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Author Correction: Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic

Nature communications 2020年第1期11卷 6292页

作者： Zhu Liu Philippe Ciais Zhu Deng Ruixue Lei Steven J Davis Sha Feng Bo Zheng Duo Cui Xinyu Dou Biqing Zhu Rui Guo Piyu Ke Taochun Sun Chenxi Lu Pan He Yuan Wang Xu Yue Yilong Wang Yadong Lei Hao Zhou Zhaonan Cai Yuhui Wu Runtao Guo Tingxuan Han Jinjun Xue Olivier Boucher Eulalie Boucher Frédéric Chevallier Katsumasa Tanaka Yiming Wei Haiwang Zhong Chongqing Kang Ning Zhang Bin Chen Fengming Xi Miaomiao Liu François-Marie Bréon Yonglong Lu Qiang Zhang Dabo Guan Peng Gong Daniel M Kammen Kebin He Hans Joachim Schellnhuber Department of Earth System Science Tsinghua University Beijing China. zhuliu@***. Laboratoire des Sciences du Climat et de l'Environnement LSCE CEA CNRS UVSQ Centre d'Etudes Orme de Merisiers Gif-sur-Yvette France. Department of Earth System Science Tsinghua University Beijing China. Department of Meteorology and Atmospheric Science The Pennsylvania State University University Park PA USA. Department of Earth System Science University of California Irvine 3232Croul Hall Irvine CA USA. Division of Geological and Planetary Sciences California Institute of Technology Pasadena CA USA. Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA. Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment Technology School of Environmental Science and Engineering Nanjing University of Information Science and Technology Nanjing China. Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China. Climate Change Research Center Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China. Key Laboratory of Middle Atmosphere and Global Environment Observation Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China. School of Environment Tsinghua University Beijing China. School of Mathematical School Tsinghua University Beijing China. Department of Mathematical Sciences Tsinghua University Beijing China. Center of Hubei Cooperative Innovation for Emissions Trading System Wuhan China. Faculty of Management and Economics Kunming University of Science and Technology 13 Kunming China. Economic Research Centre of Nagoya University Furo-cho Chikusa-ku Nagoya Japan. Institut Pierre-Simon Laplace Sorbonne Université/CNRS Paris France. Université Paris Dauphine Place du Maréchal de Lattre de Tassigny 75016 Paris France.

A Correction to this paper has been published: https://***/10.1038/s41467-020-20254-5.

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Theranostics: Boosting Postsurgical Outcomes of Orthotopic Hepatocellular Carcinoma via an EpCAM-Targeting Theranostic Nanoparticle (Part. Part. Syst. Charact. 8/2019)

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Particle & Particle systems Characterization 2019年第8期36卷

作者： Min Hu Kun Wang Wenting Shang Ziyu Han Hongmei Jiang Chihua Fang Jie Tian Department of Hepatobiliary Surgery Zhujiang Hospital Southern Medical University Guangzhou 510280 China CAS Key Laboratory of Molecular Imaging The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Department of Hepatobiliary Surgery The First Affiliated Hospital of Jinan University Guangzhou 510630 China Institute of Automation University of Chinese Academy of Sciences Beijing 100080 China Guangdong Provincial Clinical and Engineering Technology Research Center of Digital Medicine Guangzhou 510280 China

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Digital LQR Steady-state Optimal control with Feedforward for Nonminimum Phase Boost DC-DC Converter

Digital LQR Steady-State Optimal Control with Feedforward fo...

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第28届中国控制与决策会议

作者： Mengting Zhang Xiuliang Li Jia Liu Hongye Su Jian Song State Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang University

ISBN: (纸本)9781467397155

This paper presents a perfect optimal control for nonminimum phase boost DC-DC converters by adopting a combination of feedback and feedforward controller.A linear quadratic regulator(LQR) feedback controller is firstly designed and proposed to have the following ***,integral control by state augmentation is adopted in order to and eliminate the steady-state ***,feedback gain and estimator gain are calculated offline based on LQR steady-state optimal control method and steady-state kalman ***,the problems of integral wind-up protection is also handled.A feedforward controller is then proposed to eliminate the phase error caused by right half phase zero(RHPZ) emerged in nonminimum phase of boost DC-DC *** feedforward controller is implemented by zero phase error tracking control(ZPETC) technique because the inverse of nonminimum phase system is ***,the proposed controller has been implemented on NI CompactRio platform for boost converter voltage *** experimental results clearly show the proposed controller offers better performance as compared to conventional digital compensation controller.

