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American Control Conference

作者： Shuang Wu Ziyang Guo Dawei Shi Karl Henrik Johansson Ling Shi Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology 100081 China ACCESS Linnaeus Centre School of Electrical Engineering KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781509045839

The security issue in cyber-physical systems has attracted growing interests in the last decades. This paper considers how false data injection attack can degrade the estimation quality of a remote state estimation system. In this system, smart sensors measure a dynamic process and send preprocessed data through a communication network to a remote estimator to estimate the process. It is assumed that there are malicious attackers in the communication network, who are able to obtain and falsify all the data sent by the sensors. It is common that the remote estimator is equipped with a residue-based detector to detect potential attacks. We propose a class of deception attack and analyze its feasibility. We show that the proposed attack enables the attacker to inject false data into the remote estimator without being detected. We derive a criterion to judge the optimality of performance of this type of attack in the sense of maximizing the estimation error covariance. Furthermore, we find that a simple linear attack strategy, which flips the sign of intercepted signal, satisfies the optimality criterion. We present numerical examples to illustrate our theoretical results.

关键词： Cyber-physical systems (CPS) deception attack remote state estimation

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A novel extended potential field controller for use on aerial robots

A novel extended potential field controller for use on aeria...

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2016 IEEE International Conference on automation science and engineering, CASE 2016

作者： Woods, Alexander C. Lay, Hung M. Ha, Quang P. Advanced Robotics and Automation Laboratory Department of Computer Science and Engineering University of Nevada RenoNV89557 United States School of Electrical Mechanical and Mechatronic Systems University of Technology Sydney Australia

ISBN: (纸本)9781509024094

Unmanned Aerial Vehicles (UAV), commonly known as drones, have many potential uses in real world applications. Drones require advanced planning and navigation algorithms to enable them to safely move through and interact with the world around them. This paper presents an extended potential field controller (ePFC) which enables an aerial robot, or drone, to safely track a dynamic target location while simultaneously avoiding any obstacles in its path. The ePFC outperforms a traditional potential field controller (PFC) with smoother tracking paths and shorter settling times. The proposed ePFC's stability is evaluated by Lyapunov approach, and its performance is simulated in a Matlab environment. Finally, the controller is implemented on an experimental platform in a laboratory environment which demonstrates the effectiveness of the controller. © 2016 IEEE.

关键词： Controllers

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Aviation BIT optimal method for reducing false alarm rate under gust environment

Aviation BIT optimal method for reducing false alarm rate un...

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2016 IEEE International Conference on Prognostics and Health Management, ICPHM 2016

作者： Quo, Yangming Zhang, Jiaqi Li, Qingdong Liu, Shan Chai, Wangxu School of Computer Science and Technology Northwestern Polytechnical University Reliability and Maintenance Research Laboratory Northwestern Polytechnical University Xi'an710072 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China School of Computer Science and Technology Northwestern Polytechnical University Xi'an710072 China

ISBN: (纸本)9781509003822

High false alarm rate is the major restrictive factors for the aviation built-in test (BIT) application in aircraft. This paper analyzes the environmental influence on the aviation BIT, and addresses that gust is the one of the principal reasons leading to false alarm from aviation BIT. Under the gust environment, the aero relative speed will be changed which changes the angle of incidence and lift force simultaneously. It inevitably makes the aviation BIT diagnosis data change and maybe cause false alarm in aviation BIT in some special situations. We gather the sample data influencing the BIT accuracy via the simulation experiment or actual test, and establish a decision network for the aviation BIT to confirm the influence degree under gust environment, which can comprehensively consider the change of various elements in the residual vector to distinguish the influence degree between the gust and real fault in the aircraft. This way, based on the original BIT diagnosis results and the output results of the decision network, the false alarm rate of aviation BIT can be effectively improved with weighting processing. The Monte Carlo simulation result shows the proposed aviation BIT optimal method is a good approach because it reduces the false alarm rate of aviation BIT under gust environment, and further improve the BIT diagnostic ability. The method will be useful in actual application. © 2016 IEEE.

