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Chinese Automation Congress

作者： Yasir Ali Zhen Shen Fenghua Zhu Gang Xiong Shichao Chen Yuanqing Xia Fei-Yue Wang The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China Cloud Computing Center Chinese Academy of Sciences Dongguan China School of Automation Beijing Institute of Technology China Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of Sciences Beijing China

The rapid development of the Cloud Computing Technologies (CCTs) has amended the conventional design of resource-constrained Network control System (NCS) to the powerful and flexible design of Cloud-Based Networked control System (CB-NCS) by relocating the processing part to the cloud server. This arrangement has produced many internets based exquisite applications. However, this new arrangement has also raised many network security challenges for the cloud-based control system related to cyber-physical part of the system. In the absence of robust verification methodology, an attacker can launch the modification attack in order to destabilize or take control of NCS. It is desirable that there shall be a solution authentication methodology used to verify whether the incoming solutions are coming from the cloud or not. This paper proposes a methodology used for the verification of the receiving solution to the local control system from the cloud using Karush-Kuhn-Tucker (KKT) conditions, which is then applied to actuator after verification and thus ensure the stability in case of modification attack.

关键词： Cloud-based control system Security of NCS Analysis of solution verification for NCS KKT conditions

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The 3D point clouds registration for human foot 3rd

The 3D point clouds registration for human foot

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3rd International Conference on Intelligence Science, ICIS 2018

作者： Xie, Yi Shang, Xiuqin Li, Yuqing Liu, Xiwei Zhu, Fenghua Xiong, Gang Pirttikangas, Susanna Zhou, Jiehan School of Electrical and Electronic Engineering The University of Manchester ManchesterM13 9PL United Kingdom The 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 DongguanGuangdong China College of Information Science and Technology Beijing University of Chemical Technology Beijing China Qingdao Academy of Intelligent Industries Qingdao China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing100190 China The Center for Ubiquitous Computing University of Oulu Oulu Finland

ISBN: (纸本)9783030013127

For personalized design it is important to be able to collect, measure and evaluate individual properties of human beings. This paper proposes registration for the point clouds during foot 3D scanning. In the experiment, we get the point clouds of the human foot from the Artec 3D scanner and complete the registration of the point clouds from different visual angles. Dealing with the customized footwear, we choose a novel algorithm, which combines the NARF key point detector and the FPFH descriptor, to improve the efficiency of the initial iteration and reduce the computation burden of matching process. © IFIP International Federation for Information Processing 2018.

关键词： Iterative methods

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Robust medical instrument segmentation challenge 2019

arXiv

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arXiv 2020年

作者： Roβ, Tobias Reinke, Annika Full, Peter M. Wagner, Martin Kenngott, Hannes Apitz, Martin Hempe, Hellena Filimon, Diana Mindroc Scholz, Patrick Tran, Thuy Nuong Bruno, Pierangela Arbelaéz, Pablo Bian, Gui-Bin Bodenstedt, Sebastian Bolmgren, Jon Lindström Bravo-Sánchez, Laura Chen, Hua-Bin González, Cristina Guo, Dong Halvorsen, Pål Heng, Pheng-Ann Hosgor, Enes Hou, Zeng-Guang Isensee, Fabian Jha, Debesh Jiang, Tingting Kirtac, Kadir Jin, Yueming Kletz, Sabrina Leger, Stefan Li, Zhixuan Maier-Hein, Klaus H. Ni, Zhen-Liang Riegler, Michael A. Schoeffmann, Klaus Shi, Ruohua Speidel, Stefanie Stenze, Michael Twick, Isabell Wang, Gutai Wang, Jiacheng Wang, Liansheng Wang, Lu Zhang, Yujie Zhou, Yan-Jie Zhu, Lei Wiesenfarth, Manuel Kopp-Schneider, Annette Müller-Stich, Beat P. Maier-Hein, Lena Heidelberg Germany University of Heidelberg Germany Heidelberg Germany Department of General Heidelberg University Hospital Visceral and Transplant Surgery Heidelberg Germany caresyntax Berlin Germany University of Chinese Academy Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China SimulaMet Oslo Norway Tromsø Norway Oslo Norway School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Partner Site Dresden Heidelberg Germany Faculty of Medicine and University Hospital Carl Gustav Carus Technische Universität Dresden Dresden Germany Dresden Germany Universidad de Los Andes Bogotá Colombia Peking University Peking China Department of Computer Science School of Informatics Xiamen University Xiamen China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong German Cancer Research Center Division of Biostatistics Heidelberg Germany Institute of Information Technology Klagenfurt University Austria

Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and roboticassisted interventions. While numerous methods for detecting, segmenting and tracking of medical instruments based on endoscopic video images have been proposed in the literature, key limitations remain to be addressed: Firstly, robustness, that is, the reliable performance of state-of-the-art methods when run on challenging images (e.g. in the presence of blood, smoke or motion artifacts). Secondly, generalization;algorithms trained for a specific intervention in a specific hospital should generalize to other interventions or institutions. In an effort to promote solutions for these limitations, we organized the Robust Medical Instrument Segmentation (ROBUST-MIS) challenge as an international benchmarking competition with a specific focus on the robustness and generalization capabilities of algorithms. For the first time in the field of endoscopic image processing, our challenge included a task on binary segmentation and also addressed multi-instance detection and segmentation. The challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures from three different types of surgery. The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap. While the average detection and segmentation quality of the best-performing algorithms is high, future research should concentrate on detection and segmentation of small, crossing, moving and transparent instrument(s) (parts). Copyright © 2020, The Authors. All rights reserved.

关键词： Endoscopy

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UAV assisted bridge defect inspection system 3rd

UAV assisted bridge defect inspection system

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3rd International Conference on Intelligence Science, ICIS 2018

作者： Yang, Shuzhan Shen, Zhen Wang, Xiao Bai, Tianxiang Ji, Yingliang Jiang, Yuyi Liu, Xiwei Dong, Xisong Li, Chuanfu Han, Qi Lu, Jian Xiong, Gang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266109 China College of Engineering Peking University Beijing100871 China University of Chinese Academy of Sciences Beijing100049 China Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Expressway Qingdao266041 China Guangdong Launca Medical Device Technology Co. Ltd. Dongguan523808 China

ISBN: (纸本)9783030013127

Traditional bridge inspection methods require lane closures, inspection equipment, and most importantly the experiences and knowledge of the inspectors. This increases not only the inspection cost and time, but also the risk to the travelling public. Due to the lengthy and costly traditional bridge inspection methods, there has been an increasing backlog of inspection activities. In this research, we design an unmanned aerial vehicle (UAV) assisted bridge defect inspection system, in which a UAV can capture the image and transmit the information to the ground station for further analysis. The system can be divided into 2 subsystems: electromechanics & communication system, and image processing system. The electromechanics & communication systems ensure the self-locating, flight control, image transmission, and human intervention functions. The image processing system performs the image preprocessing, defect extraction, and provides the inspection report. This system, if put into practice, can save the cost up to 70%. We believe that the UAV assisted bridge inspection can be popular in the future. © IFIP International Federation for Information Processing 2018.

关键词： Image processing

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Detecting social bots by jointly modeling deep behavior and content information 17

Detecting social bots by jointly modeling deep behavior and ...

引用

26th ACM International Conference on Information and Knowledge Management, CIKM 2017

作者： Cai, Chiyu Li, Linjing Zeng, Daniel State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences China Department of Management Information Systems University of Arizona TucsonAZ85721 United States

ISBN: (纸本)9781450349185

Bots are regarded as the most common kind of malwares in the era of Web 2.0. In recent years, Internet has been populated by hundreds of millions of bots, especially on social media. Thus, the demand on effective and efficient bot detection algorithms is more urgent than ever. Existing works have partly satisfied this requirement by way of laborious feature engineering. In this paper, we propose a deep bot detection model aiming to learn an effective representation of social user and then detect social bots by jointly modeling social behavior and content information. The proposed model learns the representation of social behavior by encoding both endogenous and exogenous factors which affect user behavior. As to the representation of content, we regard the user content as temporal text data instead of just plain text as be treated in other existing works to extract semantic information and latent temporal patterns. To the best of our knowledge, this is the first trial that applies deep learning in modeling social users and accomplishing social bot detection. Experiments on real world dataset collected from Twitter demonstrate the effectiveness of the proposed model. © 2017 Association for Computing Machinery.

关键词： Semantics

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The Smart Street Lighting System Based on NB-IoT

The Smart Street Lighting System Based on NB-IoT

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Chinese Automation Congress

作者： Shichao Chen Shuangshuang Han Gang Xiong Fei-Yue Wang Jia Xu Kun Wang Macau University of Science and Technology Chinese Academy of Sciences Macau China The Cloud Computing Center Chinese Academy of Sciences Dongguan China Beijing University of Chemical Technology Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries The State Key laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Qingdao China Beijing University of Chemical Technology Beijing China

With the development of communication technology, in order to meet the smart city's demand for intelligent infrastructure, the smart street lighting system based on NB-IoT is an effective method to realize the goal. This paper elaborates on the smart street lighting system's architecture, including the perception and control layer, the transport layer, the platform layer and the application layer. In addition, the actual system is built to verify. This system uses STM32 to build the perception and controller, and relies on China Telecom's NB-IoT communication network for the data transmission, finally using the IoT management platform-Huawei OceanConnect- to implement the connection management, the device management, the data calculation and the remote control functions. This system verifies the feasibility of the smart street lighting system based on NB-IoT technology, which provides the reference for the promotion and application. It is expected to provide intelligent, diversified, efficient and economical services for the smart cities.

