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Application of video image processing in sports action recognition based on particle swarm optimization algorithm

PREVENTIVE MEDICINE 2023年第1期173卷 107592-107592页

作者： Zhang, Youming Hou, Xingchen Mudanjiang Normal Univ Sch Phys Educ & Hlth Sci Mudanjiang 157011 Peoples R China

The existing sports training methods are mainly aimed at the sports field environment. The traditional sports training is only based on the coaches' visual inspection and combined with their own experience to put forward suggestions, which is relatively inefficient, thus limiting the rise of athletes' sports training level to a certain extent. Based on this background, combining traditional physical education teaching methods with video image processing technology, especially using particle swarm optimization algorithm, can promote the application of human motion recognition technology in physical training. This paper mainly investigates the optimization process of particle swarm optimization algorithm and discusses the development of particle swarm optimization algorithm;Secondly, through video decoding, image noise removal, video enhancement and other methods, complete video image processing and establish the structure of the manikin to achieve the collection of key points of the target, and then collect relevant data with experimental methods The results show that the motion recognition system proposed in this paper can effectively detect the changes of athletes' sampling point path, and can be compared with standard movements, which has a good auxiliary role. With the application of video image processing technology in sports training becoming more and more common, athletes can analyze their training videos in a more intuitive way and find out shortcomings, so as to improve the training effect. This paper studies particle swarm optimization algorithm and applies it to the field of video image processing, which promotes the development of sports action recognition technology based on video processing.

关键词： particle swarm optimization algorithm Video image processing Physical education teaching Action recognition

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Research on the Bi-level Programming Model for Ticket Fare Pricing of Urban Rail Transit based on particle swarm optimization algorithm

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Procedia - Social and Behavioral Sciences 2013年 96卷 633-642页

作者： Zhao Xueyu Yang Jiaqi Wuhan University of Technology School of Transportation Heping Avenue 1040 Wuchang District Wuhan City 430063

As a public service facility, the social and economic benefits of urban rail transit ticket fare are both important, so reasonable ticket fare is a key for the solid development of urban rail transit. The social and economic benefits should be taken into account under the competitive condition led by various modes of transportation in order to get an optimal strategy in ticket fare pricing of urban rail transit on the premise of meeting the service quality standard. Here, the factors considered in the ticket fares fare pricing of urban rail transit in the domestic and foreign cities are summarized, after which the Logit model of the mode split within the public transit system is established. With considering both the respective benefits of the urban rail transit company and the travellers, a bi-level programming model is established together with the solution idea to the model with the particle swarm optimization algorithm. The example demonstrates the feasibility and effectiveness of the bi-level programming model and the related measures and the particle swarm ooptimization aalgorithm is fittable for the urban rail transit fare pricing. The suggestions proposed from the result of the example are helpful for the decision making of ticket fare pricing of urban rail transit.

关键词： Ticket fare pricing Urban rail transit Logit model Bi-level programming model particle swarm optimization algorithm

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Estimation of Induction Motor Equivalent Circuit Parameters from Manufacturer's Datasheet by particle swarm optimization algorithm for Variable Frequency Drives

ELECTRICA

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ELECTRICA 2022年第1期22卷 16-26页

作者： Gulbahce, Mehmet Onur Karaaslan, Muhammed Emin Fatih Sultan Mehmet Vakif Univ Dept Elect & Elect Engn Istanbul Turkey

In recent years, industrial developments have made it necessary to control Induction motors, used in both industrial and household applications, over a wide range of speeds. Thanks to vector-control algorithms, in order to control the torque in high-performance operations over wide-ranging speeds, the equivalent circuit parameters of the induction motor have to be known precisely. In this study, the equivalent circuit parameters of the induction motor are estimated only with the limited information shared by the manufacturer's datasheets. The estimation method is based on the principle of solving nonlinear equations derived from the equivalent circuit of an induction motor by the particle swarm optimization algorithm. The proposed equation set and the algorithmic solution have been tested for 20 different induction motors and presented in comparison with the experimentally obtained equivalent circuit parameters. Moreover, the speed-torque characteristics obtained experimentally and calculated from the estimated equivalent circuit parameters for ten different selected motors are compared and the performance of the proposed algorithm is examined.

