检索结果-内蒙古大学图书馆

Estimates, trends, and drivers of the global burden of type 2 diabetes attributable to PM_2·5 air pollution, 1990–2019: an analysis of data from the Global Burden of Disease Study 2019

学校读者我要写书评

暂无评论

The Lancet Planetary Health 2022年第7期6卷 e586-e600页

作者： Burkart, Katrin Causey, Kate Cohen, Aaron J Wozniak, Sarah S Salvi, Devashri Digvijay Abbafati, Cristiana Adekanmbi, Victor Adsuar, Jose C Ahmadi, Keivan Alahdab, Fares Al-Aly, Ziyad Alipour, Vahid Alvis-Guzman, Nelson Amegah, Adeladza Kofi Andrei, Catalina Liliana Andrei, Tudorel Ansari, Fereshteh Arabloo, Jalal Aremu, Olatunde Aripov, Timur Babaee, Ebrahim Banach, MacIej Barnett, Anthony Bärnighausen, Till Winfried Bedi, Neeraj Behzadifar, Masoud Béjot, Yannick Bennett, Derrick A Bensenor, Isabela M Bernstein, Robert S Bhattacharyya, Krittika Bijani, Ali Biondi, Antonio Bohlouli, Somayeh Breitner, Susanne Brenner, Hermann Butt, Zahid A Cámera, Luis Alberto Cantu-Brito, Carlos Carvalho, Felix Cerin, Ester Chattu, Vijay Kumar Chauhan, Bal Govind Choi, Jee-Young Jasmine Chu, Dinh-Toi Dai, Xiaochen Dandona, Lalit Dandona, Rakhi Daryani, Ahmad Davletov, Kairat De Courten, Barbora Demeke, Feleke Mekonnen Denova-Gutiérrez, Edgar Dharmaratne, Samath Dhamminda Dhimal, Meghnath Diaz, Daniel Djalalinia, Shirin Duncan, Bruce B El Sayed Zaki, Maysaa Eskandarieh, Sharareh Fareed, Mohammad Farzadfar, Farshad Fattahi, Nazir Fazlzadeh, Mehdi Fernandes, Eduarda Filip, Irina Fischer, Florian Foigt, Nataliya A Freitas, Marisa Ghashghaee, Ahmad Gill, Paramjit Singh Ginawi, Ibrahim Abdelmageed Gopalani, Sameer Vali Guo, Yuming Gupta, Rajat Das Habtewold, Tesfa Dejenie Hamadeh, Randah R Hamidi, Samer Hankey, Graeme J Hasanpoor, Edris Hassen, Hamid Yimam Hay, Simon I Heibati, Behzad Hole, Michael K Hossain, Naznin Househ, Mowafa Irvani, Seyed Sina Naghibi Jaafari, Jalil Jakovljevic, Mihajlo Jha, Ravi Prakash Jonas, Jost B Jozwiak, Jacek Jerzy Kasaeian, Amir Kaydi, Neda Khader, Yousef Saleh Khafaie, Morteza Abdullatif Khan, Ejaz Ahmad Khan, Junaid Khan, Md Nuruzzaman Khatab, Khaled Khater, Amir M Kim, Yun Jin Kimokoti, Ruth W Kisa, Adnan Kivimäki, Mika Knibbs, Luke D Kosen, Soewarta Koul, Parvaiz A Koyanagi, Ai Defo, Barthelemy Kuate Kugbey, Nuworza Lauriola, Paolo Lee, Paul H Leili, Mostafa Lewycka, Sonia Li, Shanshan Lim, Lee-Ling Li Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Division of Plastic and Reconstructive Surgery University of Washington Seattle WA United States Research Insights and Innovation Seattle WA United States Health Effects Institute Boston MA United States School of Public Health Boston University Boston MA United States Department of Juridical and Economic Studies La Sapienza University Rome Italy Department of Population Medicine Cardiff University Cardiff United Kingdom Sport Science Department University of Extremadura Badajoz Spain School of Public Health Imperial College London London United Kingdom Department of Primary Care and Public Health Imperial College London London United Kingdom Mayo Evidence-based Practice Center Mayo Clinic Foundation for Medical Education and Research Rochester MN United States John T. Milliken Department of Internal Medicine Washington University in St. Louis St. Louis MO United States Clinical Epidemiology Center US Department of Veterans Affairs (VA) St Louis MO United States Health Management and Economics Research Center Iran University of Medical Sciences Tehran Iran Department of Health Economics Iran University of Medical Sciences Tehran Iran Preventive Medicine and Public Health Research Center Iran University of Medical Sciences Tehran Iran Pars Advanced and Minimally Invasive Medical Manners Research Center Iran University of Medical Sciences Tehran Iran Department of Community and Family Medicine Iran University of Medical Sciences Tehran Iran Research Group in Hospital Management and Health Policies Universidad de la Costa (University of the Coast) Barranquilla Colombia Research Group in Health Economics University of Cartagena Cartagena Colombia Department of Biomedical Science University of Cape Coast Cape Coast Ghana Cardiology Department Ca

