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A motor imagery EEG signal optimized processing algorithm

ALEXANDRIA ENGINEERING JOURNAL 2024年 101卷 38-51页

作者： Geng, Xiaozhong Zhang, Xi Yue, Mengzhe Hu, Weixin Wang, Linen Zhang, Xintong Yu, Ping Long, Duo Yan, Hui Changchun Inst Technol Sch Comp Technol & Engn Changchun 130012 Peoples R China Jilin Inst Chem Technol Sch Informat & Control Engn Jilin 132022 Peoples R China Jilin Univ Finance & Econ Sch Accounting Changchun 130117 Peoples R China Suqian Univ Sch Management Suqian 223800 Peoples R China

Feature extraction and classification is a difficult area in motor imagery electroencephalogram (EEG) signal processing. In order to improve the classification accuracy of EEG signals, both a feature extraction method based on the combination of LMD-CSP and a classification algorithm based on the fusion of PSO-SVM are proposed. Firstly, the extended informax ICA algorithm is used to denoise the signal and reduce the influence of noise on the signal. Then, the pre-processed EEG signals are decomposed into multiple Product Function (PF) components by Local Mean Decomposition (LMD), and the most discriminative PF component is selected. Next, feature extraction is carried out from the selected PF components using the Common Space Pattern (CSP). Finally, the obtained features are input into a Support Vector Machine (SVM) classifier improved by particle swarm optimization (PSO) for classification recognition. The experimental results show that compared with the traditional CSP-SVM method, CapsNet method, WPT-CSP+CNN method, FDCSP-SVM method, and EMD-CNN method, the classification accuracy of the proposed method is increased by 26.94 %, 14.9 %, 13.34 %, 8.34 % and 4.04 %, respectively. This further proves the superiority of the proposed method for EEG signal processing technology.

关键词： Local mean decomposition Common spatial pattern particle swarm optimization algorithm Support vector machine

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Multi-temperature capable enhanced bidirectional long short term memory-multilayer perceptron hybrid model for lithium-ion battery SOC estimation

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ENERGY 2024年 312卷

作者： Zhou, Yifei Wang, Shunli Feng, Renjun Xie, Yanxin Fernandez, Carlos Southwest Univ Sci & Technol Engn & Technol Ctr Sch Informat Engn Mianyang Peoples R China Robert Gordon Univ Sch Pharm & Life Sci Aberdeen Scotland

In this study, we propose a novel hybrid modeling framework for State of Charge (SOC) estimation across a broad temperature spectrum. First, we build a hybrid model to optimize stacked layers of stacked bidirectional long short term memory networks by introducing dropout mechanisms. At the same time, we also optimize the traditional multi-layer perceptron model to ResMLP, which is improved by introducing residual linkage, and then integrate the two optimization models. Finally, the synergistic effect and attention mechanism of genetic algorithm and particle swarm optimization are used to optimize its parameters. We then rigorously tested the model on nine datasets, including HPPC, DST and BBDST, at different temperatures of 5 degrees C, 15 degrees C and 35 degrees C. Using MAE, RMSE and MAXE benchmarks, our research results show that the proposed hybrid model outperforms the benchmark algorithm, achieving significantly enhanced performance and higher accuracy, and the maximum SOC estimation error is kept below 4.53 %. In addition, experimental evaluation at different temperatures shows the robustness and adaptability of the proposed algorithm.

关键词： State of charge Lithium-ion battery BiLSTM Multiple perceptrons particle swarm optimization algorithm Genetic algorithm

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Structure Deterioration Identification and Model Updating for Prestressed Concrete Bridges Based on Massive Point Cloud Data

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APPLIED SCIENCES-BASEL 2024年第21期14卷 10007页

作者： Sun, Zhe Zhao, Sihan Liang, Bin Liu, Zhansheng Beijing Univ Technol Coll Architecture & Civil Engn Beijing 100124 Peoples R China Beijing Univ Technol Chongqing Res Inst Chongqing 401121 Peoples R China Natl Key Lab Bridge Safety & Resilience Beijing 100124 Peoples R China

