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IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Wei Wang Xiaobo Lu Jiaji Song Cong Chen School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

ISBN: (纸本)9781509034857

This paper proposes a two-column convolutional neural network algorithm for face point detection, which proves better performance than single-column. Because of the deep level structure, global texture features are extracted at higher layers of the network, and the results of keypoint location remains high accuracy. In the first column, we take the R, G, B channel component of original image as input to train the neural network respectively and calculate the average location results. In the second one, we use Sobel operator to extract first-order derivative feature of original image and regard it as the input to train another convolutional network. The location results of two columns above are fused with appropriate proportion to ensure better results. Experimental result on LEW dataset shows the performance of the proposed parallel structure better than single convolutional network, and it stays robust due to occlusions, large pose variations and extreme lighting.

关键词： Neural networks

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Human physical activity recognition based on computer vision with deep learning model

Human physical activity recognition based on computer vision...

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IEEE Instrumentation and measurement Technology Conference

作者： Lingfei Mo Fan Li Yanjia Zhu Anjie Huang School of Instrument Science and Engineering Southeast University Nanjing China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing China

ISBN: (纸本)9781467392211

Human activity recognition is an active research area in the computer science because it is widely used in the fields of the security monitoring, health assessment, human machine interaction and other human related content searching. In this paper, a computer vision model based on the deep learning algorithm is proposed, which can recognize the human physical activity based on the skeleton data of the human body from the sensor of Microsoft Kinect. This model uses the human skeletons data from the CAD-60 dataset to recognize the human physical activity without using any prior knowledge. It can reduce the works on the stage of data preprocessing and feature extraction. It can also improve the generalization performance and robustness of the model, and give a better understanding of the human physical activity. Different tricks which can improve the performance of the neural networks, such as some regularization methods and other activation functions are tested. Finally, a convolutional neural network is used for the feature extraction, and a multilayer perceptron is used as the following classifier. The model can recognize twelve types of activities and the accuracy rate is 81.8%. It demonstrates that it is very effective to use the convolutional neural network to supervised learning and this model applies to human physical activity recognition.

关键词： Feature extraction Training Skeleton Neurons Computational modeling Biological neural networks Machine learning

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Environment-Dependent Payoffs in Finite Populations

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Chinese Physics Letters 2015年第2期32卷 160-162页

作者：徐微鸿张艳玲谢广明封雪 Center for Systems and Control State Key Laboratory for Turbulence and Complex SystemsCollege of EngineeringPeking University

In finite population games with weak selection and large population size, when the payoff matrix is constant, the one-third law serves as the condition of a strategy to be advantageous. We generalize the result to the cases of environment-dependent payoff matrices which exhibit the feedback from the environment to the population. Finally, a more general law about cooperation-dominance is obtained.

关键词： Environment-Dependent Payoffs in Finite Populations

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Optimal Innovation-Based Deception Attack on Remote State Estimation

Optimal Innovation-Based Deception Attack on Remote State Es...

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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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7.3: Compare Variation of User's EEG Owing to Parallax Cue

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SID Symposium Digest of Technical Papers 2018年第S1期49卷

作者： Haichen Hu Danli Wang Kang Yue Xinpan Yang Steve C. Chiu Beijing Key Lab of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China The State Key Laboratory of Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China College of Science and Engineering Idaho State University Pocatello America

3D display has become increasingly popular in many fields. It can provide incredible immersion compared with 2D display. There are many researches referring to the change of user about no matter subjective experience or objective physiology signal while watching 3D content. Similarly, in this paper, we study the variation of user’s EEG when the user watched 3D content. However, we only focus on parallax cue and avoid other cues of 3D content by using Random Dot Stereogram(RDS) as visual stimulus. We designed an experiment which asked the subject to watch RDS with nine kinds of parallaxes(0,±0.5,±1.1,±1.3,±1.7). The users watched one RDS for 1 minute and then closed eyes and rest for 1 minute every time. During watching, EEG signal with 30-channel was recorded. The result suggests that significant variation of the user’s EEG data occurred in the Alpha (8–13Hz) and Beta(14–30Hz) frequency band by different parallax’s comparison. And we found not only parallax but also electrode position can impact the user’s EEG data significantly. Accurately, the EEG data has a significant change in six electrodes position in both frequency bands including CZ, PZ, OZ, O2 in international 10–20 system. It means those electrode positions we detect can be regarded as typical positions where we record EEG signal. It will simplify the process of EEG signal acquisition when we need to study the relationship between parallax cue of 3D display and the user further.

