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Equivalent circuit modeling and state-of-charge estimation of lithium titanate battery under low ambient pressure

JOURNAL OF ENERGY STORAGE 2024年 77卷

作者： Li, Guishu Xie, Song Guo, Wenqi Wang, Qilin Tao, Xin Civil Avit Flight Univ China Civil Aircraft Fire Sci & Safety Engn Key Lab Sichuan Prov Guanghan 618307 Sichuan Peoples R China

The low ambient pressure during the flight of aircraft has a significant impact on the performance and safety of the onboard power battery. In order to ensure the safe operation of the battery system, it is very important to accurately estimate and manage the state-of-charge (SOC) of the battery. In this work, the equivalent circuit modeling (ECM) of lithium titanate battery (LTB) is studied in detail, and the influence of analog circuit model parameters in low ambient pressures is discussed for the first time. The forgetting factor recursive least square algorithm is introduced to accurately identify the ECM parameters of the LTB under different pressures online. The particle swarm optimization algorithm is innovatively proposed to optimize the covariance matrix of the Kalman filter algorithm. The verification shows that the root mean square error of the ECM of LTB under different ambient pressures is less than 0.025. In the SOC estimation process, the noise covariance matrixes of the extended Kalman filter and the unscented Kalman filter are optimized by the particle swarm algorithm. The optimized SOC estimation absolute error is less than 3 %, especially at 96 kPa and 30 kPa, where the absolute error is less than 2 %.

关键词： Low-pressure environment State of charge Kalman filtering particle swarm optimization algorithm Recursive least square

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Abnormal Heart Sound Detection using Time-Frequency Analysis and Machine Learning Techniques

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年 90卷

作者： Nia, Parastoo Sadeghi Hesar, Hamed Danandeh Sahand Univ Technol Fac Biomed Engn Tabriz Iran

Phonocardiogram (PCG) signals contains valuable information pertaining to heart valve functionality, rendering them potentially useful for early detection of cardiovascular diseases. Automated classification of heart sounds harbors great promise for identifying cardiac pathologies. This paper introduces a novel automated approach to classify normal and abnormal heart sounds. Our methodology involves partitioning heart sounds into four segments: S1, S2, systolic, and diastolic, followed by extraction of time-frequency and time-statistical features. Prior to data classification, we employ two techniques - particle swarm optimization (PSO) and Sequential Forward Feature Selection (SFFS) - for efficient feature selection. We assess the performance of the proposed method on the Physionet Challenge 2016 database, utilizing the 10-fold cross-validation method. To address the issue of dataset imbalance, we apply the synthetic minority over-sampling technique (SMOTE) to create balanced datasets. Our approach surpasses existing methods in the literature, as evidenced by its superior accuracy, sensitivity, and specificity metrics. Specifically, our method achieves an accuracy of 98.03%, a sensitivity of 97.64%, and a specificity of 98.43% in distinguishing normal from abnormal heart sounds on the Physionet database. These findings outperform the results obtained by previously established methods evaluated on the Physionet 2016 challenge database.

关键词： Phonocardiogram particle swarm optimization algorithm Feature Selection Classification

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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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Solving a new mathematical model for the integrated cockpit crew pairing and rostering problem by meta-heuristic algorithms under the COVID-19 pandemic

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2024年第8期75卷 1493-1509页

作者： Saemi, Saeed Komijan, Alireza Rashidi Tavakkoli-Moghaddam, Reza Islamic Azad Univ Dept Ind Engn Cent Tehran Branch Tehran Iran Islamic Azad Univ Dept Ind Engn Firoozkooh Branch Firoozkooh Iran Univ Tehran Coll Engn Sch Ind Engn Tehran Iran

This study examines the problem of crew pairing and rostering under COVID-19 conditions. To keep the cockpit crew members safe against COVID-19, the problem is investigated in such a way that each cockpit crew member spends less daily sit time period in the airports and returns to his/her home base at the end of the day to take a rest. This study aims to introduce a Mixed-Integer Linear Programming (MILP) formulation for the problem. In order to solve the problem, three meta-heuristic algorithms including Genetic algorithm (GA), Firefly algorithm (FA), and particle swarm optimization (PSO) are applied based on a new chromosome representation. The proposed algorithms could obtain solutions with the least possible number of cockpit crew members to cover the existing flights by considering some rules and regulation related to employing cockpits. Moreover, the findings indicate that the algorithms can provide solutions near the optimal solutions (1.94%, 2.49%, and 2.43% gaps for the GA, FA, and PSO on average, respectively) for the small-scale instances extracted from the data sets. Additionally, the proposed GA can find lower-cost solutions in comparison to the FA and PSO in approximately similar CPU time for problem instances with different sizes.