As a critical component of the transportation system, the safety of bridges is directly related to public safety and the smooth flow of traffic. This study addresses the aforementioned issues by focusing on the identification of bridge structure deterioration and the updating of finite element models, proposing a systematic research framework. First, this study presents a preprocessing method for bridge point cloud data and determines the parameter ranges for key algorithms through parameter tuning. Subsequently, based on the massive point cloud data, this research explores and optimizes the methods for identifying bridge cracks and spatial deformations, significantly enhancing the accuracy and efficiency of identification. On this basis, the particle swarm optimization algorithm is employed to optimize the key parameters in crack detection, ensuring the reliability and precision of the algorithm. Additionally, the study summarizes the methods for detecting bridge structural deformations based on point cloud data and establishes a framework for updating the bridge model. Finally, by integrating the results of bridge crack and deformation detection and combining Bayesian model correction and adaptive nested sampling methods, this research sets up the process for updating finite element model parameters and applies it to the analysis of actual bridge point cloud data.

关键词： point cloud model crack detection particle swarm optimization algorithm spatial deformation detection bridge finite element model updating

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Optimal Scheduling of Island Microgrids with Seawater Pumped Storage Plants for Multi-Energy Complementarity

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2025年第2期20卷 185-196页

作者： Su, Shiwei Chai, Huarui Lin, Jianbo Yi, Chengming Li, Xin China Three Gorges Univ Hubei Prov Engn Res Ctr Intelligent Energy Technol Yichang 443002 Hubei Peoples R China Three Gorges Univ Sch Elect Engn & Renewable Energy Yichang 443002 Hubei Peoples R China

The rapid development of new energy sources, such as offshore wind power and photovoltaic power, has provided a new solution to the problem of power supply for islands far from the mainland. Wave energy is a kind of renewable energy originated from the ocean, but the existing island power supply programs seldom consider this favorable natural condition. In addition, seawater variable-speed pumped storage is a new idea to consume offshore wind power and improve the reliability of coastal and island power systems. In view of the stochastic and intermittent nature of new energy sources, this paper adopts seawater variable-speed pumped storage power plants as energy storage equipment, and put forward an island power supply scheme with wind power, photovoltaic power generation, wave power generation, pumped storage power plants and diesel generator sets as a multifunctional complementary isolated grid. Firstly, wave energy generators, wind farms, photovoltaic farms, pumped storage power stations and diesel generator sets are modeled separately. Then, considering their respective operating conditions, constraints and load requirements, the optimal scheduling of island microgrids with multi-energy complementarity is constructed. Finally, based on the improved particle swarm optimization algorithm, the model is solved. According to the wind power photovoltaic and wave power output curves of several typical scenarios in an island far away from the mainland, the cost and benefit of different schemes are compared to verify the effectiveness of the optimal scheduling in this paper. (c) 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

关键词： seawater pumped storage plant wave energy generation optimal scheduling particle swarm optimization algorithm

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MPSO: An optimization algorithm for Task Offloading in Cloud-Edge Aggregated Computing Scenarios for Autonomous Driving

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MOBILE NETWORKS & APPLICATIONS 2024年 1-15页

作者： Liu, Xuanyan Yan, Rui Kim, Jung Yoon Xu, Xiaolong Nanjing Univ Posts & Telecommun Jiangsu Key Lab Big Data Secur & Intelligent Proc Nanjing Peoples R China Gachon Univ Coll Future Ind Seongnam Si South Korea

