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arXiv 2018年

作者： Zheng, Bolun Sun, Rui Tian, Xiang Chen, Yaowu Zhejiang University Institute of Advanced Digital Technology and Instrument No. 38 Zheda Road Hangzhou310027 China Sichuan University College of Physical Science and Technology No.24 South Section 1 Yihuan Road Chengdu China Zhejiang Provincial Key Laboratory for Network Multimedia Technologies Zhejiang University China State Key Laboratory of Industrial Control Technology Zhejiang University China Zhejiang University Embedded System Engineering Research Center Ministry of Education of China

Recent studies have used deep residual convolutional neural networks (CNNs) for JPEG compression artifact reduction. This study proposes a scalable CNN called S-Net. Our approach effectively adjusts the network scale dynamically in a multitask system for real-time operation with little performance loss. It offers a simple and direct technique to evaluate the performance gains obtained with increasing network depth, and it is helpful for removing redundant network layers to maximize the network efficiency. We implement our architecture using the Keras framework with the TensorFlow backend on an NVIDIA K80 GPU server. We train our models on the DIV2K dataset and evaluate their performance on public benchmark datasets. To validate the generality and universality of the proposed method, we created and utilized a new dataset, called WIN143, for over-processed images evaluation. Experimental results indicate that our proposed approach outperforms other CNN-based methods and achieves state-of-the-art performance. Copyright © 2018, The Authors. All rights reserved.

关键词： Network layers

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Correction: Photoluminescence tuning in carbon dots: surface passivation or/and functionalization, heteroatom doping

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Journal of Materials Chemistry C 2019年第10期7卷 3105-3105页

作者： Linbo Li Tao Dong Institute of Applied Micro-Nano Science and Technology – IAMNST Chongqing Key Laboratory of Colleges and Universities on Micro-Nano Systems Technology and Smart Transducing Chongqing Engineering Laboratory for Detection Control and Integrated System National Research Base of Intelligent Manufacturing Service Chongqing Technology and Business University Nan'an District 400067 Chongqing China Institute for Microsystems – IMS Faculty of Technology Natural Sciences and Maritime Sciences University of South-Eastern Norway Postboks 235 3603 Kongsberg Norway

Correction for ‘Photoluminescence tuning in carbon dots: surface passivation or/and functionalization, heteroatom doping’ by Linbo Li et al., J. Mater. Chem. C, 2018, 6, 7944–7970.

Correction for ‘Photoluminescence tuning in carbon dots: surface passivation or/and functionalization, heteroatom doping’ by Linbo Li et al., J. Mater. Chem. C, 2018, 6, 7944–7970.

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Discrete time position feedback based steering control for autonomous homing of a mobile robot 12

Discrete time position feedback based steering control for a...

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12th IEEE International Conference on control and Automation, ICCA 2016

作者： Dwaracherla, Vikranth Reddy Thakar, Shantanu Kumar, G. K. Arun Vachhani, Leena Department of Electrical Engineering Indian Institute of Technology Bombay India Department of Aerospace Engineering Indian Institute of Science Bangalore India System and Control Engineering Indian Institute of Technology Bombay India

ISBN: (纸本)9781509017386

This paper presents a steering control law based on discrete time position feedback to drive the mobile robot to a predefined home position. The controller relies on controlling the rotation direction of the robot for homing, which works even for an erroneous feedback. The proposed control law steers the robot to an interior of a small circle centred at the home position in the presence of bounded random position measurement errors. Position feedback without the need of accurate measurement and guarantee in autonomous homing in the presence of sensor errors by merely changing the steering direction makes the proposed controller suitable for low-cost robot applications. Theoretical results presented in this paper are verified using Matlab simulations and experiments. © 2016 IEEE.