关键词： DC-DC converter LQR Feedforward control ZPETC Nonminimum phase system

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A Digital Dual-Mode LQR Optimal controller for Boost Converter over a Wide Operation Range

A Digital Dual-Mode LQR Optimal Controller for Boost Convert...

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第35届中国控制会议

作者： Mengting Zhang Xiuliang Li Jia Liu Hongye Su Jian Song State Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang University

ISBN: (纸本)9781509009107

A new dual-mode discrete-time linear quadratic regulator(LQR) optimal controller is presented,which allows voltage control of a boost converter for wide-load-range condition,whether in continuous conduction mode(CCM) or in discontinuous conduction mode(DCM).A linear quadratic regulator(LQR) controller combined with kalman filter is designed for the voltage control of boost *** gain and estimator gain can be calculated offline based on LQR steady-state optimal control method and steady-state kalman *** control gains calculated based on plant models will be obtained since the modeling of DC-DC converters in CCM and DCM are *** proposed control strategy makes it possible for boost converter to autonomously operate in CCM or DCM control gain by utilizing a mode *** proposed mode detector greatly enhances the control performance in different operating mode,even at the boundary ***,the proposed controller has been implemented on NI CompactRio platform for boost converter voltage *** experimental results clearly show the proposed controller offers better performance in both transient response and frequency response.

关键词： Boost converter continuous conduction mode discontinuous conduction mode dual-mode control LQR

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ADRC methodology for a quadrotor UAV transporting hanged payload

ADRC methodology for a quadrotor UAV transporting hanged pay...

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International Conference on Information and Automation (ICIA)

作者： Hongda Wang Yongbin Huang Chao Xu State Key Laboratory of Industrial Control Technology and the Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

ISBN: (纸本)9781509041039

The research of a quadrotor delivering hanged payload is meaningful in many applications, but it's also a very challenging task because the payload will significantly change the dynamics of the quadrotor. Since the states of payload is hard to measure and the mass may change, the control strategy depending on precise models may not work. Thus, we propose the Active Disturbance Rejection control strategy in this case. Using the Extended state Observer, we can estimate the disturbance in the quadrotor plant, which is caused by the dynamics of payload. By counteracting the disturbance in the system, we can achieve favorable performance of the quadrotor even the payload swing is introduced for experimental validation artificially.

关键词： Payloads Observers Mathematical model Aircraft Uncertainty Vehicle dynamics

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CASCIRE surveillance network and work on avian influenza viruses

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Science China(Life Sciences) 2017年第12期60卷 1386-1391页

