CERN Document Server

    Krob, Daniel; Morel, Gérard; Roussel, Jean-Claude

    2015-01-01

    This book contains all refereed papers that were accepted to the fifth edition of the « Complex Systems Design & Management » (CSD&M 2014) international conference which took place in Paris (France) on the November 12-14, 2014. These proceedings cover the most recent trends in the emerging field of complex systems sciences & practices from an industrial and academic perspective, including the main industrial domains (aeronautic & aerospace, transportation & systems, defense & security, electronics & robotics, energy & environment, health & welfare services, software & e-services), scientific & technical topics (systems fundamentals, systems architecture & engineering, systems metrics & quality, systemic tools) and system types (transportation systems, embedded systems, software & information systems, systems of systems, artificial ecosystems). The CSD&M 2014 conference is organized under the guidance of the CESAMES non-profit organization, addres...

  17. 2013 International Conference on Mechatronics and Automatic Control Systems

    CERN Document Server

    2014-01-01

    This book examines mechatronics and automatic control systems. The book covers important emerging topics in signal processing, control theory, sensors, mechanic manufacturing systems and automation. The book presents papers from the 2013 International Conference on Mechatronics and Automatic Control Systems held in Hangzhou, China on August 10-11, 2013. .

  18. Neural Network for Optimization of Existing Control Systems

    DEFF Research Database (Denmark)

    Madsen, Per Printz

    1995-01-01

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

  19. Fourth International Conference on Complex Systems Design & Management

    CERN Document Server

    Boulanger, Frédéric; Krob, Daniel; Marchal, Clotilde

    2014-01-01

    This book contains all refereed papers that were accepted to the fourth edition of the « Complex Systems Design & Management » (CSD&M 2013) international conference which took place in Paris (France) from December 4-6, 2013. These proceedings cover the most recent trends in the emerging field of complex systems sciences & practices from an industrial and academic perspective, including the main industrial domains (transport, defense & security, electronics, energy & environment, e-services), scientific & technical topics (systems fundamentals, systems architecture & engineering, systems metrics & quality, systemic tools) and system types (transportation systems, embedded systems, software & information systems, systems of systems, artificial ecosystems). The CSD&M 2013 conference is organized under the guidance of the CESAMES non-profit organization

  20. 7th International Conference on Complex Systems Design & Management

    CERN Document Server

    Goubault, Eric; Krob, Daniel; Stephan, François

    2017-01-01

    This book contains all refereed papers that were accepted to the seventh edition of the international conference « Complex Systems Design & Management Paris» (CSD&M Paris 2016) which took place in Paris (France) on the December 13-14, 2016 These proceedings cover the most recent trends in the emerging field of complex systems sciences & practices from an industrial and academic perspective, including the main industrial domains (aeronautic & aerospace, defense & security, electronics & robotics, energy & environment, healthcare & welfare services, software & e-services, transportation), scientific & technical topics (systems fundamentals, systems architecture & engineering, systems metrics & quality, system is modeling tools) and system types (artificial ecosystems, embedded systems, software & information systems, systems of systems, transportation systems). The CSD&M Paris 2016 conference is organized under the guidance of the CESAMES non-profit orga...

  1. 6th International Conference on Complex Systems Design & Management

    CERN Document Server

    Bocquet, Jean-Claude; Bonjour, Eric; Krob, Daniel

    2016-01-01

    This book contains all refereed papers that were accepted to the sixth edition of the « Complex Systems Design & Management Paris » (CSD&M Paris 2015) international conference which took place in Paris (France) on November 23-25, 2015. These proceedings cover the most recent trends in the emerging field of complex systems sciences & practices from an industrial and academic perspective, including the main industrial domains (aeronautics & aerospace, defense & security, electronics & robotics, energy & environment, health & welfare, software & e-services, transportation), scientific & technical topics (systems fundamentals, systems architecture & engineering, systems metrics & quality, systems modeling tools) and systems types (artificial ecosystems, embedded systems, software & information systems, systems of systems, transportation systems). The CSD&M Paris 2015 conference is organized under the guidance of the CESAMES non-profit organization, address...

  2. 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

  3. 20th Annual Systems Engineering Conference. Volume 1, Monday-Tuesday

    Science.gov (United States)

    2017-10-26

    20th Annual Systems Engineering Conference October 23-26, 2017 | Waterford at Springfield | Springfield, VA NDIA.org/systemsengineering...Conference Program SYSTEMS ENGINEERING CONFERENCE 2 Welcome to the NDIA Systems Engineering Conference On behalf of the National Defense Industrial...Association’s Systems Engineering Division, I would like to extend a very warm welcome to the 20th Annual Systems Engineering Conference. Yes, the 20th Annual

  4. 1st International Conference on Advanced Intelligent System and Informatics

    CERN Document Server

    Hassanien, Aboul; El-Bendary, Nashwa; Dey, Nilanjan

    2016-01-01

    The conference topics address different theoretical and practical aspects, and implementing solutions for intelligent systems and informatics disciplines including bioinformatics, computer science, medical informatics, biology, social studies, as well as robotics research. The conference also discuss and present solutions to the cloud computing and big data mining which are considered hot research topics. The conference papers discussed different topics – techniques, models, methods, architectures, as well as multi aspect, domain-specific, and new solutions for the above disciplines. The accepted papers have been grouped into five parts: Part I—Intelligent Systems and Informatics, addressing topics including, but not limited to, medical application, predicting student performance, action classification, and detection of dead stained microscopic cells, optical character recognition, plant identification, rehabilitation of disabled people. Part II—Hybrid Intelligent Systems, addressing topics including, b...

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

    NARCIS (Netherlands)

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

    2014-01-01

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

  6. Spiking Neural P Systems with Communication on Request.

    Science.gov (United States)

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

    2017-12-01

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

  7. Support for Conference Entitled "The Role of Neural Plasticity in Chemical Intolerance"

    National Research Council Canada - National Science Library

    Shaikh, Rashid

    2000-01-01

    .... Other disorders that overlap with CI, such as Gulf War Syndrome, chronic fatigue and fibromyalgia, were also be discussed. The conference was attended by toxicologists, basic neuroscientists, environmental health professionals and clinicians, including general practitioners and psychiatrists.

  8. Bifurcation and chaos in neural excitable system

    International Nuclear Information System (INIS)

    Jing Zhujun; Yang Jianping; Feng Wei

    2006-01-01

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

  9. Eighth International Conference on Intelligent Systems and Knowledge Engineering

    CERN Document Server

    Li, Tianrui; ISKE 2013; Foundations of Intelligent Systems; Knowledge Engineering and Management; Practical Applications of Intelligent Systems

    2014-01-01

    "Foundations of Intelligent Systems" presents selected papers from the 2013 International Conference on Intelligent Systems and Knowledge Engineering (ISKE2013). The aim of this conference is to bring together experts from different expertise areas to discuss the state-of-the-art in Intelligent Systems and Knowledge Engineering, and to present new research results and perspectives on future development. The topics in this volume include, but not limited to: Artificial Intelligence Theories, Pattern Recognition, Intelligent System Models, Speech Recognition, Computer Vision, Multi-Agent Systems, Machine Learning, Soft Computing and Fuzzy Systems, Biological Inspired Computation, Game Theory, Cognitive Systems and Information Processing, Computational Intelligence, etc. The proceedings are benefit for both researchers and practitioners who want to utilize intelligent methods in their specific research fields. Dr. Zhenkun Wen is a Professor at the College of Computer and Software Engineering, Shenzhen University...

  10. 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.

  11. Ninth International Conference on Dependability and Complex Systems

    CERN Document Server

    Mazurkiewicz, Jacek; Sugier, Jarosław; Walkowiak, Tomasz; Kacprzyk, Janusz

    2014-01-01

    DepCoS – RELCOMEX is an annual series of conferences organized by Wrocław University of Technology to promote a comprehensive approach to evaluation of system performability which is now commonly called dependability. In contrast to classic analyses which were concentrated on reliability of technical resources and structures built from them, dependability is based on multi-disciplinary approach to theory, technology, and maintenance of a system considered to be a multifaceted amalgamation of technical, information, organization, software and human (users, administrators, supervisors, etc.) resources. Diversity of processes being realized (data processing, system management, system monitoring, etc.), their concurrency and their reliance on in-system intelligence often severely impedes construction of strict mathematical models and calls for application of intelligent and soft computing methods. This book presents the proceedings of the Ninth International Conference on Dependability and Complex Systems DepC...

  12. International Conference on Information and Communication Technology for Intelligent Systems

    CERN Document Server

    Das, Swagatam

    2016-01-01

    This volume contains 60 papers presented at ICTIS 2015: International Conference on Information and Communication Technology for Intelligent Systems. The conference was held during 28th and 29th November, 2015, Ahmedabad, India and organized communally by Venus International College of Technology, Association of Computer Machinery, Ahmedabad Chapter and Supported by Computer Society of India Division IV – Communication and Division V – Education and Research. This volume contains papers mainly focused on ICT and its application for Intelligent Computing, Cloud Storage, Data Mining, Image Processing and Software Analysis etc.

  13. Video Conference System that Keeps Mutual Eye Contact Among Participants

    Directory of Open Access Journals (Sweden)

    Masahiko Yahagi

    2011-10-01

    Full Text Available A novel video conference system is developed. Suppose that three people A, B, and C attend the video conference, the proposed system enables eye contact among every pair. Furthermore, when B and C chat, A feels as if B and C were facing each other (eye contact seems to be kept among B and C. In the case of a triangle video conference, the respective video system is composed of a half mirror, two video cameras, and two monitors. Each participant watches other participants' images that are reflected by the half mirror. Cameras are set behind the half mirror. Since participants' image (face and the camera position are adjusted to be the same direction, eye contact is kept and conversation becomes very natural compared with conventional video conference systems where participants' eyes do not point to the other participant. When 3 participants sit at the vertex of an equilateral triangle, eyes can be kept even for the situation mentioned above (eye contact between B and C from the aspect of A. Eye contact can be kept not only for 2 or 3 participants but also any number of participants as far as they sit at the vertex of a regular polygon.

  14. International Conference on Strongly Correlated Electron Systems 2017 (SCES2017)

    Science.gov (United States)

    2018-05-01

    The 2017 International Conference on Strongly Correlated Electron Systems, SCES 2017, took place at the Clarion Congress Hotel in Prague, Czech Republic from July 17 to 21, 2017. The meeting was held under the auspices of the Department of Condensed Matter Physics of the Faculty of Mathematics and Physics of the Charles University.

  15. International Conference on Intelligent Technologies and Engineering System (ICITES 2012)

    CERN Document Server

    Huang, Yi-Cheng; Intelligent Technologies and Engineering Systems

    2013-01-01

    This book concentrates on intelligent technologies as it relates to engineering systems. The book covers the following topics: networking, signal processing, artificial intelligence, control and software engineering, intelligent electronic circuits and systems, communications, and materials and mechanical engineering. The book is a collection of original papers that have been reviewed by technical editors. These papers were presented at the International Conference on Intelligent Technologies and Engineering Systems, held Dec. 13-15, 2012.

  16. 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

  17. 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.

  18. 36th International Conference on Information Systems Architecture and Technology

    CERN Document Server

    Grzech, Adam; Świątek, Jerzy; Wilimowska, Zofia

    2016-01-01

    This four volume set of books constitutes the proceedings of the 36th International Conference Information Systems Architecture and Technology 2015, or ISAT 2015 for short, held on September 20–22, 2015 in Karpacz, Poland. The conference was organized by the Computer Science and Management Systems Departments, Faculty of Computer Science and Management, Wroclaw University of Technology, Poland. The papers included in the proceedings have been subject to a thorough review process by highly qualified peer reviewers. The accepted papers have been grouped into four parts: Part I—addressing topics including, but not limited to, systems analysis and modeling, methods for managing complex planning environment and insights from Big Data research projects. Part II—discoursing about topics including, but not limited to, Web systems, computer networks, distributed computing, and multi-agent systems and Internet of Things. Part III—discussing topics including, but not limited to, mobile and Service Oriented Archi...

  19. 2016 37th International Conference Information Systems Architecture and Technology

    CERN Document Server

    Grzech, Adam; Świątek, Jerzy; Wilimowska, Zofia

    2017-01-01

    This four volume set of books constitutes the proceedings of the 2016 37th International Conference Information Systems Architecture and Technology (ISAT), or ISAT 2016 for short, held on September 18–20, 2016 in Karpacz, Poland. The conference was organized by the Department of Management Systems and the Department of Computer Science, Wrocław University of Science and Technology, Poland. The papers included in the proceedings have been subject to a thorough review process by highly qualified peer reviewers. The accepted papers have been grouped into four parts: Part I—addressing topics including, but not limited to, systems analysis and modeling, methods for managing complex planning environment and insights from Big Data research projects. Part II—discoursing about topics including, but not limited to, Web systems, computer networks, distributed computing, and mulit-agent systems and Internet of Things. Part III—discussing topics including, but not limited to, mobile and Service Oriented Architect...

  20. Proceedings: Cooling tower and advanced cooling systems conference

    International Nuclear Information System (INIS)

    1995-02-01

    This Cooling Tower and Advanced Cooling Systems Conference was held August 30 through September 1, 1994, in St. Petersburg, Florida. The conference was sponsored by the Electric Power Research Institute (EPRI) and hosted by Florida Power Corporation to bring together utility representatives, manufacturers, researchers, and consultants. Nineteen technical papers were presented in four sessions. These sessions were devoted to the following topics: cooling tower upgrades and retrofits, cooling tower performance, cooling tower fouling, and dry and hybrid systems. On the final day, panel discussions addressed current issues in cooling tower operation and maintenance as well as research and technology needs for power plant cooling. More than 100 people attended the conference. This report contains the technical papers presented at the conference. Of the 19 papers, five concern cooling tower upgrades and retrofits, five to cooling tower performance, four discuss cooling tower fouling, and five describe dry and hybrid cooling systems. Selected papers are indexed separately for inclusion in the Energy Science and Technology Database

  1. 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.

  2. 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

  3. 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.

  4. 2014 World Conference on Information Systems and Technologies

    CERN Document Server

    Correia, Ana; Tan, Felix; Stroetmann, Karl

    2014-01-01

    This book contains a selection of articles from The 2014 World Conference on Information Systems and Technologies (WorldCIST'14), held between the 15th and 18th of April in Funchal, Madeira, Portugal, a global forum for researchers and practitioners to present and discuss recent results and innovations, current trends, professional experiences and challenges of modern Information Systems and Technologies research, technological development and applications. The main topics covered are: Information and Knowledge Management; Organizational Models and Information Systems; Intelligent and Decision Support Systems; Software Systems, Architectures, Applications and Tools; Computer Networks, Mobility and Pervasive Systems; Radar Technologies; Human-Computer Interaction; Health Informatics; and Information Technologies in Education.

  5. 12th International Conference on Intelligent Autonomous Systems

    CERN Document Server

    Cho, Hyungsuck; Yoon, Kwang-Joon; Lee, Jangmyung

    2013-01-01

    Intelligent autonomous systems are emerged as a key enabler for the creation of a new paradigm of services to humankind, as seen by the recent advancement of autonomous cars licensed for driving in our streets, of unmanned aerial and underwater vehicles carrying out hazardous tasks on-site, and of space robots engaged in scientific as well as operational missions, to list only a few. This book aims at serving the researchers and practitioners in related fields with a timely dissemination of the recent progress on intelligent autonomous systems, based on a collection of papers presented at the 12th International Conference on Intelligent Autonomous Systems, held in Jeju, Korea, June 26-29, 2012. With the theme of “Intelligence and Autonomy for the Service to Humankind, the conference has covered such diverse areas as autonomous ground, aerial, and underwater vehicles, intelligent transportation systems, personal/domestic service robots, professional service robots for surgery/rehabilitation, rescue/security ...

  6. 7th International Conference on Intelligent Systems and Knowledge Engineering

    CERN Document Server

    Li, Tianrui; Li, Hongbo

    2014-01-01

    These proceedings present technical papers selected from the 2012 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2012), held on December 15-17 in Beijing. The aim of this conference is to bring together experts from different fields of expertise to discuss the state-of-the-art in Intelligent Systems and Knowledge Engineering, and to present new findings and perspectives on future developments. The proceedings introduce current scientific and technical advances in the fields of artificial intelligence, machine learning, pattern recognition, data mining, knowledge engineering, information retrieval, information theory, knowledge-based systems, knowledge representation and reasoning, multi-agent systems, and natural-language processing, etc. Furthermore they include papers on new intelligent computing paradigms, which combine new computing methodologies, e.g., cloud computing, service computing and pervasive computing with traditional intelligent methods. By presenting new method...

  7. International Conference on Systems, Control and Information Technologies 2016

    CERN Document Server

    Kaliczyńska, Małgorzata

    2017-01-01

    This book presents the proceedings of the International Conference on Systems, Control and Information Technologies 2016. It includes research findings from leading experts in the fields connected with INDUSTRY 4.0 and its implementation, especially: intelligent systems, advanced control, information technologies, industrial automation, robotics, intelligent sensors, metrology and new materials. Each chapter offers an analysis of a specific technical problem followed by a numerical analysis and simulation as well as the implementation for the solution of a real-world problem.

  8. 4th International Conference on Communications, Signal Processing, and Systems

    CERN Document Server

    Mu, Jiasong; Wang, Wei; Zhang, Baoju

    2016-01-01

    This book brings together papers presented at the 4th International Conference on Communications, Signal Processing, and Systems, which provides a venue to disseminate the latest developments and to discuss the interactions and links between these multidisciplinary fields. Spanning topics ranging from Communications, Signal Processing and Systems, this book is aimed at undergraduate and graduate students in Electrical Engineering, Computer Science and Mathematics, researchers and engineers from academia and industry as well as government employees (such as NSF, DOD, DOE, etc).

  9. 2nd International Conference on Intelligent Technologies and Engineering Systems

    CERN Document Server

    Chen, Cheng-Yi; Yang, Cheng-Fu

    2014-01-01

    This book includes the original, peer reviewed research papers from the 2nd International Conference on Intelligent Technologies and Engineering Systems (ICITES2013), which took place on December 12-14, 2013 at Cheng Shiu University in Kaohsiung, Taiwan. Topics covered include: laser technology, wireless and mobile networking, lean and agile manufacturing, speech processing, microwave dielectrics, intelligent circuits and systems, 3D graphics, communications, and structure dynamics and control.

  10. International Conference on Intelligent and Interactive Systems and Applications

    CERN Document Server

    Patnaik, Srikanta; Yu, Zhengtao

    2017-01-01

    This book provides the latest research findings and developments in the field of interactive intelligent systems, addressing diverse areas such as autonomous systems, Internet and cloud computing, pattern recognition and vision systems, mobile computing and intelligent networking, and e-enabled systems. It gathers selected papers from the International Conference on Intelligent and Interactive Systems and Applications (IISA2016) held on June 25–26, 2016 in Shanghai, China. Interactive intelligent systems are among the most important multi-disciplinary research and development domains of artificial intelligence, human–computer interaction, machine learning and new Internet-based technologies. Accordingly, these systems embrace a considerable number of application areas such as autonomous systems, expert systems, mobile systems, recommender systems, knowledge-based and semantic web-based systems, virtual communication environments, and decision support systems, to name a few. To date, research on interactiv...

  11. 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

  12. 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.

  13. 3rd International Conference on Intelligent Technologies and Engineering Systems

    CERN Document Server

    2016-01-01

    This book includes the original, peer reviewed research from the 3rd International Conference on Intelligent Technologies and Engineering Systems (ICITES2014), held in December, 2014 at Cheng Shiu University in Kaohsiung, Taiwan. Topics covered include: Automation and robotics, fiber optics and laser technologies, network and communication systems, micro and nano technologies, and solar and power systems. This book also Explores emerging technologies and their application in a broad range of engineering disciplines Examines fiber optics and laser technologies Covers biomedical, electrical, industrial, and mechanical systems Discusses multimedia systems and applications, computer vision and image & video signal processing.

  14. 2nd International Conference on Health Care Systems Engineering

    CERN Document Server

    Sahin, Evren; Li, Jingshan; Guinet, Alain; Vandaele, Nico

    2016-01-01

    In this volume, scientists and practitioners write about new methods and technologies for improving the operation of health care organizations. Statistical analyses play an important role in these methods with the implications of simulation and modeling applied to the future of health care. Papers are based on work presented at the Second International Conference on Health Care Systems Engineering (HCSE2015) in Lyon, France. The conference was a rare opportunity for scientists and practitioners to share work directly with each other. Each resulting paper received a double blind review. Paper topics include: hospital drug logistics, emergency care, simulation in patient care, and models for home care services. Discusses statistical analysis and operations management for health care delivery systems based on real case studies Papers in this volume received a double blind review Brings together the work of scientists, practitioners, and clinicians to unite research and practice in the future of these systems Top...

  15. 3rd International Conference on Health Care Systems Engineering

    CERN Document Server

    Li, Jingshan; Matta, Andrea; Sahin, Evren; Vandaele, Nico; Visintin, Filippo

    2017-01-01

    This book presents statistical processes for health care delivery and covers new ideas, methods and technologies used to improve health care organizations. It gathers the proceedings of the Third International Conference on Health Care Systems Engineering (HCSE 2017), which took place in Florence, Italy from May 29 to 31, 2017. The Conference provided a timely opportunity to address operations research and operations management issues in health care delivery systems. Scientists and practitioners discussed new ideas, methods and technologies for improving the operations of health care systems, developed in close collaborations with clinicians. The topics cover a broad spectrum of concrete problems that pose challenges for researchers and practitioners alike: hospital drug logistics, operating theatre management, home care services, modeling, simulation, process mining and data mining in patient care and health care organizations.

  16. Europhysics conference on control systems for experimental physics

    International Nuclear Information System (INIS)

    Kuiper, B.

    1990-01-01

    This volume contains the proceedings of a conference dealing with computer control of particle accelerators and other larger experimental-physics installations. This conference in Villars was the second in a now-established biennial series starting in 1985 in Los Alamos and continuing in 1989 in Vancouver. It included 9 invited papers, presented orally, 61 contributed papers displayed as posters, 6 topical workshops, and 7 tutorials. With few exceptions, all papers appear in the proceedings. Topics include functioning or proposed control systems of several large accelerators (LEP, SSC, GSI, INP, IHEP) and the UA1 experiment at CERN, overviews and current status of control systems for other accelerators and associated equipment, software, modelling, use of expert systems, maintenance, interfaces, network procedures and communications, and timing. Transcripts of the workshops have been reproduced in full, each followed by a summary. (orig.)

  17. Conference on Earth Observation and Information Systems

    CERN Document Server

    Morley, Lawrence

    1977-01-01

    The NATO Science Committee and its subsidiary Programme Panels provide support for Advanced Research Institutes (ARI) in various fields. The idea is to bring together scientists of a chosen field with the hope that they will achieve a consensus on research direc­ tions for the future, and make recommendations for the benefit of a wider scientific community. Attendance is therefore limited to those whose experience and expertise make the conclusions significant and acceptable to the wider community. Participants are selected on the basis of substantial track records in research or in the synthesis of research results to serve mankind. The proposal for a one-week ARIon Earth Observation and In­ formation Systems was initiated by the NATO Special Programme Panel on Systems Science (SPPOSS). In approving the ARI, the senior NATO Science Committee identified the subject as one of universal impor­ tance, requiring a broad perspective on the development of opera­ tional systems based on successful experimental s...

  18. 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.

  19. Conference on Environmental Assessment of Socioeconomic Systems

    CERN Document Server

    Ittelson, William

    1978-01-01

    Neglect of the relation between the socio-economic system and its natural environment has had detrimental consequences in the past, for example - the pollution of the natural environment (water, air and soil) by producing, using and consuming the products of our industrialized economy, - the forseeable exhaustion of natural resources by continuing the increase of industrial production. Most of the recent activities, both in research and in adminis­ tration, against these impacts have been technically oriented, with the aim of stimulating and introducing new technologies of produc­ tion and new products to diminish the environmental pollution. But these efforts, which are of course necessary, cannot be successful in approaching the aim - which should and must in the long-term view be defined as the development of society in balance with the natural environment. Therefore, in addition to an assess­ ment of technologies, emphasis should be put on an assessment of socio-economic systems. On di~~erent levels, i...

  20. 3rd Conference on Ignition Systems for Gasoline Engines

    CERN Document Server

    Sens, Marc

    2017-01-01

    The volume includes selected and reviewed papers from the 3rd Conference on Ignition Systems for Gasoline Engines in Berlin in November 2016. Experts from industry and universities discuss in their papers the challenges to ignition systems in providing reliable, precise ignition in the light of a wide spread in mixture quality, high exhaust gas recirculation rates and high cylinder pressures. Classic spark plug ignition as well as alternative ignition systems are assessed, the ignition system being one of the key technologies to further optimizing the gasoline engine.

  1. SIGEF Conference

    CERN Document Server

    Terceño-Gómez, Antonio; Ferrer-Comalat, Joan; Merigó-Lindahl, José; Linares-Mustarós, Salvador

    2015-01-01

    This book is a collection of selected papers presented at the SIGEF conference, held at the Faculty of Economics and Business of the University of Girona (Spain), 06-08 July, 2015. This edition of the conference has been presented with the slogan “Scientific methods for the treatment of uncertainty in social sciences”. There are different ways for dealing with uncertainty in management. The book focuses on soft computing theories and their role in assessing uncertainty in a complex world. It gives a comprehensive overview of quantitative management topics and discusses some of the most recent developments in all the areas of business and management in soft computing including Decision Making, Expert Systems and Forgotten Effects Theory, Forecasting Models, Fuzzy Logic and Fuzzy Sets, Modelling and Simulation Techniques, Neural Networks and Genetic Algorithms and Optimization and Control. The book might be of great interest for anyone working in the area of management and business economics and might be es...

  2. International conference in electrical engineering and intelligent systems

    CERN Document Server

    Gelman, Len; Electrical Engineering and Intelligent Systems

    2013-01-01

    The revised and extended papers collected in this volume represent the cutting-edge of research at the nexus of electrical engineering and intelligent systems. They were selected from well over 1000 papers submitted to the high-profile international World Congress on Engineering held in London in July 2011. The chapters cover material across the full spectrum of work in the field, including computational intelligence, control engineering, network management, and wireless networks. Readers will also find substantive papers on signal processing, Internet computing, high performance computing, and industrial applications.   The Electrical Engineering and Intelligent Systems conference, as part of the 2011 World Congress on Engineering was organized under the auspices of the non-profit International Association of Engineers (IAENG). With more than 30 nations represented on the conference committees alone, the Congress features the best and brightest scientific minds from a multitude of disciplines related to eng...

  3. 6th International Conference on Knowledge and Systems Engineering

    CERN Document Server

    Le, Anh-Cuong; Huynh, Van-Nam

    2015-01-01

    This volume contains papers presented at the Sixth International Conference on Knowledge and Systems Engineering (KSE 2014), which was held in Hanoi, Vietnam, during 9–11 October, 2014. The conference was organized by the University of Engineering and Technology, Vietnam National University, Hanoi. Besides the main track of contributed papers, this proceedings  feature the results of four special sessions focusing on specific topics of interest and three invited keynote speeches. The book gathers a total of 51 carefully reviewed papers describing recent advances and development on various topics including knowledge discovery and data mining, natural language processing, expert systems, intelligent decision making, computational biology, computational modeling, optimization algorithms, and industrial applications.

  4. 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

  5. International Conference on Power Electronics and Renewable Energy Systems

    CERN Document Server

    Suresh, L; Dash, Subhransu; Panigrahi, Bijaya

    2015-01-01

    The book is a collection of high-quality peer-reviewed research papers presented in Proceedings of International Conference on Power Electronics and Renewable Energy Systems (ICPERES 2014) held at Rajalakshmi Engineering College, Chennai, India. These research papers provide the latest developments in the broad area of Power Electronics and Renewable Energy. The book discusses wide variety of industrial, engineering and scientific applications of the emerging techniques. It presents invited papers from the inventors/originators of new applications and advanced technologies.

