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arXiv 2021年

作者： Antonelli, Michela Reinke, Annika Bakas, Spyridon Farahani, Keyvan Kopp-Schneider, Annette Landman, Bennett A. Litjens, Geert Menze, Bjoern Ronneberger, Olaf Summers, Ronald M. van Ginneken, Bram Bilello, Michel Bilic, Patrick Christ, Patrick F. Do, Richard K.G. Gollub, Marc J. Heckers, Stephan H. Huisman, Henkjan Jarnagin, William R. McHugo, Maureen K. Napel, Sandy Golia Pernicka, Jennifer S. Rhode, Kawal Tobon-Gomez, Catalina Vorontsov, Eugene Hulsmon, Hankan Meakin, James A. Ourselin, Sebastien Wiesenfarth, Manuel Arbeláez, Pablo Bae, Byeonguk Chen, Sihong Daza, Laura Feng, Jianjiang He, Baochun Isensee, Fabian Ji, Yuanfeng Jia, Fucang Kim, Namkug Kim, Ildoo Merhof, Dorit Pai, Akshay Park, Beomhee Perslev, Mathias Rezaiifar, Ramin Rippel, Oliver Sarasua, Ignacio Shen, Wei Son, Jaemin Wachinger, Christian Wang, Liansheng Wang, Yan Xia, Yingda Xu, Daguang Xu, Zhanwei Zheng, Yefeng Simpson, Amber L. Maier-Hein, Lena Cardoso, M. Jorge School of Biomedical Engineering & Imaging Sciences King’s College London London United Kingdom Heidelberg Germany Heidelberg Germany Faculty of Mathematics and Computer Science University of Heidelberg Heidelberg Germany University of Pennsylvania PhiladelphiaPA United States BethesdaMD United States Heidelberg Germany Electrical Engineering and Computer Science Vanderbilt University NashvilleTN United States Radboud University Medical Center Radboud Institute for Health Sciences Nijmegen Netherlands Quantitative Biomedicine University of Zurich Zurich Switzerland DeepMind London United Kingdom School of Computing Department of Biomedical and Molecular Sciences Queen’s University KingstonON Canada Department of Surgery Memorial Sloan Kettering Cancer Center New YorkNY United States BethesdaMD United States Department of Informatics Technische Universität München Germany Department of Radiology Memorial Sloan Kettering Cancer Center New YorkNY United States Department of Psychiatry & Behavioral Sciences Vanderbilt University Medical Center NashvilleTN United States Department of Radiology Stanford University StanfordCA United States Department of Computer Science and Software Engineering École Polytechnique de Montréal Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Medical Faculty University of Heidelberg Heidelberg Germany VUNO Inc. Seoul Korea Republic of Universidad de los Andes Colombia Department of Automation Tsinghua University Beijing China Heidelberg Germany Department of Computer Science Xiamen University Xiamen361005 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Kakao Brain Korea Republic of Institute of Imaging & Computer Vision RWTH Aachen University Aachen Germany Cere

International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks;(2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability;(3) the training of accurate AI segmentation models is now commoditized to non AI *** Codes 68T07. © 2021, CC BY-NC-SA.

关键词： Image segmentation

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A multidimensional space based fault-tolerant control system design method for commercial aircraft

A multidimensional space based fault-tolerant control system...

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CSAA/IET International Conference on Aircraft Utility Systems, AUS 2018

作者： Wang, Jun Wang, Shaoping Wang, Xingjian Tomovic, Mileta M. Shi, Cun School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang University Beijing100191 China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou310027 China Engineering Technology Department Old Dominion University NorfolkVA United States

ISBN: (纸本)9781785617911

The fault-tolerant control techniques can maintain the safety of aircraft under fault conditions. Different strategies should be used to response to specific fault conditions according to the fault diagnosis information. To improve the fault-tolerant control design efficiency, this paper propose a multidimensional space based method, the flight/actuation system is divided and modeled by using the multidimensional space first, and then operator norm is used to map and quantize the fault information, finally, the decision making algorithm is designed to match the fault-tolerant control strategies to specific fault conditions. © 2018 IET Conference Publications. All rights reserved.

