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2017 IEEE International Conference on Robotics and Automation, ICRA 2017

作者： Sariyildiz, Emre Wang, Huiming Yu, Haoyong Department of Biomedical Engineering Faculty of Engineering National University of Singapore 4 Engineering Drive 3 Singapore117583 Singapore Research Center of Analysis and Control for Complex Systems Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9781509046331

In this paper, a new robust position controller is proposed for Series Elastic Actuators (SEAs) by using Sliding Mode control (SMC) and a second order Disturbance Observer (DOb). The latter estimates not only disturbances but also their first and second order successive derivatives. A simple yet efficient dynamic model of the position control system is derived by using the analogy of a two-mass-spring-damper system. It is of fourth order and suffers from collocated and non-collocated disturbances. The former is directly cancelled by feeding-back its estimation through control input. The latter is suppressed by treating the estimations of disturbances and their first and second order time derivatives in the design of the SMC-based robust position controller. By cancelling disturbances via their estimations, not only the robustness of the position control system is improved but also the control signal chattering is lowered. The proposed robust controller significantly improves the position control performance of SEAs by suppressing plant uncertainties and external disturbances, such as friction, backlash, inertia variation and load. The validity of the proposed robust position controller is verified by giving experimental results of an SEA. © 2017 IEEE.

关键词： Sliding mode control

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CPSS-based Signal Forwarding Method at Relays for Full-duplex Cooperative Vehicular Networks

CPSS-based Signal Forwarding Method at Relays for Full-duple...

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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Han, Shuangshuang Ma, Houxue Wang, Xiao Liu, Hong Cao, Dongpu Wang, Fei-Yue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Shandong266000 China Qingdao Huituo Intelligent Machine Company Qingdao China JIANGSU XCMG CONSTRUCTION MACHINERY RESEARCH INSTITUTE LTD Xuzhou Jiangsu221004 China Mechanical and Mechatronics Engineering University of Waterloo 200 University Avenue Wes WaterlooONN2L 3G1 Canada Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781538644522

With increasing popularity of Internet of Vehicles (IoV), concerns for reliable and low complexity communication techniques are proposed due to the requirements of signal reliability and transmission delay for vehicles. Meanwhile, the explosive and pervasive use of social network applications further adds drivers' social relationships and behavioural characteristics into it, and makes it a cyber-physical-social system (CPSS). This paper proposes and analyzes an improved forward scheme for full deplex cooperative vehicular networks in terms of its CPSS features. The proposed CPSS-based forwarding (CPSS-F) strategy forwards a soft estimate of the received signal based on social historic data at the relay node (vehicle/infrastructure) to the destination node (vehicle/infrastructure), which achieves improved reliability than the two conventional strategies in cooperative networks, i.e., amplify-and-forward (AF) and decode-and-forward (DF). Furthermore, the proposed CPSS-F relay achieves performance gains and complexity reduction compared to the conventional AF and DF. Experimental results further confirm the advantages of the proposed CPSS-F for cooperative vehicular networks. The proposed CPSS-F approach is easily extended to other cooperative vehicular social networks, for example, multi-way, half-duplex, or large-antenna networks. © 2018 IEEE.

关键词： complex networks

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Improving Auction Mechanisms for Online Real-Time Bidding Advertising with a Two-stage Resale Model

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IFAC-PapersOnLine 2017年第1期50卷 13575-13580页

作者： Qin, Rui Yuan, Yong Wang, Fei-Yue The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China Research Center of Military Computational Experiments and Parallel System National University of Defense Technology Changsha China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China

Real Time Bidding (RTB), widely publicized as one of the most promising big-data-driven business models for online computational advertising, has the potential of effectively monetizing user clicks and impressions via two-stage auctions with resale. Demand side platforms (DSPs) play a key role as intermediators in this auction process. Typically, the first-stage auctions will be conducted separately in each DSP to determine one winning advertiser registered on it, and in the second-stage auction, each DSP submits a bid based on its winning advertiser's bid to the ad exchange platform (AdX). The highest-bid DSP wins the ad impression from AdX and resells it to its winning advertiser in pursuit of the intermediate fee. This two-stage resale auction is a critical component in maintaining the effectiveness and efficiency of RTB ecosystems. In this paper, we strive to identify and design potential improvements for this auction mechanism with the aim of enhancing the total revenue of both advertisers and DSPs. We also validated our proposed auction mechanisms using the computational experiment approach. The experimental results indicate that our proposed mechanisms can make both advertisers and DSPs better off. Our work represents the first step towards a new research area of optimal mechanism design for RTB auctions, and is expected to provide useful managerial insights in RTB market practice. © 2017

