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Structures Congress

作者： Cancelli, A. Micheli, L. Alipour, A. Laflamme, S. Sritharan, S. Iowa State Univ Dept Civil Construct & Environm Engn Ames IA 50011 USA

ISBN: (数字)9780784480427

ISBN: (纸本)9780784480427

This paper aims to develop a new methodology for damage localization and estimation, through an algorithm that utilizes the ambient vibration data. More precisely, the data collected from sensors are analyzed with a subspace system identification algorithm (SSID) to retrieve frequencies and mode shapes of the structure. This information is then used to construct reduced order stiffness matrices for the system, using two different types of order reduction techniques;Guyan and SEREP. Using these matrices and the retrieved frequencies of the system, optimization functions are constructed and then analyzed with a particle swarm algorithm to locate and estimate the damage. To validate the proposed algorithm, a simply supported reinforced concrete (RC) girder was simulated. The obtained results show that the method is capable of estimating both the position and the severity of the damage in the system with a high accuracy.

关键词： Reinforced concrete beams particle swarm algorithm Concrete beams and girders monitoring data algorithms matrices

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Decision - making of Direct Customers Based on Available Transfer Capability 2

Decision - making of Direct Customers Based on Available Tra...

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2nd Asia Conference on Power and Electrical Engineering (ACPEE)

作者： Tang Quan Lin Zhaohang Li Huaqiang Sichuan Elect Power Corp Power Econ Res Inst Chengdu 61000 Sichuan Peoples R China Sichuan Univ Chengdu 61000 Sichuan Peoples R China

ISBN: (纸本)9781510840744

Large customer direct-power-purchasing is a hot spot in the electricity market reform. In this paper, the author established an Available Transfer Capability (ATC) model which takes uncertain factors into account, applied the model into large customer direct-power-purchasing transactions and improved the reliability of power supply during direct-power-purchasing by introducing insurance theory. The author also considered the customers loss suffered from power interruption when building ATC model, established large customer decision model, took purchasing quantity of power from different power plants and reserved capacity insurance as variables, targeted minimum power interruption loss as optimization goal and best solution by means of particle swarm algorithm to produce optimal power purchasing decision of large consumers. Simulation was made through IEEE57 system finally and proved that such method is effective.

关键词： power interruption Purchasing particle swarm algorithm power markets customers automatic tool changer decision making Electric Power Source Power Plants heat transfer capability insurance Energy sources

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A QoE-Aware Video Quality Guarantee Mechanism in 5G Network 9th

A QoE-Aware Video Quality Guarantee Mechanism in 5G Network

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9th International Conference on Image and Graphics (ICIG)

作者： Wang, Ruyan Yang, Yan Wu, Dapeng Chongqing Univ Posts & Telecommun Key Lab Opt Commun & Networks Chongqing 400065 Peoples R China

ISBN: (纸本)9783319715896;9783319715889

Future Fifth-Generation networks are expected to predominantly carry multimedia traffic. Video consume an enormous amount of scarce resources in mobile devices and cellular wireless networks due to the demand for high data rates of video streaming. The limited resource of wireless media and unreliable nature of wireless channels in cellular networks make challenging to deliver videos at high quality of experience. Therefore, this paper proposed an a QoE-aware video quality guarantee Mechanism. Considered link state information and scalable video coding structure, established a QoE evaluation mode, and used the particle swarm optimization algorithm to select optimal video transmission rate. The results show that the proposed method can effectively improve the utilization rate of network resources and reduce network congestion while improving the quality of the user experience.

关键词： 5G network Scalable video coding Quality of experience particle swarm algorithm

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A CUDA-based Multi-Channel particle swarm algorithm

A CUDA-based Multi-Channel Particle Swarm Algorithm

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2011 International Conference on Control, Automation and Systems Engineering(CASE)

作者： Li Wenna Zhang Zhenyu School of Electrical Engineering & Automation Tianjin Polytechnic University School of Information & Communication Engineering Tianjin polytechnic University

ISBN: (纸本)9781457708596

In order to improve the efficiency and accuracy of the particle swarm optimization algorithm in optimization, the parallel CUDA-based particle swarm algorithm is proposed and developed. With the Compute Unified Device Architecture(CUDA) technology, the parallel data structure is defined, and the mechanism of computing tasks mapping to CUDA is described. From the optimization experiments results of 4 benchmark functions it shows that the CUDAbased parallel algorithm can greatly save computing time and improve computing accuracy. This new PSO algorithm is more suitable for the relevant application of the particle swarm algorithm.