关键词： Decision making

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Sound Source Localization Using Time Differences of Arrival;Euclidean Distance Matrices Based Approach

Sound Source Localization Using Time Differences of Arrival;...

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International Symposium on Telecommunications, IST

作者： Ali Kafaei Zad Tehrani Bahador Makkiabadi Ali Pourmohammad Seyed Hani Hozhabr Department of Electrical Engineering Amirkabir University of Technology Tehran Iran Department of Medical Physics and Biomedical Engineering Tehran University of Medical Sciences Tehran Iran Electrical Engineering Department Amirkabir University of Technology Tehran Iran Research Center for Biomedical Technologies and Robotics Institute for Advanced Medical Technologies (IAMT) Tehran Iran

ISBN: (纸本)9781538682753;9781538682746

In the source localization problem, time differences of arrival (TDOA) and intensity level differences (ILD) of microphones can be employed to estimate the source location. Due to existing additive noise in real applications the ILD measurement provides less reliable information compared to the TDOA. Therefore, this study is focused on developing algorithms employing the TDOA information only. In the past studies, TDOA were used mostly for estimation of direction of the arrival (DOA) parameter. To find the source location from TDOA of different microphones, the intersection of several equations must be calculated which this solving process requires complex numerical analysis. The solving processes, which generally ignore the noise existence, are not robust to noise and might not converge to the true answer in real-world applications. This paper tackles the source localization problem by converting the numerical analysis approach to an iterative minimization one in order to improve localization accuracy in noisy conditions. The performance of the proposed iterative minimization algorithm is seen to be sensitive to the initial values. To address this issue, another algorithm, based on Euclidean Distance theory, is developed to obtain stable and accurate results. The proposed framework works properly in different SNR conditions. The results show that the proposed methods are more accurate than the existing numerical analysis based methods in different noisy conditions even in very low SNR conditions.

关键词： Position measurement Microphones Cost function Euclidean distance Numerical analysis Noise measurement

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Bounded Control of Robotic Manipulators via Fuzzy Logic Systems 36

Bounded Control of Robotic Manipulators via Fuzzy Logic Syst...

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第36届中国控制会议

作者： Linghuan Kong Wei He Yiting Dong Chenguang Yang Guang Li School of Automation Engineering and Center for Robotics University of Electronic Science and Technology of China School of Automation and Electrical Engineering University of Science and Technology Beijing Department of Mechanical Engineering Texas Tech University Zienkiewicz Centre for Computational Engineering Swansea University School of Engineering and Materials Science Queen Mary University of London

ISBN: (纸本)9781538629185

In this paper, a fuzzy neural network controller is proposed for a class of robotic manipulators with unknown dynamics and input constraints. First, to cope with the unknown dynamics, fuzzy logic systems are designed to approximate it. Second,considering the case where an aggressive control input may lead to the poor performance, even resulting in instability in practice,the controller is designed with the function tanh（·）, and the function has the ability to make the controller keep in the predefined range. At last, evidence based on the Lyapunov theory proves the system errors converge to a small range near zero. Simulations are carried out to show the effectiveness of the designed control.

关键词： Fuzzy Logic System Neural Networks Input Constraints Robotic Manipulators

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Wearable Flexible Hybrid Electronics: advanced Soft Materials, Sensor Integrations, and Applications of Wearable Flexible Hybrid Electronics in Healthcare, Energy, and Environment (Adv. Mater. 15/2020)

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advanced Materials 2020年第15期32卷

作者： Hyo‐Ryoung Lim Hee Seok Kim Raza Qazi Young‐Tae Kwon Jae‐Woong Jeong Woon‐Hong Yeo George W. Woodruff School of Mechanical Engineering Institute for Electronics and Nanotechnology Georgia Institute of Technology Atlanta GA 30332 USA Department of Mechanical Engineering University of South Alabama Mobile AL 36608 USA Department of Electrical Computer & Energy Engineering University of Colorado Boulder Boulder CO 80309 USA School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon 34141 South Korea George W. Woodruff School of Mechanical Engineering Wallace H. Coulter Department of Biomedical Engineering Institute for Electronics and Nanotechnology Parker H. Petit Institute for Bioengineering and Biosciences Center for Flexible and Wearable Electronics Advanced Research Neural Engineering Center Institute for Materials Institute for Robotics and Intelligent Machines Georgia Institute of Technology Atlanta GA 30332 USA