关键词： Sales Commerce Machine design Auction mechanisms Computational advertisings Computational experiment Critical component Demand side Effectiveness and efficiencies Optimal mechanism designs Real time

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Coordinated Adaptive Flowfield Estimation for Second-Order Agents Formation Tracking Given Orbits 35

Coordinated Adaptive Flowfield Estimation for Second-Order A...

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第35届中国控制会议

作者： CHEN Yang-Yang TIAN Yu-Ping WANG Zan-Zan School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University

ISBN: (纸本)9781509009107

This paper deals with cooperative estimation and control for second-order agents formation tracking a set of given orbit in an external flowfield,where only direction of flow inertial velocity is spatiotemporal and known.A novel coordinated adaptive estimator based on local neighbor-to-neighbor information is proposed to estimate the flow *** is shown that our previous geometric extension design,consensus and adaptive backstepping method can be combined together to construct the robust formation tracking controller under bidirectional *** theoretical result is proved by the numerical simulation.

关键词： Coordinated Adaptive Estimation Second-Order Agents Formation Tracking control

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Relation Model of Burden Operation and State Variables of Blast Furnace Based on Low Frequency Feature Extraction*

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IFAC-PapersOnLine 2017年第1期50卷 13796-13801页

作者： Zhang, Kexin Wu, Min An, Jianqi Cao, Weihua Liu, Zhentao Ning, Fulong School of Automation China University of Geosciences Wuhan430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Faculty of Engineering China University of Geosciences wuhan430074 China Department of Computer Science School of Computing Tokyo Institute of Technology Yokohama226-8502 Japan

In the blast furnace ironmaking process, the running state of the blast furnace can be directly adjusted by the burden operation. Therefore, the explicit relation between the burden operation and the running state variables is vital for the blast furnace operators. However, the state variables of the blast furnace are affected by many factors and the burden matrix is complicated. In this paper, a support vector regression(SVR) predicting model based on low frequency feature extraction is proposed to solve these problems. First, the low frequency components of state variables time series which are mostly affected by the burden operation are extracted. Then, the dimensions of the burden matrix are reduced by a devised simple expression method. Finally, the relation model based on SVR is proposed to predict the values of the expected state variables according to the simplified burden matrix and the value of initial state variables. The simulation on real factory data indicates that the model reflects the accurate quantitative relation between the burden operation and the state variables. © 2017

关键词： Blast furnaces Blast furnace practice Extraction Feature extraction Support vector regression Blast furnace iron making Blast furnace operators burden operation Initial state variables Low frequency components Relation models State variables Support vector regression (SVR)

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Inside Cover: A novel in vivo Cerenkov luminescence image-guided surgery on primary and metastatic colorectal cancer (J. Biophotonics 3/2020)

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Journal of Biophotonics 2020年第3期13卷

作者： Zeyu Zhang Yawei Qu Yu Cao Xiaojing Shi Hongbo Guo Xiaojun Zhang Sheng Zheng Haifeng Liu Zhenhua Hu Jie Tian Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and Technology Xidian University Xi'an China 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 Beijing China Department of Gastroenterology the Third Medical Centre Chinese PLA General Hospital Beijing China Department of Control Science and Engineering Harbin Institute of Technology Harbin China Department of Anorectal the Third medical Centre Chinese PLA General Hospital Beijing China University of Chinese Academy of Sciences Beijing China School of Information Sciences and Technology Northwest University Xi'an China Department of Nuclear Medicine Chinese PLA General Hospital Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine Beihang University Beijing China

This study presents a novel intraoperative in vivo imaging approach which harnessed Cerenkov luminescence (CL) to detect primary and metastatic colorectal cancer (CRC) using clinically approved radiopharmaceuticals. In the mice and swine experiments, the proposed approach effectively improved the effect of CRC surgery. The approach is believed to be promising for utilizing CL in open surgery. Further details can be found in the article by Zeyu Zhang, Yawei Qu, Yu Cao et al. ( e201960152 )

