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

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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Task scheduling in a cloud computing environment using HGPSO algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第1期22卷 2179-2185页

作者： Kumar, A. M. Senthil Venkatesan, M. Tejaa Shakthi Inst Technol Women Dept CSE Coimbatore Tamil Nadu India KSR Inst Engn & Technol Tiruchengode Tamil Nadu India

Cloud computing delivers computing resources like software and hardware as a service to the users through a network. The main idea of cloud computing is to share the tremendous power of storage, computation and information to the scientific applications. In cloud computing, the user tasks are organized and executed with suitable resources to deliver the services effectively. There are plenty of task allocation techniques that are used to accomplish task scheduling. In order to enhance the task scheduling technique, an efficient task scheduling algorithm is proposed in this paper. optimization techniques are very popular in solving NP-hard problems. In this proposed technique, user tasks are stored in the queue manager. The priority is calculated and suitable resources are allocated for the task if it is a repeated task. New tasks are analyzed and stored in the on-demand queue. The output of the on-demand queue is given to the Hybrid Genetic-particle swarm optimization (HGPSO) algorithm. To implement HGPSO technique, genetic algorithm and particle swarm optimization algorithm are combined and used. HGPSO algorithm evaluates suitable resources for the user tasks which are in the on-demand queue.

关键词： Cloud computing optimization techniques Task scheduling Genetic algorithm particle swarm optimization algorithm

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An accumulative time-delay multivariate grey model with nonlinear structure and its application

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APPLIED MATHEMATICAL MODELLING 2025年 147卷

作者： Wang, Ying Li, Shuliang Chongqing Technol & Business Univ Sch Management Sci & Engn Chongqing 400067 Peoples R China

Existing studies on new energy vehicle sales forecasting are mostly based on univariate models, failing to fully consider external factors and their complex relationships. In addition, the traditional time-delay models and nonlinear models are limited to researching single effects among variables, ignoring the combined role of multiple effects in the actual system. Therefore, an accumulative time-delay multivariate model with a nonlinear structure is proposed by introducing time-delay and nonlinear parameters. The new model is a further extension of the common time-delay and nonlinear models, which can effectively capture the time-lag relationship and nonlinear effects among variables. Furthermore, the compatibility and scalar multiplication properties of the novel model are proven. Subsequently, the superior particle swarm optimization algorithm for optimizing parameters is determined by a Monte Carlo simulation and two statistical test methods. The comparative analysis and noise sensitivity test show that the novel model's accuracy and stability are superior to the other nine competing models. Ultimately, the new model is leveraged to anticipate the new energy vehicle sales in China and some suggestions are proposed.

关键词： Grey multivariate model Time-delay effect Nonlinear structure particle swarm optimization algorithm New energy vehicle sales

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Optimal Scheduling Method for Cogeneration System Based on Heat Storage Device

Optimal Scheduling Method for Cogeneration System Based on H...

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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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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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Optimal design and performance analysis of solar hybrid CCHP system considering influence of building type and climate condition

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ENERGY 2019年 174卷 647-663页

作者： Yang, G. Zhai, X. Q. Shanghai Jiao Tong Univ Inst Refrigerat & Cryogen Shanghai 200240 Peoples R China

Incorporating solar energy technologies to the conventional combined cooling, heating and power (CCHP) system has been considered as an effective solution to mitigate the looming energy and environmental challenges. The mathematical model of a conventional CCHP system hybridized with photovoltaic (PV) panels and solar thermal collectors is built in this study. The particle swarm optimization (PSO) algorithm is employed to find the optimal size of key components of the solar hybrid CCHP system. The simulation work of solar hybrid CCHP systems in three building prototypes across seven climate zones is carried out to find appropriate design schemes of these cases. Besides, some guiding principles of the design of the solar hybrid CCHP system in early stage are summarized. The results show that the system under the following thermal load (FTL) strategy is the first choice in most cases of hospitals and hotels, except for the systems of hotels in the cold and very cold zones. On the contrary, the systems in offices perform better in the following electric load (FEL) mode in the majority of climate zones except for the hot-humid zone. Generally, the average optimal integrated performance (S) values of hospitals, hotels and offices can reach 28.95%, 28.20% and 22.69%, respectively. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Combined cooling heating and power Solar energy Hybrid system particle swarm optimization algorithm Building energy consumption Climate condition