关键词： Monte Carlo methods

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5G ENTERING INTO A NEW ERA

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China Communications 2017年第12期14卷 I0002-I0004页

作者： Feifei Gao Changle Li Jun Fang Kaizhi Huang Zhi Quan Chengwen Xing Jianhua Zhang Zhongshan Zhang Liqiang Zhao Yulong Zou Department of Automation Tsinqhua UniversityBeiiingChina the State Key Laboratory of Integrated Services Networks Xidian University china communications ieee communications magazine ieee signal processing letters national digital switching system engineering&technological research center the college of information engineering shenzhen university the school of information and electronics beijing institute of technology bupt itu-r imt-2020 channel model ieee comm the school of computer and communication engineering in the university of science and technology beijing(ustb) state key lab.of integrated ser vice networks xidian university the nanjing university of posts and telecommunications(nupt)

The ever increasing wireless data services, such asimaging, video, audio, multimedia, etc., have de-mands for the very high speed wireless communicationsand network, which are unfortunately a bottleneck whencombining with the wireline core network. Users＇ arenow expecting high quality of experience with low-costdevices, ubiquitous connectivity, energy efficiency, highreliability, or even ultra-low latency if a vehicle terminalis applied.

关键词： 5G ENTERING A NEW ERA

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3D action recognition using multi-temporal depth motion maps and fisher vector 25

3D action recognition using multi-temporal depth motion maps...

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25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Chen, Chen Liu, Mengyuan Zhang, Baochang Han, Jungong Junjun, Jiang Liu, Hong Department of Electrical Engineering University of Texas at Dallas United States Key Laboratory of Machine Perception Shenzhen Graduate School Peking University China School of Automation Science and Electrical Engineering Beihang University China Department of Computer Science and Digital Technologies Northumbria University United Kingdom School of Computer Science China University of Geosciences China

This paper presents an effective local spatiotemporal descriptor for action recognition from depth video sequences. The unique property of our descriptor is that it takes the shape discrimination and action speed variations into account, intending to solve the problems of distinguishing different pose shapes and identifying the actions with different speeds in one goal. The entire algorithm is carried out in three stages. In the first stage, a depth sequence is divided into temporally overlapping depth segments which are used to generate three depth motion maps (DMMs), capturing the shape and motion cues. To cope with speed variations in actions, multiple frame lengths of depth segments are utilized, leading to a multi-temporal DMMs representation. In the second stage, all the DMMs are first partitioned into dense patches. Then, the local binary patterns (LBP) descriptor is exploited to characterize local rotation invariant texture information in those patches. In the third stage, the Fisher kernel is employed to encode the patch descriptors for a compact feature representation, which is fed into a kernel-based extreme learning machine classifier. Extensive experiments on the public MSRAction3D, MSRGesture3D and DHA datasets show that our proposed method outperforms state-of-the-art approaches for depth-based action recognition.

关键词： Local binary pattern

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Linear perspective shape-from-shading method with two images

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Journal of Systems engineering and Electronics 2015年第5期26卷 1080-1087页

作者： Lei Yang Ning Zhang Yanyun Ren Huosheng Hu Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronics Engineering and AutomationShanghai University School of Computer Science and Electrical Engineering University of Essex

Shape-from-shading(SFS) is one of the important approaches of 3-D surface reconstruction in computer vision. Since reflectance map equation in SFS is a nonlinear partial differential equation(PDE) with two unknown variables, SFS with one image is ill-posed in mathematical sense. A linear perspective SFS method with two images is proposed to deal with the problem. We assume that two images with different light source directions are captured firstly. Orthogonal projection is not as accurate as perspective one to simulate imaging processes. Two reflectance map equations are established based on the Lambertian model under perspective projection, and the equations are further transformed into one linear PDE. Then the iterative semi-Lagrangian algorithm is used to approximate the solution. Finally, 3-D height values of pixel points in imaging planes are solved by the numerical interpolation method. Experimental results of both hemisphere and complex surfaces show that the proposed method can reconstruct surfaces accurately.

关键词： shape-from-shading(SFS) perspective projection semi-Lagrangian algorithm numerical interpolation method

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Image classification using spatial difference descriptor under spatial pyramid matching framework 22nd

Image classification using spatial difference descriptor und...

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22nd International Conference on MultiMedia Modeling, MMM 2016

作者： Li, Yuhui Xu, Jiucheng Zhang, Yifan Zhang, Chunjie Yin, Hongsheng Lu, Hanqing School of Computer and Information Engineering Henan Normal University Xinxiang China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Science Beijing China School of Computer and Control Engineering University of China Academy of Sciences Beijing China China University of Mining and Technology Xuzhou China

ISBN: (纸本)9783319276700

Spatial pyramid matching (SPM) model is an extension of the bagof-visual words (BoW) model for local feature encoding. It firstly partitions the image into increasingly fine sub-regions, and then concatenates the histograms within each sub-region. However, the SPM model does not consider the spatial information differences between sub-regions explicitly. To make use of this information, we exploit a novel descriptor called spatial difference. In the process of promoting the performance of image classification, this descriptor is mainly used to concatenate the histograms of bag-of-visual words model under spatial pyramid matching framework. Finally, we conduct image classification experiments on several public datasets to demonstrate the effectiveness of the proposed scheme. © Springer International Publishing Switzerland 2016.