Background: Experimental and epidemiological studies indicate an association between exposure to particulate matter (PM) air pollution and increased risk of type 2 diabetes. In view of the high and increasing prevalence of diabetes, we aimed to quantify the burden of type 2 diabetes attributable to PM_2·5 originating from ambient and household air pollution. Methods: We systematically compiled all relevant cohort and case-control studies assessing the effect of exposure to household and ambient fine particulate matter (PM_2·5) air pollution on type 2 diabetes incidence and mortality. We derived an exposure–response curve from the extracted relative risk estimates using the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. The estimated curve was linked to ambient and household PM_2·5 exposures from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, and estimates of the attributable burden (population attributable fractions and rates per 100 000 population of deaths and disability-adjusted life-years) for 204 countries from 1990 to 2019 were calculated. We also assessed the role of changes in exposure, population size, age, and type 2 diabetes incidence in the observed trend in PM_2·5-attributable type 2 diabetes burden. All estimates are presented with 95% uncertainty intervals. Findings: In 2019, approximately a fifth of the global burden of type 2 diabetes was attributable to PM_2·5 exposure, with an estimated 3·78 (95% uncertainty interval 2·68–4·83) deaths per 100 000 population and 167 (117–223) disability-adjusted life-years (DALYs) per 100 000 population. Approximately 13·4% (9·49–17·5) of deaths and 13·6% (9·73–17·9) of DALYs due to type 2 diabetes were contributed by ambient PM2·5, and 6·50% (4·22–9·53) of deaths and 5·92% (3·81–8·64) of DALYs by household air pollution. High burdens, in terms of numbers as well as rates, were estimated in Asia, sub-Saharan Africa, and South Am

关键词：

Modeling and analysis of secondary controlled virtual synchronous generator with dynamic droop for microgrid

学校读者我要写书评

暂无评论

Modeling and analysis of secondary controlled virtual synchr...

IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Weihang Yan David Wenzhong Gao Fang Zhang Lin Cheng Shijie Yan Department of Electrical and Computer Engineering University of Denver Denver USA State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Tsinghua University Beijing China College of Information Science and Technology Northeastern University Shenyang China

ISBN: (纸本)9781538614280

In this paper, a dynamic droop mechanism is proposed to damp the synchronizing oscillation that exists in a virtual synchronous generator based distributed generator system without varying the steady-state active-power-loop droop coefficient. In order to eliminate the steady-state error introduced due to load changes or generator trips, a distributed secondary controller based on extremum seeking is employed so that microgrid frequency can be restored to nominal value. Besides, the small-signal model of virtual synchronous generator system is presented under d-q reference frame where virtual synchronous generator is deployed as a PV node. Moreover, the linearized system model of virtual synchronous generator with dynamic droop mechanism is derived and its advantage is discussed. Finally, a modified IEEE 14 bus microgrid system is established in Matlab/Simulink platform. Simulation results demonstrate the validity and feasibility of the proposed method.

