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Concentration Retrieval in a Calibration-Free Wavelength Modulation Spectroscopy System Using particle swarm optimization algorithm

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SENSORS 2023年第14期23卷 6374-6374页

作者： Zhang, Tingting Sun, Yongjie Wang, Pengpeng Zhu, Cunguang Liaocheng Univ Sch Phys Sci & Informat Technol Liaocheng 252000 Peoples R China Univ Jinan Sch Phys & Technol Jinan 250022 Peoples R China

This paper develops a concentration retrieval technique based on the particle swarm optimization (PSO) algorithm, which is used for a calibration-free wavelength modulation spectroscopy system. As compared with the commonly used technique based on the Levenberg-Marquardt (LM) algorithm, the PSO-based method is less dependent on the pre-characterization of the laser tuning parameters. We analyzed the key parameters affecting the performance of the PSO-based technique and determined their optimal parameter values through testing. Furthermore, we conducted a comparative analysis of the efficacy of two techniques in detecting C2H2 concentration. The results showed that the PSO-based concentration retrieval technique is about 63 times faster than the LM-based one in achieving the same accuracy. Within 5 s, the PSO-based technique can produce findings that are generally consistent with the values anticipated.

关键词： particle swarm optimization algorithm wavelength modulated spectroscopy calibration-free spectral fitting

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

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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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An improved particle swarm optimization algorithm for P300 Neural network recognizer 23

An improved particle swarm optimization algorithm for P300 N...

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Proceedings of the 2023 13th International Conference on Communication and Network Security

作者： Wei Zou Shanshan Tu Zhenhu Ning Jiawei Xu Shuaikun Xing Xue Liao Department of Computer Science Beijing University of Technology China

ISBN: (纸本)9798400707964

P300 is an important control system signal in the brain, so there is an urgent need and practical significance to work on the efficient classification of P300 event-related potentials. In this article, we design a convolutional neural network CNNnet based on chaotic adaptive particle swarm optimization (CAPSO) algorithm for efficient and accurate detection and classification of P300 EEG signals. The chaotic adaptive particle swarm optimization algorithm uses Logistic chaotic mapping to initialize the initial position of particles, and adopts a dynamic adaptive weighting strategy. Compared with traditional particle swarm optimization algorithms, it can effectively improve the optimization speed and convergence speed of particles. The experimental results show that compared with other P300 detection neural networks and traditional particle swarm optimization algorithms, this algorithm has faster convergence speed and higher convergence accuracy, and can effectively avoid the problem of particle swarm falling into local optima.

关键词： P300 detection adaptive inertia weights chaotic mapping convolutional neural network 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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A particle-swarm-optimization-algorithm-Improved Jiles-Atherton Model for Magnetorheological Dampers Considering Magnetic Hysteresis Characteristics

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INFORMATION 2024年第2期15卷 101页

作者： Guo, Ying-Qing Li, Meng Yang, Yang Xu, Zhao-Dong Xie, Wen-Han Nanjing Forestry Univ Coll Mech & Elect Engn Nanjing 210037 Peoples R China Southeast Univ China Pakistan Belt & Rd Joint Lab Smart Disaster Nanjing 210096 Peoples R China

As a typical intelligent device, magnetorheological (MR) dampers have been widely applied in vibration control and mitigation. However, the inherent hysteresis characteristics of magnetic materials can cause significant time delays and fluctuations, affecting the controllability and damping performance of MR dampers. Most existing mathematical models have not considered the adverse effects of magnetic hysteresis characteristics, and this study aims to consider such effects in MR damper models. Based on the magnetic circuit analysis of MR dampers, the Jiles-Atherton (J-A) model is adopted to characterize the magnetic hysteresis properties. Then, a weight adaptive particle swarm optimization algorithm (PSO) is introduced to the J-A model for efficient parameter identifications of this model, in which the differential evolution and the Cauchy variation are combined to improve the diversity of the population and the ability to jump out of the local optimal solution. The results obtained from the improved J-A model are compared with the experimental data under different working conditions, and it shows that the proposed J-A model can accurately predict the damping performance of MR dampers with magnetic hysteresis characteristics.

关键词： magnetorheological damper Jiles-Atherton hysteresis model magnetic hysteresis characteristic particle swarm optimization algorithm

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A Hybrid Whale optimization and particle swarm optimization algorithm 8

A Hybrid Whale Optimization and Particle Swarm Optimization ...

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IEEE International Conference on Progress in Informatics and Computing (IEEE PIC)

作者： Yuan, Zijing Li, Jiayi Yang, Haichuan Zhang, Baohang Univ Toyama Fac Engn Toyama 9308555 Japan

ISBN: (纸本)9781665426558

In the field of optimization algorithms, hybrid algorithms are increasingly valued by researchers for their effectiveness in improving algorithmic *** recent years, a new type of natural meta-heuristic algorithm called whale optimization algorithm has been proposed. The algorithm refers to whales in nature and imitates their three different feeding methods to solve realistic optimization problems. The particle swarm algorithm, on the other hand, is an algorithm proposed by imitating the way a flock of birds transmits information. As population intelligence algorithms, the accuracy of these two algorithms are not high enough in the convergence process. At the same time, they tend to fall into the local optimum. In this paper, a hybrid algorithm based on whale optimization algorithm and particle swarm algorithm is proposed to update the population by a kind of selection iteration. The experimental results confirm that the algorithm has excellent superiority in convergence accuracy and convergence speed.

