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IEEE Electric Ship Technologies Symposium, ESTS

作者： Pinaki Mitra Ganesh K. Venayagamoorthy Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla Missouri USA

The naval electric ship is often subject to severe damages under battle conditions. The damages or faults might even affect the generators and as a result, critical loads might suffer from power deficiency which may lead to an eventual collapse of rest of the system. In order to serve the critical loads and maintain a proper power balance without excessive generation, the ship power system requires a fast reconfiguration of the remaining system under fault conditions. A fast intelligent algorithm using the small population based particle swarm optimization (SPPSO) for dynamic reconfiguration of the available generators and loads when a fault in the ship power system is detected is presented in this paper. SPPSO is a variant of PSO which operates with fewer particles and a regeneration concept, where new potential solutions are generated every few iterations. This concept of regeneration makes the algorithm fast and enhances its exploration capability to a large extent. The strength of the proposed reconfiguration strategy is first illustrated with Matlab results and then with a real-time implementation on a real time digital simulator and a digital signal processor.

关键词： real time systems Marine vehicles Power systems Signal processing algorithms Power system faults Power generation Power system dynamics Particle swarm optimization electrical fault detection Fault detection

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Comparison of feedforward and feedback neural network architectures for short term wind speed prediction

Comparison of feedforward and feedback neural network archit...

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International Joint Conference on Neural Networks (IJCNN)

作者： Richard L. Welch Stephen M. Ruffing Ganesh K. Venayagamoorthy Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering Missouri S & T MO USA

This paper compares three types of neural networks trained using particle swarm optimization (PSO) for use in the short term prediction of wind speed. The three types of neural networks compared are the multi-layer perceptron (MLP) neural network, Elman recurrent neural network, and simultaneous recurrent neural network (SRN). Each network is trained and tested using meteorological data of one week measured at the National Renewable Energy laboratory National Wind Technology Center near Boulder, CO. Results show that while the recurrent neural networks outperform the MLP in the best and average case with a lower overall mean squared error, the MLP performance is comparable. The better performance of the feedback architectures is also shown using the mean absolute relative error. While the SRN performance is superior, the increase in required training time for the SRN over the other networks may be a constraint, depending on the application.

关键词： Neurofeedback Neural networks Feedforward neural networks Wind speed Recurrent neural networks Multi-layer neural network Particle swarm optimization Multilayer perceptrons Testing Meteorology

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Generalized neuron based secure media access control protocol for wireless sensor networks

Generalized neuron based secure media access control protoco...

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IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, MCDM

作者： Raghavendra V. Kulkarni Ganesh K. Venayagamoorthy Abhishek V. Thakur Sanjay K. Madria Real-Time Power and Intelligent Systems Laboratory Missouri University of Science and Technology Rolla MO USA Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA Department of Computer Science Missouri University of Science and Technology Rolla MO USA

Security plays a pivotal role in most applications of wireless sensor networks. It is common to find inadequately secure networks confined only to controlled environments. The issue of security in wireless sensor networks is a hot research topic for over a decade. This paper presents a compact generalized neuron (GN) based medium access protocol that renders a CSMA/CD network secure against denial-of-service attacks launched by adversaries. The GN enhances the security by constantly monitoring multiple parameters that reflect the possibility that an attack is launched by an adversary. Particle swarm optimization, a popular bio-inspired evolutionary-like optimization algorithm is used for training the GN. The wireless sensor network is simulated using Vanderbilt Prowler, a probabilistic wireless network simulator. Simulation results show that the choice of threshold suspicion parameter impacts on the tradeoff between network effectiveness and lifetime.

关键词： Media Access Protocol Neurons Wireless application protocol Wireless sensor networks Biomedical monitoring Routing Remote monitoring Condition monitoring Intelligent sensors Access protocols

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Computational intelligence approach to load forecasting - a practical application for the desert of Saudi Arabia

Computational intelligence approach to load forecasting - a ...

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International Conference on computer and Information Technology (ICCIT)

作者： Suman Ahmmed Dewan Md. Fayzur Rahman Md. Khairul Hasan Ahmed Yousuf Saber Mohammad Zahidur Rahman Department of Computer Science and Engineering United International University Dhaka Bangladesh Department of Computer Science and Engineering Ahsanullah University of Science and Technology Bangladesh Real-Time Power and Intelligent Systems Laboratory Missouri University of Science and Technology USA Department of Computer Science and Engineering Jahangir Nagar University Dhaka Bangladesh

