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Quantum Gaussian particle swarm optimization approach for PID controller design in AVR system

Quantum Gaussian particle swarm optimization approach for PI...

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IEEE International Conference on systems, Man and Cybernetics

作者： Leandro dos Santos Coelho Bruno Avila de Meirelles Herrera Industrial and Systems Engineering Graduate Program PPGEPS Pontifical Catholic University of Paraná Curitiba Parana Brazil

During the history of science of computational intelligence, many evolutionary algorithms approaches were proposed having more or less success in solving various optimization problems. In this context, the Particle Swarm Optimization (PSO) is a bio-inspired optimization mechanism based on the metaphor of social behaviour of birds flocking and fish schooling in search for food. Inspired by the classical PSO method and quantum mechanics theories, this work presents a quantum-behaved PSO (QPSO) approach using Gaussian probability distribution function (G-QPSO). Numerical simulations based on optimized proportional-integral-derivative (PID) control of an automatic regulator voltage system for nominal system parameters and step reference voltage input demonstrate the effectiveness and efficiency of G-QPSO approach. Simulation results of G-QPSO to determine the PID parameters are compared with the classical PSO and QPSO.

关键词： Particle swarm optimization Three-term control Control systems Quantum mechanics History Computational intelligence Evolutionary computation Birds Marine animals Educational institutions

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Cultural differential evolution approach to optimize the economic dispatch of electrical energy using thermal generators

Cultural differential evolution approach to optimize the eco...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Leandro dos Santos Coelho Adriano Del Vigna de Almeida Viviana Cocco Mariani Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Paraná Curitiba Parana Brazil Mechanical Engineering Program Pontifical Catholic University of Paraná Curitiba Parana Brazil

Differential evolution (DE) is a powerful population-based algorithm of evolutionary computation field designed for solving global optimization problems. The potentialities of DE are its simple structure, easy use, convergence speed and robustness. However, the control parameters and learning strategies involved in DE are highly dependent on the problems under consideration. Choosing suitable parameter values requires also previous experience of the user. Despite its crucial importance, there is no consistent methodology for determining the control parameters of a DE. In this paper, different DE approaches combined with a cultural algorithm technique based on normative and situational knowledge are proposed as alternative methods to solving the economic load dispatch problem of thermal generators with valve-point effect. The DE approaches are validated for a test system consisting of 13 thermal generators whose nonsmooth fuel cost function takes into account the valve-point loading effects. Numerical results indicate that performance of the cultural DE present best results when compared with previous optimization approaches in solving load dispatch problems with the valve-point effect.

关键词： Cultural differences Optimization Evolution (biology) Generators Economics Mathematical model Evolutionary computation

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Integration of computer based problem solving into engineering curricula

Integration of computer based problem solving into engineeri...

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2008 ASEE Annual Conference and Exposition

作者： Raubenheimer, Dianne Brent, Rebecca Joines, Jeff Craig, Amy North Carolina State University Department of Adult and Higher Education College of Education NCSU Education Designs Inc. Education Designs Inc. Cary NC United States College of Engineering North Carolina State University National Effective Teaching Institute Chemistry and Science Department NC State University Textile Engineering Program College of Engineering Student-Owned Computing College of Engineering Department of Industrial and Systems Engineering NC State University

The primary objectives of this engineering project are (1) to examine how to develop students' problem solving and computational skills early in their program of study and (2) to further enhance these skills by building upon critical computing concepts semester after semester. The project is a component of NC State University's quality enhancement plan, which focuses on the use of technology in enhancing student learning. The project stems from new introductory computer-based modeling courses that were created in two engineering departments, and has expanded to include other departments. We give an overview of the project, provide an example of how a problem is modeled and broken apart, present some assessment results, and discuss the emerging lessons being learned. © American Society for engineering Education, 2008.

关键词： Problem solving

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Recommending mobile content using content diffusion and user preference transition

Recommending mobile content using content diffusion and user...