关键词： CUDA particle swarm algorithm parallel computing

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A Bio-inspired Neural Network Based PSO Method For Robot Path Planning 13

A Bio-inspired Neural Network Based PSO Method For Robot Pat...

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13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Ni, Jianjun Zhang, Zhitong Su, Baiyan Fan, Xinnan Liang, Wei Hohai Univ Coll IOT Engn Changzhou 213022 Peoples R China Jining Vocat Technol Coll Dept Elect & Informat Engn Jining 272037 Peoples R China Hohai Univ Coll Business Adm Changzhou 213022 Peoples R China

ISBN: (纸本)9781538621653

Robot path planning in complex and unknown environment is a challenging task. To deal with this problem, an improved PSO method based on bio-inspired neural network is proposed, where a bio-inspired neural network is used to optimize the fitness function of the PSO based method. A dynamic environment modeling method is introduced to realize the path planning in unknown environment. Finally, some simulation experiments on various environments are conducted. The results show the efficiency of the proposed method.

关键词： Mobile Robot Path Planing particle swarm algorithm Bio-inspire Neural Network

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Study on Ice Regime Forecast Based on SVR Optimized by particle swarm Optimization algorithm

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Meteorological and Environmental Research 2012年第11期3卷 36-40页

作者： WANG Fu-qiang RONG Fei North China University of Water Resources and Electric Power

[Objective] The research aimed to study forecast models for frozen and melted dates of the river water in Ningxia-Inner Mongolia section of the Yellow River based on SVR optimized by particle swarm optimization algorithm. [Method] Correlation analysis and cause analysis were used to select suitable forecast factor combination of the ice regime. particle swarm optimization algorithm was used to determine the optimal parameter to construct forecast model. The model was used to forecast frozen and melted dates of the river water in Ningxia-Inner Mongolia section of the Yellow River. [Result] The model had high prediction accuracy and short running time. Average forecast error was 3.51 d, and average running time was 10.464 s. Its forecast effect was better than that of the support vector regression optimized by genetic algorithm (GA) and back propagation type neural network (BPNN). It could accurately forecast frozen and melted dates of the river water. [Conclusion] SVR based on particle swarm optimization algorithm could be used for ice regime forecast.

关键词： particle swarm algorithm Support vector machine SVR Ice regime forecast China

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Form Error Evaluation of Noncontact Scan Data Using Constriction Factor particle swarm Optimization

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JOURNAL OF ADVANCED MANUFACTURING SYSTEMS 2017年第3期16卷 205-226页

作者： Pathak, Vimal Kumar Singh, Amit Kumar Malaviya Natl Inst Technol Dept Mech Engn Jaipur 302017 Rajasthan India

Form error evaluation of manufactured parts is one of the crucial aspects of precision coordinate metrology. With the advent of technology, the noncontact data acquisition techniques are replacing the conventional machines like coordinate measuring machine (CMM). This paper presents an optimization technique to evaluate minimum zone form errors, namely straightness, circularity, flatness and cylindricity using constriction factor-based particle swarm optimization (CFPSO) algorithm. Addition of constriction factor helps in accelerating the convergence property of CFPSO. Initially, a simple minimum zone objective function is formulated mathematically for each form error and then optimized using the proposed CFPSO. Primarily, the results of the proposed method for form error evaluation are compared with the literature results. Furthermore, the data obtained from noncontact 3D scanner is processed and the results of form error evaluation using CFPSO algorithm are compared with Steinbichler's INSPECT PLUS software results. It was found that the results obtained using the proposed CFPSO algorithm are fast and better as compared with other evolutionary techniques like genetic algorithm (GA), previous literatures and software results. Furthermore, to ensure effectiveness of the proposed method statistical analysis (t-test) was performed. CFPSO results for large dimension of problem show significant difference in computation time as compared with GA. The CFPSO algorithm provides 27.25%, 7.5% and 6.38% improvements in circularity, flatness and cylindricity, respectively, in comparison to RE software results, for determination of minimum zone error. Thus, the methodology presented helps in improving the accuracy and for speeding up of the automated inspection process generally performed by CMMs in industries.