关键词： Smart street lighting system NB-IoT IoT Smart city

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Hemorrhagic fever with renal syndrome in China： Mechanisms on two distinct annual peaks and control measures

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International Journal of Biomathematics 2018年第2期11卷 319-340页

作者： Li Li Cui-Hua Wang Shi-Fu Wang Ming-Tao Li Laith Yakob Bernard Cazelles Zhen Jin Wen-Yi Zhang School of Computer and Information Technology Shanxi University Taiyuan Shanxi 030006 P. R. China Science and Technology on Electronic Testand Measurement Laboratory North University of China Taiyuan 030051 P. R. China Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention Shanxi University Taiyuan Shanxi 030006 P. R. China Complex Systems Research Center Shanxi University Taiyuan Shanxi 030006 P. R. China Department of Children's Medical LaboratoryDiagnosis Center Qilu Children's Hospital of Shandong University Jinan 250022 P. R. China Department of Disease ControlLondon School of Hygiene and Tropical Medicine London UK UMMISCO UMI 209 IRD- UPMC 93142 Bondy France Eco- Evolutionary MathematicsIBENS UMR 8197 ENS Paris France Institute of Disease Control and Prevention Academy of Military Medical Science Beijing 100071 P. R. China

Hemorrhagic fever with renal syndrome （HFRS） is a rodent-borne disease caused by several serotypes of hantavirus and 90% of all reported HFRS cases occur in China. However, the dynamics of such outbreak, particularly the characteristics of two distinct annual peaks in China, are not well understood. Here, we investigate several of the biologically plausible causes for the peaks in monthly HFRS cases, and find that the key factor is the interplay between periodic transmission rates and rodent periodic birth rate. Analysis of dynamicM model reveals that vaccination plays a significant role in the control of HFRS in China. Sensitive analysis of different interventions demonstrates that integrating rodent culling and environmental management with the current vaccination program is effective for HFRS control. Our results suggest that for diseases from animals to human beings, the features of both animals and humans beings should be taken into account in the control and prevention strategies.

关键词： HFRS two annual peaks periodic transmission and birth rate controlstrategies.

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An Improved-WM Method Based on Optimization of Centers of Output Fuzzy Subsets for Fuzzy Rules

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IFAC-PapersOnLine 2017年第1期50卷 7369-7374页

作者： Jin Y. Cao W. Wu M. She J. Yuan Y. School of Automation China University of Geosciences Wuhan 430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China School of Engineering Tokyo University of Technology Hachioji Tokyo 192-0982 Japan

The generation of fuzzy rules from samples is significant for fuzzy modelling. To improve the robustness of Wang-Mendel (WM) method, an improved WM method to extract fuzzy rules from all the regularized sample data was proposed. However, the accuracy of the model with this method is degraded for the conflicting rules with small difference between support degrees. And the output subsets can only be chosen from the pre-defined ones. To solve these problems, we develop an improved-WM method based on optimization of centers of output fuzzy subsets for fuzzy rules (COiWM). This method adopts the fuzzy c-means (FCM) clustering algorithm to divide the input and output spaces, and the improved WM method which replaces the original data by regularized data is used to calculate the support degrees. Then the support degrees are used as weights to optimize the centers of output fuzzy subsets with a method of weighted averages, so as to enhance the accuracy of a fuzzy model. Experimental results of a case study on short term daily maximum electric load forecasting prove that our proposed method enhances the accuracy of a fuzzy model. © 2017

关键词： Accuracy Conflicting rules Fuzzy c-means clustering algorithm Fuzzy rules Wang-Mendel method

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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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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Zhu, Jianliang Ai, Yunfeng Tian, Bin Cao, Dongpu Scherer, Sebastian School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100190 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 Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada Robotics Institute Carnegie Mellon University PittsburghPA15213 United States

ISBN: (纸本)9781538644522

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. © 2018 IEEE.

关键词： Hamming distance

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Design and Implementation of General Source Simulation System Architecture

Design and Implementation of General Source Simulation Syste...

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2017 International Conference on Applied Mathematics, Modeling and Simulation (AMMS2017)

作者： Wei Chu Songyi Liu Key Laboratory of Information Systems Engineering Department of China Satellite Launch Measurement and Control System

Equipment development and daily training of C2 system need simulation intelligence which are provided by simulation system. This paper put forward a whole system architecture and integration framework which can improve the develop efficiency of source simulation system on the basis of the using demand analysis of simulation system. This may satisfy the demand of system flexibility and rapid deployment.

关键词： simulation system system architecture integration framework

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