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Stochastic search strategies in 2D using agents with limited perception

IFAC Proceedings Volumes 2012年第22期45卷 650-654页

作者： David Puljiz Maja Varga Stjepan Bogdan University of Zagreb Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering LARICS–Laboratory for Robotics and Intelligent Control Systems Zagreb Croatia Laboratory of Intelligent Systems Ecole Polytechnique Federale de Lausanne 1015 Lausanne Switzerland

Animals use stochastic search strategies to find a potential nest or to localize the food source. Animals do not rely much on the environmental cues or their sensory inputs. Since their cognitive capabilities are rather limited, search strategies are usually simplistic and stochastic. And yet such simple search strategies result in high efficiency in finding potential targets in limited time intervals. Similar problems with perception of the environment and limited computational power and sensory inputs are observed on the mobile robots used in search scenarios. In spite of their limitations, such robotic systems usually perform deterministic search strategies which heavily depend on environmental cues and sensory input. The research question we are addressing in this paper is which stochastic search strategy has the best performance in terms of area coverage and can stochastic search strategies outperform deterministic search strategies.

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Investigation on different learning techniques for weighted kernel regression in solving small sample problem

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ICIC Express Letters 2012年第3期6卷 705-710页

作者： Shapiai, Mohd Ibrahim Ibrahim, Zuwairie Sudin, Shahdan Mohamad, Mohd Saberi Khalid, Marzuki Centre of Artificial Intelligent and Robotics Faculty of Electrical Engineering Malaysia Faculty of Computer Science and Information Systems Universiti Teknologi Malaysia Skudai 81310 Malaysia

Previously, weighted kernel regression (WKR) for solving small sample problems has been reported. The proposed WKR has been successfully employed to solve rational functions with very few samples. The design and development of WKR is important in order to extend the capability of the technique with various learning techniques. Based on WKR, a simple iteration technique is employed to estimate the weight parameters before WKR can be used in predicting the unseen test samples. In this paper, we investigate two learning techniques in estimating the weight parameters. For this purpose, a Ridge Regression (RR) and a guided search based on Particle Swarm Optimization (PSO) are used to investigate the capability of WKR in solving small sample problems. It is found that RR and PSO are better than iteration technique in terms of computational time and exibility of defining the objective function to estimate weight parameters, respectively, without sacrificing the quality of prediction, as supported by the conducted experiments. © 2012 ICIC International.

关键词： Rational functions

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Tracker design using composite nonlinear feedback with application to a DC servomotor

Tracker design using composite nonlinear feedback with appli...

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Power Electronic and Drive systems and Technologies Conference (PEDSTC)

作者： Saleh Mobayen Vahid Johari Majd Intelligent Control Systems Laboratory School of Electrical and Computer Engineering Tarbiat Moalem University Tehran Iran

In this paper, the theory of a nonlinear control technique, i.e., the composite nonlinear feedback control is considered for robust tracking of a class of linear systems with time varying uncertain parameters and disturbances. This controller can guarantee that the tracking error decreases asymptotically to zero in the presence of time varying uncertain parameters and disturbances. For performance improvement of dynamical system, this proposed robust tracking controller consists of linear and nonlinear feedback parts without any switching element. The linear feedback law is designed to yield a closed loop system with a small damping ratio for the existence of a quick response. On the other hand, the nonlinear feedback law is designed to increase the damping ratio as the system output approaches the output of the reference model. Finally, the simulations of applying the control law on a DC servomotor positioning system are given to illustrate the validity of the results developed in this paper.

关键词： Servomotors

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A Scalable Wide Area Monitoring System using Cellular Neural Networks

A Scalable Wide Area Monitoring System using Cellular Neural...

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International Joint Conference on Neural Networks

作者： Karthikeyan Balasubramaniam Bipul Luitel Ganesh Kumar Venayagamoorthy Real-Time Power and Intelligent Systems Laboratory Holcombe Department of Electrical and Computer Engineering Clemson University

ISBN: (纸本)9781467314886

Synchrophasor systems make power grids more observable by collecting data from various locations, time-align and process them as a coherent data set. Better observability results in better control actions. A limiting factor to this approach is communication delays. Power system wide area communication delays range from several milliseconds to several seconds depending on the communication media and distance. One way to deal with this is to have an intelligent system which can predict state values for one or more time steps ahead of time. A novel four dimensional scalable multirate cellular neural network (CNN) architecture for use as wide area monitoring system (WAMS) is proposed. Recurrent neural network (RNN) is used as computational engine for each cell as RNNs have dynamic memory. By using information from phasor measurement units (PMUs) that are optimally located in a power system, each layer predicts a state variable for one or more time steps. Data from remote PMUs are replaced by the respective CNN cells' time delayed predicted state values for next time step. This enables local controllers to take real-time control action with system wide information. A 12-bus test power system is used to develop and demonstrate the effectiveness of the proposed CNN framework for WAMS.

