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Intelligent recognition of steel plate surface defect based on deep convolutional GAN

Neural Computing and Applications 2025年第14期37卷 8331-8345页

作者： Jiang, Benyi Zhou, Ping Sun, Xiaoyang Chai, Tianyou State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 Thailand

The production of hot rolled strip steel plates is affected by various uncertainties, resulting in numerous defects on the steel plate surface, such as scratches, cracks, and inclusions, which significantly impact the surface quality and performance of the steel plates. Considering the high rates of missed and false detections by traditional manual visual inspections, along with the limited accuracy of existing defect recognition algorithms due to insufficient defect sample data, a novel intelligent identification method is proposed for the recognition of steel plate surface defects based on deep convolutional generative adversarial network (DCGAN). Aiming at the problem of insufficient samples of actual industrial surface defect images, the proposed method generates steel plate surface image data with a similar distribution to real data based on DCGAN, which will expand the original sample data. Moreover, batch normalization is used to process the data, and an appropriate activation function is selected to better combine the original GAN and the deep convolutional neural network (DCNN) model in structure, thereby effectively improving the quality of the generated image data. Finally, the extended dataset is used to train the DCNN, and the recognition accuracy of the trained model on the test set is 96.16%. To the high error rate of manual inspection and the difficulty of creating and labeling datasets, the experimental results on actual industrial data demonstrate that the proposed method has good engineering application for the recognition of steel plate surface defects. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Hot rolling

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Dynamic event-triggered data-driven iterative learning bipartite tracking control for nonlinear MASs with prescribed performance

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Science China(Information Sciences) 2025年第1期68卷 292-305页

作者： Tao SHI Wei-Wei CHE School of Mechatronic Engineering and Automation Shanghai University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University College of Information Science and Engineering Northeastern University

This article proposes a distributed dynamic event-triggered data-driven iterative learning control(DET-DDILC)scheme under a predefined performance to tackle the bipartite tracking control problem for multiagent systems(MASs). An improved dynamic linearization technique is utilized to convert the nonlinear MASs into an iterative linear data model. First,a peer-to-peer mapping function is introduced to map the constrained distributed system output homeomorphism to an unconstrained one. In addition, a DET mechanism based on a time-iteration-varying function is devised to conserve network communication resources. Based on the unconstrained transformation and the designed DET mechanism, the DET-DDILC algorithm is devised to ensure that the bipartite tracking performance of MASs can be within the preset range. Finally, the effectiveness and feasibility of the designed control scheme are demonstrated via a simulation case by a comparison.

关键词： bipartite tracking control prescribed performance event-triggered iterative learning control multiagent systems

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Data-Driven Fault-Tolerant Bipartite Consensus Tracking for Multi-Agent Systems With a Non-Autonomous Leader

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 279-281页

作者： Yan Zhou Guanghui Wen Jialing Zhou Tao Yang the Department of Systems Science Southeast University the State Key Laboratory of CNS/ATM Beijing Institute of Technology the Beijing Institute of Technology (Zhuhai) the State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

Dear Editor,This letter studies the bipartite consensus tracking problem for heterogeneous multi-agent systems with actuator faults and a leader's unknown time-varying control input. To handle such a problem, the continuous fault-tolerant control protocol via observer design is developed. In addition, it is strictly proved that the multi-agent system driven by the designed controllers can still achieve bipartite consensus tracking after faults occur.

关键词： Time varying control systems

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Multi-scale Sequential Bayesian Calibration of Digital Twin Model Parameters 24

Multi-scale Sequential Bayesian Calibration of Digital Twin ...

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2nd International Conference on Frontiers of Intelligent Manufacturing and automation, CFIMA 2024

作者： Wang, Han Lu, Shaowen State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

ISBN: (纸本)9798400710681

Digital twins have been increasingly applied in the optimization of industrial production, especially in scenarios such as process monitoring, metric prediction, and retrospective analysis. The calculation model in digital twins is often based on the physical and chemical mechanisms of the process, and there are still many undetermined parameters that need to be calibrated based on actual data. To address this issue, a model parameter calibration algorithm based on multi-scale sequential Bayesian is proposed, which unifies the parameter uncertainty, model bias, and observation error in the Bayesian framework, and uses the difference between the model output and actual data for rolling calibration. Considering the problem of high computational cost for posterior distribution of high-dimensional unknown parameters in Bayesian analysis, a multi-scale model representation method based on Gaussian process convolution is introduced. Additionally, the proposed method has been tested through simulation examples and an actual industrial process model. The results show that the algorithm can achieve independent analysis of model output and model bias, significantly reduce the complexity of calibration calculations, improve calibration efficiency, and ensure a high degree of agreement between predicted and actual outputs. © 2024 Copyright held by the owner/author(s).