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Fairness evaluation method of resource allocation based on BPSO multidimensional perspective

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第2期22卷 S4283-S4290页

作者： Lei, Jiao de Menezes, Rui Manuel Campilho Pereira ISCTE Univ Inst Lisbon Dept Quantitat Methods Management & Econ Lisbon Portugal Southern Med Univ Sch Hlth Serv Management Guangzhou Guangdong Peoples R China

In order to improve the effectiveness of fairness evaluation algorithm for public health resources, a kind of fairness evaluation method for public health resources based on BPSO dimension reduction under multi-perspective was proposed in the thesis. Firstly, data sources and research methods for fairness evaluation of public health resources was introduced;computational steps of Lorenz index were given;then, in order to reduce the complexity of data processing, particle swarm optimization was used to implement dimension reduction processing for software failure data;character string (0 or 1) of binary system was used to show particle position so as to realize simplification of data processing. Finally, effectiveness of methods mentioned in the thesis is verified in the positive analysis of fairness evaluation for regional distribution of health resources in Shaoguan city, Guangdong province.

关键词： particle swarm optimization algorithm Dimension reduction Multi-perspective Public health resources Fairness

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MULTI-WAYPOINT-BASED PATH PLANNING FOR FREE-FLOATING SPACE ROBOTS

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INTERNATIONAL JOURNAL OF ROBOTICS & AUTOMATION 2019年第5期34卷 461-467页

作者： Zhao, Suping Siciliano, Bruno Zhu, Zhanxia Gutierrez-Giles, Alejandro Luo, Jianjun Northwestern Polytech Univ Natl Key Lab Aerosp Flight Dynam 127 Youyi West Rd Xian 710072 Shaanxi Peoples R China Univ Naples Federico II Dept Elect Engn & Informat Technol Via Claudio 21 I-80125 Naples Italy

This paper studies the multi-waypoint-based path planning problem (MWPP) for redundant space robots. The end-effector of a space robot should visit a set of predefined waypoints with optimal distance, and the free-floating base should suffer minimum attitude disturbances from the manipulator during manoeuver. The MWPP is decomposed into two sub-problems: the problem of optimal waypoint-sequence and the problem of optimal joint-movements. First, the Hybrid Self-adaptive particle swarm optimization algorithm is proposed for optimal waypoint-sequence. Second, an Improved particle swarm optimization algorithm, combined with direct kinematics of the space robot, is proposed for optimal jointmovements. Finally, simulations are presented to validate the approach, including comparisons with other approaches.

关键词： Multi-waypoint-based path planning problem free-floating base redundant space robots waypoint-sequence particle swarm optimization algorithm

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Thermodynamic, economic analysis and optimization of a heat pump driven desalination system with open-air humidification dehumidification configurations

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ENERGY 2019年 174卷 768-778页

作者： He, W. F. Chen, J. J. Zhen, M. R. Han, D. Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn ECRG Nanjing 210016 Jiangsu Peoples R China

In this paper, the heat pump is coupled to a humidification dehumidification desalination system, with open-air configurations, to enhance the energy conversion efficiency. After establishing the energetic and entropic equations for all the thermal processes, the correlations between the desalination performance and the critical parameters, including the compression pressure ratio, pinch temperature difference of the condenser, terminal temperature difference of the evaporator, are revealed. Afterwards, the mass flow rate ratio and effectiveness during humidification and dehumidification are treated as the decision variables to optimize the energy conversion efficiency of the heat pump driven desalination system. The simulation results show that the actual top water production and gained-output-ratio of the desalination system reach 88.34 kgh(-1) and 3.72 at the balance condition of the humidifier. It is also obtained that raising the compression pressure ratio and reducing the pinch temperature difference of the condenser and terminal temperature difference of the evaporator, can promote the desalination performance. Furthermore, based on the particle swarm optimization algorithm, the best desalination performance, with 151.03 kgh(-1) for the water production, and 5.95 for the gained-output-ratio, is optimized within the prescribed range of the decision variables, while the corresponding cost of produced water arrives at 0.015$L-1 through the economic analysis. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Heat pump Humidification dehumidification desalination system Energy conversion efficiency Critical parameters particle swarm optimization algorithm

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