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IEEE International Conference on Industrial Technology (ICIT)

作者： Xin Wen Zhen-Tao Liu Dan Chen School of Automation China University of Geosciences Wuhan 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

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

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.

关键词： Knowledge engineering Training Human computer interaction Accuracy Face recognition Computational modeling Noise Neural networks Interference Data models

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

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

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IEEE International Conference on Industrial Technology (ICIT)

作者： Nanxin Huang Chi Xu 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: (数字)9798331521950

ISBN: (纸本)9798331521967

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.

关键词： Training Solid modeling Convolution Service robots Pose estimation Zero shot learning Virtual reality Benchmark testing Feature extraction Robustness

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An Online Cross-layered Defense Strategy with Bandwidth Allocation for Multi-channel systems under DoS Attacks

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IEEE Transactions on Automatic control 2025年

作者： Wan, Liheng Li, Panshuo Lam, James Guangdong University of Technology School of Automation Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control China Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangzhou510006 China The University of Hong Kong Department of Mechanical Engineering Hong Kong

This paper proposes an online cross-layered defense strategy for multi-channel systems with switched dynamics under DoS attacks. The enabling condition of a channel under attacks is formulated with respect to attack flow and channel bandwidth, then a new networked control system model bridging the gap between system dynamics and network deployment is built. Based on this, the cross-layered defense strategy is proposed. It jointly optimizes the controller gain and bandwidth allocation of channels according to the real-time attack flow and system dynamics, by solving a mixed-integer semidefinite programming online. A smart enumeration algorithm for non-convex bi-level optimization is proposed to analyze the stability under the strategy. Numerical examples are given to illustrate the high resilience from the cross-layered feature. © 1963-2012 IEEE.

关键词： Integer programming

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Stability and Stabilization of T-S Fuzzy systems with Sampled-Data controller: A Fuzzy-Related Matrix Injection Method

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IEEE Transactions on Fuzzy systems 2025年

作者： Xiong, Du Zhang, Chuan-Ke Jin, Li Fan, Yu-Long He, Yong China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China

This paper addresses the stability and stabilization issues of Takagi-Sugeno (T-S) fuzzy systems under sampled-data control. In this paper, efforts are dedicated to developing a stability criterion and control strategy with high accuracy and low complexity, leveraging an improved looped functional and a fuzzy-related matrix injection method. First, an improved looped function containing quadratic information about the sampling sawtooth characteristic is employed to include as much system state information as possible in a simple form. In response to the nonlinear terms encountered in the functional derivatives, a fuzzy-related matrix injection method is proposed on the basis of linearization transformation. This method enables the unified treatment of reciprocally convex and quadratic terms, which is conducive to reducing estimation gaps. Based on the aforementioned techniques, stability and stabilization criteria for T-S fuzzy systems are derived. The efficacy and superiority of the proposed method are substantiated through rigorous theoretical analysis and illustrative instances. © 1993-2012 IEEE.

关键词： Sampled data control systems

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H∞ state estimation for delayed neural networks via variable-augmented-based free-weighting matrices method

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Journal of the Franklin Institute 2025年第7期362卷

作者： Chang, Xu-Kang He, Yong 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

The issue of H∞ state estimation for neural networks with time-varying delays is investigated in this study. Firstly, an augmented Lyapunov-Krasovskii functional (LKF) with two delay-product-type terms is constructed to enhance the consideration of system state, delay, and delay derivative information. Furthermore, for taking into account more effective information, the LKF is augmented with both single and double integral variables. Accordingly, the LKF derivative becomes a higher-order term that exhibits nonlinear characteristics of the time delay. To solve the nonlinear problem, a variable-augmented-based free-weighting-matrices (VAFWMs) approach is employed to transform the nonlinear term into a linear form and provides more freedom in obtaining less-conservative results. Consequently, two improved H∞ state estimation criteria are derived. Lastly, a numerical example is provided to demonstrate the merits and effectiveness of the presented methods. Meanwhile, to prove the practical viability of the presented methods, this study extends the methods to a real-world quadruple-tank process system. © 2025 The Franklin Institute

关键词： Lyapunov functions

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End-to-end multi-task reinforcement learning-based UAV swarm communication attack detection and area coverage

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Knowledge-Based systems 2025年 316卷

作者： Yu, Jin Zhang, Ya Sun, Changyin School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

This paper proposes an innovative end-to-end multi-task reinforcement learning framework designed to enhance the security and operational stability of UAVs in complex communication attacks environments. In scenarios involving multiple UAV coverage, the framework classifies attack types and predicts UAV behaviors, which enables rapid response and compromised data recovery. First, a spatiotemporal attention mechanism is employed to strengthen the network by capturing temporal dependencies in the communication data and improving the recognition of hybird attack mode, thereby facilitating the integration of detection and repair. Second, the causal inference mechanism introduced in this study extracts true causal relationships from interference, which ensures the accuracy and stability of action predictions for area coverage under attack conditions. Finally, the entire process is modeled as a multi-task Markov decision process, optimizing overall decision-making efficiency. Additionally, the dynamic pheromone induction mechanism is proposed to realize the adaptive regional cooperative coverage of multiple UAVs. The experimental results demonstrate that the proposed algorithm can effectively detect hybrid attacks and predict UAV actions, protecting the multi-UAV cooperative area coverage. © 2025