关键词： Inference engines

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Smart fiber-optic masks: real-time respiratory monitoring and health analysis

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Science China(Information Sciences) 2025年第4期68卷 216-227页

作者： Qi YU Ya-Nan ZHANG Jiaqi ZHANG Shuang YANG Lingxiao JIANG Xuegang LI College of Information Science and Engineering Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Affiliated Hospital of North Sichuan Medical College

In this study, we design a smart mask based on an ultrathin retractable flexible fiber optic sensor with high sensitivity(0.56 V/m-1), ultra-light mass（0.24 g）, and excellent tensile properties. The sensor was integrated with a medical mask to recognize multiple breathing patterns（standard, fast, slow, shallow, deep, breath-holding）, coughing, and speaking,as well as to study gender characteristics and physiological differences in the respiratory system. By comparing the nasal-oral respiratory waveforms and frequencies, guidance for respiratory correction was provided. In addition, affixing flexible fiber optic sensors to the outside of the mask reduces the risk of cross-infection and significantly reduces temperature and humidity disturbances. Studies have shown that nine sensor locations placed on the outside of the mask responded significantly to respiratory waveforms. For personalized health management, the system is also equipped with a communication module,web and mobile apps to support data downloads, real-time monitoring, and exception alerts.

关键词： smart mask respiratory monitoring fiber optic sensor health management anti-interference

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Stealthy Attacks with Historical Data on Distributed state Estimation

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4541-4550页

作者： Xing, Jitao Ye, Dan Li, Pengyu Northeastern University College of Information Science and Engineering Shenyang110819 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China

This paper addresses the problem of designing stealthy attacks on distributed estimation using historical data. The distributed sensors transmit innovations to remote state estimators and neighboring nodes, which attackers can intercept and tamper with. To bypass the configured false data detectors, the attack parameters must satisfy the stealthiness constraints. The determination of the optimal stealthy attack strategy is reformulated as a series of convex optimization problems. Additionally, a lower bound on the compromised estimation error covariance is derived, and analytical solutions for the suboptimal stealthy attack strategy that maximizes the bound are provided. These solutions are proven to be piecewise constant with smaller computational complexity. Finally, numerical simulations validate the theoretical results. © 2005-2012 IEEE.

关键词： Convex optimization

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Hybrid Model and Data-Driven Optimal PID Control with Enhanced Disturbance Rejection

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IEEE Transactions on automation Science and Engineering 2025年 22卷 15286-15298页

作者： Liu, Ning Zhang, Yajun Wang, Zhuqing Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Taiyuan University of Technology College of Computer Science and Technology Taiyuan030600 China

This paper proposes an optimal proportional-integral-derivative (PID) control method with improved disturbance rejection for nonlinear uncertain systems. The controller achieves optimal closed-loop dynamics by integrating constant and time-varying control gains. Initially, the system is transformed into a linear model with a nonlinear uncertainty term that lumps unmodeled dynamics and disturbances. Constant PID gains are subsequently derived using an optimal tuning rule based on the frequency and gain of the linear model at −180° and −120° phase lag points for systems with and without ultimate points, respectively, by employing a fractional-order integrator and Tchebyshev polynomials to simplify computation. Then an extended state observer (ESO)-assisted recursive optimization approach is introduced to adaptively adjust the time-varying PID gains in real time based on the uncertainty estimation from the ESO and operational data, improving adaptation under nonlinear uncertainties. This hybrid tuning enhances the robustness of the closed-loop system to uncertain dynamics and mitigates the instability caused by poor initial parameters, outperforming traditional model and data-driven methods. The stability of the proposed approach is proven, and its effectiveness is validated through simulations and an industrial case study on the pressure control of the hematite regrinding process. © 2004-2012 IEEE.