  6. 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

  7. 11th International Conference on Dependability and Complex Systems

    CERN Document Server

    Mazurkiewicz, Jacek; Sugier, Jarosław; Walkowiak, Tomasz; Kacprzyk, Janusz

    2016-01-01

    These proceedings present the results of the Eleventh International Conference on Dependability and Complex Systems DepCoS-RELCOMEX which took place in a picturesque Brunów Palace in Poland from 27th June to 1st July, 2016. DepCoS-RELCOMEX is a series of international conferences organized annually by Department of Computer Engineering of Wrocław University of Science and Technology since 2006. The roots of the series go as far back as to the seventies of the previous century – the first RELCOMEX conference took place in 1977 – and now its main aim is to promote a multi-disciplinary approach to dependability problems in theory and engineering practice of complex systems. Complex systems, nowadays most often computer-based and distributed, are built upon a variety of technical, information, software and human resources. The challenges in their design, analysis and maintenance not only originate from the involved technical and organizational structures but also from the complexity of the information proce...

  8. International Conference on Dynamical Systems : Theory and Applications

    CERN Document Server

    2016-01-01

    The book is a collection of contributions devoted to analytical, numerical and experimental techniques of dynamical systems, presented at the international conference "Dynamical Systems: Theory and Applications," held in Lódz, Poland on December 7-10, 2015. The studies give deep insight into new perspectives in analysis, simulation, and optimization of dynamical systems, emphasizing directions for future research. Broadly outlined topics covered include: bifurcation and chaos in dynamical systems, asymptotic methods in nonlinear dynamics, dynamics in life sciences and bioengineering, original numerical methods of vibration analysis, control in dynamical systems, stability of dynamical systems, vibrations of lumped and continuous sytems, non-smooth systems, engineering systems and differential equations, mathematical approaches to dynamical systems, and mechatronics.

  9. International Conference on Dynamical Systems : Theory and Applications

    CERN Document Server

    2016-01-01

    The book is the second volume of a collection of contributions devoted to analytical, numerical and experimental techniques of dynamical systems, presented at the international conference "Dynamical Systems: Theory and Applications," held in Lódz, Poland on December 7-10, 2015. The studies give deep insight into new perspectives in analysis, simulation, and optimization of dynamical systems, emphasizing directions for future research. Broadly outlined topics covered include: bifurcation and chaos in dynamical systems, asymptotic methods in nonlinear dynamics, dynamics in life sciences and bioengineering, original numerical methods of vibration analysis, control in dynamical systems, stability of dynamical systems, vibrations of lumped and continuous sytems, non-smooth systems, engineering systems and differential equations, mathematical approaches to dynamical systems, and mechatronics.

  10. Conference on development of JAEA knowledge management system, 2010 (Conference report)

    International Nuclear Information System (INIS)

    Notoya, Shin; Sasao, Eiji; Ota, Kunio; Shimizu, Kazuhiko

    2010-11-01

    In March 2010, a prototype of 'next generation' knowledge management system (KMS) and a status report (CoolRep H22) synthesizing the R and D results were made publicly available at the conclusion of R and D for the first Midterm Plan (October 2005 to March 2010). This could further reinforce the technical knowledge base and thus enable the geological disposal project and associated safety regulations to be continuously supported. The conference on 'Development of JAEA Knowledge Management System, 2010' was held on 16th June 2010 in Tokyo, with main aims of introducing KMS and CoolRep H22 to broader stakeholders, promoting their understanding and having input from them for further improvement of KMS. This report compiles presentation materials and comments to KMS and CoolRep H22 from participants. (author)

  11. 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...

  12. 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.)

  13. 15th International conference on Hybrid Intelligent Systems

    CERN Document Server

    Han, Sang; Al-Sharhan, Salah; Liu, Hongbo

    2016-01-01

    This book is devoted to the hybridization of intelligent systems which is a promising research field of modern computational intelligence concerned with the development of the next generation of intelligent systems. This Volume contains the papers presented in the Fifteenth International conference on Hybrid Intelligent Systems (HIS 2015) held in Seoul, South Korea during November 16-18, 2015. The 26 papers presented in this Volume were carefully reviewed and selected from 90 paper submissions. The Volume will be a valuable reference to researchers, students and practitioners in the computational intelligence field.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 2016 World Conference on Information Systems and Technologies

    CERN Document Server

    Correia, Ana; Adeli, Hojjat; Reis, Luis; Teixeira, Marcelo

    2016-01-01

    This book contains a selection of articles from The 2016 World Conference on Information Systems and Technologies (WorldCIST'16), held between the 22nd and 24th of March at Recife, Pernambuco, Brazil. WorldCIST is a global forum for researchers and practitioners to present and discuss recent results and innovations, current trends, professional experiences and challenges of modern Information Systems and Technologies research, together with their technological development and applications. The main topics covered are: Information and Knowledge Management; Organizational Models and Information Systems; Software and Systems Modeling; Software Systems, Architectures, Applications and Tools; Multimedia Systems and Applications; Computer Networks, Mobility and Pervasive Systems; Intelligent and Decision Support Systems; Big Data Analytics and Applications; Human-Computer Interaction; Health Informatics; Information Technologies in Education; Information Technologies in Radiocommunications.

  19. 2017 World Conference on Information Systems and Technologies

    CERN Document Server

    Correia, Ana; Adeli, Hojjat; Reis, Luís; Costanzo, Sandra

    2017-01-01

    This book presents a selection of papers from the 2017 World Conference on Information Systems and Technologies (WorldCIST'17), held between the 11st and 13th of April 2017 at Porto Santo Island, Madeira, Portugal. WorldCIST is a global forum for researchers and practitioners to present and discuss recent results and innovations, current trends, professional experiences and challenges involved in modern Information Systems and Technologies research, together with technological developments and applications. The main topics covered are: Information and Knowledge Management; Organizational Models and Information Systems; Software and Systems Modeling; Software Systems, Architectures, Applications and Tools; Multimedia Systems and Applications; Computer Networks, Mobility and Pervasive Systems; Intelligent and Decision Support Systems; Big Data Analytics and Applications; Human–Computer Interaction; Ethics, Computers & Security; Health Informatics; Information Technologies in Education; and Information Tec...

  20. FOREWORD: 9th International Conference on Compressors and their Systems

    Science.gov (United States)

    Kovacevic, Ahmed, Prof

    2015-08-01

    The 9th International Conference on Compressors and their Systems will be held in London from 5th - 9th September 2015, and as its Chairman, it is my pleasure to welcome you. This series of conferences started in 1999 organised by the Fluid Machinery Group of the Institution of Mechanical Engineers (IMechE) but since 2009 it has been managed by City University London in conjunction with the IMechE and the Institute of Refrigeration, both of which have been very proactive in promoting it. The Organising committee is grateful for their support and continued encouragement. This year, after rigorous reviewing, we have accepted over 80 technical papers for publication, the highest number in the conference history. On behalf of the organising committee I would like to thank the reviewers for their hard work and assistance. In addition to the main technical sessions, this year we have introduced a third day, specifically for Industry, to consider technology, business and market drivers on compressor developments. The traditional series of the short courses is this year continuing prior to the main event with the second short course/forum on Computational Fluid Dynamics in rotating positive displacement machines. I would like to extend my special thanks to our main sponsors, Holroyd PTG, Howden and Kapp Niels for their continuing support for the conference. With their generous contributions we have managed to keep the conference fees at the same level as in 2013, despite extending it to 3 days and holding it outside the University this year. The welcome reception on Sunday 6th September 2015 is dedicated to the celebration of the 20th anniversary of the Centre for Positive Displacement Compressors Technology which was formed at City University in 1995 with support from the Royal Academy of Engineering and Holroyd; its main aim being to assist British manufacturers of screw compressors. The Centre has since made a significant impact on the screw compressor world, far beyond

  1. 2015 World Conference on Information Systems and Technologies

    CERN Document Server

    Correia, Ana; Costanzo, Sandor; Reis, Luis

    2015-01-01

    This book contains a selection of articles from The 2015 World Conference on Information Systems and Technologies (WorldCIST'15), held between the 1st and 3rd of April in Funchal, Madeira, Portugal, a global forum for researchers and practitioners to present and discuss recent results and innovations, current trends, professional experiences and challenges of modern Information Systems and Technologies research, technological development and applications. The main topics covered are: Information and Knowledge Management; Organizational Models and Information Systems; Intelligent and Decision Support Systems; Big Data Analytics and Applications; Software Systems, Architectures, Applications and Tools; Multimedia Systems and Applications; Computer Networks, Mobility and Pervasive Systems; Human-Computer Interaction; Health Informatics; Information Technologies in Education; Information Technologies in Radiocommunications.

  2. 1st International Conference on Signal, Networks, Computing, and Systems

    CERN Document Server

    Mohapatra, Durga; Nagar, Atulya; Sahoo, Manmath

    2016-01-01

    The book is a collection of high-quality peer-reviewed research papers presented in the first International Conference on Signal, Networks, Computing, and Systems (ICSNCS 2016) held at Jawaharlal Nehru University, New Delhi, India during February 25–27, 2016. The book is organized in to two volumes and primarily focuses on theory and applications in the broad areas of communication technology, computer science and information security. The book aims to bring together the latest scientific research works of academic scientists, professors, research scholars and students in the areas of signal, networks, computing and systems detailing the practical challenges encountered and the solutions adopted.

  3. Fifth International Conference on the Dynamics of Information Systems

    CERN Document Server

    Walteros, Jose; Pardalos, Panos

    2014-01-01

    The contributions of this volume stem from the “Fifth International Conference on the Dynamics of Information Systems” held in Gainesville, FL in February 2013, and discuss state-of the-art  techniques in handling problems and solutions in the broad field of information systems. Dynamics of Information Systems: Computational and Mathematical Challenges presents diverse aspects of modern information systems with an emphasis on interconnected network systems and related topics, such as signal and message reconstruction, network connectivity, stochastic network analysis, cyber and computer security, community and cohesive structures in complex networks. Information systems are a vital part of modern societies. They are essential to our daily actions, including social networking, business and bank transactions, as well as sensor communications. The rapid increase in these capabilities has enabled us with more powerful systems, readily available to sense, control, disperse, and analyze information.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 15th IEEE International Conference on Intelligent Engineering Systems

    CERN Document Server

    Živčák, Jozef; Aspects of Computational Intelligence Theory and Applications

    2013-01-01

    This volume covers the state-of-the art of the research and development in various aspects of computational intelligence and gives some perspective directions of development. Except the traditional engineering areas that contain theoretical knowledge, applications, designs and projects, the book includes the area of use of computational intelligence in biomedical engineering. „Aspects of Computational Intelligence: Theory and Applications” is a compilation of carefully selected extended papers written on the basis of original contributions presented at the 15th IEEE International Conference on Intelligent Engineering Systems 2011, INES 2011 held at June 23.-26. 2011 in AquaCity Poprad, Slovakia.    

  9. 9th Asian Conference on Intelligent Information and Database Systems

    CERN Document Server

    Nguyen, Ngoc; Shirai, Kiyoaki

    2017-01-01

    This book presents recent research in intelligent information and database systems. The carefully selected contributions were initially accepted for presentation as posters at the 9th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2017) held from to 5 April 2017 in Kanazawa, Japan. While the contributions are of an advanced scientific level, several are accessible for non-expert readers. The book brings together 47 chapters divided into six main parts: • Part I. From Machine Learning to Data Mining. • Part II. Big Data and Collaborative Decision Support Systems, • Part III. Computer Vision Analysis, Detection, Tracking and Recognition, • Part IV. Data-Intensive Text Processing, • Part V. Innovations in Web and Internet Technologies, and • Part VI. New Methods and Applications in Information and Software Engineering. The book is an excellent resource for researchers and those working in algorithmics, artificial and computational intelligence, collaborative systems, decisio...

  10. 8th Asian Conference on Intelligent Information and Database Systems

    CERN Document Server

    Madeyski, Lech; Nguyen, Ngoc

    2016-01-01

    The objective of this book is to contribute to the development of the intelligent information and database systems with the essentials of current knowledge, experience and know-how. The book contains a selection of 40 chapters based on original research presented as posters during the 8th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2016) held on 14–16 March 2016 in Da Nang, Vietnam. The papers to some extent reflect the achievements of scientific teams from 17 countries in five continents. The volume is divided into six parts: (a) Computational Intelligence in Data Mining and Machine Learning, (b) Ontologies, Social Networks and Recommendation Systems, (c) Web Services, Cloud Computing, Security and Intelligent Internet Systems, (d) Knowledge Management and Language Processing, (e) Image, Video, Motion Analysis and Recognition, and (f) Advanced Computing Applications and Technologies. The book is an excellent resource for researchers, those working in artificial intelligence, mu...

  11. Summary of third international executive conference on photovoltaic power systems

    Energy Technology Data Exchange (ETDEWEB)

    Gillett, W.

    2001-07-01

    towards sustainable buildings fully into account. A further aim was to share the lessons learned from recent market experience on the full range of additional values that arise from the use of photovoltaic power systems and how those values impact on customer choice. Further, the promotion of international co-operation between the private and public sectors on policies for the removal of key constraints and for the promotion, financing and implementation of solar photovoltaic electricity projects was discussed. The conference was expected to achieve the following outcomes: Stronger relationships and networks between the participants and, through them, also between the sectors represented; A better definition of the added values of PV which influence customer choice; Recommendations which can be implemented by each of the business sectors represented at the conference for the orderly future development of the most important future PV markets; Recommendations to the IEA for ways in which it could enhance collaboration with both governments and industry, using its unique position to assist the future development of PV markets.

  12. 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....

  13. 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 ...

  14. 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.

  15. 14th International Conference on Intelligent Autonomous Systems

    CERN Document Server

    Hosoda, Koh; Menegatti, Emanuele; Shimizu, Masahiro; Wang, Hesheng

    2017-01-01

    This book describes the latest research advances, innovations, and visions in the field of robotics as presented by leading researchers, engineers, and practitioners from around the world at the 14th International Conference on Intelligent Autonomous Systems (IAS-14), held in Shanghai, China in July 2016. The contributions amply demonstrate that robots, machines and systems are rapidly achieving intelligence and autonomy, attaining more and more capabilities such as mobility and manipulation, sensing and perception, reasoning, and decision-making. They cover a wide range of research results and applications, and particular attention is paid to the emerging role of autonomous robots and intelligent systems in industrial production, which reflects their maturity and robustness. The contributions were selected by means of a rigorous peer-review process and highlight many exciting and visionary ideas that will further galvanize the research community and spur novel research directions. The series of biennial IAS ...

  16. 10th International Conference on Dependability and Complex Systems

    CERN Document Server

    Mazurkiewicz, Jacek; Sugier, Jarosław; Walkowiak, Tomasz; Kacprzyk, Janusz

    2015-01-01

    Building upon a long tradition of scientifi c conferences dealing with problems of reliability in technical systems, in 2006 Department of Computer Engineering at Wrocław University of Technology established DepCoS-RELCOMEX series of events in order to promote a comprehensive approach to evaluation of system performability which is now commonly called dependability. Contemporary complex systems integrate variety of technical, information, soft ware and human (users, administrators and management) resources. Their complexity comes not only from involved technical and organizational structures but mainly from complexity of information processes that must be implemented in specific operational environment (data processing, monitoring, management, etc.). In such a case traditional methods of reliability evaluation focused mainly on technical levels are insufficient and more innovative, multidisciplinary methods of dependability analysis must be applied. Selection of submissions for these proceedings exemplify di...

  17. 16th International Conference on Intelligent Systems Design and Applications

    CERN Document Server

    Abraham, Ajith; Gamboa, Dorabela; Novais, Paulo

    2017-01-01

    This book comprises selected papers from the 16th International Conference on Intelligent Systems Design and Applications (ISDA’16), which was held in Porto, Portugal from December 1 to16, 2016. ISDA 2016 was jointly organized by the Portugual-based Instituto Superior de Engenharia do Porto and the US-based Machine Intelligence Research Labs (MIR Labs) to serve as a forum for the dissemination of state-of-the-art research and development of intelligent systems, intelligent technologies, and applications. The papers included address a wide variety of themes ranging from theories to applications of intelligent systems and computational intelligence area and provide a valuable resource for students and researchers in academia and industry alike. .

  18. 36th national systems conference on recent advancements in system modelling applications

    CERN Document Server

    Krishnan, J

    2013-01-01

    The book is a collection of peer-reviewed scientific papers submitted by active researchers in the 36th National System Conference (NSC 2012). NSC is an annual event of the Systems Society of India (SSI), primarily oriented to strengthen the systems movement and its applications for the welfare of humanity. A galaxy of academicians, professionals, scientists, statesman and researchers from different parts of the country and abroad are invited to attend the Conference. The book presents various research articles in the area of system modelling in all disciplines of engineering sciences as well as socio-economic systems. The book can be used as a tool for further research.

  19. 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.

  20. 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.

  1. 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)

  2. 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.

  3. 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.

  4. 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

  5. Joint conference of iMEC 2015 (2nd International Manufacturing Engineering Conference & APCOMS 2015 (3rd Asia-Pacific Conference on Manufacturing Systems)

    Science.gov (United States)

    2016-02-01

    The iMEC 2015 is the second International Manufacturing Engineering Conference organized by the Faculty of Manufacturing, Universiti Malaysia Pahang (UMP), held from 12-14th November 2015 in Kuala Lumpur, Malaysia, with a theme "Materials, Manufacturing and Systems for Tomorrow". For the first time, iMEC is organized together with 3rd Asia- Pacific Conference on Manufacturing System (APCOMS 2015) which owned by Fakulti Teknologi Industri, Institut Teknologi Bandung (ITB), Indonesia. This is an extended collaboration between UMP and ITB to intensify knowledge sharing and experiences between higher learning institutions. This conference (iMEC & APCOMS 2015) is a platform for knowledge exchange and the growth of ideas, particularly in manufacturing engineering. The conference aims to bring researchers, academics, scientists, students, engineers and practitioners from around the world together to present their latest findings, ideas, developments and applications related to manufacturing engineering and other related research areas. With rapid advancements in manufacturing engineering, iMEC is an appropriate medium for the associated community to keep pace with the changes. In 2015, the conference theme is “Materials, Manufacturing and Systems for Tomorrow” which reflects the acceleration of knowledge and technology in global manufacturing. The papers in these proceedings are examples of the work presented at the conference. They represent the tip of the iceberg, as the conference attracted over 200 abstracts from Malaysia, Indonesia, Japan, United Kingdom, Australia, India, Bangladesh, South Africa, Turkey and Morocco and 151 full papers were accepted in these proceedings. The conference was run in four parallel sessions with 160 presenters sharing their latest finding in the areas of manufacturing process, systems, advanced materials and automation. The first keynote presentation was given by Prof. B. S. Murthy (IIT, Madras) on "Nanomaterials with Exceptional

  6. Joint conference of iMEC 2015 (2nd International Manufacturing Engineering Conference and APCOMS 2015 (3rd Asia-Pacific Conference on Manufacturing Systems)

    International Nuclear Information System (INIS)

    2016-01-01

    The iMEC 2015 is the second International Manufacturing Engineering Conference organized by the Faculty of Manufacturing, Universiti Malaysia Pahang (UMP), held from 12-14th November 2015 in Kuala Lumpur, Malaysia, with a theme 'Materials, Manufacturing and Systems for Tomorrow'. For the first time, iMEC is organized together with 3rd Asia- Pacific Conference on Manufacturing System (APCOMS 2015) which owned by Fakulti Teknologi Industri, Institut Teknologi Bandung (ITB), Indonesia. This is an extended collaboration between UMP and ITB to intensify knowledge sharing and experiences between higher learning institutions. This conference (iMEC and APCOMS 2015) is a platform for knowledge exchange and the growth of ideas, particularly in manufacturing engineering. The conference aims to bring researchers, academics, scientists, students, engineers and practitioners from around the world together to present their latest findings, ideas, developments and applications related to manufacturing engineering and other related research areas. With rapid advancements in manufacturing engineering, iMEC is an appropriate medium for the associated community to keep pace with the changes. In 2015, the conference theme is “Materials, Manufacturing and Systems for Tomorrow” which reflects the acceleration of knowledge and technology in global manufacturing. The papers in these proceedings are examples of the work presented at the conference. They represent the tip of the iceberg, as the conference attracted over 200 abstracts from Malaysia, Indonesia, Japan, United Kingdom, Australia, India, Bangladesh, South Africa, Turkey and Morocco and 151 full papers were accepted in these proceedings. The conference was run in four parallel sessions with 160 presenters sharing their latest finding in the areas of manufacturing process, systems, advanced materials and automation. The first keynote presentation was given by Prof. B. S. Murthy (IIT, Madras) on &apos

  7. 12th International Conference on Intelligent Autonomous Systems (IAS-12)

    CERN Document Server

    Yoon, Kwang-Joon; Lee, Jangmyung; Frontiers of Intelligent Autonomous Systems

    2013-01-01

    This carefully edited volume aims at providing readers with the most recent progress on intelligent autonomous systems, with its particular emphasis on intelligent autonomous ground, aerial and underwater vehicles as well as service robots for home and healthcare under the context of the aforementioned convergence. “Frontiers of Intelligent Autonomous Systems” includes thoroughly revised and extended papers selected from the 12th International Conference on Intelligent Autonomous Systems (IAS-12), held in Jeju, Korea, June 26-29, 2012. The editors chose 35 papers out of the 202 papers presented at IAS-12 which are organized into three chapters: Chapter 1 is dedicated to autonomous navigation and mobile manipulation, Chapter 2 to unmanned aerial and underwater vehicles and Chapter 3 to service robots for home and healthcare. To help the readers to easily access this volume, each chapter starts with a chapter summary introduced by one of the editors: Chapter 1 by Sukhan Lee, Chapter 2 by Kwang Joon Yoon and...

  8. SAI Intelligent Systems Conference (IntelliSys) 2015

    CERN Document Server

    Kapoor, Supriya; Bhatia, Rahul

    2016-01-01

    This book is a remarkable collection of chapters covering a wider range of topics, including unsupervised text mining, anomaly and Intrusion Detection, Self-reconfiguring Robotics, application of Fuzzy Logic to development aid, Design and Optimization, Context-Aware Reasoning, DNA Sequence Assembly and Multilayer Perceptron Networks. The twenty-one chapters present extended results from the SAI Intelligent Systems Conference (IntelliSys) 2015 and have been selected based on high recommendations during IntelliSys 2015 review process. This book presents innovative research and development carried out presently in fields of knowledge representation and reasoning, machine learning, and particularly in intelligent systems in a more broad sense. It provides state - of - the - art intelligent methods and techniques for solving real world problems along with a vision of the future research.

  9. 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.)

  10. 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

  11. Neural network retrievals of Karenia brevis harmful algal blooms in the West Florida Shelf (Conference Presentation)

    Science.gov (United States)

    Ahmed, Samir; El-Habashi, Ahmed

    2016-10-01

    Effective detection and tracking of Karenia brevis Harmful Algal Blooms (KB HAB) that frequently plague the coasts and beaches of the West Florida Shelf (WFS) is important because of their negative impacts on ecology. They pose threats to fisheries, human health, and directly affect tourism and local economies. Detection and tracking capabilities are needed for use with the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite, so that HABs monitoring capabilities, which previously relied on imagery from the Moderate Resolution Imaging Spectroradiometer Aqua, can be extended to VIIRS. Unfortunately, VIIRS, unlike its predecessor MODIS-A, does not have a 678 nm channel to detect chlorophyll fluorescence, which is used in the normalized fluorescence height (nFLH) algorithm, or in the Red Band Difference (RBD) algorithm. Both these techniques have demonstrated that the remote sensing reflectance signal from the MODIS-A fluorescence band (Rrs 678 nm) helps in effectively detecting and tracking KB HABs in the WFS. To overcome the lack of a fluorescence channel on VIIRS, the approach described here, bypasses the need for measurements at 678nm, and permits extension of KB HABs satellite monitoring to VIIRS. The essence of the approach is the application of a standard multiband neural network (NN) inversion algorithm, previously developed and reported by us, that takes VIIRS Rrs measurements at the 486, 551 and 671nm bands as inputs, and produces as output the related Inherent Optical Properties (IOPs), namely: absorption coefficients of phytoplankton (aph443) dissolved organic matter (ag) and non-algal particulates (adm) as well as the particulate backscatter coefficient, (bbp) all at 443nm. We next need to relate aph443 in the VIIRS NN retrieved image to equivalent KB HABs concentrations. To do this, we apply additional constraints, defined by (i) low backscatter manifested as a maximum Rrs551 value and (ii) a minimum [Chla] threshold (and hence an equivalent

  12. Selected papers from the 2nd IEEEE Nordic Circuits and Systems Conference (NorCAS), 2016

    DEFF Research Database (Denmark)

    Sparsø, Jens

    2018-01-01

    This special issue includes selected papers from the 2nd IEEEE Nordic Circuits and Systems Conference (NorCAS), held in Linköping, Sweden, October 24-25, 2016. The IEEE NorCAS conference is the main circuits and systems event of the Nordic and Baltic countries representing both academia and the e......This special issue includes selected papers from the 2nd IEEEE Nordic Circuits and Systems Conference (NorCAS), held in Linköping, Sweden, October 24-25, 2016. The IEEE NorCAS conference is the main circuits and systems event of the Nordic and Baltic countries representing both academia...

  13. Proceedings of the tenth International Scientific Conference on Control of Power Systems

    International Nuclear Information System (INIS)

    Janicek, F.; Eleschova, Z.

    2012-01-01

    The conference scope covers control of production, transmission and consumption of energy, design, diagnostics, implementation, economic and environmental impact of control and information systems used in power engineering.

  14. 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.

  15. Complex Systems Design & Management : Proceedings of the Third International Conference on Complex Systems Design & Management

    CERN Document Server

    Caseau, Yves; Krob, Daniel; Rauzy, Antoine

    2013-01-01

    This book contains all refereed papers that were accepted to the third edition of the « Complex Systems Design & Management » (CSD&M 2012) international conference that took place in Paris (France) from December 12-14, 2012. (Website: http://www.csdm2012.csdm.fr)  These proceedings cover the most recent trends in the emerging field of complex systems sciences & practices from an industrial and academic perspective, including the main industrial domains (transport, defense & security, electronics, energy & environment, e-services), scientific & technical topics (systems fundamentals, systems architecture& engineering, systems metrics & quality, systemic  tools) and system types (transportation systems, embedded systems, software & information systems, systems of systems, artificial ecosystems). The CSD&M 2012 conference is organized under the guidance of the CESAMES non-profit organization (http://www.cesames.net).

  16. 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.

  17. 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

  18. 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.

  19. 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.

  20. International Conference on Advances in Tribology and Engineering Systems

    CERN Document Server

    Deheri, Gunamani; Patel, Harshvadan; Mehta, Shreya

    2014-01-01

    This book contains advanced-level research material in the area of lubrication theory and related aspects, presented by eminent researchers during the International Conference on Advances in Tribology and Engineering Systems (ICATES 2013) held at Gujarat Technological University, Ahmedabad, India during October 15–17, 2013. The material in this book represents the advanced field of tribology and reflects the work of many eminent researchers from both India and abroad. The treatment of the presentations is the result of the contributions of several professionals working in the industry and academia. This book will be useful for students, researchers, academicians, and professionals working in the area of tribology, in general, and bearing performance characteristics, in particular, especially from the point-of-view of design. This book will also appeal to researchers and professionals working in fluid-film lubrication and other practical applications of tribology. A wide range of topics has been included des...

  1. ICENES '91:Sixth international conference on emerging nuclear energy systems

    International Nuclear Information System (INIS)

    1991-01-01

    This document contains the program and abstracts of the sessions at the Sixth International Conference on Emerging Nuclear Energy Systems held June 16--21, 1991 at Monterey, California. These sessions included: The plenary session, fission session, fission and nonelectric session, poster session 1P; (space propulsion, space nuclear power, electrostatic confined fusion, fusion miscellaneous, inertial confinement fusion, μ-catalyzed fusion, and cold fusion); Advanced fusion session, space nuclear session, poster session 2P, (nuclear reactions/data, isotope separation, direct energy conversion and exotic concepts, fusion-fission hybrids, nuclear desalting, accelerator waste-transmutation, and fusion-based chemical recycling); energy policy session, poster session 3P (energy policy, magnetic fusion reactors, fission reactors, magnetically insulated inertial fusion, and nuclear explosives for power generation); exotic energy storage and conversion session; and exotic energy storage and conversion; review and closing session

  2. Multiphysics Modelling and Simulation for Systems Design Conference

    CERN Document Server

    Abbes, Mohamed; Choley, Jean-Yves; Boukharouba, Taoufik; Elnady, Tamer; Kanaev, Andrei; Amar, Mounir; Chaari, Fakher

    2015-01-01

    This book reports on the state of the art in the field of multiphysics systems. It consists of accurately reviewed contributions to the MMSSD’2014 conference, which was held from December 17 to 19, 2004 in Hammamet, Tunisia. The different chapters, covering new theories, methods and a number of case studies, provide readers with an up-to-date picture of multiphysics modeling and simulation. They highlight the role played by high-performance computing and newly available software in promoting the study of multiphysics coupling effects, and show how these technologies can be practically implemented to bring about significant improvements in the field of design, control and monitoring of machines.  In addition to providing a detailed description of the methods and their applications, the book also identifies new research issues, challenges and opportunities, thus providing researchers and practitioners with both technical information to support their daily work and a new source of inspiration for their future...