关键词： Mathematical model Microgrids Synchronous generators Frequency control Inverters Power system stability Power system dynamics

Event-triggered active-passive dynamic consensus filters

学校读者我要写书评

暂无评论

Event-triggered active-passive dynamic consensus filters

American control Conference

作者： J. Daniel Peterson Tansel Yucelen S. Jagannathan Eduardo Pasiliao the Mechanical and Aerospace Engineering Department and a member of the Advanced Systems Research Laboratory at the Missouri University of Science and Technology Rolla MO 65409 USA the Mechanical Engineering Department and the Director of the Laboratory for Autonomy Control Information and Systems at the University of South Florida Tampa FL 33620 USA the William A. Rutledge Distinguished Professor of the Electrical and Computer Engineering Department and Director of the Embedded Control Systems and Networking Laboratory at the Missouri University of Science and Technology Rolla MO 65409 USA the Air Force Research Laboratory Munitions Directorate Eglin Air Force Base FL 32542 USA

ISBN: (纸本)9781509045839

Multiagent systems consist of groups of agents that exchange local information to achieve given system-level goals. Traditionally, these systems assumed that all agents have some knowledge (e.g., observations of a process of interest) to contribute in order to achieve a common goal of interest, which may not be the case in many scenarios. Motivated from this standpoint, a new class of multiagent systems, active-passive multiagent systems, were recently proposed, which consists of agents subject to observations of a process of interest (i.e., active agents) and agents without any observations (i.e., passive agents). However, in this class of multiagent systems, agents need to continuously exchange information among themselves, incurring a high cost of interagent information exchange, and requiring that agents synchronize their update times. To address these shortcomings, this paper presents a new class of event-triggered active-passive dynamic consensus filters, in which agents schedule information exchange dependent on errors exceeding user-defined thresholds, significantly reducing the overall cost of interagent information exchange, and allowing agents to determine when to broadcast their information to their neighbors thus eliminating the need to synchronize their states. An illustrative numerical example is presented to demonstrate the efficacy of our approach.

关键词： consist groups agents

Critical science plan for the Daniel K. Inouye Solar Telescope (DKIST)

