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Stability and Stabilization of Sampled-Data Based LFC for Power systems:A Data-Driven Method

IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 291-293页

作者： Yu-Long Fan Chuan-Ke Zhang Yong He the School of Automation China University of Geosciences the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems the Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education IEEE

Dear Editor,This letter is concerned with stability analysis and stabilization design for sampled-data based load frequency control(LFC) systems via a data-driven method. By describing the dynamic behavior of LFC systems based on a data-based representation, a stability criterion is derived to obtain the admissible maximum sampling interval(MSI) for a given controller and a design condition of the PI-type controller is further developed to meet the required MSI. Finally, the effectiveness of the proposed methods is verified by a case study.

关键词： Sampled data control systems

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Conditional Diffusion Model for Skeleton-based Gesture Recognition with Severe Occlusions

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IEEE Signal Processing Letters 2025年 32卷 1970-1974页

作者： Liu, Jinting Gan, Minggang Du, Yao Guan, keyi Guo, Jia Beijing Institute of Technology Key Laboratory of Complex System Intelligent Control and Decision School of Automation Beijing China

In the field of skeleton-based gesture recognition, occlusion remains a significant challenge, significantly degrading performance when key joints are occluded or disturbed. To tackle this issue, we propose DiffTrans, a practical conditional diffusion model for occlusion recognition, which enables skeleton-based gesture recognition under high occlusion by generating more likely samples. This study addresses the hand skeleton occlusion problem by framing it as a conditional denoising problem, where unoccluded data serve as observations and occluded data as repair targets. We employ a conditional diffusion model to impute the missing skeleton data and the DSTANet model, which is based on the transformer, to learn the skeleton feature representations. Research results show that the DiffTrans outperforms existing methods under various occlusion modes, maintaining high performance even in scenarios with a high missing rate. © 1994-2012 IEEE.

关键词： Musculoskeletal system

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Non-contact video-based vital signs continuous monitor for uncontrained conscious rodents

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IEEE Sensors Journal 2025年第10期25卷 16628-16639页

作者： He, Yuke Gao, Tong Lv, Yong Zhang, Jingjing China University of Geoscienceis Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation Wuhan430074 China

Animal experiments should minimize pain and discomfort and maximize animal welfare to satisfy the experimental animal 3R principle. The quality of vital signs monitor results collected from conscious and stress-free rodents is superior, ensuring experimental data's reliability, accuracy, and scientificity. This work proposes a non-contact video-based vital signs continuous monitoring method for uncontrained conscious rodents. Firstly, rodents are tracked using keypoint detection, and Regions of Interest (ROI) on the rodents are selected based on signal-to-noise ratio (SNR) distribution of vital signs. Secondly, corner motion within the ROI region is tracked to synthesize skin pulsations, from which respiratory signals are extracted. Finally, a method is proposed to eliminate the influence of respiratory harmonics, thereby removing respiratory interference and extracting the heart rate. Four rats and four mice were used to measure the performance of the proposed method. This work is the first to use a camera to continuously monitor the heart rate of unrestrained and conscious rodents. Compared with the ground truth (semi-implantable electrocardiogram), the relative heart rate (HR) error was 1.86 ± 0.82% and 1.20 ± 0.33% for rats and mice, respectively. This method is used for uncontrained conscious rodents and has high applicability, allowing it to be used in various experimental environments and conditions. At the same time, this work contributes to the development of new methods for continuously and objectively evaluating animal welfare, thereby promoting the development of modern biomedical and ethical research. © 2001-2012 IEEE.

关键词： Invertebrates

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Attention-Modulated Feature Fusion Neural Network for Inverse Modeling of Microwave Filters

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IEEE Microwave and Wireless Technology Letters 2025年第4期35卷 376-379页

作者： Guo, Linwei Cao, Weihua Hu, Wenkai Lu, Zhengyang Wu, Min China University of Geosciences School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

Inverse modeling is extensively applied in the design and tuning of microwave filters (MFs). Inverse models (IMs) take the features extracted from the high-dimensional electromagnetic parameters as input. How to make full use of features from multiple perspective is a critical issue for improving model accuracy. To solve it, this letter proposes an attention-modulated feature fusion neural network (AMFFNN). AMFFNN achieves multiperspective feature fusion (MPFF) at the input side and independent feature fusion (IFF) at the output side. In addition, feature fusion in AMFFNN is enhanced by attention modules to dynamically identify the importance of each feature. Statistical and comparative results of simulations demonstrate that AMFFNN outperforms existing methods in terms of accuracy, stability, and generalization. © 2023 IEEE.