关键词： Disturbance rejection

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Decentralized Federated Learning Algorithm Under Adversary Eavesdropping

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 448-456页

作者： Lei Xu Danya Xu Xinlei Yi Chao Deng Tianyou Chai Tao Yang the State Key Laboratory of Synthetical Automation for Process Industries Northeastern University the Department of Mechanical Engineering University of Victoria Shanghai Institute of Intelligent Science and Technology Tongji University IEEE the Institute of Advanced Technology Nanjing University of Posts and Telecommunications

In this paper, we study the decentralized federated learning problem, which involves the collaborative training of a global model among multiple devices while ensuring data *** classical federated learning, the communication channel between the devices poses a potential risk of compromising private information. To reduce the risk of adversary eavesdropping in the communication channel, we propose TRADE(transmit difference weight) concept. This concept replaces the decentralized federated learning algorithm's transmitted weight parameters with differential weight parameters, enhancing the privacy data against eavesdropping. Subsequently, by integrating the TRADE concept with the primal-dual stochastic gradient descent(SGD)algorithm, we propose a decentralized TRADE primal-dual SGD algorithm. We demonstrate that our proposed algorithm's convergence properties are the same as those of the primal-dual SGD algorithm while providing enhanced privacy protection. We validate the algorithm's performance on fault diagnosis task using the Case Western Reserve University dataset, and image classification tasks using the CIFAR-10 and CIFAR-100 datasets,revealing model accuracy comparable to centralized federated learning. Additionally, the experiments confirm the algorithm's privacy protection capability.

关键词： Adversary eavesdropping decentralized federated learning privacy protection stochastic gradient descent

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Consensus Control Strategy for the Treatment of Tumour With Neuroadaptive Cellular Immunotherapy

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 575-584页

作者： Jiayue Sun Dongni Li Huaguang Zhang Lu Liu Wenyue Zhao IEEE the State Key Laboratory of Synthetical Automation for Process Industries the College of Information Science and Engineering Northeastern University the Department of Breast Surgery The First Hospital of China Medical University the Department of Thoracic Surgery The First Hospital of China Medical University

This paper presents a novel neuro-adaptive cellular immunotherapy control strategy that leverages the high efficiency and applicability of chimeric antigen receptor-engineered T(CAR-T) cells in treating cancer. The proposed real-time control strategy aims to maximize tumor regression while ensuring the safety of the treatment. A dynamic growth model of cancer cells under the influence of cellular immunotherapy is established for the first time, which aligns with clinical experimental *** the backstepping method, a novel consensus reference model is designed to consider the characteristics of cancer cell changes during the treatment process and conform to clinical rules. The model is segmented and continuous, with cancer cells expected to decrease in a step-like manner. Furthermore, a prescribed performance mechanism is constructed to maintain the therapeutic effect of the proposed scheme while ensuring the transient performance of the system. Through the analysis of Lyapunov stability, all signals within the closed-loop system are proven to be semiglobally uniformly ultimately bounded(SGUUB). Simulation results demonstrate the effectiveness of the proposed control strategy, highlighting its potential for clinical application in cancer treatment.

关键词： Cellular immunotherapy therapy chimeric antigen receptor-engineered T (CAR-T) cells neuro-adaptive control novel consensus reference model prescribed performance mechanism

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Robust Predictive Bipartite Bumpless Control for Markov Jump MASs Under Switching Topology: Application to Rotary-Wing Air Vehicles

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 7020-7032页

作者： Wang, Jian Dong, Jiuxiang The Liaoning Province Key Laboratory of Safe Operation Technique for Autonomous Unmanned Systems College of Information Science and Engineering Shenyang110819 China Northeastern University State Key Laboratory of Synthetical Automation of Process Industries Shenyang110819 China

This article addresses the issue of bipartite consensus for Markov jump multi-agent systems subjected to bounded disturbance and input constraints under mode-dependent switching topology. To surmount the challenge of handling disturbance, hard constraints and bump suppression, which are not considered and difficult to deal with in existing literature on solutions to algebraic Riccati equation, a novel bipartite robust predictive bumpless control algorithm is proposed with integrating H∞ performance. To be specific, the offline designing process of bipartite controller parameters is firstly performed by investigating terminal constraint set and robust invariant set. In this sense, H∞ type cost function and consensus error state bump cost are constructed for achieving disturbance attenuation and suppressing bump in error state, respectively. This further avoids possible counterproductive results caused by setting constraints on bump with a small gain. Next, based on the optimization prediction dynamics, perturbation state of controller is optimized online for each agent, which efficiently reduces the computational burden compared with conventional online and efficient model predictive control. Furthermore, the recursive feasibility of the proposed method and stochastic stability of error dynamics are theoretically guaranteed. Finally, an application to rotary-wing air vehicles is utilized to verify the potential of theoretic results. © 1967-2012 IEEE.

关键词： Cost functions

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