关键词： Mobile robots

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Corrigendum to: The TianQin project: current progress on science and technology

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Progress of Theoretical and Experimental Physics 2021年第5期2021卷

作者： Mei, Jianwei Bai, Yan-Zheng Bao, Jiahui Barausse, Enrico Cai, Lin Canuto, Enrico Cao, Bin Chen, Wei-Ming Chen, Yu Ding, Yan-Wei Duan, Hui-Zong Fan, Huimin Feng, Wen-Fan Fu, Honglin Gao, Qing Gao, TianQuan Gong, Yungui Gou, Xingyu Gu, Chao-Zheng Gu, De-Feng He, Zi-Qi Hendry, Martin Hong, Wei Hu, Xin-Chun Hu, Yi-Ming Hu, Yuexin Huang, Shun-Jia Huang, Xiang-Qing Jiang, Qinghua Jiang, Yuan-Ze Jiang, Yun Jiang, Zhen Jin, Hong-Ming Korol, Valeriya Li, Hong-Yin Li, Ming Li, Pengcheng Li, Rongwang Li, Yuqiang Li, Zhu Li, Zhulian Li, Zhu-Xi Liang, Yu-Rong Liang, Zheng-Cheng Liao, Fang-Jie Liu, Qi Liu, Shuai Liu, Yan-Chong Liu, Li Liu, Pei-Bo Liu, Xuhui Liu, Yuan Lu, Xiong-Fei Lu, Yang Lu, Ze-Huang Luo, Yan Luo, Zhi-Cai Milyukov, Vadim Ming, Min Pi, Xiaoyu Qin, Chenggang Qu, Shao-Bo Sesana, Alberto Shao, Chenggang Shi, Changfu Su, Wei Tan, Ding-Yin Tan, Yujie Tan, Zhuangbin Tu, Liang-Cheng Wang, Bin Wang, Cheng-Rui Wang, Fengbin Wang, Guan-Fang Wang, Haitian Wang, Jian Wang, Lijiao Wang, Panpan Wang, Xudong Wang, Yan Wang, Yi-Fan Wei, Ran Wu, Shu-Chao Xiao, Chun-Yu Xu, Xiao-Shi Xue, Chao Yang, Fang-Chao Yang, Liang Yang, Ming-Lin Yang, Shan-Qing Ye, Bobing Yeh, Hsien-Chi Yu, Shenghua Zhai, Dongsheng Zhang, Caishi Zhang, Haitao Zhang, Jian-dong Zhang, Jie Zhang, Lihua Zhang, Xin Zhang, Xuefeng Zhou, Hao Zhou, Ming-Yue Zhou, Ze-Bing Zhu, Dong-Dong Zi, Tie-Guang Luo, Jun TianQin Research Center for Gravitational Physics & School of Physics and Astronomy Sun Yat-sen University (Zhuhai Campus) Zhuhai 519082 P.R. China Centre for Gravitational Experiments School of Physics MOE Key Laboratory of Fundamental Physical Quantities Measurement & Hubei Key Laboratory of Gravitation and Quantum Physics PGMF Huazhong University of Science and Technology Wuhan 430074 P.R. China SISSA Via Bonomea 265 34136 Trieste Italy and INFN Sezione di Trieste & IFPU – Institute for Fundamental Physics of the Universe Via Beirut 2 34014 Trieste Italy Former Faculty Politecnico di Torino Corso Duca degli Abruzzi 24 10129 Torino Italy Yunnan Observatories Chinese Academy of Sciences Kunming 650011 China School of Physical Science and Technology Southwest University Chongqing 400715 China Beijing Institute of Control Engineering Beijing 100094 P.R. China SUPA School of Physics and Astronomy University of Glasgow Glasgow G12 8QQ UK DFH Satellite Co. Ltd. Beijing 100094 P.R. China National Astronomical Observatories Chinese Academy of Sciences Beijing 100012 China School of Astronomy and Space Science University of Chinese Academy of Sciences Beijing 100049 China School of Physics and Astronomy University of Birmingham Birmingham B15 2TT United Kingdom Center for High Energy Physics & Department of Physics and State Key Laboratory of Nuclear Physics and Technology Peking University No. 5 Yiheyuan Rd Beijing 100871 P.R. China Lomonosov Moscow State University Sternberg Astronomical Institute Moscow 119992 Russia Dipartimento di Fisica “G. Occhialini” Universitá degli Studi Milano Bicocca Piazza della Scienza 3 I-20126 Milano Italy School of Aeronautics and Astronautics Shanghai Jiao Tong University Shanghai 200240 China Purple Mountain Observatory Chinese Academy of Sciences Nanjing 210023 & School of Astronomy and Space Science University of Science and Technology of China Hefei Anhui 230026 P.R. China Max-Planck-Institut für Gravitationsphysik (Al

