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3rd International Conference on Applied Mathematics, Modeling and Computer Simulation, AMMCS 2023

作者： Hao, Faqiang Guo, Jining School of Control Science and Engineering Bohai University Liaoning Jinzhou121013 China

ISBN: (纸本)9781643684581

LLC resonant converter, which is widely used in the charging of new energy vehicles, can achieve zero-voltage switching (ZVS) of the primary side MOSFET and zero-current switching (ZCS) of the secondary side rectifying diode. They also have the advantages of high efficiency and high-power density. The output voltage is still affected by input voltage fluctuation, reference voltage change, load mutation and other factors. The charging mode of automobile batteries with different materials is also different. In order to stabilize the output voltage and realize automatic switching of constant voltage and current, this paper proposes a double closed-loop linear active disturbance rejection control (LADRC) scheme. Firstly, the transfer functions of the system are obtained by extended description function (EDF) method and simulation method respectively. The transfer functions of the two methods are compared and analysed. Finally, the LADRC controller and PI controller are simulated and compared, and the dynamic performance of the control system is evaluated by ITAE index. The experimental results show that the output voltage of LADRC control is more stable. © 2023 The Authors.

关键词： Zero voltage switching

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A Detection Method of Bolts on Axlebox Cover Based on Cascade Deep Convolutional Neural Network

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Computer Modeling in engineering & sciences 2023年第3期134卷 1671-1706页

作者： Ji Wang Liming Li Shubin Zheng Shuguang Zhao Xiaodong Chai Lele Peng Weiwei Qi Qianqian Tong School of Urban Railway Transportation Shanghai University of Engineering ScienceShanghai201620China School of Information Science and Technology Donghua UniversityShanghai201620China Shanghai Engineering Research Centre of Vibration and Noise Control Technologies for Rail Transit Shanghai University of Engineering ScienceShanghai201620China

This paper proposes a cascade deep convolutional neural network to address the loosening detection problem of bolts on axlebox ***,an SSD network based on ResNet50 and CBAM module by improving bolt image features is proposed for locating bolts on axlebox *** then,theA2-PFN is proposed according to the slender features of the marker lines for extracting more accurate marker lines regions of the ***,a rectangular approximationmethod is proposed to regularize themarker line regions asaway tocalculate the angle of themarker line and plot all the angle values into an angle table,according to which the criteria of the angle table can determine whether the bolt with the marker line is in danger of ***,our improved algorithm is compared with the pre-improved algorithmin the object localization *** results show that our proposed method has a significant improvement in both detection accuracy and detection speed,where ourmAP(IoU=0.75)reaches 0.77 and fps reaches *** in the saliency detection stage,after qualitative comparison and quantitative comparison,our method significantly outperforms other state-of-the-art methods,where our MAE reaches 0.092,F-measure reaches 0.948 and AUC reaches ***,according to the angle table,out of 676 bolt samples,a total of 60 bolts are loose,69 bolts are at risk of loosening,and 547 bolts are tightened.

关键词： Loosening detection cascade deep convolutional neural network object localization saliency detection

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Deep Co-Training Partial Least Squares Model for Semi-Supervised Industrial Soft Sensing

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3363-3371页

作者： Zheng, Junhua Zhou, Le Ye, Lingjian Ge, Zhiqiang Zhejiang University of Science and Technology School of Automation and Electrical Engineering Hangzhou310023 China Huzhou University Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems School of Engineering Huzhou313000 China Southeast University School of Mathematics Nanjing210096 China

Data-driven soft sensing has become quite popular in recent years, which can provide real-time estimations of key variables in industrial processes. While the introduction of deep learning does improve the prediction performance, it is highly restricted to the number of labeled training data, as well as large computational burden and cumbersome parameter tuning procedures. How to break through the bottleneck of data-drive models in terms of limited labeled data and high computational complexity should be one of the main recent focuses in the field of industrial soft sensing. In this article, a deep co-training PLS (deep CT-PLS) model is proposed to extend the ordinary PLS model to the semi-supervised deep form. While the deep model can efficiently extract inherent natures of process data, the co-training strategy makes lots of unlabeled data useful through a two-view cross training and annotation process. In this case, the performance restriction of the deep PLS model can be greatly relieved, with the incorporation of additional unlabeled data, while at the same time the designed model structure keeps in a low computational complexity. Based on the case study on a real industrial production process, the deep CT-PLS model can significantly improve the soft sensing performance. © 2013 IEEE.