关键词： Form error evaluation non-contact inspection GD&T 3D point cloud particle swarm algorithm minimum zone method

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Design for sustainability of industrial symbiosis based on emergy and multi-objective particle swarm optimization

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SCIENCE OF THE TOTAL ENVIRONMENT 2016年 562卷 789-801页

作者： Ren, Jingzheng Liang, Hanwei Dong, Liang Sun, Lu Gao, Zhiqiu Nanjing Univ Informat Sci & Technol Sch Geog & Remote Sensing Collaborat Innovat Ctr Forecast & Evaluat Meteoro Nanjing 210044 Jiangsu Peoples R China Univ Southern Denmark Dept Technol & Innovat Ctr Engn Operat Management Campusvej 55 DK-5230 Odense M Denmark Leiden Univ CML Leiden Netherlands Natl Inst Environm Studies Ctr Social & Environm Syst Res Onogawa 16-2 Tsukuba Ibaraki 3058506 Japan Univ Tokyo Grad Sch Frontier Sci Dept Environm Syst 5-1-5 Kashiwanoha Kashiwa Chiba 2778563 Japan Chinese Acad Sci Inst Atmospher Phys State Key Lab Atmospher Boundary Layer Phys & Atm Beijing Peoples R China

Industrial symbiosis provides novel and practical pathway to the design for the sustainability. Decision support tool for its verification is necessary for practitioners and policy makers, while to date, quantitative research is limited. The objective of this work is to present an innovative approach for supporting decision-making in the design for the sustainability with the implementation of industrial symbiosis in chemical complex. Through incorporating the emergy theory, the model is formulated as a multi-objective approach that can optimize both the economic benefit and sustainable performance of the integrated industrial system. A set of emergy based evaluation index are designed. Multi-objective particle swarm algorithm is proposed to solve the model, and the decision-makers are allowed to choose the suitable solutions form the Pareto solutions. An illustrative case has been studied by the proposed method, a few of compromises between high profitability and high sustainability can be obtained for the decision-makers/stakeholders to make decision. (C) 2016 Elsevier B. V. All rights reserved.

关键词： Design for sustainability Industrial symbiosis Emergy particle swarm algorithm Chemical complex

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Biomedical classification application and parameters optimization of mixed kernel SVM based on the information entropy particle swarm optimization

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COMPUTER ASSISTED SURGERY 2016年第sup1期21卷 133-142页

作者： Li, Mi Lu, Xiaofeng Wang, Xiaodong Lu, Shengfu Zhong, Ning Beijing Univ Technol Fac Informat Technol Dept Automat Beijing Peoples R China Beijing Int Collaborat Base Brain Informat & Wisd Beijing Peoples R China Beijing Key Lab MRI & Brain Informat Beijing Peoples R China Maebashi Inst Technol Dept Life Sci & Informat Maebashi Gunma Japan

The types of kernel function and relevant parameters' selection in support vector machine (SVM) have a major impact on the performance of the classifier. In order to improve the accuracy and generalization ability of the model, we used mixed kernel function SVM classification algorithm based on the information entropy particle swarm optimization (PSO): on the one hand, the generalization ability of classifier is effectively enhanced by constructing a mixed kernel function with global kernel function and local kernel function;on the other hand, the accuracy of classification is improved through optimization for related kernel parameters based on information entropy PSO. Compared with PSO-RBF kernel and PSO-mixed kernel, the improved PSO-mixed kernel SVM can effectively improve the classification accuracy through the classification experiment on biomedical datasets, which would not only prove the efficiency of this algorithm, but also show that the algorithm has good practical application value in biomedicine prediction.

关键词： Mixed kernel function information entropy particle swarm algorithm SVM kernel function

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Reactive Power Optimization Simulation of Active Distribution Network Based on particle swarm Optimization

Reactive Power Optimization Simulation of Active Distributio...

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2017 International Conference on Applied Mathematics, Modeling and Simulation (AMMS2017)

作者： HaoZhang HongjuanLi MinXu State Grid Jiangxi Electric Power Company Shangrao Power Supply Branch School of Information Engineering Nanchang University

The access of a large number of renewable energy makes the active distribution network become the inevitable trend of development, coordination of renewable distributed power and traditional reactive power compensation device can effectively achieve the active distribution network reactive power optimization. The simulation results of IEEE33 node system show that the mathematical model is correct and accurate for the reactive power optimization of the distribution network, and the particle swarm optimization algorithm can effectively search the global optimal.

关键词： active distribution network mathematical model particle swarm algorithm simulation

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