关键词： whale optimization algorithm meta-heuristic algorithm particle swarm optimization algorithm exploration and exploitation

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Quantitative study of the defects of ferromagnetic materials using the magnetic charge model and particle swarm optimization algorithm

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JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS 2022年第Part1期564卷

作者： Su, Sanqing Li, Junting Wang, Wei Liu, Xinwei Zuo, Fuliang Deng, Ruize Xian Univ Architecture & Technol Sch Civil Engn Xian 710055 Shaanxi Peoples R China

Local defects in components are an important factor responsible for causing damage to steel structures. Hence, metal magnetic memory (MMM) has been used to investigate this issue in recent years. MMM can detect defects based on variations in the self-magnetic leakage of ferromagnetic materials. However, there have been problems of difficulty in quantifying the theoretical parameters of the magnetic charge model and the single character-ization of magnetic feature parameters, when using MMM for quantitative analysis of defects. Therefore, the particle swarm optimization (PSO) algorithm was introduced to quicky and accurately quantify the parameters of the magnetic charge model. Theoretical values were compared with experimental data to verify the accuracy of the proposed method. Then, the defect information (defect width and defect depth) was changed and the variation patterns of the magnetic signal and characteristic parameters were analyzed. Finally, the weight of each characteristic parameter was calculated using the entropy value method. A theoretical formula to comprehen-sively describe defects using multi-feature parameters was proposed. The results show that the theoretical values calculated based on the parameter identification of the PSO algorithm well agreed with the experimental data. Moreover, the proposed formula well described the relationship between the defect width and characteristic parameters. This study is expected to provide a basis for improving the quantitative analysis of MMM.

关键词： Metal magnetic memory Magnetic charge model particle swarm optimization algorithm Parameter identification Local defect Characteristic parameter

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Hybrid particle swarm optimization algorithm for Area Minimization in 65 nm Technology 53

Hybrid Particle Swarm Optimization Algorithm for Area Minimi...

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IEEE International Symposium on Circuits and Systems (IEEE ISCAS)

作者： Rashid, Ria Nambath, Nandakumar Indian Inst Technol Goa Sch Elect Sci Ponda 403401 India

ISBN: (纸本)9781728192017

Analog design can be considered as a multidimensional optimization problem since it involves trade-offs between several circuit parameters. Various optimization techniques have been proposed to reduce the cycle time of analog design. We propose a hybrid particle swarm optimization algorithm with linearly decreasing inertia weight for the optimization of analog circuit design. The proposed method is validated in a differential amplifier circuit with a current mirror load. Promising simulation results demonstrate that the proposed method can significantly reduce the design time required for analog circuits.

关键词： Analog circuit design area optimization particle swarm optimization algorithm

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Trajectory tracking control strategy of manipulator based on improved particle swarm optimization algorithm 4

Trajectory tracking control strategy of manipulator based on...

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4th Artificial Intelligence and Cloud Computing Conference (AICCC)

作者： Gang, Mingyi Pan, Xiaobo Tang, Kaiyuan Xia, Xingguo Feng, Benxiu Maanshan Tech Coll Dept Elect Engn Maanshan Peoples R China Sinosteel Maanshan Gen Inst Min Res Co LTD Maanshan Peoples R China

ISBN: (纸本)9781450384162

In view of the manipulator system is a highly coupled, nonlinear dynamic characteristics and the system structure and parameters, such as there are many unpredictable factors in the practical work of multiple input multiple output system, designed a fuzzy neural network controller, and combined with particle swarm optimization algorithm for fuzzy neural network controller parameter setting. Through MATLAB simulation, it is proved that the scheme has strong robustness and stability for the control system, and effectively solves the trajectory tracking problem of manipulator.

关键词： Manipulator Fuzzy neural network particle swarm optimization algorithm

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A particle swarm optimization algorithm based on an improved deb criterion for constrained optimization problems

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PEERJ COMPUTER SCIENCE 2022年 8卷 e1178页

作者： Sun, Ying Shi, Wanyuan Gao, Yuelin North Minzu Univ Collaborat Innovat Ctr Sci Comp & Intelligent Pro Yinchuan Ningxia Peoples R China North Minzu Univ Sch Math & Informat Sci Yinchuan Ningxia Peoples R China

To solve the nonlinear constrained optimization problem, a particle swarm optimization algorithm based on the improved Deb criterion (CPSO) is proposed. Based on the Deb criterion, the algorithm retains the information of 'excellent' infeasible solutions. The algorithm uses this information to escape from the local best solution and quickly converge to the global best solution. Additionally, to further improve the global search ability of the algorithm, the DE strategy is used to optimize the personal best position of the particle, which speeds up the convergence speed of the algorithm. The performance of our method was tested on 24 benchmark problems from IEEE CEC2006 and three real-world constraint optimization problems from CEC2020. The simulation results show that the CPSO algorithm is effective.

关键词： particle swarm optimization algorithm Constrained optimization problems Deb criterion

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