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Rast, Mark P. Bello González, Nazaret Bellot Rubio, Luis Cao, Wenda Cauzzi, Gianna DeLuca, Edward de Pontieu, Bart Fletcher, Lyndsay Gibson, Sarah E. Judge, Philip G. Katsukawa, Yukio Kazachenko, Maria D. Khomenko, Elena Landi, Enrico Martínez Pillet, Valentin Petrie, Gordon J.D. Qiu, Jiong Rachmeler, Laurel A. Rempel, Matthias Schmidt, Wolfgang Scullion, Eamon Sun, Xudong Welsch, Brian T. Andretta, Vincenzo Antolin, Patrick Ayres, Thomas R. Balasubramaniam, K.S. Ballai, Istvan Berger, Thomas E. Bradshaw, Stephen J. Carlsson, Mats Casini, Roberto Centeno, Rebecca Cranmer, Steven DeForest, Craig Deng, Yuanyong Erdélyi, Robertus Fedun, Viktor Fischer, Catherine E. González Manrique, Sergio J. Hahn, Michael Harra, Louise Henriques, Vasco M.J. Hurlburt, Neal E. Jaeggli, Sarah Jafarzadeh, Shahin Jain, Rekha Jefferies, Stuart M. Keys, Peter H. Kowalski, Adam F. Kuckein, Christoph Kuhn, Jeffrey R. Liu, Jiajia Liu, Wei Longcope, Dana James McAteer, R.T. McIntosh, Scott W. McKenzie, David E. Paz Miralles, Mari Morton, Richard J. Muglach, Karin Nelson, Chris J. Panesar, Navdeep K. Parenti, Susanna Parnell, Clare E. Poduval, Bala Reardon, Kevin P. Reep, Jeffrey W. Schad, Thomas A. Schmit, Donald Sharma, Rahul Socas-Navarro, Hector Srivastava, Abhishek K. Sterling, Alphonse C. Suematsu, Yoshinori Tarr, Lucas A. Tiwari, Sanjiv Alexandra, Tritschler Verth, Gary Vourlidas, Angelos Wang, Haimin Wang, Yi-Ming Department of Astrophysical and Planetary Sciences University of Colorado BoulderCO80309 United States Laboratory for Atmospheric and Space Physics University of Colorado BoulderCO80303 United States Kiepenheuer-Institut für Sonnenphysik Freiburg79104 Germany GranadaE-18080 Spain Big Bear Solar Observatory New Jersey Institute of Technology Big Bear CityCA92314 United States National Solar Observatory BoulderCO80303 United States Harvard-Smithsonian Center for Astrophysics CambridgeMA02138 United States Lockheed Martin Solar & Astrophysics Laboratory Palo AltoCA94304 United States Institute of Theoretical Astrophysics Rosseland Centre for Solar Physics University of Oslo OsloN-0315 Norway School of Physics and Astronomy University of Glasgow GlasgowG12 8QQ United Kingdom High Altitude Observatory National Center for Atmospheric Research BoulderCO80307 United States Department of Astronomical Science School of Physical Sciences The Graduate University for Advanced Studies SOKENDAI Tokyo181-8588 Japan National Astronomical Observatory of Japan National Institutes of Natural Science Tokyo181-8588 Japan Instituto de Astrofísica de Canarias La Laguna Tenerife38205 Spain Department of Climate and Space Sciences and Engineering University of Michigan Ann ArborMI48109 United States Department of Physics Montana State University BozemanMT59717 United States NOAA National Centers for Environmental Information BoulderCO80305 United States Department of Mathematics Physics and Electrical Engineering Northumbria University Newcastle upon TyneNE1 8ST United Kingdom Institute for Astronomy University of Hawaii PukalaniHI96768 United States Department of Natural and Applied Sciences University of Wisconsin Green BayWI54311 United States INAF Osservatorio Astronomico di Capodimonte Naples80131 Italy Center for Astrophysics and Space Astronomy University of Colorado BoulderCO80309 United States AFRL Battlespace Environment Division

The Daniel K. Inouye Solar Telescope (DKIST) will revolutionize our ability to measure, understand and model the basic physical processes that control the structure and dynamics of the Sun and its atmosphere. The first-light DKIST images, released publicly on 29 January 2020, only hint at the extraordinary capabilities which will accompany full commissioning of the five facility instruments. With this Critical Science Plan (CSP) we attempt to anticipate some of what those capabilities will enable, providing a snapshot of some of the scientific pursuits that the Daniel K. Inouye Solar Telescope hopes to engage as start-of-operations nears. The work builds on the combined contributions of the DKIST Science Working Group (SWG) and CSP Community members46, who generously shared their experiences, plans, knowledge and dreams. Discussion is primarily focused on those issues to which DKIST will uniquely contribute. Copyright © 2020, The Authors. All rights reserved.

关键词：

Optimal power management for failure mode of MVDC microgrids in all-electric ships

学校读者我要写书评

暂无评论

arXiv 2017年

作者： Xu, Qimin Yang, Bo Han, Qiaoni Yuan, Yazhou Chen, Cailian Guan, Xinping Department of Automation Shanghai Jiao Tong University Shanghai200240 China Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Institute of Electrical Engineering Yanshan University Qinhuangdao066004 China

