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arXiv 2018年

作者： Wang, Chen Xia, Weiguo Sun, Jinan Fan, Ruifeng Xie, Guangming State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing100871 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China National Engineering Research Center for Software Engineering Peking University Beijing100871 China

We study the general formation problem for a group of mobile agents in a plane, in which the agents are required to maintain a distribution pattern, as well as to rotate around or remain static relative to a static/moving target. The prescribed distribution pattern is a class of general formations that the distances between neighboring agents or the distances from each agent to the target do not need to be equal. Each agent is modeled as a double integrator and can merely perceive the relative information of the target and its neighbors. A distributed control law is designed using the limit-cycle based idea to solve the problem. One merit of the controller is that it can be implemented by each agent in its Frenet-Serret frame so that only local information is utilized without knowing global information. Theoretical analysis is provided of the equilibrium of the N-agent system and of the convergence of its converging part. Numerical simulations are given to show the effectiveness and performance of the proposed controller. Copyright © 2018, The Authors. All rights reserved.

关键词： Mobile agents

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Multi-view laplacian eigenmaps based on bag-of-neighbors for RGBD human emotion recognition

arXiv

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arXiv 2018年

作者： Liu, Shenglan Guo, Shuai Qiao, Hong Wang, Yang Wang, Bin Luo, Wenbo Zhang, Mingming Zhang, keye Du, Bixuan School of Computer Science and Technology Dalian University of Technology Dalian116024 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Research Center for Brain and Cognitive Neuroscience Liaoning Normal University Dalian116024 China

Human emotion recognition is an important direction in the field of biometric and information forensics. However, most existing human emotion research are based on the single RGB view. In this paper, we introduce a RGBD video-emotion dataset and a RGBD face-emotion dataset for research. To our best knowledge, this may be the first RGBD video-emotion dataset. We propose a new supervised nonlinear multi-view laplacian eigenmaps (MvLE) approach and a multihidden-layer out-of-sample network (MHON) for RGB-D human emotion recognition. To get better representations of RGB view and depth view, MvLE is used to map the training set of both views from original space into the common subspace. As RGB view and depth view lie in different spaces, a new distance metric bag of neighbors (BON) used in MvLE can get the similar distributions of the two views. Finally, MHON is used to get the low-dimensional representations of test data and predict their labels. MvLE can deal with the cases that RGB view and depth view have different size of features, even different number of samples and classes. And our methods can be easily extended to more than two views. The experiment results indicate the effectiveness of our methods over some state-of-art methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Laplace transforms

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Design of AI + Curriculum for Primary and Secondary Schools in Qingdao

Design of AI + Curriculum for Primary and Secondary Schools ...

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Chinese Automation Congress (CAC)

作者： Xiao Han Fang Hu Gang Xiong Xiwei Liu Xiaoyan Gong Xiaojie Niu Wanruo Shi Xinzhu Wang Qingdao Institute of Education Sciences Qingdao China Qingdao No.2 Middle School of Shandong Province Qingdao China The Cloud Computing Center Chinese Academy of Sciences Dongguan China The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao China Faculty of Education Beijing Normal University Beijing China

We are entering a new AI era, and the development of AI technology and popularization of AI education has become the national strategy of China. The primary and secondary school students are the builder of future society, and have strong curiosity and passion to learn new things; therefore, we should make a good preparation of AI education for them and cultivate their basic AI literacy and computational thinking to adapt to the future. In this paper, from the viewpoint of thinking mode and literacy and skill training, a curriculum including basic, project, and practical types is designed. Integrating with the iSTREAM educational system proposed by Chinese Academy of Sciences, exploratory works were shown with some case studies. The applications show that AI education is helpful to enrich the study of primary and secondary school students and to prompt comprehensive quality.

关键词： Artificial intelligence Education Industries Technological innovation Programming profession Task analysis

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Three Dimensional Lenticular Display Rendering Based on Light Field Acquisition

Three Dimensional Lenticular Display Rendering Based on Ligh...

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SID Symposium, Seminar, and Exhibition 2017, Display Week 2017

作者： Pei, Renjing Geng, Zheng Ma, Kui Mei, Zhang Rong, Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences East Zhongguanchun Road No. 95 Haidian District Beijing100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing101408 China

ISBN: (纸本)9781510848757

Crosstalk is a critical defect affecting quality in 3D displays. Existing methods require tedious computations or device-specific optical measurements and results are often sub-optimal for 3D productions. We propose a method based on light field acquisition and optimization for crosstalk reduction. Algorithms were developed and experimental results showed superior performance. © 2017 SID.

关键词： Crosstalk

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Event-triggered integral reinforcement learning for nonlinear continuous-time systems

Event-triggered integral reinforcement learning for nonlinea...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Qichao Zhang Dongbin Zhao The state Key Laboratory of Management and Control for Complex Systems University of Chinese Academy of Sciences Beijing China

In this paper, the optimal control problem for the continuous-time nonlinear systems with partially unknown dynamics is investigated. The event-triggered internal reinforcement learning (IRL) is proposed to approach the solution of the Hamilton-Jacobi-Bellman (HJB) equation. Note that the knowledge of internal dynamics is relaxed, and the event-triggered control scheme is adopted to reduce the computational burden and communication resources. For the online implementation purpose, a single-critic neural network (NN) structure is constructed to approach the optimal value function and the optimal policy with convergence analysis. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed algorithm.

