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2024 China Automation Congress, CAC 2024

作者： Wu, Xuejin Wu, Yali Hu, Yanghu Department of Information and Control Engineering Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China State Key Laboratoiy of Metal Extrusion and Forging Equipment Technology China National Heavy Machinery Research Institute Co. Ltd Xi'an710016 China

ISBN: (纸本)9798350368604

The scarcity of fault samples presents a significant challenge for fault diagnosis in industrial processes. Conventional methods often fail to deliver satisfactory results when dealing with small sample sizes. In addressing the issue of small sample fault diagnosis in industrial processes, this paper utilizes the Variational Mode Reconstruction(VMR) algorithm to screen the best components decomposed by the Variational Mode Decomposition(VMD) through the envelope entropy then random weighting them and signal reconstruction to achieve data enhancement, to enrich the sample size and to prevent the model overfitting phenomenon due to the scarcity of samples. Meanwhile, this paper integrates Convolutional Neural Network (CNN) and Long Short-term Memory Neural Network (LSTM) to propose a network model of spatio-temporal feature fusion for feature extraction and classification. The experiments demonstrate that this model not only enhances data diversity but also ensures consistency before and after enhancement, effectively mitigating overfitting. Furthermore, it guarantees high recognition and generalization rates in fault diagnosis. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep convolutional autoencoder thermography for artwork defect detection

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Quantitative InfraRed Thermography Journal 2024年第6期21卷 367-383页

作者： Liu, Yi Wang, Fumin Liu, Kaixin Mostacci, Miranda Yao, Yuan Sfarra, Stefano Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou China Shanxi Key Laboratory of Signal Capturing & Processing North University of China Taiyuan China Professional Restorer Celano Italy Department of Chemical Engineering National Tsing Hua University Hsinchu Taiwan Department of Industrial and Information Engineering and Economics University of L’Aquila L’Aquila Italy

Infrared thermography is a cost-effective non-destructive evaluation technique that plays a critical role in extracting information about defects in cultural heritage such as works of art. However, in-depth studies on the internal structure of contemporary artworks using infrared thermography remain lacking, and therefore, a deep convolutional autoencoder thermography (DCAT) data analysis method is proposed for defect detection of contemporary artworks. In the proposed method, original data are enhanced by convolution to reduce the noise and inhomogeneous background in the original thermal images;subsequently, a deep autoencoder is used to extract nonlinear features from the enhanced thermographic data, and the results of the hidden layer are visualised. These visualised images highlight information regarding the internal structure and defects of the artwork. The case study on a handmade replica of Picasso’s ‘La Bouteille de Suze’, reproduced exactly as the original, has shown that DCAT significantly improved the accuracy of defect detection when compared with that of commonly used methods. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Nondestructive examination

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Energy management of solid oxide fuel cell/lithium battery hybrid system

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CSEE Journal of Power and Energy systems 2022年 1-14页

作者： Li, Dong Zhong, Xiao Bo Xu, Yuan Wu Wei, Chong Yang Zhao, Song Jiang, Jianhua Deng, Zhonghua Fu, Xiaowei Li, Xi School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Hubei Wuhan430074 China College of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan430081 China

The research of fuel cell and lithium battery hybrid system has attracted more and more researchers because of its advantages of low emission. However, the lower efficiency of energy management has been a critical factor that obstruct the commercialization of hybrid system. In this work, based on finite element ideas, a solid oxide full cell node model is developed to accurately estimate the operating status of the fuel cell. As for lithium battery, previous energy management studies generally only focused on state of charge, however, state of health is also a key parameter for lithium battery. In this paper, considering the strong coupling between variables we use particle filter algorithm to jointly estimate the state of charge and state of health. To reasonably distribute and manage the energy in the hybrid system, a T-S fuzzy controller for the hybrid system is designed by controlling the state variables in the SOFC model and the state of lithium battery. Finally, our algorithm is verified by DSPACE simulation platform. The result shows that the hybrid system with the energy management strategy not only meets the real-time sharply changing demand but also ensures the hybrid system working efficiently and safely. © 2022 China Electric Power Research Institute. All rights reserved.

关键词： Hybrid systems

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Global Path Planning for Unmanned Surface Vessels based on Risk-Penalty-related A* Algorithm 41

Global Path Planning for Unmanned Surface Vessels based on R...

