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26th International Conference on Industrial Technology, ICIT 2025

作者： Yi, Kang Liu, Xin Hu, Wenkai Li, Yupeng School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9798331521950

Accurately and promptly detecting the pipeline anomaly is crucial to the safe operation of pipeline systems, while a difficulty lies in that many existing methods require massive data for training models. However, pipelines are running under normal state for the most of the time, and labeled pipeline anomaly data is usually scarce. Among the commonly used sensors, vibration sensors are widely utilized in pipeline detection because of their advantages such as easy installation and high sensitivity. However, the vibration signal shows non-stationary characteristics when anomalies occur, and are contaminated by noises, making it difficult to represent the actual state with features extracted from either the time or frequency domain. Accordingly, this paper proposes a pipeline anomaly detection method based on the KPCA (kernel principal component analysis) and cosine distance prototypical network. First, features are extracted from original signals;then, the feature dimension is reduced by KPCA;last, the cosine distance is introduced to the prototypical network for anomaly detection. The effectiveness of the proposed method is demonstrated by case studies involving experimental data. © 2025 IEEE.

关键词： Frequency domain analysis

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A multiple-time-scale control strategy for burden distribution and blast supply of a BF

A multiple-time-scale control strategy for burden distributi...

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Chinese control and Decision Conference, CCDC

作者： Yunpeng Guo Jianqi An Guoyu Zhao Min Wu Qingyi Wang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

The operations of blast furnaces (BFs) are very vital for the long-term stability of the iron making process. The burden distribution and blast supply are the two major operation systems of BFs. At present, the researches are lack effective adjustment methods for the BF operations combined with burden distribution and blast supply. The burden distribution affects the iron making process on a long time scale, while the blast supply affects the iron making process on a short time scale. This paper presents a multi-time sampling-data adjustment strategy for the BF operations aiming at optimizing GUR on multiple time scales. First, this paper analyzes the relationship between the gas utilization ratio (GUR) and the burden distribution, the blast supply on multiple time scales. Then, this paper establishes a prediction model of GUR on the long time scale and the short time scale based on autoregressive integrated moving average (ARIMA). Next, this paper provides a control strategy of burden distribution and a control strategy of blast supply by a prediction model based on support vector regression (SVR). Finally, this paper makes experiments and applies this method in a real-world BF. The analysis of the results shows the control strategy of the BF operations provides a good guide on making a suitable decision for burden distribution and blast supply.

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Adaptive fixed-time anti-synchronization of neural networks: Potential application in active noise control

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IFAC-PapersOnLine 2023年第2期56卷 6400-6405页

作者： Haoyu Li Leimin Wang School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan 430074 China

This paper discusses active noise control (ANC) through the dynamics analysis of neural networks. By designing an adaptive controller on the controlled neural network to generate a signal opposite to the noise source, the latter is considered neural networks with unknown parameters. To leave only the propagating signal sources in the channel, the controlled neural network is achieved the anti-synchronization with the unknown parameter neural network (fitted to noise source). Considering different noise environments, fixed-time control is introduced in ANC, i.e., the fixed-time stability of the error system is proved by the Lyapunov method. Since NNs-based ANC can directly obtain the anti-signal, our control scheme can significantly simplify the existing ANC scheme. Finally, a simulation example is provided to demonstrate the feasibility of the results in this paper.

关键词： Active noise control adaptive control fixed-time anti-synchronization Lyapunov methods

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Rebalancing Multi-Label Class-Incremental Learning 39

Rebalancing Multi-Label Class-Incremental Learning

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Du, Kaile Zhou, Yifan Lyu, Fan Li, Yuyang Xie, Junzhou Shen, Yixi Hu, Fuyuan Liu, Guangcan School of Automation Southeast University China Key Laboratory of Measurement and Control of CSE Ministry of Education China NLPR MAIS CASIA China School of Electronic and Information Engineering Suzhou University of Science and Technology China Suzhou Key Laboratory of Intelligent Low Carbon Technology Application China Jiangsu Industrial Intelligent Low Carbon Technology Engineering Center China