This paper presents the development of an Artificial Neural Networks and Particle Swarm Optimization (ANN-PSO) based short-term load forecasting model with improved generalization technique for the Regional Power Control Center of Saudi Electricity Company, Western Operation Area (SEC-WOA). Weather, load demand, wind speed, wind direction, heat, sunlight, etc. are quite different in a desert land than other places. Thus this model is different from a typical forecasting model considering inputs and outputs. In this research paper two steps have been introduced, first load forecasting made by mapping mechanism and then optimization technique applied to improve its accuracy. This paper includes ANN and PSO models for 24-hours ahead load forecasting. ANN is an effective mathematical tool for mapping complex relationships. It is also successful for doing forecasting, categorization, classification, and so forth. On the other hand, PSO is the most promising optimization tool. It has better information sharing and conveying mechanism; it has better balance of local and global searching abilities; and can handle huge multi-dimensional optimization problems efficiently with hundreds of thousands of constraints. Thus PSO is chosen as the optimization model of the weight matrix of ANN. Results show that the proposed ANN-PSO performs much better than ANN for the load forecasting in a desert like Saudi Arabia.

关键词： Computational intelligence Load forecasting Artificial neural networks Predictive models Weather forecasting Wind speed Particle swarm optimization Load modeling Power control Solar heating

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Effects of learning rate on the performance of the population based incremental learning algorithm

Effects of learning rate on the performance of the populatio...

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International Joint Conference on Neural Networks (IJCNN)

作者： Komla A. Folly Ganesh K. Venayagamoorthy Department of Electrical Engineering University of Cape Town Cape Town South Africa Real-Time Power and Intelligent Systems Laboratory Missouri University of Science and Technology Rolla MO USA

The effect of learning rate (LR) on the performance of a newly introduced evolutionary algorithm called population-based incremental learning (PBIL) is investigated in this paper. PBIL is a technique that combines a simple genetic algorithm (GA) with competitive learning (CL). Although CL is often studied in the context of artificial neural networks (ANNs), it plays a vital role in PBIL in that the idea of creating a prototype vector in learning vector quantization (LVQ) is central to PBIL. In PBIL, the crossover operator of GAs is abstracted away and the role of population is redefined. PBIL maintains a real-valued probability vector (PV) or prototype vector from which solutions are generated. The probability vector controls the random bitstrings generated by PBIL and is used to create other individuals through learning. The setting of the learning rate (LR) can greatly affect the performance of PBIL. However, the effect of the learning rate in PBIL is not yet fully understood. In this paper, PBIL is used to design power system stabilizers (PSSs) for a multi-machine power system. Four cases studies with different learning rate patterns are investigated. These include fixed LR; purely adaptive LR; fixed LR followed by adaptive LR; and adaptive LR followed by fixed LR. It is shown that a smaller learning rate leads to more exploration of the algorithm which introduces more diversity in the population at the cost of slower convergence. On the other hand, a higher learning rate means more exploitation of the algorithm and hence, this could lead to a premature convergence in the case of fixed LR. Therefore, in setting the LR, a trade-off is needed between exploitation and exploration.

关键词： Space exploration Electronic design automation and methodology Genetic algorithms Convergence Prototypes Power systems Power system modeling Cities and towns real time systems Intelligent systems

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TSS: Applying two-stage sampling in micro-architecture simulations

TSS: Applying two-stage sampling in micro-architecture simul...

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International Symposium on Modeling, Analysis and Simulation of computer and Telecommunication Systems (MASCOTS)

作者： Zhibin Yu Hai Jin Jian Chen Lizy K. John Service Computing Technology and System Laboratory Cluster and Grid Computing Laboratory Huazhong University of Science and Technology Wuhan China Department of Electrical and Computer Engineering University of Texas Austin Austin TX USA

ISBN: (纸本)9781424449279

Accelerating micro-architecture simulation is becoming increasingly urgent as the complexity of workload and simulated processor increases. This paper presents a novel two-stage sampling (TSS) scheme to accelerate the sampling-based simulation. It firstly selects some large samples from a dynamic instruction stream as candidates of detail simulation and then samples some small groups from each selected first stage sample to do detail simulation. Since the distribution of standard deviation of cycle per instruction (CPI) is insensitive to microarchitecture, TSS could be used to speedup design space exploration by splitting the sampling process into two stages, which is able to remove redundant instruction samples from detail simulation when the program is in stable program phase (standard deviation of CPI is near zero). It also adopts systematic sampling to accelerate the functional warm-up in sampling simulation. Experimental results show that, by combining these two techniques, TSS achieves an average and maximum speedup of 1.3 and 2.29 over SMARTS, with the average CPI relative error is less than 3%. TSS could significantly accelerate the time consuming iterative early design evaluation process.