关键词： EEG Parallax RDS 3D

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On power allocation for jamming attack against wireless state estimation in cyber-physical systems

On power allocation for jamming attack against wireless stat...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Hongbao Shi Xianghui Cao Lianghong Peng Changyin Sun School of Automation and the Key Lab of Measurement and Control of Complex Systems of Engineering Southeast University Nanjing Jiangsu China

ISBN: (纸本)9781509044245

Recently, the security issues of Cyber-Physical systems (CPS) have attracted considerable interest from both academic and industrial communities. Due to the open accessibility of wireless channels, wireless remote state estimation is vulnerable to various cyber attacks including jamming attacks with which adversaries can significantly impact the estimation performance by jamming the wireless communications. Moreover, by using higher power, the effect of jamming attack may be greater. In this paper, we consider remotely estimating the states of multiple systems and investigate the power allocation problem for the jamming attacker under its energy constraint. Through theoretical analysis, we show that, under a simple condition, the optimal power allocation scheme is to concentrate the attack power on one single system. Finally, numerical results are presented which validates the theoretical results.

关键词： Resource management Jamming Wireless communication State estimation Wireless sensor networks Estimation error

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Semi-Global Stabilization for a Class of Nonlinear Time-Delay systems by Linear Output Feedback

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IFAC-PapersOnLine 2015年第12期48卷 185-190页

作者： Wang, Pan Chai, Lin Fei, Shumin Liu, Guquan Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Ministry of Education School of Automation Nanjing 210096 China

The semi-global stabilization problem for a class of nonlinear systems with state time-delay is addressed in this paper. Sufficient conditions are derived by using Lyapunov-Krasovskii functional method and adjusting the scaling gain to guarantee the systems as well as the resulted closed-loop systems under a linear output feedback controller to be semi-globally stable. Numerical example is presented to illustrate the effectiveness of the obtained results in this paper. © 2015, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Closed loop systems

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Spherical Formation Tracking control of Second-Order Nonlinear Agents With Directed Communication

Spherical Formation Tracking Control of Second-Order Nonline...

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IEEE International Conference on control and Automation

作者： Yang-Yang Chen Kai-Xuan Wang Ya Zhang School of Automation and Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Chien-Shiung Wu College Southeast University

ISBN: (纸本)9781509017393

This article proposes a solution to the problem of forcing multiple agents to formation tracking a family of given concentric spheres when the communication topology is directed. Each agent under consideration has intrinsic second-order nonlinear dynamics. By using our previous concentric compression method, the cooperative control system can be regarded as a cascade system composed of the spherical tracking subsystem and the formation subsystem with the spherical tracking error as input. The algorithm for directed interaction is constructed by separately designing the control input projected onto the normal vector to the surface of sphere and the direction of spherical meridian for the spherical tracking subsystem, and the control input projected onto the direction of spherical parallel for formation motion. The asymptotic stability of the system is proved by using the tools of Input-to-State stability (ISS) theory. The effectiveness of the analytical results is verified by numerical simulations.

关键词： control input cascade systems Sphericity Normal vector SUB-SYSTEM Mentoring Agents tracking control System stability Nonlinear Dynamics PROBLEM SOLUTION

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Framework of future innovative urban transport 19

Framework of future innovative urban transport

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19th IEEE International Conference on Intelligent Transportation systems, ITSC 2016

作者： Dong, Xisong Zhou, Jiehan Hu, Bin Riekki, Jukka Xiong, Gang Wang, Feiyue Zhu, Fenghua State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China Center for Ubiquitous Computing University of Oulu Oulu Finland State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Cloud Computing Center Chinese Academy of Sciences Dongguan China Qingdao Academy of Intelligent Industries Qingdao China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781509018895

Rapidly growing urbanization is putting more pressure on the well-being of citizens and the environment. Applying the latest ICT technology to address urban transport challenges is a key strategy to release the new innovation powerhouse of urbanization. This paper introduces a framework of future Innovative Urban Transport (IUT), to support next generation urban transport, based on IoT, 4G, Big Data, Cloud Computing and other novel ICT technologies. We present the construction of the framework, its reference architecture, and contribution to the environmental and socially important impacts. IUT aims to advance the existing smart city transport, to foster urban innovations that maximize the well-being of citizens while minimizing the negative effects like traffic congestion, air pollution high resource congestion, and traffic accidents. © 2016 IEEE.

关键词： Traffic congestion

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Input optics systems of the KAGRA detector during O3GK (vol 2023, 023F01, 2023)

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PROGRESS OF THEORETICAL AND EXPERIMENTAL PHYSICS 2023年第5期2023卷

