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Chinese automation Congress (CAC)

作者： XiaoYu Dou Yan Yuan Weihua Cao Peilun Ouyang School of Automation China University of Geosciences Wuhan P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

The computer room management system is a management system for network monitoring, bottom control, information filtering and security monitoring of computer room. It has the characteristics of multi-user and multi module. Scientific and intelligent management and control is the premise of efficient and stable work in computer room. Aiming at the problems of power supply security and efficiency, user authority configuration and management security of traditional computer room, a centralized management system of computer room based on Role Based of Access control(RBAC) is designed and implemented. This paper first introduces the structure and function of the system, then focuses on the detailed design of the user management subsystem of the system, and finally gives the realization and actual operation of the system. At present, the system has been successfully applied to a power room equipment, which proves the security, stability and efficiency of the system.

关键词： Computer security Security Encryption Databases Real-time systems Personnel complexity theory

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Graph Convolutional Extreme Learning Machine

Graph Convolutional Extreme Learning Machine

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International Joint Conference on Neural Networks (IJCNN)

作者： Zijia Zhang Yaoming Cai Wenyin Gong Xiaobo Liu Zhihua Cai School of Computer Science China University of Geosciences Wuhan China School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Extreme Learning Machine (ELM) has gained lots of research interest due to its universal approximation capability and fast learning speed. However, traditional ELMs are devised for regular Euclidean data, such as 2D grid and 1D sequence, and thus don't apply to non-Euclidean data, e.g., graph-structured data. To overcome this shortcoming, this paper presents a Graph Convolutional Extreme Learning Machine (termed as GCELM) for semi-supervised classification. Technically, a random graph convolutional layer is introduced to replace the random projection of original ELM, which endues ELM with the capability of dealing with graph-structured data directly. To generate a robust graph from the raw dataset, a self-representation model is adopted to construct a weighted graph. Extensive experiments on 27 UCI datasets demonstrate that GCELM outperforms many popular semi-supervised methods, and with faster learning speed. To the best of our knowledge, this is the first work that combines graph convolution with ELM.

关键词： Convolution Robustness Closed-form solutions Neural networks Training Convolutional codes Optimization

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A Comparison Study of Three Active Disturbance Rejection Methods

A Comparison Study of Three Active Disturbance Rejection Met...

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第三十九届中国控制会议

作者： Youwu Du Weihua Cao Jinhua She Mingxing Fang School of Automation China University of Geosciences College of Physics and Electronic Information Anhui Normal University Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Engineering Tokyo University of Technology

This paper investigates the performance of three active disturbance-rejection methods: disturbance-observer-based（DOB） control, active disturbance rejection control（ADRC）, and equivalent-input-disturbance-based（EID） control. Compared to the classic feedback control, these methods provide an additional degree of freedom to reject disturbances effectively. First, the disturbance-rejection mechanism of each of the methods is analyzed. For a proper design, the transfer function from disturbances to the output of a system approximates zero using these methods. This implies the effect of disturbances is blocked to the ***, the three methods are applied to the position control of a ball-and-beam system for comparison. The simulation results show that all these methods are insensitive to disturbances and robust to system uncertainties. And the error of the system output is smaller for the EID method than for the DOB and ADRC methods.

关键词： Active disturbance rejection control（ADRC） disturbance observer（DOB） disturbance estimation equivalent input disturbance（EID） robust control

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Fluidspread: A New Method of Maximizing Positive Influence in Online Social Networks via Fluid Dynamics

Fluidspread: A New Method of Maximizing Positive Influence i...

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International Conference on control and automation (ICCA)

作者： Feng Wang Jinhua She Yasuhiro Ohyama Min Wu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (数字)9781728190938

ISBN: (纸本)9781728190945

Influence maximization aims at detecting the top-k influential users in online social networks. Almost all previous models of influence spread cannot simultaneously incorporate users' attitudes, interactions between users, and dynamic influences. However, in this study, we established a new model of influence spread via fluid dynamics, which reveals the time-evolving process for influence spread. We modeled the spread of influence as the process of fluid update based on three dimensions: the difference of fluid height, the temperature of fluids, and the difference of temperature. Moreover, we formulated the problem of maximizing positive influence and devised a Fluid-spread greedy algorithm to solve it. We conducted extensive comparisons between our approach and several baselines, and experimental results illustrate the effectiveness and efficiency of the Fluidspread model and algorithm.

