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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Zhang, Youmin Li, Bai Zhang, Yue Jia, Ning Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'An University of Technology Xi'an710048 China Division of Systems Engineering Center for Information and Systems Engineering Boston University Boston United States Department of Mechanical Industrial and Aerospace Engineering Concordia Institute of Aerospace Design and Innovation Concordia University Montreal Canada Institute of Systems Engineering Tianjin University Tianjin China

ISBN: (纸本)9781538644522

This paper focuses on the scheme of cooperative lane change motion planning of multiple connected and automated vehicles, so as to minimize the time for lane change while penalizing large steering angles subject to hard collision avoidance constraints. Nominally this scheme should be formulated in a centralized way with the constraints of all the vehicles considered simultaneously. In order to facilitate the numerical solving process of this centralized optimization problem, we propose a stepwise computation framework. Starting with a sub-problem with all of the collision avoidance constraints removed, a sequence of sub-problems are defined by adding back the removed collision avoidance constraints gradually until the original problem takes shape in the end. The optimum of one sub-problem is always used as the initial guess when solving the next sub-problem. This iterative process continues until the optimum of the original problem is obtained. In this way, the difficulties in the original centralized problem are divided into multiple parts, and every progress made to address the partial difficulties is 'solidified' by the initial guess. © 2018 IEEE.

关键词： Collision avoidance

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Performance analysis of a photovoltaics aided coal-fired power plant

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Energy Procedia 2019年 158卷 1348-1353页

作者： Mingkun Jiang Yuexia Lv Tiankun Wang Zunqiang Sun Jianmin Liu Xinhai Yu Jinyue Yan Key Laboratory of Pressure Systems and Safety (MOE) School of Mechanical Engineering East China University of Science and Technology Shanghai 200237 China State Key Laboratory of Clean and Efficient Coal-fired Power Generation and Pollution Control Guodian Science and Technology Research Institute Nanjing 210023 China Applied Energy Innovation Institute Ningbo 315201 China School of Mechanical & Automotive Engineering Qilu University of Technology (Shandong Academy of Sciences) Jinan China China Energy Group Beijing 100011 China School of Business Society and Technology Mälardalen University Västerås Sweden School of Chemical Science and Engineering Royal Institute of Technology Stockholm Sweden

In this article, integration of photovoltaics (PV) into a coal-fired power plant was proposed. The performance including economic analysis and environmental impact was conducted by a case study in the northwest area of China. The results show that the PV system can replace part of auxiliary power consumption using renewable electricity to reduce internal power consumption and the emissions. Due to the feature of the integration into a power plant, the curtailment of solar PV electricity does not occur compared to stand-alone PV system. The investment cost, operation and maintenance (O&M) expenditure were feasible compared with other PV power generation plants.

关键词： photovoltaics coal-fired power plants auxiliary power consumption

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Deep learning for object detection and grasping: A survey

Deep learning for object detection and grasping: A survey

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2018 IEEE International Conference on Information and Automation, ICIA 2018

作者： Jia, Qun Cai, Jun Cao, Zhiqiang Wu, Yelan Zhao, Xionglei 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 Beijing101408 China School of Computer and Information Engineering Beijing Technology and Business University Beijing100048 China

ISBN: (纸本)9781538680698

Detecting and grasping objects in unstructured environments is an important yet difficult task. Fortunately, the breakthroughs from deep convolutional networks stimulate the development of object detection and grasping. The survey aims to serve as a comparison for region-based and region-free detection framework based on deep learning, and supplies the latest research results of object grasping with deep learning. Firstly, we briefly analyze the object detection and grasping. Then, the representative object detection methods based on deep learning are overviewed. Thirdly, we introduce the application of convolutional neural networks in object grasping. Finally, the potential trends in object detection and grasping based on deep learning are discussed. © 2018 IEEE.

