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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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Active Disturbance Rejection Based Adaptive Dynamic Surface Control for Nonlinear Systems

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 8607-8617页

作者： Zhou, Lan Sun, Yongbo He, Yong Li, Hongyi Zhang, Xian-Ming Hunan Univ Sci & Technol Sch Informat & Elect Engn Xiangtan 411201 Peoples R China Hunan Univ Sci & Technol Sch Mech Engn Xiangtan 411201 Peoples R China China Univ Geosci Sch Automat Wuhan 430074 Hubei Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Swinburne Univ Technol Sch Sci Comp & Engn Technol Melbourne Vic 3122 Australia

This paper presents an adaptive dynamic surface control (DSC) method for nonlinear systems with multiple disturbances, parameter uncertainties, and unknown nonlinear dynamics, using a reduced-order extended state observer (ROESO). Compared to related methods, this proposed approach offers several distinct advantages: (i) The method does not require knowledge of the upper bound function for unknown nonlinear dynamics, allowing the controlled plant to not be bounded-input bounded-state, thus expanding the applicability of the DSC method;(ii) By utilizing known model information, an ROESO is designed to handle mismatched uncertainties and disturbances, reducing the load on the observer and enhancing its capability to suppress unknown nonlinear dynamics;(iii) This method employs an adaptive output-feedback DSC approach, which is more practical and easier to implement than state-feedback methods;and (iv) The observer gain, adaptive gain, and DSC gain are simultaneously optimized using a particle swarm optimization algorithm. Additionally, detailed stability analysis is provided, and simulations and comparative experiments are conducted on a rotational system to demonstrate the efficacy and superiority of the proposed method.

关键词： Uncertainty Nonlinear dynamical systems Backstepping Observers Heuristic algorithms Adaptive control Complexity theory Attenuation Adaptation models Uncertain systems Mismatched disturbances dynamic surface control reduced-order extended state observer particle swarm optimization algorithm

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Optimizing Image Watermarking with Dual-Tree Complex Wavelet Transform and particle swarm Intelligence for Secure and High-Quality Protection

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APPLIED SCIENCES-BASEL 2025年第3期15卷 1315-1315页

作者： Bsoul, Abed Al Raoof Ismail, Alaa Bani Yarmouk Univ Informat Syst Dept Irbid 21163 Jordan Yarmouk Univ Comp Sci Dept Irbid 21163 Jordan

Watermarking is a technique used to address issues related to the widespread use of the internet, such as copyright protection, tamper localization, and authentication. However, most watermarking approaches negatively affect the quality of the original image. In this research, we propose an optimized image watermarking approach that utilizes the dual-tree complex wavelet transform and particle swarm optimization algorithm. Our approach focuses on maintaining the highest possible quality of the watermarked image by minimizing any noticeable changes. During the embedding phase, we break down the original image using a technique called dual-tree complex wavelet transform (DTCWT) and then use particle swarm optimization (PSO) to choose specific coefficients. We embed the bits of a binary logo into the least significant bits of these selected coefficients, creating the watermarked image. To extract the watermark, we reverse the embedding process by first decomposing both versions of the input image using DTCWT and extracting the same coefficients to retrieve those corresponding bits (watermark). In our experiments, we used a common dataset from watermarking research to demonstrate the functionality against various watermarked copies and peak signal-to-noise ratio (PSNR) and normalized cross-correlation (NCC) metrics. The PSNR is a measure of how well the watermarked image maintains its original quality, and the NCC reflects how accurately the watermark can be extracted. Our method gives mean PSNR and NCC of 80.50% and 92.51%, respectively.

关键词： watermarking dual-tree complex wavelet transform particle swarm optimization algorithm imperceptibility

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Cultural algorithm Based on particle swarm optimization for Partner Selection of Virtual Enterprise

Cultural Algorithm Based on Particle Swarm Optimization for ...

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第三十一届中国控制会议

作者： ZHOU Wei~1,BU Yan-ping~2 1.School of Business,East China University of Science and Technology,Shanghai,200237,P.R.China 2.School of Technology,Shanghai Jiaotong University,Shanghai,201101,P.R.China

A novel cultural algorithm based on particle swarm optimization(PSO) algorithm was proposed in this *** analyzing the partner selection problems of virtual enterprise,the CPSO algorithm was presented to solve enterprise alliance problem within reasonable time and *** are certain number partners of each sub-task in virtual enterprise environment. The objective is,by selecting the optimal combination of partners,to minimize project's completion time and project's total cost. We tested the CPSO algorithm against the PSO *** results demonstrate that it can be superior to the regular PSO. We also tested the CPSO algorithm with the exhaustion method to show the algorithm's efficiency.

