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32nd Chinese Control And Decision Conference (CCDC)

作者： Zhang Xu Pang Xinfu Zhao Wenjing Liu Li Zhang Yifan Mou Zurong Shenyang Inst Engn Key Lab Energy Saving & Controlling Power Syst Li Shenyang 110136 Peoples R China Shenyang Inst Engn Sch Elect Power Shenyang 110136 Peoples R China Shenyang Inst Engn Sch Automat Shenyang 110136 Peoples R China State Grid Liaocheng Elect Power Supply Co Liaocheng 252000 Shandong Peoples R China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158556

In the north of China, the problem of wind abandonment is very serious. In order to improve the ability of wind power consumption, a cogeneration system with an electric boiler is proposed. First, the mathematical model of the minimum power generation cost of the traditional unit and the optimized model are established. Second, the model is solved by using the particle swarm optimization algorithm. In addition, a power structure of combined heat and power system (CHP) is constructed for simulation experiments. Through experimental analysis, proving the feasibility of the model. Finally, Simulation results show that making the electric boiler work in the period of wind abandonment can effectively alleviate the wind abandonment phenomenon. It also can provide more space for the wind power and enhance the wind power consumption.

关键词： Combined heat and power generation Wind abandonment Electric boiler Wind power particle swarm optimization algorithm

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Research on Tennis Recognition Technology Based on Computer Vision

Research on Tennis Recognition Technology Based on Computer ...

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2021 International Conference On Electronics, Circuits And Information Engineering(2021年电子、电路与信息工程国际会议)

作者： Zhang Lu Information Engineering University Luoyang Henan China 471003

ISBN: (纸本)9781665448109

Visual attention mechanism is one of the important means for human beings to perceive the external *** mathematical models to introduce visual attention mechanism into computer vision to simulate human visual perception system is a hot research topic in the field of computer *** research of visual attention model is not only helpful for human beings to better explore the working mechanism of human visual attention,but also has very important significance for solving large-scale data screening and improving image processing efficiency,which has important application value in moving object detection,machine vision,image information matching,image compression and other *** visual attention model is used to preprocess the video sequence,and the region of interest is found as the candidate region for target *** the color and shape matching algorithm is used to match the candidate *** on tennis video show that the algorithm can recognize the target well when the color and shape of the target are relatively single and the significance in the background is high.

关键词： Visual Attention Vector Processor Moving Target Detection particle swarm optimization algorithm Tennis Recognition Model

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Optimal Tuning Of PID Controller For Switched Reluctance Motor Speed Control Using particle swarm optimization

Optimal Tuning Of PID Controller For Switched Reluctance Mot...

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International Conference on Control, Power, Communication and Computing Technologies (ICCPCCT)

作者： Nimisha, K. K. Senthilkumar, R. Vimal Jyothi Engn Coll Dept EEE Power Elect Chemperi Kerala India Vimal Jyothi Engn Coll Dept EEE Chemperi Kerala India

ISBN: (纸本)9781538607961

This paper describes the Design of controllers for Switched Reluctance Motor using technique such as particle swarm optimization. Conventional PID controller is nowadays used in most Engineering being acknowledged its ability to give up a superior control in power electronic system. The purpose of this work is to design a speed controller for the motor to achieve minimum time domain integral squared error. This work concentrates mainly on the design of feedback PID controller to achieve the minimum integral squared error and hence the controller parameters k(p), k(i), and k(d) are identified. It can be done through PSO algorithm. The model of a converter works along with the algorithm which results with a robust feedback PID controller was developed using MATLAB/SIMULINK software.

关键词： Active boost converter Switched Reluctance Motor PID controller particle swarm optimization algorithm

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Secure and optimal authentication framework for cloud management using HGAPSO algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第2期22卷 S4007-S4016页

作者： Selvarani, P. Suresh, A. Malarvizhi, N. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Sch Comp Dept Comp Sci & Engn Chennai 600062 Tamil Nadu India Nehru Inst Engn & Technol Dept Comp Sci & Engn Coimbatore 641105 Tamil Nadu India

Data security is the major problem in cloud computing. To overcome this problem in the existing work Password can be used as a key to encrypt and decrypt the data in cloud environment. Some of the limitations having Password system because it is not secured, and easily forgotten. In order to overcome these problems the proposed technique utilizes effective data storage using biometric-based authentication to support the user authentication for the cloud environment. For user authentication here we are considering iris and fingerprint. Initially the feature values are extracted from the iris and fingerprint using local binary pattern. In order to improve the security Extracting the feature value of fingerprint and iris and it is given input to the hybrid Genetic algorithm and particle swarm optimization algorithm to find the best solution using Cross over mutation technique. Best solution value can be act as a key for encrypting and decrypting data using Triple Data Encryption Standard algorithm. Finally encrypted data can be stored in cloud using cloud simulator in the Working platform of net beans in java. Finally randomly tested with 5 fingerprint and 5 Iris image for the purpose of man in the middle attack. After tested with fingerprint and iris proposed Hybrid Genetic algorithm with particle swarm optimization algorithm having less attack compared with the existing particle swarm optimization algorithm. So the intruder cannot be able to access the data in cloud environment.