关键词： Inverse problems

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Research on Urban Air Quality Prediction Method Based on the Fusion of Wide-area Spatiotemporal Distribution Data 26

Research on Urban Air Quality Prediction Method Based on the...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Tao, Zixing Cao, Weihua Gan, Chao Wuhan China School of Future Technology School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (纸本)9798331521950

Air quality data exhibit nonlinearity, sensitivity to environmental factors, and long-term dependencies. Numerous factors influence air quality, making accurate predictions based on a single-dimensional dataset impractical. This study proposes a method for urban air quality prediction that integrates wide-area spatiotemporal data, designed to address the unique characteristics of air quality datasets. First, relevant wide-area spatiotemporal data are selected, and their correlations with air quality are systematically analyzed. Second, a Long Short-Term Memory (LSTM) network-based Transformer model is utilized to capture the long-term dependencies in the air quality sequences. Finally, the model successfully generates hourly multi-step predictions for six air pollutants. The experimental results show that the proposed method outperforms the method that relies on air quality data alone for multi-step air quality prediction. © 2025 IEEE.

关键词： Air quality

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Bearing Fault Diagnosis Under Variable Working Conditions Based on Domain Adaptation and Improved Depthwise Separable Convolution Network 26

Bearing Fault Diagnosis Under Variable Working Conditions Ba...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Kuang, Zehui Mao, Fan Zhao, Xingyu Wan, Xiongbo 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 School of Future Technology 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

ISBN: (纸本)9798331521950

Domain adaptation methods and appropriate feature extractors are usually applied to solve the problem that the variable working conditions of bearings affect the effectiveness of the fault diagnostic framework. However, the existing feature extractors are too complex to be deployed in real industries. In this paper, we propose a lightweight framework called improved depthwise separable convolution network (IDSCN) for the diagnosis of bearing faults in variable operating conditions. IDSCN is built on wavelet convolution and depthwise separable residual convolution (DSRC), where the wavelet convolution is used to extract frequency features from the source and target domains, and DSRC guarantees the performance of feature extraction with reduced parameters and computation. To minimise the distribution difference between the source and target domains, the local maximum mean difference is incorporated into the proposed framework. The performance of the proposed framework is validated on the Paderborn University dataset, involving a total of 6 transfer tasks. The experiment shows that the proposed framework achieves satisfactory results and outperforms other frameworks in bearing fault diagnosis. © 2025 IEEE.

关键词： Bearing fault diagnosis Depthwise separable residual convolution Domain adaptation Wavelet convolution

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Multi-Level Features Fusion for Zero-Shot Object Pose Estimation 26

Multi-Level Features Fusion for Zero-Shot Object Pose Estima...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Huang, Nanxin Xu, Chi 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

ISBN: (纸本)9798331521950

Driven by advancements in industrial production and artificial intelligence, the need for pose estimation of new ob-jects in areas like robotic manipulation and virtual reality is increasing. We introduce a zero-shot object pose estimation approach that identifies the poses of objects excluded from the training dataset, removing the requirement for re-modeling. The method is built around a multi-level features fusion framework de-signed to enhance generalization. First, a trainable feature extraction module filters and selects multi-level features extracted by the backbone network. Unlike traditional convolutional ker-nels, we incorporate a dynamic convolution kernel to enhance the feature extraction capability. Second, in the feature fusion module, we adopt a dynamic weight generation strategy to perform weighted fusion of multi-level features. This method enhances template matching by effectively describing similarities between unseen objects (those absent from the training set) and templates, leveraging robust and adaptive feature representations to narrow the gap with seen objects. Experimental results demonstrate that our approach achieves state-of-the-art performance on two popu-lar benchmark datasets, LineMod and LineMod-Occlusion, proves that our method has better generalization than previous models. © 2025 IEEE.