关键词： cultural algorithm particle swarm optimization algorithm virtual enterprise partner selection

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Application of Hybrid Engine Modeling Method Based on Neural Network Group and PSO with Adaptive Inertia Factor in Engine Calibration

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INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY 2025年第3期26卷 829-846页

作者： Shi, Xiuyong Wei, Jiande Wang, Haoyu Liu, Hua Jiang, Degang Tongji Univ Sch Automot Studies Shanghai 201804 Peoples R China Nanchang Automot Inst Intelligence & New Energy Nanchang 330052 Peoples R China

The traditional hybrid engine calibration method has low efficiency and high cost of manpower and material resources, which can not meet the calibration requirements of complex engine electronic control system. The calibration method based on a mathematical model can greatly reduce the test workload and improve efficiency. Therefore, the black-box model of the engine is constructed by using the results of Spearman correlation analysis, and nine variables are selected as input, at the same time five variables are used as outputs. The improved RSR-BPNNG neural network group method is used to construct the hybrid engine economy and emission model. The model prediction results show that the R2 value of fuel consumption prediction reaches 0.9975, and the R2 value of NOx emission prediction reaches 0.9933, which achieves high precision modeling. On this basis, the performance of the engine under the WHSC cycle is simulated and optimized by the improved adaptive PSO algorithm. The optimization results show that the NOx emission of the engine is reduced by 8.16%, and the fuel consumption is reduced by 4.55%.

关键词： Hybrid vehicles Engine calibration Neural network particle swarm optimization algorithm

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Research on Offshore Vessel Trajectory Prediction Based on PSO-CNN-RGRU-Attention

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APPLIED SCIENCES-BASEL 2025年第7期15卷 3625-3625页

作者： Liu, Wei Cao, Yu Liaoning Petrochem Univ Sch Informat Sci & Control Engn Fushun 113005 Peoples R China

In busy offshore waters with high vessel density and intersecting shipping lanes, the risk of collisions and accidents is significantly increased. To address the problem of insufficient feature extraction capability of traditional recurrent neural networks (RNNs) in ship trajectory prediction in busy nearshore areas, this paper proposes a hybrid model based on particle swarm optimization (PSO), Convolutional Neural Networks (CNN), Residual Networks, Attention Mechanism, and Gated Recurrent Units (GRU), named PSO-CNN-RGRU-Attention, for ship trajectory prediction. This study utilizes real Automatic Identification System (AIS) data and applies the PSO algorithm to optimize the model and determine the optimal parameters, using a sliding window method for input and output prediction. The effectiveness and practicality of the model have been fully verified. Experimental results show that, compared to the PSO-CNN-GRU model, the proposed model improves the longitude by 7.8%, 3.4%, and 1.7% in terms of Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), respectively, and improves the latitude by 48.3%, 62.9%, and 39.2%, respectively. This has significantly contributed to enhancing the safety of ship navigation in the Bohai Strait.

关键词： AIS information particle swarm optimization algorithm convolutional neural network attention mechanism trajectory prediction

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Prediction of the Remaining Useful Life of Lithium-Ion Batteries Based on Mode Decomposition and ED-LSTM

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BATTERIES-BASEL 2025年第3期11卷 86-86页

作者： Song, Bingzeng Yue, Guangzhao Guo, Dong Wu, Hanming Sun, Yonghai Li, Yuhua Zhou, Bin Shandong Univ Technol Sch Transportat & Vehicle Engn Zibo 255000 Peoples R China Natl Engn Lab Mobile Source Emiss Control Technol Tianjin 300300 Peoples R China Jinan Heating Grp CO LTD Jinan 250011 Peoples R China Shandong Agr Univ Coll Mech & Elect Engn Tai An 271017 Peoples R China State Key Lab Intelligent Transportat Syst Beijing 100088 Peoples R China

