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第34届中国控制与决策会议

作者： Jianfeng Zhang Chunhui Zhao Jinliang Ding Youxian Sun Xianling Li State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Science and Technology on Thermal Energy and Power Laboratory Wuhan 2nd Ship Design and Research Institute

ISBN: (纸本)9781665478977

With the increasing complexity of modern industrial processes,the high-dimensional industrial data exhibits strong nonlinearity,bringing considerable challenges to the fault diagnosis of *** efficiently extract deep meaningful features that are crucial for fault diagnosis,a sparse Gaussian feature extractor(SGFE) is designed to learn a nonlinear mapping that projects the raw data into the feature space with the fault label *** feature space is described by the one-hot encoding of the fault category label as an orthogonal *** this way,the sparse Gaussian features related to fault categories can be gradually learned from the raw data by *** the feature space,the sparse Gaussian(SG) loss function is designed to constrain the distribution of features to multiple sparse multivariate Gaussian *** sparse Gaussian features are linearly separable in the feature space,which can improve the accuracy of the downstream fault classification *** effectiveness of the proposed SGFE is verified by the Tennessee-Eastman(TE) benchmark process.

关键词： Nonlinear fault diagnosis sparse Gaussian feature learning Gaussian feature extractor

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Billet Number Recognition Based on ESRGAN and Improved YOLOv5

Billet Number Recognition Based on ESRGAN and Improved YOLOv...

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Youth Academic Annual Conference of Chinese Association of automation (YAC)

作者： Zijia Wang Yichao Dong Dan Niu Minghao Liu Qi Li Xisong Chen Ministry of Education School of Automation Nanjing China Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Nanjing China Nanjing Sciyon Wisdom Technology Group Co. Ltd.Nanjing Nanjing China Jiangsu Province Key Laboratory of Thermal Process Intelligent Control Nanjing Nanjing China

ISBN: (纸本)9781665465373

Aiming at solving the problem of billet number recognition and further improve the rate of recognition, this paper proposes a billet number recognition algorithm based on ESRGAN and improved YOLOv5, which is referred to as YOLOv5-Billet in this paper. According to the actual situation, we propose a target recognition algorithm that uses the lightweight network MobileNetv3 to replace the backbone feature extraction network of YOLOv5. We also introduce ESRGAN to improve the quality of the input image and further improve the recognition effect. A series of rigorous experiments show that YOLOv5-Billet has a certain improvement in billet recognition speed and precision. Specifically, the average precision of YOLOv5-Billet is improved by 10.8%, and the detection speed is also improved by 5.12fps, reaching 40.35fps. Through the experimental verification of real-time collection of data sets, compared with a variety of classical target detection methods, the recognition precision and detection speed are improved to varying degrees. This model maintains the lightweight characteristics of YOLOv5 and meets the requirements of real-time monitoring of the number of blanks.

关键词： Billets Image recognition Target recognition Object detection Inventory management Turning Real-time systems

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A New Learning Paradigm for Stochastic Configuration Network: SCN+

arXiv

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arXiv 2022年

作者： Ao, Yanshuang Zhou, Xinyu Dai, Wei School Of Information And Control Engineering China University Of Mining And Technology Xuzhou221116 China State Key Laboratory Of Synthetical Automation For Process Industries Northeastern University Shenyang110819 China

Learning using privileged information (LUPI) paradigm, which pioneered teacher-student interaction mechanism, makes the learning models use additional information in training stage. This paper is the first to propose an incremental learning algorithm with LUPI paradigm for stochastic configuration network (SCN), named SCN+. This novel algorithm can leverage privileged information into SCN in the training stage, which provides a new method to train SCN. Moreover, the convergences have been studied in this paper. Finally, experimental results indicate that SCN+ indeed performs favorably. Copyright © 2022, The Authors. All rights reserved.

