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arXiv 2017年

作者： Sun, Shiliang Zhang, Changshui Zhang, Yi State Key Laboratory of Intelligent Technology and Systems Department of Automation Tsinghua University BeijingChina

A novel predictor for traffic flow forecasting, namely spatiotemporal Bayesian network predictor, is proposed. Unlike existing methods, our approach incorporates all the spatial and temporal information available in a transportation network to carry our traffic flow forecasting of the current site. The Pearson correlation coefficient is adopted to rank the input variables (traffic flows) for prediction, and the best-first strategy is employed to select a subset as the cause nodes of a Bayesian network. Given the derived cause nodes and the corresponding effect node in the spatio-temporal Bayesian network, a Gaussian Mixture Model is applied to describe the statistical relationship between the input and output. Finally, traffic flow forecasting is performed under the criterion of Minimum Mean Square Error (M.M.S.E.). Experimental results with the urban vehicular flow data of Beijing demonstrate the effectiveness of our presented spatio-temporal Bayesian network predictor. Copyright © 2017, The Authors. All rights reserved.

关键词： Bayesian networks

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A Real-time Hand Postures Estimation Method

A Real-time Hand Postures Estimation Method

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IEEE International Conference on Cyber technology in automation, Control, and intelligent systems

作者： Sunjie Chen Hongbin Ma Cong Han School of Automation Beijing Institute of Technology State Key Laboratory of Complex System Intelligent Control and Decision Beijing 100081 China

ISBN: (纸本)9781538604915

Hand postures can be used to achieve friendly human-machine interaction (HMI) for the advantages of simple expressions, informative instructions and unconstrained operations. However, most previous postures estimation methods have problems such as poor real time performance, low estimation accuracy, being sensitive to the varying illumination and relying on marks on hands or special assistant devices. In this paper, we will introduce an efficient real-time method to estimate hand postures with an ordinary monocular camera. This method contains two stages, which are hand segmentation and postures recognition. Rather segment objects from the whole image, we locate the hand within a local area first and then adapt motion, color and contour informations to segment the hand region accurately. After that, template matching algorithm is used to recognize hand postures with fingertips and shape features. The experimental results demonstrated that the introduced method realizes high estimation accuracy and fast processing speed. Furthermore, the method is robust to varying illumination conditions.

关键词： Microsoft Windows Image segmentation Image color analysis Estimation Motion segmentation Support vector machines Lighting Motion segmentation image colour analysis Microsoft Windows(software) Lighting Hand body position Support Vector Network image segmentation Subdivision of hand

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Optimization Algorithms for Predictive Control Approach to Networked Bilinear systems

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自动化学报 2017年第7期43卷 1234-1240页

作者： Binglin Wang Yu Kang Jiahu Qin Yanmei Li Department of Automation University of Science and Technology of China Hefei 230027 China State Key Laboratory of Fire Science Department of Automation Institute of Advanced Technology University of Science and Technology of China Hefei 230027 China and also with the Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chi- nese Academy of Sciences Beijing 100190 China Physics and Electronic Engineering Anqing Normal University Anqing 246011 China

This paper is concerned with the networked predictive control of discrete-time bilinear *** deal with the network-induced communication delay that exists in both forward channel（controller to actuator）and feedback channel（sensor to controller）,a bilinear networked predictive control scheme is *** a non-convex optimization problem of solving the predictive control sequence is presented,for which two gradually-optimized algorithms are proposed based on the special structure of bilinear system dynamics *** numerical simulation indicates that the resulting predictive control sequence can compensate for the network-induced issues actively,which proves the effectiveness of the proposed predictive control strategy.

关键词：离散双线性系统预测控制策略逐步优化算法网络化系统动力学模型控制序列反馈通道通信延迟

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Optimal tracking cooperative control for multi-agent systems with periodic sampling via robust model predictive control approach 36

Optimal tracking cooperative control for multi-agent systems...

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第36届中国控制会议

作者： Bohui Wang Weisheng Chen Jingcheng Wang Bin Zhang Zhengqiang Zhang Hai Lin Bin Ma the School of Aerospace Science and Technology Xidian University the Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of China the Autonomous Systems and Intelligent Control international Joint Research Center Xian Technological University the Department of Electrical Engineering University of South Carolina the with the School of Engineering Qufu Normal University

ISBN: (纸本)9781538629185

This paper addresses the optimized tracking cooperative control problem for multi-agent systems with periodic sampling and directed communication topology via robust model predictive control *** proposed optimized tracking cooperative control strategy relaxes the assumptions in existing works that the control gain and the local input must be continuous and the states information exchange has no recourse *** the conditions of the optimized consensus and the communication cost being satisfied,the tracking cooperative control law with bounded parameters is developed based on the periodic *** shows that if the sampling condition is satisfied,the multi-agent systems will reach the optimized *** results are provided to verify the proposed approach.