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2008 International Conference on e-Learning, e-Business, Enterprise Information systems, and e-Government, EEE 2008

作者： Lee, Tong Queue Lee, Eunchang Park, Young Park, Yongtae Department of Mobile Internet Dongyang Technical College Seoul Korea Republic of Graduate Program in Technology and Management Seoul National University Seoul Korea Republic of Department of Computer Science and Information Systems Bradley University Peoria IL United States Department of Industrial Engineering Seoul National University Seoul Korea Republic of

ISBN: (纸本)1601320639

Recommendation accuracy is especially important in mobile e-commerce environments due to the limited screen size of mobile devices and relatively expensive connection costs. Mobile content tends to be fashionable and are geared for young users. This paper presents a novel method of building a more accurate recommender system for mobile content in a mobile ecommerce environment. The method is based on collaborative filtering, and models content diffusion and user preference transition and incorporates them in constructing pseudo ratings from implicit feedback data. In a variety of experiments, recommender systems based on the method showed significantly better recommendation accuracy than a pure collaborative filtering-based recommender system.

关键词： Collaborative filtering

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DLM: A distributed Large Memory System using remote memory swapping over cluster nodes

DLM: A distributed Large Memory System using remote memory s...

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IEEE International Conference on Cluster computing

作者： Hiroko Midorikawa Motoyoshi Kurokawa Ryutaro Himeno Mitsuhisa Sato Department of Computer and Information Science Seikei University Musashino Tokyo Japan Graduate School of Systems and Information Engineering University of Tsukuba Tennodai Tsukuba Ibaraki Japan Advanced Center of Computing and Communication Wako Saitama Japan Research Program for Computational Science RIKEN Wako Saitama Japan

Emerging 64 bitOSpsilas supply a huge amount of memory address space that is essential for new applications using very large data. It is expected that the memory in connected nodes can be used to store swapped pages efficiently, especially in a dedicated cluster which has a high-speed network such as 10 GbE and Infiniband. In this paper, we propose the distributed large memory system (DLM), which provides very large virtual memory by using remote memory distributed over the nodes in a cluster. The performance of DLM programs using remote memory is compared to ordinary programs using local memory. The results of STREAM, NPB and Himeno benchmarks show that the DLM achieves better performance than other remote paging schemes using a block swap device to access remote memory. In addition to performance, DLM offers the advantages of easy availability and high portability, because it is a user-level software without the need for special hardware. To obtain high performance, the DLM can tune its parameters independently from kernel swap parameters. We also found that DLMpsilas independence of kernel swapping provides more stable behavior.

关键词： Kernel Arrays Memory management Servers Bandwidth Performance evaluation Benchmark testing

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Economic dispatch optimization using hybrid chaotic particle swarm optimizer

Economic dispatch optimization using hybrid chaotic particle...

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2007 IEEE International Conference on systems, Man, and Cybernetics, SMC 2007

作者： Dos Santos Coelho, Leandro Mariani, Viviana Coceo Industrial and Systems Engineering Graduate Program - PPGEPS Automation and Systems Laboratory Pontifical Catholic University of Paraná Imaculada Conceição 1155 80215-901 Curitiba Paraná Brazil Pontifical Catholic University of Paraná Mechanical Engineering Graduate Program PUCPR/PPGEM Imaculada Conceição 1155 80215-901 Curitiba Paraná Brazil

ISBN: (纸本)1424409918

Particle swarm optimization (PSO) is a population-based stochastic optimization technique, originally developed by Eberhart and Kennedy, inspired by simulation of a social psychological metaphor instead of the survival of the fittest individual. In PSO, the system (swarm) is initialized with a population of random solutions (particles) and searches for optima using cognitive and social factors by updating generations. PSO has been successfully applied to a wide range of applications, mainly in solving continuous nonlinear optimization problems. Based on the PSO and chaos theories, this paper discusses the use of a chaotic PSO approach hybridized with an Implicit Filtering (IF) technique to optimize performance of economic dispatch problems. The chaotic PSO with chaos sequences is the global optimizer and the IF is used to fine-tune the chaotic PSO run in sequential manner. The hybrid methodology is validated for a test system consisting of 13 thermal units whose incremental fuel cost function takes into account the valve-point loading effects. © 2007 IEEE.

关键词： Particle swarm optimization (PSO)

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On selecting the MIMO Generalized Orthonormal Basis Functions poles by using Particle Swarm Optimization

On selecting the MIMO Generalized Orthonormal Basis Function...