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第41届中国控制会议

作者： Siyuan Xu Weilin Li Zheyu Zhang Jing Wu Chengnian Long UM-SJTU Joint Institute Shanghai Jiao Tong University Department of Automation and Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,an optimal path planning of unmanned surface vessels（USVs） is presented with the consideration of the practical marine *** from the traditional open/barrier classification,an improved riskpenalty-related A algorithm is proposed based on a new obstacle modelling,where the risk penalty in the potential risk region is defined according to boat weight and marine ***,an unreal engine is used to verify the effectiveness of the proposed *** given different boat weight and weather condition,our improved A*algorithm can provide different navigation path for the same sea area,which shows our proposed method is more efficient and safe.

关键词： Risk-penalty-related A~*Algorithm Map Division Global path planning Unmanned Surface Vessels

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Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous Driving

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第5期9卷 1-19页

作者： Li, Yueyuan Yuan, Wei Zhang, Songan Yan, Weihao Shen, Qiyuan Wang, Chunxiang Yang, Ming Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Innovation Center of Intelligent Connected Vehicle Global Institute of Future Technology Shanghai Jiao Tong University Shanghai China Global Institute of Future Technology Shanghai Jiao Tong University Shanghai China

Simulators play a crucial role in autonomous driving, offering significant time, cost, and labor savings. Over the past few years, the number of simulators for autonomous driving has grown substantially. However, there is a growing concern about the validity of algorithms developed and evaluated in simulators, indicating a need for a thorough analysis of the development status of the simulators. To address existing gaps in research, this paper undertakes a comprehensive review of the history of simulators, proposes a utility-based taxonomy, and investigates the critical issues within open-source simulators. Analysis of the past thirty years' development trajectory reveals a trend characterized by an increase in open-source simulators and an expansion of their functionality scope. The categorization of simulators based on feature functionalities delineates five primary classes: traffic flow, sensory data, driving policy, vehicle dynamics, and comprehensive simulators. Furthermore, the paper identifies critical unresolved issues in open-source simulators, including concerns regarding the fidelity of sensory data, representation of traffic scenarios, and accuracy in vehicle dynamics simulation, all of which have the potential to undermine experimental confidence. Additionally, challenges in data format inconsistency, labor-intensive map construction processes, sluggish step updating, and insufficient support for Hardware-In-the-Loop testing are discussed as hindrances to experimental efficiency. In light of these findings, the survey furnishes task-oriented recommendations to aid in the selection of simulators, taking into account factors such as accessibility, maintenance status, and quality, while highlighting the inherent limitations of existing open-source simulators in validating algorithms and facilitating real-world experimentation. IEEE

关键词： Autonomous vehicles

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An Adaptive Physics-Informed Neural Network with Two-Stage Learning Strategy to Solve Partial Differential Equations

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Numerical Mathematics(Theory,Methods and Applications) 2023年第2期16卷 298-322页

作者： Shuyan Shi Ding Liu Ruirui Ji Yuchao Han School of Automation and Information Engineering Xi‘an University of TechnologyXi‘an 710048P.R.China National&Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xi‘an 710048P.R.China Xi’an University of Technology Shannxi Key Laboratory of Complex System Control and Intelligent Information ProcessingXi‘an 710048P.R.China

Physics-Informed Neural Network(PINN)represents a new approach to solve Partial Differential Equations(PDEs).PINNs aim to solve PDEs by integrating governing equations and the initial/boundary conditions(I/BCs)into a loss ***,the imbalance of the loss function caused by parameter settings usually makes it difficult for PINNs to converge,*** they fall into local *** other words,the presence of balanced PDE loss,initial loss and boundary loss may be critical for the *** addition,existing PINNs are not able to reveal the hidden errors caused by non-convergent boundaries and conduction errors caused by the PDE near the ***,these problems have made PINN-based methods of limited use on practical *** this paper,we propose a novel physics-informed neural network,*** adaptive physics-informed neural network with a two-stage training *** algorithm adds spatio-temporal coefficient and PDE balance parameter to the loss function,and solve PDEs using a two-stage training process:pre-training and formal *** pre-training step ensures the convergence of boundary loss,whereas the formal training process completes the solution of PDE by balancing various loss *** order to verify the performance of our method,we consider the imbalanced heat conduction and Helmholtz equations often appearing in practical *** Klein-Gordon equation,which is widely used to compare performance,reveals that our method is able to reduce the hidden *** results confirm that our algorithm can effectively and accurately solve models with unbalanced loss function,hidden errors and conduction *** codes developed in this manuscript are publicy available at https://***/callmedrcom/ATPINN.