关键词： Self-supervised learning

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Dislocation me diate d dynamic tension-compression asymmetry of a Ni_(2)CoFeV_(0.5)Mo_(0.2) medium entropy alloy

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Journal of Materials science & Technology 2023年第28期159卷 204-218页

作者： Ao Meng Xiang Chen Yazhou Guo Yiping Lu Yonghao Zhao Nano and Heterogeneous Materials Center School of Materials Science and EngineeringNanjing University of Science and TechnologyNanjing 210094China School of Aeronautics Northwestern Polytechnical UniversityXi’an 710072China Key Laboratory of Solidification Control and Digital Preparation Technology(Liaoning Province) School of Materials Science and EngineeringDalian University of TechnologyDalian 116024China

Although tension-compression(T-C)asymmetry in yield strength was rarely documented in coarse-grained face centered cubic(FCC)metals as critical resolved shear stress(CRSS)for dislocation slip differs little between tension and compression,the T-C asymmetry in strength,i.e.,higher strength when loaded in compression than in tension,was reported in some FCC high entropy alloys(HEAs)due to twinning and phase transitions activated at high strain regimes in *** this paper,we demonstrate a reversed and atypical tension-compression asymmetry(tensile strength markedly exceeds compressive strength)in a non-equiatomic FCC Ni_(2)CoFeV_(0.5)Mo_(0.2) medium entropy alloy(MEA)under dynamic loading,wherein dislocation slip governs dynamic deformation without twins or phase *** asymme-try can be primarily interpreted as higher CRSS and more hard slip modes(lower average Schmid factor)activated in grains under dynamic tension than ***,larger strain rate sensitivity in dy-namic tension overwhelmingly contributes to the higher flow stress,thanks to the occurrence of more immobile Lomer-locks,narrower spacing of planar slip bands and higher dislocation *** finding may provide some insights into designing MEAs/HEAs with desired properties under extreme conditions such as blast,impact and crash.

关键词： Medium entropy alloy Dynamic deformation Tension-compression asymmetry Slip trace analysis EBSD and TEM

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Microbes team with nanoscale zero-valent iron: A robust route for degradation of recalcitrant pollutants

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Journal of Environmental sciences 2022年第8期34卷 140-146页

作者： Nuo Liu Jing Liu Hong Wang Shaolin Li Wei-xian Zhang State Key Laboratory for Pollution Control and Resource Reuse School of Environmental Science and EngineeringTongji UniversityShanghai 200092China

Integrating nanoscale zero-valent iron(nZVI) with biological treatment processes holds the promise of inheriting significant advantages from both environmental nano-and biotechnologies. nZVI and microbes can perform in coalition in direct contact and act simultaneously, or be maintained in separate reactors and operated sequentially. Both modes can generate enhanced performance for wastewater treatment and environmental remediation. nZVI scavenges and eliminates toxic metals, and enhances biodegradability of some recalcitrant contaminants while bioprocesses serve to mineralize organic compounds and further remove impurities from wastewater. This has been demonstrated in a number of recent works that nZVI can substantially augment the performance of conventional biological treatment for wastewaters from textile and nonferrous metal industries. Our recent laboratory and field tests show that COD of the industrial effluents can be reduced to a record-low of 50 ppm. Recent literature on the theory and applications of the nZVI-bio system is highlighted in this mini review.

