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SSRN 2024年

作者： Xie, Yuan Zou, Tao Yang, Junjie Sun, Weijun Xie, Shengli School of Mechanical and Electrical Engineering Guangzhou University Guangdong-Hong Kong-Macao Key Laboratory of Multi-scale Information Fusion and Collaborative Optimization Control of Complex Manufacturing Process Guangzhou510006 China School of Automation Guangdong University of Technology Joint International Research Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education of the P.R.C. Guangzhou510006 China School of Automation Guangdong University of Technology Guangdong Key Laboratory of IoT Information Processing Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China School of Automation Guangdong University of Technology Key Laboratory of Intelligent Detection and the IoT in Manufacturing Ministry of Education 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China

Blind source separation under an underdetermined reverberation environment is a very challenging issue. The classical method is based on the expectation maximization algorithm. However, it is limited to high reverberation environments, resulting in bad or even invalid separation performance. To eliminate this restriction, a room impulse response reshaping-based expectation maximization method is designed to solve the problem of blind signal separation in an underdetermined reverberant environment. Firstly, a room impulse response reshaping technology is designed to eliminate the influence of audible echo on the reverberant environment, improving the quality of the received signals. Then, a new mathematical model of time-frequency mixing signals is established to reduce the approximation error of model transformation caused by high reverberation. Furthermore, an improved expectation maximization method is proposed for real-time update learning rules of model parameters, and then the sources are separated using the estimators provided by the improved expectation maximization method. Experimental results based on source separation of speech and music mixtures demonstrate that the proposed algorithm achieves better separation performance while maintaining much better robustness than popular expectation maximization methods. © 2024, The Authors. All rights reserved.

关键词： Reverberation

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Blind Separation of Heart Sound Convolutive Mixtures Utilizing Independent Vector Analysis 40

Blind Separation of Heart Sound Convolutive Mixtures Utilizi...

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40th Chinese Control Conference, CCC 2021

作者： Xie, Yuan Xie, Kan Xie, Shengli Guangdong University of Technology Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing School of Automation Guangzhou510006 China Ministry of Education of the P.R.C. Guangdong Key Laboratory of IoT Information Processing Jt. Intl. Research Laboratory of Intelligent Information Processing and System Integration of IoT Guangzhou510006 China Ministry of Education 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Key Laboratory of Intelligent Detection and the IoT in Manufacturing Guangzhou510006 China

ISBN: (纸本)9789881563804

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 methods. Especially, it has better superiority in the strong reverberant environment. © 2021 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Time domain analysis

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Broadband low-frequency sound absorption of multifunctional composite metastructure

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Science China(Technological Sciences) 2025年第2期68卷 114-124页

作者： Yilong YANG Shanshan LIU Yan LI Tong GAO Yongdong PAN Yong LI Yabin JIN School of Aerospace Engineering and Applied Mechanics Tongji UniversityShanghai 200092China State International Joint Research Center of Aerospace Design and Additive Manufacturing Northwestern Polytechnical UniversityXi’an 710072China Institute of Acoustics School of Physics Science and EngineeringTongji UniversityShanghai 200092China Shanghai Key Laboratory of Intelligent Sensing and Detection Technology School of Mechanical and Power EngineeringEast China University of Science and TechnologyShanghai 200237China Shanghai Institute of Aircraft Mechanics and Control Shanghai 200092China

In this study,we fabricated multifunctional metastructures from carbon fiber-reinforced plastic composites using additive manufacturing *** metastructures are characterized by their lightweight,load-bearing capacity,and broadband low-frequency sound absorption *** metastructure consists of 36 unit cells,and non-local coupling mechanism was considered for designing the sound absorption *** developed an acoustic impedance theory tailored for the metastructure,facilitating an analysis of thermal and viscous dissipation *** is proven theoretically and experimentally that the proposed composite metastructure can achieve a noise reduction with an average sound absorption coefficient greater than 0.9 across frequencies in the rage of 330-1500 *** also studied the metastructure’s quasi-static and cyclic compression performance,confirming its efficient absorption capabilities after cyclic *** proposed design and additive manufacturing method for composite metastructures provides a novel pathway for creating lightweight,multifunctional structures with diverse applications,such as aerospace engineering.

