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2023 IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Xiao, Han Chen, Lijia Dai, Zhiyi Wenhua College Wuhan China

ISBN: (纸本)9798350309614

In fresh food supermarkets, the freshness period of vegetable products is very important, in order to ensure the quality and freshness of the goods, supermarkets need to focus on the daily sales situation to carry out timely replenishment operations. This paper assists merchants to make the best replenishment and pricing strategy to maximize the benefits. First of all, this paper analyzes the correlation and distribution law between each vegetable category and single product according to the sales data of a superstore over the past three years. And then this paper analyzes the relationship between sales volume and cost plus pricing, establishes the price elasticity of demand model, and combines it with the ARIMA model to establish the automatic pricing and replenishment optimization model for vegetables. Finally, we use the optimization algorithm to get the optimal pricing and replenishment strategy. © 2023 IEEE.

关键词： ARIMA Model Automatic Pricing Replenishment Strategy Correlation particle swarm optimization algorithm

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Application Evaluation of particle swarm optimization algorithm in Organic Pollution Control Prediction System 1

Application Evaluation of Particle Swarm Optimization Algori...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： He, Zixin University of Washington Seattle United States

ISBN: (纸本)9798350316469

Pollution control is a complex system engineering, and in order to truly achieve comprehensive environmental management and protection, it is necessary to do a good job in controlling pollution at the source. However, due to historical reasons and economic development levels, many places still have serious pollution phenomena. This paper mainly analyzes and discusses the problem of organic pollution, uses particle swarm optimization (PSO) algorithm to screen pollutants, and combines BP (back propagation) neural network to establish a pollution source prediction system. According to the selected optimal solution, a new mathematical model is constructed, which can better serve environmental protection, improve work efficiency, and reduce operating costs, It has high practical value. Comparing the PSO algorithm model with the BP neural network model in the treatment of organic pollutants, experimental data shows that the application effect of the PSO algorithm is more ideal, the prediction results are more accurate, and the pollutant removal rate has also increased by about 3.53%, providing new ideas and methods for environmental science and engineering exploration. The research results show that applying PSO algorithm to organic pollution control prediction systems can achieve certain results. By optimizing model parameters or control strategy parameters, the accuracy and control effectiveness of organic pollutant prediction can be improved. The PSO algorithm can also be used to optimize risk assessment models and improve the accuracy of organic pollutant risk assessment. © 2023 IEEE.

关键词： BP Neural Network Management Method Organizational Polling Control particle swarm optimization algorithm Prediction System

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Research on Optimal Allocation of Distributed Power Supply Based on particle swarm optimization algorithm 3

Research on Optimal Allocation of Distributed Power Supply B...

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3rd International Conference on New Energy and Power Engineering, ICNEPE 2023

作者： Huang, Guanglei Zhou, Baorong Mao, Tian Jiang, Jiru Wang, Tao Zhao, Wenmeng Chen, Shuyao China Southern Power Grid Shenzhen Power Supply Company Shenzhen518067 China China Southern Power Grid Electric Power Research Institute Guangzhou510530 China

ISBN: (纸本)9798350393224

The distribution network is connected to a multitude of dispersed power sources and electric car clusters, which results in a diverse range of load types. The distribution network planning is greatly challenged by dispersed generation's randomness and instability. In order to adjust to the distribution network, the planning method of varied load access is therefore suggested. In order to maximize the capacity of dispersed generation permitted to access the line, the distribution network planning model is first constructed. Then, a variety of alternate strategies are produced by gaining access to the distributed power supply at every node;In order to direct the distribution network's planning, the best access strategy for the created enormous access scheme is ultimately chosen using the particle swarm optimization (PSO) technique. The model's rationality and validity are confirmed by the simulation results of real-world situations. © 2023 IEEE.

关键词： capacity configuration dispersed power supply distribution network planning particle swarm optimization algorithm

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Residual Vibration Suppression of Piezoelectric Inkjet Printing Based on particle swarm optimization algorithm

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MICROMACHINES 2024年第10期15卷 1192-1192页

作者： Zhu, Huixuan Li, Song Zhu, Runyang Gao, Feiyang Yin, Zhenyu Liu, Lianqing Zheng, Xiongfei Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Shenyang Inst Comp Technol Shenyang 110168 Peoples R China Liaoning Key Lab Domest Ind Control Platform Techn Shenyang 110168 Peoples R China

