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IEEE Region 10 Conference on TENCON 2010

作者： Mahbub, Upal Acharjee, Partha Pratim Fattah, Shaikh Anowarul Bangladesh Univ Engn & Technol Dept Elect & Elect Engn Dhaka 1000 Bangladesh

ISBN: (纸本)9781424468904

This paper deals with the problem of echo cancellation of speech signals in an acoustic environment. In this regard, generally, different adaptive filter algorithms are employed, which may lack the flexibility of controlling the convergence rate, number of iterations, range of variation of filter coefficients, and tolerance consistency. In order to overcome these problems, unlike conventional approaches, we formulate the task of echo cancelation as a coefficient optimization problem whereby we introduce the particle swarm optimization (PSO) algorithm. In this case, the PSO is designed to perform the error minimization in frequency domain. From extensive experimentations, it is shown that the proposed PSO based acoustic echo cancellation method provides high echo cancellation performance in terms of echo return loss enhancement with a faster convergence rate in comparison to that obtained by some of the state-of-the-art methods.

关键词： Echo cancellation adaptive filter particle swarm optimization algorithm speech analysis frequency domain optimization

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Economic Dispatch Considering Ancillary Service Based on Revised particle swarm optimization algorithm

Economic Dispatch Considering Ancillary Service Based on Rev...

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6th International Conference on Intelligent Computing

作者： Ma, Xin Liu, Yong North China Univ Water Conservancy & Elect Power Sch Management & Econ Zhengzhou 450011 Henan Province Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9783642149214

A revised strategy particle swarm optimization algorithm is proposed to solve the economic dispatch problems in power systems Many constraints such as ramp rate limits and prohibited zones are taken into account and the loss is also calculated On the basis of strategy particle swarm optimization algorithm a new revised strategy is provided to handle the constraints and make sure the particles to satisfy the constraints The strategy can guarantee the particles to search in or around the feasible solutions area combined with penalty functions The accuracy and speed of the algorithm are improved for the particles will rarely search in the infeasible solutions area and the results also show that the new algorithm has a fast speed high accuracy and good convergence

关键词： Electricity market economic dispatch ancillary service particle swarm optimization algorithm revise strategy

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Utilization Efficiency Forecasting of Moisture Content in Maize Based on particle swarm optimization algorithm and RBF Neural Network

Utilization Efficiency Forecasting of Moisture Content in Ma...

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2nd International Conference on Computer and Automation Engineering (ICCAE)

作者： Yu Xiaofang Gao Jvlin Song Guodong Inner Mongol Agr Univ Hohhot 010010 Peoples R China Inner Mongolia Autonomous Reg Seed Control Stn Hohhot 010010 Peoples R China

ISBN: (纸本)9781424455690

Utilization efficiency forecasting of moisture content in maize has a great importance to maize production. RBF neural network is able to universal approximation. PSO-RBF neural network which combines particle swarm optimization (PSO) with RBF neural network is proposed to utilization efficiency forecasting of moisture content in maize. Maize fields of the farms in Henan province are applied to study the utilization efficiency forecasting ability of moisture content in maize by the proposed PSO-RBF neural network method. And BP neural network and normal RBF neural network are applied to compare the PSO-RBF neural network method. By analyzing the experimental results, it is indicated that utilization efficiency forecasting ability of moisture content in maize by PSO-RBF neural network than that by RBF neural network and BP neural network.

关键词： utilization efficiency moisture content RBF neural network particle swarm optimization algorithm

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Grey Model Based particle swarm optimization algorithm for Anticorrosion Reliability Design of Underground Pipelines

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2nd International Conference on Advances in Product Development and Reliability (PDR)

作者： Liu, Qinming Dong, Ming Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Coll Econ & Management Shanghai 200052 Peoples R China

ISBN: (纸本)9780878492541

This paper explores the grey model based PSO (particle swarm optimization) algorithm for anti-cauterization reliability design of underground pipelines. First, depending on underground pipelines' corrosion status, failure modes such as leakage and breakage are studied. Then, a grey GM(1,1) model based PSO algorithm is employed to the reliability design of the pipelines. One important advantage of the proposed algorithm is that only fewer data is used for reliability design. Finally, applications are used to illustrate the effectiveness and efficiency of the proposed approach.