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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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Decentralized adaptive neural network sliding mode position/force control of constrained reconfigurable manipulators

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Journal of Central South University 2016年第11期23卷 2917-2925页

作者：李元春丁贵彬赵博 Department of Control Science and Engineering Changchun University of Technology Department of Control Science and Engineering Jilin University State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

A decentralized adaptive neural network sliding mode position/force control scheme is proposed for constrained reconfigurable manipulators. Different from the decentralized control strategy in multi-manipulator cooperation, the proposed decentralized position/force control scheme can be applied to series constrained reconfigurable manipulators. By multiplying each row of Jacobian matrix in the dynamics by contact force vector, the converted joint torque is obtained. Furthermore, using desired information of other joints instead of their actual values, the dynamics can be represented as a set of interconnected subsystems by model decomposition technique. An adaptive neural network controller is introduced to approximate the unknown dynamics of subsystem. The interconnection and the whole error term are removed by employing an adaptive sliding mode term. And then, the Lyapunov stability theory guarantees the stability of the closed-loop system. Finally, two reconfigurable manipulators with different configurations are employed to show the effectiveness of the proposed decentralized position/force control scheme.

关键词： constrained reconfigurable manipulators position/force control model decomposition decentralized control neural network

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GPIO based backstepping control for electronic throttle

GPIO based backstepping control for electronic throttle

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Annual Conference of Industrial Electronics Society

作者： Ke Shi Zuo Wang Chao Wu Shihua Li Senior Member IEEE School of Automation Southeast University Nanjing PR China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering PR China

This paper mainly studies the angle tracking problem in electronic throttle system with unknown time-varying disturbances. Firstly, generalized proportional integral observer (GPIO) is used to estimate the time-varying disturbances including model uncertainties, friction torque and spring initial torque. Secondly, a composite controller combining disturbance estimation and backstepping control method is designed, which is called the BSC+GPIO method. This control method is convenient to implement and the disturbance rejection capability is enhanced by feedforward compensating the estimated value of system disturbances. The simulation results show the effectiveness of the proposed method.

关键词： Torque Observers DC motors Friction Backstepping Springs

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End-to-End Driving Activities and Secondary Tasks Recognition Using Deep Convolutional Neural Network and Transfer Learning

End-to-End Driving Activities and Secondary Tasks Recognitio...

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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Xing, Yang Tang, Jianlin Liu, Hong Lv, Chen Cao, Dongpu Velenis, Efstathios Wang, Fei-Yue Advanced Vehicle Engineering Centre Cranfield University BedfordMK43 0AL United Kingdom Vehicle Intelligence Pioneers Ltd Qingdao China Jiangsu XCMG Construction Machinery Research Institute Ltd. No. 26 Tuolanshan Road Economic Development Zone Xuzhou Jiangsu221004 China School of Mechanical and Mechatronics Engineering University of Waterloo 200 University Avenue West WaterlooONN2L 3G1 Canada State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781538644522

Drivers' decision and their corresponding behaviors are important aspects that can affect the driving safety, and it is necessary to understand the driver behaviors in real-time. In this study, an end-to-end driving-related tasks recognition system is proposed. Specifically, seven common driving activities are identified, which are normal driving, right mirror checking, rear mirror checking, left mirror checking, using in-vehicle video device, texting, and answering mobile phone. Among these, the first four activities are regarded as normal driving tasks, while the rest three are divided into distraction group. The images are collected using a consumer range camera, namely, Kinect. In total, five drivers are involved in the naturalistic data collection. Before training the identification model, the raw images are first segmented using a Gaussian mixture model (GMM) to extract the driver region from the background. Then, a pre-trained deep convolutional neural network (CNN) model is trained to classify the behaviors, which directly takes the processed RGB images as the input and outputs the identified label. In this work, the AIexNet is selected as the pre-trained CNN model. Then, to reduce the training cost, the transfer learning mechanism is applied to the CNN model. An average of 79\% detection accuracy is achieved for the seven driving tasks. The proposed integration model can be used as a low-cost driver distraction and dangerous tasks recognition modeL. © 2018 IEEE.

关键词： Neural network models

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