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Research Square 2021年

作者： Zhang, Yijin Huang, Jie Lin, Zongbing Key Lab of Intelligent Analysis and Decision on Complex Systems Chongqing Municipal Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China School of Mathematics and Computer Science Panzhihua University Panzhihua617000 China

Small World Effect (or Six Degrees of Separation theory) has generated significant influence in the world. Many researchers and institutes have done lots of work on the study of it. We model Small World Effect by random process and Graph, calculate the probability that any two people i and j in the world can contact each other after n steps (forwarding messages by intermediaries) based on different R (average number of acquaintances everyone has in the world).When R=50 or 80, if, the probability is 0.848 and the search is very likely to happen. When R=150, n=4, this probability is 0.99994, that is, after 4 steps, any two people in the world will establish connection almost surely. In the sense of Dunbar's number (R=150), six degrees of separation becomes four degrees of separation. We propose the concepts of (directed) Random Fuzzy Graph for the very first time which can describe the fact of recognition among people best, because the relation among persons is random and fuzzy. © 2021, CC BY.

关键词： Random processes

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A Learning Convolutional Neural Network Approach for Network Robustness Prediction

arXiv

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arXiv 2022年

作者： Lou, Yang Wu, Ruizi Li, Junli Wang, Lin Li, Xiang Chen, Guanrong The Department of Computing and Decision Sciences Lingnan University Hong Kong The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The College of Computer Science Sichuan Normal University Chengdu610066 China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Network robustness is critical for various societal and industrial networks again malicious attacks. In particular, connectivity robustness and controllability robustness reflect how well a networked system can maintain its connectedness and controllability against destructive attacks, which can be quantified by a sequence of values that record the remaining connectivity and controllability of the network after a sequence of node- or edge-removal attacks. Traditionally, robustness is determined by attack simulations, which are computationally very time-consuming or even practically infeasible. In this paper, an improved method for network robustness prediction is developed based on learning feature representation using convolutional neural network (LFR-CNN). In this scheme, higher-dimensional network data are compressed to lower-dimensional representations, and then passed to a CNN to perform robustness prediction. Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors;2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability;3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations;4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures. © 2022, CC BY.

关键词： complex networks

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Precedence-constrained path planning of messenger UAV for air-ground coordination

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control Theory and Technology 2019年第1期17卷 13-23页

作者： Yulong DING Bin XIN Jie CHEN School of Automation Beijing Institute of Technology Beijing 700087 China Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing 100081 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing 100081 China

This paper addresses an unmanned aerial vehicle (UAV) path planning problem for a team of cooperating heterogeneous vehicles composed of one UAV and multiple unmanned ground vehicles (UGVs). The UGVs are used as mobile actuators and scattered in a large area. To achieve multi-UGV communication and collaboration, the UAV serves as a messenger to fly over all task points to collect the task information and then flies all UGVs to transmit the information about tasks and UGVs. The path planning of messenger UAV is formulated as a precedence-constrained dynamic Dubins traveling salesman problem with neighborhood (PDDTSPN). The goal of this problem is to find the shortest route enabling the UAV to fly over all task points and deliver information to all requested UGVs. When solving this path planning problem, a decoupling strategy is proposed to sequentially and rapidly determine the access sequence in which the UAV visits task points and UGVs as well as the access location of UAV in the com mu nication n eighborhood of each task point and each UGV. The effectiveness of the proposed approach is corroborated through computational experiments on randomly generated instances. The computational results on both small and large in stances dem on strate that the proposed approach can generate high-quality solutions in a reasonable time as compared with two other heuristic algorithms.

关键词： Air-ground coordination curvature-constrained path planningz precedence constraints Dubins traveling salesman problem

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SPP-CNN: An Efficient Framework for Network Robustness Prediction

arXiv

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arXiv 2023年

作者： Wu, Chengpei Lou, Yang Wang, Lin Li, Junli Li, Xiang Chen, Guanrong The College of Computer Science Sichuan Normal University Chengdu610066 China The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China The Department of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

This paper addresses the robustness of a network to sustain its connectivity and controllability against malicious attacks. This kind of network robustness is typically measured by the time-consuming attack simulation, which returns a sequence of values that record the remaining connectivity and controllability after a sequence of node- or edge-removal attacks. For improvement, this paper develops an efficient framework for network robustness prediction, the spatial pyramid pooling convolutional neural network (SPP-CNN). The new framework installs a spatial pyramid pooling layer between the convolutional and fully-connected layers, overcoming the common mismatch issue in the CNN-based prediction approaches and extending its generalizability. Extensive experiments are carried out by comparing SPP-CNN with three state-of-the-art robustness predictors, namely a CNN-based and two graph neural networks-based frameworks. Synthetic and real-world networks, both directed and undirected, are investigated. Experimental results demonstrate that the proposed SPP-CNN achieves better prediction performances and better generalizability to unknown datasets, with significantly lower time-consumption, than its counterparts. © 2023, CC BY.