With the development of cloud computing and edge computing technologies, these technologies have come to play a crucial role in the field of autonomous driving. The autonomous driving sector faces unresolved issues, with one key problem being the handling of latency-sensitive applications within vehicles. Cloud computing and edge computing provide a solution by segmenting unresolved computing tasks and offloading them to different computing nodes, effectively addressing the challenges of high concurrency through distributed computing. While the academic literature addresses computation offloading issues, it often focuses on static scenarios and does not fully leverage the advantages of cloud computing and edge computing. To address these challenges, a multivariate particle swarm optimization (MPSO) algorithm tailored for the cloud-edge aggregated computing environment in the autonomous driving domain is proposed. The algorithm, grounded in real-world scenarios, considers factors that may impact computation latency, abstracts them into quantifiable attributes, and determines the priority of each task. Tasks are then assigned to optimal computing nodes to achieve a balance between computation time and waiting time, resulting in the shortest total average weighted computation latency time for all tasks. To validate the effectiveness of the algorithm, experiments were conducted using the self-designed CETO-Sim simulation platform. The algorithm's results were compared with those of simulated annealing, traditional particle swarm optimization, purely local computation, and purely cloud-based computation. Additionally, comparisons with traditional algorithms were considered in terms of iteration count and result stability. The results indicate that the MPSO algorithm not only achieves optimal computation offloading strategies within specified time constraints when addressing computation offloading issues in the autonomous driving domain but also exhibits high stability. F

关键词： Autonomous driving Cloud-edge aggregated computing Computation offloading particle swarm optimization algorithm Latency optimization

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COVID-19 Vaccines distribution routes with bioinspired metaheuristic algorithms: Resoluteness

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IEEE LATIN AMERICA TRANSACTIONS 2024年第6期22卷 484-493页

作者： Estrada Moreno, Leonardo Daniel Valdovinos Rosas, Rosa Maria Hernandez, Lourdes Loza Alejo, Roberto Univ Autonoma Estado Mexico Toluca Mexico Inst Tecnol Toluca Metepec Mexico

The global emergency of COVID-19 caused by the SARS-CoV-2 virus at the end of 2019, was without a doubt a critical and historical point for society in general;for instance, the effective development of vaccines, as well as the efficient distribution of them;They were an unprecedented challenge to slow down the spread or mitigate its impact on societies around the world. This article specifies three bio-inspired metaheuristic algorithms (genetic algorithm, particle swarm optimization algorithm, and artificial bee colony algorithm) that were used in the context of the capacitated vehicle routing problem to generate vaccine distribution routes, specifically, COVID-19 vaccine for over 18 years old the first and the second doses applications in Mexico, particularly in the State of Mexico. The quality of the solutions obtained by these algorithms is compared, as a result of the performance of the particle swarm optimization (PSO) algorithm being superior in solution quality.

关键词： Vaccines Costs Metaheuristics COVID-19 Vehicle routing Transportation Supply chains Artificial Bee Colony algorithm Bio-inspired Capacitated Vehicle Routing Problem Genetic algorithm particle swarm optimization algorithm

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An Optimal Rotating Machinery Fault Feature Extraction Method Based on the Variational Mode Extraction and Synthetic Detection Index

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JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES 2025年第1期13卷 1-18页

作者： Lu, Na Jiang, Shuangyun Li, Zhongliang Cao, Chaofan Zhang, Guangtao Chen, Xudong Zhengzhou Univ Zhengzhou Peoples R China

PurposeSetting parameters for variational mode extraction (VME) has been a challenge in fault feature extraction. Previous studies have often optimized these parameters using fitness functions like entropy value or kurtosis to evaluate the VME decomposition results. However, these methods overlook the impact on subsequent fault classification. This paper aims to propose a more effective method for fault feature extraction in rotating *** fault feature extraction method based on VME and the synthetic detection index (SDI) is introduced. The VME parameters are optimized using the particle swarm optimization (PSO) algorithm, with the maximum value of SDI as the objective function. The optimized VME is then used to extract features from the original signals and build a fault feature set for fault *** proposed method was tested on two datasets, and the numerical results demonstrate its superior performance in fault classification compared to other *** results show that the proposed method, which optimizes VME parameters using PSO, enhances fault feature extraction and classification accuracy in rotating machinery, outperforming other comparative techniques.

