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Data-Driven Attack Detection and Resilient Control for Heterogeneous Nonlinear Nonaffine MASs

IEEE Transactions on Network Science and engineering 2025年

作者： Yang, Chen Che, Wei-Wei Qingdao University School of Automation Shandong Key Laboratory of Industrial Control Technology Qingdao266071 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries College of Information Science and Engineering Shenyang110819 China Northeastern University College of Information Science and Engineering Shenyang China

The data-driven attack detection and resilient control problems for heterogeneous nonlinear nonaffine multi-agent systems (MASs) in the presence of false data injection (FDI) attacks are investigated in this paper. The heterogeneous nonlinear nonaffine MASs are firstly translated to an equivalent linear data model by using the dynamic linearization method. Then, based on the obtained linear data model, a novel attack detector, by using the nonlinear autoregressive with exogenous input neural network (NARXNN), is designed to detect whether MASs are under FDI attacks. Further, a data-driven resilient switched control scheme with the attack compensation mechanism is designed to achieve the leaderless consensus control objective. Finally, a simulation is given to illustrate the efficiency of the proposed method by comparisons. © 2013 IEEE.

关键词： Authorization

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Operating Performance Assessment of Plant-Wide processes Based on Hierarchical Multiblock Performance-Relevant Kernel Independent Component Analysis

Operating Performance Assessment of Plant-Wide Processes Bas...

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第40届中国控制会议

作者： Yan Liu Fei Chu Fuli Wang Dakuo He College of Information Science&Engineering Northeastern University College of Information and Control Engineering China University of Mining and Technology State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

The operating performance assessment of plant-wide processes is very important for improving product quality and comprehensive economic *** this study,a new operating performance assessment method based on hierarchical multiblock performance-relevant kernel independent component analysis(HMPRKICA) is proposed for nonlinear and non-Gaussian plant-wide *** proposed approach divides the process variables into several sub-processes according to the physical structure and incorporates sub-process information into high level for operating performance assessment of plant-wide *** the new method,the difficulty of process analysis is reduced and the interpretability of the assessment results are *** process operating performance is nonoptimal,the responsible sub-process is first located by the sub-process assessment result,and then the variable contribution rates are calculated in the corresponding sub-process to narrow the search for the nonoptimal ***,a case study of gold hydrometallurgy process evaluates the efficiency of the proposed method.

关键词： Plant-wide process operating performance assessment HMPRKICA non-Gaussianity nonlinearity

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Dehydration drives damage in the freezing of brittle hydrogels

arXiv

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arXiv 2024年

作者： Yang, Shaohua Gerber, Dominic Feng, Yanxia Bain, Nicolas Kuster, Matthias de Lorenzis, Laura Xu, Ye Dufresne, Eric R. Style, Robert W. School of Mechanical Engineering and Automation Beihang University Beijing China Department of Materials ETH Zürich Zürich8093 Switzerland University of Lyon Université Claude Bernard Lyon 1 CNRS Institut Lumière Matière VilleurbanneF-69622 France Department of Mechanical and Process Engineering ETH Zürich Zürich8093 Switzerland Department of Materials Science & Engineering Cornell University Ithaca United States Laboratory of Atomic and Solid State Physics Cornell University Ithaca United States

It is widely known that freezing breaks soft, wet materials. However, the mechanism underlying this damage is still not clear. To understand this process, we freeze model, brittle hydrogel samples, while observing the growth of ice-filled cracks that break these apart. We show that damage is not caused by the expansion of water upon freezing, or the growth of ice-filled cavities in the hydrogel. Instead, local ice growth dehydrates the surrounding hydrogel, leading to drying-induced fracture. This dehydration is driven by the process of cryosuction, whereby undercooled ice sucks nearby water towards itself, feeding its growth. Our results highlight the strong analogy between freezing damage and desiccation cracking, which we anticipate being useful for developing an understanding of both topics. Our results should also give useful insights into a wide range of freezing processes, including cryopreservation, food science and frost heave. Copyright © 2024, The Authors. All rights reserved.

