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Multi-Objective Cooperative Salvo Attack Against Group Target

Journal of systems Science & complexity 2018年第1期31卷 244-261页

作者： ZENG Jie DOU Lihua XIN Bin School of Automation State Key Laboratory of Intelligent Control and Decision of Complex SystemsBeijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology

This paper proposes a solution for the problem of cooperative salvo attack of multiple cruise missiles against targets in a group. Synchronization of the arrival time of missiles to hit their common target, minimizing the time consumption of attack and maximizing the expected damage to group targets are taken into consideration simultaneously. These operational objectives result in a hierarchical mixed-variable optimization problem which includes two types of subproblems, namely the multi-objective missile-target assignment（MOMTA） problem at the upper level and the time-optimal coordinated path planning（TOCPP） problems at the lower level. In order to solve the challenging problem, a recently proposed coordinated path planning method is employed to solve the TOCPP problems to achieve the soonest salvo attack against each target. With the aim of finding a more competent solver for MOMTA, three state-of-the-art multi-objective optimization methods（MOMs）,namely NSGA-II, MOEA/D and DMOEA-εC, are adopted. Finally, a typical example is used to demonstrate the advantage of the proposed method. A simple rule-based method is also employed for comparison. Comparative results show that DMOEA-εC is the best choice among the three MOMs for solving the MOMTA problem. The combination of DMOEA-εC for MOMTA and the coordinated path planning method for TOCPP can generate obviously better salvo attack schemes than the rule-based method.

关键词： Coordinated path planning missile-target assignment multi-missile salvo attack multiobjective optimization

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Graph Attention Model Embedded With Multi-Modal Knowledge For Depression Detection

Graph Attention Model Embedded With Multi-Modal Knowledge Fo...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Wenbo Zheng Lan Yan Chao Gou Fei-Yue Wang School of Software Engineering Xi’an Jiaotong University Xi’an China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Intelligent Systems Engineering Sun Yat-sen University

ISBN: (数字)9781728113319

ISBN: (纸本)9781728113326

With more than 300 million people depressed worldwide annually, depression is a global problem. The goal of depression detection is to improve diagnostic accuracy and availability, leading to faster intervention. The most important and challenging problem here is to design an effective and robust depression detection model. To this end, there are two challenges to overcome: 1) Multi-modal (audio, image, text, etc.) information must be jointly considered to make accurate inferences. 2) Existing deep learning-based work suffers from multi-modal data sufficiency problem. To address these issues, we propose a graph attention model embedded with multi-modal knowledge for depression detection. This approach learns not only reasonable embeddings for nodes in the knowledge graph, but also exploits medical knowledge to improve the performance of classification and prediction with the knowledge attention mechanism. Experimental results on the real-world datasets show that the proposed approach significantly improves the classification and prediction performance compared with other major state-of-the-art approaches, with guaranteed the robustness with each modality of multi-modal data. Overall, this paper shows how multi-modal knowledge attention mechanism and deep-learning-based networks can be combined to assist mental health patients and practitioners.

关键词： Biomedical imaging Robustness Data models Machine learning Task analysis Knowledge engineering

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Parallel Hospital: ACP-Based Hospital Smart Operating System

Parallel Hospital: ACP-Based Hospital Smart Operating System

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Yongjun Wang Fei-Yue Wang Xiao Wang Zhongjun Guan Liwei Ouyang Jiangong Wang Lan Yan Wenbo Zheng Wenwen Zhang Beijing Tiantan Hospital Capital Medical University Beijing Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Software Engineering Xi'an Jiaotong University Xi'an China

This article aims to propose a parallel hospital based on parallel medicine. The parallel hospital takes the parallel idea of virtual and real interaction as its core concept and uses parallel idea's artificial scenarios, computational experiments, and parallel execution (ACP) theory as the guiding theory to construct a new generation of intelligent hospital management system with virtual and real interaction. We conducted a case study of the parallel hospital practice case of Tiantan Hospital-Tiantan Smart Brain, so as to confirm the feasibility and scientificity of the proposed parallel hospital.

关键词： Training Hospitals Operating systems Digital twin Conferences

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Fast and Robust RGB-D Multiple Human Tracking Based on Part Model for Mobile Robots

Fast and Robust RGB-D Multiple Human Tracking Based on Part ...

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第三十八届中国控制会议

作者： Shiying Sun Xiaoguang Zhao Min Tan State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

In this paper, we address the problem of multiple human tracking for mobile robots and proposed a fast and robust RGB-D multiple human tracking approach based on part model. Firstly, a simplified deformable part model method is used for fast human detection using RGB-D information. Then the partial occlusion is detected with depth information. Finally, part model and partial occlusion information are combined into data association process and an on-line appearance classification method with dynamic occlusion handling is proposed for robust tracking. The performance of the proposed method is evaluated on the public dataset, and experiment results have demonstrated the effectiveness of the proposed method.