关键词： Sampling methods Computational modeling Acceleration Microarchitecture computer simulation Redundancy Grid computing Space exploration computer errors Process design

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AAAI 2008 workshop reports

AAAI 2008 workshop reports

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作者： Anand, Sarabjot Singh Bunescu, Razvan Carvcdho, Vitor Chomicki, Jan Conitzer, Vincent Cox, Michael T. Dignum, Virginia Dodds, Zachary Dredze, Mark Furcy, David Gabrilovich, Evgeniy Göker, Mehmet H. Guesgen, Hans Hirsh, Haym Jannach, Dietmar Junker, Ulrich Ketter, Wolfgang Kobsa, Alfred Koenig, Sven Lau, Tessa Lewis, Lundy Matson, Eric Metzler, Ted Mihalcea, Rada Mobasher, Bamshad Pineau, Joelle Poupart, Pascal Raja, Anita Ruml, Wheeler Sadeh, Norman Shani, Guy Shapiro, Daniel Smith, Trey Taylor, Matthew E. Wagstaff, Kiri Walsh, William Zhou, Rong Department of Computer Science University of Warwick United Kingdom School of Electrical Engineering and Computer Science Ohio University United States Microsoft Live Labs United States computer science and engineering University at Buffalo United States Computer science and economics Duke University United States Intelligent Computing group of BBN Technologies Utrecht University Netherlands Department of Computer science Harvey Mudd College United States University of Pennsylvania United States University of Wisconsin United States Yahoo Research PricewaterhouseCoopers Center for Advanced Research United States Department of Computer science School of Engineering and Advanced Technology Massey University New Zealand Departent of Computer science Rutgers University United States Department of Computer Science Dortmund University of Technology. Germany ILOG Rotterdam School of Management Erasmus University Netherlands Donald Bren School of Information and Computer Sciences University of California Irvine United States Depatrment of Computer science University of Southern California United States IBM Almaden Research Center United States Department of Computer Information Technology Southern New Hampshire University United States Computer and Information Technology Program College of Technology Purdue University United States Hughes Program for Religion and Science Dialogue Oklahoma City University United States Department of Computer Science and Engineering University of North Texas United States School of Computing DePaul University United States Department of Computer Science McGill University Canada School of Computer Science University of Waterloo Canada Department of Software and Information Systems University of North Carolina Charlotte United States University of New Hampshire United States Microsoft Research Institute for the Study of Learning and Expertise Applied Reactivity Inc. United States NASA Ames Research Center Carnegie Mellon University West

AAAI was pleased to present the AAAI-08 Workshop Program, held Sunday and Monday, July 13-14, in Chicago, Illinois, USA. The program included the following 15 workshops: Advancements in POMDP Solvers;AI Education Workshop Colloquium;Coordination, Organizations, Institutions, and Norms in Agent Systems, Enhanced Messaging;Human Implications of Human-Robot Interaction;Intelligent Techniques for Web Personalization and Recommender Systems;Metareasoning: Thinking about Thinking;Multidisciplinary Workshop on Advances in Preference Handling;Search in Artificial Intelligence and Robotics;Spatial and Temporal Reasoning;Trading Agent Design and Analysis;Transfer Learning for Complex Tasks;What Went Wrong and Why: Lessons from AI Research and Applications;and Wikipedia and Artificial Intelligence: An Evolving Synergy. Copyright © 2009, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Human robot interaction

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Reports of the AAAI 2009 spring symposia

Reports of the AAAI 2009 spring symposia

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作者： Bao, Jie Bojars, Uldis Choudhury, Tanzeem Ding, Li Greaves, Mark Kapoor, Ashish Louchart, Sandy Mehta, Manish Nebel, Bernhard Nirenburg, Sergei Oates, Tim Roberts, David L. Sanfilippo, Antonio Stojanovic, Nenad Stubbs, Kristen Thomaz, Andrea L. Tsui, Katherine Woelfl, Stefan Tetherless World Constellation Rensselaer Polytechnic Institute United States Social Software Unit of DERI Galway Ireland Computer Science Department Dartmouth College United States Knowledge Systems at Vulcan Inc United States Adaptive Systems and Interaction Group Microsoft Research Redmond United States School for Mathematics and Computer Sciences Heriot-Watt University United Kingdom School ot Interactive Computing Georgia Institute of Technology United States Department of Computer Science University of Freiburg Germany Computer Science and Electrical Engineering Department University of Maryland Baltimore County United States Pacific Northwest National Laboratory Richland WA United States University of Karlsruhe Germany IRobot Corporation United States University of Massachusetts Lowell United States

