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Annual Conference of Industrial Electronics Society

作者： Fei Long Chuan-Ke Zhang Yong He Min Wu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

In networked load frequency control (LFC) systems, time-varying delays could come from two aspects: measurements' transmission from power plant to control center and control signals from the control center to plant side, because of the use of open communication networks. Those delays can affect the dynamic performance and the stability of the system. In this paper, the two delays are considered in one-area LFC system, instead of combining these two delays into one, when the delay-dependent stability analysis is carried out by using Lyaponuvtheory and linear matrix inequality (LMI) technique. Moreover, the dynamic delay interval method is employed to derive less conservative delay-dependent stability criteria for such time-delay system compared with the existing results. Finally, the effectiveness of the proposed criteria are verified by two case studies.

关键词： Delays Stability criteria Time-varying systems Power system stability Frequency control Symmetric matrices

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Compressive phase retrieval via reweighted amplitude flow

arXiv

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arXiv 2017年

作者： Zhang, Liang Wang, Gang Giannakis, Georgios B. Chen, Jie Digital Technology Center Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing100081 China School of Automation State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China

The problem of reconstructing a sparse signal vector from magnitude-only measurements (a.k.a., compressive phase retrieval), emerges naturally in diverse applications, but it is NP-hard in general. Building on recent advances in nonconvex optimization, this paper puts forth a new algorithm that is termed compressive reweighted amplitude flow and abbreviated as CRAF, for compressive phase retrieval. Specifically, CRAF operates in two stages. The first stage seeks a sparse initial guess via a new spectral procedure. In the second stage, CRAF implements a few hard thresholding based iterations using reweighted gradients. When there are sufficient measurements, CRAF provably recovers the underlying signal vector exactly with high probability under suitable conditions. Moreover, its sample complexity coincides with that of the state-of-the-art procedures. Finally, substantial simulated tests showcase remarkable performance of the new spectral initialization, as well as improved exact recovery relative to competing alternatives. Copyright © 2017, The Authors. All rights reserved.

关键词： Iterative methods

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Visual Place Recognition in Long-term and Large-scale Environment based on CNN Feature

Visual Place Recognition in Long-term and Large-scale Enviro...

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IEEE intelligent Vehicles Symposium

作者： Jianliang Zhu Yunfeng Ai Bin Tian Dongpu Cao Sebastian Scherer School of Artificial Intelligence University of Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Department of Mechanical & Mechatronics Engineering University of Waterloo Robotics Institute Carnegie Mellon University

ISBN: (纸本)9781538644539

With the universal application of camera in intelligent vehicles, visual place recognition has become a major problem in intelligent vehicle localization. The traditional solution is to make visual description of place images using hand-crafted feature for matching places, but this description method is not very good for extreme variability, especially for seasonal transformation. In this paper, we propose a new method based on convolutional neural network (CNN), by putting images into the pre-trained network model to get automatically learned image descriptors, and through some operations of pooling, fusion and binarization to optimize them, then the similarity result of place recognition is presented with the Hamming distance of the place sequence. In the experimental part, we compare our method with some state-of-the-art algorithms, FABMAP, ABLE-M and SeqSLAM, to illustrate its advantages. The experimental results show that our method based on CNN achieves better performance than other methods on the representative public datasets.

关键词： Hamming distance intelligent vehicles binarization Recognition (Psychology) Visualization network model Cameras images

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Siamese-ResNet: Implementing Loop Closure Detection based on Siamese Network

Siamese-ResNet: Implementing Loop Closure Detection based on...

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2018 IEEE intelligent Vehicles Symposium, IV 2018

作者： Qiu, Kai Ai, Yunfeng Tian, Bin Wang, Bin Cao, Dongpu School of Aritificial Intelligence University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China School of Software Engineering University of Science and Technology of China HeFei230026 China Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada

ISBN: (纸本)9781538644522

Deep learning has made significant breakthroughs in the tasks of image classification, detection, segmentation, etc. However, the application of deep learning in robotics is still scarce. SLAM is a fundamental problem in robotics and loop closure detection is an important part of SLAM. This paper attempts to use supervised learning methods to solve the loop closure detection problem in vision SLAM. We proposed Siamese-ResNet network, which combines Siamese network with ResNet to detect loop closure. To show the effectiveness of Siamese-ResNet, we evaluate Siamese-ResNet and FabMap2.0 on several open published datasets, like TUM SLAM dataset and FabMap SLAM dataset. Compared with FabMap2.0, Siamese-ResNet shows higher accuracy, better robustness and shorter time-consuming. © 2018 IEEE.