关键词： Equivalent circuit induction motors parameter estimation particle swarm optimization algorithm vector control

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Inverse design of a binary waveguide crossing by the particle swarm optimization algorithm

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RESULTS IN PHYSICS 2022年第0期34卷 105268-105268页

作者： Goudarzi, Kiyanoush Lee, Moonjoo Pohang Univ Sci & Technol POSTECH Dept Elect Engn Pohang 37673 South Korea

This paper describes the inverse design of the particle swarm optimization algorithm combined with the three-dimensional finite-difference time-domain simulation to design a waveguide crossing that resembles a binary code. The device consists of 15 x 15 air holes in a 220-nm silicon slab on a 3-mu m-thick SiO2 substrate with one input port and three output waveguide ports. The designed device has a small footprint of 4 mu m(2) and a short simulation time of 1.7 h. In the wavelength range of 1.5-1.6 mu m, the device has insertion loss IL < 0.85 dB and crosstalk XT < -14.5 dB. This device tolerates air hole-position disordering of eta(p) = 23%, and hole-radius disordering of eta = 35%, so it is appropriate to use in the complementary metal-oxide-semiconductor fabrication process. The paper also proposes integrated interconnections that contain 2 x 2 waveguide crossings and have IL < 2.9 dB and XT < -13 dB, and 3 x 3 waveguide crossings that have IL < 5 and XT < -12.5 dB.

关键词： particle swarm optimization algorithm Waveguide crossing Disordering

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Research on Location Problem of Multi-distribution Center based on Chaos Adaptive Mutation particle swarm optimization algorithm

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INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING 2016年第12期9卷 283-294页

作者： Du, Tiaotiao Wu, Kaijun Wang, Tiejun LanZhou Jiao Tong Univ Sch Elect & Informat Engn Lanzhou 730070 Peoples R China Northwestern Univ National Sch Math & Comp Sci Inst Lanzhou 730030 Peoples R China

Location problem of multi-distribution center is a kind of NP hard problem. To solve such problems, this paper proposes a chaos adaptive mutation particle swarm optimization algorithm. The algorithm uses the ergodic property of chaos to initialize the particle swarm to enhance the diversity of the population, according to the variance of population fitness to adjust the probability of mutation, and adjust the inertia weight factor to improve the global and local search capability of the whole population. In this paper, the algorithm is applied to the location problem of multi-distribution center, established the multi-factor constraints of mathematical model which aiming at timeliness, and on this basis, the corresponding algorithm is designed. It can be seen from the location instance simulation results that the optimization results and efficiency of the adaptive mutation particle swarm optimization algorithm is better than the genetic algorithm and the standard particle swarm optimization algorithm.

关键词： Distribution center location particle swarm optimization algorithm Chaos adaptive mutation

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Novel particle swarm optimization algorithm Based on President Election: Applied to a Renewable Hybrid Power System Controller

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INTERNATIONAL JOURNAL OF ENGINEERING 2021年第1期34卷 97-109页

作者： Yahyazadeh, M. Johari, M. S. HosseinNia, S. H. Vali E Asr Univ Rafsanjan Fac Engn Rafsanjan Iran Fac 3mE Dept Precis & Microsyst Engn Mekelweg 2 NL-2628 CD Delft Netherlands

particle swarm optimization has been a popular and common met heuristic algorithm from its genesis time. However, some problems such as premature convergence, weak exploration ability and great number of iterations have been accompanied with the nature of this algorithm. Therefore, in this paper we proposed a novel classification for particles to organize them in a different way. This new method which is inspired from president election is called President Election particle swarm optimization (PEPSO). This algorithm is trying to choose useful particles and omit functionless ones at initial steps of algorithm besides considering the effects of all generated particles to get a directed and fast convergence. Some preparations are also done to escape from premature convergence. To validate the applicability of our proposed PEPSO, it is compared with the other met heuristic algorithm including GAPSO, Logistic PSO, Tent PSO, and PSO to estimate the parameters of the controller for a hybrid power system. Results verify that PEPSO has a better reaction in worst conditions in finding parameters of the controller.