关键词： Environmental microbiology Nanoscale zero-valent iron(nZVI) Remediation Wastewater nZVI-bio system

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Asynchronous Output Regulation control for Continuous-Time Markovian Jump Systems with Colored-Noise

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Journal of Systems science & Complexity 2023年第4期36卷 1463-1479页

作者： WANG Shitong WU Zheng-Guang WU Zhaojing The Institute of Cyber-Systems and Control Zhejiang UniversityHangzhou 310027China The Institute of Intelligence Science and Engineering Shenzhen PolytechnicShenzhen 518055China The School of Mathematics and Informational Science Yantai UniversityYantai 264005China

The output regulation control problem for the Markovian jump systems with colored-noise is proposed in this *** the modes mismatch problem often exists in the Markovian switching system,the authors design a modes asynchronous controller with an internal model form,which can not only deal with external disturbance,but also reduce the influence of stochastic noise on the system,such that the mean square of the regulation error can be made arbitrarily small by turning the designed ***,a criterion of the m-th moment noise-to-state exponential stability is presented for continuous-time Markovian jump systems under the asynchronous control.

关键词： Asynchronous control colored-noise Markovian jump systems output regulation control

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Data-Driven Group Formation control of Cyber-Physical Systems via Distributed Cloud Computing

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 341-350页

作者： Ren, Hongru Long, Yinren Li, Hongyi Huang, Tingwen Guangdong University of Technology School of Automation Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control Guangzhou510006 China Southwest University College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

This paper investigates the group formation control problem for cyber-physical systems (CPSs) with random communication constraints. The distributed cloud computing system is constructed to divide agents into groups and establish communication between agents. A data-driven predictive control strategy is proposed by combining networked predictive control and model-free adaptive control method. The desired group formation control performance can be achieved and the three-channel random communication constraints of CPSs are actively compensated. Thisstrategy does not require the system model and relies solely on the system's I/O data for adaptive learning. Further analyses concludes the conditions for simultaneous reach stability and group formation of the closed-loop CPSs using the data-driven predictive control strategy. The effectiveness of the proposed strategy is validated by simulation results. © 2023 IEEE.

关键词： Distributed cloud

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A Novel Reliable Three-Way Multi-Classification Model under Intuitionistic Fuzzy Environment

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IEEE Transactions on Fuzzy Systems 2025年第6期33卷 1726-1739页

作者： Zhang, Libo Guo, Cong Wang, Tianxing Liu, Dun Li, Huaxiong Southwest University School of Artificial Intelligence Chongqing400715 China Nanjing University Department of Control Science and Intelligence Engineering School of Management and Engineering China Nanjing Audit University School of Business Nanjing211815 China Southwest Jiaotong University School of Economics and Management Chengdu610031 China

Three-way decision (3WD) is a trisecting-and-acting model that is in line with the decision-making logic of human beings. Bayesian risk decision (BRD) theory is a statistical method that optimizes decisions by quantifying and minimizing uncertainty-induced losses. As a typical BRD method, cost-sensitive 3WD aims to reduce decision risks and enhance decision credibility by incorporating an uncertainty decision item, known as boundary decision. However, in most existing 3WD-based multi-classification models, there is redundancy in both the decision item set and cost matrix, which may lead to decision conflict, ambiguity, and redundancy. Moreover, determining the cost matrix under the intuitionistic fuzzy (IF) environment remains a challenging task. To address these problems, we propose a reliable three-way multi-classification (R3WMC) model under the IF environment. Specifically, an IF cost transformation mechanism is proposed, and a novel boundary decision associated with the corresponding compact cost matrix is established to achieve a conflict-free decision set. Motivated by confidence-based classifiers, we propose an effective decision rule which is time-efficient and able to solve decision conflicts. We also demonstrate that our model is an enhanced BRD model, contributing to a solid theoretical basis for the proposed R3WMC method. Finally, a comparative analysis of multiple models across various datasets validates the reliability and superior performance of our model. © 1993-2012 IEEE.

