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International Conference on Power Electronics and Motion control (IPEMC)

作者： Yang Zhou Jin Zhao Yang Liu Lijun Sun Ministry of Education School of Artificial Intelligence and Automation Key Laboratory of Imaging Processing and Intelligence Control Huazhong University of Science and Technology Wuhan China China Electronic Product Reliability and Environmental Testing Research Institute Guangdong Provincial Key Laboratory of Electronic Information Products Reliability Technology Guangzhou China

ISBN: (数字)9798350351330

ISBN: (纸本)9798350351347

This paper studies the power switch open-circuit(OC) fault in two-level three-phase (2L3P) voltage source inverter (VSI). According to the topological symmetry of the inverter, when it works in healthy conditions, its phase currents are standard sine waves with only 120-degree phase difference, and the similarity between phase currents is high. When OC fault occurs, the current waveforms of both normal and fault phases will be distorted, the similarity will be changed. Therefore, this paper introduces the Gram matrix in image processing to measure the similarity and proposes a fast and computationally efficient diagnostic method based on the eigenvector of Gram-Matrix. Physical experiment shows that both single-switch fault and multiple-switch fault on the same leg can be effectively detected and located.

关键词： Legged locomotion Fault diagnosis Voltage source inverters Switches Computational efficiency Maintenance Velocity measurement

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Cascading Failures in Interdependent Directed Networks Under Localized Attacks

SSRN

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SSRN 2023年

作者： Lv, Mengyu Pan, Linqiang Liu, Xueming Key Laboratory of Image Information Processing and Intelligent Control Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

Many real-world systems interact with one another through dependency links, which reduces the system robustness. Most previous studies on the robustness of interdependent networks focus on undirected networks, and the related studies on directed networks are limited to random or targeted attacks. However, some failure scenarios can not be described by these two kinds of attacks, such as earthquakes, floods, and epidemics, where systems are attacked in a local range. In this work, we develop a theoretical framework for analyzing the robustness of interdependent directed networks under localized attacks. We find that for degree homogeneous networks, network robustness under localized attacks is similar to that under random attacks. There are four phase transitions in the phase diagram of the network, and a four-phase transition point and a two-phase transition point are found. For degree heterogeneous networks, localized attacks are more likely to lead to collapse than random attacks. As the coupling strength between networks increases, the interdependent networks first show a second order phase transition, and then a hybrid phase transition, and a first order phase transition at last. Furthermore, as the degree heterogeneity decreases, the robustness of networks first increases and then decreases, showing a local robustness maximum. The findings could help understand network robustness and enable better design of robust interdependent systems. © 2023, The Authors. All rights reserved.

关键词： Phase diagrams

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A novel method of constructing high-dimensional digital chaotic systems on finite-state automata

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Chinese Physics B 2020年第9期29卷 233-243页

作者： Jun Zheng Han-Ping Hu School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Image Information Processing and Intelligent Control Ministry of EducationWuhan 430074China

When chaotic systems are implemented on finite precision machines, it will lead to the problem of dynamical degradation. Aiming at this problem, most previous related works have been proposed to improve the dynamical degradation of low-dimensional chaotic maps. This paper presents a novel method to construct high-dimensional digital chaotic systems in the domain of finite computing precision. The model is proposed by coupling a high-dimensional digital system with a continuous chaotic system. A rigorous proof is given that the controlled digital system is chaotic in the sense of Devaney's definition of chaos. Numerical experimental results for different high-dimensional digital systems indicate that the proposed method can overcome the degradation problem and construct high-dimensional digital chaos with complicated dynamical properties. Based on the construction method, a kind of pseudorandom number generator (PRNG) is also proposed as an application.

关键词： high-dimensional digital chaotic system dynamical degradation anti-control pseudorandom number generator

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A Method for Detecting the Reducers’ Grease Failure Based on Vibration Signal Analysis

A Method for Detecting the Reducers’ Grease Failure Based o...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Ditong Wang Hong Zhang Ying Zheng Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Wuhan China National Key Laboratory of Science and Technology on Multispectral Information Processing and School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

Real time detection of the lubricating grease’s effectiveness is the best method to solve wear faults, which can achieve the purpose of predictive maintenance and improve the service life of mechanical equipment. A new online detection method for gearbox lubricating grease failure is proposed to address the issues about long offline detection cycles and high labor costs associated with phased sampling analysis. Compared with online detection methods such as inductive and photoelectric, our method is difficult to detect the specific physicochemical parameters of the grease. However, it has the advantage of simple operation without touching the grease or changing the structure of the tested object. It only needs to attach the vibration sensor table to the mechanical arm reducer to measure and analyze the vibration signal. This article briefly analyzes the impact of grease failure on vibration signals, and then we introduce a novel approach that uses support vector machines along with feature extraction through wavelet packet decomposition for our detection methodology. Next transfer the commonly used spectral image recognition algorithms for temporal signal classification to achieve failed grease detection as a baseline and compare with our traditional machine learning methods. The experimental findings indicate that the recognition accuracy of both methods are above 90%, with traditional machine learning performing better on this task.