关键词： Object detection

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Experimental Study on Thermal Runaway of LiFePO4/C Battery under Heating Condition

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IOP Conference Series: Earth and Environmental Science 2020年第4期546卷

作者： Fei Gao Yang Kai Congjie Wang Wei Liu Yanli Zhu State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute. Beijing 100192 China Beijing Institute of Technology 100081 China State Grid Anhui Electric Power CO. LTD Hefei 230000 China

The safety of lithium ion batteries is a hot topic in battery application and research at present. LiFePO4/C batteries are generally believed to be safe and have been widely used. The LiFePO4/C batteries are taken as the research object, the thermal runaway characteristics of the LiFePO4/C batteries are analyzed from the changes of battery temperature and voltage during the test process. The results show that the battery volatge fluctuated greatly before the thermal runaway occurred for the battery under one side heating, however, the battery voltage did not fluctuate and dropt to 0 V for the battery under two sides heating. For the battery group under one side heating, the thermal runaway chain reaction did not occur, which meant that the LiFePO4/C battery possessed good safety.

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Guidance Vector Field Encoding based on Contraction Analysis

Guidance Vector Field Encoding based on Contraction Analysis

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European control Conference

作者： Chengshuai Wu Jian Chen Dimitri Jeltsema Chenxi Dai State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Section Control Systems Engineering HAN University of Applied Science Arnhem CE The Netherlands Ohio State University Columbus USA

The guidance vector field encoding problem is revisited via exploiting the recently developed differential Lyapunov framework for contraction analysis. In control applications, a designed guidance vector field is utilized to guide a particle (such as a robot, or an end actuator) to converge to and circulate along a target curve, such as robot navigation, or motion planing. This paper proposes some new methods for guidance vector filed encoding. The methods analyze the guidance vector field encoding problem from a theoretical perspective providing insights into the problem, and it shows that the contraction analysis is a natural theoretical tool for guidance vector encoding. Furthermore, the differences and relations between the proposed result and the previous methods are discussed. Simulation examples are presented to illustrate the effectiveness of the proposed methods.

关键词： Encoding Manifolds Optimization Navigation Robots Task analysis Trajectory

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Ensemble of Extreme Learning Machines for Regression

Ensemble of Extreme Learning Machines for Regression

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Data Driven control and Learning systems (DDCLS)

作者： Atmane Khellal Hongbin Ma Qing Fei School of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology China

ISBN: (纸本)9781538626191

Regression, as a particular task of machine learning, performs a vital part in data-driven modeling, by finding the connections between the system state variables without any explicit knowledge about the system, using a collection of input-output data. To enhance the prediction performance and maximize the training speed, we propose a fully learnable ensemble of Extreme Learning Machines (ELMs) for regression. The developed approach learns the combination of different individual models, using the ELM algorithm, which is applied to minimize both the prediction error and the norm of the network parameters, which leads to higher generalization performance under Bartlett's theory. Moreover, the average based ELM ensemble may be viewed as a particular case of our model. Extensive experiments on many standard regression benchmark datasets have been carried out, and comparison with different models has been performed. The experimental findings confirm that the proposed ensemble can reach competitive results in term of the generalization performance, and the training speed. Furthermore, the influence of different hyper parameters on the performance, in term of the prediction error and the training speed, of the developed model has been investigated to provide a meaningful guideline to practical applications.

关键词： Computational modeling Training Mathematical model Predictive models Neural networks Prediction algorithms Approximation algorithms

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An Automatic Labeling Strategy for Locomotion Mode Recognition with Robotic Transtibial Prosthesis

An Automatic Labeling Strategy for Locomotion Mode Recogniti...

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IEEE International Conference on cyber technology in Automation, control, and Intelligent systems

作者： Enhao Zheng Qining Wang Hong Qiao The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences No. 95 of Zhongguancun East Road Beijing China The Beijing Innovation Center for Engineering Science and Advanced Technology (BIC-ESAT) Peking University Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728107707

ISBN: (纸本)9781728107714

Doffing and Donning the prosthetic socket seriously influenced the performances of the locomotion mode recognition. To make the recognition algorithm adaptive to the disturbances, labeling the new coming data without human intervene was a key step. In this study, we proposed an automatic labeling method with on-prosthesis mechanical signals. The strategy was designed based on the dynamic time warping (DTW) to measure the similarities between the data of a whole completed gait cycle and the pre-defined templates. The automatic labeling algorithm was validated on 6 unilateral transtibial subjects wearing the robotic prosthesis. 2 experimental sessions with 5 locomotion mode and 8 locomotion transition tasks were investigated. Between the sessions, donning and doffing the prosthetic socket were done by the subjects without expertise manual configuration. We evaluated two template generation methods, i.e. the fixed template and the sliding template. The average accuracy of the automatic labeling after re-wearing the socket achieved 96.2% across the subjects, which was comparable to existing sEMG-based studies. The strategy provided a promising tool to accumulate training with inertial signals for locomotion mode recognition.