Piezoelectric inkjet printing technology, known for its high precision and cost-effectiveness, has found extensive applications in various fields. However, the issue of residual vibration significantly limits its printing quality and efficiency. This paper presents a method for suppressing residual vibration based on the particle swarm optimization (PSO) algorithm. Initially, an improved PI model considering the nonlinear hysteresis characteristics of piezoelectric ceramics is established, and the model is identified through a strain gauge circuit to ensure its accuracy in describing the nonlinear hysteresis characteristics. Subsequently, a dynamic model of the piezoelectric inkjet printing system is constructed, with precise parameter identification achieved using the self-induction principle. This enables precise simulation of residual vibration. Finally, the driving waveform is optimized based on the PSO algorithm, with iterative calculations employed to find the optimal combination of driving waveform parameters, effectively suppressing residual vibration while ensuring sufficient injection energy. The results indicate that this method significantly reduces the amplitude of residual vibration, thereby effectively enhancing printing quality and stability. This research offers a novel solution for residual vibration suppression in piezoelectric inkjet printing technology, potentially advancing its applications in printing and biofabrication.

关键词： piezoelectric inkjet printing residual vibration suppression nonlinear hysteresis characteristics particle swarm optimization algorithm self-induction principle

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Convergence and Performance Analysis of a particle swarm optimization algorithm for Optical Tuning of Gold Nanohole Arrays

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MATERIALS 2024年第4期17卷 807页

作者： Angelini, Margherita Zagaglia, Luca Marabelli, Franco Floris, Francesco Univ Pavia Dept Phys Via Bassi 6 I-27100 Pavia Italy

Gold nanohole arrays, hybrid metal/dielectric metasurfaces composed of periodically arranged air holes in a thick gold film, exhibit versatile support for both localized and propagating surface plasmons. Leveraging their capabilities, particularly in surface plasmon resonance-oriented applications, demands precise optical tuning. In this study, a customized particle swarm optimization algorithm, implemented in Ansys Lumerical FDTD, was employed to optically tune gold nanohole arrays treated as bidimensional gratings following the Bragg condition. Both square and triangular array dispositions were considered. Convergence and evolution of the particle swarm optimization algorithm were studied, and a mathematical model was developed to interpret its outcomes.

关键词： FDTD simulation particle swarm optimization algorithm gold nanohole arrays

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Joint Inversion Method for Ionospheric Properties Based on particle swarm optimization algorithm 23

Joint Inversion Method for Ionospheric Properties Based on P...

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Yang, Yangqiang Wang, Menglin Zhang, Xueqing College of Information and Communication National University of Defense Technology Wuhan China

ISBN: (纸本)9798350325959

The ionosphere with charged particles such as free electrons and positive ions, is one of the most important factors affecting the accuracy of satellite navigation system, the quality of short-wave communication and the safety of power transmission. The detection and inversion methods of the ionosphere have always been the focus of ionospheric research. Obtaining data through various ionospheric detection systems and then using corresponding inversion models and algorithms is an important method to obtain ionospheric properties. By comprehensively using ionospheric altimeter data, typical regional TEC data, and IRI empirical model data, this paper proposed a joint ionospheric property inversion method based on particle swarm optimization algorithm to overcome the large errors that may be caused by using single source data. Simulation results showed that the proposed method can be used for ionospheric properties inversion. © 2023 IEEE.

关键词： ionospheric inversion IRI model particle swarm optimization algorithm QPS model TEC data

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Kinematic Parameter Identification for a Parallel Robot with an Improved particle swarm optimization algorithm

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APPLIED SCIENCES-BASEL 2024年第15期14卷 6557页

作者： Yu, Dayong Univ Shanghai Sci & Technol Sch Mech Engn Shanghai 200093 Peoples R China

The spacecraft docking motion simulation system for on-orbit docking plays a very important role in some theoretical research and engineering application fields. The parallel robot utilized in the spacecraft docking simulation system requires high positioning and orientation accuracy to achieve better simulation results. A novel kinematic parameter identification method with an improved particle swarm optimization (PSO) algorithm is proposed to enhance positioning and orientation accuracy of the parallel robot. A fitness function is established using these residuals between the measured and computed poses by a coordinate measuring machine and forward kinematics. The kinematic parameter identification problem is turned into a high-dimensional nonlinear optimization in which the unknown kinematic parameter errors are regarded as optimal variables. The optimal variables are solved by the proposed improved PSO algorithm. The mean values of the positioning and orientation errors are reduced from 4.3268 mm and 0.2221 deg to 0.7692 mm and 0.0674 deg, respectively. The proposed kinematic parameter identification method increases the positioning accuracy mean by 22.26% and the orientation accuracy mean by 32.80% compared with the least squares method. The kinematic parameter identification method with the improved PSO algorithm can effectively enhance positioning and orientation accuracy of the parallel robot for docking motion simulation.