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Optogenetic Probes: Rapidly Customizable, Scalable 3D‐Printed Wireless Optogenetic Probes for Versatile Applications in Neuroscience (Adv. Funct. Mater. 46/2020)

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advanced Functional Materials 2020年第46期30卷

作者： Juhyun Lee Kyle E. Parker Chinatsu Kawakami Jenny R. Kim Raza Qazi Junwoo Yea Shun Zhang Choong Yeon Kim John Bilbily Jianliang Xiao Kyung‐In Jang Jordan G. McCall Jae‐Woong Jeong School of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon 34141 Republic of Korea Department of Anesthesiology Washington University Pain Center Department of Psychiatry Washington University in St. Louis St. Louis MO 63130 USA Department of Pharmaceutical and Administrative Sciences St. Louis College of Pharmacy St. Louis MO 63130 USA Center for Clinical Pharmacology St. Louis College of Pharmacy and Washington University School of Medicine St. Louis MO 63130 USA Department of Electrical and Electronic Information Engineering Toyohashi University of Technology Toyohashi 441‐8580 Japan Department of Robotics Engineering Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu 42988 Republic of Korea Department of Mechanical Engineering University of Colorado Boulder Boulder CO 80309 USA

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Common Limitations of Image Processing Metrics: A Picture Story

arXiv

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arXiv 2021年

作者： Reinke, Annika Tizabi, Minu D. Sudre, Carole H. Eisenmann, Matthias Rädsch, Tim Baumgartner, Michael Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Bankhead, Peter Benis, Arriel Blaschko, Matthew Buettner, Florian Cardoso, M. Jorge Chen, Jianxu Cheplygina, Veronika Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Engelhardt, Sandy Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hamprecht, Fred Hashimoto, Daniel A. Heckmann-Nötzel, Doreen Hirsch, Peter Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kavur, A. Emre Kenngott, Hannes Kleesiek, Jens Kleppe, Andreas Koehler, Sven Kofler, Florian Kopp-Schneider, Annette Kooi, Thijs Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moher, David Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Noyan, M. Alican Petersen, Jens Polat, Gorkem Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Rieke, Nicola Riegler, Michael A. Rivaz, Hassan Saez-Rodriguez, Julio Sánchez, Clara I. Schroeter, Julien Saha, Anindo Selver, M. Alper Sharan, Lalith Shetty, Shravya Smeden, Maarten V.A.N. Stieltjes, Bram Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben V.A.N. Varoquaux, Gaël Wiesenfarth, Manuel Yaniv, Ziv R. Jäger, Paul Maier-Hein, Lena Division of Intelligent Medical Systems and HI Helmholtz Imaging Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Intelligent Medical Systems Heidelberg Germany NCT Heidelberg DKFZ University Medical Center Heidelberg Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Division of Medical Image Computing Heidelberg Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Institute of Genetics and Cancer University of Edinburgh Edinburgh United Kingdom Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Kasteelpark Arenberg 10 - box 2441 Leuven3001 Belgium Frankfurt/Mainz DKFZ UCT Frankfurt-Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany Frankfurt Cancer Insititute Germany Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for St

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide. © 2021, CC BY.

关键词： Medical imaging

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Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the heichole benchmark