关键词： Cellular neural networks Recurrent neural networks State estimation Synchrophasors Wide area monitoring and control Cellular neural networks Wide area measurement systems Communications Media Phasor measurement units recurrent neural nets area monitoring State variable power systems Dynamic storage local controller Power grids intelligent systems Data Collection Methods Scalability MILLISECOND

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Agent-based cyber control strategy design for resilient control systems: Concepts, architecture and methodologies

Agent-based cyber control strategy design for resilient cont...

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2012 5th International Symposium on Resilient Control systems, ISRCS 2012

作者： Rieger, Craig Zhu, Quanyan Başar, Tamer Instrumentation Control and Intelligent Systems Idaho National Laboratory Idaho Falls ID United States Coordinated Science Laboratory Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign 1308. W. Main St. Urbana IL United States

ISBN: (纸本)9781467301633

The implementation of automated regulatory control has been around since the middle of the last century through analog means. It has allowed engineers to operate the plant more consistently by focusing on overall operations and settings instead of individual monitoring of local instruments (inside and outside of a control room). A similar approach is proposed for cyber security, where current border-protection designs have been inherited from information technology developments that lack consideration of the high-reliability, high consequence nature of industrial control systems. Instead of an independent development, however, an integrated approach is taken to develop a holistic understanding of performance. This performance takes shape inside a multi-agent design, which provides a notional context to model highly decentralized and complex industrial process control systems, the nervous system of critical infrastructure. The resulting strategy will provide a framework for researching solutions to security and unrecognized interdependency concerns with industrial control systems. © 2012 IEEE.

关键词： Networked control systems

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Biologically inspired intrinsically motivated learning for service robots based on novelty detection and habituation

Biologically inspired intrinsically motivated learning for s...

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IEEE International Conference on robotics and Biomimetics

作者： Yiannis Gatsoulis Chris Burbridge T. Martin McGinnity Intelligent Systems Research Centre Faculty of Computing and Engineering University of Ulster Derry UK Intelligent Robotics Laboratory School of Computer Science University of Birmingham Birmingham UK

The effective operation of service robots relies on developmental programs that allow the robot to expand its knowledge about its dynamic operating environment. Motivation theories from neuroscience and neuropsychology study the underlying mechanisms that drive the engagement of biological creatures to certain activities, such as learning. This research uses a physical Willow Garage PR2 robot, which is equipped with a cumulative learning mechanism driven by the intrinsic motivation of novelty detection based on computational models of biological habituation. It cumulatively learns the 360° appearance of novel real-world objects by picking them up. This paper discusses the theoretical motivations and background information on intrinsic motivations as novelty detection. The results and conclusions from the experimental study are presented.

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SmartParks for short term power flow control in smart grids

SmartParks for short term power flow control in smart grids

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IEEE International Electric Vehicle Conference (IEVC)

作者： Ganesh Kumar Venayagamoorthy Real-Time Power and Intelligent Systems Laboratory Holcombe Department of Electrical and Computer Engineering Clemson University SC USA

Among other functions, the smart grid is a vehicle to maximize the penetration of wind power by exploiting the use of energy storage devices in order to maximize the utilization of renewable energy and bring about maximum reduction in emissions. Large number of electric vehicles in a parking lot enabled to carry out vehicle-to-grid (V2G) transactions has been termed as a `SmartPark' by the author. The use of vehicle-to-grid technology, information technology and advanced computational methods can provide short term real and reactive power support to overcome the drawback of the intermittent nature of renewable sources of electricity. This paper presents the intelligent control of SmartParks to enhance a smart grid's capability to compensate and mitigate active and reactive power fluctuations caused by the variability in wind power penetration in selected transmission lines and network buses.