  3. 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

  4. 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.

  5. 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.

  6. International Conference on Intelligent Robots and Systems - IROS 2011

    CERN Document Server

    Rosen, Jacob; Redundancy in Robot Manipulators and Multi-Robot Systems

    2013-01-01

    The trend in the evolution of robotic systems is that the number of degrees of freedom increases. This is visible both in robot manipulator design and in the shift of focus from single to multi-robot systems. Following the principles of evolution in nature, one may infer that adding degrees of freedom to robot systems design is beneficial. However, since nature did not select snake-like bodies for all creatures, it is reasonable to expect the presence of a certain selection pressure on the number of degrees of freedom. Thus, understanding costs and benefits of multiple degrees of freedom, especially those that create redundancy, is a fundamental problem in the field of robotics. This volume is mostly based on the works presented at the workshop on Redundancy in Robot Manipulators and Multi-Robot Systems at the IEEE/RSJ International Conference on Intelligent Robots and Systems - IROS 2011. The workshopwas envisioned as a dialog between researchers from two separate, but obviously relatedfields of robotics: on...

  7. 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.

  8. 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

  9. 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

  10. 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.

  11. 4th International Joint Conference on Computational Intelligence

    CERN Document Server

    Correia, António; Rosa, Agostinho; Filipe, Joaquim

    2015-01-01

    The present book includes extended and revised versions of a set of selected papers from the Fourth International Joint Conference on Computational Intelligence (IJCCI 2012)., held in Barcelona, Spain, from 5 to 7 October, 2012. The conference was sponsored by the Institute for Systems and Technologies of Information, Control and Communication (INSTICC) and was organized in cooperation with the Association for the Advancement of Artificial Intelligence (AAAI). The conference brought together researchers, engineers and practitioners in computational technologies, especially those related to the areas of fuzzy computation, evolutionary computation and neural computation. It is composed of three co-located conferences, each one specialized in one of the aforementioned -knowledge areas. Namely: - International Conference on Evolutionary Computation Theory and Applications (ECTA) - International Conference on Fuzzy Computation Theory and Applications (FCTA) - International Conference on Neural Computation Theory a...

  12. 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.

  13. Proceedings of the 13th Energy System and Economy Conference; Dai 13 kai energy system keizai conference koen ronbunshu

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1997-01-30

    The Energy System and Economy Conference was held in Tokyo on January 30 and 31, 1997, including 80 papers made public, keynote speeches having a theme of the international situation in the climate change issue and the Japanese condition, and panel discussions. In the special session on the global environmental problem, speeches were given on CO2 emission/reduction, CO2 recovery/fixation, environmental policy, comprehensive analyses, etc. In the electric power economy section, reports were made on effects of load characteristics of the power system on the effectiveness of power supply, the free electric power market and DSM (demand-side management), etc. As to the energy system, reported were a study on increasing efficiency of the hydrogen use regional heat supply system, samples of the hydrogen energy system for local core cities, etc. Relating to the energy technology, a paper on a proposal of solid phase heat engines using shape memory alloys and others were read. Papers were more added on urban and commercial/residential energy, natural energy, traffic, energy economy, etc.

  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. 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.

  18. 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.

  19. 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.

  20. 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

  1. Conference on the flexibilization of the electric power system

    International Nuclear Information System (INIS)

    Laure Kaelble; Lantrain, Aurore; Pienisch, Kerstin; Behrens, Uwe; Renaud, Arnaud; Bena, Michel; Levacher, Ralf; Broves, Antoine de; Langrock, Thomas; Bureau, Cedric

    2015-01-01

    The French-German office for Renewable energies (OFAEnR) organised a conference on the flexibilization of the electric power system in France and in Germany. In the framework of this French-German exchange of experience, about 100 participants discussed the different existing flexibility offers and shared information about the regulatory and economical context in both countries. This document brings together the available presentations (slides) made during this event: 1 - Flexibility in Germany: Status Quo and Perspectives (Laure Kaelble); 2 - Flexibility and French regulation (Aurore Lantrain); 3 - Virtual Power Plants: Contributions to flexibility? (Kerstin Pienisch); 4 - Wind power as player in the market for flexibility (Uwe Behrens); 5 - Energy storage potential in France - PEPS study for 2030 (Arnaud Renaud); 6 - More Flexibilities for TSOs to operate the electric System (Michel Bena); 7 - 'PolyenergyNet' project: how to flexibilize the low voltage grid by making it more autonomous (Ralf Levacher, in German); 8 - Promotion of industrial Demand Response through aggregation (Antoine de Broves); 9 - Using urban load: an economical model for companies? (Thomas Langrock); 10 - How to Involve All Consumers? ENGIe's Demand Side Management Offers (Cedric Bureau)

  2. 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...

  3. 5th CIRP international conference on industrial product-service systems

    CERN Document Server

    2013-01-01

    “An Industrial Product-Service System is characterized by the integrated and mutually  determined planning, development, provision and use of product and service shares including its immanent software components in Business-to-Business applications and represents a knowledge-intensive socio-technical system.” – Meier, Roy, Seliger (2010) Since the first conference in 2009, the CIRP International Conference on Industrial Product-Service Systems has become a well-established international forum for the review and discussion of advances, research results and industrial improvements. Researchers from all over the world have met at previous IPS² conferences in Cranfield (2009), Linköping (2010), Braunschweig (2011) and Tokyo (2012). In 2013, the 5th CIRP International Conference on Industrial Product-Service Systems is held in Bochum. Important topics of IPS² research presented at the conference are: planning and development, sustainability, business models, operation, service engineering, knowledge mana...

  4. 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.

  5. Neural network application to aircraft control system design

    Science.gov (United States)

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

    1991-01-01

    The feasibility of using artificial neural 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.

  6. 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.

  7. FISA 2009 - 7th European Commission conference on EURATOM research and training in reactor systems. Conference proceedings

    International Nuclear Information System (INIS)

    Goethem, G. van; Manolatos, P.; Hugon, M.; Bhatnagar, V.; Deffrennes, M.; Webster, S.

    2010-01-01

    The main achievements of the first series of projects under EURATOM FP-7 for nuclear research and training activities (2007 to 2011) were discussed. Approximately 500 participants were registered at FISA 2009 and at the 7 post-conference workshops, representing a wide audience of nuclear scientists and decision makers coming from 32 countries worldwide. The focus of the conference was on scientific and technological research in the following areas: nuclear plant life management for existing reactors (Generation II), severe accident management (Generation III), assessment of future nuclear fission systems (Generation IV), partitioning and transmutation systems (innovative fuels), access to large research infrastructures, and nuclear education and training. Special attention was devoted to the societal and industrial goals of GIF: sustainability, industrial competitiveness, safety and reliability, proliferation resistance. (orig.)

  8. Neural Network Based Intrusion Detection System for Critical Infrastructures

    Energy Technology Data Exchange (ETDEWEB)

    Todd Vollmer; Ondrej Linda; Milos Manic

    2009-07-01

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

  9. Neural Network Target Identification System for False Alarm Reduction

    Science.gov (United States)

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

    2009-01-01

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

  10. 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

  11. 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

  12. Learning about the Earth as a System. International Conference on Geoscience Education Conference Proceedings (2nd, Hilo, HI, July 28-31, 1997).

    Science.gov (United States)

    Fortner, Rosanne W., Ed.; Mayer, Victor J., Ed.

    Learning about the earth as a system was the focus of the 1997 International Conference on Geoscience Education. This proceedings contains details on the organization of the conference as well as five general sessions by various participants. The interactive poster sessions are organized according to three themes: (1) Earth Systems/Science…

  13. 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.

  14. Realization on the interactive remote video conference system based on multi-Agent

    Directory of Open Access Journals (Sweden)

    Zheng Yan

    2016-01-01

    Full Text Available To make people at different places participate in the same conference, speak and discuss freely, the interactive remote video conferencing system is designed and realized based on multi-Agent collaboration. FEC (forward error correction and tree P2P technology are firstly used to build a live conference structure to transfer audio and video data; then the branch conference port can participate to speak and discuss through the application of becoming a interactive focus; the introduction of multi-Agent collaboration technology improve the system robustness. The experiments showed that, under normal network conditions, the system can support 350 branch conference node simultaneously to make live broadcasting. The audio and video quality is smooth. It can carry out large-scale remote video conference.

  15. 77 FR 22312 - Geomagnetic Disturbances to the Bulk-Power System; Notice of Technical Conference

    Science.gov (United States)

    2012-04-13

    ...-Power System on Monday, April 30, 2012, from 11 a.m. to 4 p.m. This staff-led conference will be held in... webcast of the meeting/ conference is also available through www.ferc.gov . Anyone with Internet access...

  16. ENVIRONMENTAL PROBLEM SOLVING WITH GEOGRAPHIC INFORMATION SYSTEMS: 1994 AND 1999 CONFERENCE PROCEEDINGS

    Science.gov (United States)

    These two national conferences, held in Cincinnati, Ohio in 1994 and 1999, addressed the area of environmental problem solving with Geographic Information Systems. This CD-ROM is a compilation of the proceedings in PDF format. The emphasis of the conference presentations were on ...

  17. 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

  18. 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

  19. Foundations of Intelligent Systems : Proceedings of the Sixth International Conference on Intelligent Systems and Knowledge Engineering

    CERN Document Server

    Li, Tianrui

    2012-01-01

    Proceedings of The Sixth International Conference on Intelligent System and Knowledge Engineering presents selected papers from the conference ISKE 2011, held December 15-17 in Shanghai, China. This proceedings doesn’t only examine original research and approaches in the broad areas of intelligent systems and knowledge engineering, but also present new methodologies and practices in intelligent computing paradigms. The book introduces the current scientific and technical advances in the fields of artificial intelligence, machine learning, pattern recognition, data mining, information retrieval, knowledge-based systems, knowledge representation and reasoning, multi-agent systems, natural-language processing, etc. Furthermore, new computing methodologies are presented, including cloud computing, service computing and pervasive computing with traditional intelligent methods. The proceedings will be beneficial for both researchers and practitioners who want to utilize intelligent methods in their specific resea...

  20. Seventh International Conference on Intelligent Systems and Knowledge Engineering - Foundations and Applications of Intelligent Systems

    CERN Document Server

    Li, Tianrui; Li, Hongbo

    2014-01-01

    These proceedings present technical papers selected from the 2012 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2012), held on December 15-17 in Beijing. The aim of this conference is to bring together experts from different fields of expertise to discuss the state-of-the-art in Intelligent Systems and Knowledge Engineering, and to present new findings and perspectives on future developments. The proceedings introduce current scientific and technical advances in the fields of artificial intelligence, machine learning, pattern recognition, data mining, knowledge engineering, information retrieval, information theory, knowledge-based systems, knowledge representation and reasoning, multi-agent systems, and natural-language processing, etc. Furthermore they include papers on new intelligent computing paradigms, which combine new computing methodologies, e.g., cloud computing, service computing and pervasive computing with traditional intelligent methods. By presenting new method...

  1. 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.

  2. 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

  3. 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.

  4. 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

  5. PREFACE: International Conference on Strongly Correlated Electron Systems (SCES 2011)

    Science.gov (United States)

    Littlewood, P. B.; Lonzarich, G. G.; Saxena, S. S.; Sutherland, M. L.; Sebastian, S. E.; Artacho, E.; Grosche, F. M.; Hadzibabic, Z.

    2012-11-01

    The Strongly Correlated Electron Systems Conference (SCES) 2011, was held from 29 August-3 September 2011, in Cambridge, UK. SCES'2011 was dedicated to 100 years of superconductivity and covered a range of topics in the area of strongly correlated systems. The correlated electronic and magnetic materials featured include f-electron based heavy fermion intermetallics and d-electron based transition metal compounds. The meeting welcomed to Cambridge 657 participants from 23 countries, who presented 127 talks (including 16 plenary, 57 invited, and 54 contributed) and 736 posters in 40 sessions over five full days of meetings. This proceedings volume contains papers reporting on the science presented at the meeting. This work deepens our understanding of the rich physical phenomena that arise from correlation effects. Strongly correlated systems are known for their remarkable array of emergent phenomena: the traditional subjects of superconductivity, magnetism and metal-insulator transitions have been joined by non-Fermi liquid phenomena, topologically protected quantum states, atomic and photonic gases, and quantum phase transitions. These are some of the most challenging and interesting phenomena in science. As well as the science driver, there is underlying interest in energy-dense materials, which make use of 'small' electrons packed to the highest possible density. These are by definition 'strongly correlated'. For example: good photovoltaics must be efficient optical absorbers, which means that photons will generate tightly bound electron-hole pairs (excitons) that must then be ionised at a heterointerface and transported to contacts; efficient solid state refrigeration depends on substantial entropy changes in a unit cell, with large local electrical or magnetic moments; efficient lighting is in a real sense the inverse of photovoltaics; the limit of an efficient battery is a supercapacitor employing mixed valent ions; fuel cells and solar to fuel conversion

  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. SIAM conference on applications of dynamical systems. Abstracts and author index

    Energy Technology Data Exchange (ETDEWEB)

    1992-12-31

    A conference (Oct.15--19, 1992, Snowbird, Utah; sponsored by SIAM (Society for Industrial and Applied Mathematics) Activity Group on Dynamical Systems) was held that highlighted recent developments in applied dynamical systems. The main lectures and minisymposia covered theory about chaotic motion, applications in high energy physics and heart fibrillations, turbulent motion, Henon map and attractor, integrable problems in classical physics, pattern formation in chemical reactions, etc. The conference fostered an exchange between mathematicians working on theoretical issues of modern dynamical systems and applied scientists. This two-part document contains abstracts, conference program, and an author index.

  9. 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

  10. 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.

  11. 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.

  12. 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

  13. Proceedings of the 10. IASTED international conference on power and energy systems (PES 2008)

    Energy Technology Data Exchange (ETDEWEB)

    Fuchs, E. (ed.)

    2008-07-01

    This conference provided a forum to review new technologies and modelling strategies for power systems and to address issues related to artificial intelligence and power system design optimization. The role of renewable energy sources such as solar, wind and biomass energy in interconnected power systems were also reviewed. In addition, conference participants presented recent advances in distributed power generation; load shedding; fault diagnosis; energy storage; power system stability; and security of supply. The sessions of the conference were entitled: fault detection, diagnosis, protection and power quality; renewable, distributed, generation and power lines; power system analysis, operation and control; and electricity markets. The conference featured 43 presentations, of which 24 have been catalogued separately for inclusion in this database. refs., tabs. figs.

  14. Proceedings of the joint contractors meeting: FE/EE Advanced Turbine Systems conference FE fuel cells and coal-fired heat engines conference

    Energy Technology Data Exchange (ETDEWEB)

    Geiling, D.W. [ed.

    1993-08-01

    The joint contractors meeting: FE/EE Advanced Turbine Systems conference FEE fuel cells and coal-fired heat engines conference; was sponsored by the US Department of Energy Office of Fossil Energy and held at the Morgantown Energy Technology Center, P.O. Box 880, Morgantown, West Virginia 26507-0880, August 3--5, 1993. Individual papers have been entered separately.

  15. Proceedings of the 4. IASTED Asian conference on power and energy systems : AsiaPES 2008

    Energy Technology Data Exchange (ETDEWEB)

    Nor, K.M. [Technological Univ. of Malaysia, Kuala Lumpur (Malaysia)] (ed.)

    2008-07-01

    Recent technological innovations related to power systems were presented at this international energy and power systems conference. New technologies and modelling strategies for power systems were identified along with issues related to artificial intelligence and design optimization. The role of renewable energy sources such as solar, wind and biomass energy in interconnected power systems were also reviewed. The conference was divided into 9 sessions entitled: (1) distribution systems, (2) electromagnetic fields, (3) power quality, (4) power system operations, (5) power system planning, (6) power system protection, (7) power system stability, (8) renewable energy, and (9); thermal systems. All 68 presentations from the conference have been catalogued separately for inclusion in this database. refs., tabs., figs.

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

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2010-01-01

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

  17. Stability Analysis of Neural Networks-Based System Identification

    Directory of Open Access Journals (Sweden)

    Talel Korkobi

    2008-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2000-07-01

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

  19. 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....

  20. 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.

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

    International Nuclear Information System (INIS)

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

    1991-01-01

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

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

    Science.gov (United States)

    Lin, Chih-Min; Hsu, C F

    2003-01-01

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

  3. 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

  4. 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.

  5. 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.

  6. 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.

  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. 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

  9. 1st International Conference on Information and Communication Technology for Intelligent Systems

    CERN Document Server

    Das, Swagatam

    2017-01-01

    This volume contains 59 papers presented at ICTIS 2015: International Conference on Information and Communication Technology for Intelligent Systems. The conference was held during 28th and 29th November, 2015, Ahmedabad, India and organized communally by Venus International College of Technology, Association of Computer Machinery, Ahmedabad Chapter and Supported by Computer Society of India Division IV – Communication and Division V – Education and Research. This volume contains papers mainly focused on ICT for Computation, Algorithms and Data Analytics etc.

  10. Symptom based diagnostic system using artificial neural networks

    International Nuclear Information System (INIS)

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

    2003-01-01

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

  11. 1st Complex Systems Digital Campus World E-Conference 2015

    CERN Document Server

    Bourgine, Paul; Collet, Pierre

    2017-01-01

    This book contains the proceedings as well as invited papers for the first annual conference of the UNESCO Unitwin Complex System Digital Campus (CSDC), which is an international initiative gathering 120 Universities on four continents, and structured in ten E-Departments. First Complex Systems Digital Campus World E-Conference 2015 features chapters from the latest research results on theoretical questions of complex systems and their experimental domains. The content contained bridges the gap between the individual and the collective within complex systems science and new integrative sciences on topics such as: genes to organisms to ecosystems, atoms to materials to products, and digital media to the Internet. The conference breaks new ground through a dedicated video-conferencing system – a concept at the heart of the international UNESCO UniTwin, embracing scientists from low-income and distant countries. This book promotes an integrated system of research, education, and training. It also aims at contr...

  12. 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.

  13. 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....

  14. 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.

  15. 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

  16. Proceedings of AsiaPES 2007 : Asian power and energy systems conference

    Energy Technology Data Exchange (ETDEWEB)

    Ongsakul, W. [Asian Inst. of Technology, Pathumthani (Thailand)] (ed.)

    2007-07-01

    This energy and power systems conference provided a forum for international researchers and power industry members to discuss recent technological innovations related to power systems. New technologies and modelling strategies for power systems were identified along with issues related to artificial intelligence and design optimization. The role of renewable energy sources such as solar, wind and biomass energy in interconnected power systems were also reviewed. The conference was divided into 8 sessions entitled: (1) control, protection, power flow and design, (2) planning and operation, (3) alternative energy, (4) stability, reliability, forecasting and load shedding, (5) phasor measurement and power quality, (6) distribution, analysis, technology and policy (7) energy efficiency, storage and pricing, and (8) a special session on the application of phasor measurement units to monitor wide area power system dynamics. The conference featured 88 presentations, of which 63 have been catalogued separately for inclusion in this database. refs., tabs., figs.

  17. 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...

  18. 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...

  19. 2nd Biennial Conference on Refrigeration for Cryogenic Sensors and Electronic Systems

    CERN Document Server

    1983-01-01

    This proceedings documents the output of the Second Biennial Conference on Refrigeration for Cryogenic Sensors and Electronic Systems held at the National Aeronautics and Space Administration's Goddard Space Flight Center, Greenbelt, Maryland, on December 7-8, 1982. Building on the first open meeting hosted by the National Bureau of Standards in 1980, the focus of this second meeting was again on low-temperature, closed-cycle cooler technology. However, higher temperature coolers (77 K), with technology applicable to the low temperature coolers, were considered to be within the scope of this meeting. This second conference consisted of 30 papers presented by representatives of industry, government, and academia. The conference proceedings reproduced here was published by the NASA Goddard Space Flight Center in Greenbelt Maryland as NASA Conference Publication 2287.

  20. INTERCARTO CONFERENCES

    OpenAIRE

    Vladimir Tikunov

    2010-01-01

    The InterCarto conferences are thematically organized to target one of the most pressing problems of modern geography—creation and use of geographical information systems (GISs) as effective tools for achieving sustainable development of territories. Over the years, from 1994 to 2009, 1872 participants from 51 countries and 156 cities, who made 1494 reports, attended the conferences. There were 1508 participants from 49 regions of Russia making 1340 presentations. The conferences hosted 31 di...

  1. 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.

  2. AMA Conferences 2015. SENSOR 2015. 17th international conference on sensors and measurement technology. IRS2 2015. 14th international conference on infrared sensors and systems. Proceedings

    International Nuclear Information System (INIS)

    2015-01-01

    This meeting paper contains presentations of two conferences: SENSOR 2015 and IRS 2 (= International conference on InfraRed Sensors and systems). The first part of SENSOR 2015 contains the following chapters: (A) SENSOR PRINCIPLES: A.1: Mechanical sensors; A.2: Optical sensors; A.3: Ultrasonic sensors; A.4: Microacoustic sensors; A.5: Magnetic sensors; A.6: Impedance sensors; A.7: Gas sensors; A.8: Flow sensors; A.9: Dimensional measurement; A.10: Temperature and humidity sensors; A.11: Chemosensors; A.12: Biosensors; A.13: Embedded sensors; A.14: Sensor-actuator systems; (B) SENSOR TECHNOLOGY: B.1: Sensor design; B.2: Numerical simulation of sensors; B.3: Sensor materials; B.4: MEMS technology; B.5: Micro-Nano-Integration; B.6: Packaging; B.7: Materials; B.8: Thin films; B.9: Sensor production; B.10: Sensor reliability; B.11: Calibration and testing; B.12: Optical fibre sensors. (C) SENSOR ELECTRONICS AND COMMUNICATION: C.1: Sensor electronics; C.2: Sensor networks; C.3: Wireless sensors; C.4: Sensor communication; C.5: Energy harvesting; C.6: Measuring systems; C.7: Embedded systems; C.8: Self-monitoring and diagnosis; (D) APPLICATIONS: D.1: Medical measuring technology; D.2: Ambient assisted living; D.3: Process measuring technology; D.4: Automotive; D.5: Sensors in energy technology; D.6: Production technology; D.7: Security technology; D.8: Smart home; D.9: Household technology. The second part with the contributions of the IRS 2 2015 is structured as follows: (E) INFRARED SENSORS: E.1: Photon detectors; E.2: Thermal detectors; E.3: Cooled detectors; E.4: Uncooled detectors; E.5: Sensor modules; E.6: Sensor packaging. (G) INFRARED SYSTEMS AND APPLICATIONS: G.1: Thermal imaging; G.2: Pyrometry / contactless temperature measurement; G.3: Gas analysis; G.4: Spectroscopy; G.5: Motion control and presence detection; G.6: Security and safety monitoring; G.7: Non-destructive testing; F: INFRARED SYSTEM COMPONENTS: F.1: Infrared optics; F.2: Optical modulators; F.3

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2010-07-01

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

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. Mendel conference

    CERN Document Server

    2015-01-01

    This book is a collection of selected accepted papers of Mendel conference that has been held in Brno, Czech Republic in June 2015. The book contents three chapters which represent recent advances in soft computing including intelligent image processing and bio-inspired robotics.: Chapter 1: Evolutionary Computing, and Swarm intelligence, Chapter 2: Neural Networks, Self-organization, and Machine Learning, and Chapter3: Intelligent Image Processing, and Bio-inspired Robotics. The Mendel conference was established in 1995, and it carries the name of the scientist and Augustinian priest Gregor J. Mendel who discovered the famous Laws of Heredity. In 2015 we are commemorating 150 years since Mendel's lectures, which he presented in Brno on February and March 1865. The main aim of the conference was to create a periodical possibility for students, academics and researchers to exchange their ideas and novel research methods.  .

  11. 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

  12. 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

  13. International Conference on Instrumentation, Measurement, Circuits and Systems (ICIMCS 2011)

    CERN Document Server

    Instrumentation, Measurement, Circuits and Systems

    2012-01-01

    The volume includes a set of selected papers extended and revised from the 2011 International Conference on Mechanical Engineering and Technology, held on London, UK, November 24-25, 2011.   Mechanical engineering technology is the application of physical principles and current technological developments to the creation of useful machinery and operation design. Technologies such as solid models may be used as the basis for finite element analysis (FEA) and / or computational fluid dynamics (CFD) of the design. Through the application of computer-aided manufacturing (CAM), the models may also be used directly by software to create "instructions" for the manufacture of objects represented by the models, through computer numerically controlled (CNC) machining or other automated processes, without the need for intermediate drawings.   This volume covers the subject areas of mechanical engineering and technology, and also covers interdisciplinary subject areas of computers, communications, control and automation...

  14. 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.

  15. 77 FR 69899 - Public Conference on Geographic Information Systems (GIS) in Transportation Safety

    Science.gov (United States)

    2012-11-21

    ... NATIONAL TRANSPORTATION SAFETY BOARD Public Conference on Geographic Information Systems (GIS) in... Geographic Information Systems (GIS) in transportation safety on December 4-5, 2012. GIS is a rapidly... visualization of data. The meeting will bring researchers and practitioners in transportation safety and GIS...

  16. 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.

  17. 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.

  18. 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

  19. 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)

  20. 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

  1. 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)

  2. 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...

  3. 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

  4. 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

  5. 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)

  6. 13th International conference on environmental degradation of materials in nuclear power systems

    International Nuclear Information System (INIS)

    2007-01-01

    The 13th International Conference on Environmental Degradation of Materials in Nuclear Power Systems was held on August 19-23, 2007 in Whistler, British Columbia, Canada. More of a scientific meeting than a convention, this conference series is the premier nuclear industry corrosion meeting where the 225 registrations consisted of world experts of the field from utilities, engineering and service organizations, manufacturers, research establishments and universities gathered to listen to 144 technical papers on new work and to explore new insights into corrosion mechanisms in the many water cooled systems in nuclear power plants. Over 225 delegates attended the conference, over 144 technical papers were presented in the following sessions: IASCC; Waste; PWR Secondary; Ni-Base Welds; Operating Experience; Low Alloy Steels; Alloy 800 Steam Generator Tubing; Zirconium Alloys; Crack Growth; SCWR; PWR Primary; BWR SCC; Irradiation Effects; Flow Accelerated Corrosion; and, Nobel Metal

  7. 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)

  8. 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)

  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. 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...

  11. 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...

  12. 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.

  13. 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.

  14. 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.

  15. 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...

  16. 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.

  17. 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.

  18. International Conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems

    CERN Document Server

    Vijayakumar, K; Panigrahi, Bijaya; Das, Swagatam

    2017-01-01

    The volume is a collection of high-quality peer-reviewed research papers presented in the International Conference on Artificial Intelligence and Evolutionary Computation in Engineering Systems (ICAIECES 2016) held at SRM University, Chennai, Tamilnadu, India. This conference is an international forum for industry professionals and researchers to deliberate and state their research findings, discuss the latest advancements and explore the future directions in the emerging areas of engineering and technology. The book presents original work and novel ideas, information, techniques and applications in the field of communication, computing and power technologies.

  19. International Conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems

    CERN Document Server

    Bhaskar, M; Panigrahi, Bijaya; Das, Swagatam

    2016-01-01

    The book is a collection of high-quality peer-reviewed research papers presented in the first International Conference on International Conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems (ICAIECES -2015) held at Velammal Engineering College (VEC), Chennai, India during 22 – 23 April 2015. The book discusses wide variety of industrial, engineering and scientific applications of the emerging techniques. Researchers from academic and industry present their original work and exchange ideas, information, techniques and applications in the field of Communication, Computing and Power Technologies.