The prediction of remaining useful life (RUL) of lithium-ion batteries is key to the reliability assessment of batteries and affects safe application of batteries. This article introduces a CEEMDAN-RF-MHA-ED-LSTM method. Using CEEMDAN, the battery capacity data were decomposed to obtain intrinsic mode functions (IMFs), and the weight of each IMF was obtained via the random forest (RF) algorithm. The LSTM neural network was used, the encoder-decoder (ED) structure was introduced, the multi-head attention (MHA) mechanism was used to construct a network model, and the particle swarm optimization (PSO) algorithm was used to optimize the model parameters. Each IMF was input into the model, and the obtained forecast results were weighted and reconstructed to obtain the final forecast data. This method was validated on the battery dataset released by NASA. Compared with that of the single LSTM model, the mean absolute error of the proposed method decreases by 74%, 62%, 71%, and 55% on the No. 05, 06, 07, and 18th battery datasets, respectively. The root mean square error decreased by 72%, 59%, 70%, and 54%, and the mean absolute percent error decreased by 75%, 65%, 71%, and 58%, respectively. This method can accurately predict battery RUL.

关键词： lithium-ion battery life prediction modal decomposition particle swarm optimization algorithm long short-term memory network

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Application of Genetic and particle swarm optimization algorithms to Portfolio optimization Problem: Borsa Istanbul and Crypto Money Exchange 4

Application of Genetic and Particle Swarm Optimization Algor...

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4th International Conference on Computer Science and Engineering (UBMK)

作者： Huseyinov, Ilham Ulucay, Samed Istanbul Aydin Univ Yazilim Muhendisligi Bolumu Istanbul Turkey Istanbul Aydin Univ Bilgisayar Muhendisligi Bolumu Istanbul Turkey

ISBN: (纸本)9781728139647

particle optimization (PSO) algorithm and genetic (GA) algorithms are widely used in a variety of optimization problems. Portfolio optimization problems offer minimum risk and maximum profit-based solutions to help investors on optimal investment. Current researches have focused on PSO and GA algorithms based on Markowitz's mean variance method. This paper examines the implementation of PSO and GA algorithms to solve the optimization problem of two portfolios, Borsa Istanbul (BIST) and Cryptocurrency Exchange (KPB). This study also uses Markowitz mean variance method, that is, variance method for risk minimization and mean method for profit maximization. PSO and GA varying coefficients are compared in terms of sharpe risk percentage and portfolio profit. The comparison result is in favor of PSO algorithm.

关键词： Portfolio optimization Level Variance Model Genetic algorithm particle swarm optimization algorithm

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Fast and accurate auto-disturbances-rejection temperature control system based on particle swarm optimized fuzzy control: Applied for particle three-dimensional (3D) printing prosthetic orthotic plate

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ADDITIVE MANUFACTURING 2025年 101卷

作者： Liu, Biaoqiang Qian, Bo Liang, Yuxin Dai, Peng Li, Ruidi Wei, Qingsong Shanghai Univ Engn Sci Sch Mech & Automot Engn Longteng Rd 333 Shanghai 201620 Peoples R China Cent South Univ State Key Lab Powder Met Changsha 410083 Peoples R China Huazhong Univ Sci & Technol Sch Mat Sci & Engn Luoyu Rd 1037 Wuhan 430074 Peoples R China

In traditional temperature control system of particle 3D printing extrusion device, it exists many issues such as slow response speed, large fluctuation, and poor anti-interference ability. The above issues cause the instability of the extruded PLA filament, affecting the mechanical properties and surface quality of the printed samples. Based on particle swarm optimization algorithm, this paper proposes a precise control method for temperature control system to iteratively optimize the quantization factor and proportion factor, finally it obtains the optimal weight factor. Compared with traditional PID control and fuzzy PID control, response speed has improved by 58.6 % and 40.0 % respectively, overshoot has reduced by 76 % and 35 % respectively, and steady-state time has shortened to 24 s. Comparison of experimental results: The tensile strength of the samples increases by 15.52 % and 7.47 % respectively, the bending strength increases by 17.93 % and 11.58 % respectively, and the internal pores are improved significantly. In summary, the method proposed in this paper can effectively solve the problems of the temperature control system for particle 3D printing, and improve the mechanical properties and surface quality of the samples. The printed prosthetic orthotic plate can well meet the fitness and comfort of the human body.

关键词： Accurate temperature control 3D printing particle swarm optimization algorithm Screw extrusion Analogue simulation

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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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