关键词： Wind power generation Wind farms Voltage control Smart grids Fluctuations Reactive power Power transmission lines

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Reports of the AAAI 2011 fall symposia

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AI Magazine 2012年第1期33卷 71-78页

作者： Blisard, Sam Carmichael, Ted Ding, Li Finin, Tim Frost, Wende Graesser, Arthur Hadzikadic, Mirsad Kagal, Lalana Kruijff, Geert-Jan M. Langley, Pat Lester, James McGuinness, Deborah L. Mostow, Jack Papadakis, Panagiotis Pirri, Fiora Prasad, Rashmi Stoyanchev, Svetlana Varakantham, Pradeep Interactive Systems Section Naval Research Laboratory Washington DC United States Department of Software and Information Systems University of North Carolina Charlotte United States Qualcomm Inc. United States Department of Computer Science and Electrical Engineering University of Maryland Baltimore County United States Department of Psychology Institute of Intelligent Systems University of Memphis United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Germany Department of Computer Science and Engineering Arizona State University Tempe AZ United States Department of Computer Science North Carolina State University United States Department of Computer Science and Cognitive Science Rensselaer Polytechnic Institute United States School of Computer Science Carnegie Mellon University United States Department of Informatics and Systems University of Sapienza Rome Italy Department of Informatics and Systems ALCOR Lab University of Sapienza Rome Italy Department of Health Informatics and Administration University of Wisconsin-Milwaukee United States Columbia University United States School of Information Systems Singapore Management University Singapore Singapore

The Association for the Advancement of Artificial Intelligence was pleased to present the 2011 Fall Symposium Series, held Friday through Sunday, November 4-6, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the seven symposia are as follows: (1) Advances in Cognitive systems;(2) Building Representations of Common Ground with intelligent Agents;(3) Complex Adaptive systems: Energy, Information, and Intelligence;(4) Multiagent Coordination under Uncertainty;(5) Open Government Knowledge: AI Opportunities and Challenges;(6) Question Generation;and (7) Robot-Human Teamwork in Dynamic Adverse Environments. The highlights of each symposium are presented in this report. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： intelligent agents

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Super-bit locality-sensitive hashing

Super-bit locality-sensitive hashing

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26th Annual Conference on Neural Information Processing systems 2012, NIPS 2012

作者： Ji, Jianqiu Li, Jianmin Yan, Shuicheng Zhang, Bo Tian, Qi State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing 100084 China Department of Electrical and Computer Engineering National University of Singapore Singapore 117576 Singapore Department of Computer Science University of Texas at San Antonio One UTSA Circle San Antonio TX 78249-1644 United States

ISBN: (纸本)9781627480031

Sign-random-projection locality-sensitive hashing (SRP-LSH) is a probabilistic dimension reduction method which provides an unbiased estimate of angular similarity, yet suffers from the large variance of its estimation. In this work, we propose the Super-Bit locality-sensitive hashing (SBLSH). It is easy to implement, which orthogonalizes the random projection vectors in batches, and it is theoretically guaranteed that SBLSH also provides an unbiased estimate of angular similarity, yet with a smaller variance when the angle to estimate is within (0, π/2]. The extensive experiments on real data well validate that given the same length of binary code, SBLSH may achieve significant mean squared error reduction in estimating pairwise angular similarity. Moreover, SBLSH shows the superiority over SRP-LSH in approximate nearest neighbor (ANN) retrieval experiments.

关键词： Nearest neighbor search

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A novel feature extraction method for the classification of SAR images

A novel feature extraction method for the classification of ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Örsan Aytekin Mehmet Koc Ilkay Ulusoy Computer Vision and Intelligent Systems Research Laboratory Department of Electrical and Electronics Engineering Middle East Technical University Ankara Turkey

This paper proposes a new method for the classification of synthetic aperture radar (SAR) images based on a novel feature vector. The method aims at combining intensity information of pixels with spatial information and structural relationships. Unlike classical approaches which define a static neighborhood and relate spatial information for each center pixel to all the pixels within that window, the local primitives (LPs) proposed in this study provide us with an adaptive neighborhood for each pixel. LPs correspond to a certain number of layers of local homogenous connected components. Using LPs, a feature vector (local primitive pattern, LPP) is constructed for each pixel. The feature vector includes information about the sizes and contrast differences of LPs within a disk as well as the repetitive frequency of LPs outside that disk. To test the efficiency of LPP, support vector machine (SVM) classification is utilized.

关键词： Quantization Synthetic aperture radar Support vector machine classification Feature extraction Remote sensing Training

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