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共 999 条记录，以下是711-720 订阅

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A Hybrid Method of GA and ACO for 2-D Entropic Automatic Thresholding of Digital Images

A Hybrid Method of GA and ACO for 2-D Entropic Automatic Thr...

引用

international workshop on Intelligent Systems and applications, ISA

作者： Xiaohong Shen Yulin Zhang Fangzhen Li School of Control Science and Engineering Shandong University Jinan China School of Computer Science and Technology Shandong Economics University Jinan China

Thresholding is an important approach of image segmentation. One of the criteria to select the threshold is two- dimensional (2-D) entropic thresholding. However this method is time-consuming. A hybrid method of GA and ACO is introduced to the 2-D entropic thresholding instead of the exhaustive search. In the proposed method, the ant which represents the threshold vector is at the same time the chromosome. The search space is equally divided into several blocks each marked by pheromone. To reflect the collaboration of ant colony, the 2-D entropy of the ant and the pheromone of its block are both used to construct the fitness function. The best threshold vector is obtained by the genetic evolution of ant colony. Experiments show the proposed method obviously reduced the search time and its accuracy, stability and search efficiency are better than that of the 2-D entropic algorithm by using GA or ACO.

关键词： Digital images Entropy Ant colony optimization Image segmentation Genetic algorithms Biological cells Stability Convergence Two dimensional displays Automatic control

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Communication Latency Tolerant Parallel Algorithm for Particle Swarm optimization

Communication Latency Tolerant Parallel Algorithm for Partic...

引用

Japan-China Joint workshop on Frontier of Computer Science and Technology (FCST)

作者： Bo Li Koichi Wada Department of Computer Science University of Tsukuba Tsukuba Ibaraki Japan

Particle swarm optimization (PSO) algorithm is a population-based algorithm for finding the optimal solution. Because of its simplicity and high efficiency, PSO is gaining attention in solving complex and large scale problems. However, PSO often requires long execution time to solve those problems. This paper proposes a parallel PSO algorithm, called delayed exchange parallelization, which improves performance of PSO on distributed environment by hiding communication latency efficiently. By overlapping communication with computation, the proposed algorithm extracts parallelism inherent in PSO. The performance of our proposed parallel PSO algorithm was evaluated using several applications. The results of evaluation showed that the proposed parallel algorithm drastically improved the performance of PSO, especially in high-latency network environment.

关键词： Delay Parallel algorithms Particle swarm optimization Computer science Large-scale systems Iterative algorithms Concurrent computing Design optimization Communications technology Parallel processing

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The JAMES II Framework for Modeling and Simulation

The JAMES II Framework for Modeling and Simulation

引用

international workshop on High Performance Computational Systems Biology, HIBI

作者： Jan Himmelspach Adelinde M. Uhrmacher Institute of Computer Science University of Rostock Rostock Germany

JAMES II is a general and open framework based on the ldquoPlug'n simulaterdquo concept, which enables developers to integrate their modeling and simulation methodological ideas into, and to create their applications upon an existing *** concept together with currently more than 400 plug-ins,and an explicit representation and storage of experiments ease developing modeling and simulation methods and contribute to a systematic experimental evaluation of methods. Modelers benefit from the flexibility that the framework provides with respect to modeling, simulation, and analysis methods, supporting effective and efficient simulation studies, and the well tested methods add to the credibility of the results achieved.

关键词： Biological system modeling Computational modeling Analytical models Computer simulation Computational systems biology Application software optimization methods Partitioning algorithms Computer science Testing

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Application of Soft Computing in the Control of Hydroelectric Generating Unit

Application of Soft Computing in the Control of Hydroelectri...

引用

international workshop on Intelligent Systems and applications, ISA

作者： Shuqing Wang Zipeng Zhang Liqin Xue Hubei University of Technology Wuhan China Shandong University of Science and Technology Qingdao China

Hydroelectric generating unit system of water plant is a non-linear and complicated system. Conventional controller cannot get good controlling performance in control. In this study, an advanced soft computing technique based on fuzzy, neural network and genetic algorithm is used in the control of hydroelectric generating unit system of hydropower plant. Fuzzy reasoning system is used as controller and genetic algorithm is employed to optimize the parameters and rules of fuzzy controller in the design of controller or real-time control process. Dynamic identification model of control system based on RBF neural networks is designed to appraise the controlling performance of fuzzy controller. In the design, improved genetic algorithm is adopted based on general genetic algorithm and the character of control system. The improved genetic algorithm quickens optimizing speed and makes fuzzy controller acquire knowledge effectively. Simulation results show that the designed soft computing optimization control system can well control hydroelectric generating unit and its control performance is superior to conventional controller.