arXiv

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arXiv 2021年

作者： Wagner, Martin Müller-Stich, Beat-Peter Kisilenko, Anna Tran, Duc Heger, Patrick Mündermann, Lars Lubotsky, David M. Müller, Benjamin Davitashvili, Tornike Capek, Manuela Reinke, Annika Yu, Tong Vardazaryan, Armine Innocent Nwoye, Chinedu Padoy, Nicolas Liu, Xinyang Lee, Eung-Joo Disch, Constantin Meine, Hans Xia, Tong Jia, Fucang Kondo, Satoshi Reiter, Wolfgang Jin, Yueming Long, Yonghao Jiang, Meirui Dou, Qi Heng, Pheng Ann Twick, Isabell Kirtac, Kadir Hosgor, Enes Bolmgren, Jon Lindström Stenzel, Michael von Siemens, Björn Kenngott, Hannes G. Nickel, Felix von Frankenberg, Moritz Mathis-Ullrich, Franziska Maier-Hein, Lena Speidel, Stefanie Bodenstedt, Sebastian Department for General Visceral and Transplantation Surgery Heidelberg University Hospital Im Neuenheimer Feld 420 Heidelberg69120 Germany Heidelberg Im Neuenheimer Feld 460 Heidelberg69120 Germany Data Assisted Solutions Corporate Research & Technology KARL STORZ SE & Co. KG Dr. Karl-Storz-Str. 34 Tuttlingen78332 Germany Im Neuenheimer Feld 223 Heidelberg69120 Germany Im Neuenheimer Feld 223 Heidelberg69120 Germany Faculty of Mathematics and Computer Science Heidelberg University Im Neuenheimer Feld 205 Heidelberg69120 Germany ICube University of Strasbourg CNRS France 300 bd Sébastien Brant - CS 10413 Illkirch CedexF-67412 France IHU Strasbourg France 1 Place de l'hôpital Strasbourg67000 France Sheikh Zayed Institute for Pediatric Surgical Innovation Children's National Hospital 111 Michigan Ave NW WashingtonDC20010 United States University of Maryland College Park 2405 A V Williams Building College ParkMD20742 United States Fraunhofer Institute for Digital Medicine MEVIS Max-von-Laue-Str. 2 Bremen28359 Germany University of Bremen FB3 Medical Image Computing Group ℅ Fraunhofer MEVIS Am Fallturm 1 Bremen28359 Germany Lab for Medical Imaging and Digital Surgery Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Konika Minolta Inc. JP TOWER 2-7-2 Marunouchi Chiyoda-ku Tokyo100-7015 Japan Wintegral GmbH Ehrenbreitsteiner Str. 36 München80993 Germany Department of Computer Science and Engineering Ho Sin-Hang Engineering Building The Chinese University of Hong Kong Sha Tin NT Hong Kong Caresyntax GmbH Komturstr. 18A Berlin12099 Germany Department of Surgery Salem Hospital of the Evangelische Stadtmission Heidelberg Zeppelinstrasse 11-33 Heidelberg69121 Germany Health Robotics and Automation Laboratory Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Geb. 40.28 KIT Campus Süd Engler-Bunte-Ring 8 Karlsruhe76131 Germany Medical Faculty Heidelberg Uni

PURPOSE: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive warnings and semi-autonomous robotic assistance or improve training of surgeons via data-driven feedback. In surgical workflow analysis up to 91% average precision has been reported for phase recognition on an open data single-center video dataset. In this work we investigated the generalizability of phase recognition algorithms in a multi-center setting including more difficult recognition tasks such as surgical action and surgical skill. METHODS: To achieve this goal, a dataset with 33 laparoscopic cholecystectomy videos from three surgical centers with a total operation time of 22 hours was created. Labels included framewise annotation of seven surgical phases with 250 phase transitions, 5514 occurences of four surgical actions, 6980 occurences of 21 surgical instruments from seven instrument categories and 495 skill classifications in five skill dimensions. The dataset was used in the 2019 international Endoscopic Vision challenge, sub-challenge for surgical workflow and skill analysis. Here, 12 research teams trained and submitted their machine learning algorithms for recognition of phase, action, instrument and/or skill assessment. RESULTS: F1-scores were achieved for phase recognition between 23.9% and 67.7% (n=9 teams), for instrument presence detection between 38.5% and 63.8% (n=8 teams), but for action recognition only between 21.8% and 23.3% (n=5 teams). The average absolute error for skill assessment was 0.78 (n=1 team). CONCLUSION: Surgical workflow and skill analysis are promising technologies to support the surgical team, but are not solved yet, as shown by our comparison of machine learning algorithms. This novel HeiChole benchmark can be used for comparable evaluation and validation of future work. In future studies, it is of utmost impo