作者： Yuhai Bi Weifeng Shi Jianjun Chen Quanjiao Chen Zhenghai Ma Gary Wong Wenxia Tian Renfu Yin Guanghua Fu Yongchun Yang William J.Liu Chuansong Quan Qianli Wang Shenghu He Xiangdong Li Qianfeng Xia Lixin Wang Zhaohui Pan Laixing Li Hong Li Wen Xu Ying Luo Hui Zeng Lianpan Dai Haixia Xiao Kirill Sharshov Alexander Shestopalov Yi Shi Jinghua Yan Xuebing Li Yingxia Liu Fumin Lei Wenjun Liu George F.Gao CAS Key Laboratory of Pathogenic Microbiology and Immunology Collaborative Innovation Center for Diagnosis and Treatment ofInfectious DiseaseInstitute of MicrobiologyCenter for Influenza Research and Early-warning(CASCIRE)Chinese Academy of SciencesBeijing 100101China Shenzhen Key Laboratory of Pathogen and Immunity State Key Discipline of Infectious DiseaseShenzhen Third People's HospitalShenzhen 518112China Shandong Universities Key Laboratory of Etiology and Epidemiology of Emerging Infectious Diseases Taishan Medical CollegeTai'an 271016China CAS Key Laboratory of Special Pathogens and Biosafety Wuhan Institute of VirologyChinese Academy of SciencesWuhan 430071China College of Life Science and Technology Xinjiang UniversityUrumchi 830046China College of Animal Science and Veterinary Medicine Shanxi Agricultural UniversityTaigu 030801China Department of Veterinary Preventive Medicine College of Veterinary MedicineJilin UniversityJilin 130062China Institute of Animal Husbandry and Veterinary Medicine Fujian Academy of Agricultural SciencesFuzhou 350013China Center of Excellence for Animal Health Inspection College of Animal Science and TechnologyZhejiang Agriculture and Forestry UniversityZhejiang 311300China National Institute for Viral Disease Control and Prevention Chinese Center for Disease Control and Prevention(China CDC)Beijing 102206China Key Laboratory of Public Health Safety Ministry of EducationSchool of Public HealthFudan UniversityShanghai 200032China Laboratory of Clinical Veterinary Medicine College of AgricultureNingxia UniversityYinchuan 750021China National Research Center for Veterinary Medicine Luoyang 471003China Laboratory of Tropical Biomedicine and Biotechnology and Faculty of Tropical Medicine and Laboratory MedicineImmunology DepartmentSchool of Basic Medicine and Life ScienceHainan Medical UniversityHaikou 571101China Xizang Agriculture and Animal Husbandry College Linzhi 860000China Key Laboratory of Adaptation and Evolution of Plateau Biota N

Studies on influenza virus by Chinese Academy of Sciences(CAS)could be traced back as early as 2005 by the CAS key laboratory of Pathogenic Microbiology and Immunology(CASPMI),who discovered that Qinghai-like Clade 2.2H5N1 subtype highly pathogenic avian influenza virus(HPAIV)first caused severe outbreak in wild birds in Qinghai Lake(Liu et al.,2005).

关键词： CASCIRE surveillance network work on avian influenza viruses

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A Dynamic Adaptive Algorithm for Statistical Up-scaling based Short-term Wind Power Prediction of Regions

A Dynamic Adaptive Algorithm for Statistical Up-scaling base...

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2017 International Electrical and Energy Conference (CIEEC 2017)

作者： Xiaosheng Peng Yuzhu chen Jianfeng Che Bo Wang School of Electrical and Electronic Engineering Huazhong University of Science and Technology State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute

Wind Power Prediction（WPP） of regions is beneficial for the department of power grid dispatching to evaluate the wind power output characteristics from a global perspective and to formulate a scientific and reasonable scheduling scheme for power *** statistical up-scaling based WPP of regions is one of the most popular methods,which has been widely applied by the industrial *** selection of reference wind farms is one of the key factors of the statistical up-scaling method based WPP of ***,most of the methods for the selection of reference wind farms are not flexible,which restrict the improvement of the WPP accuracy,as the weather of wind farm sub-regions are always *** overcome the challenge,a dynamic adaptive algorithm based WPP of regions is presented in the paper,in terms of the dynamic selection of reference wind farms,the dynamic selection of the WPP methods based on the classification of the weather types of the wind farms and the dynamic selection of the up-scaling *** results show that the proposed method is with higher accuracy than the traditional statistical up-scaling based WPP of regions and is applicable for industrial WPP of regional wind farms.

关键词： Wind Power Prediction of Regions Dynamic Adaptive Algorithm Statistical Up-scaling Short-term

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A Short-term Wind Power Prediction Approach Based On the Dynamic Classification of the Weather Types of Wind Farms

A Short-term Wind Power Prediction Approach Based On the Dyn...