关键词： Prosthetics Robot sensing systems Labeling Sockets Heuristic algorithms

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Distributed Full Order Sliding Mode control for Finite-time Attitude Synchronization and Tracking of Spacecraft

Distributed Full Order Sliding Mode Control for Finite-time ...

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第37届中国控制会议

作者： Nsubuga Latifu Zhuoyue Song Chao Duan Zhen Li Housheng Su Qinghe Wu Xiangdong Liu State Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology Key Laboratory of Drive and Control of Servo Motion System Ministry of Industry and Information Technology School of Automation Huazhong University of Science and Technology

This paper investigates a distributed finite-time attitude synchronization and tracking problem of multiple spacecraft when a time varying reference signal is available to only a subset of group *** Rodrigues parameters are used for attitude representation.A two layer sliding mode surface with fast non-singular terminal sliding mode surface（FNTSMS） as the inner layer and terminal sliding mode surface（TSMS） as the outer layer is used to create a full order sliding mode *** proposed distributed attitude coordination control law guarantees attitude synchronization and tracking as long as their exists atleast one spacecraft that has a direct access to the virtual leader’s time varying ***-time stability of the closed loop system is guaranteed by Lyapunov-based stability *** results are presented to validate the performance of the proposed control strategy.

关键词： Attitude synchronization Attitude tracking Finite-time Full-order sliding mode（FOSM） Distributed control

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Adaptive and azimuth-aware fusion network of multimodal local features for 3D object detection

arXiv

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arXiv 2019年

作者： Tian, Yonglin Wang, Kunfeng Wang, Yuang Tian, Yulin Wang, Zilei Wang, Fei-Yue Department of Automation University of Science and Technology of China Hefei230027 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology Beijing University of Chemical Technology Beijing100029 China University of Science and Technology Beijing Beijing100083 China North China University of Technology Beijing100144 China

This paper focuses on the construction of stronger local features and the effective fusion of image and LiDAR data. We adopt different modalities of LiDAR data to generate richer features and present an adaptive and azimuth-aware network to aggregate local features from image, bird's eye view maps and point cloud. Our network mainly consists of three subnetworks: ground plane estimation network, region proposal network and adaptive fusion network. The ground plane estimation network extracts features of point cloud and predicts the parameters of a plane which are used for generating abundant 3D anchors. The region proposal network generates features of image and bird's eye view maps to output region proposals. To integrate heterogeneous image and point cloud features, the adaptive fusion network explicitly adjusts the intensity of multiple local features and achieves the orientation consistency between image and LiDAR data by introduce an azimuth-aware fusion module. Experiments are conducted on KITTI dataset and the results validate the advantages of our aggregation of multimodal local features and the adaptive fusion network. Copyright © 2019, The Authors. All rights reserved.

关键词： Object recognition

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PDP: Parallel Dynamic Programming

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IEEE/CAA Journal of Automatica Sinica 2017年第1期4卷 1-5页

作者： Fei-Yue Wang Jie Zhang Qinglai Wei Xinhu Zheng Li Li IEEE State Key Laboratory of Management and Control for Complex Systems(SKL-MCCS) Institute of AutomationChinese Academy of Sciences(CASIA) School of Computer and Control Engineering University of Chinese Academy of Sciences Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences(SKL-MCCSCASIA) Qingdao Academy of Intelligent Industries Department of Computer Science and Engineering University of Minnesota Department of Automation Tsinghua University

Deep reinforcement learning is a focus research area in artificial intelligence. The principle of optimality in dynamic programming is a key to the success of reinforcement learning methods. The principle of adaptive dynamic programming ADP is first presented instead of direct dynamic programming DP , and the inherent relationship between ADP and deep reinforcement learning is developed. Next, analytics intelligence, as the necessary requirement, for the real reinforcement learning, is discussed. Finally, the principle of the parallel dynamic programming, which integrates dynamic programming and analytics intelligence, is presented as the future computational intelligence. © 2014 Chinese Association of Automation.

关键词： Artificial intelligence Neural networks Reinforcement learning

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