作者： Akutsu, T. Ando, M. Arai, K. Arai, Y. Araki, S. Araya, A. Aritomi, N. Asada, H. Aso, Y. Bae, S. Bae, Y. Baiotti, L. Bajpai, R. Barton, M. A. Cannon, K. Cao, Z. Capocasa, E. Chan, M. Chen, C. Chen, K. Chen, Y. Chiang, C-I Chu, H. Chu, Y-K Eguchi, S. Enomoto, Y. Flaminio, R. Fujii, Y. Fujikawa, Y. Fukunaga, M. Fukushima, M. Furuhata, T. Gao, D. Ge, G-G Ha, S. Hagiwara, A. Haino, S. Han, W-B Hasegawa, K. Hattori, K. Hayakawa, H. Hayama, K. Himemoto, Y. Hiranuma, Y. Hirata, N. Hirose, E. Hong, Z. Hsieh, B-H Huang, G-Z Huang, H-Y Huang, P. Huang, Y-C Huang, Y-J Hui, D. C. Y. Ide, S. Ikenoue, B. Imam, S. Inayoshi, K. Inoue, Y. Ioka, K. Ito, K. Itoh, Y. Izumi, K. Jeon, C. Jin, H-B Jung, K. Jung, P. Kaihotsu, K. Kajita, T. Kakizaki, M. Kamiizumi, M. Kanbara, S. Kanda, N. Kang, G. Kataoka, Y. Kawaguchi, K. Kawai, N. Kawasaki, T. Kim, C. Kim, J. Kim, J. C. Kim, Ws Kim, Y-M Kimura, N. Kita, N. Kitazawa, H. Kojima, Y. Kokeyama, K. Komori, K. Kong, A. K. H. Kotake, K. Kozakai, C. Kozu, R. Kumar, R. Kume, J. Kuo, C. Kuo, H-S Kuromiya, Y. Kuroyanagi, S. Kusayanagi, K. Kwak, K. Lee, H. K. Lee, H. W. Lee, R. Leonardi, M. Li, K. L. Lin, L. C-C Lin, C-Y Lin, F-K Lin, F-L Lin, H. L. Liu, G. C. Luo, L-W Majorana, E. Marchio, M. Michimura, Y. Mio, N. Miyakawa, O. Miyamoto, A. Miyazaki, Y. Miyo, K. Miyoki, S. Mori, Y. Morisaki, S. Moriwaki, Y. Nagano, K. Nagano, S. Nakamura, K. Nakano, H. Nakano, M. Nakashima, R. Nakayama, Y. Narikawa, T. Naticchioni, L. Negishi, R. Quynh, L. Nguyen Ni, W-T Nishizawa, A. Nozaki, S. Obuchi, Y. Ogaki, W. Oh, J. J. Oh, S. H. Ohashi, M. Ohishi, N. Ohkawa, M. Ohta, H. Okutani, Y. Okutomi, K. Oohara, K. Ooi, C. Oshino, S. Otabe, S. Pan, K-C Pang, H. Parisi, A. Park, J. Arellano, F. E. Pena Pinto, I. Sago, N. Saito, S. Saito, Y. Sakai, K. Sakai, Y. Sakuno, Y. Sato, S. Sato, T. Sawada, T. Sekiguchi, T. Sekiguchi, Y. Shao, L. Shibagaki, S. Shimizu, R. Shimoda, T. Shimode, K. Shinkai, H. Shishido, T. Shoda, A. Somiya, K. Son, E. J. Sotani, H. Sugimoto, R. Suresh, J. Suzuki, T. Suzuki, T. Tagoshi, H. Takahashi, H Gravitational Wave Science Project National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Department of Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Research Center for the Early Universe The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Institute for Cosmic Ray Research KAGRA Observatory The University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa City Chiba 277-8582 Japan Accelerator Laboratory High Energy Accelerator Research Organization (KEK) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Earthquake Research Institute The University of Tokyo 1-1-1 Yayoi Bunkyo-ku Tokyo 113-0032 Japan Department of Mathematics and Physics Graduate School of Science and Technology Hirosaki University 3 Bunkyo-cho Hirosaki Aomori 036-8561 Japan Kamioka Branch National Astronomical Observatory of Japan 238 Higashi-Mozumi Kamioka-cho Hida City Gifu 506-1205 Japan The Graduate University for Advanced Studies (SOKENDAI) 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Korea Institute of Science and Technology Information 245 Daehak-ro Yuseong-gu Daejeon 34141 Republic of Korea National Institute for Mathematical Sciences 70 Yuseong-daero 1689 Beon-gil Yuseong-gu Daejeon 34047 Republic of Korea International College Osaka University 1-1 Machikaneyama-cho Toyonaka City Osaka 560-0043 Japan School of High Energy Accelerator Science The Graduate University for Advanced Studies (SOKENDAI) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Department of Astronomy Beijing Normal University Xinjiekouwai Street 19 Haidian District Beijing 100875 China Department of Applied Physics Fukuoka University 8-19-1 Nanakuma Jonan Fukuoka City Fukuoka 814-0180 Japan Department of Physics Tamkang University No. 151 Yingzhuan Road Danshui Dist. New Taipei City 25137 Taiwan Department of Physics

KAGRA, the underground and cryogenic gravitational-wave detector, was operated for its solo observation from February 25 to March 10, 2020, and its first joint observation with the GEO 600 detector from April 7 to April 21, 2020 (O3GK). This study presents an overview of the input optics systems of the KAGRA detector, which consist of various optical systems, such as a laser source, its intensity and frequency stabilization systems, modulators, a Faraday isolator, mode-matching telescopes, and a high-power beam dump. These optics were successfully delivered to the KAGRA interferometer and operated stably during the observations. The laser frequency noise was observed to limit the detector sensitivity above a few kilohertz, whereas the laser intensity did not significantly limit the detector sensitivity.

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