关键词： Hybrid optimization algorithm Chaotic Function Hybrid Power System particle swarm optimization algorithm President Election algorithm

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optimization Management of Storage Location in Stereoscopic Warehouse by Integrating Genetic algorithm and particle swarm optimization algorithm

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JOURNAL OF APPLIED MATHEMATICS 2024年第1期2024卷

作者： Zhang, Shuhong Zheng, Xianghui Xu, Fan Wang, Suzhen Zhang, Qixia Cao, Yuan Anhui Sanlian Univ Fac Engn Hefei 230601 Peoples R China Hefei Coll Finance & Econ Dept Artificial Intelligence Hefei 230601 Peoples R China Anhui Honglu Steel Struct Grp Co Ltd Operat Dept Hefei 230601 Peoples R China Hefei Coll Finance & Econ Dept Bldg & Mapping Hefei 230601 Peoples R China

The management of a three-dimensional warehouse is a key part of the upper computer monitoring and management system for three-dimensional warehouses. Currently, there are problems such as unreasonable planning and inability to respond to real-time demands. Therefore, further improvement is needed to optimize the management of storage locations. The main purpose of the research is to achieve better storage stability and improve storage and retrieval efficiency. First, the study constructed a multiobjective mathematical model based on the weight, frequency of use, and category of the goods. Three objective functions were constructed. Therefore, the operational efficiency of the stacker crane and the turnover rate of items were improved. Meanwhile, the overall stability of the shelves was ensured, and the management efficiency of the warehouse was improved. At the same time, the study introduced the GA-PSO algorithm to solve the mathematical model and optimize the goods location planning. These results confirmed that the proposed algorithm had significantly lower iteration times than traditional particle swarm optimization in different warehouse sizes and types of goods. The iteration required to reach the optimal value in Situation 1 was only 80, which was 90 fewer than PSO. Meanwhile, in Situation 2, the optimization results of the proposed algorithm in four objective functions were as high as 42.94%, 26.03%, 30.72%, and 46.15%, respectively, which increased by 1.20%, 8.04%, 5.61%, and 7.38% compared to PSO. The proposed algorithm can achieve more efficient and intelligent warehouse management, improving the efficiency and accuracy of logistics operations. It is significant for logistics industry development and enterprise competitiveness enhancement.

关键词： automated stereoscopic warehouse GA-PSO genetic algorithm monitoring and management system particle swarm optimization algorithm

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Research on Enterprise Supply Chain Anti-Disturbance Management Based on Improved particle swarm optimization algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第8期14卷 927-936页

作者： Dai, Tongqing Huanggang Normal Univ Business Sch Huanggang 438000 Peoples R China

A supply chain that is effective and of the highest caliber boosts customer happiness as well as sales and earnings, increasing the company's competitiveness in the market. It has been discovered that the standard supply chain management technique leaves the supply chain with weak supply chain stability because it has a low ability to withstand the manufacturer's production behaviour. An enterprise supply chain resistance management model is built using the study's proposed particle swarm optimisation technique, which is based on a genetic algorithm with stochastic neighbourhood structure, to solve this issue. The suggested technique outperformed the other two algorithms utilised for comparison in a performance comparison test, with a stable particle swarm fitness value of 0.016 after 800 iterative iterations and the fastest convergence. The proposed model was then empirically examined, and the results revealed that the production team using the model completed the same volume of orders in 32 days while making $460,000 more in profit. With scores of 4.5, 4.5, 4.3, 4.3, 4.2, and 4.2, respectively, the team also had the lowest values of the six forms of employee anti-production conduct, outperforming the comparative management style. In summary, the study proposes an anti-disturbance management model for enterprise supply chains that can rationalise the scheduling of manufacturers' production behaviour and thus improve the stability of the supply chain.