关键词： Image classification

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Face alignment across large poses: A 3D solution

Face alignment across large poses: A 3D solution

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2016 IEEE Conference on computer Vision and Pattern Recognition, CVPR 2016

作者： Zhu, Xiangyu Lei, Zhen Liu, Xiaoming Shi, Hailin Li, Stan Z. Center for Biometrics and Security Research National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China Department of Computer Science and Engineering Michigan State University United States

ISBN: (纸本)9781467388511

Face alignment, which fits a face model to an image and extracts the semantic meanings of facial pixels, has been an important topic in CV community. However, most algorithms are designed for faces in small to medium poses (below 45°), lacking the ability to align faces in large poses up to 90°. The challenges are three-fold: Firstly, the commonly used landmark-based face model assumes that all the landmarks are visible and is therefore not suitable for profile views. Secondly, the face appearance varies more dramatically across large poses, ranging from frontal view to profile view. Thirdly, labelling landmarks in large poses is extremely challenging since the invisible landmarks have to be guessed. In this paper, we propose a solution to the three problems in an new alignment framework, called 3D Dense Face Alignment (3DDFA), in which a dense 3D face model is fitted to the image via convolutional neutral network (CNN). We also propose a method to synthesize large-scale training samples in profile views to solve the third problem of data labelling. Experiments on the challenging AFLW database show that our approach achieves significant improvements over state-of-the-art methods.

关键词： Alignment

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23rd International Conference on Pattern Recognition, ICPR 2016

作者： Wang, Jin Zhu, Junkang Wang, Zheng Gao, Changxin Sang, Nong Rui, Huang National Key Laboratory of Science and Technology on Multispectral Information Processing School of Automation Huazhong University of Science and Technology China School of Physics Nanjing University China School of Computer Wuhan University China School of Science and Engineering Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9781509048472

Person re-identification, aiming to match a specific person among non-overlapping cameras, has attracted plenty of attention in recent years. It can be regarded as a visual retrieval task, namely given a query person image, ranking all gallery images according to their similarities to the query. Conventionally, this similarity function is learnt by forcing intra-distances to be small while inter-distances to be large, which are referred to as individual similarity constraints. In this paper, we propose to learn the similarity function by taking into account of both individual similarity constraints and contextual similarity constraints. The context of a query is defined as its k-nearest neighbors in the gallery. We argue that if two images are from the same person, apart from the visual likeness between them, denoted as the individual similarity, they should also possess similar k-nearest neighbors in the gallery, denoted as the contextual similarity. Motivated by this assumption, we propose a new Contextual Similarity Regularized Metric Learning (CSRML) method for person re-identification. The contextual similarity regularization term forces two images of the same person to share similar context. Both individual and contextual similarity constraints are encoded by a large margin logistic loss function and the final problem is solved by the stochastic gradient descent algorithm. Experiments on the challenging VIPeR and CUHK01 datasets show that our approach achieves very competitive performance. © 2016 IEEE.

关键词： Stochastic systems

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A hierarchical urban network control with integration of demand balance and traffic signal coordination

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IFAC-PapersOnLine 2016年第3期49卷 31-36页

作者： Zhou, Zhao Lin, Shu Xi, Yugeng Li, Dewei Zhang, Jun Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control Information Processing Ministry of Education of China Shanghai200240 China School of Computer and Control Engineering University of Chinese Academy of Science Beijing100049 China Joint Institute of UM-SJTU Shanghai Jiao Tong University Shanghai200240 China

This paper concerns the integration of demand balance control and traffic flow coordination control in a hierarchical framework for complex urban traffic networks. At the first level, a complex traffic network is first partitioned into several subnetworks. The traffic dynamics in each subnetwork is modeled by utilizing the concept of Macroscopic Fundamental Diagram (MFD), which relates the network-wide space-mean flow and the number of vehicles at the aggregated level. This model aims at designing a demand balance model predictive controller (MPC), which can improve the internal flow inside the subnetwork by regulating the input flows from outside. At the second level, based on a more detailed traffic flow model, the signal timings of all intersections for each subnetwork are determined by a flow coordination MPC controller, which aims at distributing the number of vehicles in each subnetwork as homogeneously as possible. The effectiveness of the proposed approach is investigated via simulation under different scenarios on a hypothetical urban traffic network, and the performance is compared with other control strategies. © 2016

关键词： Street traffic control

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