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Generalised Error Functions from the Kerr Metric

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Chinese Physics Letters 2016年第3期33卷 12-16页

作者：唐文林罗子人刘润球 Science and Technology on Aerospace Flight Dynamics Laboratory Beijing Aerospace Control Center Department of Aerospace Guidance Navigation and Control School of AstronauticsBeihang University QUEST Center of Quantum Engineering and Space-Time Research Leibniz Universitt Hannover Max Planck Institute for Gravitational Physics(Albert Einstein Institute) Institute of Applied Mathematics Morningside Center of Mathematics and LESCInstitute of Computational MathematicsAcademy of Mathematics and System ScienceChinese Academy of Sciences

Motivated by the effort to understand the mathematical structure underlying the Teukolsky equations in a Kerr metric background, a homogeneous integral equation related to the prolate spheroidal function is studied. From the consideration of the Fredholm determinant of the integral equation, a family of generalized error function is defined, with which the Fredholm determinant of the sinc kernel is also evaluated. An analytic solution of a special ease of the fifth Painlev~ transcendent is then worked out explicitly.

关键词： of is exp in Generalised Error Functions from the Kerr Metric from

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Integral augmented nonlinear differentiator for noisy signals

Integral augmented nonlinear differentiator for noisy signal...

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Asian control Conference

作者： Zikang Su Honglun Wang Na Li Yue Yu Jianfa Wu SHENYUAN Honors College Beihang University Beijing China School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing China Unmanned System Research Institute Beihang University Beijing China

ISBN: (纸本)9781509015740;9781509015733

This paper proposed an arctangent function based integral augmented nonlinear differentiator (IAND) for obtaining of the noise-less derivative signal from a noisy measurement signal. The basic idea of proposed IAND with high noise-attenuation effect is to establish the differentiator by expanding the signal dynamics with extra state variable representing the integrated noisy measurement, then with the integral of measurement as input, the IAND is formulated to achieve better noise suppression ability without appreciable delay. Moreover, the convergence characteristic and anti-noise property in the frequency domain are analyzed via singular perturbation theory and describing function method, respectively. The simulation comparison results with other differentiators show the superiority of IAND in noise suppression.

关键词： Noise measurement Pollution measurement Convergence Frequency-domain analysis Perturbation methods Estimation Acceleration

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Robot Teaching by Teleoperation Based on Visual Interaction and Neural Network Learning

Robot Teaching by Teleoperation Based on Visual Interaction ...

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2017 9th International Conference on Modelling, Identification and control (ICMIC 2017)

作者： Yang Xu Chenguang Yang Junpei Zhong Hongbin Ma Lijun Zhao Min Wang Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and EngineeringSouth China University of Technology National Institute of Advanced Industrial Science and Technology School of Automation Beijing Institution of Technology State Key Laboratory of Robotics and System Harbin Institute of Technology

Traditional methods of Robot teaching require human demonstrators to program with a teaching pendant,which is a complex and time-consuming *** this paper,we propose a novel method based on teleoperation which allows a demonstrator to train robot in an intuitive *** specifically,at the beginning the demonstrator controls a robot by visual *** then a learning algorithm based on radial basis function(RBF) network is used to transfer the demonstrator's motions to the *** verify the effectiveness of this developed methods,several simulation experiments have been carried out which based on Microsoft Kinect Sensor and the Virtual Robot Experimentation Platform(V-REP).The experimental results show that this method has achieved satisfactory *** the help of this method,the robot can not only complete the task autonomously after teaching,but also can learn the details of demonstrator's behavior.