关键词： Supply chain particle swarm optimization algorithm genetic algorithm inverse production behaviour neighbourhood structure

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Performance evaluation of the particle swarm optimization algorithm to unambiguously estimate plasma parameters from incoherent scatter radar signals

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EARTH PLANETS AND SPACE 2020年第1期72卷 1-25页

作者： Martinez-Ledesma, Miguel Jaramillo Montoya, Francisco Univ Santiago Chile Phys Dept Av Ecuador 3493 Santiago Chile Univ Concepcion CePIA Dept Astron Av Esteban Iturra S-NCasilla 160-C Concepcion Chile Univ Chile Dept Elect Engn Av Tupper 2007 Santiago Chile

Simultaneously estimating plasma parameters of the ionosphere presents a problem for the incoherent scatter radar (ISR) technique at altitudes between similar to 130 and similar to 300 km. Different mixtures of ion concentrations and temperatures generate almost identical backscattered signals, hindering the discrimination between plasma parameters. This temperature-ion composition ambiguity problem is commonly solved either by using models of ionospheric parameters or by the addition of parameters determined from the plasma line of the radar. Some studies demonstrated that it is also possible to unambiguously estimate ISR signals with very low signal fluctuation using the most frequently used non-linear least squares (NLLS) fitting algorithm. In a previous study, the unambiguous estimation performance of the particle swarm optimization (PSO) algorithm was evaluated, outperforming the standard NLLS algorithm fitting signals with very small fluctuations. Nevertheless, this study considered a confined search range of plasma parameters assuming a priori knowledge of the behavior of the ion composition and signals with very large SNR obtained at the Arecibo Observatory, which are not commonly feasible at other ISR facilities worldwide. In the present study, we conduct Monte Carlo simulations of PSO fittings to evaluate the performance of this algorithm at different signal fluctuation levels. We also determine the effect of adding different combinations of parameters known from the plasma line, different search ranges, and internal configurations of PSO parameters. Results suggest that similar performances are obtained by PSO and NLLS algorithms, but PSO has much larger computational requirements. The PSO algorithm obtains much lower convergences when no a priori information is provided. The a priori knowledge of N-e and Te/Ti\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{

关键词： Temperature-ion composition ambiguity Ionospheric plasma parameters Incoherent scatter radar Monte Carlo simulation optimization algorithms particle swarm optimization algorithm

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The optimization of Marine Diesel Engine Rotational Speed Control Process by Fuzzy Logic Control Based on particle swarm optimization algorithm

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FUTURE INTERNET 2018年第10期10卷 99-99页

作者： Tien Anh Tran Vietnam Maritime Univ Fac Marine Engn Haiphong 180000 Vietnam

The marine main diesel engine rotational speed automatic control plays a significant role in determining the optimal main diesel engine speed under impacting on navigation environment conditions. In this article, the application of fuzzy logic control theory for main diesel engine speed control has been associated with particle swarm optimization (PSO). Firstly, the controller is designed according to fuzzy logic control theory. Secondly, the fuzzy logic controller will be optimized by particle swarm optimization (PSO) in order to obtain the optimal adjustment of the membership functions only. Finally, the fuzzy logic controller has been completely innovated by particle swarm optimization algorithm. The study results will be represented under digital simulation form, as well as comparison between traditional fuzzy logic controller with fuzzy logic control-particle swarm optimization speed controller being obtained.

关键词： main diesel engine fuzzy logic control theory particle swarm optimization algorithm optimal engine speed navigation environment

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