关键词： Robotics

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Smooth controller design for non-linear systems using multiple fixed models

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IET control Theory & Applications 2017年第9期11卷 1467-1473页

作者： Jie Chen Wei Chen Jian Sun The Key Laboratory of Intelligent Control and Decision of Complex System School of Automation Beijing Institute of Technology Beijing People's Republic of China Science and Technology on Electronic Information Control Laboratory Chengdu People's Republic of China

Multiple model adaptive control (MMAC) with second-level adaptation is a recently proposed methodology for dealing with systems where the parametric uncertainty is large. Compared with the multiple model switching scheme, the new scheme can lead to significant improvements in performance. Some research has been conducted using the new scheme, but all of the results concern linear systems with an adaptive identification model set. In this study, MMAC with second-level adaptation scheme is extended to non-linear systems in strict feedback form, and the fixed identification model set is under consideration. This is motivated by the fact that a smooth controller can lead to smooth performance and the fixed identification model set gains potential advantages over the adaptive identification model set, especially for the case that the parameters of the system change over the time. Design details are presented and the stability of MMAC with second-level adaptation using a fixed identification model set for non-linear systems is given, which has not been discussed before. Finally, two simulations are performed to show that this scheme performs much better than conventional schemes, including adaptive control and multiple-model switching schemes, in terms of convergence speed and transient performance.

关键词： nonlinear control systems control system synthesis adaptive control uncertain systems linear systems identification convergence transient performance convergence speed fixed identification model set second-level adaptation scheme adaptive identification model set linear systems multiple model switching scheme parametric uncertainty MMAC multiple model adaptive control multiple fixed models nonlinear systems smooth controller design

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Proposal of Initiative Service Model for Service Robot

Proposal of Initiative Service Model for Service Robot

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第十二届中国智能机器人大会

作者： Man Hao Weihua Cao Min Wu Zhentao Liu Luefeng Chen Ri Zhang School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Service robots are becoming a part of our *** realize friendly human-robot interaction,an initiative service model for service robot is proposed,which is composed of three layers,i.e.,information acquisition layer,in which communication information and environmental information are collected by sensors;decision layer,in which personal intention and emotional states are obtained by multi-modal emotion recognition and intention understanding,and users' demands are obtained by demand analysis model;initiative service execution layer,in which the task execution and emotion adaption of robot are carried *** service can sense motive of people,by which a humanized and real-time service could be *** service experiments are performed,including passive service and initiative service of *** results show that the drinking time of passive service lags behind the initiative service 30 minutes on average,and the initiative service renders drinking service 2 times more than passive service on average,besides,the water quantity provided by initiative service is more reasonable,which demonstrates the feasibility of the *** prospect,initiative service model could be applied to many occasions in our daily life,e.g.,family service,caring for the elderly,and medical rehabilitation.

关键词： service robots service pattern fuzzy production rules emotion recognition intention understanding

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End-to-End Driving Activities and Secondary Tasks Recognition Using Deep Convolutional Neural Network and Transfer Learning

End-to-End Driving Activities and Secondary Tasks Recognitio...

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IEEE intelligent Vehicles Symposium

作者： Yang Xing Jianlin Tang Hong Liu Chen Lv Dongpu Cao Efstathios Velenis Fei-Yue Wang Advanced Vehicle Engineering Centre Cranfield University JIANGSU XCMG CONSTRUCTION MACHINERY RESEARCH INSTITUTE LTD School of Mechanical and Mechatronics Engineering University of Waterloo State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781538644539