关键词： Robot Teaching Teleoperation radial basis function(RBF) network

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The Liver Tumor Segmentation Benchmark (LiTS)

arXiv

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arXiv 2019年

作者： Bilic, Patrick Christ, Patrick Li, Hongwei Bran Vorontsov, Eugene Ben-Cohen, Avi Kaissis, Georgios Szeskin, Adi Jacobs, Colin Mamani, Gabriel Efrain Humpire Chartrand, Gabriel Lohöfer, Fabian Holch, Julian Walter Sommer, Wieland Hofmann, Felix Hostettler, Alexandre Lev-Cohain, Naama Drozdzal, Michal Amitai, Michal Marianne Vivanti, Refael Sosna, Jacob Ezhov, Ivan Sekuboyina, Anjany Navarro, Fernando Kofler, Florian Paetzold, Johannes C. Shit, Suprosanna Hu, Xiaobin Lipková, Jana Rempfler, Markus Piraud, Marie Kirschke, Jan Wiestler, Benedikt Zhang, Zhiheng Hülsemeyer, Christian Beetz, Marcel Ettlinger, Florian Antonelli, Michela Bae, Woong Bellver, Míriam Bi, Lei Chen, Hao Chlebus, Grzegorz Dam, Erik B. Dou, Qi Fu, Chi-Wing Georgescu, Bogdan Giró-I-Nieto, Xavier Gruen, Felix Han, Xu Heng, Pheng-Ann Hesser, Jürgen Moltz, Jan Hendrik Igel, Christian Isensee, Fabian Jäger, Paul Jia, Fucang Kaluva, Krishna Chaitanya Khened, Mahendra Kim, Ildoo Kim, Jae-Hun Kim, Sungwoong Kohl, Simon Konopczynski, Tomasz Kori, Avinash Krishnamurthi, Ganapathy Li, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Lowengrub, John Ma, Jun Maier-Hein, Klaus Maninis, Kevis-Kokitsi Meine, Hans Merhof, Dorit Pai, Akshay Perslev, Mathias Petersen, Jens Pont-Tuset, Jordi Qi, Jin Qi, Xiaojuan Rippel, Oliver Roth, Karsten Sarasua, Ignacio Schenk, Andrea Shen, Zengming Torres, Jordi Wachinger, Christian Wang, Chunliang Weninger, Leon Wu, Jianrong Xu, Daguang Yang, Xiaoping Yu, Simon Chun-Ho Yuan, Yading Yue, Miao Zhang, Liping Cardoso, Jorge Bakas, Spyridon Braren, Rickmer Heinemann, Volker Pal, Christopher Tang, An Kadoury, Samuel Soler, Luc van Ginneken, Bram Greenspan, Hayit Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland Ecole Polytechnique de Montréal Canada Department of Medical Imaging Radboud University Medical Center Nijmegen Netherlands Department of Biomedical Engineering Tel-Aviv University Israel Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Philips Research China Philips China Innovation Campus Shanghai China School of Biomedical Engineering & Imaging Sciences King0s College London London United Kingdom Institute for AI in Medicine Technical University of Munich Germany Department of Computer Science Guangdong University of Foreign Studies China Institute for diagnostic and interventional radiology Klinikum rechts der Isar Technical University of Munich Germany Institute for Diagnostic and Interventional Neuroradiology Klinikum Rechts der Isar Technical University of Munich Germany Department of Hepatobiliary Surgery The Affiliated Drum Tower Hospital Nanjing University Medical School China Department of Computing Imperial College London London United Kingdom Institute for Tissue Engineering and Regenerative Medicine Helmholtz Zentrum München Neuherberg Germany Brigham and Women's Hospital Harvard Medical School United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel University of Pennsylvania PA United States Pte. Ltd. Singapore Medical Imaging and Reconstruction Lab Department of Engineering Design Indian Institute of Technology Madras India Sensetime Shanghai China Department of Radiology Perelman School of Medicine University of Pennsylvania United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PA United States Co. Ltd China MontréalQC Canada Department of Radiology Radiation Oncology and Nuclear Medicine University of Montréal

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://***/. In addition, both data and online evaluation are accessible via ***. © 2019, CC BY-NC-ND.