关键词： composite metastructure broadband sound absorption additive manufacturing

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Robust tensor decomposition via orientation invariant tubal nuclear norms

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Science China(Technological Sciences) 2022年第6期65卷 1300-1317页

作者： WANG AnDong ZHAO QiBin JIN Zhong LI Chao ZHOU GuoXu School of Automation Guangdong University of TechnologyGuangzhou 510006China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Tensor Learning Team RIKEN Center for Advanced Intelligence ProjectTokyo 103-0027Japan Key Laboratory of Intelligent Perception and System for High-Dimensional Information Ministry of EducationNanjing 210094China Key Laboratory of Intelligent Detection and the Internet of Things in Manufacturing Ministry of EducationGuangzhou 510006China

Aiming at recovering an unknown tensor(i.e.,multi-way array)corrupted by both sparse outliers and dense noises,robust tensor decomposition(RTD)serves as a powerful pre-processing tool for subsequent tasks like classification and target detection in many computer vision and machine learning ***,tubal nuclear norm(TNN)based optimization is proposed with superior performance as compared with other tensorial nuclear norms for tensor ***,one major limitation is its orientation sensitivity due to low-rankness strictly defined along tubal orientation and it cannot simultaneously model spectral low-rankness in multiple *** this end,we introduce two new tensor norms called OITNN-O and OITNN-L to exploit multi-orientational spectral low-rankness for an arbitrary K-way(K≥3)*** further formulate two RTD models via the proposed norms and develop two algorithms as the ***,we establish non-asymptotic error bounds which can predict the scaling behavior of the estimation *** on real-world datasets demonstrate the superiority and effectiveness of the proposed norms.

关键词： tensor recovery t-SVD estimation error tensor completion

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Dynamic Sparse Coding-Based Value Estimation Network for Deep Reinforcement Learning

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SSRN 2023年

作者： Zhao, Haoli Li, Zhenni Su, Wensheng Xie, Shengli School of Automation Guangdong University of Technology Guangzhou510006 China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology GDUT Guangzhou510006 China Key Laboratory of Intelligent Detection The Internet of Things in Manufacturing GDUT Ministry of Education Guangzhou510006 China Key Laboratory of Intelligent Information Processing and System Integration of IoT GDUT Ministry of Education Guangzhou510006 China

Deep Reinforcement Learning (DRL) is one powerful tool for varied control automation problems. Performances of DRL highly depend on the accuracy of value estimation for states from environments. However, the Value Estimation Network (VEN) in DRL can be easily influenced by the phenomenon of catastrophic interference from environments and training, and moreover, the efficiency of DRL is restricted by the training with redundant parameters. In this paper, we propose a Dynamic Sparse Coding-based (DSC) DRL model to obtain precise sparse representations and sparse parameters in VEN through training, i.e., DSC-VEN, to improve the control performance, which is not only applicable in Q-learning but also in actor-critic structured DRL. In detail, to alleviate interference in VEN, we propose to employ dynamic sparse coding to learn sparse representations for accurate value estimation with a dynamic sparsity regularizer. To avoid influences from redundant parameters, we employ dynamic sparse coding to prune weights with dynamic thresholds. Therefore, we develop a dynamic sparse coding-based VEN algorithm, which can improve control performances not only for Q-learning structured discrete-action DRL but also actor-critic structured continuous-action DRL. We have validated the proposed DSC-VEN in both discrete and continuous action benchmark environments. Experiments demonstrate that the proposed algorithms with dynamic sparse coding can obtain higher control performances than existing benchmark DRL algorithms in both discrete-action and continuous-action environments, e.g., over 25% increase in Puddle World and about 10% increase in Hopper. Moreover, the proposed algorithm can reach convergence efficiently with fewer episodes in different environments. © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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RHNet: A Real-Time High-Resolution Network for Surface Defect detection

RHNet: A Real-Time High-Resolution Network for Surface Defec...

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第43届中国控制会议

作者： Yijie Wang Zhigang Ren Jianpu Cai Zongze Wu Shengli Xie School of Automation Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangdong University of Technology Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong University of Technology College of Mechatronics and Control Engineering Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)Shenzhen University Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing (GDUT) Ministry of Education

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

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 ***, 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.

关键词： Surface defect inspection Anomaly detection Semantic segmentation Deep learning Pattern recognition

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Optimal Tracking Control of Injection Speed in Injection Molding Machine with Gaussian Process Learning and Model Predictive Control

Optimal Tracking Control of Injection Speed in Injection Mol...

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第43届中国控制会议

作者： Jianpu Cai Zhigang Ren Bo Zhang Zongze Wu School of Automation Guangdong Key Laboratory of IoT Information Technology Guangdong University of Technology Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong University of Technology Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing (GDUT) Ministry of Education College of Mechatronics and Control Engineering Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)Shenzhen University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Injection molding, as a common plastic processing technology, has significant importance in modern manufacturing industry. However, due to the nonlinear dynamic characteristics of injection molding machines(IMMs) and the influence of various factors such as raw materials, equipment, and environment during the injection molding process(IMP), uncertainty residuals are generated, which makes it difficult to achieve optimal tracking control of injection speed, thereby affecting product quality and production efficiency. To address this issue, we propose a learning-based model predictive control(LMPC) *** scheme uses Gaussian process regression(GPR) to predict and model uncertainty residuals in the IMP. The residual model of IMM is learned through historical injection molding data, and the model predictive control(MPC) is used to achieve optimal tracking control of injection speed based on the predicted model, thereby automatically adjusting the injection speed according to requirements. In addition, we use differential dynamic programming with control constraints to solve the optimization ***, we conduct comprehensive numerical simulation experiments to prove the effectiveness and efficiency of the proposed LMPC scheme in injection speed control of IMM.