ISBN: (纸本)157735897X

Multi-label class-incremental learning (MLCIL) is essential for real-world multi-label applications, allowing models to learn new labels while retaining previously learned knowledge continuously. However, recent MLCIL approaches can only achieve suboptimal performance due to the oversight of the positive-negative imbalance problem, which manifests at both the label and loss levels because of the task-level partial label issue. The imbalance at the label level arises from the substantial absence of negative labels, while the imbalance at the loss level stems from the asymmetric contributions of the positive and negative loss parts to the optimization. To address the issue above, we propose a Rebalance framework for both the Loss and Label levels (RebLL), which integrates two key modules: asymmetric knowledge distillation (AKD) and online relabeling (OR). AKD is proposed to rebalance at the loss level by emphasizing the negative label learning in classification loss and down-weighting the contribution of overconfident predictions in distillation loss. OR is designed for label rebalance, which restores the original class distribution in memory by online relabeling the missing classes. Our comprehensive experiments on the PASCAL VOC and MS-COCO datasets demonstrate that this rebalancing strategy significantly improves performance, achieving new state-of-the-art results even with a vanilla CNN backbone. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Multi-task learning

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Impulsive Formation control of Nonlinear Leader-Following Multi-agent Systems with Input Saturation*

Impulsive Formation Control of Nonlinear Leader-Following Mu...

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Industrial Cyber-Physical Systems (ICPS)

作者： Ni Zhang Xiaowei Jiang Xianhe Zhang Le You School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China School of Electrical Engineering and Automation Hubei Normal University Huangshi China

Leader-following formation analysis problem for a second-order nonlinear multi-agent system(MAS) with input saturation is investigated in this paper. And the impulsive formation control algorithm is introduced in the designed protocol which only works at the impulse times. Owing to the real-world limited communication channels, input saturation is considered in the impulsive controller. Furthermore, based on Lyapunov stability theories, Kronecker properties, eigenvalue and so on, some sufficient conditions that guarantee the leader-following consensus of MAS are obtained. Lastly, several simulations are worked out to verify the correctness and effectiveness of the theoretical results.

关键词： Sufficient conditions Protocols Simulation Communication channels Cyber-physical systems control systems Formation control

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An Extended-Matrix-Separation-Based Summation Inequality on Stability of Discrete-Time Delayed Lur'e System*

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IFAC-PapersOnLine 2023年第2期56卷 6976-6981页

作者： Ke-You Xie Cui Zhu Wen-Hu Chen Chuan-Ke Zhang Yong He School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan 430074 China

This paper investigates the stability of discrete-time Lur'e systems with a time-varying delay. Firstly, an extended-matrix-separated-based summation inequality is established to estimate the summation term containing the system state and the forward difference of the state. Then, an improved delay-product-type Lyapunov-Krasovskii functional (LKF) is proposed, which involves the characteristics of matrices refined and the information of delay squared. By using a quadratic function negative transformation lemma based on matrix-injection, a delay-dependent stability criterion is obtained. Finally, a numerical example demonstrates the advantages of the proposed criterion.

关键词： Discrete-time system Lur'e system stability time-varying delay summation inequality delay-product-type LKF

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Knowledge transfer for tuning microwave filters with unknown individual differences

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IFAC-PapersOnLine 2023年第2期56卷 10180-10185页

作者： Linwei Guo Weihua Cao Leyu Bi Wenkai Hu Yan Yuan Min Wu School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan 430074 China

Improving the production efficiency of Microwave Filters (MFs) is of great practical significance for constructing modern communication systems. The characteristics of MFs are with various degrees of individual differences caused by materials. The most tuning techniques have to start from scratch for tuning different MFs, which is time-consuming. The knowledge from previous tuning processes can be used in the current tuning due to the relevance of MFs. Motivated by this, a knowledge transfer method for tuning MFs with unknown individual differences is proposed. The main contributions are threefold: 1) An Knowledge Transfer technique for Optimization Tuning (KTOT) is created to tune various MFs efficiently; 2) an Evaluation mechanism of Difference Degree (EDD) is proposed to guide transfer; 3) a Guiding strategy of Knowledge Transfer Intensity (KTIG) in accordance with the difference degree is presented to reuse knowledge reasonably. The high-efficiency of KTOT and the effectiveness of KTIG based on EDD are demonstrated.

关键词： Knowledge transfer difference degree evaluation microwave filters tuning processes intelligent optimization

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Motion Analysis and Experiment of Sheet Small Magnetron Soft Robot 8

Motion Analysis and Experiment of Sheet Small Magnetron Soft...