关键词： Index Terms-multi-agent systems tracking cooperative control periodic sampling optimized consensus robust model predictive control

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MEMS-based Human Activity Recognition Using Smartphone 35

MEMS-based Human Activity Recognition Using Smartphone

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第35届中国控制会议

作者： TIAN Ya CHEN Wenjie Department of Automation Beijing Institute of Technology Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education

ISBN: (纸本)9781509009107

Data mining is one hot orientation in today’s research *** activity recognition is meaningful in our daily living and is a significant aspect in data *** previously research is almost based on tri-axial *** paper presents a novel method to collect data from both accelerometer and gyroscope using *** daily activities including Walking,Running,Upstairs,Downstairs,Standing,Sitting and Cycling,a total of seven categories are *** raw data from MEMS are recorded by smartphone according to different daily *** improve the accuracy of classification for daily activities,this paper combines time-series features with wavelet coefficients to extract *** recognize these activities,the support vector machine is used to finish this ***,we compare the accuracy with other machine learning methods,such as k-nearest neighbor algorithm and neural network or decision *** result indicates that our method can achieve nearly 96%classification accuracy for the seven kinds of daily activities.

关键词： classification activity recognition wavelet transform support vector machine feature extraction

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Model-driven deep learning for physical layer communications

arXiv

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arXiv 2018年

作者： He, Hengtao Jin, Shi Wen, Chao-Kai Gao, Feifei Li, Geoffrey Ye Xu, Zongben National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Institute of Communications Engineering National Sun Yat-sen University Kaohsiung804 Taiwan State Key Lab of Intelligent Technologies and Systems Tsinghua University Department of Automation Tsinghua University Beijing China School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Institute for Information and System Sciences Xian Jiaotong University Xi'an710049 China

intelligent communication is gradually becoming a mainstream direction. As a major branch of machine learning, deep learning (DL) has been applied in physical layer communications and demonstrated an impressive performance improvement in recent years. However, most of the existing works related to DL focus on data-driven approaches, which consider the communication system as a black box and train it by using a huge volume of data. Training a network requires sufficient computing resources and extensive time, both of which are rarely found in communication devices. By contrast, modeldriven DL approaches combine communication domain knowledge with DL to reduce the demand for computing resources and training time. This article discusses the recent advancements in model-driven DL approaches in physical layer communications, including transmission schemes, receiver design, and channel information recovery. Several open issues for future research are also highlighted. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep learning

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Image-based visual tracking of a moving target for a quadrotor

Image-based visual tracking of a moving target for a quadrot...

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Asian Control Conference

作者： Dongliang Zheng Hesheng Wang Weidong Chen Department of Automation Ministry of Education of China Shanghai China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

ISBN: (纸本)9781509015740;9781509015733

In this paper, the control problem of a quadrotor tracking a moving target is investigated. The method is based on image-based visual servoing (IBVS) scheme. The target is assumed to be moving with unknown, time varying and bounded linear velocity, linear acceleration, and angular velocity and acceleration of the yaw angle. First, appropriate moment features defined in a rotated image plane are chosen as image features, and a decoupled image feature dynamics is achieved. Then, using these features, a nonlinear controller is proposed for the quadrotor to track a moving target. The controller is proved uniformly ultimately bounded (UUB) by means of Lyapunov analysis. With this image-based control formulization, several state variables are not needed by the controller. These variables are: the depths of the point features which are usually needed in traditional IBVS schemes, the relative position between the quadrotor and the moving target, the velocity of the moving target, and the translational velocity of the quadrotor itself. Finally, several simulations of a quadrotor tracking a moving target are conducted to validate this method.