关键词： Multiple Human Tracking Deformable Part Model Human Detection Occlusion Handling On-line Classification

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Renal Clearing Carbon Dots-Based Near-Infrared Fluorescent Super-Small Nanoprobe for Renal Imaging

SSRN

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SSRN 2022年

作者： Gao, Pengli Hui, Hui Guo, Chanjuan Liu, Yu Su, Ya Huang, Xiazi Shang, Wenting Jiang, Jingying Tian, Jie Guo, Kunxiong CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imaging The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Biological Science and Medicine Engineering School of Engineering Medicine Beihang University No. 37 Xueyuan Road Beijing100191 China Key Laboratory of Big Data-Based Precision Medicine Beihang University Ministry of Industry and Information Technology of the People’s Republic of China No. 37 Xueyuan Road Beijing100191 China School of Engineering Medicine Beihang University No. 37 Xueyuan Road Beijing100191 China Department of Hepatobiliary Surgery Zhujiang Hospital Southern Medical University Guangzhou China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine China

Real-time renal imaging is indispensable for monitoring kidney-related disease processes and evaluating the nephrotoxicity of nanoprobes. Owing to their small size, tunable photoluminescence properties, and low biotoxicity, carbon dots (CDs) are widely employed as nanoprobes for imaging-guided disease diagnosis. However, the preparation of near-infrared CDs is a stupendous challenging. Here, CDs with near-infrared fluorescence (NIR-CDs ) were prepared by one-pot pyrolysis and were comprehensively evaluated for real-time renal imaging. The NIR-CDs possessed super-small size (1.8 ± 0.2 nm) and high quantum yield (14.3% in aqueous solution) at 696 nm. NIR-CDs exhibited high-efficiency renal clearance at 24 h post-treatment (97.47-99.27% ID) via glomerular filtration. The irreversible clearance of NIR-CDs enables effective real-time monitoring of kidney function in the normal and acute kidney injury (AKI) mouse models. Consequently, NIR-CDs can be utilized for imaging analyses of impaired kidney function, pioneering a serviceable guideline for neoapplications of NIR-CDs. © 2022, The Authors. All rights reserved.

关键词： Nanoprobes

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Optimal and stable control for two-player zero-sum game using adaptive dynamic programming 8

Optimal and stable control for two-player zero-sum game usin...

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8th IEEE Data Driven control and Learning systems Conference, DDCLS 2019

作者： Zhu, Liao Song, Ruizhuo Wei, Qinglai Yang, Zhanyu Zhao, Bo State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China School of Systems Science Beijing Normal University Beijing100875 China

ISBN: (纸本)9781728114545

In this paper, an optimal and stable iteration learning scheme is developed for two-player zero-sum game (ZSG) in the discrete-time nonlinear systems. In the implementation of developed algorithm, the optimal and stable solution of Hamilton-Jacobi-Isaacs can be obtained based on adaptive dynamic programming (ADP). First, in order to obtain the optimal control policies, value iteration approach is employed with the proof of convergence given. Second, based on Lyapunov theory, an easy condition is proposed to get the stable control laws. Third, neural networks are used to implement the developed algorithm. Finally, a simulation example is included to verify the present method. © 2019 IEEE.

关键词： Neural networks

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Enhancing rumor detection in social media using dynamic propagation structures

arXiv

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arXiv 2020年

作者： Wang, Shuai Kong, Qingchao Wang, Yuqi Wang, Lei State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China University of Chinese Academy of Sciences China Shenzhen Artificial Intelligence and Data Science Institute Longhua） China State Information Center Beijing100045 China

Social media, such as Facebook and Twitter, has become one of the most important channels for information dissemination. However, these social media platforms are often misused to spread rumors, which has brought about severe social problems, and consequently, there are urgent needs for automatic rumor detection techniques. Existing work on rumor detection concentrates more on the utilization of textual features, but diffusion structure itself can provide critical propagating information in identifying rumors. Previous works which have considered structural information, only utilize limited propagation structures. Moreover, few related research has considered the dynamic evolution of diffusion structures. To address these issues, in this paper, we propose a Neural Model using Dynamic Propagation Structures (NM-DPS) for rumor detection in social media. Firstly, we propose a partition approach to model the dynamic evolution of propagation structure and then use temporal attention based neural model to learn a representation for the dynamic structure. Finally, we fuse the structure representation and content features into a unified framework for effective rumor detection. Experimental results on two real-world social media datasets demonstrate the salience of dynamic propagation structure information and the effectiveness of our proposed method in capturing the dynamic structure. Copyright © 2020, The Authors. All rights reserved.

