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2024 China Automation Congress, CAC 2024

作者： Cai, Mingxuan Liu, Qi Liu, Yuxiang Yang, Bo Department of Automation Shanghai Jiao Tong University shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9798350368604

Accurate fault diagnosis plays a key role in the safe and efficient operation of electrical motors. Aiming at multi-class motor fault diagnosis under complex working conditions, a signal fusion-based fault diagnosis framework is proposed. Measurement parameters from different sensors are first transformed by wavelet denoising and fast Fourier transform (FFT). Then, multiple parameters spectrum and working condition information are integrated and converted into high-dimension matrices. Finally, a channel-attention-based convolution neural network (CA-CNN) is proposed to extract fault patterns from a reconstructed matrix and output diagnosis results. Experiment results on both simulated and experimental datasets verify the effectiveness of the proposed method. © 2024 IEEE.

关键词： Convolutional neural networks

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State-of-Charge Balancing control for Battery Energy Storage systems of Near-Space Vehicles with an Electric-Thermal-Aging Coupling Model 42

State-of-Charge Balancing Control for Battery Energy Storage...

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42nd Chinese control Conference, CCC 2023

作者： Zuo, Zhiqiang Du, Yunhao Wang, Yijing Zhang, Zhicheng Li, Peng School of Electrical and Information Engineering Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System Tianjin University Tianjin300072 China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai200240 China

ISBN: (纸本)9789887581543

State-of-charge (SoC) balancing is crucial for improving the efficiency and lifetime of the battery energy storage system in near-space vehicles. In this paper, the SoC balancing control problem is investigated by a coupling battery model with electric-thermal-aging dynamics. Firstly, a system identification experiment is carried out to obtain the internal parameters of the battery. Secondly, a weight optimization index is designed by integrating the balancing speed and battery state-of-health (SoH). Then, a receding horizon control algorithm is proposed to realize multi-unit battery SoC balancing with partial swarm optimization (PSO). Finally, the effectiveness of the proposed strategy is verified by simulation results. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Battery management systems

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Online Identification of Boost Converter Components with Multiple Updating RLS Algorithm 42

Online Identification of Boost Converter Components with Mul...

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42nd Chinese control Conference, CCC 2023

作者： Zhou, Tong Zhang, Zhicheng Zuo, Zhiqiang Wang, Yijing Li, Peng School of Electrical and Information Engineering Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System Tianjin University Tianjin300072 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China

ISBN: (纸本)9789887581543

In this paper, component parameters of the boost converter are identified online using a multiple updating recursive least squares (MURLS) algorithm. The component parameters, such as resistance, inductor inductance and capacitor capacitance, are obtained directly through the identification procedure rather than transfer function coefficients. The MURLS algorithm is applied to improve the rapidity of system identification compared to the traditional recursive least squares (RLS) algorithm, which is verified by a comparative simulation between MURLS and RLS and the simulation of a load-switching scenario. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Parameter estimation

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An Error Correcting Output Code Algorithm Based on Hierarchical Clustering for Lithology Identification 43

An Error Correcting Output Code Algorithm Based on Hierarchi...

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43rd Chinese control Conference, CCC 2024

作者： Li, Ao Chen, Xi Hunan University of Technology School of Rail Transit Zhuzhou412007 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China

ISBN: (纸本)9789887581581

Lithology identification serves as a crucial foundation for the precise exploration and safe exploitation of geological resources. However, challenges arise from the inherent imbalance and overlap present in logging data, making the accurate identification of lithology a complex task. Addressing these challenges effectively, this paper introduces a novel algorithm, the Error Correcting Output Code algorithm based on Hierarchical Clustering(HC-ECOC). The proposed algorithm initially formulates a coding matrix using distance and overlap measures between class clusters. Subsequently, it extends the length of the coding matrix by considering both the accuracy of the integrated classifier and the diversity among base classifiers. To evaluate the efficacy of the proposed method, comparisons were conducted with alternative algorithms using UCI datasets and an authentic lithology dataset. The results unequivocally demonstrate the superior performance of our algorithm. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Lithology