  20. MAGLEV `95. 14th international conference on magnetically levitated systems. Proceedings

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1996-12-31

    The proceedings present the papers of the 14th International Conference on Magnetically Levitated Systems held in Bremen, FRG, in November 1995. For 70 of 74 papers a separate subject analysis has been carried out. 4 papers are not available in the proceedings. (HW)

  1. 6th Annual Earth System Grid Federation Face to Face Conference Report

    Energy Technology Data Exchange (ETDEWEB)

    Williams, D. N. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2017-03-06

    The Sixth Annual Face-to-Face (F2F) Conference of the Earth System Grid Federation (ESGF), a global consortium of international government agencies, institutions, and companies dedicated to the creation, management, analysis, and distribution of extreme-scale scientific data, was held December 5–9, 2016, in Washington, D.C.

  2. Review of Lean Construction Conference Proceedings and Relationship to the Toyota Production System Framework

    Science.gov (United States)

    Jacobs, Gideon Francois

    2010-01-01

    The objective of this study was to align the International Group of Lean Construction (IGLC) conference proceedings against the Toyota Production System (TPS) to determine how well research themes in construction studies align with the TPS framework. Factories around the world that have implemented the TPS framework have experienced impressive…

  3. 77 FR 24950 - Midwest Independent Transmission, System Operator, Inc.; Supplemental Notice of Technical Conference

    Science.gov (United States)

    2012-04-26

    ... Conference. The Midwest Independent Transmission System Operator, Inc. (MISO) and/or Potomac Economics, Inc... that is less than 50 percent of the applicable Reference Level. Provide a justification for this... committed? Please explain. b. Can units committed based on economics in the SCUC and SCED processes be...

  4. Practical Action Programs in Education: Highlights of the Third National Conference on General Systems Education.

    Science.gov (United States)

    Southern Connecticut State Coll., New Haven. Center for Interdisciplinary Creativity.

    In this collection of papers Harold G. Cassidy outlines the conceptual framework for the conference which is based on a systems approach to development of practical action programs in education. A basic model is presented as a basis for shifting from the post-crisis to the pre-crisis approach to curriculum development and educational…

  5. 75 FR 52528 - Mandatory Reliability Standards for the Bulk-Power System; Notice of Technical Conference

    Science.gov (United States)

    2010-08-26

    ...] Mandatory Reliability Standards for the Bulk-Power System; Notice of Technical Conference August 19, 2010... Order No. 693 the Commission approved Reliability Standard BAL-003-0 as mandatory and enforceable and directed the ERO to develop a modification to BAL-003-0 through the Reliability Standards development...

  6. 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.

  7. International Conference on Emerging Technologies for Information Systems, Computing, and Management

    CERN Document Server

    Ma, Tinghuai; Emerging Technologies for Information Systems, Computing, and Management

    2013-01-01

    This book aims to examine innovation in the fields of information technology, software engineering, industrial engineering, management engineering. Topics covered in this publication include; Information System Security, Privacy, Quality Assurance, High-Performance Computing and Information System Management and Integration. The book presents papers from The Second International Conference for Emerging Technologies Information Systems, Computing, and Management (ICM2012) which was held on December 1 to 2, 2012 in Hangzhou, China.

  8. 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...

  9. 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

  10. 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

  11. INTERCARTO CONFERENCES

    Directory of Open Access Journals (Sweden)

    Vladimir Tikunov

    2010-01-01

    Full Text Available The InterCarto conferences are thematically organized to target one of the most pressing problems of modern geography—creation and use of geographical information systems (GISs as effective tools for achieving sustainable development of territories. Over the years, from 1994 to 2009, 1872 participants from 51 countries and 156 cities, who made 1494 reports, attended the conferences. There were 1508 participants from 49 regions of Russia making 1340 presentations. The conferences hosted 31 different sections, most popular of which were Environmental GIS-Projects: Development and Experience, Sustainable Development and Innovative Projects, GIS: the Theory and Methodology, Projects for Russia and Regions, and GIS-Technologies and Digital Mapping. The next annual InterCarto-InterGIS conference will take place in December 2011. The Russian component of the conference will be held in the Altay Kray followed by another meeting on Bali, Indonesia

  12. 2005 8th Annual Systems Engineering Conference. Volume 1, Tuesday

    Science.gov (United States)

    2005-10-27

    systems engineering – Based on ISO 12207 – software engineering – Measure using best practices of CMMI® • Benefits – Facilitates sharing of tools... 12207 Software Life-Cycle Processes IEEE SSC-C Software Engineering Process SECNAV 5000.2C DoD Architecture Framework ISO 9001:2000 Quality Systems GIG...Comparisons and Contrasts Between ISO 14001, OHSAS 18001, and MIL-STD-882D and their Suitability for the Systems Engineering Process Mr. Kenneth Dormer

  13. 20th Annual Systems Engineering Conference. Volume 3, Thursday

    Science.gov (United States)

    2017-10-26

    Program (HPCMP) The DoD HPCMP CREATE Program is a Tri-Service Program launched in 2006 by OSD and the HPCMP to develop and deploy eleven physics -based...best practices, System of Systems guide and capstone marketplace to development of technical leaders. ENTERPRISE HEALTH MANAGEMENT/PROGNOSTICS...DIAGNOSTICS/RELIABILTY Track Chairs: Chris Resig, The Boeing Company The health of the system as a whole – the enterprise – is a critical function of

  14. 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

  15. International Conference on Effective Nuclear Regulatory Systems: Sustaining Improvements Globally. Book of Abstracts

    International Nuclear Information System (INIS)

    2016-01-01

    The objective of this conference is to review and assess ways of further improving the effectiveness of regulatory systems for nuclear facilities and activities for both nuclear safety and nuclear security. The action items in the summary presented by the President of the conference held in 2013 in Ottawa, the lessons of the Fukushima Daiichi accident, the discussions at other international conferences and at international experts’ meetings conducted within the framework of the IAEA Action Plan on Nuclear Safety, as well as the CNS and the principles outlined in the Vienna Declaration on Nuclear Safety, will continue to have a significant impact on regulatory systems. All the aforementioned need to be taken into account to sustain improvements to regulatory systems. The expected outcomes of the conference are: - Enhanced safety and security of nuclear installations worldwide; - Challenges in regulating radiation sources and radioactive waste addressed; - Enhanced international cooperation for sustaining regulatory effectiveness; - Strengthened and sustained regulatory competence for nuclear safety and security; and - Strategies and actions for the future identified, as well as issues for consideration by governments, regulatory bodies and international organizations.

  16. 13th Annual Systems Engineering Conference: Tues- Wed

    Science.gov (United States)

    2010-10-28

    afknprod/ASPs/Reg/ GroupAdmin.asp?Filter=23843&EventID=1464 7 &GroupID=19841 No-Host Social, 1700-1900 Hamlet Restaurant I n t e g r i t y - S e r v i c e...Engineering Relationship Between SE and other Disciplines Systems People • Behaviours (The Head) – Understanding system concepts and viewpoints...level, and used/ consumed by SEs at both the SoS and single system level in the process of developing, maintaining, enhancing, deploying, and assessing

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

    Science.gov (United States)

    Kuljaca, Ognjen

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

  18. 6th Conference on Design and Modeling of Mechanical Systems

    CERN Document Server

    Fakhfakh, Tahar; Daly, Hachmi; Aifaoui, Nizar; Chaari, Fakher

    2015-01-01

    This book offers a collection of original peer-reviewed contributions presented at the 6th International Congress on Design and Modeling of Mechanical Systems (CMSM’2015), held in Hammamet, Tunisia, from the 23rd to the 25th of March 2015. It reports on both recent research findings and innovative industrial applications in the fields of mechatronics and robotics, dynamics of mechanical systems, fluid structure interaction and vibroacoustics, modeling and analysis of materials and structures, and design and manufacturing of mechanical systems. Since its first edition in 2005, the CMSM Congress has been held every two years with the aim of bringing together specialists from universities and industry to present the state-of-the-art in research and applications, discuss the most recent findings and exchange and develop expertise in the field of design and modeling of mechanical systems. The CMSM Congress is jointly organized by three Tunisian research laboratories: the Mechanical Engineering Laboratory of th...

  19. Autopolyreactivity Confers a Holistic Role in the Immune System.

    Science.gov (United States)

    Avrameas, S

    2016-04-01

    In this review, we summarize and discuss some key findings from the study of naturally occurring autoantibodies. The B-cell compartment of the immune system appears to recognize almost all endogenous and environmental antigens. This ability is accomplished principally through autopolyreactive humoral and cellular immune receptors. This extended autopolyreactivity (1) along immunoglobulin gene recombination contributes to the immune system's ability to recognize a very large number of self and non-self constituents; and (2) generates a vast immune network that creates communication channels between the organism's interior and exterior. Thus, the immune system continuously evolves depending on the internal and external stimuli it encounters. Furthermore, this far-reaching network's existence implies activities resembling those of classical biological factors or activities that modulate the function of other classical biological factors. A few such antibodies have already been found. Another important concept is that natural autoantibodies are highly dependent on the presence or absence of commensal microbes in the organism. These results are in line with past and recent findings showing the fundamental influence of the microbiota on proper immune system development, and necessitate the existence of a host-microbe homeostasis. This homeostasis requires that the participating humoral and cellular receptors are able to recognize self-antigens and commensal microbes without damaging them. Autopolyreactive immune receptors expressing low affinity for both types of antigens fulfil this role. The immune system appears to play a holistic role similar to that of the nervous system. © 2016 The Foundation for the Scandinavian Journal of Immunology.

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

    Science.gov (United States)

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

    1996-08-01

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

  1. 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.

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

    Science.gov (United States)

    Mitani, S; Okamoto, H

    1991-05-01

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

  3. 2012 Proceedings of the International Conference on Communications, Signal Processing, and Systems

    CERN Document Server

    Wang, Wei; Mu, Jiasong; Liang, Jing; Zhang, Baoju; Pi, Yiming; Zhao, Chenglin

    2012-01-01

    Communications, Signal Processing, and Systems is a collection of contributions coming out of the International Conference on Communications, Signal Processing, and Systems (CSPS) held October 2012. This book provides the state-of-art developments of Communications, Signal Processing, and Systems, and their interactions in multidisciplinary fields, such as Smart Grid. The book also examines Radar Systems, Sensor Networks, Radar Signal Processing, Design and Implementation of Signal Processing Systems and Applications. Written by experts and students in the fields of Communications, Signal Processing, and Systems.

  4. Book of abstracts: International Conference on Smart Energy Systems and 4th Generation District Heating

    DEFF Research Database (Denmark)

    , which takes a sole focus on the electricity sector, the smart energy systems approach includes the entire energy system in its identification of suitable energy infrastructure designs and operation strategies. Focusing solely on the smart electricity grid often leads to the definition of transmission......It is a great pleasure to welcome you to the first International Conference on Smart Energy Systems and 4th Generation District Heating at Aalborg University, Copenhagen Campus on 25-26 August 2015. The conference is organised by the 4DH Strategic Research Centre in collaboration with Aalborg...... University and offers more than 70 presentations in 3 parallel sessions with more than 180 participants from 25 countries around the world. The aim is to present and discuss scientific findings and industrial experiences related to the development of Smart Energy Systems and future 4th Generation District...

  5. 2001 Joint ADVISOR/PSAT Vehicle Systems Modeling User's Conference Proceedings (CD)

    International Nuclear Information System (INIS)

    Markel, T.

    2001-01-01

    The 2001 Joint ADVISOR/PSAT Vehicle Systems Modeling User Conference provided an opportunity for engineers in the automotive industry and the research environment to share their experiences in vehicle systems modeling using ADVISOR and PSAT. ADVISOR and PSAT are vehicle systems modeling tools developed and supported by the National Renewable Energy Laboratory and Argonne National Laboratory respectively with the financial support of the US Department of Energy. During this conference peers presented the results of studies using the simulation tools and improvements that they have made or would like to see in the simulation tools. Focus areas of the presentations included Control Strategy, Model Validation, Optimization and Co-Simulation, Model Development, Applications, and Fuel Cell Vehicle Systems Analysis. Attendees were offered the opportunity to give feedback on future model development plans

  6. 2001 Joint ADVISOR/PSAT Vehicle Systems Modeling User's Conference Proceedings (CD)

    Energy Technology Data Exchange (ETDEWEB)

    Markel, T.

    2001-08-01

    The 2001 Joint ADVISOR/PSAT Vehicle Systems Modeling User Conference provided an opportunity for engineers in the automotive industry and the research environment to share their experiences in vehicle systems modeling using ADVISOR and PSAT. ADVISOR and PSAT are vehicle systems modeling tools developed and supported by the National Renewable Energy Laboratory and Argonne National Laboratory respectively with the financial support of the US Department of Energy. During this conference peers presented the results of studies using the simulation tools and improvements that they have made or would like to see in the simulation tools. Focus areas of the presentations included Control Strategy, Model Validation, Optimization and Co-Simulation, Model Development, Applications, and Fuel Cell Vehicle Systems Analysis. Attendees were offered the opportunity to give feedback on future model development plans.

  7. 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.

  8. 2014 Earth System Grid Federation and Ultrascale Visualization Climate Data Analysis Tools Conference Report

    Energy Technology Data Exchange (ETDEWEB)

    Williams, Dean N. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2015-01-27

    The climate and weather data science community met December 9–11, 2014, in Livermore, California, for the fourth annual Earth System Grid Federation (ESGF) and Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT) Face-to-Face (F2F) Conference, hosted by the Department of Energy, National Aeronautics and Space Administration, National Oceanic and Atmospheric Administration, the European Infrastructure for the European Network of Earth System Modelling, and the Australian Department of Education. Both ESGF and UVCDATremain global collaborations committed to developing a new generation of open-source software infrastructure that provides distributed access and analysis to simulated and observed data from the climate and weather communities. The tools and infrastructure created under these international multi-agency collaborations are critical to understanding extreme weather conditions and long-term climate change. In addition, the F2F conference fosters a stronger climate and weather data science community and facilitates a stronger federated software infrastructure. The 2014 F2F conference detailed the progress of ESGF, UV-CDAT, and other community efforts over the year and sets new priorities and requirements for existing and impending national and international community projects, such as the Coupled Model Intercomparison Project Phase Six. Specifically discussed at the conference were project capabilities and enhancements needs for data distribution, analysis, visualization, hardware and network infrastructure, standards, and resources.

  9. Trends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection - 15th International Conference

    OpenAIRE

    Fernando De la Prieta; Zita Vale; Luis Antunes; Tiago Pinto; Andrew T. Campbell; Vicente Julián; Antonio J.R. Neves; María N. Moreno

    2017-01-01

    PAAMS, the International Conference on Practical Applications of Agents and Multi-Agent Systems is an evolution of the International Workshop on Practical Applications of Agents and Multi-Agent Systems. PAAMS is an international yearly tribune to present, to discuss, and to disseminate the latest developments and the most important outcomes related to real-world applications. It provides a unique opportunity to bring multi-disciplinary experts, academics and practitioners together to exchange...

  10. 9th International Conference on Knowledge, Information and Creativity Support Systems

    CERN Document Server

    Papadopoulos, George; Skulimowski, Andrzej; Kacprzyk  , Janusz

    2016-01-01

    This volume consists of a number of selected papers that were presented at the 9th International Conference on Knowledge, Information and Creativity Support Systems (KICSS 2014) in Limassol, Cyprus, after they were substantially revised and extended. The 27 regular papers and 19 short papers included in this proceedings cover all aspects of knowledge management, knowledge engineering, intelligent information systems, and creativity in an information technology context, including computational creativity and its cognitive and collaborative aspects. .

  11. 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.

  12. International conference on sub-critical accelerator driven systems. Proceedings

    International Nuclear Information System (INIS)

    Litovkina, L.P.; Titarenko, Yu.E.

    1999-01-01

    The International Meeting on Sub-Critical Accelerator Driven Systems was organized by the State Scientific Center - Institute for Theoretical and Experimental Physics with participation of Atomic Ministry of RF. The Meeting objective was to analyze the recent achievements and tendencies of the accelerator-driven systems development. The Meeting program covers a broad range of problems including the accelerator-driven systems (ADS) conceptual design; analyzing the ADS role in nuclear fuel cycle; accuracy of modeling the main parameters of ADS; conceptual design of high-current accelerators. Moreover, the results of recent experimental and theoretical studies on nuclear data accumulation to support the ADS technologies are presented. About 70 scientists from the main scientific centers of Russia, as well as scientists from USA, France, Belgium, India, and Yugoslavia, attended the meeting and presented 44 works [ru

  13. 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

  14. Monitoring osseointegration and developing intelligent systems (Conference Presentation)

    Science.gov (United States)

    Salvino, Liming W.

    2017-05-01

    Effective monitoring of structural and biological systems is an extremely important research area that enables technology development for future intelligent devices, platforms, and systems. This presentation provides an overview of research efforts funded by the Office of Naval Research (ONR) to establish structural health monitoring (SHM) methodologies in the human domain. Basic science efforts are needed to utilize SHM sensing, data analysis, modeling, and algorithms to obtain the relevant physiological and biological information for human-specific health and performance conditions. This overview of current research efforts is based on the Monitoring Osseointegrated Prosthesis (MOIP) program. MOIP develops implantable and intelligent prosthetics that are directly anchored to the bone of residual limbs. Through real-time monitoring, sensing, and responding to osseointegration of bones and implants as well as interface conditions and environment, our research program aims to obtain individualized actionable information for implant failure identification, load estimation, infection mitigation and treatment, as well as healing assessment. Looking ahead to achieve ultimate goals of SHM, we seek to expand our research areas to cover monitoring human, biological and engineered systems, as well as human-machine interfaces. Examples of such include 1) brainwave monitoring and neurological control, 2) detecting and evaluating brain injuries, 3) monitoring and maximizing human-technological object teaming, and 4) closed-loop setups in which actions can be triggered automatically based on sensors, actuators, and data signatures. Finally, some ongoing and future collaborations across different disciplines for the development of knowledge automation and intelligent systems will be discussed.

  15. 6TH Saint Petersburg International Conference on Integrated Navigation Systems.

    Science.gov (United States)

    1999-10-01

    pp. 7/1-12. 4. Kharisov V.N., Perov A.I., Boldin VA. (editors). The global satelllite radio-navigational system GLONASS [in Russian]. Moskow: IPRZhR...The need for oil and natural gas requires the techniques for their searching and extracting in deep seas. These techniques have yielded particular

  16. Identification and adaptive neural network control of a DC motor system with dead-zone characteristics.

    Science.gov (United States)

    Peng, Jinzhu; Dubay, Rickey

    2011-10-01

    In this paper, an adaptive control approach based on the neural networks is presented to control a DC motor system with dead-zone characteristics (DZC), where two neural networks are proposed to formulate the traditional identification and control approaches. First, a Wiener-type neural network (WNN) is proposed to identify the motor DZC, which formulates the Wiener model with a linear dynamic block in cascade with a nonlinear static gain. Second, a feedforward neural network is proposed to formulate the traditional PID controller, termed as PID-type neural network (PIDNN), which is then used to control and compensate for the DZC. In this way, the DC motor system with DZC is identified by the WNN identifier, which provides model information to the PIDNN controller in order to make it adaptive. Back-propagation algorithms are used to train both neural networks. Also, stability and convergence analysis are conducted using the Lyapunov theorem. Finally, experiments on the DC motor system demonstrated accurate identification and good compensation for dead-zone with improved control performance over the conventional PID control. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

  17. Psychological Processing in Chronic Pain: A Neural Systems Approach

    OpenAIRE

    Simons, Laura; Elman, Igor; Borsook, David

    2013-01-01

    Our understanding of chronic pain involves complex brain circuits that include sensory, emotional, cognitive and interoceptive processing. The feed-forward interactions between physical (e.g., trauma) and emotional pain and the consequences of altered psychological status on the expression of pain have made the evaluation and treatment of chronic pain a challenge in the clinic. By understanding the neural circuits involved in psychological processes, a mechanistic approach to the implementati...

  18. IFLA General Conference, 1986. Pre-Conference Seminar on Automated Systems for Access to Multilingual and Multiscript Library materials: Problems and Solutions. Papers.

    Science.gov (United States)

    International Federation of Library Associations and Institutions, The Hague (Netherlands).

    A seminar which considered problems and solutions regarding automated systems for access to multilingual and multiscript library materials was held as a pre-session before the IFLA general conference in 1986. Papers presented include: (1) "Romanized and Transliterated Databases of Asian Language Materials--History, Problems, and…

  19. 8th International Conference on Computer Recognition Systems

    CERN Document Server

    Jackowski, Konrad; Kurzynski, Marek; Wozniak, Michał; Zolnierek, Andrzej

    2013-01-01

    The computer recognition systems are nowadays one of the most promising directions in artificial intelligence. This book is the most comprehensive study of this field. It contains a collection of 86 carefully selected articles contributed by experts of pattern recognition. It reports on current research with respect to both methodology and applications. In particular, it includes the following sections: Biometrics Data Stream Classification and Big Data Analytics  Features, learning, and classifiers Image processing and computer vision Medical applications Miscellaneous applications Pattern recognition and image processing in robotics  Speech and word recognition This book is a great reference tool for scientists who deal with the problems of designing computer pattern recognition systems. Its target readers can be the as well researchers as students of computer science, artificial intelligence or robotics.

  20. 9th International Conference on Computer Recognition Systems

    CERN Document Server

    Jackowski, Konrad; Kurzyński, Marek; Woźniak, Michał; Żołnierek, Andrzej

    2016-01-01

    The computer recognition systems are nowadays one of the most promising directions in artificial intelligence. This book is the most comprehensive study of this field. It contains a collection of 79 carefully selected articles contributed by experts of pattern recognition. It reports on current research with respect to both methodology and applications. In particular, it includes the following sections: Features, learning, and classifiers Biometrics Data Stream Classification and Big Data Analytics Image processing and computer vision Medical applications Applications RGB-D perception: recent developments and applications This book is a great reference tool for scientists who deal with the problems of designing computer pattern recognition systems. Its target readers can be the as well researchers as students of computer science, artificial intelligence or robotics.  .

  1. 46th Annual Gun and Missile Systems Conference and Exhibition

    Science.gov (United States)

    2011-09-01

    is concerned (no negative results for environment when dumping of substances is prevented) General Dynamics Ordnance and Tactical Systems...displacement capability of electro dynamic shaker, hydraulic shaker has displacement capability but not force capability  Requested waiver for...support equipment were developed. J75189 Ground Handling Adapter J75192 Hoisting Beam J75199 Gun Transfer Adapter J75196 Mount Rail J75197 Gun Mounting

  2. 20th Annual Systems Engineering Conference. Volume 2, Wednesday

    Science.gov (United States)

    2017-10-26

    Roadmap Key Capability Areas C yber M odeling, Sim ulation, and Experim entation (M SE) Em bedded, M obile, and Tactical System s ( EM T) (MSE & EMT) cross...Space Weapons Missile Bomb Sensors WS Characteristics WS Quality Properties Defining Themes WS Engineering Methods WS Types | 10 | This technical data...aircraft 10 Aircraft depart initial point (IP) 11 JTAC controls CAS aircraft 12 Bombs on target 13 Assessment < 3min < 2 min > 95% Acrcy ASOC/ DASC CAS

  3. Annual Systems Engineering Conference (12th). Volume 1

    Science.gov (United States)

    2009-10-29

    This Presentation will discuss : • Modularity: what it is, Pros and Cons , how it is used on LCS • Overview of extended systems – What’s the concern...is Export Approval # ACK219 (assigned IAW PRO -4527, PRO -3439). Marion L. Butterfield, Alaka Shivananda, and Dennis Schwarz (The Boeing Company...Other Tech Maturity Assessment Methods 7. SWOT (Strength, Weakness, Threat, Opportunity) Analysis 8. Conclusions & Recommendations GWU 3 Introduction The

  4. 9th Annual Systems Engineering Conference: Volume 4 Thursday

    Science.gov (United States)

    2006-10-26

    ISO /IEC 27000 series – Information Security Management System (ISMS) – ISO /IEC 17799:2005 – Code of Practice for Information Security Management...October 2006, Track 2 Standards Organizations Supporting Assurance ISO IEC JTC1TC176 SC1 SC22 Terminology Software Engineering Language, OS SC7...Information Assurance IEEE CS ISO IEC IEEE CS NIST FISMA Projects U.S. Gov’t DoD MIL-STDsPolicy Memos OMG Knowledge Discovery Models OMG 259th Annual

  5. 17th East European Conference on Advances in Databases and Information Systems and Associated Satellite Events

    CERN Document Server

    Cerquitelli, Tania; Chiusano, Silvia; Guerrini, Giovanna; Kämpf, Mirko; Kemper, Alfons; Novikov, Boris; Palpanas, Themis; Pokorný, Jaroslav; Vakali, Athena

    2014-01-01

    This book reports on state-of-art research and applications in the field of databases and information systems. It includes both fourteen selected short contributions, presented at the East-European Conference on Advances in Databases and Information Systems (ADBIS 2013, September 1-4, Genova, Italy), and twenty-six papers from ADBIS 2013 satellite events. The short contributions from the main conference are collected in the first part of the book, which covers a wide range of topics, like data management, similarity searches, spatio-temporal and social network data, data mining, data warehousing, and data management on novel architectures, such as graphics processing units, parallel database management systems, cloud and MapReduce environments. In contrast, the contributions from the satellite events are organized in five different parts, according to their respective ADBIS satellite event: BiDaTA 2013 - Special Session on Big Data: New Trends and Applications); GID 2013 – The Second International Workshop ...

  6. 2nd Asia-Pacific Conference on Complex Systems Design & Management

    CERN Document Server

    Fong, Saik; Krob, Daniel; Lui, Pao; Tan, Yang

    2016-01-01

    This book contains all refereed papers that were accepted to the second edition of the Asia-Pacific conference on « Complex Systems Design & Management Asia» (CSD&M Asia 2016) that took place in Singapore from February 24 to February 26, 2016 (Website: http://www.2016.csdm-asia.net/). These proceedings cover the most recent trends in the emerging field of Complex Systems, both from an academic and a professional perspective. A special focus is put on Smart Nations: Designing and Sustaining. The CSD&M Asia 2016 conference is organized under the guidance of the Singapore division of the Center of Excellence on Systems Architecture, Management, Economy and Strategy (CESAMES) – Legal address: C.E.S.A.M.E.S. Singapore – 16 Raffles Quay – #38-03 Hong Leong Building – Singapore 048581 (website : http://www.cesames.net/en – email: contact@cesames.net).

  7. Remote Education Using Web Conference System in a Company of Coin Parking Business

    Science.gov (United States)

    Yoshioka, Yoshio; Mito, Hiroyuki; Azuma, Kouji

    Maintenance jobs at coin parking places (CP) are very important for keeping trouble free operation. Such maintenance jobs include special inspection at the initiation of new CP and ordinal maintenance works. In order to level up the skill of maintenance people in the company, education of the basic knowlege of electricity, facility and maintenance skills are required. We made an original text for maintenance people, and practiced education by use of web conference system, because they are distributed in whole country, This paper describes a content of text on fundamental knowledge of electricity, facility of coin parking system and trouble experiences, and also a practice of remote education using web conference system. Problems of remote education which were found by practice and the future education plan of practical skill are also described.

  8. 3rd International Conference on Particle Systems and Partial Differential Equations

    CERN Document Server

    Soares, Ana

    2016-01-01

    The main focus of this book is on different topics in probability theory, partial differential equations and kinetic theory, presenting some of the latest developments in these fields. It addresses mathematical problems concerning applications in physics, engineering, chemistry and biology that were presented at the Third International Conference on Particle Systems and Partial Differential Equations, held at the University of Minho, Braga, Portugal in December 2014. The purpose of the conference was to bring together prominent researchers working in the fields of particle systems and partial differential equations, providing a venue for them to present their latest findings and discuss their areas of expertise. Further, it was intended to introduce a vast and varied public, including young researchers, to the subject of interacting particle systems, its underlying motivation, and its relation to partial differential equations. This book will appeal to probabilists, analysts and those mathematicians whose wor...