关键词： Stochastic systems

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Deep self-organizing map neural networks for plantar pressure image segmentation employing marr-hildreth features

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International Journal of Ambient Computing and Intelligence 2021年第4期12卷 1-21页

作者： Han, Jianlin Wang, Dan Li, Zairan Shi, Fuqian Glorious Sun Guangdong School of Fashion Huizhou University Huizhou China Huidong Shoes Science and Technology Innovation Center Huizhou China Tianjin Key Laboratory of Process Measurement and Control School of Electrical Engineering and Automation Tianjin University China Wenzhou Polytechnic Wenzhou China Rutgers Cancer Institute of New Jersey New Brunswick United States

Using the plantar pressure imaging analysis method to realize the optimization design of shoe last is still relatively preliminary. The analysis and utilization of imaging data still have problems such as single processing, incomplete information acquisition, and poor processing model robustness. A deep self-organizing map neural network based on Marr-Hildreth filter (dSOM-wh) is developed in this research. The structure and learning algorithms were optimized by learning vector quantization (LVQ) and count propagation (CP). As a kind of Marr-Hildreth filter, Laplacian of Gaussian (LoG) was developed for the preprocessing. The proposed method performed high effectiveness in accuracy (AC) (92.88%), sensitive (SE) (0.8941), and f-measurement (F1) (0.8720) by comparing with ANN, CNN, SegNet, ResNet, and pre-trained inception-v neural networks. The classification-based plantar pressure biomedical functional zoning technologies have potential applications in the comfort shoe production industry. Copyright © 2021, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

关键词： Image segmentation

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H∞Asynchronous control for Discrete-Time Semi-Markov Jump Systems 16th

H∞Asynchronous Control for Discrete-Time Semi-Markov Jump S...

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16th IFAC Symposium on Large Scale Complex Systems: Theory and Applications LSS 2022

作者： Jin, Liqiang Yin, Yanyan Liu, Yanqing Teo, Kok Lay Liu, Fei National Key Laboratory of Advanced Process Control for Light Industry Ministry of Education Institute of Automation Jiangnan University Wuxi214122 China School of Electrical Engineering Computing and Mathematical Sciences Curtin University PerthWA6102 Australia School of Mathematical Sciences Sunway University Kuala Lumpur47500 Malaysia Tianjin University of Finance and Economics Tianjin China

This paper investigates the problem of H∞asynchronous control for discrete-time semi-Markov jump systems. In practice, it is difficult to ensure that controller modes and system modes are updated synchronously. Therefore, a hidden semi-Markov model is introduced to characterize such an asynchronous phenomenon between system modes and controller modes. Meanwhile, the switching signal of the controller is generated by a given conditional probability related to the system mode. Based on the mode-dependent and elapsed-time-dependent Lyapunov function, together with the semi-Markov kernel (SMK) method, sufficient conditions constructed by linear matrix inequalities (LMIs) are derived to ensure the existence of an asynchronous controller such that the resulting system is σ-error mean square stable and satisfies an H∞performance index. Finally, a numerical example is given to illustrate the effectiveness and feasibility of the method proposed. © 2022 Elsevier B.V.. All rights reserved.

关键词： controllers

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Research on Target Detection Method Based on Improved YOLOv5 19th

Research on Target Detection Method Based on Improved YOLOv5

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19th International Conference on Web Information Systems and Applications, WISA 2022

作者： Wang, Rongzhuang Liu, Jun Zhang, Hualiang School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang110142 China Liaoning Key Laboratory of Intelligent Technology for Chemical Process Industry Shenyang110142 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

ISBN: (纸本)9783031203084

At present, various target detection algorithms are used in detection and classification. It is a problem to improve the accuracy and speed of target detection by using deep learning and neural network model to train the parameters of the data set. Aiming at the problems of insufficient detection, inaccurate detection of complex background and loss of small target feature information in yolov5 target detection, an improved method combining hole convolution, moving exponential average and retraining of positive samples is proposed in this paper. This method effectively solves the problems of complex background, occlusion and incomplete feature extraction, reduces the loss rate of target features, and improves the detection accuracy of each category. The improved yolov5 target detection algorithm in this paper can not only use multi feature fusion and accelerate positive sample matching, but also use cavity convolution to increase receptive field, improve the detection effect of small targets and reduce the occurrence of missed detection and false detection. Experiments show that the improved yolov5 algorithm has better detection rate and lower loss rate than many existing target detection algorithms. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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Data-Driven Distributionally Robust Mixed-Integer control through Lifted control Policy

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IEEE Transactions on Automatic control 2025年

作者： Ma, Xutao Ning, Chao Du, Wenli Shi, Yang Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China University of Victoria Department of Mechanical Engineering VictoriaBCV8W 3P6 Canada

This paper investigates the finite-horizon distributionally robust mixed-integer control (DRMIC) of uncertain linear systems. However, deriving an optimal causal feedback control policy to this DRMIC problem is computationally formidable for most ambiguity sets. To address the computational challenge, we propose a novel distributionally robust lifted control policy (DR-LCP) method to derive a high-quality approximate solution to this DRMIC problem for a rich class of Wasserstein metric-based ambiguity sets, including the Wasserstein ambiguity set and its variants. In theory, we analyze the asymptotic performance and establish a tight non-asymptotic bound of the proposed method. In numerical experiments, the proposed DR-LCP method empirically demonstrates superior performance compared with existing methods in the literature. ©1963-2012 IEEE.