关键词： Deep learning

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Design and application of electric oil pump in automatic transmission for efficiency improvement and start–stop function

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Journal of Central South University 2016年第3期23卷 570-580页

作者：刘艳芳董鹏刘洋徐向阳 Department of Automotive Engineering School of Transportation Science and EngineeringBeihang UniversityBeijing 100191China Beijing Key Laboratory for High-efficient Power Transmission and System Control of New Energy Resource Vehicle(Beihang University) Beijing 100191China Institute of Industrial and Automotive Drivetrain Department of Mechanical EngineeringRuhr-University BochumBochum 44801Germany

For the purpose of improving efficiency and realizing start–stop function, an electric oil pump(EOP) is integrated into an 8-speed automatic transmission(AT). A mathematical model is built to calculate the transmission power loss and the hydraulic system leakage. Based on this model, a flow-based control strategy is developed for EOP to satisfy the system flow requirement. This control strategy is verified through the forward driving simulation. The results indicate that there is a best combination for the size of mechanical oil pump(MOP) and EOP in terms of minimum energy consumption. In order to get a quick and smooth starting process, control strategies of the EOP and the on-coming clutch are proposed. The test environment on a prototype vehicle is built to verify the feasibility of the integrated EOP and its control strategies. The results show that the selected EOP can satisfy the flow requirement and a quick and smooth starting performance is achieved in the start–stop function. This research has a high value for the forward design of EOP in automatic transmissions with respect to efficiency improvement and start–stop function.

关键词： automatic transmission electric oil pump mechanical oil pump start-stop function efficiency improvement forwarddriving simulation control strategy

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Distributed trajectory optimization for multiple solar-powered UAVs target tracking in urban environment by Adaptive Grasshopper Optimization Algorithm

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Aerospace science and Technology 2017年 70卷 497-510页

作者： Jianfa Wu Honglun Wang Na Li Peng Yao Yu Huang Zikang Su Yue Yu School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China Shenyuan Honors College of Beihang University Beijing 100191 China Science and Technology on Aircraft Control Laboratory Beihang University Beijing 100191 China Unmanned System Research Institute Beihang University Beijing 100191 China College of Engineering Ocean University of China Qingdao 266100 China

Aiming at the trajectory optimization of the solar-powered UAVs (SUAVs) cooperative target tracking in urban environment, the distributed model predictive control (DMPC) method based on Adaptive Grasshopper Optimization Algorithm (AGOA) is proposed in this paper. Firstly, the cooperative target tracking problem in urban environment is modeled by formulating the SUAVs kinematic and target models, the urban environment constraints, solar power harvesting and consumption models for SUAV, and sight occlusions by constructions. Especially, the sight occlusions in urban environment for SUAV are taken into consideration for the first time in this paper. A judgment method of sight occlusions for SUAV is proposed to make the calculation of the energy index more precise. Second, based on the precise modeling, the DMPC method is adopted as the framework for trajectory optimization in real time. Third, AGOA, a novel intelligent algorithm to mimic the behaviors of grasshoppers, is proposed to be the DMPC solver. The proposed AGOA has a better searching ability than the traditional GOA and some other intelligent algorithms by introducing some improvement measures e.g. the natural selection strategy, the democratic decision-making mechanism, and the dynamic feedback mechanism based on the 1/5 Principle. Finally, the effectiveness of the proposed method is demonstrated by the simulations.

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