  9. 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

  10. Nuclear power plant conference 2010 (NPC 2010): International conference on water chemistry of nuclear reactor systems and 8th International radiolysis, electrochemistry and materials performance workshop

    International Nuclear Information System (INIS)

    2010-01-01

    The Nuclear Plant Chemistry Conference was held in Quebec City, Quebec, Canada on October 3-7, 2010. It was hosted by the Canadian Nuclear Society and was held in Canada for the first time. This international event hosted over 300 attendees, two thirds from outside of Canada, mostly from Europe and and Far East. The conference is formally known as the International Conference on Water Chemistry of Nuclear Reactor Systems and is the 15th of a series that began in 1977 in Bournemouth, UK. The conference focussed on the latest developments in the science and technology of water chemistry control in nuclear reactor systems. Utility scientists, engineers and operations people met their counterparts from research institutes, service organizations and universities to address the challenges of chemistry control and degradation management of their complex and costly plants for the many decades that they are expected to operate. Following the four day conference, the 8th International Radiolysis, Electrochemistry and Materials Performance Workshop was held as associated, but otherwise free-standing event on Friday, October 8, 2010. It was also well attended and the primary focus was the effect of radiation on corrosion. When asked about the importance of chemistry in operating nuclear power plants, the primary organizers summarized it in the following statement: 'Once a nuclear plant is in operation, chemistry improvement is the only way to increase the longevity of the plant and its equipment'. The organisers of the 2010 Workshop and the NPC 2010 conference decided that these two events would be held consecutively, as previous, but for the first time the organization and registration would be shared, which proved to be a winning combination by the attendance.

  11. 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

  12. Fault detection and classification in electrical power transmission system using artificial neural network.

    Science.gov (United States)

    Jamil, Majid; Sharma, Sanjeev Kumar; Singh, Rajveer

    2015-01-01

    This paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB(®) environment.

  13. Adaptive Control of Nonlinear Discrete-Time Systems by Using OS-ELM Neural Networks

    Directory of Open Access Journals (Sweden)

    Xiao-Li Li

    2014-01-01

    Full Text Available As a kind of novel feedforward neural network with single hidden layer, ELM (extreme learning machine neural networks are studied for the identification and control of nonlinear dynamic systems. The property of simple structure and fast convergence of ELM can be shown clearly. In this paper, we are interested in adaptive control of nonlinear dynamic plants by using OS-ELM (online sequential extreme learning machine neural networks. Based on data scope division, the problem that training process of ELM neural network is sensitive to the initial training data is also solved. According to the output range of the controlled plant, the data corresponding to this range will be used to initialize ELM. Furthermore, due to the drawback of conventional adaptive control, when the OS-ELM neural network is used for adaptive control of the system with jumping parameters, the topological structure of the neural network can be adjusted dynamically by using multiple model switching strategy, and an MMAC (multiple model adaptive control will be used to improve the control performance. Simulation results are included to complement the theoretical results.

  14. A Comparative Study of Neural Networks and Fuzzy Systems in Modeling of a Nonlinear Dynamic System

    Directory of Open Access Journals (Sweden)

    Metin Demirtas

    2011-07-01

    Full Text Available The aim of this paper is to compare the neural networks and fuzzy modeling approaches on a nonlinear system. We have taken Permanent Magnet Brushless Direct Current (PMBDC motor data and have generated models using both approaches. The predictive performance of both methods was compared on the data set for model configurations. The paper describes the results of these tests and discusses the effects of changing model parameters on predictive and practical performance. Modeling sensitivity was used to compare for two methods.

  15. Artificial frame filling using adaptive neural fuzzy inference system for particle image velocimetry dataset

    Science.gov (United States)

    Akdemir, Bayram; Doǧan, Sercan; Aksoy, Muharrem H.; Canli, Eyüp; Özgören, Muammer

    2015-03-01

    Liquid behaviors are very important for many areas especially for Mechanical Engineering. Fast camera is a way to observe and search the liquid behaviors. Camera traces the dust or colored markers travelling in the liquid and takes many pictures in a second as possible as. Every image has large data structure due to resolution. For fast liquid velocity, there is not easy to evaluate or make a fluent frame after the taken images. Artificial intelligence has much popularity in science to solve the nonlinear problems. Adaptive neural fuzzy inference system is a common artificial intelligence in literature. Any particle velocity in a liquid has two dimension speed and its derivatives. Adaptive Neural Fuzzy Inference System has been used to create an artificial frame between previous and post frames as offline. Adaptive neural fuzzy inference system uses velocities and vorticities to create a crossing point vector between previous and post points. In this study, Adaptive Neural Fuzzy Inference System has been used to fill virtual frames among the real frames in order to improve image continuity. So this evaluation makes the images much understandable at chaotic or vorticity points. After executed adaptive neural fuzzy inference system, the image dataset increase two times and has a sequence as virtual and real, respectively. The obtained success is evaluated using R2 testing and mean squared error. R2 testing has a statistical importance about similarity and 0.82, 0.81, 0.85 and 0.8 were obtained for velocities and derivatives, respectively.

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

    Science.gov (United States)

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

    2014-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Agustín Flores

    2014-01-01

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

  18. Final report for Conference Support Grant "From Computational Biophysics to Systems Biology - CBSB12"

    Energy Technology Data Exchange (ETDEWEB)

    Hansmann, Ulrich H.E.

    2012-07-02

    This report summarizes the outcome of the international workshop From Computational Biophysics to Systems Biology (CBSB12) which was held June 3-5, 2012, at the University of Tennessee Conference Center in Knoxville, TN, and supported by DOE through the Conference Support Grant 120174. The purpose of CBSB12 was to provide a forum for the interaction between a data-mining interested systems biology community and a simulation and first-principle oriented computational biophysics/biochemistry community. CBSB12 was the sixth in a series of workshops of the same name organized in recent years, and the second that has been held in the USA. As in previous years, it gave researchers from physics, biology, and computer science an opportunity to acquaint each other with current trends in computational biophysics and systems biology, to explore venues of cooperation, and to establish together a detailed understanding of cells at a molecular level. The conference grant of $10,000 was used to cover registration fees and provide travel fellowships to selected students and postdoctoral scientists. By educating graduate students and providing a forum for young scientists to perform research into the working of cells at a molecular level, the workshop adds to DOE's mission of paving the way to exploit the abilities of living systems to capture, store and utilize energy.

  19. International Conference on Harmonisation; guidance on Q10 Pharmaceutical Quality System; availability. Notice.

    Science.gov (United States)

    2009-04-08

    The Food and Drug Administration (FDA) is announcing the availability of a guidance entitled "Q10 Pharmaceutical Quality System." The guidance was prepared under the auspices of the International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH). The guidance describes a model for an effective quality management system for the pharmaceutical industry, referred to as the Pharmaceutical Quality System. The guidance is intended to provide a comprehensive approach to an effective pharmaceutical quality system that is based on International Organization for Standardization (ISO) concepts, includes applicable good manufacturing practice (GMP) regulations and complements ICH guidances on "Q8 Pharmaceutical Development" and "Q9 Quality Risk Management."

  20. International Conference on Grey Systems and intelligent Services (IEEE GSIS 2009)

    CERN Document Server

    Liu, Sifeng; Advances in Grey Systems Research

    2010-01-01

    This book contains contributions by some of the leading researchers in the area of grey systems theory and applications. All the papers included in this volume are selected from the contributions physically presented at the 2009 IEEE International Conference on Grey Systems and Intelligent Services, November 11 – 12, 2009, Nanjing, Jiangsu, People’s Republic of China. This event was jointly sponsored by IEEE Systems, Man, and Cybernetics Society, Natural Science Foundation of China, and Grey Systems Society of China. Additionally, Nanjing University of Aeronautics and Astronautics also invested heavily in this event with its direct and indirect financial and administrative supports.

  1. Coupling Strength and System Size Induce Firing Activity of Globally Coupled Neural Network

    International Nuclear Information System (INIS)

    Wei Duqu; Luo Xiaoshu; Zou Yanli

    2008-01-01

    We investigate how firing activity of globally coupled neural network depends on the coupling strength C and system size N. Network elements are described by space-clamped FitzHugh-Nagumo (SCFHN) neurons with the values of parameters at which no firing activity occurs. It is found that for a given appropriate coupling strength, there is an intermediate range of system size where the firing activity of globally coupled SCFHN neural network is induced and enhanced. On the other hand, for a given intermediate system size level, there exists an optimal value of coupling strength such that the intensity of firing activity reaches its maximum. These phenomena imply that the coupling strength and system size play a vital role in firing activity of neural network

  2. Fuzzy wavelet plus a quantum neural network as a design base for power system stability enhancement.

    Science.gov (United States)

    Ganjefar, Soheil; Tofighi, Morteza; Karami, Hamidreza

    2015-11-01

    In this study, we introduce an indirect adaptive fuzzy wavelet neural controller (IAFWNC) as a power system stabilizer to damp inter-area modes of oscillations in a multi-machine power system. Quantum computing is an efficient method for improving the computational efficiency of neural networks, so we developed an identifier based on a quantum neural network (QNN) to train the IAFWNC in the proposed scheme. All of the controller parameters are tuned online based on the Lyapunov stability theory to guarantee the closed-loop stability. A two-machine, two-area power system equipped with a static synchronous series compensator as a series flexible ac transmission system was used to demonstrate the effectiveness of the proposed controller. The simulation and experimental results demonstrated that the proposed IAFWNC scheme can achieve favorable control performance. Copyright © 2015 Elsevier Ltd. All rights reserved.

  3. Teleradiology network system using the web medical image conference system with a new information security solution

    Science.gov (United States)

    Satoh, Hitoshi; Niki, Noboru; Eguchi, Kenji; Ohmatsu, Hironobu; Kusumoto, Masahiro; Kaneko, Masahiro; Kakinuma, Ryutaru; Moriyama, Noriyuki

    2012-02-01

    We have developed the teleradiology network system with a new information security solution that provided with web medical image conference system. In the teleradiology network system, the security of information network is very important subjects. We are studying the secret sharing scheme and the tokenization as a method safely to store or to transmit the confidential medical information used with the teleradiology network system. The confidential medical information is exposed to the risk of the damage and intercept. Secret sharing scheme is a method of dividing the confidential medical information into two or more tallies. Individual medical information cannot be decoded by using one tally at all. Our method has the function of automatic backup. With automatic backup technology, if there is a failure in a single tally, there is redundant data already copied to other tally. Confidential information is preserved at an individual Data Center connected through internet because individual medical information cannot be decoded by using one tally at all. Therefore, even if one of the Data Centers is struck and information is damaged due to the large area disaster like the great earthquake of Japan, the confidential medical information can be decoded by using the tallies preserved at the data center to which it escapes damage. Moreover, by using tokenization, the history information of dividing the confidential medical information into two or more tallies is prevented from lying scattered by replacing the history information with another character string (Make it to powerlessness). As a result, information is available only to those who have rightful access it and the sender of a message and the message itself are verified at the receiving point. We propose a new information transmission method and a new information storage method with a new information security solution.

  4. Proceedings of the 2009 CIGRE Canada conference on power systems : innovation and renewal : building the new power system

    International Nuclear Information System (INIS)

    2009-01-01

    The Conseil International des Grands Reseaux Electriques (CIGRE) is the International Council on Large Electric Systems. It promotes technical, economic and environmental developments in electricity transmission and generation. CIGRE Canada is the Canadian National Committee which fosters the participation of Canadian members in CIGRE activities. CIGRE Canada organizes an annual conference that provides a forum for power system engineers, decision makers,economists, and academics to discuss technological developments in electrical power systems. The presentations at this conference addressed issues regarding the use of renewable energy sources in power transmission and distribution systems, with particular reference to control and protection; HVDC and MVDC; modelling tools; interface technologies; and reduced carbon generation and sustainability. The use of active distribution systems was also discussed in terms of future trends; the role of information technology and communications; and the role of energy storage. The session on smart grids addressed issues such as power utility perspectives; sensing, measurements and controls; advanced interfaces and decision support systems; open-architecture; distributed energy resources; and regulatory issues. Issues concerning the interconnection of non traditional energy sources to the power systems were also discussed along with recent research initiatives related to renewable energy source development. The sessions were entitled: smart grids; distributed energy resources; wind and solar PV; AC systems and HV lines; wide area measurements; power system operation and control; modelling and analysis; substation automation; and HVDC and facts. The conference featured 66 presentations, of which 35 have been catalogued separately for inclusion in this database

  5. PREFACE: International Conference on Strongly Correlated Electron Systems 2014 (SCES2014)

    Science.gov (United States)

    2015-03-01

    The 2014 International Conference on Strongly Correlated Electron Systems (SCES) was held in Grenoble from the 7th to 11th of July on the campus of the University of Grenoble. It was a great privilege to have the conference in Grenoble after the series of meetings in Sendai (1992), San Diego (1993), Amsterdam (1994), Goa (1995), Zürich (1996), Paris (1998), Nagano (1999), Ann Arbor (2001), Krakow (2002), Karlsruhe (2004), Vienna (2005), Houston (2007), Buzios (2008), Santa Fe (2010), Cambridge (2011) and Tokyo (2013). Every three years, SCES joins the triennial conference on magnetism ICM. In 2015, ICM will take place in Barcelona. The meeting gathered an audience of 875 participants who actively interacted inside and outside of conference rooms. A large number of posters (530) was balanced with four parallel oral sessions which included 86 invited speakers and 141 short oral contributions. A useful arrangement was the possibility to put poster presentations on the website so participants could see them all through the conference week. Each morning two plenary sessions were held, ending on Friday with experimental and theoretical summaries delivered by Philipp Gegenwart (Augsburg) and Andrew Millis (Columbia). The plenary sessions were given by Gabriel Kotliar (Rutgers), Masashi Kawasaki (Tokyo), Jennifer Hoffman (Harvard), Mathias Vojta (Dresden), Ashvin Vishwanath (Berkeley), Andrea Cavalleri (Hamburg), Marc-Henri Julien (Grenoble), Neil Mathur (Cambridge), Giniyat Khaliullin (Stuttgart), and Toshiro Sakakibara (Tokyo). The parallel oral sessions were prepared by 40 symposium organizers selected by the chairman (Antoine Georges) and co-chairman (Kamran Behnia) of the Program Committee with the supplementary rule that speakers had not delivered an invited talk at the previous SCES conference held in 2013 in Tokyo. Special attention was given to help young researchers via grants to 40 overseas students. Perhaps due to the additional possibility of cheap

  6. Coding of level of ambiguity within neural systems mediating choice.

    Science.gov (United States)

    Lopez-Paniagua, Dan; Seger, Carol A

    2013-01-01

    Data from previous neuroimaging studies exploring neural activity associated with uncertainty suggest varying levels of activation associated with changing degrees of uncertainty in neural regions that mediate choice behavior. The present study used a novel task that parametrically controlled the amount of information hidden from the subject; levels of uncertainty ranged from full ambiguity (no information about probability of winning) through multiple levels of partial ambiguity, to a condition of risk only (zero ambiguity with full knowledge of the probability of winning). A parametric analysis compared a linear model in which weighting increased as a function of level of ambiguity, and an inverted-U quadratic models in which partial ambiguity conditions were weighted most heavily. Overall we found that risk and all levels of ambiguity recruited a common "fronto-parietal-striatal" network including regions within the dorsolateral prefrontal cortex, intraparietal sulcus, and dorsal striatum. Activation was greatest across these regions and additional anterior and superior prefrontal regions for the quadratic function which most heavily weighs trials with partial ambiguity. These results suggest that the neural regions involved in decision processes do not merely track the absolute degree ambiguity or type of uncertainty (risk vs. ambiguity). Instead, recruitment of prefrontal regions may result from greater degree of difficulty in conditions of partial ambiguity: when information regarding reward probabilities important for decision making is hidden or not easily obtained the subject must engage in a search for tractable information. Additionally, this study identified regions of activity related to the valuation of potential gains associated with stimuli or options (including the orbitofrontal and medial prefrontal cortices and dorsal striatum) and related to winning (including orbitofrontal cortex and ventral striatum).

  7. Child Maltreatment and Neural Systems Underlying Emotion Regulation.

    Science.gov (United States)

    McLaughlin, Katie A; Peverill, Matthew; Gold, Andrea L; Alves, Sonia; Sheridan, Margaret A

    2015-09-01

    The strong associations between child maltreatment and psychopathology have generated interest in identifying neurodevelopmental processes that are disrupted following maltreatment. Previous research has focused largely on neural response to negative facial emotion. We determined whether child maltreatment was associated with neural responses during passive viewing of negative and positive emotional stimuli and effortful attempts to regulate emotional responses. A total of 42 adolescents aged 13 to 19 years, half with exposure to physical and/or sexual abuse, participated. Blood oxygen level-dependent (BOLD) response was measured during passive viewing of negative and positive emotional stimuli and attempts to modulate emotional responses using cognitive reappraisal. Maltreated adolescents exhibited heightened response in multiple nodes of the salience network, including amygdala, putamen, and anterior insula, to negative relative to neutral stimuli. During attempts to decrease responses to negative stimuli relative to passive viewing, maltreatment was associated with greater recruitment of superior frontal gyrus, dorsal anterior cingulate cortex, and frontal pole; adolescents with and without maltreatment down-regulated amygdala response to a similar degree. No associations were observed between maltreatment and neural response to positive emotional stimuli during passive viewing or effortful regulation. Child maltreatment heightens the salience of negative emotional stimuli. Although maltreated adolescents modulate amygdala responses to negative cues to a degree similar to that of non-maltreated youths, they use regions involved in effortful control to a greater degree to do so, potentially because greater effort is required to modulate heightened amygdala responses. These findings are promising, given the centrality of cognitive restructuring in trauma-focused treatments for children. Copyright © 2015 American Academy of Child and Adolescent Psychiatry

  8. 7th International Conference in Methodologies and Intelligent Systems for Technology Enhanced Learning

    CERN Document Server

    Gennari, Rosella; Mascio, Tania; Rodríguez, Sara; Prieta, Fernando; Ramos, Carlos; Silveira, Ricardo

    2017-01-01

    This book presents the outcomes of the 7th International Conference in Methodologies and Intelligent Systems for Technology Enhanced Learning (MIS4TEL'17), hosted by the Polytechnic of Porto, Portugal from 21 to 23 June 2017. Expanding on the topics of the previous conferences, it provided an open forum for discussing intelligent systems for technology enhanced learning (TEL) and their roots in novel learning theories, empirical methodologies for their design or evaluation, stand-alone and web-based solutions, and makerspaces. It also fostered entrepreneurship and business startup ideas, bringing together researchers and developers from industry, education and the academic world to report on the latest scientific research, technical advances and methodologies.

  9. Proceedings of the International Conference on Information Systems Design and Intelligent Applications 2012

    CERN Document Server

    Avadhani, P; Abraham, Ajith

    2012-01-01

    This volume contains the papers presented at INDIA-2012: International conference on  Information system Design and Intelligent Applications held on January 5-7, 2012 in Vishakhapatnam, India. This conference was organized by Computer Society of India (CSI), Vishakhapatnam chapter well supported by Vishakhapatnam Steel, RINL, Govt of India. It contains 108 papers contributed by authors from six different countries across four continents. These research papers mainly focused on intelligent applications and various system design issues. The papers cover a wide range of topics of computer science and information technology discipline ranging from image processing, data base application, data mining, grid and cloud computing, bioinformatics among many others. The various intelligent tools like swarm intelligence, artificial intelligence, evolutionary algorithms, bio-inspired algorithms have been applied in different papers for solving various challenging IT related problems.

  10. 6th International Conference in Methodologies and intelligent Systems for Technology Enhanced Learning

    CERN Document Server

    Prieta, Fernando; Mascio, Tania; Gennari, Rosella; Rodríguez, Javier; Vittorini, Pierpaolo

    2016-01-01

    The 6th International Conference in Methodologies and intelligent Systems for Technology Enhanced Learning held in Seville (Spain) is host by the University of Seville from 1st to 3rd June, 2016. The 6th edition of this conference expands the topics of the evidence-based TEL workshops series in order to provide an open forum for discussing intelligent systems for TEL, their roots in novel learning theories, empirical methodologies for their design or evaluation, stand-alone solutions or web-based ones. It intends to bring together researchers and developers from industry, the education field and the academic world to report on the latest scientific research, technical advances and methodologies.

  11. Science and Information Conference 2013 : Intelligent Systems for Science and Information

    CERN Document Server

    Kapoor, Supriya; Bhatia, Rahul

    2014-01-01

    The book Intelligent Systems for Science and Information is the remarkable collection of extended chapters from the selected papers that were published in the proceedings of Science and Information (SAI) Conference 2013. It contains twenty-four chapters in the field of Intelligent Systems, which received highly recommended feedback during SAI Conference 2013 review process. All chapters have gone through substantial extension and consolidation and were subject to another round of rigorous review and additional modification. These chapters represent the state of the art of the cutting-edge research and technologies in related areas, and can help inform relevant research communities and individuals of the future development in Science and Information.    

  12. Sign Language Recognition System using Neural Network for Digital Hardware Implementation

    International Nuclear Information System (INIS)

    Vargas, Lorena P; Barba, Leiner; Torres, C O; Mattos, L

    2011-01-01

    This work presents an image pattern recognition system using neural network for the identification of sign language to deaf people. The system has several stored image that show the specific symbol in this kind of language, which is employed to teach a multilayer neural network using a back propagation algorithm. Initially, the images are processed to adapt them and to improve the performance of discriminating of the network, including in this process of filtering, reduction and elimination noise algorithms as well as edge detection. The system is evaluated using the signs without including movement in their representation.

  13. On-line identification of hybrid systems using an adaptive growing and pruning RBF neural network

    DEFF Research Database (Denmark)

    Alizadeh, Tohid

    2008-01-01

    This paper introduces an adaptive growing and pruning radial basis function (GAP-RBF) neural network for on-line identification of hybrid systems. The main idea is to identify a global nonlinear model that can predict the continuous outputs of hybrid systems. In the proposed approach, GAP......-RBF neural network uses a modified unscented kalman filter (UKF) with forgetting factor scheme as the required on-line learning algorithm. The effectiveness of the resulting identification approach is tested and evaluated on a simulated benchmark hybrid system....

  14. Skin condition measurement by using multispectral imaging system (Conference Presentation)

    Science.gov (United States)

    Jung, Geunho; Kim, Sungchul; Kim, Jae Gwan

    2017-02-01

    There are a number of commercially available low level light therapy (LLLT) devices in a market, and face whitening or wrinkle reduction is one of targets in LLLT. The facial improvement could be known simply by visual observation of face, but it cannot provide either quantitative data or recognize a subtle change. Clinical diagnostic instruments such as mexameter can provide a quantitative data, but it costs too high for home users. Therefore, we designed a low cost multi-spectral imaging device by adding additional LEDs (470nm, 640nm, white LED, 905nm) to a commercial USB microscope which has two LEDs (395nm, 940nm) as light sources. Among various LLLT skin treatments, we focused on getting melanin and wrinkle information. For melanin index measurements, multi-spectral images of nevus were acquired and melanin index values from color image (conventional method) and from multi-spectral images were compared. The results showed that multi-spectral analysis of melanin index can visualize nevus with a different depth and concentration. A cross section of wrinkle on skin resembles a wedge which can be a source of high frequency components when the skin image is Fourier transformed into a spatial frequency domain map. In that case, the entropy value of the spatial frequency map can represent the frequency distribution which is related with the amount and thickness of wrinkle. Entropy values from multi-spectral images can potentially separate the percentage of thin and shallow wrinkle from thick and deep wrinkle. From the results, we found that this low cost multi-spectral imaging system could be beneficial for home users of LLLT by providing the treatment efficacy in a quantitative way.

  15. 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.

  16. 15th International Conference on Accelerator and Large Experimental Physics Control Systems

    CERN Document Server

    2015-01-01

    ICALEPCS is a biennial series of conferences that is intended to: * Provide a forum for the interchange of ideas and information between control system specialists working on large experimental physics facilities around the world (accelerators, particle detectors, fusion reactors, telescopes, etc.); * Create an archival literature of developments and progress in this rapidly changing discipline; * Promote, where practical, standardization in both hardware and software; Promote collaboration between laboratories, institutes and industry.

  17. Proceedings: USACERL/ASCE First Joint Conference on Expert Systems, 29-30 June 1988

    Science.gov (United States)

    1989-01-01

    another paper at this conference [HARANDI&LANGE&KEARNEY88]. Second is the linkage of a Graphical Abstract Domain Model (GADM), to the production rule...representation of the domain knowledge and its associated heuristics. This report deals with this, the second method. 39 Basically, a Graphical ... Abstract Domain Model (GADM) is a graphical representation of the elements in a physical system and its interconnections. For the purposes of this project

  18. International Conference on VLSI, Communication, Advanced Devices, Signals & Systems and Networking

    CERN Document Server

    Shirur, Yasha; Prasad, Rekha

    2013-01-01

    This book is a collection of papers presented by renowned researchers, keynote speakers and academicians in the International Conference on VLSI, Communication, Analog Designs, Signals and Systems, and Networking (VCASAN-2013), organized by B.N.M. Institute of Technology, Bangalore, India during July 17-19, 2013. The book provides global trends in cutting-edge technologies in electronics and communication engineering. The content of the book is useful to engineers, researchers and academicians as well as industry professionals.

  19. Nostradamus conference 2013

    CERN Document Server

    Chen, Guanrong; Rössler, Otto; Snasel, Vaclav; Abraham, Ajith; Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems

    2013-01-01

    Prediction of behavior of the dynamical systems, analysis and modeling of its structure is vitally important problem in engineering, economy and science today. Examples of such systems can be seen in the world around us and of course in almost every scientific discipline including such “exotic” domains like the earth’s atmosphere, turbulent fluids, economies (exchange rate and stock markets), population growth, physics (control of plasma), information flow in social networks and its dynamics, chemistry and complex networks. To understand such dynamics and to use it in research or industrial applications, it is important to create its models. For this purpose there is rich spectra of methods, from classical like ARMA models or Box Jenkins method to such modern ones like evolutionary computation, neural networks, fuzzy logic, fractal geometry, deterministic chaos and more. This proceeding book is a collection of the accepted papers to conference Nostradamus that has been held in Ostrava, Czech Republic. P...

  20. 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.

  1. 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

  2. Color Image Encryption Algorithm Based on TD-ERCS System and Wavelet Neural Network

    Directory of Open Access Journals (Sweden)

    Kun Zhang

    2015-01-01

    Full Text Available In order to solve the security problem of transmission image across public networks, a new image encryption algorithm based on TD-ERCS system and wavelet neural network is proposed in this paper. According to the permutation process and the binary XOR operation from the chaotic series by producing TD-ERCS system and wavelet neural network, it can achieve image encryption. This encryption algorithm is a reversible algorithm, and it can achieve original image in the rule inverse process of encryption algorithm. Finally, through computer simulation, the experiment results show that the new chaotic encryption algorithm based on TD-ERCS system and wavelet neural network is valid and has higher security.

  3. Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition

    CERN Document Server

    Melin, Patricia

    2012-01-01

    This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural ne...

  4. Neural network modeling of nonlinear systems based on Volterra series extension of a linear model

    Science.gov (United States)

    Soloway, Donald I.; Bialasiewicz, Jan T.

    1992-01-01

    A Volterra series approach was applied to the identification of nonlinear systems which are described by a neural network model. A procedure is outlined by which a mathematical model can be developed from experimental data obtained from the network structure. Applications of the results to the control of robotic systems are discussed.

  5. Absolute stability of nonlinear systems with time delays and applications to neural networks

    Directory of Open Access Journals (Sweden)

    Xinzhi Liu

    2001-01-01

    Full Text Available In this paper, absolute stability of nonlinear systems with time delays is investigated. Sufficient conditions on absolute stability are derived by using the comparison principle and differential inequalities. These conditions are simple and easy to check. In addition, exponential stability conditions for some special cases of nonlinear delay systems are discussed. Applications of those results to cellular neural networks are presented.