关键词： Underground pipeline Reliability design Grey theory particle swarm optimization algorithm

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Integration of particle swarm optimization and Immune Genetic algorithm-Based Dynamic Clustering for Customer Clustering

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2015年第5期24卷 1550019-1550019页

作者： Kuo, R. J. Lin, S. H. Chen, Zhen-Yao Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 10607 Taiwan Taiwan Semicond Mfg Co Hsinchu 30078 Taiwan De Lin Inst Technol Dept Business Adm New Taipei City 23654 Taiwan

This study intends to present a dynamic clustering (DC) approach based on particle swarm optimization (PSO) and immune genetic (IG) (DCPIG) algorithm, which is able to cluster the data into adequate clusters through data characteristics with pre-specified numbers of clusters. The proposed DCPIG algorithm is compared with three DC algorithms in the literature using Iris, Wine, Glass and Vowel benchmark data sets. The experiment results show that the DCPIG algorithm can achieve higher stability and accuracy than the other algorithms. In addition, the DCPIG algorithm is also applied to a real-world problem considering the customer clustering for a cyber flower shop. Lastly, we recommend different products and services to customers based on the clustering results.

关键词： Cluster analysis dynamic clustering particle swarm optimization algorithm immune genetic algorithm

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Fault Diagnosis Analysis with Support Vector Regression and particle swarm optimization algorithm

Fault Diagnosis Analysis with Support Vector Regression and ...

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22nd Chinese Control and Decision Conference

作者： Tian, WenJie Liu, JiCheng Beijing Union Univ Beijing Automat Inst Beijing 100101 Peoples R China

ISBN: (纸本)9781424451821

The fault diagnosis model with support vector regression (SVR) and particle swarm optimization algorithm (POSA) for is proposed. The novel structure model has higher accuracy and faster convergence speed. We construct the network structure, and give the algorithm flow. The impact factor of fault behaviors is discussed. With the ability of strong self-learning and faster convergence, this fault detection method can detect various fault behaviors rapidly and effectively. by learning the typical fault characteristic information. Utilizing the character that principal components analysis algorithm can keep the discern ability of original dataset after reduction, the reduces of the original dataset are calculated and used to train individual SVR for ensemble, and consequently, increase the detection accuracy. To validate the effectiveness of the proposed method, simulation experiments are performed based on the electronic circuit dataset. The results show that the proposed method is a promised method owning to its high diversity, high detection accuracy and faster speed in fault diagnosis.

关键词： Support Vector Regression Principal Components Analysis Fault Diagnosis Reduction particle swarm optimization algorithm

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Intelligent Maintenance Scheduling System for Maximum Performance of Solar-Energy-Generating System

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SENSORS AND MATERIALS 2017年第11期29卷 1579-1588页

作者： Huang, Chung-Chi Far East Univ Dept Automat & Control Engn 49 Zhonghua Rd Tainan 74448 Taiwan

In this research, an intelligent maintenance scheduling system for gaining the maximum performance of a solar-energy-generating system is proposed. In the system, the particle swarm optimization (PSO) algorithm is used for the intelligent maintenance scheduling of the solar energy -generating system. The maintenance center receives many maintenance prescription requests from all solar-energy-generating stations. For various uncertainties, the optimal solution of maintenance scheduling is considered. To reduce maintenance costs and improve maintenance efficiency, the intelligent scheduling system using the PSO method is constructed. In the maintenance scheduling model, the maintenance time and prework time are set to be the optimal computing parameters, and the lowest cost is set to be the optimal target. By design and implementation, we can determine the advantages of the PSO-based intelligent system. It meets both requirements of real time and integration for intelligent systems. Experimental results show the advantages of the intelligent maintenance scheduling system for the maximum performance of the solar-energy-generating system;that is, such a scheduling system meets both requirements of efficiency and lowest cost for the intelligent maintenance of the solar-energy-generating system.

