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43rd Chinese Control Conference, CCC 2024

作者： Wang, Yijie Ren, Zhigang Cai, Jianpu Wu, Zongze Xie, Shengli Shenzhen518060 China Guangdong University of Technology School of Automation Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong University of Technology Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Guangzhou510006 China

ISBN: (纸本)9789887581581

In the industrial domain, surface defect detection after multiple processing steps is crucial for improving the outgoing quality of products. However, due to the characteristics of surface defects, such as low contrast, irregular shapes, small and elongated features, and significant noise. To address these issues, this paper proposes a novel real-time high-resolution network (RHNet). Unlike previous models, RHNet emphasizes the importance of modeling spatial information to capture small defects. Therefore, the output feature maps of each module are maintained at 1/4 resolution of the original image. We introduce a short-term dual-branch module (SDBM) to handle real-time processing of high-resolution feature maps. Additionally, FPAPPM is proposed for swift extraction of deep information and multi-scale context. Through extensive experiments, we demonstrate that our proposed model exhibits robust defect modeling capabilities and detection performance while still meeting real-time requirements. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Semantic Segmentation

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A deep learning-based approach for motion-induced error reduction in fringe projection profilometry 8

A deep learning-based approach for motion-induced error redu...

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8th Symposium on Novel Photoelectronic Detection Technology and Applications

作者： Su, Wenqing Tan, Ji He, Zhaoshui School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong University of Technology Key Laboratory for IoT Intelligent Information Processing and System Integration of Ministry of Education Guangzhou510006 China Guangdong University of Technology Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China

ISBN: (数字)9781510653122

ISBN: (纸本)9781510653115

Fringe projection profilometry (FPP) is one of popular 3D measurement techniques, which can be divided into two categories: Fourier transform profilometry (FTP) and phase-shifting profilometry (PSP). Compared to FTP, PSP has been increasingly appealing to researchers due to its merits of higher accuracy and sensitivity. Although PSP works well with the assumption that the object stays quasi-static, it is sensitive to the object motion and causes the motion-induced error in dynamic measurement due to the multi-frame measurement mechanism. However, the multi-frequency phase unwrapping is always utilized to solve the problem of surface discontinuities, which limits the reduction of numbers of projected patterns. Besides, using the high-speed camera will increase the cost of the hardware. Recently, researchers have demonstrated that the phase unwrapping, fringe denoising and dynamic range can be improved with the assistance of deep learning technique. Therefore, in this paper, a deep learning-based method is proposed for motion-induced error reduction. With the aid of strong fitting capability of the neural network, the motion-induced errors can be significantly reduced even under low capture frame rate. The proposed method is experimentally verified on its applicability for dynamic 3D measurement. © 2022 SPIE

关键词： Errors

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Differentiator-Based Adaptive H8 Tracking Control of Fully Actuated systems 3

Differentiator-Based Adaptive H8 Tracking Control of Fully A...

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3rd Conference on Fully Actuated system Theory and Applications, FASTA 2024

作者： Feng, Yuxin Li, Zhiqiang Liu, Yang He, Zhaoshui Li, Hongyi Guangdong University of Technology School of Automation Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangzhou510006 China Qingdao University of Science and Technology School of Automation and Electronics Engineering Qingdao266061 China Guangdong University of Technology School of Automation Guangzhou510006 China Key Laboratory for IoT Intelligent Information Processing and System Integration of Ministry of Education Guangzhou510006 China

ISBN: (纸本)9798350373691

This article addresses an adaptive tracking control problem for uncertain high-order fully actuated (HOFA) systems with unknown parameters and disturbances. Under the framework of backstepping, the unknown parameter is estimated and the external disturbance is handled by using adaptive control and H8 technique, respectively. Moreover, the Levant differentiator is employed to reduce the computation burden. Additionally, the boundedness of signals in the closed-loop system is proven using the Lyapunov theory. Finally, the effectiveness of the proposed scheme is validated through the simulation study. © 2024 IEEE.

关键词： Backstepping

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Adaptive neural consensus tracking control for nonlinear multiagent systems with dead zone and sensor faults

Adaptive neural consensus tracking control for nonlinear mul...