  6. Practical Applications of Intelligent Systems : Proceedings of the Sixth International Conference on Intelligent Systems and Knowledge Engineering

    CERN Document Server

    Li, Tianrui

    2012-01-01

    Proceedings of The Sixth International Conference on Intelligent System and Knowledge Engineering presents selected papers from the conference ISKE 2011, held December 15-17 in Shanghai, China. This proceedings doesn’t only examine original research and approaches in the broad areas of intelligent systems and knowledge engineering, but also present new methodologies and practices in intelligent computing paradigms. The book introduces the current scientific and technical advances in the fields of artificial intelligence, machine learning, pattern recognition, data mining, information retrieval, knowledge-based systems, knowledge representation and reasoning, multi-agent systems, natural-language processing, etc. Furthermore, new computing methodologies are presented, including cloud computing, service computing and pervasive computing with traditional intelligent methods. The proceedings will be beneficial for both researchers and practitioners who want to utilize intelligent methods in their specific res...

  7. 1st International Conference on Machine Learning for Cyber Physical Systems and Industry 4.0

    CERN Document Server

    Beyerer, Jürgen

    2016-01-01

    The work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Lemgo, October 1-2, 2015. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.

  8. An Improved Recurrent Neural Network for Complex-Valued Systems of Linear Equation and Its Application to Robotic Motion Tracking.

    Science.gov (United States)

    Ding, Lei; Xiao, Lin; Liao, Bolin; Lu, Rongbo; Peng, Hua

    2017-01-01

    To obtain the online solution of complex-valued systems of linear equation in complex domain with higher precision and higher convergence rate, a new neural network based on Zhang neural network (ZNN) is investigated in this paper. First, this new neural network for complex-valued systems of linear equation in complex domain is proposed and theoretically proved to be convergent within finite time. Then, the illustrative results show that the new neural network model has the higher precision and the higher convergence rate, as compared with the gradient neural network (GNN) model and the ZNN model. Finally, the application for controlling the robot using the proposed method for the complex-valued systems of linear equation is realized, and the simulation results verify the effectiveness and superiorness of the new neural network for the complex-valued systems of linear equation.

  9. Efficient decoding with steady-state Kalman filter in neural interface systems.

    Science.gov (United States)

    Malik, Wasim Q; Truccolo, Wilson; Brown, Emery N; Hochberg, Leigh R

    2011-02-01

    The Kalman filter is commonly used in neural interface systems to decode neural activity and estimate the desired movement kinematics. We analyze a low-complexity Kalman filter implementation in which the filter gain is approximated by its steady-state form, computed offline before real-time decoding commences. We evaluate its performance using human motor cortical spike train data obtained from an intracortical recording array as part of an ongoing pilot clinical trial. We demonstrate that the standard Kalman filter gain converges to within 95% of the steady-state filter gain in 1.5±0.5 s (mean ±s.d.). The difference in the intended movement velocity decoded by the two filters vanishes within 5 s, with a correlation coefficient of 0.99 between the two decoded velocities over the session length. We also find that the steady-state Kalman filter reduces the computational load (algorithm execution time) for decoding the firing rates of 25±3 single units by a factor of 7.0±0.9. We expect that the gain in computational efficiency will be much higher in systems with larger neural ensembles. The steady-state filter can thus provide substantial runtime efficiency at little cost in terms of estimation accuracy. This far more efficient neural decoding approach will facilitate the practical implementation of future large-dimensional, multisignal neural interface systems.

  10. Neural networks

    International Nuclear Information System (INIS)

    Denby, Bruce; Lindsey, Clark; Lyons, Louis

    1992-01-01

    The 1980s saw a tremendous renewal of interest in 'neural' information processing systems, or 'artificial neural networks', among computer scientists and computational biologists studying cognition. Since then, the growth of interest in neural networks in high energy physics, fueled by the need for new information processing technologies for the next generation of high energy proton colliders, can only be described as explosive

  11. Artificial neural systems for interpretation and inversion of seismic data

    Science.gov (United States)

    Calderon-Macias, Carlos

    The goal of this work is to investigate the feasibility of using neural network (NN) models for solving geophysical exploration problems. First, a feedforward neural network (FNN) is used to solve inverse problems. The operational characteristics of a FNN are primarily controlled by a set of weights and a nonlinear function that performs a mapping between two sets of data. In a process known as training, the FNN weights are iteratively adjusted to perform the mapping. After training, the computed weights encode important features of the data that enable one pattern to be distinguished from another. Synthetic data computed from an ensemble of earth models and the corresponding models provide the training data. Two training methods are studied: the backpropagation method which is a gradient scheme, and a global optimization method called very fast simulated annealing (VFSA). A trained network is then used to predict models from new data (e.g., data from a new location) in a one-step procedure. The application of this method to the problems of obtaining formation resistivities and layer thicknesses from resistivity sounding data and 1D velocity models from seismic data shows that trained FNNs produce reasonably accurate earth models when observed data are input to the FNNs. In a second application, a FNN is used for automating the NMO correction process of seismic reflection data. The task of the FNN is to map CMP data at control locations along a seismic line into subsurface velocities. The network is trained while the velocity analyses are performed at the control locations. Once trained, the computed weights are used as an operator that acts on the remaining CMP data as a velocity interpolator, resulting in a fast method for NMO correction. The second part of this dissertation describes the application of a Hopfield neural network (HNN) to the problems of deconvolution and multiple attenuation. In these applications, the unknown parameters (reflection coefficients

  12. Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Wang, L [School of Aeronautics and Astronautics, Tongji University, Shanghai (China); Zhang, Y Y [Chinese-German School of Postgraduate Studies, Tongji University (China); Ding, L [Chinese-German School of Postgraduate Studies, Tongji University (China)

    2006-10-15

    The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module.

  13. Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks

    Science.gov (United States)

    Wang, L.; Zhang, Y. Y.; Ding, L.

    2006-10-01

    The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module.

  14. Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks

    International Nuclear Information System (INIS)

    Wang, L; Zhang, Y Y; Ding, L

    2006-01-01

    The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module

  15. 4th CIRP International Conference on Industrial Product-Service Systems

    CERN Document Server

    Kimita, Koji

    2013-01-01

    Industrial Product-Service Systems (IPS2), which is defined as “an integrated industrial product and service offering that delivers value in use,” has expanded rapidly over the last decade. IPS2 has allowed us to achieve both high added value and high productivity and has enriched our QOL by improving the performance of products and services. We are now struggling with many awkward issues related to sustainability, but IPS2 is expected to be the “philosopher’s stone” for solving these issues. Following the pattern of conferences held in Cranfield in 2009, Linköping in 2010, and Braunschweig in 2011, the fourth International CIRP Conference on Industrial Product-Service Systems, held on November 8-9, 2012, in Tokyo, will cover various aspects of IPS2. Topics planned for this year’s conference reflect the latest IPS2 information in both the natural sciences and humanities and include case studies from various industries. IPS2 is still a relatively new field, so it is important to keep track of the ...

  16. ISC feedforward control of gasoline engine. Adaptive system using neural network; Jidoshayo gasoline engine no ISC feedforward seigyo. Neural network wo mochiita tekioka

    Energy Technology Data Exchange (ETDEWEB)

    Kinugawa, N; Morita, S; Takiyama, T [Osaka City University, Osaka (Japan)

    1997-10-01

    For fuel economy and a good driver`s feeling, it is necessary for idle-speed to keep at a constant low speed. But keeping low speed has danger of engine stall when the engine torque is disturbed by the alternator, and so on. In this paper, adaptive feedforward idle-speed control system against electrical loads was investigated. This system was based on the reversed tansfer functions of the object system, and a neural network was used to adapt this system for aging. Then, this neural network was also used for creating feedforward table map. Good experimental results were obtained. 2 refs., 11 figs.

  17. 1st International Conference on Computational Advancement in Communication Circuits and Systems

    CERN Document Server

    Dalapati, Goutam; Banerjee, P; Mallick, Amiya; Mukherjee, Moumita

    2015-01-01

    This book comprises the proceedings of 1st International Conference on Computational Advancement in Communication Circuits and Systems (ICCACCS 2014) organized by Narula Institute of Technology under the patronage of JIS group, affiliated to West Bengal University of Technology. The conference was supported by Technical Education Quality Improvement Program (TEQIP), New Delhi, India and had technical collaboration with IEEE Kolkata Section, along with publication partner by Springer. The book contains 62 refereed papers that aim to highlight new theoretical and experimental findings in the field of Electronics and communication engineering including interdisciplinary fields like Advanced Computing, Pattern Recognition and Analysis, Signal and Image Processing. The proceedings cover the principles, techniques and applications in microwave & devices, communication & networking, signal & image processing, and computations & mathematics & control. The proceedings reflect the conference’s emp...

  18. Evolving networks and the development of neural systems

    International Nuclear Information System (INIS)

    Johnson, Samuel; Marro, J; Torres, Joaquín J

    2010-01-01

    It is now generally assumed that the heterogeneity of most networks in nature probably arises via preferential attachment of some sort. However, the origin of various other topological features, such as degree–degree correlations and related characteristics, is often not clear, and they may arise from specific functional conditions. We show how it is possible to analyse a very general scenario in which nodes can gain or lose edges according to any (e.g., nonlinear) function of local and/or global degree information. Applying our method to two rather different examples of brain development—synaptic pruning in humans and the neural network of the worm C. Elegans—we find that simple biologically motivated assumptions lead to very good agreement with experimental data. In particular, many nontrivial topological features of the worm's brain arise naturally at a critical point

  19. Neural Networks for Self-tuning Control Systems

    Directory of Open Access Journals (Sweden)

    A. Noriega Ponce

    2004-01-01

    Full Text Available In this paper, we presented a self-tuning control algorithm based on a three layers perceptron type neural network. The proposed algorithm is advantageous in the sense that practically a previous training of the net is not required and some changes in the set-point are generally enough to adjust the learning coefficient. Optionally, it is possible to introduce a self-tuning mechanism of the learning coefficient although by the moment it is not possible to give final conclusions about this possibility. The proposed algorithm has the special feature that the regulation error instead of the net output error is retropropagated for the weighting coefficients modifications. 

  20. System Identification Using Multilayer Differential Neural Networks: A New Result

    Directory of Open Access Journals (Sweden)

    J. Humberto Pérez-Cruz

    2012-01-01

    Full Text Available In previous works, a learning law with a dead zone function was developed for multilayer differential neural networks. This scheme requires strictly a priori knowledge of an upper bound for the unmodeled dynamics. In this paper, the learning law is modified in such a way that this condition is relaxed. By this modification, the tuning process is simpler and the dead-zone function is not required anymore. On the basis of this modification and by using a Lyapunov-like analysis, a stronger result is here demonstrated: the exponential convergence of the identification error to a bounded zone. Besides, a value for upper bound of such zone is provided. The workability of this approach is tested by a simulation example.

  1. Study on algorithm of process neural network for soft sensing in sewage disposal system

    Science.gov (United States)

    Liu, Zaiwen; Xue, Hong; Wang, Xiaoyi; Yang, Bin; Lu, Siying

    2006-11-01

    A new method of soft sensing based on process neural network (PNN) for sewage disposal system is represented in the paper. PNN is an extension of traditional neural network, in which the inputs and outputs are time-variation. An aggregation operator is introduced to process neuron, and it makes the neuron network has the ability to deal with the information of space-time two dimensions at the same time, so the data processing enginery of biological neuron is imitated better than traditional neuron. Process neural network with the structure of three layers in which hidden layer is process neuron and input and output are common neurons for soft sensing is discussed. The intelligent soft sensing based on PNN may be used to fulfill measurement of the effluent BOD (Biochemical Oxygen Demand) from sewage disposal system, and a good training result of soft sensing was obtained by the method.

  2. A Wavelet Neural Network Optimal Control Model for Traffic-Flow Prediction in Intelligent Transport Systems

    Science.gov (United States)

    Huang, Darong; Bai, Xing-Rong

    Based on wavelet transform and neural network theory, a traffic-flow prediction model, which was used in optimal control of Intelligent Traffic system, is constructed. First of all, we have extracted the scale coefficient and wavelet coefficient from the online measured raw data of traffic flow via wavelet transform; Secondly, an Artificial Neural Network model of Traffic-flow Prediction was constructed and trained using the coefficient sequences as inputs and raw data as outputs; Simultaneous, we have designed the running principium of the optimal control system of traffic-flow Forecasting model, the network topological structure and the data transmitted model; Finally, a simulated example has shown that the technique is effectively and exactly. The theoretical results indicated that the wavelet neural network prediction model and algorithms have a broad prospect for practical application.

  3. Neural Stem Cells: Implications for the Conventional Radiotherapy of Central Nervous System Malignancies

    International Nuclear Information System (INIS)

    Barani, Igor J.; Benedict, Stanley H.; Lin, Peck-Sun

    2007-01-01

    Advances in basic neuroscience related to neural stem cells and their malignant counterparts are challenging traditional models of central nervous system tumorigenesis and intrinsic brain repair. Neurogenesis persists into adulthood predominantly in two neurogenic centers: subventricular zone and subgranular zone. Subventricular zone is situated adjacent to lateral ventricles and subgranular zone is confined to the dentate gyrus of the hippocampus. Neural stem cells not only self-renew and differentiate along multiple lineages in these regions, but also contribute to intrinsic brain plasticity and repair. Ionizing radiation can depopulate these exquisitely sensitive regions directly or impair in situ neurogenesis by indirect, dose-dependent and inflammation-mediated mechanisms, even at doses <2 Gy. This review discusses the fundamental neural stem cell concepts within the framework of cumulative clinical experience with the treatment of central nervous system malignancies using conventional radiotherapy

  4. Towards an Irritable Bowel Syndrome Control System Based on Artificial Neural Networks

    Science.gov (United States)

    Podolski, Ina; Rettberg, Achim

    To solve health problems with medical applications that use complex algorithms is a trend nowadays. It could also be a chance to help patients with critical problems caused from nerve irritations to overcome them and provide a better living situation. In this paper a system for monitoring and controlling the nerves from the intestine is described on a theoretical basis. The presented system could be applied to the irritable bowel syndrome. For control a neural network is used. The advantages for using a neural network for the control of irritable bowel syndrome are the adaptation and learning. These two aspects are important because the syndrome behavior varies from patient to patient and have also concerning the time a lot of variations with respect to each patient. The developed neural network is implemented and can be simulated. Therefore, it can be shown how the network monitor and control the nerves for individual input parameters.

  5. Global neural dynamic surface tracking control of strict-feedback systems with application to hypersonic flight vehicle.

    Science.gov (United States)

    Xu, Bin; Yang, Chenguang; Pan, Yongping

    2015-10-01

    This paper studies both indirect and direct global neural control of strict-feedback systems in the presence of unknown dynamics, using the dynamic surface control (DSC) technique in a novel manner. A new switching mechanism is designed to combine an adaptive neural controller in the neural approximation domain, together with the robust controller that pulls the transient states back into the neural approximation domain from the outside. In comparison with the conventional control techniques, which could only achieve semiglobally uniformly ultimately bounded stability, the proposed control scheme guarantees all the signals in the closed-loop system are globally uniformly ultimately bounded, such that the conventional constraints on initial conditions of the neural control system can be relaxed. The simulation studies of hypersonic flight vehicle (HFV) are performed to demonstrate the effectiveness of the proposed global neural DSC design.

  6. 2nd International Conference on Electrical Systems, Technology and Information 2015

    CERN Document Server

    Tanoto, Yusak; Lim, Resmana; Santoso, Murtiyanto; Pah, Nemuel

    2016-01-01

    This book includes the original, peer-reviewed research papers from the 2nd International Conference on Electrical Systems, Technology and Information (ICESTI 2015), held in September 2015 at Patra Jasa Resort & Villas Bali, Indonesia. Topics covered include: Mechatronics and Robotics, Circuits and Systems, Power and Energy Systems, Control and Industrial Automation, and Information Theory.    It explores emerging technologies and their application in a broad range of engineering disciplines, including communication technologies and smart grids. It examines hybrid intelligent and knowledge-based control, embedded systems, and machine learning. It also presents emerging research and recent application in green energy system and storage. It discusses the role of electrical engineering in biomedical, industrial and mechanical systems, as well as multimedia systems and applications, computer vision and image and signal processing. The primary objective of this series is to provide references for disseminat...

  7. Bioinformatics and systems biology research update from the 15th International Conference on Bioinformatics (InCoB2016).

    Science.gov (United States)

    Schönbach, Christian; Verma, Chandra; Bond, Peter J; Ranganathan, Shoba

    2016-12-22

    The International Conference on Bioinformatics (InCoB) has been publishing peer-reviewed conference papers in BMC Bioinformatics since 2006. Of the 44 articles accepted for publication in supplement issues of BMC Bioinformatics, BMC Genomics, BMC Medical Genomics and BMC Systems Biology, 24 articles with a bioinformatics or systems biology focus are reviewed in this editorial. InCoB2017 is scheduled to be held in Shenzen, China, September 20-22, 2017.

  8. Biological neural networks as model systems for designing future parallel processing computers

    Science.gov (United States)

    Ross, Muriel D.

    1991-01-01

    One of the more interesting debates of the present day centers on whether human intelligence can be simulated by computer. The author works under the premise that neurons individually are not smart at all. Rather, they are physical units which are impinged upon continuously by other matter that influences the direction of voltage shifts across the units membranes. It is only the action of a great many neurons, billions in the case of the human nervous system, that intelligent behavior emerges. What is required to understand even the simplest neural system is painstaking analysis, bit by bit, of the architecture and the physiological functioning of its various parts. The biological neural network studied, the vestibular utricular and saccular maculas of the inner ear, are among the most simple of the mammalian neural networks to understand and model. While there is still a long way to go to understand even this most simple neural network in sufficient detail for extrapolation to computers and robots, a start was made. Moreover, the insights obtained and the technologies developed help advance the understanding of the more complex neural networks that underlie human intelligence.

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

    Science.gov (United States)

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

    1997-01-01

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

  10. Neural mirroring and social interaction: Motor system involvement during action observation relates to early peer cooperation.

    Science.gov (United States)

    Endedijk, H M; Meyer, M; Bekkering, H; Cillessen, A H N; Hunnius, S

    2017-04-01

    Whether we hand over objects to someone, play a team sport, or make music together, social interaction often involves interpersonal action coordination, both during instances of cooperation and entrainment. Neural mirroring is thought to play a crucial role in processing other's actions and is therefore considered important for social interaction. Still, to date, it is unknown whether interindividual differences in neural mirroring play a role in interpersonal coordination during different instances of social interaction. A relation between neural mirroring and interpersonal coordination has particularly relevant implications for early childhood, since successful early interaction with peers is predictive of a more favorable social development. We examined the relation between neural mirroring and children's interpersonal coordination during peer interaction using EEG and longitudinal behavioral data. Results showed that 4-year-old children with higher levels of motor system involvement during action observation (as indicated by lower beta-power) were more successful in early peer cooperation. This is the first evidence for a relation between motor system involvement during action observation and interpersonal coordination during other instances of social interaction. The findings suggest that interindividual differences in neural mirroring are related to interpersonal coordination and thus successful social interaction. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  11. Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

    Science.gov (United States)

    Williams-Hayes, Peggy S.

    2004-01-01

    The NASA F-15 Intelligent Flight Control System project team developed a series of flight control concepts designed to demonstrate neural network-based adaptive controller benefits, with the objective to develop and flight-test control systems using neural network technology to optimize aircraft performance under nominal conditions and stabilize the aircraft under failure conditions. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to baseline aerodynamic derivatives in flight. This open-loop flight test set was performed in preparation for a future phase in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed - pitch frequency sweep and automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. Flight data examination shows that addition of flight-identified aerodynamic derivative increments into the simulation improved aircraft pitch handling qualities.

  12. Estimating the behavior of RC beams strengthened with NSM system using artificial neural networks

    Directory of Open Access Journals (Sweden)

    Seyed Rohollah Hosseini Vaez

    2017-12-01

    Full Text Available In the last decade, conventional materials such as steel and concrete are being replaced by fiber reinforced polymer (FRP materials for the strengthening of concrete structures. Among the strengthening techniques based on Fiber Reinforced Polymer composites, the use of near-surface mounted (NSM FRP rods is emerging as a promising technology for increasing flexural and shear strength of deficient concrete, masonry and timber members. An artificial neural network is an information processing tool that is inspired by the way biological nervous systems (such as the brain process the information. The key element of this tool is the novel structure of the information processing system. In engineering applications, a neural network can be a vector mapper which maps an input vector to an output one. In the present study, a new approach is developed to predict the behavior of strengthened concrete beam using a large number of experimental data by applying artificial neural networks. Having parameters used as input nodes in ANN modeling such as elastic modulus of the FRP reinforcement, the ratio of the steel longitudinal reinforcement, dimensions of the beam section, the ratio of the NSM-FRP reinforcement and characteristics of concrete, the output node was the flexural strength of beams. The idealized neural network was employed to generate empirical charts and equations to be used in design. The aim of this study is to investigate the behavior of strengthened RC beam using artificial neural networks.

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

    Science.gov (United States)

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

    2000-01-01

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

  14. International Conference on Artificial Intelligence and Evolutionary Algorithms in Engineering Systems

    CERN Document Server

    Dash, Subhransu; Panigrahi, Bijaya

    2015-01-01

      The book is a collection of high-quality peer-reviewed research papers presented in Proceedings of International Conference on Artificial Intelligence and Evolutionary Algorithms in Engineering Systems (ICAEES 2014) held at Noorul Islam Centre for Higher Education, Kumaracoil, India. These research papers provide the latest developments in the broad area of use of artificial intelligence and evolutionary algorithms in engineering systems. The book discusses wide variety of industrial, engineering and scientific applications of the emerging techniques. It presents invited papers from the inventors/originators of new applications and advanced technologies.

  15. Proceedings of the 9. IASTED international conference on power and energy systems

    International Nuclear Information System (INIS)

    Domijan, A.Jr.

    2007-01-01

    This conference provided an international forum for researchers and practitioners to exchange ideas and practical experience in the field of energy and power systems. The purpose was to strengthen relations between the energy sector, research laboratories and universities. Discussions focused on reliability issues in the next generation of power systems. Environmental factors and customer concerns regarding power quality issues were noted as the driving force for new concepts in electricity demand. Global and regional energy challenges ranging from energy production to power delivery were also discussed. The sessions of the conference were entitled: power system planning, investment, real time management and operations; infrastructure, reliability and weather effects; alternate energy developments and sustainability using biomass, solar and other renewable energy sources; modelling power systems, demand response and end use, and tariff issues; power quality, filtering, and experimental performance evaluations; forecasting computational methods and electronics; distribution system issues, microturbines and microgrids; and, nuclear power plants, modelling and evaluation of energy systems. All 72 presentations were catalogued separately for inclusion in this database. refs., tabs., figs

  16. Proceedings of the 9. IASTED international conference on power and energy systems

    Energy Technology Data Exchange (ETDEWEB)

    Domijan, A.Jr. [Univ. of South Florida, Tampa, FL (United States)

    2007-07-01

    This conference provided an international forum for researchers and practitioners to exchange ideas and practical experience in the field of energy and power systems. The purpose was to strengthen relations between the energy sector, research laboratories and universities. Discussions focused on reliability issues in the next generation of power systems. Environmental factors and customer concerns regarding power quality issues were noted as the driving force for new concepts in electricity demand. Global and regional energy challenges ranging from energy production to power delivery were also discussed. The sessions of the conference were entitled: power system planning, investment, real time management and operations; infrastructure, reliability and weather effects; alternate energy developments and sustainability using biomass, solar and other renewable energy sources; modelling power systems, demand response and end use, and tariff issues; power quality, filtering, and experimental performance evaluations; forecasting computational methods and electronics; distribution system issues, microturbines and microgrids; and, nuclear power plants, modelling and evaluation of energy systems. All 72 presentations were catalogued separately for inclusion in this database. refs., tabs., figs.

  17. Current Scientific Approaches to Decision Making in Complex Systems: 3. Volume 1. Conference Proceedings. Third Conference, Richmond, Surrey, England, 6-8 August 1978

    Science.gov (United States)

    1980-01-01

    good decisions. What usually prevents him from implementing those rational decisions is either an overstrong conflict, leading to behavioural paralysis...of Prosocial Behaviour in the group situation. It assumes a typology of people, and attempts to show sequentially how people are influenced in their...COVERED CURRENT SCIENTIFIC APPROACHES TO DECISION MAKING IN COMPLEX SYSTEMS: III. Volume I, Conference Proceedings 6. PERFORMING ORG. REPORT NUMBER 7

  18. Real-time camera-based face detection using a modified LAMSTAR neural network system

    Science.gov (United States)

    Girado, Javier I.; Sandin, Daniel J.; DeFanti, Thomas A.; Wolf, Laura K.

    2003-03-01

    This paper describes a cost-effective, real-time (640x480 at 30Hz) upright frontal face detector as part of an ongoing project to develop a video-based, tetherless 3D head position and orientation tracking system. The work is specifically targeted for auto-stereoscopic displays and projection-based virtual reality systems. The proposed face detector is based on a modified LAMSTAR neural network system. At the input stage, after achieving image normalization and equalization, a sub-window analyzes facial features using a neural network. The sub-window is segmented, and each part is fed to a neural network layer consisting of a Kohonen Self-Organizing Map (SOM). The output of the SOM neural networks are interconnected and related by correlation-links, and can hence determine the presence of a face with enough redundancy to provide a high detection rate. To avoid tracking multiple faces simultaneously, the system is initially trained to track only the face centered in a box superimposed on the display. The system is also rotationally and size invariant to a certain degree.

  19. Differences between otolith- and semicircular canal-activated neural circuitry in the vestibular system.

    Science.gov (United States)

    Uchino, Yoshio; Kushiro, Keisuke

    2011-12-01

    In the last two decades, we have focused on establishing a reliable technique for focal stimulation of vestibular receptors to evaluate neural connectivity. Here, we summarize the vestibular-related neuronal circuits for the vestibulo-ocular reflex, vestibulocollic reflex, and vestibulospinal reflex arcs. The focal stimulating technique also uncovered some hidden neural mechanisms. In the otolith system, we identified two hidden neural mechanisms that enhance otolith receptor sensitivity. The first is commissural inhibition, which boosts sensitivity by incorporating inputs from bilateral otolith receptors, the existence of which was in contradiction to the classical understanding of the otolith system but was observed in the utricular system. The second mechanism, cross-striolar inhibition, intensifies the sensitivity of inputs from both sides of receptive cells across the striola in a single otolith sensor. This was an entirely novel finding and is typically observed in the saccular system. We discuss the possible functional meaning of commissural and cross-striolar inhibition. Finally, our focal stimulating technique was applied to elucidate the different constructions of axonal projections from each vestibular receptor to the spinal cord. We also discuss the possible function of the unique neural connectivity observed in each vestibular receptor system. Copyright © 2011 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

  20. Container-code recognition system based on computer vision and deep neural networks

    Science.gov (United States)

    Liu, Yi; Li, Tianjian; Jiang, Li; Liang, Xiaoyao

    2018-04-01

    Automatic container-code recognition system becomes a crucial requirement for ship transportation industry in recent years. In this paper, an automatic container-code recognition system based on computer vision and deep neural networks is proposed. The system consists of two modules, detection module and recognition module. The detection module applies both algorithms based on computer vision and neural networks, and generates a better detection result through combination to avoid the drawbacks of the two methods. The combined detection results are also collected for online training of the neural networks. The recognition module exploits both character segmentation and end-to-end recognition, and outputs the recognition result which passes the verification. When the recognition module generates false recognition, the result will be corrected and collected for online training of the end-to-end recognition sub-module. By combining several algorithms, the system is able to deal with more situations, and the online training mechanism can improve the performance of the neural networks at runtime. The proposed system is able to achieve 93% of overall recognition accuracy.

