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第三十三届中国控制会议

作者： LIU Jianzhi HAN Hui CHEN Cailian LI Li'an GUAN Xinping The State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE) Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of China

ISBN: (纸本)9789881563842

Radio Environment Map(REM)is a map that indicates the radio signal strength(RSS)over a finite geographical *** enables cognitive radio(CR)users to opportunistically access to the spectrum unoccupied by the primary users(PUs)who are licensed to use specific *** this paper,a basis expansion based scheme is presented to estimate *** take the sparsity of preselected basis functions and the inherent time-varying spectrum usage of primary users into account such that the RSS estimation is formulated as a recursive least squares(RLS)problem with an l1norm constraint.A distributed algorithm is presented to solve the RLS *** SU only need to exchange information with its one-hop neighbours to sketch the REM with radio signal ***,simulation studies are presented to validate the performance of the proposed method.

关键词： Cognitive radio,Basis expansion,Cooperative spectrum sensing,Distributed estimation

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Self-triggered Based Semi-global Consensus Tracking of Multi-agent systems with Input Saturation

Self-triggered Based Semi-global Consensus Tracking of Multi...

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第三十四届中国控制会议

作者： Xiaoling Wang Housheng Su Jiwei Li Xiaofan Wang Department of Automation Shanghai Jiaotong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 P. R. China School of Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Luoyu Road 1037 Wuhan 430074 P. R. China

ISBN: (纸本)9781467374439

Self-triggered control is a technique which can determine the next sampling instant by virtue of the last sampling information. Combining this technique and the low-gain feedback one, we investigate the consensus tracking of general linear multi-agent systems with input saturation and updating delays in this paper. Specific formulations of heterogenous updating delays and heterogenous sampling periods are acquired to guarantee the semi-global consensus tracking of multi-agent systems. Numerical simulations are provided to verify the main results.

关键词： Multi-agent systems Consensus Tracking Self-triggered Technique Input Saturation Low-gain Feedback Multi-agent systems sampling time tracking

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The Liver Tumor Segmentation Benchmark (LiTS)

arXiv

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arXiv 2019年

作者： Bilic, Patrick Christ, Patrick Li, Hongwei Bran Vorontsov, Eugene Ben-Cohen, Avi Kaissis, Georgios Szeskin, Adi Jacobs, Colin Mamani, Gabriel Efrain Humpire Chartrand, Gabriel Lohöfer, Fabian Holch, Julian Walter Sommer, Wieland Hofmann, Felix Hostettler, Alexandre Lev-Cohain, Naama Drozdzal, Michal Amitai, Michal Marianne Vivanti, Refael Sosna, Jacob Ezhov, Ivan Sekuboyina, Anjany Navarro, Fernando Kofler, Florian Paetzold, Johannes C. Shit, Suprosanna Hu, Xiaobin Lipková, Jana Rempfler, Markus Piraud, Marie Kirschke, Jan Wiestler, Benedikt Zhang, Zhiheng Hülsemeyer, Christian Beetz, Marcel Ettlinger, Florian Antonelli, Michela Bae, Woong Bellver, Míriam Bi, Lei Chen, Hao Chlebus, Grzegorz Dam, Erik B. Dou, Qi Fu, Chi-Wing Georgescu, Bogdan Giró-I-Nieto, Xavier Gruen, Felix Han, Xu Heng, Pheng-Ann Hesser, Jürgen Moltz, Jan Hendrik Igel, Christian Isensee, Fabian Jäger, Paul Jia, Fucang Kaluva, Krishna Chaitanya Khened, Mahendra Kim, Ildoo Kim, Jae-Hun Kim, Sungwoong Kohl, Simon Konopczynski, Tomasz Kori, Avinash Krishnamurthi, Ganapathy Li, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Lowengrub, John Ma, Jun Maier-Hein, Klaus Maninis, Kevis-Kokitsi Meine, Hans Merhof, Dorit Pai, Akshay Perslev, Mathias Petersen, Jens Pont-Tuset, Jordi Qi, Jin Qi, Xiaojuan Rippel, Oliver Roth, Karsten Sarasua, Ignacio Schenk, Andrea Shen, Zengming Torres, Jordi Wachinger, Christian Wang, Chunliang Weninger, Leon Wu, Jianrong Xu, Daguang Yang, Xiaoping Yu, Simon Chun-Ho Yuan, Yading Yue, Miao Zhang, Liping Cardoso, Jorge Bakas, Spyridon Braren, Rickmer Heinemann, Volker Pal, Christopher Tang, An Kadoury, Samuel Soler, Luc van Ginneken, Bram Greenspan, Hayit Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland Ecole Polytechnique de Montréal Canada Department of Medical Imaging Radboud University Medical Center Nijmegen Netherlands Department of Biomedical Engineering Tel-Aviv University Israel Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Philips Research China Philips China Innovation Campus Shanghai China School of Biomedical Engineering & Imaging Sciences King0s College London London United Kingdom Institute for AI in Medicine Technical University of Munich Germany Department of Computer Science Guangdong University of Foreign Studies China Institute for diagnostic and interventional radiology Klinikum rechts der Isar Technical University of Munich Germany Institute for Diagnostic and Interventional Neuroradiology Klinikum Rechts der Isar Technical University of Munich Germany Department of Hepatobiliary Surgery The Affiliated Drum Tower Hospital Nanjing University Medical School China Department of Computing Imperial College London London United Kingdom Institute for Tissue Engineering and Regenerative Medicine Helmholtz Zentrum München Neuherberg Germany Brigham and Women's Hospital Harvard Medical School United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel University of Pennsylvania PA United States Pte. Ltd. Singapore Medical Imaging and Reconstruction Lab Department of Engineering Design Indian Institute of Technology Madras India Sensetime Shanghai China Department of Radiology Perelman School of Medicine University of Pennsylvania United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PA United States Co. Ltd China MontréalQC Canada Department of Radiology Radiation Oncology and Nuclear Medicine University of Montréal

