检索结果-内蒙古大学图书馆

11th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM 2015)

作者： Jun Zhao Zhong Ma Wuhan Digital Engineering Institute China

ISBN: (纸本)9781510821279

Existing virtual network mapping algorithms does not consider resource consumption of intermediate node on communication path usually. Minimum resource consumption or shortest path of physical network is regarded as objective, thereby leading to bottleneck due to insufficient resource of intermediate node on communication path, and affecting performance of the whole physical network and subsequent success rate of virtual network. A virtual network mapping algorithm based on load balancing multi-objective particle swarm optimization is proposed in the paper aiming at the problem. Resource consumption of intermediate node is sufficiently considered in the algorithm, double balance of node load and link load is regarded as objective. Meanwhile, the optimal path of particle swarm optimization algorithm is adopted. Experiments show that the algorithm proposed in the paper can not only realize double balance of node load and link load, but also effectively improve request receiving success rate, overall resource load balance and long-term operation income.

关键词： Load Balancing particle swarm optimization algorithm Virtual Network Mapping Load balancing intermediate node Link Load Telecommunication lines particle swarm optimization Virtual networks particle swarm algorithm Resource utilization physical network

来源：评论

学校读者我要写书评

暂无评论

particle swarm optimization algorithm Improvement and Application

Particle Swarm Optimization Algorithm Improvement and Applic...

引用

2010 3rd IEEE International Conference on Computer Science and Information Technology—ICCSIT 2010

作者： Xiaoli Baojunjie and Kuanghang Department of Computer Science,Chongqing Education College Chongqing,400067,China

in this paper,through the research of the existing particle swarm optimization algorithm and its improved algorithm,a particle swarm optimization algorithm improvement program is proposed,and the experimental results show that this improved algorithm not only does not increase the complexity,but also has greater improvement in the convergence speed and stability comparing with the original algorithm.

关键词： particle swarm optimization algorithm dynamic weight aggregation population diversity convergence speed

来源：评论

学校读者我要写书评

暂无评论

An Acoustic Echo Cancellation Scheme Based on particle swarm optimization algorithm

An Acoustic Echo Cancellation Scheme Based on Particle Swarm...

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

Economic Dispatch Considering Ancillary Service Based on Rev...

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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...

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

Fault Diagnosis Analysis with Support Vector Regression and ...

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

A Parallel Markov Cerebrovascular Segmentation algorithm Based on Statistical Model

引用

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

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：