关键词： Redundancy

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Multiscale Convolution and Attention based Denoising Autoencoder for Motion Artifact Removal in ECG Signals 24

Multiscale Convolution and Attention based Denoising Autoenc...

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7th International Conference on Image and Graphics Processing, ICIGP 2024

作者： Chen, Meng Li, Yongjian Zhang, Xiuxin Gao, Jiahui Sun, Yiheng Shi, Wenzhuo Wei, Shoushui School of Control Science and Engineering Shandong University Jinan China Institute of Marine Science and Technology Shandong University Qingdao China

ISBN: (纸本)9798400716720

Motion interference is a major issue in the process of acquiring electrocardiogram (ECG) signals. It introduces noise and artifacts into the ECG signal, causing signal distortion and deformation, thus affecting the accuracy of subsequent analyses and diagnoses. This study introduces a deep learning-based model for motion artifact removal in ECG signals, namely MSCT, which combines Multi-Scale Convolution with Transformer encoder to extract the local and global features of ECG signals and motion artifacts. The comparative experiments are conducted on the MIT-BIH arrythmia database which is contaminated with the motion artifact from MIT-BIH noise stress test database. Our MSCT model shows significantly higher Cosine Similarity and output signal-to-noise ratio (SNR), and lower Root Mean Square Error (RMSE) and Multi-Scale Entropy Based Weighted Percentage Root-mean-square Difference (MSEWPRD), compared with that of the three previous methods on the motion artifact-corrupted data with various input SNR (-6 dB, 0 dB and 6 dB). Especially, when the input SNR is -6 dB, the proposed MSCT achieves the results 6.15±2.83%, 0.25±0.14 mV, 0.91±0.08, 9.20±3.74 dB for MSEWPRD, RMSE, Cosine Similarity and SNR, respectively. All the four metrics, including quantitative and qualitative metrics, have demonstrated the superiority and robust of the MSCT model proposed in this study. Furthermore, the time-domain and frequency-domain plots of a randomly selected denoising segment at -6 dB effectively showcase the superior performance of our denoising method. © 2024 ACM.

关键词： Signal to noise ratio

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An Intelligent PID controller Based on Ultra-Local Model for Trajectory Tracking in Lower Limb Exoskeleton Robot 8

An Intelligent PID Controller Based on Ultra-Local Model for...

引用

8th International Conference on Robotics and Automation engineering, ICRAE 2023

作者： Alsaied, Alrawda Li, Ke Institute of Intelligent Medical Engineering School of Control Science and Engineering Shandong University Jinan China

ISBN: (纸本)9798350327656

Aiming to enhance trajectory tracking for lower limb exoskeleton (LLEX) robot, this study proposes an intelligent proportional, integral, derivative (iPID) controller based ultra-local model (ULM) to achieve precise trajectory tracking for LLEX, moreover to optimize the controller performance, the particle swarm optimization (PSO) is utilized for controller gains tuning. The LLEX robot with four degrees of freedom (4-DOF) is created using SOLIDWORKS software and then transferred to MATLAB/SIMULINK for simulation and control purposes. Compared with the conventional proportional, integral, derivative (PID) controller, the iPID controller reduced trajectory tracking errors while achieving faster time convergence of less than 0.0001 and 0.00012 seconds for the hip and knee joints, respectively. Specifically, the estimated root mean square (RMS) of the tracking error for the hip and knee joints were 0.0012 and 0.013, respectively, indicating smaller error values achieved by the iPID based ULM model. This performance was further confirmed using integral time absolute error (ITAE), and integral absolute of the error (IAE) metrics. Based on the results obtained, it can be concluded that the iPID-based ULM model outperformed the conventional PID controller and has the potential to improve trajectory tracking for the LLEX robot. © 2023 IEEE.

关键词： Three term control systems

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