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's department of computer science, was pleased to present the 2009 Spring Symposium Series, held Monday through Wednesday, March 23-25, 2009, at Stanford University. The titles of the nine symposia were Agents That Learn from Human Teachers, Benchmarking of Qualitative Spatial and Temporal Reasoning Systems, Experimental Design for real- World Systems, Human Behavior Modeling, Intelligent Event Processing, Intelligent Narrative Technologies II, Learning by Reading and Learning to Read, Social Semantic Web: Where Web 2.0 Meets Web 3.0, and Technosocial Predictive Analytics. The goal of the Agents That Learn from Human Teachers symposium was to investigate how we can enable software and robotics agents to leam from real-time interaction with an everyday human partner. The aim of the Benchmarking of Qualitative Spatial and Temporal Reasoning Systems symposium was to initiate the development of a problem repository in the field of qualitative spatial and temporal reasoning and identify a graded set of challenges for future midterm and long-term research. The Experimental Design symposium discussed the challenges of evaluating AI systems. The Human Behavior Modeling symposium explored reasoning methods for understanding various aspects of human behavior, especially in the context of designing intelligent systems that interact with humans. The Intelligent Event Processing symposium discussed the need for more Al-based approaches in event pro-cessing and defined a kind of research agenda for the field, coined as intelligent complex event processing (iCEP). The Intelligent Narrative Technologies IIAAAI symposium discussed innovations, progress, and novel techniques in the research domain. The Learning by Reading and Learning to Read symposium explored two aspects of making natural language texts semantically accessible to, and processable by, machines. The Social Semantic

关键词： Benchmarking

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ERRATUM: 'THE FIRST FERMI LARGE AREA TELESCOPE CATALOG OF GAMMA-RAY PULSARS' (2010, ApJS, 187, 460)

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The Astrophysical Journal Supplement Series 2011年第1期193卷

作者： A. A. Abdo M. Ackermann M. Ajello W. B. Atwood M. Axelsson L. Baldini J. Ballet G. Barbiellini M. G. Baring D. Bastieri B. M. Baughman K. Bechtol R. Bellazzini B. Berenji R. D. Blandford E.D. Bloom E. Bonamente A. W. Borgland J. Bregeon A. Brez M. Brigida P. Bruel T. H. Burnett S. Buson G. A. Caliandro R. A. Cameron F. Camilo P. A. Caraveo J. M. Casandjian C. Cecchi Ö. Çelik E. Charles A. Chekhtman C. C. Cheung J. Chiang S. Ciprini R. Claus I. Cognard J. Cohen-Tanugi L. R. Cominsky J. Conrad R. Corbet S. Cutini P. R. den Hartog C. D. Dermer A. de Angelis A. de Luca F. de Palma S. W. Digel M. Dormody E. do Couto e Silva P. S. Drell R. Dubois D. Dumora C. Espinoza C. Farnier C. Favuzzi S. J. Fegan E. C. Ferrara W. B. Focke P. Fortin M. Frailis P. C. C. Freire Y. Fukazawa S. Funk P. Fusco F. Gargano D. Gasparrini N. Gehrels S. Germani G. Giavitto B. Giebels N. Giglietto P. Giommi F. Giordano T. Glanzman G. Godfrey E. V. Gotthelf I. A. Grenier M.-H. Grondin J. E. Grove L. Guillemot S. Guiriec C. Gwon Y. Hanabata A. K. Harding M. Hayashida E. Hays R. E. Hughes M. S. Jackson G. Jóhannesson A. S. Johnson R. P. Johnson T. J. Johnson W. N. Johnson S. Johnston T. Kamae G. Kanbach V. M. Kaspi H. Katagiri J. Kataoka N. Kawai M. Kerr J. Knödlseder M. L. Kocian M. Kramer M. Kuss J. Lande L. Latronico M. Lemoine-Goumard M. Livingstone F. Longo F. Loparco B. Lott M. N. Lovellette P. Lubrano A. G. Lyne G. M. Madejski A. Makeev R. N. Manchester M. Marelli M. N. Mazziotta W. McConville J. E. McEnery S. McGlynn C. Meurer P. F. Michelson T. Mineo W. Mitthumsiri T. Mizuno A. A. Moiseev C. Monte M. E. Monzani A. Morselli I. V. Moskalenko S. Murgia T. Nakamori P. L. Nolan J. P. Norris A. Noutsos E. Nuss T. Ohsugi N. Omodei E. Orlando J. F. Ormes M. Ozaki D. Paneque J. H. Panetta D. Parent V. Pelassa M. Pepe M. Pesce-Rollins F. Piron T. A. Porter S. Rainò R. Rando S. M. Ransom P. S. Ray M. Razzano N. Rea A. Reimer O. Reimer T. Reposeur S. Ritz A. Y. Rodriguez R. W. Romani M. Roth F. Ryde H. F.-W. Sadrozinski D. Sanchez A. Sander P. M. Sa Space Science Division Naval Research Laboratory Washington DC 20375 USA National Research Council Research Associate National Academy of Sciences Washington DC 20001 USA W. W. Hansen Experimental Physics Laboratory Kavli Institute for Particle Astrophysics and Cosmology Department of Physics and SLAC National Accelerator Laboratory Stanford University Stanford CA 94305 USA Santa Cruz Institute for Particle Physics Department of Physics and Department of Astronomy and Astrophysics University of California at Santa Cruz Santa Cruz CA 95064 USA Department of Astronomy Stockholm University SE-106 91 Stockholm Sweden The Oskar Klein Centre for Cosmoparticle Physics AlbaNova SE-106 91 Stockholm Sweden Istituto Nazionale di Fisica Nucleare Sezione di Pisa I-56127 Pisa Italy Laboratoire AIM CEA-IRFU/CNRS/Université Paris Diderot Service d'Astrophysique CEA Saclay 91191 Gif sur Yvette France Istituto Nazionale di Fisica Nucleare Sezione di Trieste I-34127 Trieste Italy Dipartimento di Fisica Università di Trieste I-34127 Trieste Italy Department of Physics and Astronomy Rice University MS-108 P.O. Box 1892 Houston TX 77251 USA Istituto Nazionale di Fisica Nucleare Sezione di Padova I-35131 Padova Italy Dipartimento di Fisica 'G. Galilei ' Università di Padova I-35131 Padova Italy Department of Physics Center for Cosmology and Astro-Particle Physics The Ohio State University Columbus OH 43210 USA Istituto Nazionale di Fisica Nucleare Sezione di Perugia I-06123 Perugia Italy Dipartimento di Fisica Università degli Studi di Perugia I-06123 Perugia Italy Dipartimento di Fisica 'M. Merlin' dell'Università e del Politecnico di Bari I-70126 Bari Italy Istituto Nazionale di Fisica Nucleare Sezione di Bari 70126 Bari Italy Laboratoire Leprince-Ringuet École polytechnique CNRS/IN2P3 Palaiseau France Department of Physics University of Washington Seattle WA 98195-1560 USA Columbia Astrophysics Laboratory Columbia University New York NY 10027 USA INAF-Isti