关键词： Reinforcement learning

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Prescribed Performance Bipartite Consensus control for MASs Under Data-Driven Strategy

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IEEE/CAA Journal of Automatica Sinica 2025年第5期12卷 937-946页

作者： Zhou, Qi Yin, Caiyun Ma, Hui Ren, Hongru Li, Hongyi School of Automation Guangdong University of Technology Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control Guangzhou 510006 China School of Mathematics and Statistics Guangdong University of Technology Guangzhou 510006 China College of Electronic and Information Engineering Southwest University Chongqing 400715 China

This paper investigates the bipartite consensus control problem for discrete time nonlinear multiagent systems (MASs) based on data-driven adaptive method. To begin with, a dynamic linearization strategy is utilized to establish the relationship between bipartite tracking error and control input for MASs. Secondly, the unknown parameter linearly associated with control input is acquired by the adaptive control approach, and a discrete time extended state observer is designed to estimate nonlinear uncertainties. Thirdly, in order to achieve the prescribed performance, the constrained bipartite consensus error is transformed through a strictly increasing function. Based on the converted equivalent unconstrained error function, a sliding mode controller using only the input and output data of the MASs is designed. Finally, the efficacy of the controller is confirmed by simulations. © 2014 Chinese Association of automation.

关键词： Data-driven nonlinear multiagent systems (MASs) prescribed performance sliding mode control

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Localization of Small Targets in Smart Grain Warehouse Based on VAE-LSTM 3rd

Localization of Small Targets in Smart Grain Warehouse Bas...

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3rd International Conference on Cyberspace Simulation and Evaluation, CSE 2024

作者： Tian, Ping Liu, Jianlong Su, Yingxian Chen, Guanxi Zhang, Bin Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangzhou China Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Yuen Long China School of Automation Guangdong University of Technology Guangzhou 510006 China

ISBN: (纸本)9789819645053

The tracing and localizing of invasive animal is becoming important for ensuring grain safety during grain storage with the improvement of ecological environment. At present, invasive animals of warehouse are detected and tracked manually by regular inspection with low efficiency and quality. This paper proposes a modified variational auto-encoder (VAE) structure with LSTM for small invasive animal localization that makes full advantage of the temporal and spatial information of trajectories. VAE-LSTM model comprises a pre-processing module and a localization model. In the former one, detected point cloud data are filtered into a moving track. And then the tracking series are fed into the last one where the dependencies cross time are learned with encoder layer with LSTM and each position dimension of the track is constructed through a decoder with another LSTM. The experiment results show the proposed model makes accurate small targets localization and has a fast localization speed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Small targets localization Smart city Variational auto-encoder(VAE)

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Probability-guaranteed secure formation for heterogeneous multi-agent systems under bit rates constrained: A security and performance balanced allocation protocol

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Information Sciences 2025年 717卷

作者： Liao, Weixiong Zhou, Jiaye Cheng, Chaojie Lin, Ming Tao, Jie School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control Guangdong University of Technology Guangzhou510006 China Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangdong University of Technology Guangzhou510006 China

This article focuses on the probability-guaranteed set-membership formation control for leader-following heterogeneous multi-agent systems (MASs) under constrained network. Firstly, a mathematical model is proposed, which is capable of simultaneously describing the security and formation performance of MASs over network subject to bit rates limited and FDI attacks. Based on this model, an improved secure coding-decoding protocol (SCDP) is constructed. Second, by SCDP, a probability-guaranteed and decoded-innovation-based online security control strategy is put forth, where the formation performance under constricted network is discovered. Then, with the help of set-membership theory, the formation error can be confined to the ellipsoids with a certain probability and associated parameters can be solved by two optimization problems. Finally, a simulation example is presented to validate the effectiveness of the proposed method. © 2025 Elsevier Inc.

关键词： Coding-decoding protocol Constrained bit rates False data injection attacks Set-membership approach

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Distributed Secure Consensus Estimation for Power systems Against False Data Injection Attacks

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International Journal of Robust and Nonlinear control 2025年第0期

作者： Cheng, Zhijian Ren, Hongru Qin, Jiahu Lu, Renquan School of Automation Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control and Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control Guangdong University of Technology Guangzhou China Department of Automation University of Science and Technology of China Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

As a result of the rapid growth of distributed state estimation in modern power systems, power networks are facing increasingly serious security problems, thus requiring the advancement of defense techniques against cyber attacks. This paper is devoted to investigating distributed consensus state estimation with a defense mechanism against false data injection (FDI) attacks for power systems. By introducing a transformation matrix, the local subsystem model associated with the mixed remote terminal unit and phasor measurement unit measurements is constructed. Taking into account historical estimation information on state variables without being attacked, a defense mechanism constructed by secure historical estimation value is presented to protect the estimator against FDI attacks while maintaining accuracy in estimation. Then a distributed Kalman consensus filter (DKCF) is hosted to estimate the dynamic states of power systems under FDI attack protector. Considering scalability in large-scale power systems, a suboptimal DKCF with the designed protector is developed. By means of the Lyapunov-based approach, a sufficient condition is provided to ensure that the proposed estimator equipped with the defense mechanism is stable. Finally, the proposed distributed state estimation algorithms are validated on an IEEE benchmark 14-bus power system. © 2025 John Wiley & Sons Ltd.

关键词： Cyber attacks

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