关键词： COVID-19 conditions scheduling integrated crew pairing and rostering problem meta-heuristics genetic algorithm particle swarm optimization algorithm

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A motor imagery EEG signal optimized processing algorithm

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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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Oral cancer detection using convolutional neural network optimized by combined seagull optimization algorithm

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年 87卷

作者： Huang, Qirui Ding, Huan Razmjooy, Navid Nanyang Inst Technol Sch Informat Engn Nanyang 473004 He Nan Sheng Peoples R China Ankara Yildirim Beyazit Univ Dept Ind Engn Ankara Turkiye Saveetha Sch Engn SIMATS Dept Comp Sci & Engn Div Res & Innovat Chennai 602105 Tamil Nadu India Islamic Univ Coll Tech Engn Najaf Iraq

Early diagnosis of oral cancer is crucial for improving patient outcomes and saving lives. However, inaccurate and improper diagnosis can hinder effective treatment. This paper presents a novel method for detecting oral cancer using an optimized version of Convolutional Neural Network (CNN). While basic CNNs have been widely used for image classification tasks, the incorporation of the Seagull optimization algorithm and particle swarm optimization algorithm in optimizing the CNN architecture specifically for oral cancer detection is a unique approach that is provided in this study. By combining these algorithms, the proposed method optimizes the CNN's architecture, parameters, and training process specifically for oral cancer detection. This optimization enhances the performance and accuracy of the CNN in identifying cancerous regions in oral images. Unlike previous approaches, our method incorporates advanced image processing techniques, including noise reduction, contrast enhancement, and data augmentation, to enhance the quality of input data extracted from the Oral Cancer (Lips and Tongue) images (OCI) dataset. The optimized CNN architecture uses its ability to learn intricate patterns and features from the enhanced images, enabling more accurate identification of cancerous regions. To evaluate the effectiveness of our approach, we compare it against Textural analysis, FCM, CNN, R-CNN, and ResNet-101 using four measurement indices. Results demonstrate that our proposed CSOA-based CNN system achieves the highest accuracy rate (96.94%) compared to other methods, indicating its superior performance in oral cancer detection. Furthermore, our precision rate of 94.65% and recall rate of 91.60% highlight the model's high correctness and positive classification ability. Finally, our proposed method achieves the highest F1-score (88.55%), emphasizing its superiority over other comparative methods. Through our innovative integration of the Seagull optimization algorithm a

关键词： Oral cancer Diagnosis Convolutional neural network Deep learning Combined seagull optimization algorithm particle swarm optimization algorithm

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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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Optimizing energy hub systems: A comprehensive analysis of integration, efficiency, and sustainability

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COMPUTERS & ELECTRICAL ENGINEERING 2024年 120卷

作者： Xu, Lei Rongke Digital Energy Informat Technol Beijing Co Beijing Peoples R China Tianjin Univ Coll Management & Econ Tianjin Peoples R China

This study introduces a novel application of modified particle swarm optimization (PSO) for optimizing multi-energy hub systems (EHSs) to enhance efficiency and sustainability. The proposed method leverages PSO to optimize the scheduling of various energy resources, including gas turbines, biomass units, and renewable sources such as solar and wind power. Unlike traditional optimization approaches that rely on genetic algorithm (GA) and complex encoding schemes, the PSO algorithm simplifies the process using real-valued vectors and direct communication within the swarm, which significantly reduces implementation complexity. Key contributions of this work include the development of a tailored PSO algorithm that integrates seamlessly with the multi-objective optimization of EHSs. The algorithm simultaneously targets a reduction in operational costs and carbon emissions, offering a comprehensive solution for energy hub design. The proposed PSO approach has demonstrated a 10.35 % reduction in operating costs and an 85.03 % decrease in CO2 emissions compared to traditional baseline setups. In comparative analysis, the integration of renewable sources using the PSO algorithm resulted in a 77.91 % reduction in total CO2 emissions and an 85.61 % decrease in operating costs, showcasing its effectiveness in advancing both economic and environmental objectives. Furthermore, the study provides a detailed evaluation of various scenarios, revealing that the PSO-optimized EHS configuration achieves a significant reduction in reliance on non-renewable energy sources (RES). For instance, the incorporation of photovoltaics and wind turbines in the EHS setup led to a 46.39 % increase in energy sold to the grid and a 26.82 % decrease in electricity purchased from external sources. These quantitative results underscore the robustness and practical benefits of the proposed PSO method in designing and optimizing energy systems for improved sustainability and cost-effectiveness.

关键词： Energy hub systems Renewable energy integration Multi-objective optimization particle swarm optimization algorithm System reliability

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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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