Drivers' decision and their corresponding behaviors are important aspects that can affect the driving safety, and it is necessary to understand the driver behaviors in real-time. In this study, an end-to-end driving-related tasks recognition system is proposed. Specifically, seven common driving activities are identified, which are normal driving, right mirror checking, rear mirror checking, left mirror checking, using in-vehicle video device, texting, and answering mobile phone. Among these, the first four activities are regarded as normal driving tasks, while the rest three are divided into distraction group. The images are collected using a consumer range camera, namely, Kinect. In total, five drivers are involved in the naturalistic data collection. Before training the identification model, the raw images are first segmented using a Gaussian mixture model (GMM) to extract the driver region from the background. Then, a pre-trained deep convolutional neural network (CNN) model is trained to classify the behaviors, which directly takes the processed RGB images as the input and outputs the identified label. In this work, the AlexNet is selected as the pre-trained CNN model. Then, to reduce the training cost, the transfer learning mechanism is applied to the CNN model. An average of 79% detection accuracy is achieved for the seven driving tasks. The proposed integration model can be used as a low-cost driver distraction and dangerous tasks recognition modeL.

关键词： Driver Behavior Religious Missions Transfer (Psychology) Automobile driving driving safety end-to-end Mirrors Drivers Neural network

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A Method of Fault Diagnosis Based on DE-DBN

A Method of Fault Diagnosis Based on DE-DBN

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2017中国智能自动化会议(CIAC 2017)

作者： Yajun Wang Jia Zhang Fang Deng School of Automation Beijing Institute of Technology Key Laboratory of Intelligent Control of Complex System

1 Introduction With the development of science and technology,it becomes difficult to deal with modern complex equipment with the traditional methods of fault *** a new kind of multi-layer neural network learning algo... 详细信息

关键词： A Method of Fault Diagnosis Based on DE-DBN

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An SNR-Adaptive Relaying Algorithm for Multi-Antenna Cooperative Networks

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China Communications 2017年第10期14卷 228-236页

作者： Shuangshuang Han Peng Zhang Feijin Shi Fei-Yue Wang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of SciencesBeijing China Qingdao Academy of Intelligent Industries Qingdao Shandong Province China The School of Computer Engineering Weifang University Shandong P. R. China Communications Headquarters Ministry of Foreign Affairs of the People's Republic of China Beijing China Research Center of Computational Experiments and Parallel Systems The National University of Defense Technology Changsha China

Multiple-input multiple-output(MIMO) and cooperative communications have been attracted great attention for the improvements of communication capacity, power consumption, and transmission coverage. The conventional fixed relaying protocols, amplify-and-forward(AF) and decode-and-forward(DF), have their own advantages and disadvantages, i.e. AF performs better than DF for low signal-to-noise ratio(SNR) region, while the reverse is true for high SNR region. Therefore, this paper proposes an SNR-adaptive forward(SAF) relaying scheme obtaining the advantages of both AF and DF. Furthermore, the proposed SAF does not need to switch between AF and DF when SNR changes. The main idea is to adaptively derive the soft information at the cooperative relay nodes based on the information of the received signal and the SNR. Besides, based on the theoretical analysis and the simulation results, it is affirmed that the proposed SAF achieves superior performance than both AF and DF for all SNRs. Moreover, the performance gain would be improved with the increasing number of parallel cooperative relay nodes.

关键词： MIMO SNR SNR-adaptive forward multi-antenna

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Token economics in energy systems: Concept, functionality and applications

arXiv

引用

arXiv 2018年

作者： Zhang, Jun Wang, Fei-Yue Chen, Si-Yuan School of Electrical Engineering Wuhan University Wuhan430072 China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences Beijing100190 China Parallel Economic Research Institute Profile of Qingdao Academy of Intelligent Industries Qingdao266071

Traditional centralized energy systems have the disadvantages of difficult management and insufficient incentives. Blockchain is an emerging technology, which can be utilized in energy systems to enhance their management and control. Integrating token economy and blockchain technology, token economic systems in energy possess the characteristics of strong incentives and low cost, facilitating integrating renewable energy and demand side management, and providing guarantees for improving energy efficiency and reducing emission. This article describes the concept and functionality of token economics, and then analyzes the feasibility of applying token economics in the energy systems, and finally discuss the applications of token economics with an example in integrated energy systems. Copyright © 2018, The Authors. All rights reserved.

关键词： Emission control

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