关键词： Hydrogels

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Multi-step Greedy Reinforcement Learning Based on Model Predictive Control

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IFAC-PapersOnLine 2021年第3期54卷 699-705页

作者： Yucheng Yang Sergio Lucia Department of Electrical Engineering and Computer Science TU Berlin Einsteinufer 17 10587 Berlin Germany Laboratory of Process Automation Systems TU Dortmund University Emil-Figge-Str. 70 44227 Dortmund Germany

Reinforcement learning aims to compute optimal control policies with the help of data from closed-loop trajectories. Traditional model-free approaches need huge number of data points to achieve an acceptable performance, rendering them not applicable in most real situations, even if the data can be obtained from a detailed simulator. Model-based reinforcement learning approaches try to leverage model knowledge to drastically reduce the amount of data needed or to enforce important constraints to the closed-loop operation, which is another important drawback of model-free approaches. This paper proposes a novel model-based reinforcement learning approach. The main novelty is the fact that we exploit all the information of a model predictive control (MPC) computing step, and not only the first input that is actually applied to the plant, to efficiently learn a good approximation of the state value function. This approximation can be included into a model predictive control formulation as a terminal cost with a short prediction horizon, achieving a similar performance to an MPC with a very long prediction horizon. Simulation results of a discretized batch bioreactor illustrate the potential of the proposed methodology.

关键词： Reinforcement learning Model predictive control Nonlinear system Batch reactor

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Improved LMI-based conditions for designing of PD-type ILC laws for linear batch processes over two-dimensional setting

Improved LMI-based conditions for designing of PD-type ILC l...

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International Conference on Control, automation, Robotics and Vision (ICARCV)

作者： Robert Maniarski Wojciech Paszke Hongfeng Tao Shoulin Hao Institute of Automation Electronic and Electrical Engineering University of Zielona Góra Zielona Góra Poland Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi China Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian China

ISBN: (纸本)9781665476881

This paper considers the problem of designing of iterative learning control (ILC) laws for linear batch processes. Unlike the majority of existing results about ILC law design for linear batch processes over repetitive/two-dimensional setting where Lyapunov theory is applied, this study is focused on formulating the ILC law design procedures by transforming it into an equivalent problem of (structural) stability analysis for a linear Roesser model for two-dimensional (2D) systems. Then, based on a non-conservative version of stability and stabilization conditions for linear 2D systems, suitable PD-type ILC laws are derived by the application of the linear matrix inequality (LMI) approach. Finally, a numerical example is given to show the validity of the proposed design procedure and some advantages are emphasized when compared to the existing alternatives.

关键词： State feedback Uncertainty Two dimensional displays Batch production systems Stability analysis Structural engineering Numerical models

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Hint-DETR: A Transfer Learning Model Based On DETR For Few-Shot Defect Detection

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Liu, Yu Zhang, Gengchen Li, Xing South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Dongguan University of Technology School of Electrical Engineering and Intelligentization Dongguan523808 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China

Defect detection, which ensures production stability and final product quality, is a critical part of modern intelligent manufacturing processes. To address the challenge of the difficulty of training the large model networks due to the scarcity of labeled negative samples and detecting defects with ambiguous semantic features, we propose a novel few-shot defect detection method based on transfer learning, called Hint-DETR. Two modules, Memory Bank of Defect and Background (MBDB) and Spatial Feature Information Prompt (SFIP), were added to the DETR network to adapt it to the few-shot defect detection task. The MBDB module can assist the model in learning a memory bank that contains all the defect class feature information to improve the defect perception ability of the model using a small number of defect samples and contains the background suppression loss function designed to reduce the interference of the complex background. The SFIP module extracts spatial feature information from multi-scale feature maps to enhance the DETR network’s detection ability for multi-scale targets, particularly small-scale targets. To assess our method’s performance, we created two few-shot defect datasets using publicly available defect datasets and compared the performance of some state-of-the-art few-shot object detection methods with Hint-DETR. The results of the experiment demonstrate that our method has superior defect detection capability in few-shot settings. © 1963-2012 IEEE.

关键词： Leak detection

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An improved normalized mutual information variable selection algorithm for neural networks 3

An improved normalized mutual information variable selection...