关键词： Data security in cloud Fingerprint and iris particle swarm optimization algorithm Genetic algorithm Triple DES algorithm

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Mission Reliability-Oriented Selective Maintenance optimization for Intelligent Multistate Manufacturing Systems With Uncertain Maintenance Quality

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IEEE ACCESS 2019年 7卷 109804-109816页

作者： Chen, Zhaoxiang He, Yihai Zhao, Yixiao Han, Xiao Liu, Fengdi Zhou, Di Wang, Wenzhuo Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China

Selective maintenance is widely used as a reliability-centered maintenance strategy due to the limited maintenance resources. However, existing selective maintenance studies only consider basic reliability, which cannot systematically describe the operating mechanism of a multistate system, thereby resulting in the inability to obtain an optimal maintenance strategy. Moreover, intelligent manufacturing systems are highly representative of typical multistate industrial systems. In this study, a mission reliability-oriented selective maintenance optimization model for intelligent manufacturing systems that considers the uncertain maintenance effect was proposed. First, a new connotation and modeling method for mission reliability based on multistate system theory was presented to comprehensively characterize the operating mechanism of intelligent manufacturing systems. Second, a quantitative model between maintenance resources and quality based on real-time data was established to reflect the uncertain characteristics caused by repairmen and tools. Third, a selective maintenance decision model of a multistate manufacturing system was developed under the constraints of maintenance cost and time. This constraint combination optimization problem was solved using the particle swarm optimization algorithm. Finally, a case study of selective maintenance optimization for a cylinder head manufacturing system was presented to verify the proposed method.

关键词： Multistate system manufacturing system selective maintenance mission reliability particle swarm optimization algorithm

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Human action tracking design of neural network algorithm based on GA-PSO in physical training

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第2期22卷 S4149-S4155页

作者： Leng Xinke Jiang Huijun Zuo Xin Hunan Univ Coll Phys Educ Changsha 410028 Hunan Peoples R China Hunan First Normal Coll Dept Phys Educ Changsha 410205 Hunan Peoples R China

In order to solve human action recognition algorithm in traditional physical training, the problem of some kind of action recognition without universality is usually solved emphatically, and a recognition system aimed at human action tracking in physical training is proposed. Firstly, local feature description of human action in physical training based on self-similarity matrix is constructed by using generalized self-similarity concept changed along with time and local feature extraction methods of optical flow field of spatio-temporal interest point;secondly, a new neural network training algorithm of particle swarm optimization (PSO) with self-adaptive genetic operator is proposed. Through probability control, at the same time of using PSO algorithm to optimize neural network, selection, intersection, variation and other genetic operation are implemented for optional particles adaptively to realize promotion of algorithm performance;simulation experiment indicates that proposed scheme can obviously improve algorithm efficiency of human action recognition and recognition accuracy in physical training.

关键词： Physical training Genetic algorithm particle swarm optimization algorithm Human action Tracking

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Fault Diagnosis and Reliable Configuration of Uncertain Strip Area Based on MPSO-SVM

Fault Diagnosis and Reliable Configuration of Uncertain Stri...

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第40届中国控制会议

作者： Mochen Li Bo Yao Fuzhong Wang College of Mathematics and System Science Shenyang Normal University Department of Basic Education Shenyang Institute of Engineering

For a class of linear uncertain flight control systems,in terms of the strip region,the problem of actuator continuous gain fault and reliable control is *** the same time,in order to solve the difficulty of selecting parameters of support vector machine,a parameter optimization algorithm based on improved particle swarm optimization(MPSO) is *** with grid search method,particle swarm optimization and genetic algorithm,this method has the advantages of fast convergence,short time consuming and high *** the problem that the pole information is difficult to obtain,the algorithm theory of pole observer is given to realize the real-time observation of unknown ***,the effectiveness of reliable controller design is further proved by numerical simulation.

