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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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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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Optimizing energy management in distribution systems using PSO and scenario analysis: insights from CHP, wind, and NG integration

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ELECTRICAL ENGINEERING 2024年 1-20页

作者： Wu, Guangqiang Fujian Vocat & Tech Coll Water Conservancy & Elect Coll Elect Power Engn Yongan 366000 Peoples R China

This paper proposes a new optimization framework for energy distribution systems using modified PSO, inspired by the flocking behavior of avian species. In this respect, the research study will be focused on energy generation and consumption optimization in different energy hubs with diverse generation sources and demand profiles. One of the key features to be included is the integration of WT-power production, energy demand, and price data into an overall model. By making use of historical wind speed data in conjunction with the Monte Carlo simulation technique, the model generates 1000 production scenarios that undergo a filtering process to select just three of the most likely scenarios using the Kantorovich distance matrix method. It therefore makes the generated scenarios at least 25% more accurate than those from traditional models. Its PSO algorithm will lead the way through complex optimization landscapes and come up with as much as a 35% reduction in operational costs when compared to conventional models. Besides this, the production of energy under this model aligns better with demand, thus reducing the mismatch between energy supply and demand by 20%. The results therefore show that the PSO-based model significantly yields a more efficient, cost-effective energy system. This work thus gives theoretical inputs into energy optimization with practical solutions to economically feasible energy distribution systems. Results recommend the best way to utilize energy resources in realistic applications, thus constituting a theoretically and practically valuable development in energy resource management.

关键词： Energy hub Integrated energy system particle swarm optimization algorithm System security Voltage stability

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Regional traffic flow combination prediction model considering virtual space of the road network

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2024年 637卷

作者： Hou, Yue Zhang, Di Li, Da Deng, Zhiyuan Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Gansu Peoples R China

Accurate traffic flow forecasting is an important technical measure to alleviate traffic congestion. Since traffic flow has spatial and temporal characteristics, thus the adequate extraction of its spatio-temporal features is an important prerequisite to promote the forecast accuracy of the model. However, a majority of existing traffic flow prediction models cannot sufficiently consider the neighborhood spatio-temporal relationship for the real road network in the modeling process, which makes it difficult to improve the model prediction accuracy. For this reason, this paper takes improved feature enhancement graph convolution (FEGC), gated recurrent unit (GRU), and improved lightweight particle swarm optimization (ILPSO) algorithm as components, respectively, to construct a combinatorial traffic flow prediction model FEGCGRU-ILPSO (FGI), aiming to achieve accurate forecast for regional traffic flow through fully learning spatio-temporal correlation characteristics. Firstly, considering that most traffic flow modeling methods are ineffective in characterizing the hidden association information within nodes, we propose the method of constructing the virtual space adjacency matrix based on the improved gray relational analysis (IGRA) algorithm, which achieves the effective characterization of road network neighborhood relationship by fusing it with the original adjacency matrix. Then, based on the idea of matrix decomposition, the weight adjacency matrix is further introduced to realize the dynamic capture of time -varying correlation of node graph structure in realistic road networks. Secondly, to address the performance degradation problem caused by feature assimilation in multi -layer graph convolution, an improved feature enhancement graph convolution component is proposed to alleviate the multi -layer graph convolution oversmoothing by enhancing salient features. Finally, considering the convex optimization problem caused by the way the hyperparameters of the mo

关键词： Traffic flow prediction Adjacency matrix Graph convolutional network Gated recurrent unit particle swarm optimization algorithm

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Distributed state estimation-based resilient controller design for IoT-enabled microgrids under deception attacks

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APPLIED ENERGY 2024年 374卷

作者： Ponnarasi, L. Pankajavalli, P. B. Lim, Yongdo Sakthivel, R. Bharathiar Univ Dept Comp Sci Coimbatore 641046 India Sungkyunkwan Univ Dept Math Suwon 440746 South Korea Bharathiar Univ Dept Appl Math Coimbatore 641046 India

This paper addresses the distributed state estimation-based centralized control design problem for Internet- of-Things (IoT)-enabled microgrid systems in the presence of deception attacks. Firstly, a microgrid with multiple synchronous generators is described in a state-space model. Furthermore, the states of the microgrid power system are monitored by using a set of sensors linked through IoT-enabled networks, where attacks are presumed to occur during measurements. In particular, the deception attacks are capable of compromising grid security through the malicious manipulation of measurement data. To tolerate this effect in this paper a resilient distributed state estimation design based control algorithm is developed. The microgrid system is then controlled by a centralized scheme that uses only estimates generated by a certain group of IoT-enabled networked nodes. Subsequently, an augmented system is developed that incorporates both the microgrid system and the estimator error dynamics. Using Lyapunov stability theory and matrix inequality approach, the proposed local and neighboring estimator based control gains are designed to ensure the accurate estimation of load and energy source output and continuous power supply. Specifically, the particle swarm optimization algorithm is used for optimizing the distributed state estimation-based controller to achieve the desired performance. Finally, simulation results demonstrate that the developed algorithm is capable of estimating the state of the microgrid and controlling its operations. The result reveals that microgrids can provide a constant flow of electricity while being resistant to deception attacks and disruptions caused by the networks.

关键词： Microgrid Resilient state estimation Deception attacks particle swarm optimization algorithm

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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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