  1. Proceedings: 1987 conference on expert-system applications in power plants

    International Nuclear Information System (INIS)

    1988-12-01

    The third EPRI conference on expert system applications for the electric utilities was held in Boston on May 27--29, 1987. The conference was co-hosted by Boston Edison Company and Niagara Mohawk Power Corporation and it was attended by more than 300 attendees from the US, Europe and Japan. The participants represented a broad spectrum of utilities, equipment manufacturers, engineering consultants, university faculty, and members of the federal and state regulatory agencies. Many applications of expert systems have emerged recently and many of the vendors now have meaningful prototypes that can demonstrate the benefits of expert systems to the utilities. Several utilities also have prototypes working at this time; but more importantly, the utilities have advanced significantly in their understanding and appreciation of this technology. Many utilities have organized teams within their organizations that have the charter of evaluating the leading applications and developing associated plans for the implementation of these applications. The conference identified several areas that are important to the utilities: training of the utility staff in the basic technology to rapidly build the appropriate infrastructure; review and recommendations in selection of hardware and software tools. This is needed since there is a very rapid change both in the market place as well as in terms of technical progress; dissemination of examples of good applications that directly relate to the power industry would be very helpful in relating to how the technology can be used; and coordination of the development of expert system applications among the utilities. This is needed since expert system applications typically are expensive to develop, but also relatively easy to transfer from one utility to another

  2. Proceedings of the Canadian Wind Energy Association's 2009 wind matters conference : wind and power systems

    International Nuclear Information System (INIS)

    2009-01-01

    This conference provided a forum for wind energy and electric power industry experts to discuss issues related to wind and power systems. An overview of wind integration studies and activities in Canada and the United States was provided. New tools and technologies for facilitating the integration of wind and improve market conditions for wind energy developers were presented. Methods of increasing wind penetration were evaluated, and technical issues related to wind interconnections throughout North America were reviewed. The conference was divided into the following 5 sessions: (1) experiences with wind integration, and lessons learned, (2) update on ongoing wind integration initiatives in Canada and the United States, (3) initiatives and tools to facilitate wind integration and market access, (4) developments in wind interconnection and grid codes, (5) wind energy and cold weather considerations, and (6) challenges to achieving the 20 per cent WindVision goal in Canada. The conference featured 21 presentations, of which 13 have been catalogued separately for inclusion in this database. refs., tabs., figs

  3. Neural coding in the visual system of Drosophila melanogaster: How do small neural populations support visually guided behaviours?

    Science.gov (United States)

    Dewar, Alex D M; Wystrach, Antoine; Philippides, Andrew; Graham, Paul

    2017-10-01

    All organisms wishing to survive and reproduce must be able to respond adaptively to a complex, changing world. Yet the computational power available is constrained by biology and evolution, favouring mechanisms that are parsimonious yet robust. Here we investigate the information carried in small populations of visually responsive neurons in Drosophila melanogaster. These so-called 'ring neurons', projecting to the ellipsoid body of the central complex, are reported to be necessary for complex visual tasks such as pattern recognition and visual navigation. Recently the receptive fields of these neurons have been mapped, allowing us to investigate how well they can support such behaviours. For instance, in a simulation of classic pattern discrimination experiments, we show that the pattern of output from the ring neurons matches observed fly behaviour. However, performance of the neurons (as with flies) is not perfect and can be easily improved with the addition of extra neurons, suggesting the neurons' receptive fields are not optimised for recognising abstract shapes, a conclusion which casts doubt on cognitive explanations of fly behaviour in pattern recognition assays. Using artificial neural networks, we then assess how easy it is to decode more general information about stimulus shape from the ring neuron population codes. We show that these neurons are well suited for encoding information about size, position and orientation, which are more relevant behavioural parameters for a fly than abstract pattern properties. This leads us to suggest that in order to understand the properties of neural systems, one must consider how perceptual circuits put information at the service of behaviour.

  4. Study on application of adaptive fuzzy control and neural network in the automatic leveling system

    Science.gov (United States)

    Xu, Xiping; Zhao, Zizhao; Lan, Weiyong; Sha, Lei; Qian, Cheng

    2015-04-01

    This paper discusses the adaptive fuzzy control and neural network BP algorithm in large flat automatic leveling control system application. The purpose is to develop a measurement system with a flat quick leveling, Make the installation on the leveling system of measurement with tablet, to be able to achieve a level in precision measurement work quickly, improve the efficiency of the precision measurement. This paper focuses on the automatic leveling system analysis based on fuzzy controller, Use of the method of combining fuzzy controller and BP neural network, using BP algorithm improve the experience rules .Construct an adaptive fuzzy control system. Meanwhile the learning rate of the BP algorithm has also been run-rate adjusted to accelerate convergence. The simulation results show that the proposed control method can effectively improve the leveling precision of automatic leveling system and shorten the time of leveling.

  5. 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

  6. Directive Nanophysical Cues for Regenerative Neural Cell Systems

    Science.gov (United States)

    Ayres, Virginia; Tiryaki, Volkan Mujdat; Ahmed, Ijaz; Shreiber, David

    Until recently, implantables such as stents, probes, wafers and scaffolds have been viewed as passive vehicles for the delivery of physical, pharmacological and cellular interventions. Recent research, however, indicates that the physical environments that implantables present supply directive cues in their own right that work in conjunction with biochemical cues and produce a jointly-directed outcome. We will present our research in CNS repairs using advanced scanning probe microscopy, electron microscopies and contact angle measurements to quantitatively describe the nanoscale elasticity, surface roughness, work of adhesion and surface polarity for investigation of scaffold environments. We will also present our research using super-resolution immunocytochemistry and atomic force microscopy to evaluate neural cell morphological responses with associated micro filament, microtubule and intermediate filament expressions, along with results on how and which integrin-family receptors are possibly involved. Finally, we will present our novel application of k-means cluster analysis applied across multiple experimental modalities for quantification of synergistic scaffold properties and cell responses.

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

    Science.gov (United States)

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

    2017-12-21

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

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

    Directory of Open Access Journals (Sweden)

    Ahnsei Shon

    2017-12-01

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

  9. The use of neural networks in the D0 data acquisition system

    International Nuclear Information System (INIS)

    Cutts, D.; Hoftun, J.S.; Sornborger, A.; Astur, R.V.; Johnson, C.R.; Zeller, R.T.

    1989-01-01

    We discuss the possible application of algorithms derived from neural networks to the D0 experiment. The D0 data acquisition system is based on a large farm of MicroVAXes, each independently performing real-time event filtering. A new generation of multiport memories in each MicroVAX node will enable special function processors to have direct access to event data. We describe an exploratory study of back propagation neural networks, such as might be configured in the nodes, for more efficient event filtering. 9 refs., 3 figs., 1 tab

  10. A Drone Remote Sensing for Virtual Reality Simulation System for Forest Fires: Semantic Neural Network Approach

    Science.gov (United States)

    Narasimha Rao, Gudikandhula; Jagadeeswara Rao, Peddada; Duvvuru, Rajesh

    2016-09-01

    Wild fires have significant impact on atmosphere and lives. The demand of predicting exact fire area in forest may help fire management team by using drone as a robot. These are flexible, inexpensive and elevated-motion remote sensing systems that use drones as platforms are important for substantial data gaps and supplementing the capabilities of manned aircraft and satellite remote sensing systems. In addition, powerful computational tools are essential for predicting certain burned area in the duration of a forest fire. The reason of this study is to built up a smart system based on semantic neural networking for the forecast of burned areas. The usage of virtual reality simulator is used to support the instruction process of fire fighters and all users for saving of surrounded wild lives by using a naive method Semantic Neural Network System (SNNS). Semantics are valuable initially to have a enhanced representation of the burned area prediction and better alteration of simulation situation to the users. In meticulous, consequences obtained with geometric semantic neural networking is extensively superior to other methods. This learning suggests that deeper investigation of neural networking in the field of forest fires prediction could be productive.

  11. Systematic Self-Regulation of the Neural System Essential for Peak Performance and Wellbeing.

    Science.gov (United States)

    Cassel, Russell N.

    1985-01-01

    Balance and harmony within one's neural system is dynamic and changing, and restoring that balance is essential for peak performance. With a minimum amount of training individuals are able to restore this delicate balance and thereby enhance their own wellbeing. Autogenic feedback training has been demonstrated to be an effective means for…

  12. Systemic Injection of Neural Stem/progenitor Cells in Mice With Chronic EAE

    OpenAIRE

    Donegà, Matteo; Giusto, Elena; Cossetti, Chiara; Schaeffer, Julia; Pluchino, Stefano

    2014-01-01

    Neural stem/precursor cells (NPCs) are a promising stem cell source for transplantation approaches aiming at brain repair or restoration in regenerative neurology. This directive has arisen from the extensive evidence that brain repair is achieved after focal or systemic NPC transplantation in several pre clinical models of neurological diseases.

  13. Comparable mechanisms for action and language: Neural systems behind intentions, goals and means

    NARCIS (Netherlands)

    Schie, H.T. van; Toni, I.; Bekkering, H.

    2006-01-01

    In this position paper we explore correspondence between neural systems for language and action starting from recent electrophysiological findings on the roles of posterior and frontal areas in goal-directed grasping actions. The paper compares the perceptual and motor organization for action and

  14. Artificial neural network decision support systems for new product development project selection

    NARCIS (Netherlands)

    Thieme, R.J.; Song, Michael; Calantone, R.J.

    2000-01-01

    The authors extend and develop an artificial neural network decision support system and demonstrate how it can guide managers when they make complex new product development decisions. The authors use data from 612 projects to compare this new method with traditional methods for predicting various

  15. A New Method for Studying the Periodic System Based on a Kohonen Neural Network

    Science.gov (United States)

    Chen, David Zhekai

    2010-01-01

    A new method for studying the periodic system is described based on the combination of a Kohonen neural network and a set of chemical and physical properties. The classification results are directly shown in a two-dimensional map and easy to interpret. This is one of the major advantages of this approach over other methods reported in the…

  16. Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2014-11-01

    Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.

  17. A Low-Cost Maximum Power Point Tracking System Based on Neural Network Inverse Model Controller

    Directory of Open Access Journals (Sweden)

    Carlos Robles Algarín

    2018-01-01

    Full Text Available This work presents the design, modeling, and implementation of a neural network inverse model controller for tracking the maximum power point of a photovoltaic (PV module. A nonlinear autoregressive network with exogenous inputs (NARX was implemented in a serial-parallel architecture. The PV module mathematical modeling was developed, a buck converter was designed to operate in the continuous conduction mode with a switching frequency of 20 KHz, and the dynamic neural controller was designed using the Neural Network Toolbox from Matlab/Simulink (MathWorks, Natick, MA, USA, and it was implemented on an open-hardware Arduino Mega board. To obtain the reference signals for the NARX and determine the 65 W PV module behavior, a system made of a 0.8 W PV cell, a temperature sensor, a voltage sensor and a static neural network, was used. To evaluate performance a comparison with the P&O traditional algorithm was done in terms of response time and oscillations around the operating point. Simulation results demonstrated the superiority of neural controller over the P&O. Implementation results showed that approximately the same power is obtained with both controllers, but the P&O controller presents oscillations between 7 W and 10 W, in contrast to the inverse controller, which had oscillations between 1 W and 2 W.

  18. Engineering applications of fpgas chaotic systems, artificial neural networks, random number generators, and secure communication systems

    CERN Document Server

    Tlelo-Cuautle, Esteban; de la Fraga, Luis Gerardo

    2016-01-01

    This book offers readers a clear guide to implementing engineering applications with FPGAs, from the mathematical description to the hardware synthesis, including discussion of VHDL programming and co-simulation issues. Coverage includes FPGA realizations such as: chaos generators that are described from their mathematical models; artificial neural networks (ANNs) to predict chaotic time series, for which a discussion of different ANN topologies is included, with different learning techniques and activation functions; random number generators (RNGs) that are realized using different chaos generators, and discussions of their maximum Lyapunov exponent values and entropies. Finally, optimized chaotic oscillators are synchronized and realized to implement a secure communication system that processes black and white and grey-scale images. In each application, readers will find VHDL programming guidelines and computer arithmetic issues, along with co-simulation examples with Active-HDL and Simulink. Readers will b...

  19. The International conference on fast reactors and related fuel cycles: next generation nuclear systems for sustainable development. Book of abstracts

    International Nuclear Information System (INIS)

    2017-01-01

    The materials of the International Conference on Fast Reactors and Related Fuel Cycles (June 26-29, 2017, Yekaterinburg) are presented. The forum was organized by the IAEA with the assistance of Rosatom State Corporation. The theme of the conference: “The New Generation of Nuclear Systems for Sustainable Development”. About 700 specialists from more than 30 countries took part in the conference. The state and prospects for the development of the direction of fast reactors in countries dealing with this topic were discussed. A wide range of scientific issues covered the concepts of prospective reactors, reactor cores, fuel and fuel cycles, operation and decommissioning, safety, licensing, structural materials, industrial implementation [ru

  20. Neural-net based unstable machine identification using individual energy functions. [Transient disturbances in power systems

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M [Institut Nikola Tesla, Belgrade (Yugoslavia); Sobajic, D J; Pao, Yohhan [Case Western Reserve Univ., Cleveland, OH (United States)

    1991-10-01

    The identification of the mode of instability plays an essential role in generating principal energy boundary hypersurfaces. We present a new method for unstable machine identification based on the use of supervised learning neural-net technology, and the adaptive pattern recognition concept. It is shown that using individual energy functions as pattern features, appropriately trained neural-nets can retrieve the reliable characterization of the transient process including critical clearing time parameter, mode of instability and energy margins. Generalization capabilities of the neural-net processing allow for these assessments to be made independently of load levels. The results obtained from computer simulations are presented using the New England power system, as an example. (author).

  1. Design and Implementation of Behavior Recognition System Based on Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    Yu Bo

    2017-01-01

    Full Text Available We build a set of human behavior recognition system based on the convolution neural network constructed for the specific human behavior in public places. Firstly, video of human behavior data set will be segmented into images, then we process the images by the method of background subtraction to extract moving foreground characters of body. Secondly, the training data sets are trained into the designed convolution neural network, and the depth learning network is constructed by stochastic gradient descent. Finally, the various behaviors of samples are classified and identified with the obtained network model, and the recognition results are compared with the current mainstream methods. The result show that the convolution neural network can study human behavior model automatically and identify human’s behaviors without any manually annotated trainings.

  2. Second Asia-Pacific Conference on the Computer Aided System Engineering

    CERN Document Server

    Chaczko, Zenon; Jacak, Witold; Łuba, Tadeusz; Computational Intelligence and Efficiency in Engineering Systems

    2015-01-01

    This carefully edited and reviewed volume addresses the increasingly popular demand for seeking more clarity in the data that we are immersed in. It offers excellent examples of the intelligent ubiquitous computation, as well as recent advances in systems engineering and informatics. The content represents state-of-the-art foundations for researchers in the domain of modern computation, computer science, system engineering and networking, with many examples that are set in industrial application context. The book includes the carefully selected best contributions to APCASE 2014, the 2nd Asia-Pacific Conference on  Computer Aided System Engineering, held February 10-12, 2014 in South Kuta, Bali, Indonesia. The book consists of four main parts that cover data-oriented engineering science research in a wide range of applications: computational models and knowledge discovery; communications networks and cloud computing; computer-based systems; and data-oriented and software-intensive systems.

  3. 12th International Conference of Dynamical Systems-Theory and Applications

    CERN Document Server

    Applied Non-Linear Dynamical Systems

    2014-01-01

    The book is a collection of contributions devoted to analytical, numerical and experimental techniques of dynamical systems, presented at the International Conference on Dynamical Systems: Theory and Applications, held in Łódź, Poland on December 2-5, 2013. The studies give deep insight into both the theory and applications of non-linear dynamical systems, emphasizing directions for future research. Topics covered include: constrained motion of mechanical systems and tracking control; diversities in the inverse dynamics; singularly perturbed ODEs with periodic coefficients; asymptotic solutions to the problem of vortex structure around a cylinder; investigation of the regular and chaotic dynamics; rare phenomena and chaos in power converters; non-holonomic constraints in wheeled robots; exotic bifurcations in non-smooth systems; micro-chaos; energy exchange of coupled oscillators; HIV dynamics; homogenous transformations with applications to off-shore slender structures; novel approaches to a qualitative s...

  4. A novel joint-processing adaptive nonlinear equalizer using a modular recurrent neural network for chaotic communication systems.

    Science.gov (United States)

    Zhao, Haiquan; Zeng, Xiangping; Zhang, Jiashu; Liu, Yangguang; Wang, Xiaomin; Li, Tianrui

    2011-01-01

    To eliminate nonlinear channel distortion in chaotic communication systems, a novel joint-processing adaptive nonlinear equalizer based on a pipelined recurrent neural network (JPRNN) is proposed, using a modified real-time recurrent learning (RTRL) algorithm. Furthermore, an adaptive amplitude RTRL algorithm is adopted to overcome the deteriorating effect introduced by the nesting process. Computer simulations illustrate that the proposed equalizer outperforms the pipelined recurrent neural network (PRNN) and recurrent neural network (RNN) equalizers. Copyright © 2010 Elsevier Ltd. All rights reserved.

  5. System specification/system design document comment review: Plutonium Stabilization and Packaging System. Notes of conference

    International Nuclear Information System (INIS)

    1996-01-01

    A meeting was held between DOE personnel and the BNFL team to review the proposed resolutions to DOE comments on the initial issue of the system specification and system design document for the Plutonium Stabilization and Packaging System. The objectives of this project are to design, fabricate, install, and start up a glovebox system for the safe repackaging of plutonium oxide and metal, with a requirement of a 50-year storage period. The areas discussed at the meeting were: nitrogen in can; moisture instrumentation; glovebox atmosphere; can marking bar coding; weld quality; NFPA-101 references; inner can swabbing; ultimate storage environment; throughput; convenience can screw-top design; furnacetrays; authorization basis; compactor safety; schedule for DOE review actions; fire protection; criticality safety; applicable standards; approach to MC and A; homogeneous oxide; resistance welder power; and tray overfill. Revised resolutions were drafted and are presented

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

    Science.gov (United States)

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

    2018-03-01

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

  7. A Red-Light Running Prevention System Based on Artificial Neural Network and Vehicle Trajectory Data

    Directory of Open Access Journals (Sweden)

    Pengfei Li

    2014-01-01

    Full Text Available The high frequency of red-light running and complex driving behaviors at the yellow onset at intersections cannot be explained solely by the dilemma zone and vehicle kinematics. In this paper, the author presented a red-light running prevention system which was based on artificial neural networks (ANNs to approximate the complex driver behaviors during yellow and all-red clearance and serve as the basis of an innovative red-light running prevention system. The artificial neural network and vehicle trajectory are applied to identify the potential red-light runners. The ANN training time was also acceptable and its predicting accurate rate was over 80%. Lastly, a prototype red-light running prevention system with the trained ANN model was described. This new system can be directly retrofitted into the existing traffic signal systems.

  8. New Solutions to the Firing Squad Synchronization Problems for Neural and Hyperdag P Systems

    Directory of Open Access Journals (Sweden)

    Michael J. Dinneen

    2009-11-01

    Full Text Available We propose two uniform solutions to an open question: the Firing Squad Synchronization Problem (FSSP, for hyperdag and symmetric neural P systems, with anonymous cells. Our solutions take e_c+5 and 6e_c+7 steps, respectively, where e_c is the eccentricity of the commander cell of the dag or digraph underlying these P systems. The first and fast solution is based on a novel proposal, which dynamically extends P systems with mobile channels. The second solution is substantially longer, but is solely based on classical rules and static channels. In contrast to the previous solutions, which work for tree-based P systems, our solutions synchronize to any subset of the underlying digraph; and do not require membrane polarizations or conditional rules, but require states, as typically used in hyperdag and neural P systems.

  9. A red-light running prevention system based on artificial neural network and vehicle trajectory data.

    Science.gov (United States)

    Li, Pengfei; Li, Yan; Guo, Xiucheng

    2014-01-01

    The high frequency of red-light running and complex driving behaviors at the yellow onset at intersections cannot be explained solely by the dilemma zone and vehicle kinematics. In this paper, the author presented a red-light running prevention system which was based on artificial neural networks (ANNs) to approximate the complex driver behaviors during yellow and all-red clearance and serve as the basis of an innovative red-light running prevention system. The artificial neural network and vehicle trajectory are applied to identify the potential red-light runners. The ANN training time was also acceptable and its predicting accurate rate was over 80%. Lastly, a prototype red-light running prevention system with the trained ANN model was described. This new system can be directly retrofitted into the existing traffic signal systems.

  10. 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

  11. A Red-Light Running Prevention System Based on Artificial Neural Network and Vehicle Trajectory Data

    Science.gov (United States)

    Li, Pengfei; Li, Yan; Guo, Xiucheng

    2014-01-01

    The high frequency of red-light running and complex driving behaviors at the yellow onset at intersections cannot be explained solely by the dilemma zone and vehicle kinematics. In this paper, the author presented a red-light running prevention system which was based on artificial neural networks (ANNs) to approximate the complex driver behaviors during yellow and all-red clearance and serve as the basis of an innovative red-light running prevention system. The artificial neural network and vehicle trajectory are applied to identify the potential red-light runners. The ANN training time was also acceptable and its predicting accurate rate was over 80%. Lastly, a prototype red-light running prevention system with the trained ANN model was described. This new system can be directly retrofitted into the existing traffic signal systems. PMID:25435870

  12. 3rd International Conference on Modelling, Computation and Optimization in Information Systems and Management Sciences

    CERN Document Server

    Dinh, Tao; Nguyen, Ngoc

    2015-01-01

    This proceedings set contains 85 selected full papers presented at the 3rd International Conference on Modelling, Computation and Optimization in Information Systems and Management Sciences - MCO 2015, held on May 11–13, 2015 at Lorraine University, France. The present part I of the 2 volume set includes articles devoted to Combinatorial optimization and applications, DC programming and DCA: thirty years of Developments, Dynamic Optimization, Modelling and Optimization in financial engineering, Multiobjective programming, Numerical Optimization, Spline Approximation and Optimization, as well as Variational Principles and Applications

  13. Stochastic Oscillation in Self-Organized Critical States of Small Systems: Sensitive Resting State in Neural Systems.

    Science.gov (United States)

    Wang, Sheng-Jun; Ouyang, Guang; Guang, Jing; Zhang, Mingsha; Wong, K Y Michael; Zhou, Changsong

    2016-01-08

    Self-organized critical states (SOCs) and stochastic oscillations (SOs) are simultaneously observed in neural systems, which appears to be theoretically contradictory since SOCs are characterized by scale-free avalanche sizes but oscillations indicate typical scales. Here, we show that SOs can emerge in SOCs of small size systems due to temporal correlation between large avalanches at the finite-size cutoff, resulting from the accumulation-release process in SOCs. In contrast, the critical branching process without accumulation-release dynamics cannot exhibit oscillations. The reconciliation of SOCs and SOs is demonstrated both in the sandpile model and robustly in biologically plausible neuronal networks. The oscillations can be suppressed if external inputs eliminate the prominent slow accumulation process, providing a potential explanation of the widely studied Berger effect or event-related desynchronization in neural response. The features of neural oscillations and suppression are confirmed during task processing in monkey eye-movement experiments. Our results suggest that finite-size, columnar neural circuits may play an important role in generating neural oscillations around the critical states, potentially enabling functional advantages of both SOCs and oscillations for sensitive response to transient stimuli.

  14. 10th International Conference on Practical Applications of Agents and Multi-Agent Systems

    CERN Document Server

    Pérez, Javier; Golinska, Paulina; Giroux, Sylvain; Corchuelo, Rafael; Trends in Practical Applications of Agents and Multiagent Systems

    2012-01-01

    PAAMS, the International Conference on Practical Applications of Agents and Multi-Agent Systems is an evolution of the International Workshop on Practical Applications of Agents and Multi-Agent Systems. PAAMS is an international yearly tribune to present, to discuss, and to disseminate the latest developments and the most important outcomes related to real-world applications. It provides a unique opportunity to bring multi-disciplinary experts, academics and practitioners together to exchange their experience in the development of Agents and Multi-Agent Systems.   This volume presents the papers that have been accepted for the 2012 in the workshops: Workshop on Agents for Ambient Assisted Living, Workshop on Agent-Based Solutions for Manufacturing and Supply Chain and Workshop on Agents and Multi-agent systems for Enterprise Integration.

  15. 16th East-European Conference on Advances in Databases and Information Systems (ADBIS 2012)

    CERN Document Server

    Härder, Theo; Wrembel, Robert; Advances in Databases and Information Systems

    2013-01-01

    This volume is the second one of the 16th East-European Conference on Advances in Databases and Information Systems (ADBIS 2012), held on September 18-21, 2012, in Poznań, Poland. The first one has been published in the LNCS series.   This volume includes 27 research contributions, selected out of 90. The contributions cover a wide spectrum of topics in the database and information systems field, including: database foundation and theory, data modeling and database design, business process modeling, query optimization in relational and object databases, materialized view selection algorithms, index data structures, distributed systems, system and data integration, semi-structured data and databases, semantic data management, information retrieval, data mining techniques, data stream processing, trust and reputation in the Internet, and social networks. Thus, the content of this volume covers the research areas from fundamentals of databases, through still hot topic research problems (e.g., data mining, XML ...

  16. 14th International Conference on Practical Applications of Agents and Multi-Agent Systems : Special Sessions

    CERN Document Server

    Escalona, María; Corchuelo, Rafael; Mathieu, Philippe; Vale, Zita; Campbell, Andrew; Rossi, Silvia; Adam, Emmanuel; Jiménez-López, María; Navarro, Elena; Moreno, María

    2016-01-01

    PAAMS, the International Conference on Practical Applications of Agents and Multi-Agent Systems is an evolution of the International Workshop on Practical Applications of Agents and Multi-Agent Systems. PAAMS is an international yearly tribune to present, to discuss, and to disseminate the latest developments and the most important outcomes related to real-world applications. It provides a unique opportunity to bring multi-disciplinary experts, academics and practitioners together to exchange their experience in the development of Agents and Multi-Agent Systems. This volume presents the papers that have been accepted for the 2016 in the special sessions: Agents Behaviours and Artificial Markets (ABAM); Advances on Demand Response and Renewable Energy Sources in Agent Based Smart Grids (ADRESS); Agents and Mobile Devices (AM); Agent Methodologies for Intelligent Robotics Applications (AMIRA); Learning, Agents and Formal Languages (LAFLang); Multi-Agent Systems and Ambient Intelligence (MASMAI); Web Mining and ...

  17. 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

  18. Proceedings of the international conference on vacuum science and technology and SRS vacuum systems. V.1: accelerators and SRS systems

    International Nuclear Information System (INIS)

    Venkatramani, N.; Sinha, A.K.

    1995-01-01

    An International Conference on Vacuum Science and Technology, INCOVAST-95 was held during January 30 - February 2, 1995 at the Centre for Advanced Technology (CAT), Indore under the aegis of the Indian Vacuum Society. Centre for Advanced Technology has a major programme of design and construction of a 450 MeV electron storage ring, synchrotron radiation source Indus-1 followed by the 1.25 GeV Indus-2. To match the activities at the centre, the present conference had ultrahigh vacuum for Synchrotron Radiation Sources (SRSs) as the main theme. Three major topics, namely accelerators and SRS systems, thin films and surfaces, vacuum components and applications were covered in detail. A short summary of the discussions is also included in the proceedings. Papers relevant to INIS are indexed separately

  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. DEVELOPMENT OF A COMPUTER SYSTEM FOR IDENTITY AUTHENTICATION USING ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Timur Kartbayev

    2017-03-01

    Full Text Available The aim of the study is to increase the effectiveness of automated face recognition to authenticate identity, considering features of change of the face parameters over time. The improvement of the recognition accuracy, as well as consideration of the features of temporal changes in a human face can be based on the methodology of artificial neural networks. Hybrid neural networks, combining the advantages of classical neural networks and fuzzy logic systems, allow using the network learnability along with the explanation of the findings. The structural scheme of intelligent system for identification based on artificial neural networks is proposed in this work. It realizes the principles of digital information processing and identity recognition taking into account the forecast of key characteristics’ changes over time (e.g., due to aging. The structural scheme has a three-tier architecture and implements preliminary processing, recognition and identification of images obtained as a result of monitoring. On the basis of expert knowledge, the fuzzy base of products is designed. It allows assessing possible changes in key characteristics, used to authenticate identity based on the image. To take this possibility into consideration, a neuro-fuzzy network of ANFIS type was used, which implements the algorithm of Tagaki-Sugeno. The conducted experiments showed high efficiency of the developed neural network and a low value of learning errors, which allows recommending this approach for practical implementation. Application of the developed system of fuzzy production rules that allow predicting changes in individuals over time, will improve the recognition accuracy, reduce the number of authentication failures and improve the efficiency of information processing and decision-making in applications, such as authentication of bank customers, users of mobile applications, or in video monitoring systems of sensitive sites.