关键词： Containers Dynamical systems Valves Plugs Time complexity Temperature distribution Social networking (online)

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Graph Optimization based Visual SLAM fusing keyPoints and Markers

Graph Optimization based Visual SLAM fusing KeyPoints and Ma...

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第三十九届中国控制会议

作者： Zhen Chen Yang Zhou Fengdi Zhang Min Xu Xiangdong Liu Zhen Li School of Automation and Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology Beijing Aerospace Automatic Control Institute National Key Laboratory of Science and Technology on Aerospace Intelligence Control

Simultaneous localization and mapping（SLAM） plays an important role in autonomous navigation for mobile *** of the visual SLAM approaches use keypoints for tracking, whose performance however suffers from the unstable landmarks during task due to uncertain light condition and frequently changeable viewpoint. The situation even becomes worse for visual SLAM in low texture environment especially in indoor buildings, where the supplementary artificial markers can be used to improve the robust detection under a wider range of circumstances. Inspired by this thought, this paper developed a visual SLAM system integrated with keypoints and artificial markers. A graph optimization problem is constructed to optimize the trajectory by taking both the reprojection error of keypoints and the influence of markers into consideration. The experimental results on SPM dataset demonstrate the superior accuracy of the proposed graph optimization algorithm compared with the start-of-the-art ORB-SLAM2.

关键词： Visual simultaneous localization and mapping（SLAM） graph optimization keypoints artificial markers

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Transient Performance Evaluation of Flexible Production Lines With Two Bernoulli Machines and Dedicated Buffers*

Transient Performance Evaluation of Flexible Production Line...

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IEEE International Conference on automation Science and Engineering (CASE)

作者： Jingchuan Chen Zhiyang Jia Longzhu Huang Yaping Dai School of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing China

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

In the contemporary manufacturing environment, flexible production is growing in popularity due to its quick adaptation and response to the market changes and customized requirements. Multi-type products under small-batch-based operations are commonly seen in flexible production. Therefore, dedicated buffers are sometimes used to separate different types of parts that are being processed on one production system. Besides, the steady-state analysis may be ineffective because of the small-batch-based operation, as a result, transient analysis becomes indispensable in investigating the transient behavior of flexible production lines. In this work, we investigate the performance evaluation issue in flexible production systems with multi-type products, two machines obeying the Bernoulli reliability model, and dedicated buffers in real-time. Based on Markovian analysis, a set of auxiliary production lines are presented to decouple the dynamics of the original system. Then, the transient performance measures of interest are approximately evaluated from the auxiliary production lines to avoid the issue of system state explosion. The state explosion is common when applying the Markovian approach. Finally, the proposed analytical algorithm is validated to be with high precision through numerical experiments in contrast with the Monte Carlo simulation.

关键词： Transient analysis Production systems Reliability Performance evaluation Numerical models Manufacturing

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Introduction

Engineering Applications of Computational Methods

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Engineering Applications of Computational Methods 2020年 3卷 1-31页

作者： Wu, Min Cao, Weihua Chen, Xin She, Jinhua China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China School of Engineering Tokyo University of Technology Tokyo Japan

The iron and steel industry is the base for the development of national economy. It directly influences the industries of construction, machinery, shipbuilding, automobile, household electrical appliances, etc. The complexity and large uncertainties of the processes in the iron and steel industry make it difficult to establish accurate mathematical models and to control the processes using conventional control methods. On the other hand, since intelligent control does not require an accurate mathematics model, and its control algorithm has self-learning and adaptive ability, this control method is playing an increasingly important role in the iron and steel industry. © 2020, Science Press.

关键词： Steelmaking

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MatchMesh: Knowledge-based 3D Point Cloud Meshing Using Divide-and-conquer Deformation

MatchMesh: Knowledge-based 3D Point Cloud Meshing Using Divi...