关键词： Physics informed neural networks partial differential equations two-stage learning scientific computing

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Mapless Path Planning of Multi-robot systems in Complex Environments via Deep Reinforcement Learning 4

Mapless Path Planning of Multi-robot Systems in Complex Envi...

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4th International Conference on Data-Driven Optimization of Complex systems, DOCS 2022

作者： Han, Wanbin Fang, Chongrong He, Jianping Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai China

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

As mobile robots are becoming more and more widely used, it is of great significance to design an efficient path planning method for multi-robot systems (MRS) that can adapt to complex and unknown environments. In this paper, we present a deep reinforcement learning (DRL) based method to navigate the MRS with collision avoidance in unknown dynamic environments, which consists of centralized learning and decentralized executing paradigm. The proposed policy maps original laser information into robot control commands without constructing global maps. The learned policy is tested in Gazebo environments with three robot systems, which shows the effective performance in terms of success rate, extra time rate, and formation maintenance rate. © 2022 IEEE.

关键词： Multipurpose robots

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A Hybrid Communication Framework Based Remote Management Architecture with OPC UA Information Model Construction 48

A Hybrid Communication Framework Based Remote Management Arc...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Chen, Yuchao Zhang, Jinglong Xu, Qimin Chen, Cailian Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Cloud manufacturing aims to carry out large-scale collaborative production through remote equipment management. To improve management efficiency, it is necessary to construct a unified information model and an adaptive communication framework for manufacturing lines with heterogeneous (high reliable/ low load) needs. OPC UA has become a prominent solution for remote configuration with extensible information models and various transport mechanisms. However, the existing solutions based on OPC UA focus on the information model and communication framework for the machine level, which is hard to support efficient remote management of the production line. In this paper, we construct the model of the process and parameter information in production-line level with a unified manner, which enables unified scheduling, configuration, and monitoring. The operational data are modeled hierarchically to reduce the amount of data transmission during monitoring. The hybrid communication framework is designed by integrating the Client-Server and PubSub models with edge assistance, which is to meet the heterogeneous communication requirements. We implement the management architecture on the physical platform to verify the performance. The experiments show that our architecture is suitable for remote configuration in terms of the combined performance of packet loss rate and CPU load with a massive connection. © 2022 IEEE.

关键词： Information theory

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A New Island Genetic Algorithm for Path Planning Problems *

A New Island Genetic Algorithm for Path Planning Problems *

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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Zhang, Jinghao Chen, Zhe Li, Ning Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Tsinghua University Department of Automation Beijing100084 China

ISBN: (纸本)9798350327182

Genetic algorithm (GA) is an effective method for path planning problems. As a powerful variant of GA, island genetic algorithm (IGA) has considerable improvement in performance. In this paper, a new island model of GA is proposed to avoid the premature phenomenon and achieve better efficiency. First, a new method of creating subpopulations is presented based on K-means to expand the searching area for the optima. Meanwhile, recombining subpopulations is proposed as a new strategy to improve the diversity of populations and save computational time. Moreover, a method is designed based on Monte Carlo sampling to handle the uncertainty of maps. Comparative experiments are presented to verify the efficiency of the proposed algorithm. Then, a proper number of samples is found by simulation to balance the accuracy and the time cost of Monte Carlo sampling. © 2023 IEEE.

关键词： Genetic algorithms

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Co-Design of Dynamic Quantizer and Small-Gain-Based Distributed Model Predictive control of Nonlinear Continuous system

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IEEE Transactions on control of Network systems 2025年

作者： Zheng, Yi Zhang, Yueyan Li, Shaoyuan Luo, Min Shanghai Jiao Tong University Department of Automation China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai China

This paper presents a co-design control scheme that integrates Distributed Model Predictive control with dynamic quantizers for networked nonlinear continuous systems operating under limited communication bandwidth. Each subsystem-based Model Predictive control formulation considers the dynamic quantizer's influence within the optimization problem in the proposed method. Both the quantizer parameters and the control law are optimized simultaneously in real time, which offers potential improvements in the performance of the closed-loop system within a certain communication limitation. A stability constraint based on small-gain Lyapunov theory is designed for each subsystem's controller, allowing for a relaxation of the restrictions on the closed-loop subsystems compared to decentralized approaches. Additionally, a sufficient condition that ensures the stability of the overall system is provided. Simulation results demonstrate the effectiveness of the proposed co-design method. © 2014 IEEE.

关键词： Predictive control systems

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