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Machine learning-based improved plastic stress distribution method for strength prediction of CFST members

STRUCTURES 2025年 74卷

作者： Zhou, Zheng Yang, Lifu Zhou, Xuhong Shi, Caijun Hunan Univ Coll Civil Engn Changsha 410082 Peoples R China

The plastic stress distribution (PSD) method is used as the primary method in typical design codes for calculating the strength of concrete-filled steel tubular (CFST) members. However, the PSD method can only be applied to members with limited ranges of material strengths and cross-sectional slenderness ratios, and the calculation methods are lacking or relatively complicated for members beyond the limitations. In this work, an improved PSD method is proposed for the strength prediction of CFST members with different cross-sectional slenderness ratios and material strengths, by using the concrete strength factor fi1 to consider the possible compressive strength increase or reduction of the concrete due to the confinement or local buckling from the steel tube and the steel strength factor fi2 to consider the potential compressive strength reduction of the steel tube due to local buckling. The direct inverse analysis using the improved particle swarm optimization algorithm is then conducted to identify the strength factors fi1 and fi2 based on the extensive experimental results. Machine learning algorithms are finally explored to train the rules between the strength factors (fi1 and fi2) and the member properties including the diameter or width and depth, wall thickness, and material strength, and the predicted results are in good agreement with the experimental results. These results further specify the accurate plastic stress distribution, and provide a strong justification for possible changes to the design codes to expand the use of the PSD method to all types of CFST members.

关键词： Concrete-filled steel tubes Plastic stress distribution method Direct inverse analysis particle swarm optimization algorithm Machine learning algorithm

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Lithium battery state of health estimation based on PSO-GWO algorithm optimization under chaotic mapping with hybrid kernel extreme learning machine model

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JOURNAL OF APPLIED ELECTROCHEMISTRY 2025年第6期55卷 1383-1398页

作者： Ding, Xvqiang Ni, Yiwei Zhu, Dandan Li, Zhiwei Jiao, Yunxiao Wang, Qi Nantong Inst Technol Sch Automot Engn Nantong 226001 Peoples R China Jiangsu Univ Sci & Technol Zhenjiang 212003 Peoples R China

In battery management systems, the health status of lithium batteries constantly affects the accurate estimation of their charging status and energy status, making the health status particularly important. However, in energy storage systems, rapidly and accurately estimating the health status of lithium batteries, as well as estimating it at an appropriate proportion, has always been a challenge. Therefore, to improve the stable operation of energy storage systems, this paper proposes a model that optimizes a hybrid kernel extreme learning machine using the circle mapping chaotic method and particle swarm optimization algorithm to improve the gray wolf algorithm. First, to address the issues of initialization instability and slow convergence speed in the gray wolf optimization algorithm, the ideas of circle mapping chaos and particle swarm optimization are proposed to replace the position update formula of the gray wolf optimization algorithm, thereby enhancing the algorithm's stability and convergence speed. Second, to address the limitation of the single learning capability of the kernel extreme learning machine model, a hybrid kernel extreme learning machine model is employed. The generalization and learning capabilities of the entire model are verified through simulation experiments. Finally, simulation predictions are conducted again under different data-splitting proportions to obtain an optimal training proportion, providing additional reference directions for practical application. Experimental results demonstrate that the proposed model maintains a fit above 0.99 for four sets of batteries, and ensures the reliability and reasonableness of the model when using a 50% training set proportion.

关键词： Lithium battery state of health Gray wolf optimization algorithm Hybrid kernel extreme learning machine particle swarm optimization algorithm Circle chaos mapping

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Development and simulation of two novel indoor odor source localization methods using a modified shark smell optimization algorithm

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MEASUREMENT 2025年 240卷

作者： Lin, Qin Wu, Sihuan Wu, Sifan Wang, Hui Zhang, Jinxiu Sun Yat Sen Univ Sch Aeronaut & Astronaut Shenzhen Campus Shenzhen 518107 Peoples R China Southern Marine Sci & Engn Guangdong Lab Zhuhai 519085 Peoples R China

Aiming to solve the problem of odor source localization (OSL) in the presence of interference sources, this paper presents two methods based on swarm intelligence algorithms. We initially introduced the shark smell optimization (SSO) algorithm and modified it for OSL tasks. Subsequently, mechanisms for collective information sharing and preventing falling into local minima were incorporated, leading to the development of the Improved Shark Smell optimization (I-SSO) algorithm. We tested both algorithms in a computational fluid dynamics (CFD) simulated environment with a single interference source and compared them to the particle swarm optimization (PSO) algorithm and whale optimization algorithm (WOA). In scenarios with one and two interference sources. The results showed that the I-SSO algorithm outperformed the other three algorithms in both environment settings, demonstrating a higher success rate and superior search distance efficiency.