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3rd IEEE International Conference on Electronic Information Technology and Computer engineering, EITCE 2019

作者： Tian, Pengxin Wu, Hao Wang, Hongxun Sun, Kai School of Electrical Engineering and Automation Jinan China State Key Laboratory of Process Automation in Mining and Metallurgy Ganization Beijing China Beijing Key Laboratory of Process Automation in Mining and Metallurgy Beijing China

ISBN: (纸本)9781728135847

In this paper, normalized mutual information feature selection (NMIFS) and tabu search (TS) are integrated to develop a new variable selection algorithm for neural networks. NMIFS is applied to select influential variables that contribute to the output variable, and avoids selecting redundant variables by calculating mutual information. TS based strategy is designed to prevent NMIFS from falling into local optimal situation. The proposed algorithm performs the variable selection by combining entropy information, mutual information, and validating error information of neural networks. Therefore, the algorithm proposed in this paper has more advantages than previous mutual information based variable selection algorithms. Several simulation datasets with different scale, correlation and noise are implemented to compare the performance of the proposed algorithm with several classical algorithms. The experiments show that developed algorithm present better model accuracy with fewer variables selected, compared to other state-of-art methods. © 2019 IEEE.

关键词： Tabu search

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Event-triggered Iterative Learning Control via Fixed Threshold Condition

Event-triggered Iterative Learning Control via Fixed Thresho...

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第32届中国过程控制会议(CPCC2021)

作者： Lin Na CHI Ronghu School of Automation & Electronic Engineering Qingdao University of Science & Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationEast China University of Science and Technology

In this paper,an event-triggered iterative learning control method is proposed for a class of nonlinear nonaffine discrete-time ***,a compact form iterative dynamic linearization method is introduced to transform the original nonlinear system into a linear data ***,the event triggering condition based on fixed threshold is *** parameter estimation algorithm and learning control law,only the output information of triggered iteration is used to reduce the sampling *** proposed method is data-driven and does not need any model *** analysis and simulation results verify the effectiveness of the proposed method.

关键词： Iterative learning control Event-triggered control Fixed threshold condition Nonlinear systems

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Two Feature Fusion Network Based on Efficient Deep Residual Shrinkage Spatial-Temporal Graph Convolution for Emotion Recognition from Gaits

Two Feature Fusion Network Based on Efficient Deep Residual ...

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Chinese Control and Decision Conference, CCDC

作者： Xiaoqing Liu Sen Zhang Wendong Xiao School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) Beijing China Shunde Graduate School University of Science and Technology Beijing Foshan China Beijing Engineering Research Center of Industrial Spectrum Imaging Beijing China

ISBN: (纸本)9781665478977

Emotion Recognition plays a key role in human social activities. Most current methods choose facial information for emotion recognition. But facial information is easily limited by distance in large scenes. Gait information can break this limitation, but the amount of publicly available data is small. This paper proposes two feature fusion network based on efficient deep residual shrinkage spatial-temporal graph convolution to achieve automatic emotion recognition from gaits in large scenes. Firstly chebyshev polynomial approximation graph convolution is combined with efficient deep residual shrinkage network to obtain efficient deep residual shrinkage spatial-temporal graph convolution network to extract deep features. Then sixteen temporal affective features extracted from gaits are used as input of efficient deep residual shrinkage network to extract new affective features. Finally the deep features is spliced with new affective features to divided emotion into happy,angry,sad and neutral. We performed a ten-flod cross-validation experiment on the E-gait real datasets. The highest recognition accuracy is 0.87, with an average of 0.85. Experimental results show that the proposed model is effective in dealing with emotion recognition promblem from gait in fewer sample situation.

关键词： Emotion recognition Convolution Chebyshev approximation Feature extraction

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重大耗能设备低碳运行智能控制方法（英文）

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engineering 2023年第8期 84-95页

作者： Tianyou Chai Mingyu Li Zheng Zhou Siyu Cheng Yao Jia Zhiwei Wu State Key Laboratory of Synthetical Automation for Process Industries Northeastern University National Engineering Technology Research Center for Metallurgical Industry Automation (Shenyang) Northeastern University

Based on an analysis of the operational control behavior of operation experts on energy-intensive equipment, this paper proposes an intelligent control method for low-carbon operation by combining mechanism analysis with deep learning, linking control and optimization with prediction, and integrating decision-making with control. This method, which consists of setpoint control, self-optimized tuning,and tracking control, ensures that the energy consumption per tonne is as low as possible, while remaining within the target range. An intelligent control system for low-carbon operation is developed by adopting the end–edge–cloud collaboration technology of the Industrial Internet. The system is successfully applied to a fused magnesium furnace and achieves remarkable results in reducing carbon emissions.

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