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OPTIMISATION OF SELF-PUNCHING RIVETING PROCESS PARAMETERS FOR HIGH-SPEED RAIL TOP PLATES

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UPB Scientific Bulletin, Series D: Mechanical Engineering 2022年第4期84卷 197-210页

作者： Zheng, Qingchun Guo, Chengmin Zhu, Peihao Liu, Jiaqi Ma, Wenpeng Zhao, Shuoshuo Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China China

To improve the quality of self-pierce riveted joints, this paper uses a multi-objective optimisation method that incorporates a quadratic polynomial response surface model and the NSGA-II algorithm to optimise the parameters of the self-pierce riveting process. First, we carry out finite element simulations of the self-pierce riveted joint to determine the main factors affecting the quality of the joint forming through sensitivity analysis, and specify the optimum range of each parameter. Second, we use central composite design experiments to establish a quadratic polynomial response surface model. Third, we optimise the multi-objective model by using the NSGA-II algorithm and obtain the Pareto optimal solution. Finally, we use cross-sectional measurement and mechanical properties experiments to verify the feasibility of optimising the self-pierce riveted joint. The experimental results show that the bottom thickness increases by 24.7%, the undercut amount by 14.5%, and the tensile shear force by 27% compared to the pre-optimisation period. The quality of the riveted joints was significantly improved after optimisation, laying a good foundation for improving the safety of high-speed railway bodies. © 2022, Politechnica University of Bucharest. All rights reserved.

关键词： Multiobjective optimization

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Network Intrusion Detection Model Based on Space-time Fusion Features and Attention Mechanism 33

Network Intrusion Detection Model Based on Space-time Fusion...

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第33届中国控制与决策会议

作者： Yali Wu Liting Huang Jinjin Qi Xiaoxiao Quan School of Automation and Information Engineering Xi'an University of Technology Shaanxi ProvinceKeyLaboratory of Complex System Control and Intelligent Information Processing Shaanxi Province Key Laboratory of Complex System Control and Intelligent Information Processing

ISBN: (纸本)9781665431293

To solve the problem of low performance of network intrusion detection,a deep learning intrusion detection model based on space-time fusion features and attention mechanism—CLT-net is *** this model,space-time fusion features are obtained by integrating convolutional neural network and long short-time memory network,and attention module is added to calculate the importance of the input features,and softmax function is used for *** a large number of simulation experiments on NSL-KDD data sets,CLT-net has significantly improved the convergence of the training set and the accuracy of the test *** with the traditional CNN model with similar structure and the space-time fusion CLSTM the accuracy of the model increased by 11.8% and 10.9%*** shows that this model has great potential in the application field of network intrusion detection.

关键词： Network Intrusion Detection Convolution Neural Network Long Short-term Memory Network Attention Mechanism CLT-net

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A copula-based reliability model for phased mission systems with dependent components (vol 39, pg 1533, 2023)

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2023年第7期39卷 3179-3179页

作者： Li, X. Zhan, K. Xiong, X. Vu, T. Q. Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan University of Technology Taiyuan China School of Media Design College of Technology and Design University of Economics Ho Chi Minh City Vietnam

Recently, researches on the reliability analysis of phased mission systems (PMSs) are very popular. And most of the existing research on PMSs assumed that all the components are statistically independent on each other, which is not meet the engineering reality. The system structure changes from phase to phase in the PMSs, so the dependency among components may also change in different phases. Meanwhile, the well-known phase dependency is also existing in the system reliability modeling. To model the varying dependency among components and phase dependency at the same time, a copula-based method is proposed for the reliability analysis of PMS with correlated components. Firstly, the system reliability of multi-state PMS with dependent components is evaluated by different copula functions. Then, multiple indicators are used to select the optimal copula functions for different phases. And an engineering case is illustrated to show the proposed copula-based method.