关键词： Injection molding model predictive control optimal control gaussian process learning control

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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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Chinese Control Conference (CCC)

作者： Chengjie Huang Ci Chen Zhi Liu Zhenni Li Guangdong University of Technology Guangzhou P. R. China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou P. R. China Guangdong Key Laboratory of IoT Information Technology Guangzhou P. R. China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou P. R. China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing Ministry of Education Guangzhou

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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Improving GNSS positioning correction using Deep Reinforcement Learning with Adaptive Reward Augmentation Method 36

Improving GNSS positioning correction using Deep Reinforceme...

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36th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2023

作者： Tang, Jianhao Li, Zhenni Guo, Rui Zhao, Haoli Wang, Qianming Liu, Ming Xie, Shengli Polycarpou, Marios School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Techtotop Microelectronics Technology Co. Ltd. Guangzhou510006 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing Guangzhou510006 China Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus

ISBN: (纸本)9780936406350

High-precision global navigation satellite system (GNSS) positioning for automatic driving in urban environments is an unsolved problem because of the influence of multipath effects. Recently, methods based on data-driven deep reinforcement learning (DRL) have been used to learn continual positioning-correction strategies without strict assumptions about model parameters, and are adaptable to nonstationary urban environments. However, these methods face two remaining challenges: 1) real-time data for training collected in nonstationary urban environments is inadequate because of issues such as response delay and signal interruption, which causes the performance degradation in DRL, 2) the existing methods use vehicle positions as the environment observations, ignoring the complex errors caused by multipath effects in urban environments. In this paper, we propose a novel DRL-based positioning-correction method with an adaptive reward augmentation method (ARAM), and use GNSS measurements instead of vehicle positions as the environment observations, for improving the GNSS positioning accuracy in nonstationary urban environments. To specify the accurate current state of the vehicle agent, we use the GNSS measurement observations, including the line-of-sight (LOS) vector and the pseudorange residual, to model complex environmental errors, and employ a long and short term memory (LSTM) module to learn the temporal aspects of the observations, which include the interference by multipath effects on the GNSS positioning in urban environments. To address the performance degradation caused by inadequate real-time training data, we employ ARAM to adaptively modify the matching of data between the source domain and target domain of the nonstationary urban environments, to leverage sufficient data from the source domain for training, and thus to improve the performance of DRL. Hence, based on ARAM and using the GNSS measurement observations, we construct an LSTM-based proximal pol

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SA-UCBSS: Sparsity-Based Adaptive Underdetermined Convolutive Blind Source Separation

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SSRN 2023年

作者： Xie, Yuan Zou, Tao Yang, Junjie Sun, Weijun Xie, Shengli School of Mechanical and Electrical Engineering Guangzhou University Guangzhou Key Laboratory of Integrated Automation of Manufacturing Process Guangzhou510006 China School of Automation Guangdong University of Technology Joint International Research Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education of the P.R.C. Guangzhou510006 China School of Automation Guangdong University of Technology Guangdong Key Laboratory of IoT Information Processing Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China School of Automation Guangdong University of Technology Key Laboratory of Intelligent Detection and the IoT in Manufacturing Ministry of Education 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China

Blind source separation is the process of separating unknown source signals only based on received mixing signals when the source signal cannot be accurately known. Traditional methods are mainly based on linear approximate transformation model, which often have large systematic errors in high reverberation environment, resulting in poor separation performance. To avoid such limitation, a sparsity-based adaptive underdetermined convolutive blind source sepparation (SA-UCBSS) algorithm is proposed by unifying a convolutive approximate transformation model and an Lp-norm regularization penalty. A crosstalk canceller is designed for room impulse response pretreatment with p-norm optimization to eliminate audible echoes, and a sparse optimization scheme is designed for the parameter initialization to make the iterative recovery process converge faster. Then, a convolutive approximate transformation model is built to achieve better accurate estimation of mixing system. SA-UCBSS algorithm is proposed to achieve better source separation by adaptively tuning the value of parameter p. Experimental results including BSS of synthesized and live-recorded speech convolutive mixtures show that SA-UCBSS algorithm has superior separating property and better robustness compared with several popular algorithms. © 2023, The Authors. All rights reserved.

关键词： Blind source separation

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