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2021 China Automation Congress, CAC 2021

作者： Shi, Shuyan Liu, Ding Zhao, Zhongdan Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Shananxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an710048 China

ISBN: (纸本)9781665426473

In this paper, a time-fractional heat conduction model is established to describe the heat transfer process of monocrystalline silicon in the Czochralski method. The numerical solution of the fractional-order model is obtained by using implicit difference scheme. In addition, an improved grey wolf algorithm is proposed to estimate the parameters of the fractional heat conduction model using the experimental temperature data. The numerical experiment results show that the time-fractional heat conduction model has a higher explanatory ability, compared with the classical heat conduction model. The Improved Grey Wolf algorithm proposed in this paper is effective in solving the parameter estimation problem of the time-fractional heat conduction equation. © 2021 IEEE

关键词： Heat conduction

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ORB-Based Fast Anti-Viewing Image Feature Matching Algorithm

ORB-Based Fast Anti-Viewing Image Feature Matching Algorithm

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2018 Chinese Automation Congress, CAC 2018

作者： Oi, Liangliang Liu, Wei Liu, Ding Crystal Growth Equipment and System Integration Engineering Research Center Laboratory for Complex System Control and Intelligent of Information Processing Xi'An University of Technology Xi'an710048 China

ISBN: (纸本)9781728113128

Aiming at the problem that the original ORB algorithm is difficult to solve the effective matching problem under large angle of view transformation. This paper proposes an image feature matching algorithm based on ORB to resist the change of view according to the principle of ASIFT. The basic idea of the algorithm is as follows, Firstly, the longitude angle and latitude angle caused by the rotation of the camera are simulated, and the simulated image sequence set which is deformed by the change of the optical axis direction of the camera is accurately simulated. Then the feature points are extracted and mapped them into the original image by combining the FAST feature point detection method with the BRISK feature descriptors. Finally, the Brute-Force Matcher and the Random Sampling Consistency (RANSAC) method were used for feature matching and optimization. The experimental results show that the proposed algorithm can solve the problem that the image features cannot be matched under the large change of viewing angle (greater than 60°), and it has a significant improvement in real-time performance compared with ASIFT. © 2018 IEEE.

关键词： Cameras

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Autonomous Tracking control for Four-Wheel Independent Steering Robot Based on Improved Pure Pursuit

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Journal of Beijing Institute of Technology 2020年第4期29卷 466-473页

作者： Jing Li Qingbin Wu Junzheng Wang Hui Qin Jiehao Li Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information TechnologySchool of AutomationBeijing Institute of TechnologyBeijing 100081China

Autonomous tracking control is one of the fundamental challenges in the field of robotic autonomous navigation,especially for future intelligent *** this paper,an improved pure pursuit control method is proposed for the path tracking control problem of a four-wheel independent steering *** on the analysis of the four-wheel independent steering model,the kinematic model and the steering geometry model of the robot are *** the path tracking control is realized by considering the correlation between the look-ahead distance and the velocity,as well as the lateral error between the robot and the reference *** experimental results demonstrate that the improved pure pursuit control method has the advantages of small steady-state error,fast response and strong robustness,which can effectively improve the accuracy of path tracking.

关键词： autonomous tracking control four-wheel independent steering robot pure pursuit lateral error

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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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Multi-Branch Spatial-Temporal Attention Graph Convolution Network for Skeleton-based Action Recognition 41

Multi-Branch Spatial-Temporal Attention Graph Convolution Ne...

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第41届中国控制会议

作者： Daoshuai Wang Dewei Li Yaonan Guan Gang Wang Haibin Shao Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Skeleton-based human action reco gnition has attracted considerable research interest due to its robustness to dynamic environments and complex *** based on graph neural networks have achieved great success in this ***,most of these graph neural network based methods adopt a single-branch or two-stream structure with limited input *** to the separation of space and time,many methods cannot pay attention to the critical information of human skeleton sequence,and multi-scale spatial-temporal dependent attention modeling becomes the key to *** paper proposes a novel multi-branch Spatio-temporal attention graph convolutional neural network(MB-STAGCN) to recognize human actions from skeleton *** proposed model adopts a multi-branch structure,fuses three-stream features,and then inputs them into the backbone network to extract features from multiple perspectives to a greater ***,we design a new temporal convolutional block for multi-scale extraction of information in the backbone *** also propose an attention block named Spatio-temporal Concat Attention(STCatAtt) to capture critical *** on benchmark datasets show remarkable performance for human action reco gnition,demonstrating the effectiveness of our method.