关键词： reliable control support vector machine pole observer particle swarm optimization algorithm grid search method genetic algorithm

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Bhattacharyya distance criterion based multibit quantizer design for cooperative spectrum sensing in cognitive radio networks

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WIRELESS NETWORKS 2019年第5期25卷 2665-2674页

作者： Fu, Yuanhua He, Zhiming Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Sichuan Peoples R China Univ Elect Sci & Technol China Guangdong Inst Elect & Informat Engn Dongguan Peoples R China

Cooperative spectrum sensing (CSS) is crucial for dynamic spectrum access in cognitive radio networks. This paper considers a CSS scheme by using a multilevel quantizer in each sensing node (SN) to quantize the local energy detector's observation. A log-likelihood ratio test detector by using quantized data received from each SN is proposed to determine the presence or absence of the primary user signal. The Bhattacharyya distance (BD) of the cooperative sensing system is derived. Then, a quantizer design scheme by maximizing the BD that as the criterion function in an optimization problem with respect to the quantization thresholds is proposed. As nonlinear and high-dimensional of the objective function, a particle swarm optimization algorithm is employed to solve operating parameters for the quantizer. Furthermore, we derive the upper bound performance on cooperative energy detection (CED) in CSS. To validate the effectiveness of the proposed approach, The quantizer is compared with other schemes in terms of detection performance. Simulation results show that 2- or 3-bit quantization of the proposed approach achieves comparable performance to upper bound of CED without quantization.

关键词： Multibit quantization Bhattacharyya distance particle swarm optimization algorithm Cooperative spectrum sensing

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Coordinated path planning for an unmanned aerial-aquatic vehicle (UAAV and an autonomous underwater vehicle (AUV) in an underwater target strike mission

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OCEAN ENGINEERING 2019年第Jun.15期182卷 162-173页

作者： Wu, Yu Chongqing Univ Coll Aerosp Engn Chongqing 400044 Peoples R China

Unmanned system has become more and more popular as it can adapt to diverse environments and has prospective applications. Especially, the coordination among heterogeneous vehicles is capable of completing complicated tasks, which is often beyond the ability of homogeneous vehicles. In this paper, the underwater target strike mission is concentrated, and the mission is completed by the coordination between a UAAV and an AUV. UAAV and AUV are deployed in this mission because UAAV has strong search ability in air and can communicate with AUV directly after it dives into water. Firstly, to decompose the problem, the mission is divided into two phases, i.e., single flying of UAAV and underwater coordination between UAAV and AUV. In the coordinated path planning model, the motion of vehicles, the constraints in different media and the optimization index in each phase are all formulated into mathematical forms. Based on the particle swarm optimization (PSO) algorithm, the collocation points are used to determine the locations of control variables. Those points can reduce the computation load and improve the solution quality, and they are distributed by height and moment according to the forms of constraints in each phase. Besides, the strategy of addressing infeasible solutions is generated to guarantee the normal operation of PSO-based algorithm. Simulation results demonstrate that the proposed two-phase coordinated path planning method can generate coordinated paths, and the obtained results is very close to the optimal solution in theory. Compared to the whole method, the two-phase method can better deal with the complicated constraints in each phase.

关键词： Unmanned system Unmanned aerial-aquatic vehicle Autonomous underwater vehicle Coordinated path planning particle swarm optimization algorithm

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Layout optimization of large-scale oil-gas gathering system based on combined optimization strategy

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NEUROCOMPUTING 2019年 332卷 159-183页

作者： Liu, Yang Chen, Shuangqing Guan, Bing Xu, Ping Northeast Petr Univ Sch Petr Engn Daqing 163318 Peoples R China

Layout optimization of large-scale oil-gas gathering system is a kind of NP-hard problem in the field of system optimization. It involves a large number of network nodes, coupled optimization variables, complex network structures and hydraulic constraints, which cause the great difficulty in constructing optimization models and solution methods. In this paper, a high-dimensional mixed integer nonlinear layout optimization mathematical model involving the pipeline network structure parameters and pipeline design parameters is established, which can be applied to large-scale oil gathering system and gas gathering system universally. A modified particle swarm optimization (MPSO) algorithm with global search ability is proposed. The convergence theorem of the stochastic optimization algorithm is established based on the Poincare cycle theory. Global convergence of MPSO algorithm is proved, and the performance of MPSO algorithm is analyzed by numerical experiments. Based on dimension reduction planning and modularization thought, the grid dissection set partition method is proposed, and the theoretical foundation and complexity of the grid dissection method are discussed. In order to reduce the dimension of the layout optimization problem, the concept of the fuzzy set of adjacent position, and a novel approach for the well-station connection mode optimization are put forward. Based on the MPSO algorithm, grid dissection set partition method and solution method by fuzzy set of adjacent position, a combined optimization strategy for layout optimization model is proposed. The reliability and practicality of the proposed layout optimization model and combined optimization strategy are verified by the successful application of a real-world large-scale oil field with 661 wells. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Layout optimization particle swarm optimization algorithm Global convergence Convergence theorem Oil-gas gathering system

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