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European Control Conference (ECC)

作者： Bruno C. Reginato Gustavo H. C. Oliveira Industrial and Systems Engineering Graduate Program (PPGEPS) of the Pontifical Catholic University of Parana (PUCPR)

This paper deals with the problem of finding the pole(s) in Orthonormal Basis Functions (OBF) models. In fact, any stable system can be exactly represented by an infinite orthonormal functions series, assuming that the basis used in such representation is complete. However, in practice a truncated basis functions expansion is used. For certain basis choice and fixed number of functions, there is a pole or set of poles that minimizes the truncation error. The problem is then to find such set. Therefore, the present paper is focused on the problem of finding the basis functions poles when more than one dynamic is involved, for instance, in SISO Generalized Orthonormal Basis Functions (GOBF) models or MIMO Laguerre, Kautz and/or GOBF models. It is proposed and analyzed here the use of Particle Swarm Optimization (PSO) for such problem. Simulation results presented shows the validity of the proposed method.

关键词： Convergence Mathematical model Particle swarm optimization Approximation methods Optimization MIMO Equations

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Economic dispatch optimization using hybrid chaotic particle swarm optimizer

Economic dispatch optimization using hybrid chaotic particle...

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IEEE International Conference on systems, Man and Cybernetics

作者： Leandro dos Santos Coelho Viviana Cocco Mariani Industrial and Systems Engineering Graduate Program-PPGEPS Automation and Systems Laboratory PUCPR/PPGEPS Pontifical Catholic University of Paraná Curitiba Parana Brazil Mechanical Engineering Graduate Program PUCPR/PPGEM Pontifical Catholic University of Paraná Curitiba Parana Brazil

Particle swarm optimization (PSO) is a population-based stochastic optimization technique, originally developed by Eberhart and Kennedy, inspired by simulation of a social psychological metaphor instead of the survival of the fittest individual. In PSO, the system (swarm) is initialized with a population of random solutions (particles) and searches for optima using cognitive and social factors by updating generations. PSO has been successfully applied to a wide range of applications, mainly in solving continuous nonlinear optimization problems. Based on the PSO and chaos theories, this paper discusses the use of a chaotic PSO approach hybridized with an implicit filtering (IF) technique to optimize performance of economic dispatch problems. The chaotic PSO with chaos sequences is the global optimizer and the IF is used to fine-tune the chaotic PSO run in sequential manner. The hybrid methodology is validated for a test system consisting of 13 thermal units whose incremental fuel cost function takes into account the valve-point loading effects.

关键词： Chaos Particle swarm optimization Fuel economy Stochastic processes Psychology Social factors Filtering theory System testing Thermal loading Cost function

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A CAN and OSEK NM based siren for automobiles

A CAN and OSEK NM based siren for automobiles

引用

2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07

作者： Qiao, Xin Wang, Zhixue Sun, Yuan He, Feng Wang, Fei-Yue IEEE Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences CO 100080 Beijing China Institute of Computing Chinese Academy of Sciences CO 100080 Beijing China Key Laboratory for Complex Systems and Intelligence Science Chinese Academy of Sciences Beijing China Program for Advanced Research in Complex Systems Department of Systems and Industrial Engineering University of Arizona Tucson AZ 85719 United States

ISBN: (纸本)1424410762

Today, more and more Electronic Control Units have been installed in vehicle, and Controller Area Network is one of the most widely used networks to connect these ECUs, also the architecture of OSEK is increasingly employed into the design of individual ECU and the network. In this paper, we propose a new framework to design the ECU with limited-storage-space. In the framework, an infinite cycle acts as a mainframe, and the code conforming to OSEK NM is inserted into the cycle. A siren of vehicle is developed with this method, and a Hardware-In-Loop test is accomplished. The results show that the framework is available, and the real-time performance of the siren is satisfied. © 2007 IEEE.

关键词： Intelligent vehicle highway systems

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Sequential sampling for solving stochastic programs 07

Sequential sampling for solving stochastic programs

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Simulation Winter Conference

作者： Guzin Bayraksan David P. Morton Department of Systems and Industrial Engineering University of Arizona Tucson Tucson AZ USA Graduate Program in Operations Research University of Texas Austin Austin TX USA

ISBN: (纸本)9781424413065

We develop a sequential sampling procedure for solving a class of stochastic programs. A sequence of feasible solutions, with at least one optimal limit point, is given as input to our procedure. Our procedure estimates the optimality gap of a candidate solution from this sequence, and if that point estimate is sufficiently small then we stop. Otherwise, we repeat with the next candidate solution from the sequence with a larger sample size. We provide conditions under which this procedure: (i) terminates with probability one and (ii) terminates with a solution which has a small optimality gap with a prespecified probability.

关键词： Sampling methods Stochastic processes Stochastic systems Mathematical programming Random variables Monte Carlo methods State estimation Operations research Decision making Uncertainty

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