关键词： Industrial robots

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Congruent-transformation-based stability criterion of T-S fuzzy systems with time-varying delay via the generalized line-integral Lyapunov function

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IEEE Transactions on automation Science and Engineering 2025年 22卷 14669-14678页

作者： Liu, Zhou-Zhou Jin, Li He, Yong China University of Geosciences School of Automation Wuhan430074 China China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China China University of Geosciences Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

This paper is concerned with the stability analysis of Takagi-Sugeno (T-S) fuzzy systems with time-varying delays. First, by constructing a generalized line-integral Lyapunov function (GLILF), applying the free-matrix-based inequality and the delay-derivative-dependent free-weighting-matrices method, a basic stability criterion is derived. Besides, the structure of the matrices in the stability criterion is reconstructed through congruent transformation, and an improved stability criterion is derived accordingly. To clarify the advancement of the proposed method, a stability condition is given without the GLILF. Finally, two numerical examples and a practical example are applied to show the advancement and merits of the presented method. © 2004-2012 IEEE.

关键词： Lyapunov functions

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Finite-Time Consensus of Second-Order Multiagent systems With Input Saturation via Hybrid Sliding-Mode control

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IEEE Transactions on automation Science and Engineering 2025年 22卷 14623-14632页

作者： Jiang, Xiaowei Jiao, Ranran Li, Bo Zhang, Xianhe Yan, Huaicheng China University of Geosciences School of Automation Wuhan430079 China Ministry of Education Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Geodetection Technology Wuhan430074 China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai200240 China Anhui University of Finance and Economics School of Finance Bengbu233030 China Hubei Normal University School of Electrical Engineering and Automation Huangshi435002 China Ministry of Education East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Shanghai200237 China

This paper addresses the finite-time consensus (FTC) issue for second-order multi-agent systems (MASs) with nonlinear disturbances. To tackle the challenges posed by increasingly complex communication environments, an innovative integral sliding-mode surface is designed using event-triggered control. Furthermore, the study explores communication constraints between the leader and followers, employing impulsive control to facilitate communication only at specific intervals. A novel hybrid integral sliding-mode control protocol is advanced for the second-order MASs, which effectively eliminates the "Zeno phenomenon" and notably reduces both communication frequency and energy consumption, thereby enhancing overall communication efficiency. Ultimately, the efficacy of the put forward protocol is verified through simulation examples including comparison experiment. © 2004-2012 IEEE.

关键词： Protocols Sliding mode control Event detection Multi-agent systems Costs Training Topology Switches Sufficient conditions Stability analysis

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Efficient Facial Expression Recognition Network Based on Spot-Adaptive Knowledge Distillation 26

Efficient Facial Expression Recognition Network Based on Spo...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Wen, Xin Liu, Zhen-Tao Chen, Dan School of Automation China University of Geosciences Wuhan430074 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)9798331521950

Facial expression recognition plays a key role in promoting the development of comprehensive intelligence and building friendly human-computer interaction. Due to the interference of feature noise in expression data, the lightweight facial expression recognition model with fewer parameters is difficult to learn more expression features through simple training, which limits the improvement of its recognition performance. An efficient facial expression recognition network based on Spot-adaptive Knowledge Distillation is proposed in this paper. Inspired by VoVNetV2, the network designed in this paper is lightweightly improved using Depthwise Separable Convolution and the parameter-free SimAM attention mechanism, reducing the number of parameters to 0.21 M. To further improve the recognition accuracy of the model, Spot-adaptive Knowledge Distillation is employed to improve the characterization ability of the model. The recognition accuracies of the student network designed in this paper on the KDEF and RAF-DB datasets are 93.05% and 81.17% respectively after spot-adaptive distillation. © 2025 IEEE.

关键词： Facial Expression Recognition Knowledge Distillation Lightweight Neural Network

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