关键词： parallel robot parameter identification particle swarm optimization algorithm pose accuracy nonlinear least squares

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A physically based constitutive model for 41CrS4 steel based on particle swarm optimization algorithm

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MATERIALS RESEARCH EXPRESS 2024年第10期11卷 106514页

作者： Wei, Xiaoxiao Tan, Fan Yang, Peipei Pan, Hongchen State Grid Gansu Elect Power Res Inst Lanzhou 730070 Peoples R China Shanghai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn Shanghai 200240 Peoples R China Lanzhou Univ Technol Sch Mat Sci & Engn Lanzhou 730050 Peoples R China State Grid Lanzhou Elect Power Supply Co Lanzhou 730050 Peoples R China Evergrande New Energy Automot R&D Inst Global Head Mat Engn Dept Shanghai 201600 Peoples R China

Utilizing the Gleeble-3500 thermal simulation apparatus, a thermal compression assay was performed on 41CrS4 steel within the temperature range of 900 degrees C to 1200 degrees C, featuring a strain rate of 0.01 to 5 s-1, to derive its flow stress curve. The evaluation of the Arrhenius equation parameters was adeptly carried out by deploying a sophisticated particle swarm optimization algorithm. Through rigorous analysis, the correlation coefficient and the mean absolute deviation were calculated to quantify the alignment between the predictive accuracy of the developed model and the empirical data. The findings demonstrate the ability of the particle swarm optimization algorithm to significantly enhance the precision of the constitutive model. This augmented level of accuracy substantively increases the model's utility and reliability for simulations of high-temperature material forming processes.

关键词： 41CrS4 steel high-temperature constitutive model particle swarm optimization algorithm

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An Improved particle swarm optimization algorithm for Parameters Optimizing of Feedforward Neural Networks 14

An Improved Particle Swarm Optimization Algorithm for Parame...

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14th International Conference on Advanced Computational Intelligence

作者： Zhang, Xiaoping Yang, Tianhang Wang, Li He, Zhonghe Liu, Shida North China Univ Technol Sch Elect & Control Engn Beijing Peoples R China

ISBN: (数字)9781665470452

ISBN: (纸本)9781665470469;9781665470452

Deep learning is an important branch of neural networks, which has high accuracy in classification and regression problems, and has been widely used. However, its performance is greatly affected by the parameters. In this paper, an improved particle swarm algorithm named as PSO-C is proposed to automatically train the parameters of the feedforward neural networks. In the proposed algorithm, the curiosity factor is introduced to divide the particles into two categories with different curiosity characteristics so as to improve the exploration ability and information mining ability of the particle swarms. At the same time, a chaotic factor is also introduced to avoid the local optimum problem during the neural network's training. The simulation results show that the PSO-C has better optimization effect on the whole.

关键词： particle swarm optimization algorithm curiosity deep learning artificial neural network parameter optimization

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Accelerometer calibration based on improved particle swarm optimization algorithm of support vector machine

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SENSORS AND ACTUATORS A-PHYSICAL 2024年 369卷

作者： Zhao, Xin Ji, Yong-xiang Ning, Xiao-lei China HuaYin Ordnance Test Ctr Huayin 714200 Peoples R China

Aiming at the problem that the deterministic errors caused by non-orthogonal installation, calibration factor, zero bias and other factors in production and in the use of accelerometers need to be calibrated by high-precision instruments, support vector machine regression is used to process the original data output by the accelerometer, and the processed data of each axis are used to establish a parameter calibration model without reference datum through the relationship between the output value of each axis of accelerometer, gravity acceleration and coaxial reversal in the paper. Then, the adaptive mutation rate is used to dynamically adjust the number of reverse learning particles, and the particles of particle swarm optimization algorithm are selected and adjusted according to the reverse learning, which solves the problems that particle swarm optimization algorithm tends to fall into localoptimum and the convergence speed is slow, through which a fast, accurate and simple calibration can be realized, and the performance of particle swarm optimization algorithm is improved. The calibration experiment shows that the improved particle swarm optimization algorithm has higher accuracy and faster convergence speed than the particle swarm optimization algorithm, and the calibration parameter accuracy is higher than that of the least square method, which does not need the datum of each axis. The calibration model proposed in this paper can realize a benchmark-free calibration outside the laboratory. At the same time, the improved particle swarm optimization algorithm can obtain calibration parameters with higher accuracy and faster speed in the rapid calibration, which provides the idea of a new model for accelerometer calibration and expands the application environment of accelerometer.

关键词： Calibration Accelerometer Reverse learning particle swarm optimization algorithm

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