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://***/. In addition, both data and online evaluation are accessible via ***. © 2019, CC BY-NC-ND.

关键词： Deep learning

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The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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Flocking of partially-informed multi-agent systems avoiding obstacles with arbitrary shape

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Autonomous Agents and Multi-Agent systems 2014年第5期29卷 943-972页

作者： Li, Jiaojie Zhang, Wei Su, Housheng Yang, Yupu Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Department of Measurement and Control Technology Shanghai Dian Ji University Shanghai China School of Automation Key Laboratory of Image Information Processing and Intelligent Control (Huazhong University of Science and Technology) Ministry of Education National Key Laboratory of Science and Technology on Multispectral Information Processing Huazhong University of Science and Technology Wuhan China

In this paper, we study the flocking problem of multi-agent systems with obstacle avoidance, in the situation when only a fraction of the agents have information on the obstacles. Obstacles of arbitrary shape are allowed, no matter if their boundary is smooth or non-smooth, and no matter it they are convex or non-convex. A novel geometry representation rule is proposed to transfer obstacles to a dense obstacle-agents lattice structure. Non-convex regions of the obstacles are detected and supplemented using a geometric rule. The uninformed agents can detect a section of the obstacles boundary using only a range position sensor. We prove that with the proposed protocol, uninformed agents which maintain a joint path with any informed agent can avoid obstacles that move uniformly and assemble around a point along with the informed agents. Eventually all the assembled agents reach consensus on their velocity. In the entire flocking process, no distinct pair of agents collide with each other, nor collide with obstacles. The assembled agents are guaranteed not to be lost in any non-convex region of the obstacles within a distance constraint. Numerical simulations demonstrate the flocking algorithm with obstacle avoidance both in 2D and 3D space. The situation when every agent is informed is considered as a special case.

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A secure distributed consensus scheme for wireless sensor networks against data falsification

A secure distributed consensus scheme for wireless sensor ne...

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World Congress on intelligent control and Automation (WCICA)

作者： Shichao Mi Hui Han Shanying Zhu Cailian Chen Bo Yang Xinping Guan Department of Automation Key Laboratory of System Control and Information Processing Shanghai P. R. China The State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System(CEMEE) Luoyang P. R. China

This paper focuses on the problem of secure distributed consensus to defend covert misbehavior in wireless sensor networks (WSNs). Distributed consensus is a promising method to improve the efficiency and precision of consensus results in WSNs, but it introduces new security issues that malicious nodes may manipulate false sensing data to degrade the sensing result of the whole network. A data falsification attack, i.e., the attacker injects random values into its neighboring nodes at each time-step of consensus process, is considered. This kind of attack cannot be defended against by most of existing detection mechanisms. We present a distributed detection mechanism with adaptive local threshold to isolate the abnormal nodes. A Weighted Averaging-based Consensus Scheme (WACS) is proposed to decrease the negative impact of the attack and make the network converge to a consensus value. It is proved that convergence property can be guaranteed by the relationship between weighted average of the noise and stochastic approximation. Simulation results are presented to show the effectiveness of the proposed secure scheme.