关键词： Artificial neural networks Heuristic algorithms Optimal control Nonlinear systems System dynamics Learning (artificial intelligence) Convergence

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Cooperative Adaptive control for Consensus of Leader-Following General Linear Multi-Agent systems in Directed Communication Topology

Cooperative Adaptive Control for Consensus of Leader-Followi...

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Data Driven control and Learning systems (DDCLS)

作者： Benkai Li Qinglai Wei Derong Liu University of Chinese Academy of Sciences Beijing P. R. China The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing P. R. China School of Automation Guangdong University of Technology Guangzhou P. R. China

ISBN: (纸本)9781538626191

This paper investigates the consensus problem for leader-following multi-agent systems with general linear dynamics in directed communication topology. The fixed directed communication topology is considered. To adjust the coupling weights of neighboring agents, an adjacent state feedback protocol with an adaptive law is developed. LaSalle's invariance principle is used to analyze the stability. The consensus for multi-agent systems under directed communication topology containing a directed spanning tree with the leader as the root can be realized. The design method is based on Riccati inequality as well as algebraic graph theory. Finally, two examples are shown to illustrate the performance of the present controller.

关键词： Multi-agent systems Topology Protocols control systems Eigenvalues and eigenfunctions Couplings

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Real-Time lane detection and forward collision warning system based on stereo vision 28

Real-Time lane detection and forward collision warning syste...

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28th IEEE intelligent Vehicles Symposium, IV 2017

作者： Song, Wenjie Fu, Mengyin Yang, Yi Wang, Meiling Wang, Xinyu Kornhauser, Alain State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institution of Technology Beijing 100081 China Princeton University United States Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781509048052

This paper presents a real-Time and robust lane detection and forward collision warning technique based on stereo cameras. First, obstacles image is obtained through stereo matching and UV-disparity segmentation algorithm. Then, Inverse Perspective Mapping(IPM) and Sobel filtering are conducted to generate a low-noise top view of the road by fusing the obstacles image and the original image. Next, Hough Transformation for the top view map is completed and the extreme points(poles) are calculated as the detected lanes according to the traffic lanes model. Besides, the host lane is selected or supplemented among all the detected lanes and the nearest obstacle in this host lane is detected for the forward collision warning. Experimental results on the public data set indicate that our method can work effectively and real-Timely in the normal structured environment. © 2017 IEEE.

关键词： Roads Cameras Image segmentation Robustness Real-time systems Automobiles Alarm systems

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A learning-based decentralized optimal control method for modular and reconfigurable robots with uncertain environment 24th

A learning-based decentralized optimal control method for mo...

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24th International Conference on Neural Information Processing, ICONIP 2017

作者： Dong, Bo Liu, Keping Li, Hui Li, Yuanchun Department of Control Science and Engineering Changchun University of Technology Yan’an Avenue. 2055 Changchun130012 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Zhongguancun East Road. 95 Beijing100190 China

ISBN: (纸本)9783319701356

This paper presents a novel decentralized control approach for modular and reconfigurable robots (MRRs) with uncertain environment contact under a learning-based optimal compensation strategy. Unlike the known optimal control methods that are merely suitable for specific classes of robotic systems without implementing dynamic compensations, in this investigation, the dynamic model of the MRR system is described as a synthesis of interconnected subsystems, in which the obtainable local dynamic information is utilized effectively to construct the feedback controller, thus making the decentralized optimal control problem of the MRR system be formulated as an optimal compensation issue of the model uncertainty. A policy iteration algorithm is employed to solve the Hamilton-Jacobi-Bellman (HJB) equation with a modified cost function, which is approximated by constructing a critic neural network, and then the approximate optimal control policy can be derived. The asymptotic stability of the closed-loop MRR system is proved by using the Lyapunov theory. At last, simulations are performed to verify the effectiveness of the proposed decentralized optimal control approach. © Springer International Publishing AG 2017.

关键词： Neural networks

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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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Incorporating Message Embedding into Co-Factor Matrix Factorization for Retweeting Prediction

Incorporating Message Embedding into Co-Factor Matrix Factor...

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International Joint Conference on Neural Networks

作者： Can Wang Qiudan Li Lei Wang Daniel Dajun Zeng The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Department of Management Information Systems University of Arizona

ISBN: (纸本)9781509061839

With the rapid growth of Web 2.0, social media has become a prevalent information sharing and spreading platform, where users can retweet interesting messages. To better understand the propagation mechanism for information diffusion, it is necessary to model the user retweeting behavior and predict future retweets. Some existing work in retweeting prediction based on matrix factorization focuses on using user-message interaction information, user information and social influence information, etc. The challenge of improving prediction performance is how to jointly perform deep representation of these information to solve the sparsity problem and then learn a more comprehensive retweeting behavior model. Inspired by word2vec and co-factor matrix factorization model, this paper proposes a hybrid model, called HCFMF, for learning users' retweeting behavior, it first computes the message content similarity by considering the message co-occurrence, the author information and word2vec based low-dimensional representation of content, then, jointly decomposes the user-message matrix and message-message similarity matrix based on a co-factorization model. We empirically evaluate the performance of the proposed model on real world weibo datasets. Experimental results show that taking the dense representation of author and content information into consideration could allow us make more accurate analysis of users' retweeting patterns. The mined patterns could serve as a feedback channel for both consumers and management departments.

关键词： Retweeting prediction Co-factor matrix factorization Word2vec Low-dimensional representation matrix decomposition Consumers feedback channel Hybrid modes information role model Information Sharing

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