关键词： complex networks

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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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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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Coordinated motion control and event-based obstacle-crossing for four wheel-leg independent motor-driven robotic system via MPC

arXiv

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arXiv 2020年

作者： Liu, Dongchen Wang, Junzheng Wang, Shoukun The National Key Laboratory of Complex System Intelligent Control and Decision School of Automation Beijing Institute of Technology Beijing10081 China The Ministry of Industry and Information Technology Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing Institute of Technology Beijing10081 China

This work investigates the coordinated motion control and obstacle-crossing problem for a four wheel-leg independent motor-driven robotic system via a model predictive control (MPC) approach based on an event-triggering mechanism. The modeling of a wheel-leg robotic control system with a dynamic supporting polygon is organized. The system dynamic model is 3 degrees of freedom ignoring the pitch, roll, and vertical motions. The single wheel dynamic is analyzed considering the characteristics of the motor-driven and the Burckhardt nonlinear tire model. As a result, an over-actuated predictive model is proposed with the motor torques as inputs and the system states as outputs. As the supporting polygon is only adjusted at certain conditions, an event-based triggering mechanism is designed to save hardware resources and energy. The MPC controller is evaluated on a virtual prototype as well as a physical prototype. The simulation results guide the parameter tuning for the controller implementation in the physical prototype. The experimental results on these two prototypes verify the efficiency of the proposed approach. © 2020, CC BY.

关键词： Geometry

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Observer-based finite time H ∞ control of nonlinear discrete time-varying systems with an adaptive event-triggered Mechanism

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Journal of the Franklin Institute 2020年第16期357卷 11668-11689页

作者： Menghua Chen Hamid Reza Karimi Jian Sun School of Automation Beijing Institute of Technology Beijing 100081 China Department of Mechanical Engineering Politecnico di Milano Milan 20156 Italy Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing 100081 China

The problem of finite time stability and synthesis is discussed for nonlinear discrete time-varying systems by using the T-S fuzzy model. To reduce the communication network resources, two adaptive event-triggered mechanisms (AETMs) are taken into consideration in sensor-to-observer (S/O) and observer-to-controller (O/C) channels. The thresholds of the AETMs are adjusted according to the estimation error rather than some fixed ones. Sufficient conditions for developing an observer-based time-varying control are obtained, which ensure the time-varying error system to be finite time stable and satisfy the H ∞ performance simultaneously. Moreover, using the singular value decomposition technique, recursive linear matrix inequalities conditions are obtained for computing the gains of the controller and observer. Finally, the superiority and applicability of the proposed method are demonstrated by examples.

关键词：

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Robust recognition of truck license plate in mine environment

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

引用

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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Forward Modeling of Shallow Transient Electromagnetic Method under Ramp Turn-off Current

Forward Modeling of Shallow Transient Electromagnetic Method...

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第三十九届中国控制会议

作者： Guangjun Wang Menglan Song School of Automation China University of Geosciences China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex System

In order to reduce the shallow detection blind zone and improve the shallow detection accuracy, this paper presents a shallow transient electromagnetic direct forward modeling method based on ramp turn-off current. The main idea is to obtain the spectrum of the ramp turn-off current signal by Fourier transform at first, then using Maxwell’s equation to solve the propagation process of the electromagnetic signal in the medium, thus obtaining the early signal of transient electromagnetic *** theory, this method is more suitable for the detection of shallow subsurface media than the ideal step signal. Simulation experiments on the ramp step signals with different turn-off times show that the complete ideal coincidence with the traditional ideal step signal when the turn-off time approaches zero, indicating the correctness of the algorithm. At the same time, compared with the current element superposition forward method, the forward curve obtained by this method is closer to the actual situation,and the calculation speed is much faster than the current element superposition method. The research results in this paper are of great value to reduce the detection blind zone, improve the detection accuracy and real-time performance of the transient electromagnetic detecting.

关键词： Ramp turn-off current shallow transient electromagnetic method forward modeling

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