关键词： intelligent maintenance scheduling system particle swarm optimization algorithm solar energy

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A Parallel Markov Cerebrovascular Segmentation algorithm Based on Statistical Model

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Journal of Computer Science & Technology 2016年第2期31卷 400-416页

作者： Rong-Fei Cao Xing-Ce Wang Zhong-Ke Wu Ming-Quan Zhou Xin-Yu Liu Department of Information Science and Technology Beijing Normal University Beijing 100875 China Engineering Research Center of Virtual Reality and Applications Ministry of Education Beijing 100875 China Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

For segmenting cerebral blood vessels from the time-of-flight magnetic resonance angiography （TOF-MRA） images accurately, we propose a parallel segmentation algorithm based on statistical model with Markov random field （MRF）. Firstly, we improve traditional non-local means filter with patch-based Fourier transformation to preprocess the TOF-MRA images. In this step, we mainly utilize the sparseness and self-similarity of the MRA brain images sequence. Secondly, we add the MRF information to the finite mixture mode （FMM） to fit the intensity distribution of medical images. We make use of the MRF in image sequence to estimate the proportion of cerebral tissues. Finally, we choose the particle swarm optimization （PSO） algorithm to parallelize the parameter estimation of FMM. A large number of experiments verify the high accuracy and robustness of our approach especially for narrow vessels. The work will offer significant assistance for physicians on the prevention and diagnosis of cerebrovascular diseases.

关键词： cerebrovascular segmentation non-local means filtering Markov random field particle swarm optimization algorithm

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PSO-BASED PID CONTROLLER DESIGN FOR AN ENERGY CONVERSION SYSTEM USING COMPRESSED AIR

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2017年第3期24卷 671-679页

作者： Atacak, Ismail Kucuk, Bayram Gazi Univ Technol Fac Comp Eng Dept TR-06500 Ankara Turkey Gazi Univ Inst Informat TR-06500 Ankara Turkey

In this study, an optimal control algorithm is proposed to overcome low performance problems arising from the non-linear characteristics of pneumatic motor in compressed air-based energy conversion systems. The effectiveness of the proposed algorithm is tested on an energy conversion system which includes a compressor, a proportional valve, a pneumatic motor (PM), a permanent magnet direct current (PMDC) generator and a control card. The control function of the system is carried out by driving the proportional valve with the control signals which is obtained depending on the PMDC generator output voltage error. In this structure, an optimal proportional-integral-derivative (PID) controller which tunes on-line its own gain parameters by particle swarm optimization (PSO) algorithm according to the operating conditions of the system used. In order to observe the effects of PSO-based PID controller on the system performance, the energy conversion system is also controlled by a discrete time PID controller. The experimental results show that PSO-based PID controller provides more robust control performance than discrete time PID controller under various operating conditions.

关键词： compressed air-based energy conversion optimal PID controller particle swarm optimization algorithm pneumatic motor

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A Fault Diagnosis Method of Power Systems Based on an Improved Adaptive Fuzzy Spiking Neural P Systems and PSO algorithms

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Chinese Journal of Electronics 2016年第2期25卷 320-327页

作者： WANG Jun PENG Hong TU Min Prez-Jimnez J. Mario SHI Peng School of Electrical and Information Engineering Xihua University Neijiang Power Supply Company 5. Department of Computer Science and Artificial Intelligence University of Seville 6. School of Electrical and Electronic Engineeringthe University of Adelaide Sichuan Province Key Laboratory of Power Electronics Energy-saving Technologies and Equipment Center for Radio Administration and Technology Development Xihua University

A new fault diagnosis method based on improved Adaptive fuzzy spiking neural P systems(in short,AFSN P systems) and particle swarm optimization(PSO)algorithm is presented to improve the efficiency and accuracy of diagnosis for power systems in this paper. AFSN P systems are a novel kind of computing models with parallel computing and learning ability. Based on our previous works, this paper focuses on AFSN P systems inference algorithms and learning algorithms and builds the fault diagnosis model using improved AFSN P systems for diagnosing effectively. The process of diagnosis based on AFSN P systems is expressed by matrix successfully to improve the rate of diagnosis eminently. Furthermore, particle swarm optimization algorithm is introduced into the learning algorithm of AFSN P systems, thus the convergence speed of diagnosis has a big progress. An example of 4-node system is given to verify the effectiveness of this method. Compared with the existing methods, this method has faster diagnosis speed, higher accuracy and strong ability to adapt to the grid topology changes.

关键词： Fault diagnosis Power systems Member Computing AFSN P systems particle swarm optimization algorithm

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