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第42届中国控制会议

作者： Chengjie Huang Ci Chen Zhi Liu Zhenni Li Guangdong University of Technology Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong Key Laboratory of IoT Information Technology 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education

In this paper, an adaptive neural tracking control design is investigated for uncertain multiagent systems(MASs)with actuator dead zone and sensor faults. The adaptive control technique based on neural networks is employed to handle the uncertainty in the systems, which, however, results in a bounded tracking error from most existing works. To achieve the asymptotic tracking performance, we proposed a direct adaptive strategy via the tuning function method. Technically, a Lyapunov function with a series of smooth functions is designed, which benefits proposing of an asymptotic controller. Then, under the framework of direct adaptive control, an inverse compensation scheme, as well as a fault-tolerant mechanism, are considered for disposing of the actuator dead zone and sensor failures. As a result, user-preset tracking performance is achieved and the system error in the MASs converges to a predefined range. An example in the simulation section illustrates the effectiveness of the proposed method.

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Learning the continuous-time optimal decision law from discrete-time rewards

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National Science Open 2024年第5期3卷 130-147页

作者： Ci Chen Lihua Xie Kan Xie Frank Leroy Lewis Yilu Liu Shengli Xie School of Automation Guangdong University of TechnologyGuangdong Key Laboratory of IoT Information TechnologyGuangzhou 510006China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangzhou 510006China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou 510006China UTA Research Institute the University of Texas at ArlingtonFort Worth 76118USA Department of Electrical Engineering and Computer Science University of TennesseeKnoxville 37996USA Oak Ridge National Laboratory Oak Ridge 37830USA Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou 510006China

The concept of reward is fundamental in reinforcement learning with a wide range of applications in natural and social *** an interpretable reward for decision-making that largely shapes the system's behavior has always been a challenge in reinforcement *** this work,we explore a discrete-time reward for reinforcement learning in continuous time and action spaces that represent many phenomena captured by applying physical *** find that the discrete-time reward leads to the extraction of the unique continuous-time decision law and improved computational efficiency by dropping the integrator operator that appears in classical results with integral *** apply this finding to solve output-feedback design problems in power *** results reveal that our approach removes an intermediate stage of identifying dynamical *** work suggests that the discrete-time reward is efficient in search of the desired decision law,which provides a computational tool to understand and modify the behavior of large-scale engineering systems using the optimal learned decision.

关键词： continuous-time state and action decision law learning discrete-time reward dynamical systems reinforcement learning

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An adaptive fault detection strategy for Cz crystal growth process

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IEEE Transactions on Instrumentation and Measurement 2025年

作者： Mu, Lingxia Feng, Nan Zhang, Zhenhua Zhang, Youmin Zhang, Jian Xian University of Technology Crystal Growth Equipment and Systems Integration National Engineering Research Center Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xian710048 China University of Science and Technology Beijing School of Intelligence Science and Technology Beijing100083 China Concordia University Department of Mechanical Industrial and Aerospace Engineering MontrealQCH3G 1M8 Canada

The monitoring and fault detection for the Czochralski (Cz) crystal growth process is important to ensure the quality of the produced mono-crystalline silicon. The Cz process is a typical multi-modes industrial process, which makes the detection difficult in distinguish the change caused by the fault or mode switching. In this paper, an adaptive fault detection strategy for Cz crystal growth process is proposed. Firstly, quality-related variables are selected from numerous process variables, based on which the training dataset is constructed. Then, a fault detection model is built based on slowly-varying feature and statics indicator. A model switching strategy and updating algorithm are designed to handle the abnormal situation caused by the mode switching or fault occurrence. Three representative experiments are conducted to verify the performance of the proposed strategy, including numerical cases, a publicly available dataset, and Cz crystal growth process from our laboratory. © 1963-2012 IEEE.

关键词： Crystal growth from melt

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Imperceptible and Sparse Adversarial Attacks via a Dual-Population-Based Constrained Evolutionary Algorithm

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第2期4卷 268-281页

作者： Tian, Ye Pan, Jingwen Yang, Shangshang Zhang, Xingyi He, Shuping Jin, Yaochu Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China Anhui University School of Computer Science and Technology Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Hefei230601 China Anhui University Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Hefei230601 China Bielefeld University Faculty of Technology Bielefeld33619 Germany

The sparse adversarial attack has attracted increasing attention due to the merit of a low attack cost via changing a small number of pixels. However, the generated adversarial examples are easily detected in vision since the perturbation to each pixel is relatively large. To achieve imperceptible and sparse adversarial attacks, this article formulates a bi-objective constrained optimization problem, simultaneously minimizing the 0 and 2 distances to the original image, and proposes a dual-population-based constrained evolutionary algorithm to solve it. The proposed method solves the optimization problem by evolving two populations, where one population is responsible for finding feasible solutions (i.e., successful attacks) and the other one is to minimize both the 0 and 2 distances. Moreover, a population initialization strategy and two genetic operators are customized to accelerate the convergence speed. Experimental results indicate that the proposed method can achieve high success rates with low attack costs, and strikes a better balance between the 0 and 2 distances than state-of-the-art methods. © 2020 IEEE.