关键词： Odor source localization particle swarm optimization algorithm Shark smell optimization algorithm Whale optimization algorithm swarm intelligence

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A Wind Speed Prediction Method Based on Signal Decomposition Technology Deep Learning Model

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ENERGIES 2025年第5期18卷

作者： Du, Jie Chen, Shuaizhi Pan, Linlin Liu, Yubao Nanjing Univ Informat Sci & Technol Sch Software Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Precis Reg Earth Modeling & Informat Ctr Nanjing 210044 Peoples R China China Elect Power Res Inst Beijing 100192 Peoples R China Nanjing Univ Informat Sci & Technol Sch Atmospher Phys Nanjing 210044 Peoples R China

Accurate and reliable wind speed prediction plays a significant role in ensuring the reasonable scheduling of wind power resources. However, wind speed sequences often exhibit complex characteristics such as instability and volatility, which create substantial challenges for prediction. In order to cope with these challenges, a multi-step wind speed prediction method based on secondary decomposition (SD) techniques and deep learning prediction models is proposed in this paper. First, the original signal was decomposed into multiple sequences by using two signal decomposition techniques, multi-scale wavelet power spectrum analysis (MWPSA) and variational mode decomposition (VMD). Second, a model was constructed by combining convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) networks, and attention mechanism to perform multi-step wind speed predicting for each sequence, and the model parameters were optimized by the particle swarm optimization (PSO) algorithm. Ultimately, the results from all sequences were combined to generate the final wind speed prediction. The predictive performance of the proposed method was evaluated using real wind speed data collected from a wind farm in China. Experimental results show that the proposed method significantly outperforms other comparison models in multi-step wind speed prediction, which highlights its accuracy and reliability.

关键词： wind speed predicting signal decomposition hybrid deep learning model particle swarm optimization algorithm

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Short-term wind speed forecasting based on a hybrid model that integrates PSO-LSSVM and XGBoost

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INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES 2024年第19期19卷 1138-1143页

作者： Shi, Yanhua Zhengzhou Univ Technol Sch Informat Engn 18 Yingcai St Zhengzhou 450053 Henan Peoples R China

A groundbreaking method is proposed to mitigate the impact of unpredictable fluctuations in wind velocity on wind power generation. This innovative approach integrates the particle swarm optimization (PSO)-least squares support vector machine (LSSVM) and XGBoost models in a harmonious manner. Initially, the raw wind speed data is subjected to wavelet threshold denoising to reduce noise and volatility. For short-term wind speed prediction, a PSO-LSSVM-XGBoost model is introduced. After the initial wind speed sequence undergoes wavelet threshold denoising, the enhanced sequence is forecasted using the LSSVM model, with its hyperparameters optimized through the PSO algorithm. The errors, obtained by subtracting the predicted values from the original data, are compensated using XGBoost. The final forecast results combine the rectified error data with the initial projected results. Experimental findings demonstrate the model's remarkable capability to enhance prediction performance and accuracy.

关键词： wind speed prediction least squares support vector machine particle swarm optimization algorithm deep learning combination model

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

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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

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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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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Research on optimization Method of Discrete Point Cloud Registration Based on Improved particle swarm optimization 23

Research on Optimization Method of Discrete Point Cloud Regi...

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Proceedings of the 7th International Conference on algorithms, Computing and Systems

作者： Rong Guo Yunsheng Mao Zuquan Xiang Xiaosa Zhao School of Naval Architecture Ocean and Energy Power Engineering Wuhan University of Technology China

ISBN: (纸本)9798400709098

The plate forming process based on a three-core rolling bending machine is a crucial technology in industries such as shipbuilding, aerospace, and boiler manufacturing. It enables the formation of single curvature plates like cylinders and cones. However, traditional sheet metal forming inspection methods rely on manual sample testing, resulting in low efficiency, poor precision, and an inability to automate or digitize the forming process. This paper introduces a novel automatic plate curvature detection device that measures discrete point data from sheet metal forming and requires rapid registration of this data for evaluating forming errors. To achieve rapid registration, a particle swarm optimization algorithm based on simulated annealing evolution principles is employed. This algorithm facilitates quick alignment between the discrete point data and theoretical values while analyzing sheet forming errors to guide subsequent automated processing steps. Experimental results demonstrate that the improved particle swarm optimization (PSO) effectively evaluates forming errors, providing valuable guidance for automating steel sheet forming processes.

关键词： curvature detection error evaluation particle swarm optimization algorithm registration calculation

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