关键词： Computer applications Hydroelectric power generation Control systems Genetic algorithms Fuzzy control Fuzzy systems Control system synthesis Fuzzy neural networks Neural networks Fuzzy reasoning

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A Novel Genetic Simulated Annealing Algorithm for the Resource-Constrained Project Scheduling Problem

A Novel Genetic Simulated Annealing Algorithm for the Resour...

引用

international workshop on Intelligent Systems and applications, ISA

作者： Xiaoguang Yu Dechen Zhan Lanshun Nie Xiaofei Xu Sch. of Comput. Sci. & Technol. Harbin Inst. of Technol. Harbin

A novel hybrid meta-heuristic algorithm, entitled as RCPSPGSA, is proposed for solving the resource-constrained project scheduling problem (RCPSP) in this paper. The algorithm incorporates the simulated annealing algorithm (SA) into genetic algorithm in order to improve local searching performance and boost up evolution capability. In each evolution iteration GA generates a new temporary population, and after that SA is used for improving every individual in it and at the mean time the next gap population is generated. For the sake of keeping the same convergence direction and speed of GA and SA, the cooling procedure occurs at the end of each evolution iteration. Simulation experiments are performed on the standard project instance sets of PSPLIB, and orthogonal experiment method is introduced to solve the parameter selection problem. Parameter combinations selected by this method are proved to be outperformed. Experimental results show that RCPSPGSA improves solution quality for J30, J60, J90 sets and not bad for J120.

关键词： Simulated annealing Scheduling algorithm Computational modeling Processor scheduling Computer science Ant colony optimization Computer simulation Genetic algorithms Convergence Cooling

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Pattern Recognition with Embedded Systems Technology: A Survey

Pattern Recognition with Embedded Systems Technology: A Surv...

引用

international workshop on Database and Expert Systems applications

作者： Juan-Carlos Perez-Cortes Jose-Luis Guardiola Alberto-Jose Perez-Jimenez Instituto Tecnológico de Informática Universidad Politécnica de Valencia Valencia Spain

Pattern Recognition (PR) tasks are natural candidates for embedded systems, since they usually interact with humans and other complex processes in the real world. Often regarded as the part of Artificial Intelligence (AI) closer to perception, a typical PR application reacts to external events that the system perceives through physical sensors or input devices and produces a response using actuators or information display subsystems. Being usually far from trivial, very demanding from the computational point of view, and requiring a fast reaction time, PR algorithms constitute a real challenge to the embedded system designer. In this talk, some of the main application domains and optimization approaches proposed to deal with these relevant issues, along with many open problems and paths to improvement, are presented.

关键词： Pattern recognition Embedded system Artificial intelligence Humans Intelligent sensors Sensor systems and applications Intelligent actuators Displays Embedded computing Algorithm design and analysis

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Topology and parameter optimization of ANN using genetic algorithm for application of textiles

Topology and parameter optimization of ANN using genetic alg...

引用

IEEE international workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications

作者： Lalita Admuthe Shaila Apte Sunil Admuthe Textile and Engineering Institute Ichalkaranji India Walchand College of Engineering and Technology Sangli India Padmabhooshan Vasantraodada Patil Institute of Technology Sangli India

The BP feed-forward neural network is popular in solving many non-linear multivariate and complex problems. The most important problem with neural network is to decide optimal structure and parameter settings. Literature presents a multitude of methods but there is no rigorous and accurate analytical method. This paper presents the hybrid approach of genetic algorithm and neural network computing for establishment of the optimum number of neurons on layers, transfer functions, learning rate, momentum and number of epochs for a given problem. The method can be used without restrictions to model a network with many inputs and outputs. The process involves GA evolving several structures, different parameters and fitness level of each structure. GA decides fitness using neural network as the fitness function. This technique can help to eliminate trial and error work for deciding the optimal network. The proposed Neuron-Genetic classifier has been successfully applied for prediction of yarn properties in spinning process of Textile industry.