关键词： Evolutionary algorithms

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Blind Separation of Heart Sound Convolutive Mixtures Utilizing Independent Vector Analysis

Blind Separation of Heart Sound Convolutive Mixtures Utilizi...

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第40届中国控制会议

作者： Yuan Xie Kan Xie Shengli Xie School of Automation Guangdong University of Technology and Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong Key Laboratory of IoT Information Processing and Joint International Research Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education of the P.R.C. Key Laboratory of Intelligent Detection and the IoT in Manufacturing Ministry of Education and 111 Center for Intelligent Batch Manufacturing Based on IoT Technology

Separation of heart sound signals from the recorded mixtures has become a hot research topic in the clinical diagnosis of heart diseases. To better understand the current health status of patients, it is of great significance to develop intelligent auscultation methods to improve the effectiveness of hearing and assist clinicians in practice. In this paper, an optimization convolutive blind source separation algorithm utilizing independent vector analysis is proposed for separation of the heart sound mixtures. In the algorithm, a denoising approach is firstly used to reduce the impact of the additive white Gaussian noise. Then,using the short time Fourier transform, the mixing signals in time domain are transmitted into the frequency domain. Afterwards,the unmixng matrix is updated using Newton’s method, and the heart sound sources are reconstructed based on the estimated unmixing matrix. Experimental results show that the algorithm obtains better separation performance than the baseline ***, it has better superiority in the strong reverberant environment.

关键词： Blind source separation（BSS） Convolutive mixtures Heart sound Independent vector analysis

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High Depth Resolution Wavelength Scanning Interferometry with Narrow Scanning Bandwidth Via Sinewaves Separation

SSRN

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SSRN 2022年

作者： Tan, Ji He, Zhaoshui Bai, Yulei Dong, Bo Xie, Shengli School of Automation Guangdong University of Technology Guangzhou510006 China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Key Laboratory of Intelligent Detection The Internet of Things in Manufacturing Ministry of Education Guangzhou510006 China Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China

Wavelength scanning interferometry (WSI) is a promising tomographic imaging technique. However, the depth resolution is fundamentally limited by a narrow wavelength scanning bandwidth, which brings challenges to the frequency extraction for depth information. In this work, we proposed a parameter estimation (PE) paradigm via sinewaves separation, which allows high depth resolution tomographic measurement with narrow-band sources. To obtain accurate depth information, a sinewaves separation model is designed to estimate the frequency of the overlapped signals and it can achieve higher depth resolution without any prior knowledge even under limited scanning bandwidth. Moreover, the estimated frequency is used for phase retrieval and the multi-layer 3D profile measurement can be further implemented. The proposed method has been applied to measure the multi-layer acrylic plastic and USAF resolution board. The experimental results show that the performance of the proposed method is comparable to DFT even if reducing about 60% scanning bandwidth, i.e., the proposed method can improve approximately 3 folds of the depth resolution. © 2022, The Authors. All rights reserved.

关键词： Bandwidth

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Learning-based Prescribed Rate Design for Output Regulation of Discrete-time systems

Learning-based Prescribed Rate Design for Output Regulation ...

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第35届中国控制与决策会议

作者： Chengye Zhang Ci Chen Shengli Xie School of Automation Guangdong University of Technology Guangdong Key Laboratory of IoT Information Technology School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing Ministry of Education

This paper presents data-driven output regulation designs for unknown discrete-time systems with the prescribed convergence *** integrating the convergence rate specified by the designer,the tracking problem is composed of learning two components:a feedback control gain and solutions to output regulator *** RL algorithm can learn the feedback control gain from system measurements from a specified auxiliary system without knowing its full *** output regulator equations associated with the convergence rate are solved as a feedforward control gain by leveraging the system data rather than the dynamic *** proposed designs assure that the tracking error asymptotically converges to zero at the prescribed convergence *** simulation results show the effectiveness of the proposed methods.

关键词： Reinforcement learning prescribed convergence rate model-free tracking design discrete-time systems

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