关键词： Mixed-integer linear programming

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Repetitive T-S Fuzzy Model-Based Iterative Learning control Law Design

Repetitive T-S Fuzzy Model-Based Iterative Learning Control ...

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IEEE Conference on Decision and control

作者： Robert Maniarski Wojciech Paszke Hongfeng Tao Eric Rogers Institute of Automation Electronic and Electrical Engineering University of Zielona Góra ul. Szafrana 2 Zielona Góra Poland Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi China School of Electronics and Computer Science University of Southampton Southampton UK

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper develops an iterative learning control law for a class of nonlinear systems. The approach used to represent the nonlinear system dynamics is a Takagi-Sugeno fuzzy repetitive process that considers the two directions of information propagation. Then, the control action investigated is a state feedback control law combined with a PD-type feed-forward learning control law. Consequently, linear matrix inequality techniques can be used for control design. Furthermore, this approach allows the design of control action to satisfy the requirements on both the error convergence and the transient dynamics. Finally, an example demonstrates the properties of the new design.

关键词： State feedback control design Takagi-Sugeno model Nonlinear dynamical systems Linear matrix inequalities Transient analysis Iterative learning control Convergence

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A New Robust Adaptive Neural Network Backstepping control for Single Machine Infinite Power System With TCSC

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IEEE/CAA Journal of Automatica Sinica 2020年第1期7卷 48-56页

作者： Yanhong Luo Shengnan Zhao Dongsheng Yang Huaguang Zhang A new robust adaptive neural network backstepping control for single machine infinite power system with TCSC Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110004 School of Information Science and Engineering Northeastern UniversityShenyang 110004China

For a single machine infinite power system with thyristor controlled series compensation(TCSC) device, which is affected by system model uncertainties, nonlinear time-delays and external unknown disturbances, we present a robust adaptive backstepping control scheme based on the radial basis function neural network(RBFNN). The RBFNN is introduced to approximate the complex nonlinear function involving uncertainties and external unknown disturbances, and meanwhile a new robust term is constructed to further estimate the system residual error,which removes the requirement of knowing the upper bound of the disturbances and uncertainty terms. The stability analysis of the power system is presented based on the Lyapunov function,which can guarantee the uniform ultimate boundedness(UUB) of all parameters and states of the whole closed-loop system. A comparison is made between the RBFNN-based robust adaptive control and the general backstepping control in the simulation part to verify the effectiveness of the proposed control scheme.

关键词： Backstepping control radial basis function neural network(RBFNN) robust adaptive control thyristor controlled series compensation(TCSC) uniform ultimate boundedness(UUB)

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Breaking Limitation of Convergence in Continuous Optimization: Predefined-Time Gradient Flows

Breaking Limitation of Convergence in Continuous Optimizatio...

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IEEE Conference on Decision and control

作者： Renyongkang Zhang Ge Guo Zhenyu Gao Miao Yu Lu Zhang Zhengsong Wang Meng Han Yanyan Yin College of Information Science and Engineering Northeastern University Shenyang China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Western Australia Australia

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper investigates the continuous-time unconstrained optimization via dynamical gradient flow systems. For strongly convex objective functions, a gradient flow with predefined time convergence is proposed, which is suitable for relaxing the objective function to only satisfy the PolyakLojasiewicz ($\mathbf{P}-\mathrm{L}$) condition. A predefined-time convergent Newton’s flow is designed for the minimization of strictly convex functions. The proposed result is extended for strongly convex objective functions with time-varying parameters. In our schemes, the upper bound of convergence time can be used as a prior choice without involving system initialization and complex parameter calculations. The convergence of algorithms is rigorously proved by using the Lyapunov method. Numerical simulation illustrates the effectiveness of the proposed methods.

关键词： Upper bound Linear programming Numerical simulation Minimization Convex functions Optimization Convergence Lyapunov methods

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