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Stretchable silicon electronics and their integration with rubber, plastic, paper, vinyl, leather and fabric substrates

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MRS Online Proceedings Library 2009年第1期1196卷 1-7页

作者： Kim, Dae-Hyeong Kim, Yun-Soung Liu, Zhuangjian Song, Jizhou Kim, Hoon-Sik Huang, Yonggang Y. Rogers, John A. Departments of Materials Science and Engineering Beckman Institute and Frederick Seitz Materials Research Laboratory University of Illinois at Urbana-Champaign Urbana USA Institute of High Performance Computing Singapore Singapore Department of Mechanical and Aerospace Engineering University of Miami Coral Gables USA Departments of Civil and Environmental Engineering and Mechanical Engineering Northwestern University Evanston USA Departments of Chemistry Mechanical Engineering Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana USA

Electronic systems that offer elastic mechanical responses to high strain deformations are of growing interest, due to their ability to enable new electrical, optical and biomedical devices and other applications whose requirements are impossible to satisfy with conventional wafer-based technologies or even with those that offer simple bendability. This paper describes materials and mechanical design strategies for classes of electronic circuits that offer extremely high flexibility and stretchability over large area, enabling them to accommodate even demanding deformation modes, such as twisting and linear stretching to ‘rubber-band’ levels of strain over 100%. The use of printed single crystalline silicon nanomaterials for the semiconductor provides performance in flexible and stretchable complementary metal-oxide-semiconductor (CMOS) integrated circuits approaching that of conventional devices with comparable feature sizes formed on silicon wafers. Comprehensive theoretical studies of the mechanics reveal the way in which the structural designs enable these extreme mechanical properties without fracturing the intrinsically brittle active materials or even inducing significant changes in their electrical properties. The results, as demonstrated through electrical measurements of arrays of transistors, CMOS inverters, ring oscillators and differential amplifiers, suggest a valuable route to high performance stretchable electronics that can be integrated with nearly arbitrary substrates. We show examples ranging from plastic and rubber, to vinyl, leather and paper, with capability for large area coverage.

关键词： electronic structure nanostructure devices

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