关键词： Social networking (online)

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A vision-based path planning and following system for a miniature robotic fish

A vision-based path planning and following system for a mini...

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2019 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2019

作者： Lu, Yue Chen, Xingyu Wu, Zhengxing Yan, Yan Yu, Junzhi State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory for Turbulence and Complex System Department of Mechanics and Engineering Science BIC-ESAT College of Engineering Peking University Beijing100871 China

ISBN: (纸本)9781728137261

Most existing path following algorithms for middle- or large-sized underwater vehicles minimize yaw difference of the control object. Breaking the canonical manner, this paper presents a vision-based path planning and following system for miniature robotic fish. As for path planning, the proposed system finds a gird path based on the AStar algorithm and selects a series of target points in the grid path. By connecting these points in order, a curve path can be planned. In terms of path following, the proposed system forgoes to give a specific yaw and only uses a global camera. It obtains coordinates of the robotic fish in real time by KCF tracking algorithm to calculate an approximate motion vector. A PID controller is employed to drive the difference angle between the motion vector and the target vector (the vector from the robotic fish to the target point) to zero. By swimming to target points one by one, the robotic fish can move to the terminus following the planned path. Finally, aquatic experiments are conducted to verify the effectiveness of the vision-based path planning and following system. The results reveal that the developed system can generate a safe and smooth path, and the robotic fish can follow the path with acceptable deviation. © 2018 IEEE.

关键词： Motion planning

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Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys

arXiv

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arXiv 2023年

作者： Chen, Long Li, Yuchen Huang, Chao Li, Bai Xing, Yang Tian, Daxin Li, Li Hu, Zhongxu Na, Xiaoxiang Li, Zixuan Teng, Siyu Lv, Chen Wang, Jinjun Cao, Dongpu Zheng, Nanning Wang, Fei-Yue The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Waytous Ltd United Kingdom BNU-HKBU United International College Zhuhai519087 China Hong Kong Baptist University Kowloon999077 Hong Kong The Department of Industrial and System Engineering Hong Kong Polytechnical University Hong Kong The College of Mechanical and Vehicle Engineering Hunan University China The School of Aerospace Transport and Manufacturing Cranfield University United Kingdom The School of Transportation Science and Engineering Beihang University China The Department of Automation Tsinghua University China The Department of Mechanical and Aerospace Engineering Nanyang Technological University Singapore The Centre for Sustainable Road Freight University of Cambridge United Kingdom The School of Mechanical Engineering Tsinghua University China The College of Artificial Intelligence Xi’an Jiaotong University China

Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits. Although a number of surveys have reviewed research achievements in this field, they are still limited in specific tasks, lack of systematic summary and research directions in the future. Here we propose a Survey of Surveys (SoS) for total technologies of AD and IVs that reviews the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions. To our knowledge, this article is the first SoS with milestones in AD and IVs, which constitutes our complete research work together with two other technical surveys. We anticipate that this article will bring novel and diverse insights to researchers and abecedarians, and serve as a bridge between past and future. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Application and Future of Modern Energy Internet Based on Artificial Intelligence

Application and Future of Modern Energy Internet Based on Ar...

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

作者： Xiaojing Yang Tianlu Gao Shiqian Ma Xiaoyan Gong Zhigang Huang Dazhi Hao Power Dispatching and Control Center State Grid Tianjin Electric Power Company Tianjin China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Science Beijing China Electric Power Research Institute State Grid Tianjin Electric Power Company Tianjin China

ISBN: (数字)9781728176840

ISBN: (纸本)9781728176857

The emergence of deep learning is undoubtedly a landmark breakthrough in artificial intelligence, and the emergence of this technology makes it possible for us to realize the real smart grid. A major challenge currently facing deep learning technology is the requirement of enough training samples to ensure the accuracy of results of the learning. But for the current power grid, training data can be obtained because a lot of real time data is collected to maintain the stability of its operation. With the deep learning technology, there is no doubt that these data can be utilized to realize the intelligent operation of the power grid. First, this paper gives a brief introduction to the current artificial intelligence, followed by the introduction of deep learning technology. Some potential applications of the deep learning in the daily operation of the power grid are proposed. And in the end, there is a brief description of some of the features that future smart grids should have.

关键词： Deep learning Training Training data Learning (artificial intelligence) Power system stability Real-time systems Smart grids

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