关键词： Skeleton-based human action recognition MB-STAGCN STCatAtt

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Distributed dimensionality reduction of industrial data based on clustering

Distributed dimensionality reduction of industrial data base...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Yongyan Zhang Guo Xie Wenqing Wang Xiaofan Wang Fucai Qian Xulong Du Jinhua Du Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing China

Large amounts of data are produced in system operation, and how to extract effective information from these data has become an important research topic in the industrial application. Dimensionality reduction is a way to refine the data. Because of the low efficiency of the existing methods, these methods can't discover the internal structure of the data. Regarding these problems, a distributed method of dimensionality reduction based on clustering is proposed, which includes the following steps:(1) Clustering the data into some small classes according to the similarity between the data variables; (2) reducing the dimension of data in a small class after being clustered respectively; (3) merging the data after being reduced dimension; (4) classifying the data after being merged by support vector machine (SVM). The data in the simulation is the test data, and the results show that the methods proposed in this paper are better than the existing dimensionality reduction methods.

关键词： Dimensionality reduction Support vector machines Clustering algorithms Distributed databases Data mining Principal component analysis Classification algorithms

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Optimization of Grouping Evacuation Strategy in High-rise Building Fires Based on Graph Theory and Computational Experiments

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IEEE/CAA Journal of Automatica Sinica 2018年第6期5卷 1104-1112页

作者： Yuling Hu Xiwei Liu School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing 100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing 100044China State Key Laboratory fo Management and Control for Complex Systems Institute of Automation Chinese Academy of SciencesBeijing 100190China Qingdao Academy of Intelligent Industries Qingdao 266109China

It is difficult to rescue people from outside, and emergency evacuation is still a main measure to decrease casualties in high-rise building fires. To improve evacuation efficiency, a valid and easily manipulated grouping evacuation strategy is proposed. Occupants escape in groups according to the shortest evacuation route is determined by graph theory. In order to evaluate and find the optimal grouping, computational experiments are performed to design and simulate the evacuation processes. A case study shown the application in detail and quantitative research conclusions is obtained. The thoughts and approaches of this study can be used to guide actual high-rise building evacuation processes in future.

关键词： Computational experiments grouping evacuation graph theory high-rise building fire management strategies

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An Optimal control-Based Distributed Reinforcement Learning Framework for A Class of Non-Convex Objective Functionals of the Multi-Agent Network

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IEEE/CAA Journal of Automatica Sinica 2023年第11期10卷 2081-2093页

作者： Zhe Chen Ning Li Department of Automation Shanghai Jiao Tong UniversityShanghai 200240 Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240 Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Department of Automation Tsinghua UniversityBeijing 100084China IEEE

This paper studies a novel distributed optimization problem that aims to minimize the sum of the non-convex objective functionals of the multi-agent network under privacy protection, which means that the local objective of each agent is unknown to others. The above problem involves complexity simultaneously in the time and space aspects. Yet existing works about distributed optimization mainly consider privacy protection in the space aspect where the decision variable is a vector with finite dimensions. In contrast, when the time aspect is considered in this paper, the decision variable is a continuous function concerning time. Hence, the minimization of the overall functional belongs to the calculus of variations. Traditional works usually aim to seek the optimal decision function. Due to privacy protection and non-convexity, the Euler-Lagrange equation of the proposed problem is a complicated partial differential ***, we seek the optimal decision derivative function rather than the decision function. This manner can be regarded as seeking the control input for an optimal control problem, for which we propose a centralized reinforcement learning(RL) framework. In the space aspect, we further present a distributed reinforcement learning framework to deal with the impact of privacy protection. Finally, rigorous theoretical analysis and simulation validate the effectiveness of our framework.

关键词： Distributed optimization multi-agent optimal control reinforcement learning(RL)

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Robust recognition of truck license plate in mine environment

Robust recognition of truck license plate in mine environmen...

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2020 International Conference on Computer Engineering and intelligent control, ICCEIC 2020

作者： Shi, Siqi Li, Nanting Ma, Yanjun Zheng, Liping Xi'An University of Technology Key Laboratory of Shaanxi Province for Complex System Control and Intelligent Information Processing Xian China Xi'An University of Technology School of Electrical Engineering Xian China Xi'An University of Technology School of Automatic and Information Engineering Xian China

ISBN: (数字)9781728185736

ISBN: (纸本)9781728185736

To improve the decreased recognition performance for truck license plate in complex mine environment, which is caused by such factors as polluted, damaged, a robust license plate recognition method is proposed. Firstly, several candidate locations of license plate in the truck image are obtained by utilizing features of both edge and color. Furthermore, the parameter of license plate area intersection ratio is defined to find the precise localization among those above candidates. Then, a character segmentation scheme based on the gray projection method and morphological operator is used to remove those interference factors, such as frame, rivet and stain and uneven illumination. Finally, to increase the robustness of characters recognition by CNN, a self-built license plate character dataset is constructed, which contains various polluted samples obtained by manual simulation. As shown by the experimental results, the proposed method has higher accuracy in license plate location, and obtains an improvement on character recognition accuracy by 4% compared to other existed methods. © 2020 IEEE.

关键词： License plates (automobile)

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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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