关键词： Learning algorithms

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Atomically Dispersed Fe-N4 Modified with Precisely Located S for Highly Efficient Oxygen Reduction

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Nano-micro letters 2020年第1期12卷 116页

作者： Yin Jia Xuya Xiong Danni Wang Xinxuan Duan Kai Sun Yajie Li Lirong Zheng Wenfeng Lin Mingdong Dong Guoxin Zhang Wen Liu Xiaoming Sun State Key Laboratory of Chemical Resource Engineering Beijing Advanced Innovation Center for Soft Matter Science and Engineering Beijing University of Chemical Technology Beijing 100029 People's Republic of China. Interdisciplinary Nanoscience Center (INANO) Sino-Danish Center for Education and Research (SDC) Aarhus University 8000 Aarhus C Denmark. Shandong University of Science and Technology Electrical Engineering and Automation Tsingtao 266590 People's Republic of China. Department of Chemical Engineering Loughborough University Loughborough Leicestershire LE11 3TU UK. Beijing Synchrotron Radiation Facility Institute of High Energy Physics Chinese Academy of Sciences Beijing 100049 People's Republic of China. Shandong University of Science and Technology Electrical Engineering and Automation Tsingtao 266590 People's Republic of China. zhanggx@***. State Key Laboratory of Chemical Resource Engineering Beijing Advanced Innovation Center for Soft Matter Science and Engineering Beijing University of Chemical Technology Beijing 100029 People's Republic of China. wenliu@***. State Key Laboratory of Chemical Resource Engineering Beijing Advanced Innovation Center for Soft Matter Science and Engineering Beijing University of Chemical Technology Beijing 100029 People's Republic of China. sunxm@***.

Immobilizing metal atoms by multiple nitrogen atoms has triggered exceptional catalytic activity toward many critical electrochemical reactions due to their merits of highly unsaturated coordination and strong metal-substrate interaction. Herein, atomically dispersed Fe-NC material with precise sulfur modification to Fe periphery (termed as Fe-NSC) was synthesized, X-ray absorption near edge structure analysis confirmed the central Fe atom being stabilized in a specific configuration of Fe(N)(N-C-S). By enabling precisely localized S doping, the electronic structure of Fe-N moiety could be mediated, leading to the beneficial adjustment of absorption/desorption properties of reactant/intermediate on Fe center. Density functional theory simulation suggested that more negative charge density would be localized over Fe-N moiety after S doping, allowing weakened binding capability to *OH intermediates and faster charge transfer from Fe center to O species. Electrochemical measurements revealed that the Fe-NSC sample exhibited significantly enhanced oxygen reduction reaction performance compared to the S-free Fe-NC material (termed as Fe-NC), showing an excellent onset potential of 1.09 V and half-wave potential of 0.92 V in 0.1 M KOH. Our work may enlighten relevant studies regarding to accessing improvement on the catalytic performance of atomically dispersed M-NC materials by managing precisely tuned local environments of M-N moiety.

关键词： Atomic dispersion Electronic structure Iron–nitrogen moiety Oxygen reduction Sulfur doping

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Global Pinning Synchronization with PI Controller in General Complex Directed Networks

Global Pinning Synchronization with PI Controller in General...

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第37届中国控制会议

作者： Peng Liu Haibo Gu Jinhu Lü Yu Kang Department of Automation University of Science and Technology of China LSC Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environmentand Beijing Advanced Innovation Center for Big Data and Brain Machine IntelligenceBeihang University

Global pinning synchronization in complex networks has been extensively studied in the last decades due to its potential *** question is how to design an available controller to achieve global synchronization when the network is *** we all know,the classical PI controller is most widely and successfully used in engineering systems with nonlinear ***,the global synchronization with PI controller in general complex directed networks still remain *** this paper we will theoretically show that the global synchronization of some directed networks can also be ensured by designing an appropriate PI controller and pioneering some selected nodes,then a simple numerical example will also be given to verify our main results.

关键词： Directed network Pining synchronization PI controller Lyapunov method

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