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A Reinforcement Learning PID Compound Protocol Design Method for Ultrasonic Motor control*

A Reinforcement Learning PID Compound Protocol Design Method...

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control Conference (ANZCC), Australian and New Zealand

作者： Qiang Wang Yunlong Yang Xiongjun Wu Jinqi Wang Guanyu Liu The 802 Institute of Shanghai Academy of Space Flight Technology The Eighth Academy of China Aerospace Science and Technology Corporation National Defense Key Laboratory of Science and Technology on Electromagnetic Scattering Shanghai Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China

Aiming at overcoming drawback brought by the nonlinear and time-varying characteristics of ultrasonic motor, this paper proposed a PID compound controller of ultrasonic motor based on reinforcement learning to improve the dynamic performance and adaptability of the system. In practice, the output control value of the reinforcement learning controller is observed in general to be uncertain, and sometimes even destroys the learning strategy, which makes the system unstable. To overcome this limitation, this paper ultilized the idea of expert rules to improve the controller performance, and the Lyapunov stability is also guaranteed. The simulation results show that the improved composite control algorithm can significantly improve the learning efficiency, and meanwhile shorten the number of learning iterations. At the same time, the composite controller based on expert rules significantly improves the control performance of the system, in terms of reducing the rise time and adjustment time of the system, as well as reducing the steady-state error of the system.

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Parameter training methods for convolutional neural networks with adaptive adjustment method based on Caputo fractional-order differences

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Chaos, Solitons and Fractals 2025年 199卷

作者： Zhao, Haiming Yang, Honggang Chen, Jiejie Jiang, Ping Zeng, Zhigang School of Computer and Information Engineering Hubei Normal University Huangshi435000 China School of Automation Wuhan University of Technology Wuhan430000 China School of Computer Hubei Polytechnic University Huangshi435000 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430000 China Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China Wuhan430000 China

As deep learning technologies continue to permeate various sectors, optimization algorithms have become increasingly crucial in neural network training. This paper introduces two adaptive momentum algorithms based on Grünwald–Letnikov and Caputo fractional-order differences—Fractional Order Adagrad (FAdagrad) and Fractional Order Adam (FAdam)—to update parameters more flexibly by adjusting momentum information. Commencing from the definitions of fractional derivatives, we propose integrating fractional-order differences with gradient algorithms in convolutional neural networks (CNNs). These adaptive momentum algorithms, leveraging Grünwald–Letnikov and Caputo fractional-order differences, offer enhanced flexibility, thereby accelerating convergence. Our nonlinear parameter tuning method for CNNs demonstrates superior performance compared to traditional integer-order momentum algorithms and the standard Adam algorithm. Experimental results on the BraTS2021 dataset and CIFAR-100 dataset reveal that the proposed fractional-order optimization algorithms significantly outperform their integer-order counterparts in model optimization. They not only expedite convergence but also improve the accuracy of image recognition and segmentation. © 2025 Elsevier Ltd

关键词： Convolutional neural networks

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Current-Constrained Finite-Time control Scheme for Speed Regulation of PMSM Systems With Unmatched Disturbances

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IEEE Transactions on automation Science and Engineering 2025年 22卷 16877-16887页

作者： Huiming Wang Zhize Zhang Yingjie Luo Junxiao Wang Jiankun Sun Yunda Yan Xianlun Tang Chongqing Key Laboratory of Complex Systems and Autonomous Control Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China College of Information Engineering Zhejiang University of Technology Hangzhou China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan China Department of Computer Science University College London London U.K.

The article investigates the speed regulation of permanent magnet synchronous motor (PMSM) systems. Existing control methods of the non-cascade structure suffer from the drawbacks of unsatisfactory anti-disturbance performance and slow convergence rate when the system is affected by disturbances, especially unmatched disturbances. Meanwhile, the requirements of current constraint and fast dynamics cannot be effectively balanced in the single-loop structure of speed and current using traditional control methods such as the PID controller. Because large transient currents induced by fast dynamics may damage the hardware of the system. Therefore, a current-constrained finite-time control approach is proposed. Specifically, a robust finite-time control scheme is developed with the assistance of the improved finite-time observer technique. The proposed method is capable of actively suppressing both matched and unmatched disturbances in non-cascade control systems. Simultaneously, an effective penalty mechanism is established to incorporate a specific gain function into the designed controller. This approach restricts the q-axis current to a predefined safe range without solving an optimization problem. Finally, comparative experiment results indicate that the newly proposed finite-time control method outperforms the baseline control methods in terms of disturbance rejection, convergence rate, and current constraint. Note to Practitioners—This paper addresses practical challenges in controlling permanent magnet synchronous motor (PMSM) systems, such as ensuring robust disturbance rejection while limiting transient currents to protect the hardware. Traditional methods often fail to balance fast dynamics with current constraints, leading to inefficiencies and potential damage. To tackle these issues, the proposed finite-time control approach introduces a practical solution that achieves robust disturbance rejection while ensuring that transient currents remain within safe o