关键词： Genetic algorithms Textiles Neural networks Network topology Artificial neural networks Feedforward systems Feedforward neural networks Computer networks Neurons Transfer functions

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Euclidean Particle Swarm optimization

Euclidean Particle Swarm Optimization

引用

international workshop on Intelligent Networks and Intelligent Systems (ICINIS)

作者： Hongbing Zhu Chengdong Pu Kei Eguchi Jinguang Gu College of Computer Science and Technology Wuhan University of Science and Engineering Wuhan China College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Department of Technology Education Shizuoka University Shizuoka Japan

Particle swarm optimization (PSO) is a swarm intelligence algorithm, has been successfully applied to many engineering optimization problems and shown its high search speed in these applications. However, as the dimension and the number of local optima of optimization problems increase, PSO and most existing improved PSO algorithms such as, the standard particle swarm optimization (SPSO) and the Gaussian particle swarm optimization (GPSO), are easily trapped in local optima. In this paper we proposed a novel algorithm based on SPSO called Euclidean particle swarm optimization (EPSO) which has greatly improved the ability of escaping from local optima. To confirm the effectiveness of EPSO, we have employed five benchmark functions to examine it, and compared it with SPSO and GPSO. The experiments results showed that EPSO is significantly better than SPSO and GPSO, especially obvious in higher-dimension problems.

关键词： Particle swarm optimization Educational technology Educational institutions Computer science Intelligent networks Euclidean distance Interference Probability distribution Intelligent systems Computer science education

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Interactive system design and end-to-end optimization in sensor networks

Interactive system design and end-to-end optimization in sen...

引用

international workshop on Wearable Micro and Nano Technologies for Personalized Health (pHealth)

作者： Knut Grythe Arne Lie Ilangko Balasingham Department of Communication Systems SINTEF ICT Norway Interventional Centre Rikshospitalet University Hospital Norway Department of Electronics and Telecommunications Norwegian University of Science and Technology Norway

This paper proposes an application oriented four layered quality of service (QoS) stack for wireless sensor networks (WSN), including a supporting system architecture for interacting with WSNs using the MPEG-21 multimedia framework in an medical application setting. The application of the WSNs is signal processing algorithms reporting elaborated observed sensor data to external users. The QoS model supports the tradeoff between end-users expected QoS combined with optimized energy consumption. The MPEG-21 standard is chosen as the media resource framework due to the fact that future hospitals will have networks comprising WSNs supporting an always increasing amount of both low rate medical sensor data as well as high rate audiovisual content. Furthermore due to the heterogeneous set of user terminals and network resources, media adaptation will become important to provide reliable and robust quality of service. In this paper, we argue for an extension of the MPEG-21 terminology for use in biomedical wireless sensor networks and incorporate the requirements needed for medical applications.

关键词： Quality of service Wireless sensor networks Transform coding Media Signal processing algorithms Degradation Wireless communication

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multilevel optimization of DSP Based SPEEX Decoder

Multilevel Optimization of DSP Based SPEEX Decoder

引用

Congress on Image and Signal Processing, CISP

作者： Wen-guang Jin Bin Zhang De-qing Zhu Kai-liang Hu Dept. of Information & Electronic Engineering Zhejiang University Hangzhou China Hangzhou Normal University Hangzhou China

SPEEX is a flexible codec based on the Code Excited Linear Prediction (CELP) algorithm and supports a wide range of speech quality. In this paper, several high-level and low-level optimization techniques have been concurrently employed to reduce the MIPS count and complexity of the SPEEX decoder in order to achieve good speech quality on an embedded DSP receiver. We reassign the decoder's stack and the memory, reconstruct the C code and reduce the redundancies. According to the way CodeWarrior compiles, we optimize C code and the algorithm of decoding bits from the bit stream, and then the assembly code of key modules is rewritten by using the special DSP instructions. The results show that the complexity of SPEEX decoder has been decreased from 31.64 MIPS to 12.40 MIPS. It can be used in voice over IP (VoIP) applications.

关键词： Digital signal processing Decoding Speech codecs Testing Wideband Prediction algorithms Internet telephony Narrowband Speech analysis Speech coding

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