关键词： Cameras Visualization Target tracking Visual servoing Task analysis Stability analysis

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Model-protected multi-task learning

arXiv

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arXiv 2018年

作者： Liang, Jian Liu, Ziqi Zhou, Jiayu Jiang, Xiaoqian Zhang, Changshui Wang, Fei Department of Automation Tsinghua University State Key Laboratory of Intelligent Technologies and Systems Tsinghua National Laboratory for Information Science and Technology Beijing100084 China Department of Computer Science Xi'an Jiaotong University Xi'an710049 China Department of Computer Science and Engineering Michigan State University East LansingMI48824 United States Department of Biomedical Informatics University of California San Diego San DiegoCA92093 United States Department of Healthcare Policy and Research Weill Cornell Medical College New York CityNY10065 United States

Multi-task learning (MTL) refers to the paradigm of learning multiple related tasks together. In contrast, in single-task learning (STL) each individual task is learned independently. MTL often leads to better trained models because they can leverage the commonalities among related tasks. However, because MTL algorithms can "leak" information from different models across different tasks, MTL poses a potential security risk. Specifically, an adversary may participate in the MTL process through one task and thereby acquire the model information for another task. The previously proposed privacy-preserving MTL methods protect data instances rather than models, and some of them may underperform in comparison with STL methods. In this paper, we propose a privacy-preserving MTL framework to prevent information from each model leaking to other models based on a perturbation of the covariance matrix of the model matrix. We study two popular MTL approaches for instantiation, namely, learning the low-rank and group-sparse patterns of the model matrix. Our algorithms can be guaranteed not to underperform compared with STL methods. We build our methods based upon tools for differential privacy, and privacy guarantees, utility bounds are provided, and heterogeneous privacy budgets are considered. The experiments demonstrate that our algorithms outperform the baseline methods constructed by existing privacy-preserving MTL methods on the proposed model-protection problem. Copyright © 2018, The Authors. All rights reserved.

关键词： Covariance matrix

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A deep learning-based remaining useful life prediction approach for bearings

arXiv

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arXiv 2018年

作者： Cheng, Cheng Ma, Guijun Zhang, Yong Sun, Mingyang Teng, Fei Ding, Han Yuan, Ye The Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China The School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Information Science and Engineering Wuhan University of Science and Technology Wuhan430081 China The College of Control Science and Engineering Zhejiang University Hangzhou310007 China The Department of Electrical & Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom

In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs is of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model-based approaches are limited by the complexity of mathematical modeling. Conventional data-driven approaches, on the other hand, require massive efforts to extract the degradation features and construct the health index. In this paper, a novel data-driven framework is proposed to exploit the adoption of deep convolutional neural networks (CNN) in predicting the RULs of bearings. More concretely, raw vibrations of training bearings are first processed using the Hilbert-Huang transform to construct a novel nonlinear degradation energy indicator which can be used as the training label. The CNN is then employed to identify the hidden pattern between the extracted degradation energy indicator and the raw vibrations of training bearings, which makes it possible to estimate the degradation of the test bearings automatically. Finally, testing bearings’ RULs are predicted through using a ǫ-support vector regression model. The superior performance of the proposed RUL estimation framework, compared with the state-of-the-art approaches, is demonstrated through the experimental results. The generality of the proposed CNN model is also validated by performance test on other bearings undergoing different operating conditions. Copyright © 2018, The Authors. All rights reserved.

关键词： Hilbert-Huang transform

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Unit commitment considering demand response behavior based on utility maximization

Unit commitment considering demand response behavior based o...

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2017 IEEE Conference on Energy Internet and Energy system Integration, EI2 2017

作者： Zhang, Yumin Han, Xueshan Xu, Bo Zhang, Li Sun, Donglei Miao, Xiaofeng Jiang, Jiayin Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Haidian District Beijing100084 China Economic and Technology Research Institute of State Grid Shandong Electric Power Company Jinan China State Grid Shandong Electric Power Company Yantai Electric Power Company Yantai264001 China

ISBN: (纸本)9781538614273

The active participation of demand side resources in power grid operation not only benefits the individual participant but also benefits the whole system. The optimal goal can promote by excavating the active behaviors of demand side. The welfare maximization of market participant is the driving force of such active behaviors. Considering the independence of each user's behavior, the consumer's utility function is selected to depict consumer's preference and demand response behavior. In order to coordinate the consumers' maximum welfare with social welfare maximization, consumers' utility function and units' operation cost are integrated into Unit Commitment (UC) model. The objective function is to maximize social welfare, by subtracting units' operation cost from consumers' total utility, and this objective can be acquired. Considering the proposed model should be solved in a distributed manner, the idea of the centralized coordination and the decentralized decision-making is proposed. Based on the Lagrangian relaxation (LR) principle and distributed real-time pricing scheme, a closed-loop feedback mechanism is constructed to reflect the relationship among generator output, price level, and load demand. The ten generating units example can illustrate the effectiveness of the proposed method. © 2017 IEEE.

关键词： Power markets

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