关键词： Sensors Wireless sensor networks Detection algorithms Distributed databases Convergence Vectors Approximation methods

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Using artificial plant optimization algorithm with dynamic population and cluster methods to optimize the performance of DV-Hop

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Sensor Letters 2014年第8期12卷 1313-1317页

作者： Wu, Xuexue Cui, Zhihua Dai, Chaohua Chen, Weirong Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Shanxi China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Electric Engineering Southwest Jiaotong University Sichuan China

Artificial plant optimization algorithm is a novel stochastic population-based evolutionary algorithm by simulating the plant growing process. In this paper, a new variant, which is called APOA-DC is proposed by incorporating with dynamic population size and cluster methods, furthermore, to investigate the performance, APOA-DC is applied to optimize the DV-Hop localization algorithm, simulation results show it achieves better performance than the DV-Hop algorithm.

关键词： Evolutionary algorithms

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Improved low energy adaptive clustering hierarchy protocol based on local centroid bat algorithm

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Sensor Letters 2014年第9期12卷 1372-1377页

作者： Cao, Yang Cui, Zhihua Li, Feixiang Dai, Chaohua Chen, Weirong Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Shanxi China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Electric Engineering Southwest Jiaotong University Sichuan China

Bat algorithm (BA) is a novel intelligent optimization algorithm. In order to improve the poor local search capability of the algorithm, we present a new variant of bat optimization algorithm based on local centroid strategy (LCBA). Meanwhile, LEACH is a low energy adaptive clustering hierarchy algorithm for wireless sensor networks (WSN). And it has many disadvantages such as random selection of cluster head, taking no account of the remaining energy and position of nodes. To solve these problems, this paper proposes an improved LEACH protocol based on local centroid bat algorithm. The improved protocol divides the cluster head selection process into optimization of temporary cluster head and formal cluster head selection. First, we generate temporary cluster heads by traditional LEACH protocol, then optimize these cluster heads based on LCBA and select formal cluster heads according to the remaining energy of nodes. The result of experiment shows that, comparing with LEACH, LEACH-LCBA can balance the network load efficiently, improve the energy utilization, prolong the network lifetime.

关键词： Energy utilization

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Coordinated obstacle avoidance with reduced interaction

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Neurocomputing 2014年 139卷 233-245页

作者： Jiaojie Li Wei Zhang Housheng Su Yupu Yang Hongtao Zhou Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China Department of Measurement and Control Technology Shanghai Dian Ji University Shanghai 200240 China School of Automation Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology Luoyu Road 1037 Wuhan 430074 China

In this paper, we study the coordinated obstacle avoidance algorithm of multi-agent systems when only a subset of agents has obstacle dynamic information, or every agent has local interaction. Each agent can get partial measuring states information from its neighboring agent and obstacle. Coordinated obstacle avoidance here represents not only the agents moving without collision with an obstacle, but also the agents bypassing and assembling at the opposite side of the obstacle collectively, where the opposite side is defined according to the initial relative position of the agents to the obstacle. We focus on the collective obstacle avoidance algorithms for both agents with first-order kinematics and agents with second-order dynamics. In the situation where only a fixed fraction of agents can sense obstacle information for agents with first-order kinematics, we propose a collective obstacle avoidance algorithm without velocity measurements. And then we extend the algorithm to the case in switched topology. We show that all agents can bypass an obstacle and converge together, and then assemble at the opposite side of the obstacle in finite time, if the agents׳ topology graph is connected and at least one agent can sense the obstacle. In the case where obstacle information is available to only a fixed fraction of agents with second-order kinematics, we propose two collective obstacle avoidance algorithms without measuring acceleration when the obstacle has varying velocity and constant velocity. The switched topology is considered and extended next. We show that agents can bypass the obstacle with their positions and velocities approaching consensus in finite time if the connectivity of switched topology is continuously maintained. Several simulation examples demonstrate the proposed algorithms.

关键词： Obstacle avoidance Consensus Reduced interaction Circular motion Multi-agents

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Distributed differential evolution algorithm with adaptive parameters

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Kongzhi yu Juece/control and Decision 2014年第4期29卷 701-706页

作者： Zhang, Chun-Mei Chen, Jie Xin, Bin School of Automation Ministry of Education Beijing Institute of Technology Beijing 100081 China Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing Institute of Technology Beijing 100081 China School of Electronic Information Engineering Taiyuan University of Science and Technology Taiyuan 030024 China

An adaptive distributed differential evolution algorithm is proposed based on the change of the individual's fitness value. Firstly, the initial population is divided into several subpopulations. When the migration condition is satisfied, the best individual in each subpopulation will replace the worst individual of its neighbor subpopulations according to the Von Neumann topology. The migration mechanism among subpopulations enables the information to be communicated in the whole population. Meanwhile, the adaptive mechanism of F and CR is presented for assigning different F and CR to each individual according to the individual's fitness. Numerical results show that, the proposed algorithm is beneficial to explore the solution space, which can avoid the premature convergence and search the excellent solutions.

关键词： Optimization

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