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2017 International Electrical and Energy Conference (CIEEC 2017)

作者： Xiaosheng Peng Wenhan Fan Fan Yang Jianfeng Che Bo Wang School of Electrical and Electronic Engineering Huazhong University of Science and Technology State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute

Wind Power Prediction（WPP） is an effective way to reduce the uncertainty and randomness of large volume of wind power,and to improve the wind power integration capacity of power ***,WPP based on the classification of weather types is one of the most popular methods to improve the WPP accuracy,but most of the research focused on the static classification of the weather types,and the dynamic classification of weather types is not *** Weather Prediction（NWP） data of a certain time is applied for the static classification of weather type,which is not effective for describing the dynamic changing process of the weather of wind *** overcome the challenge,a short-term WPP approach based on the dynamic classification of the weather types,dividing the weather types into stable type,trending type and fluctuating type,on the basis of wind speed change in a period of time,is presented in the *** dynamic classification of the weather types based WPP is with significant advantage to describe the wind power characteristics of different weather types,which is an effective method to improve the short-term WPP accuracy,and is recommended for the industrial applications.

关键词： Wind Power Prediction Classification of Weather Types Back Propagation Neural Network ARMA Model Hybrid Model

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Adaptive Dynamic Programming and Optimal Stabilization for Linear systems with Time-varying Uncertainty

Adaptive Dynamic Programming and Optimal Stabilization for L...

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Asian control Conference

作者： Meng Zhang Ming-Gang Gan Jie Chen Zhong-Ping Jiang State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China Department of Electrical and Computer Engineering New York University Brooklyn NY

ISBN: (纸本)9781509015740;9781509015733

This paper focuses on the optimal control of continuous-time linear time-varying uncertain systems with completely unknown internal dynamics and proposes a novel approach which leads to an optimal controller with guaranteed stability. A model-free algorithm of adaptive dynamic programming is employed to deal with the uncertainty of system parameters, yielding an optimal feedback controller for the system subject to a predefined cost. Then the stability of the system in time-varying uncertain situation which may undergo parameter changes or jumps is analyzed from the perspective of finite-time stability. On the basis of these results, a switching control strategy is presented to ensure the stability of the time-varying uncertain system with desired optimality properties in the long run. The effectiveness of the strategy is verified by simulations on a DC torque motor servo system.

关键词： Time-varying systems Stability analysis Symmetric matrices Asymptotic stability Uncertainty Optimal control

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A Semantic Labeling Strategy to Reject Unknown Objects in Large Scale 3D Point Clouds

A Semantic Labeling Strategy to Reject Unknown Objects in La...

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第35届中国控制会议

作者： Huifang Ma Lei Shi Sarath Kodagoda Rong Xiong State Key Laboratory of Industrial Control Technology and Institute of Cyber-Systems and Control Zhejiang University Centre for Autonomous Systems The University of Technology

ISBN: (纸本)9781509009107

In recent years,there has been a growing interest in the research of semantic labeling of indoor scenes represented by3 D point clouds.A fundamental problem that has largely been oversighted in the current research is the way of dealing with the unknown class which collectively includes all the objects that are of no interest to the application *** the training stage,these objects are either completely removed or labeled as unknown,resulting in a trained model which is not fully and fairly exposed to the actual sample *** the test stage,the unknown objects are naturally present and provided to the classifier,causing a significant drop of the classification accuracy- usually 20%0%.Simply improving the features or the classifier will not address the root cause *** this paper,we propose a labeling framework combining both Conditional Random Field（CRF） and P-SVM to specifically solve the problem caused by the unknown ***,we use a CRF to model the contextual relations in the 3D space,for which the parameters for both node potential and edge potential are learned from training ***,we make use of the rejection strategy of the P-S VM,which estimates an unnormalized probability for each ***,we reinforce the result of CRF with the belief provided by the P-SVM,and the labeling result is based on the agreement of the two *** proposed method takes advantage of the global optimization of CRF and the advantage of unknown rejection of *** results on publicly available data set show that this method has improved the classification accuracy by 10.7%given the accuracy drop of 19.23%caused by the unknown.

关键词： Semantic labeling CRF PI-SVM unknown rejection

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