关键词： Convergence Observers control systems automation Transient analysis Asymptotic stability Stability analysis Regulation Permanent magnet motors Optimization

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A Single-Cell Clustering Algorithm Based on Structure Perturbation Non-Negative Matrix Factorization

A Single-Cell Clustering Algorithm Based on Structure Pertur...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Jiang, Hanjing Wang, Meineng Zhang, Luping Huang, Yu-An Li, Yangyuan Huang, Yabing Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Hubei Wuhan430074 China China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China Yichun University School of Mathematics and Computer Science Yichun336000 China East China University of Technology School of Information Engineering Nanchang330013 China Northwestern Polytechnical University School of Computer Science Xi'an710129 China Renmin Hospital Wuhan University Department of Pathology Wuhan430060 China

ISBN: (纸本)9798350386226

The advent of single-cell RNA sequencing (scRNA-seq) has facilitated the acquisition of high-resolution data regarding cell heterogeneity across various tissues. A fundamental and critical step in the analysis of scRNA-seq data is cell type identification. An appropriate graph construction strategy can reflect the topological structure between cells and allows for detailed exploration of the relationships between cells and genes. Given the sparsity of scRNA-seq data and the pronounced noise generated by shallow sequencing, choosing an appropriate graph construction strategy has become a significant challenge. In this paper, we propose a single-cell clustering method, named Sc-PNNMF, based on structural perturbation of nonnegative matrix factorization. Sc-PNNMF employs a structural perturbation algorithm to optimize the gene expression matrix. The optimized matrix guides the matrix factorization process, effectively overcoming the impact of data sparsity and significant noise in gene expression data on the graph construction strategy. We compared the clustering abilities of Sc-PNNMF with five other methods on ten real datasets. © 2024 IEEE.

关键词： Non-negative matrix factorization

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Incipient Gradual Fault Detection via Transformed Component and Dissimilarity Analysis

Incipient Gradual Fault Detection via Transformed Component ...

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industrial Cyber-Physical Systems (ICPS)

作者： Lingxia Mu Wenzhe Sun Youmin Zhang Nan Feng Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Department of Mechanical Industrial & Aerospace Engineering Concordia University Montreal Canada

This paper proposes a novel method named recursive transformed component dissimilarity analysis (RTCDA) combining dissimilarity analysis algorithm and traditional sliding window technique for detecting incipient gradual faults. Firstly, orthogonal transformed components (TCs) corresponding to a new set of data in the sliding window are obtained using a recursive algorithm based on rank-one modification. Then, to quantitatively estimate the distribution difference of TCs, the dissimilarity index between TCs of the new dataset and that of referenced dataset is calculated. The distribution of TCs changes more dramatically than that of original data after a small quantitative bias in the original data. Compared with original data, TCs are more sensitive to tiny quantitative variation of dataset. Finally, case studies on a numerical example and a practical industrial fed-batch penicillin fermentation process are carried out to evaluate the performance of RTCDA method for incipient gradual fault detection.

关键词： Fault detection Cyber-physical systems Indexes Data mining

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Streaming-Data-Driven Multi-Objective Optimal Operation for Ammonia-based Integrated Energy Chemical Systems:An Event-Triggered Scheme

Streaming-Data-Driven Multi-Objective Optimal Operation for ...

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第42届中国控制会议

作者： Longyan Li Chao Ning Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationEast China University of Science and Technology

As an important component in the industry sector,the integrated energy chemical system is inescapably responsible for carbon reduction in addition to economic and reliability *** propose a novel streaming-data-driven multi-objective optimal operation for ammonia-based integrated energy chemical systems（AIECS）.In the day ahead scheduling,a detailed mathematical model for AIECS is presented,upon which a multi-objective optimization considering cost,CO emission and reliability is *** the intra-day operation,an event-triggered mechanism is firstly developed in the real-time operation of AIECS,and it enables the AIECS to be risk-aware just in *** monitoring the difference between the predicted state of charge and the capacity limitations of battery,the AIECS identifies whether it is in hazardous ***,the intelligent switch between certainty equivalence model predictive control and online-learning based distributionally robust model predictive control is proposed,such that their respective advantages are *** studies demonstrate that the proposed approach guarantees the system safety,and brings good revenue.

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