关键词： Rotating machinery Feature extraction Fault diagnosis Variational mode extraction particle swarm optimization algorithm Synthetic detection index

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Construction of Agricultural Economic-Level Prediction Model Based on GWO-SVM algorithm

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INTERNATIONAL JOURNAL OF AGRICULTURAL AND ENVIRONMENTAL INFORMATION SYSTEMS 2025年第1期16卷

作者： Li, Menghua Hebi Automot Engn Profess Coll Hebi Peoples R China

In response to the inability of traditional economic predication models to accurately predict agricultural economy, a method based on improved grey wolf optimization algorithm combined with support vector machine is proposed to establish an agricultural economy predication model. By analyzing the advantages and disadvantages of the grey wolf optimization algorithm, particle swarm optimization algorithm is used to improve the grey wolf optimization algorithm and enhance its search ability. Combined with support vector mechanism, a grey wolf optimization algorithm support vector machine prediction model is established. The results indicate that the proposed model can effectively perform economic forecasting, with a fast convergence speed and the ability to find the global optimal solution after 150 iterations. Overall, the research proposed model can raise the accuracy and efficiency of economic predication.

关键词： GWO SVM Agricultural Economic Level Predictive Model particle swarm optimization algorithm

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optimization of Control Strategy for Fuel Cell Vehicles by Integrating Fuzzy algorithm

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IEEE ACCESS 2024年 12卷 141952-141965页

作者： Wu, Qiong Chen, Hua Liu, Baolong Xian Technol Univ Sch Mechatron Engn Xian 710021 Peoples R China

Fuel cell vehicles have rapidly occupied the market with advantages such as environmental protection and energy conservation. However, their battery technology is insufficient and their endurance is poor, making them unsuitable for use over long distances. To address the aforementioned issues, a fuel cell vehicle energy storage system based on super-capacitors was constructed. Meanwhile, a proportional integral derivative controller based on fuzzy algorithms was established. Finally, the particle swarm optimization algorithm was used to optimize the fuzzy control strategy that integrated the fuzzy algorithm. When using the optimized fuzzy control strategy for simulation, the peak power of the fuel cell output power was reduced from 3.8kW to 2.0kW. The remaining power of the super-capacitor remained stable within a reasonable range throughout the entire operating condition. Under the new European urban road cycle, the optimized control strategy improved energy recovery performance by 4.3% and reduced hydrogen consumption by 0.9964%. Under the United States federal environmental protection agency standardized urban cycle conditions, the optimized control strategy improved the braking energy recovery efficiency index and effective braking energy recovery efficiency by 8.9% and 6.3%, respectively. The percentage reduction in hydrogen consumption was 0.9433%. Therefore, this research method can effectively reduce hydrogen consumption and improve the product economy and market competitiveness of enterprises.

关键词： Batteries Fuel cells Energy storage Mathematical models Supercapacitors Fuel cell vehicles Hydrogen Voltage Periodic structures optimization Environmental monitoring Fuzzy algorithm fuel cell vehicles optimization of control strategy particle swarm optimization algorithm super-capacitor

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Torque ripple reduction strategy for switched reluctance motor based on segmented non-linear correction torque sharing function

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JOURNAL OF POWER ELECTRONICS 2024年第12期24卷 1933-1943页

作者： Ben, Tong Wang, Hongliang Chen, Long Zhang, Yongchao Jing, Libing China Three Gorges Univ Coll Elect Engn & New Energy Yichang Peoples R China China Three Gorges Univ Hubei Prov Engn Technol Res Ctr Power Transmiss Li Yichang Peoples R China

The traditional torque sharing function (TSF) strategy can lead to large torque ripple and copper consumption in switched reluctance motors (SRMs) due to the limitation of the voltage in the phase change interval. In this paper, an improvement scheme is proposed to address this problem. First, a segmented non-linear correction TSF (SN-TSF) is proposed. Then with the torque ripple and copper consumption of the SRM as optimization objectives, the proposed SN-TSF values are optimized using the velocity control particle swarm optimization (VCPSO) algorithm based on the particle velocity-boundary relationship. The optimal parameters are used to obtain the optimal SN-TSF curves with different loading torques and speeds. Experimental results show that the proposed SN-TSF strategy reduces the torque ripple by 40.17% and 66.5%, and the peak phase currents by 5.058 A and 5.371 A under two different operating conditions, which demonstrates the effectiveness of this strategy.

关键词： SRM Torque sharing function particle swarm optimization algorithm Copper consumption

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