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8th International Conference on control Science and Systems engineering, ICCSSE 2022

作者： Ma, Haoyun Zhang, Pan Wu, Jundong Wang, Yawu School of Automation China University of Geosciences Hubei Wuhan430 074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Hubei Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Hubei Wuhan430074 China

ISBN: (纸本)9781665484138

In the past, most robots used rigid structures. With the development of intelligent materials, more and more soft materials began to be used in the manufacture of robots. Magnetic-driven soft robot is one of its important branches. Because of the flexibility of magnetoelastic composite robots and the harmlessness of magnetic field to human body, a magnetically-controlled soft robot has broad application prospects in medical fields such as disease diagnosis and treatment in human body. A small lamellate magnetic soft robot is designed in this paper with the advantages of small volume, light weight, high deformation, fast and simple manufacture. And it can be driven without wire under the control of the magnetic field formed by the three-dimensional Helmholtz coil. The robot can walk and crawl on the surface of magnetically-controlled. The motion characteristics of the magnetic soft robot such as crawling step length and bending height are analyzed. The data are collected through experiments to analyze the relationship between the control signal and the motion characteristics. © 2022 IEEE.

关键词： Motion analysis

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Gesture Recognition Using FMCW Radar Based on Deep Neural Network

Gesture Recognition Using FMCW Radar Based on Deep Neural Ne...

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Chinese Automation Congress (CAC)

作者： Rui Wang Wei Xue Jiao Peng Xinya Yang Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

In recent years, radar based gesture recognition technology has attracted more and more attention. Among different types of radars, frequency modulated continuous wave (FMCW) radar has the greatest application potential due to its high range and velocity resolution and low cost. However, insufficient gesture information extraction and interference signals affect the performance of FMCW radar gesture recognition. To address the problems, a gesture recognition method based on multi-dimensional features and deep neural network is proposed in this paper. First, the range-time map (RTM) and velocity-time map (VTM) of the gesture are constructed from the range-Doppler map (RDM) with two-dimensional fast Fourier transform (2D-FFT). Then, the angle-time map (ATM) is constructed with the multiple signal classification (MUSIC) algorithm. Subsequently, RTM, VTM and ATM are normalized and adaptively filtered to suppress interference signals, and the three maps are fused to construct the range-velocity-angle-time map (RV ATM) of the gesture. Finally, an improved VGG16 network with self-attention mechanism module is used for feature extraction and recognition. The experimental results show that the proposed method achieves an average accuracy of 98.3% for six gestures, and the improved VGG16 network outperforms other traditional convolutional neural networks.

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Decentralized Zeno-Free Event-Triggered control For Multiple Networks Subject to Stochastic Network Delays and Poisson Pulsing Attacks

arXiv

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arXiv 2024年

作者： Zhang, Dandan Jin, Xin Su, Hongye The State Key Laboratory of Industrial Control Technology The Institute of Cyber-Systems and Control The College of Control Science and Engineering Zhejiang University Hangzhou310027 China The Research Institute of Intelligent Complex Systems Fudan University Shanghai200433 China The Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China

By designing the decentralized time-regularized (Zeno-free) event-triggered strategies for the state-feedback control law, this paper considers the stochastic stabilization of a class of networked control systems, where two sources of randomness exist in multiple decentralized networks that operate asynchronously and independently: the communication channels are constrained by the stochastic network delays and also by Poisson pulsing denial-of-service (Pp-DoS) attacks. The time delay in the network denotes the length from a transmission instant to the corresponding update instant, and is supposed to be a continuous random variable subject to certain continuous probability distribution;while the attacks' cardinal number is a discrete random variable supposed to be subject to Poisson distribution, so the inter-attack time, i.e., the time between two consecutive attack instants, is subject to exponential distribution. The considered system is modeled as a stochastic hybrid formalism, where the randomness enters through the jump map into the reset value (the inter-attack time directly related) of each triggered strategy. By only sampling/transmitting state measurements when needed and simultaneously by taking the specific medium access protocols into account, the designed event-triggered strategies are synthesized in a state-based and decentralized form, which are robust (tolerable well) to stochastic network delays, under different tradeoff-conditions between the minimum inter-event times, maximum allowable delays (i.e., potentially tolerable delays) and the frequencies of attacks. Using stochastic hybrid tools to combine attack-active parts with attack-over parts, the designed triggered strategies, if designed well according to the actual system needs, can tolerate (be resilient to) the Pp-DoS attacks and stochastic network delays without jeopardizing the stability and Zeno-freeness, which is verified by the robust global attitude stabilization of flexible combined ro

关键词： Networked control systems

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