  1. On control of Hopf bifurcation in time-delayed neural network system

    International Nuclear Information System (INIS)

    Zhou Shangbo; Liao Xiaofeng; Yu Juebang; Wong Kwokwo

    2005-01-01

    The control of Hopf bifurcations in neural network systems is studied in this Letter. The asymptotic stability theorem and the relevant corollary for linearized nonlinear dynamical systems are proven. In particular, a novel method for analyzing the local stability of a dynamical system with time-delay is suggested. For the time-delayed system consisting of one or two neurons, a washout filter based control model is proposed and analyzed. By employing the stability theorems derived, we investigate the stability of a control system and state the relevant theorems for choosing the parameters of the stabilized control system

  2. Sliding mode synchronization controller design with neural network for uncertain chaotic systems

    Energy Technology Data Exchange (ETDEWEB)

    Mou Chen [College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016 (China)], E-mail: chenmou@nuaa.edu.cn; Jiang Changsheng; Bin Jiang; Wu Qingxian [College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016 (China)

    2009-02-28

    A sliding mode synchronization controller is presented with RBF neural network for two chaotic systems in this paper. The compound disturbance of the synchronization error system consists of nonlinear uncertainties and exterior disturbances of chaotic systems. Based on RBF neural networks, a compound disturbance observer is proposed and the update law of parameters is given to monitor the compound disturbance. The synchronization controller is given based on the output of the compound disturbance observer. The designed controller can make the synchronization error convergent to zero and overcome the disruption of the uncertainty and the exterior disturbance of the system. Finally, an example is given to demonstrate the availability of the proposed synchronization control method.

  3. Command Filtered Adaptive Fuzzy Neural Network Backstepping Control for Marine Power System

    Directory of Open Access Journals (Sweden)

    Xin Zhang

    2014-01-01

    Full Text Available In order to retrain chaotic oscillation of marine power system which is excited by periodic electromagnetism perturbation, a novel command-filtered adaptive fuzzy neural network backstepping control method is designed. First, the mathematical model of marine power system is established based on the two parallel nonlinear model. Then, main results of command-filtered adaptive fuzzy neural network backstepping control law are given. And the Lyapunov stability theory is applied to prove that the system can remain closed-loop asymptotically stable with this controller. Finally, simulation results indicate that the designed controller can suppress chaotic oscillation with fast convergence speed that makes the system return to the equilibrium point quickly; meanwhile, the parameter which induces chaotic oscillation can also be discriminated.

  4. Neural net based determination of generator-shedding requirements in electric power systems

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M [Electrical Engineering Inst. ' Nikola Tesla' , Belgrade (Yugoslavia); Sobajic, D J; Pao, Y -H [Case Western Reserve Univ., Cleveland, OH (United States). Dept. of Electrical Engineering and Applied Physics Case Western Reserve Univ., Cleveland, OH (United States). Dept. of Computer Engineering and Science AI WARE Inc., Cleveland, OH (United States)

    1992-09-01

    This paper presents an application of artificial neural networks (ANN) in support of a decision-making process by power system operators directed towards the fast stabilisation of multi-machine systems. The proposed approach considers generator shedding as the most effective discrete supplementary control for improving the dynamic performance of faulted power systems and preventing instabilities. The sensitivity of the transient energy function (TEF) with respect to changes in the amount of dropped generation is used during the training phase of ANNs to assess the critical amount of generator shedding required to prevent the loss of synchronism. The learning capabilities of neural nets are used to establish complex mappings between fault information and the amount of generation to be shed, suggesting it as the control signal to the power system operator. (author)

  5. Adaptive Neural Tracking Control for Discrete-Time Switched Nonlinear Systems with Dead Zone Inputs

    Directory of Open Access Journals (Sweden)

    Jidong Wang

    2017-01-01

    Full Text Available In this paper, the adaptive neural controllers of subsystems are proposed for a class of discrete-time switched nonlinear systems with dead zone inputs under arbitrary switching signals. Due to the complicated framework of the discrete-time switched nonlinear systems and the existence of the dead zone, it brings about difficulties for controlling such a class of systems. In addition, the radial basis function neural networks are employed to approximate the unknown terms of each subsystem. Switched update laws are designed while the parameter estimation is invariable until its corresponding subsystem is active. Then, the closed-loop system is stable and all the signals are bounded. Finally, to illustrate the effectiveness of the proposed method, an example is employed.

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

    Science.gov (United States)

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

    2017-10-01

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

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

    Science.gov (United States)

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

    2014-01-01

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

  8. New neural-networks-based 3D object recognition system

    Science.gov (United States)

    Abolmaesumi, Purang; Jahed, M.

    1997-09-01

    Three-dimensional object recognition has always been one of the challenging fields in computer vision. In recent years, Ulman and Basri (1991) have proposed that this task can be done by using a database of 2-D views of the objects. The main problem in their proposed system is that the correspondent points should be known to interpolate the views. On the other hand, their system should have a supervisor to decide which class does the represented view belong to. In this paper, we propose a new momentum-Fourier descriptor that is invariant to scale, translation, and rotation. This descriptor provides the input feature vectors to our proposed system. By using the Dystal network, we show that the objects can be classified with over 95% precision. We have used this system to classify the objects like cube, cone, sphere, torus, and cylinder. Because of the nature of the Dystal network, this system reaches to its stable point by a single representation of the view to the system. This system can also classify the similar views to a single class (e.g., for the cube, the system generated 9 different classes for 50 different input views), which can be used to select an optimum database of training views. The system is also very flexible to the noise and deformed views.

  9. Novel Modified Elman Neural Network Control for PMSG System Based on Wind Turbine Emulator

    OpenAIRE

    Lin, Chih-Hong

    2013-01-01

    The novel modified Elman neural network (NN) controlled permanent magnet synchronous generator (PMSG) system, which is directly driven by a permanent magnet synchronous motor (PMSM) based on wind turbine emulator, is proposed to control output of rectifier (AC/DC power converter) and inverter (DC/AC power converter) in this study. First, a closed loop PMSM drive control based on wind turbine emulator is designed to generate power for the PMSG system according to different wind speeds. Then, t...

  10. An analog VLSI real time optical character recognition system based on a neural architecture

    International Nuclear Information System (INIS)

    Bo, G.; Caviglia, D.; Valle, M.

    1999-01-01

    In this paper a real time Optical Character Recognition system is presented: it is based on a feature extraction module and a neural network classifier which have been designed and fabricated in analog VLSI technology. Experimental results validate the circuit functionality. The results obtained from a validation based on a mixed approach (i.e., an approach based on both experimental and simulation results) confirm the soundness and reliability of the system

  11. An analog VLSI real time optical character recognition system based on a neural architecture

    Energy Technology Data Exchange (ETDEWEB)

    Bo, G.; Caviglia, D.; Valle, M. [Genoa Univ. (Italy). Dip. of Biophysical and Electronic Engineering

    1999-03-01

    In this paper a real time Optical Character Recognition system is presented: it is based on a feature extraction module and a neural network classifier which have been designed and fabricated in analog VLSI technology. Experimental results validate the circuit functionality. The results obtained from a validation based on a mixed approach (i.e., an approach based on both experimental and simulation results) confirm the soundness and reliability of the system.

  12. Review of Data Preprocessing Methods for Sign Language Recognition Systems based on Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Zorins Aleksejs

    2016-12-01

    Full Text Available The article presents an introductory analysis of relevant research topic for Latvian deaf society, which is the development of the Latvian Sign Language Recognition System. More specifically the data preprocessing methods are discussed in the paper and several approaches are shown with a focus on systems based on artificial neural networks, which are one of the most successful solutions for sign language recognition task.

  13. Identification of chaotic systems by neural network with hybrid learning algorithm

    International Nuclear Information System (INIS)

    Pan, S.-T.; Lai, C.-C.

    2008-01-01

    Based on the genetic algorithm (GA) and steepest descent method (SDM), this paper proposes a hybrid algorithm for the learning of neural networks to identify chaotic systems. The systems in question are the logistic map and the Duffing equation. Different identification schemes are used to identify both the logistic map and the Duffing equation, respectively. Simulation results show that our hybrid algorithm is more efficient than that of other methods

  14. A neural network approach to the study of internal energy flow in molecular systems

    International Nuclear Information System (INIS)

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

    1992-01-01

    Neural networks are used to develop a new technique for efficient analysis of data obtained from molecular-dynamics calculations and is applied to the study of mode energy flow in molecular systems. The methodology is based on teaching an appropriate neural network the relationship between phase-space points along a classical trajectory and mode energies for stretch, bend, and torsion vibrations. Results are discussed for reactive and nonreactive classical trajectories of hydrogen peroxide (H 2 O 2 ) on a semiempirical potential-energy surface. The neural-network approach is shown to produce reasonably accurate values for the mode energies, with average errors between 1% and 12%, and is applicable to any region within the 24-dimensional phase space of H 2 O 2 . In addition, the generic knowledge learned by the neural network allows calculations to be made for other molecular systems. Results are discussed for a series of tetratomic molecules: H 2 X 2 , X=C, N, O, Si, S, or Se, and preliminary results are given for energy flow predictions in macromolecules

  15. 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.

  16. Plasticity and Neural Stem Cells in the Enteric Nervous System

    NARCIS (Netherlands)

    Schaefer, Karl-Herbert; Van Ginneken, Chris; Copray, Sjef

    2009-01-01

    The enteric nervous system (ENS) is a highly organized part of the autonomic nervous system, which innervates the whole gastrointestinal tract by several interconnected neuronal networks. The ENS changes during development and keeps throughout its lifespan a significant capacity to adapt to

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. A wireless transmission neural interface system for unconstrained non-human primates.

    Science.gov (United States)

    Fernandez-Leon, Jose A; Parajuli, Arun; Franklin, Robert; Sorenson, Michael; Felleman, Daniel J; Hansen, Bryan J; Hu, Ming; Dragoi, Valentin

    2015-10-01

    Studying the brain in large animal models in a restrained laboratory rig severely limits our capacity to examine brain circuits in experimental and clinical applications. To overcome these limitations, we developed a high-fidelity 96-channel wireless system to record extracellular spikes and local field potentials from the neocortex. A removable, external case of the wireless device is attached to a titanium pedestal placed in the animal skull. Broadband neural signals are amplified, multiplexed, and continuously transmitted as TCP/IP data at a sustained rate of 24 Mbps. A Xilinx Spartan 6 FPGA assembles the digital signals into serial data frames for transmission at 20 kHz though an 802.11n wireless data link on a frequency-shift key-modulated signal at 5.7-5.8 GHz to a receiver up to 10 m away. The system is powered by two CR123A, 3 V batteries for 2 h of operation. We implanted a multi-electrode array in visual area V4 of one anesthetized monkey (Macaca fascicularis) and in the dorsolateral prefrontal cortex (dlPFC) of a freely moving monkey (Macaca mulatta). The implanted recording arrays were electrically stable and delivered broadband neural data over a year of testing. For the first time, we compared dlPFC neuronal responses to the same set of stimuli (food reward) in restrained and freely moving conditions. Although we did not find differences in neuronal responses as a function of reward type in the restrained and unrestrained conditions, there were significant differences in correlated activity. This demonstrates that measuring neural responses in freely moving animals can capture phenomena that are absent in the traditional head-fixed paradigm. We implemented a wireless neural interface for multi-electrode recordings in freely moving non-human primates, which can potentially move systems neuroscience to a new direction by allowing one to record neural signals while animals interact with their environment.

  2. A wireless transmission neural interface system for unconstrained non-human primates

    Science.gov (United States)

    Fernandez-Leon, Jose A.; Parajuli, Arun; Franklin, Robert; Sorenson, Michael; Felleman, Daniel J.; Hansen, Bryan J.; Hu, Ming; Dragoi, Valentin

    2015-10-01

    Objective. Studying the brain in large animal models in a restrained laboratory rig severely limits our capacity to examine brain circuits in experimental and clinical applications. Approach. To overcome these limitations, we developed a high-fidelity 96-channel wireless system to record extracellular spikes and local field potentials from the neocortex. A removable, external case of the wireless device is attached to a titanium pedestal placed in the animal skull. Broadband neural signals are amplified, multiplexed, and continuously transmitted as TCP/IP data at a sustained rate of 24 Mbps. A Xilinx Spartan 6 FPGA assembles the digital signals into serial data frames for transmission at 20 kHz though an 802.11n wireless data link on a frequency-shift key-modulated signal at 5.7-5.8 GHz to a receiver up to 10 m away. The system is powered by two CR123A, 3 V batteries for 2 h of operation. Main results. We implanted a multi-electrode array in visual area V4 of one anesthetized monkey (Macaca fascicularis) and in the dorsolateral prefrontal cortex (dlPFC) of a freely moving monkey (Macaca mulatta). The implanted recording arrays were electrically stable and delivered broadband neural data over a year of testing. For the first time, we compared dlPFC neuronal responses to the same set of stimuli (food reward) in restrained and freely moving conditions. Although we did not find differences in neuronal responses as a function of reward type in the restrained and unrestrained conditions, there were significant differences in correlated activity. This demonstrates that measuring neural responses in freely moving animals can capture phenomena that are absent in the traditional head-fixed paradigm. Significance. We implemented a wireless neural interface for multi-electrode recordings in freely moving non-human primates, which can potentially move systems neuroscience to a new direction by allowing one to record neural signals while animals interact with their environment.

  3. 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.

  4. 18th East European Conference on Advances in Databases and Information Systems and Associated Satellite Events

    CERN Document Server

    Ivanovic, Mirjana; Kon-Popovska, Margita; Manolopoulos, Yannis; Palpanas, Themis; Trajcevski, Goce; Vakali, Athena

    2015-01-01

    This volume contains the papers of 3 workshops and the doctoral consortium, which are organized in the framework of the 18th East-European Conference on Advances in Databases and Information Systems (ADBIS’2014). The 3rd International Workshop on GPUs in Databases (GID’2014) is devoted to subjects related to utilization of Graphics Processing Units in database environments. The use of GPUs in databases has not yet received enough attention from the database community. The intention of the GID workshop is to provide a discussion on popularizing the GPUs and providing a forum for discussion with respect to the GID’s research ideas and their potential to achieve high speedups in many database applications. The 3rd International Workshop on Ontologies Meet Advanced Information Systems (OAIS’2014) has a twofold objective to present: new and challenging issues in the contribution of ontologies for designing high quality information systems, and new research and technological developments which use ontologie...

  5. 3rd International Conference on INformation Systems Design and Intelligent Applications

    CERN Document Server

    Mandal, Jyotsna; Udgata, Siba; Bhateja, Vikrant

    2016-01-01

    The third international conference on INformation Systems Design and Intelligent Applications (INDIA – 2016) held in Visakhapatnam, India during January 8-9, 2016. The book covers all aspects of information system design, computer science and technology, general sciences, and educational research. Upon a double blind review process, a number of high quality papers are selected and collected in the book, which is composed of three different volumes, and covers a variety of topics, including natural language processing, artificial intelligence, security and privacy, communications, wireless and sensor networks, microelectronics, circuit and systems, machine learning, soft computing, mobile computing and applications, cloud computing, software engineering, graphics and image processing, rural engineering, e-commerce, e-governance, business computing, molecular computing, nano-computing, chemical computing, intelligent computing for GIS and remote sensing, bio-informatics and bio-computing. These fields are not...

  6. 2nd International Conference on INformation Systems Design and Intelligent Applications

    CERN Document Server

    Satapathy, Suresh; Sanyal, Manas; Sarkar, Partha; Mukhopadhyay, Anirban

    2015-01-01

    The second international conference on INformation Systems Design and Intelligent Applications (INDIA – 2015) held in Kalyani, India during January 8-9, 2015. The book covers all aspects of information system design, computer science and technology, general sciences, and educational research. Upon a double blind review process, a number of high quality papers are selected and collected in the book, which is composed of two different volumes, and covers a variety of topics, including natural language processing, artificial intelligence, security and privacy, communications, wireless and sensor networks, microelectronics, circuit and systems, machine learning, soft computing, mobile computing and applications, cloud computing, software engineering, graphics and image processing, rural engineering, e-commerce, e-governance, business computing, molecular computing, nano computing, chemical computing, intelligent computing for GIS and remote sensing, bio-informatics and bio-computing. These fields are not only ...

  7. 8th International KES Conference on Intelligent Interactive Multimedia : Systems and Services

    CERN Document Server

    Howlett, Robert; Jain, Lakhmi; Gallo, Luigi; Pietro, Giuseppe

    2015-01-01

    Intelligent interactive multimedia systems and services will be ever more important in computer systems. Nowadays, computers are widespread and computer users range from highly qualified scientists to non-computer expert professionals. Therefore, designing dynamic personalization and adaptivity methods to store, process, transmit and retrieve information is critical for matching the technological progress with the consumer needs. This book contains the contributions presented at the eighth international KES conference on Intelligent Interactive Multimedia: Systems and Services, which took place in Sorrento, Italy, June 17-19, 2015. It contains 33 peer-reviewed scientific contributions that focus on issues ranging from intelligent image or video storage, retrieval, transmission and analysis to knowledge-based technologies, from advanced information technology architectures for video processing and transmission to advanced functionalities of information and knowledge-based services. We believe that this book w...

  8. International conference on national infrastructures for radiation safety: Towards effective and sustainable systems. Contributed papers

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2003-07-01

    The International Atomic Energy Agency (IAEA), in co-operation with the World Health Organization (WHO), the International Labour Office (ILO), the European Commission (EC), and the OECD Nuclear Energy Agency (NEA), organized the International Conference on National Infrastructures for Radiation Safety: Towards Effective and Sustainable Systems. This book contains contributed papers submitted on pertinent issues, including stakeholder involvement, IAEA Model Projects on Upgrading Radiation Protection Infrastructure, Quality Assurance, education and training, regulatory activities, performance evaluation, source security, and emergency preparedness. The material in this book has not been edited by the IAEA. These contributed papers will be published on a CD ROM as part of the Proceedings of the Conference, along with the invited papers and discussions. The papers are grouped by topical sessions: Stakeholder Involvement in Building and Maintaining National Radiation Safety Infrastructure (National and International); Implementation Experience with The Model Projects (Views From The Countries, Positive and Negative Experiences); Resources and Services (Systematic Approach), Quality Assurance, International Support Of Services; Sustainable Education And Training: Developing Skills (National Systems And Regional Solutions); Needs for Education And Training at The International Level (Including IAEA Programmes Assisting in Establishing Adequate Infrastructures); Authorization, Inspection and Enforcement (Effectiveness and Efficiency Of The Activities Of The Regulatory Bodies), Independence of Regulatory Authorities; Performance Evaluation; Source Security and Emergency Preparedness (Infrastructure Requirements at the International, National And User's Level)

  9. International conference on national infrastructures for radiation safety: Towards effective and sustainable systems. Contributed papers

    International Nuclear Information System (INIS)

    2003-01-01

    The International Atomic Energy Agency (IAEA), in co-operation with the World Health Organization (WHO), the International Labour Office (ILO), the European Commission (EC), and the OECD Nuclear Energy Agency (NEA), organized the International Conference on National Infrastructures for Radiation Safety: Towards Effective and Sustainable Systems. This book contains contributed papers submitted on pertinent issues, including stakeholder involvement, IAEA Model Projects on Upgrading Radiation Protection Infrastructure, Quality Assurance, education and training, regulatory activities, performance evaluation, source security, and emergency preparedness. The material in this book has not been edited by the IAEA. These contributed papers will be published on a CD ROM as part of the Proceedings of the Conference, along with the invited papers and discussions. The papers are grouped by topical sessions: Stakeholder Involvement in Building and Maintaining National Radiation Safety Infrastructure (National and International); Implementation Experience with The Model Projects (Views From The Countries, Positive and Negative Experiences); Resources and Services (Systematic Approach), Quality Assurance, International Support Of Services; Sustainable Education And Training: Developing Skills (National Systems And Regional Solutions); Needs for Education And Training at The International Level (Including IAEA Programmes Assisting in Establishing Adequate Infrastructures); Authorization, Inspection and Enforcement (Effectiveness and Efficiency Of The Activities Of The Regulatory Bodies), Independence of Regulatory Authorities; Performance Evaluation; Source Security and Emergency Preparedness (Infrastructure Requirements at the International, National And User's Level)

  10. A comparison of neural tube defects identified by two independent routine recording systems for congenital malformations in Northern Ireland.

    Science.gov (United States)

    Nevin, N C; McDonald, J R; Walby, A L

    1978-12-01

    The efficiency of two systems for recording congenital malformations has been compared; one system, the Registrar General's Congenital Malformation Notification, is based on registering all malformed infants, and the other, the Child Health System, records all births. In Northern Ireland for three years [1974--1976], using multiple sources of ascertainment, a total of 686 infants with neural tube defects was identified among 79 783 live and stillbirths. The incidence for all neural tube defects in 8 60 per 1 000 births. The Registrar General's Congenital Malformation Notification System identified 83.6% whereas the Child Health System identified only 63.3% of all neural tube defects. Both systems together identified 86.2% of all neural tube defects. The two systems are suitable for monitoring of malformations and the addition of information from the Genetic Counselling Clinics would enhance the data for epidemiological studies.

  11. Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System

    Directory of Open Access Journals (Sweden)

    Min-Seok Park

    2009-10-01

    Full Text Available This paper describes the procedures for development of signal analysis algorithms using artificial neural networks for Bridge Weigh-in-Motion (B-WIM systems. Through the analysis procedure, the extraction of information concerning heavy traffic vehicles such as weight, speed, and number of axles from the time domain strain data of the B-WIM system was attempted. As one of the several possible pattern recognition techniques, an Artificial Neural Network (ANN was employed since it could effectively include dynamic effects and bridge-vehicle interactions. A number of vehicle traveling experiments with sufficient load cases were executed on two different types of bridges, a simply supported pre-stressed concrete girder bridge and a cable-stayed bridge. Different types of WIM systems such as high-speed WIM or low-speed WIM were also utilized during the experiments for cross-checking and to validate the performance of the developed algorithms.

  12. Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System.

    Science.gov (United States)

    Kim, Sungkon; Lee, Jungwhee; Park, Min-Seok; Jo, Byung-Wan

    2009-01-01

    This paper describes the procedures for development of signal analysis algorithms using artificial neural networks for Bridge Weigh-in-Motion (B-WIM) systems. Through the analysis procedure, the extraction of information concerning heavy traffic vehicles such as weight, speed, and number of axles from the time domain strain data of the B-WIM system was attempted. As one of the several possible pattern recognition techniques, an Artificial Neural Network (ANN) was employed since it could effectively include dynamic effects and bridge-vehicle interactions. A number of vehicle traveling experiments with sufficient load cases were executed on two different types of bridges, a simply supported pre-stressed concrete girder bridge and a cable-stayed bridge. Different types of WIM systems such as high-speed WIM or low-speed WIM were also utilized during the experiments for cross-checking and to validate the performance of the developed algorithms.

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

    Directory of Open Access Journals (Sweden)

    Y. D. Song

    2013-01-01

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

  14. An automatic system for Turkish word recognition using Discrete Wavelet Neural Network based on adaptive entropy

    International Nuclear Information System (INIS)

    Avci, E.

    2007-01-01

    In this paper, an automatic system is presented for word recognition using real Turkish word signals. This paper especially deals with combination of the feature extraction and classification from real Turkish word signals. A Discrete Wavelet Neural Network (DWNN) model is used, which consists of two layers: discrete wavelet layer and multi-layer perceptron. The discrete wavelet layer is used for adaptive feature extraction in the time-frequency domain and is composed of Discrete Wavelet Transform (DWT) and wavelet entropy. The multi-layer perceptron used for classification is a feed-forward neural network. The performance of the used system is evaluated by using noisy Turkish word signals. Test results showing the effectiveness of the proposed automatic system are presented in this paper. The rate of correct recognition is about 92.5% for the sample speech signals. (author)

  15. Optimization of workflow scheduling in Utility Management System with hierarchical neural network

    Directory of Open Access Journals (Sweden)

    Srdjan Vukmirovic

    2011-08-01

    Full Text Available Grid computing could be the future computing paradigm for enterprise applications, one of its benefits being that it can be used for executing large scale applications. Utility Management Systems execute very large numbers of workflows with very high resource requirements. This paper proposes architecture for a new scheduling mechanism that dynamically executes a scheduling algorithm using feedback about the current status Grid nodes. Two Artificial Neural Networks were created in order to solve the scheduling problem. A case study is created for the Meter Data Management system with measurements from the Smart Metering system for the city of Novi Sad, Serbia. Performance tests show that significant improvement of overall execution time can be achieved by Hierarchical Artificial Neural Networks.

  16. Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems.

    Science.gov (United States)

    González-Gutiérrez, Carlos; Santos, Jesús Daniel; Martínez-Zarzuela, Mario; Basden, Alistair G; Osborn, James; Díaz-Pernas, Francisco Javier; De Cos Juez, Francisco Javier

    2017-06-02

    Many of the next generation of adaptive optics systems on large and extremely large telescopes require tomographic techniques in order to correct for atmospheric turbulence over a large field of view. Multi-object adaptive optics is one such technique. In this paper, different implementations of a tomographic reconstructor based on a machine learning architecture named "CARMEN" are presented. Basic concepts of adaptive optics are introduced first, with a short explanation of three different control systems used on real telescopes and the sensors utilised. The operation of the reconstructor, along with the three neural network frameworks used, and the developed CUDA code are detailed. Changes to the size of the reconstructor influence the training and execution time of the neural network. The native CUDA code turns out to be the best choice for all the systems, although some of the other frameworks offer good performance under certain circumstances.

  17. CloudScan - A Configuration-Free Invoice Analysis System Using Recurrent Neural Networks

    DEFF Research Database (Denmark)

    Palm, Rasmus Berg; Winther, Ole; Laws, Florian

    2017-01-01

    We present CloudScan; an invoice analysis system that requires zero configuration or upfront annotation. In contrast to previous work, CloudScan does not rely on templates of invoice layout, instead it learns a single global model of invoices that naturally generalizes to unseen invoice layouts....... The model is trained using data automatically extracted from end-user provided feedback. This automatic training data extraction removes the requirement for users to annotate the data precisely. We describe a recurrent neural network model that can capture long range context and compare it to a baseline...... logistic regression model corresponding to the current CloudScan production system. We train and evaluate the system on 8 important fields using a dataset of 326,471 invoices. The recurrent neural network and baseline model achieve 0.891 and 0.887 average F1 scores respectively on seen invoice layouts...

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

    Directory of Open Access Journals (Sweden)

    Yong Tao

    2016-01-01

    Full Text Available 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 parameters are derived by a Koski function algorithm to ensure the network convergences and enacts stable control. The simulations and experimental results of the deburring robot system are provided to illustrate the effectiveness of the proposed RBFNN-SMC control method. The advantages of the proposed RBFNN-SMC method are also evaluated by comparing it to existing control schemes.

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

    OpenAIRE

    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 ...

  20. Drug release control and system understanding of sucrose esters matrix tablets by artificial neural networks.

    Science.gov (United States)

    Chansanroj, Krisanin; Petrović, Jelena; Ibrić, Svetlana; Betz, Gabriele

    2011-10-09

    Artificial neural networks (ANNs) were applied for system understanding and prediction of drug release properties from direct compacted matrix tablets using sucrose esters (SEs) as matrix-forming agents for controlled release of a highly water soluble drug, metoprolol tartrate. Complexity of the system was presented through the effects of SE concentration and tablet porosity at various hydrophilic-lipophilic balance (HLB) values of SEs ranging from 0 to 16. Both effects contributed to release behaviors especially in the system containing hydrophilic SEs where swelling phenomena occurred. A self-organizing map neural network (SOM) was applied for visualizing interrelation among the variables and multilayer perceptron neural networks (MLPs) were employed to generalize the system and predict the drug release properties based on HLB value and concentration of SEs and tablet properties, i.e., tablet porosity, volume and tensile strength. Accurate prediction was obtained after systematically optimizing network performance based on learning algorithm of MLP. Drug release was mainly attributed to the effects of SEs, tablet volume and tensile strength in multi-dimensional interrelation whereas tablet porosity gave a small impact. Ability of system generalization and accurate prediction of the drug release properties proves the validity of SOM and MLPs for the formulation modeling of direct compacted matrix tablets containing controlled release agents of different material properties. Copyright © 2011 Elsevier B.V. All rights reserved.