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2020 IEEE International Conference on Networking, Sensing and control, ICNSC 2020

作者： Tang, Ying Sun, Shengtao Wu, Ben Department of Electrical and Computer Engineering Rowan University GlassboroNJ08028 United States State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute of Smart Education Qingdao Academy of Intelligent Industries Qingdao China

ISBN: (纸本)9781728168531

The reconstruction of surface mesh from point cloud is compute-intensive but also very important step in the remanufacturing and personalization industries. With more 3D scanners providing lower cost and higher resolution, further detailed point clouds can be gathered without so much effort as before. In manufacturing, there are databases which contain the origin 3D design models of the products. How to utilize the design model data for swift production of related products remains a problem for remanufacturing and customization. In order to develop a knowledge-based way of handling this problem, editing or deforming an existing mesh to match the target is an effective way of easing the workload. In this paper, we introduce a divide-and -conquer process which segments the depth scan data and then find the best match in the database as its source of deformation. The segmentation is performed on 3D point level using global features extracted by 3D CNN. After that we find best match to our knowledge with the same features to acquire a fast meshing of the target object by deforming the existing parts from the match. The deformation of parts are being done sequentially. For further performance improvement, we present a deformation training method employing transfer learning on segment editing process. © 2020 IEEE.

关键词： Image reconstruction

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Data Augment Using Deep Convolutional Generative Adversarial Networks for Transient Stability Assessment of Power systems

Data Augment Using Deep Convolutional Generative Adversarial...

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第三十九届中国控制会议

作者： Jiamin Li Hongying Yang Liping Yan Zonghan Li Daowei Liu Yuanqing Xia Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology China Electric Power Research Institute

Real–time and accurate transient stability assessment(TSA) is essential for planning, operation and control of power systems. As a data–driven technology, deep learning method plays an important role in TSA. Nevertheless, the fact that instability situations rarely occur would lead to a challenging class–imbalanced issue, which brings great difficulties to the deep learning methods. Besides, feature extraction from high dimensional input data and transient stability classification seem extremely difficult for conventional classification methods. To address these problems, this paper develops a class–imbalanced TSA method by combining nonlinear data synthesis method with the deep learning classification model. Firstly, deep convolutional generative adversarial network(DCGAN) is conducted to generate unstable instances based on the existing samples to balance the proportion of different classes. Furthermore, the convolutional neural network(CNN) is utilized to extract the nonlinear mapping relationship between the disturbance features and the stability category and realize TSA. Finally, the IEEE 10–machine, 39–bus New England system is utilized to verify the validity and effectiveness of the proposed method.

关键词： Class–imbalanced Data Generation DCGAN CNN TSA Power systems

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The Multi-Blockchain Based Data Trading Markets with Novel Pricing Mechanisms

SSRN

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SSRN 2022年

作者： Li, Juanjuan Li, Junqing Wang, Xiao Qin, Rui Yuan, Yong Wang, Fei-Yue The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Qingdao Academy of Intelligent Industries Qingdao266109 China School of Mathematics Renmin University of China Beijing100872 China Macao Institute of Systems Engineering Macau University of Science and Technology Macau 999078 China

Aiming to deal with the challenge of data security and data pricing in data trading markets, we propose the novel multi-blockchain-based framework and design the related pricing mechanisms. Specifically, data recording and trading are conducted separately in the Data Blockchain (DChain) and the Value Blockchain (VChain). Based on these blockchains, two-layer data trading markets are established to manage the initial data trading in the primary market and the data resale in the secondary market. Pricing mechanisms are proposed to protect the formulated data trading markets against strategic trading behaviors and balance the payoffs of both suppliers and users. In the regular data trading on VChain-S2D, two auction models are used to deal with users' strategic bidding in terms of the demand scale, where the incentive-compatible Vickrey-Clarke-Groves (VCG) model is dedicated to the low-demand trading scenario, while the nearly incentive-compatible Monopolistic Price (MP) model is for the high-demand trading scenario. In the temporary data trading on VChain-D2S, a reverse auction mechanism named Two-Stage Obscure Selection (TSOS) is designed to regulate both suppliers' quoting and users' valuation strategies. Moreover, experiments are carried out to validate our theoretical analysis. Our work is expected to enhance the data security and improve the trading efficiency. © 2022, The Authors. All rights reserved.

关键词： Blockchain

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