关键词： copula dependent components modular method phased mission system

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control, Modeling and Stability Analysis of DRU-based Grid-forming Converter

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Chinese Journal of Electrical Engineering 2025年第1期11卷 83-92页

作者： Wencong Wu Minzhi Wang Liang Tu Haiqing Cai Guanglei Yan Xiaohui Qu Haohan Gu Wei Chen State Key Laboratory of HVDC China Southern Power GridGuangzhou 510663China National Energy Power Grid Technology R&D Center Guangzhou 510663China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power Systems China Southern Power GridGuangzhou 510663China CSG Key Laboratory for Power System Simulation China Southern Power GridGuangzhou 510663China School of Electrical Engineering Southeast UniversityNanjing 210096China

Diode rectifier unit(DRU)-based high-voltage direct current(HVDC)transmissionsystems areeffectivein achieving the stableandeconomical operation of offshore *** theuncontrollable characteristicsof DRUs,a grid-forming(GFM)strategy forwind-turbine converters isnecessary to support offshore AC voltageand ***,the active power-synchronization control in traditional GFM converters is unsuitable for DRU-based GFM ***,the stability issue for DRU-based HVDC systems involving DRU-based GFM and grid-following(GFL)converters has not yet been *** solve these issues,this study begins with the characteristics of a DRU-based HVDC system and presents a control scheme for DRU-based GFM converters for power ***,the dq-frame impedance model of the DRU-based GFM converteris proposed for the stability analysis of the entire HVDC ***,a simulation platform is built to verify the model accuracy and system stability.

关键词： Diode rectifier unit offshore wind-powergeneration grid-forming converter impedance model stabilityanalysis

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Prediction of NO_(x)concentration using modular long short-term memory neural network for municipal solid waste incineration

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Chinese Journal of Chemical Engineering 2023年第4期56卷 46-57页

作者： Haoshan Duan Xi Meng Jian Tang Junfei Qiao Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Laboratory of Smart Environmental Protection Beijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124China Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing 100124China

Air pollution control poses a major problem in the implementation of municipal solid waste incineration(MSWI).Accurate prediction of nitrogen oxides(NO_(x))concentration plays an important role in efficient NO_(x)emission *** this study,a modular long short-term memory(M-LSTM)network is developed to design an efficient prediction model for NO_(x)***,the fuzzy C means(FCM)algorithm is utilized to divide the task into several sub-tasks,aiming to realize the divide-and-conquer ability for complex ***,long short-term memory(LSTM)neural networks are applied to tackle corresponding sub-tasks,which can improve the prediction accuracy of the ***,a cooperative decision strategy is designed to guarantee the generalization performance during the testing or application ***,after being evaluated by a benchmark simulation,the proposed method is applied to a real MSWI *** the experimental results demonstrate the considerable prediction ability of the M-LSTM network.

关键词： Municipal solid waste incineration NO_(x)concentration prediction Modular neural network Model

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Designing high-Q Fano resonance structures for enhancing fluorescence in all-dielectric metasurfaces

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Optics Letters 2025年第9期50卷 2930-2933页

作者： He, Shuai Wang, Zhiyuan Han, Bin Li, Shan Yao, Zhenyu Guo, Yulong Chen, Zhihui Wang, Yang Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education College of Physics and Optoelectronics Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Chemical Biology and Molecular Engineering of Ministry of Education Institute of Biotechnology Shanxi University Taiyuan030006 China

Highly sensitive fluorescence detection technology is crucial for biological sensing, imaging, and photonic technologies. The fluorescence enhancement effect with the same enhancement factor at multi-wavelengths is important for highly sensitive detection. However, achieving multiwavelength fluorescence enhancement remains challenging. Here, we introduce an all-dielectric metasurface composed of symmetry-broken silicon nanocylinders integrated with graphene, which is designed to significantly enhance and dynamically modulate the fluorescence emission of a single quantum dot. By fine-tuning structural parameters and adjusting the graphene Fermi level, we achieve a tunable high-Q Fano resonance, leading to a nearly 4000-fold fluorescence enhancement. Structural parameter adjustments enable consistent fluorescence enhancement across different wavelengths. Furthermore, at specific wavelengths, fluorescence enhancement can be similarly adjusted. The proposed structure can greatly expand the design strategy of heterogeneous all-dielectric metasurfaces with the same enhancement factor at multi-wavelengths. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Magnetic resonance

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