Optimal power management of shipboard power system for failure mode (OPMSF) is a significant and challenging problem considering the safety of system and person. Many existing works focused on the transient-time recovery without consideration of the operating cost and the voyage plan. In this paper, the OPMSF problem is formulated considering the mid-time scheduling and the faults at bus and generator. Two-side adjustment methods including the load shedding and the reconfiguration are coordinated for reducing the fault effects. To address the formulated non-convex problem, the travel equality constraint and fractional energy efficiency operation indicator (EEOI) limitation are transformed into the convex forms. Then, considering the infeasibility scenario affected by faults, a further relaxation is adopted to formulate a new problem with feasibility guaranteed. Furthermore, a sufficient condition is derived to ensure that the new problem has the same optimal solution as the original one. Because of the mixed-integer nonlinear feature, an optimal management algorithm based on Benders decomposition (BD) is developed to solve the new one. Due to the slow convergence caused by the time-coupled constraints, a low-complexity near-optimal algorithm based on BD (LNBD) is proposed. The results verify the effectivity of the proposed methods and algorithms. Copyright © 2017, The Authors. All rights reserved.

关键词： Relaxation processes

Secrecy performance analysis of single-input multiple-output generalized-K fading channels

学校读者我要写书评

暂无评论

Frontiers of information Technology and Electronic engineering 2016年第10期17卷 1074-1084页

作者： Lei, Hong-jiang Ansari, Imran Shafique Gao, Chao Guo, Yong-cai Pan, Gao-feng Qaraqe, Khalid A. Chongqing Key Laboratory of Mobile Communications Technology Chongqing University of Posts and Telecommunications Chongqing400065 China MOE Key Laboratory of Optoelectronic Technology and Systems Chongqing University Chongqing400044 China Department of Electrical and Computer Engineering Texas A&M University at Qatar Doha Qatar Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing400715 China

In this paper, the transmission of confidential messages through single-input multiple-output (SIMO) independent and identically generalized-K (KG) fading channels is considered, where the eavesdropper overhears the transmission from the transmitter to the receiver. Both the receiver and the eavesdropper are equipped with multiple antennas, and both active and passive eavesdroppings are considered where the channel state information of the eavesdropper’s channel is or is not available at the transmitter. The secrecy performance of SIMO KG systems is investigated. Analytical expressions for secrecy outage probability and average secrecy capacity of SIMO systems are derived via two different methods, in which KG distribution is approximated by the Gamma and mixture Gamma distributions, respectively. Numerical results are presented and verified via the Monte-Carlo simulation. © 2016, Journal of Zhejiang University Science Editorial Office and Springer-Verlag Berlin Heidelberg.

关键词： Fading channels

Distributed kalman filter in a network of linear dynamical systems

学校读者我要写书评

暂无评论

arXiv 2017年

作者： Marelli, Damián Zamani, Mohsen Fu, Minyue Department of Control Science and Engineering State Key Laboratory of Industrial Control Technology Zhejiang University 388 Yuhangtang Road Hangzhou Zhejiang Province310058 French-Argentinean International Center for Information and Systems Sciences National Scientific and Technical Research Council Ocampo Esmeralda Rosario2000 Argentina School of Electrical Engineering and Computer Science University of Newcastle University Drive CallaghanNSW2308 Australia

This paper is concerned with the problem of distributed Kalman filtering in a network of interconnected subsystems with distributed control protocols. We consider networks, which can be either homogeneous or heterogeneous, of linear time-invariant subsystems, given in the state-space form. We propose a distributed Kalman filtering scheme for this setup. The proposed method provides, at each node, an estimation of the state parameter, only based on locally available measurements and those from the neighbor nodes. The special feature of this method is that it exploits the particular structure of the considered network to obtain an estimate using only one prediction/update step at each time step. We show that the estimate produced by the proposed method asymptotically approaches that of the centralized Kalman filter, i.e., the optimal one with global knowledge of all network parameters, and we are able to bound the convergence rate. Moreover, if the initial states of all subsystems are mutually uncorrelated, the estimates of these two schemes are identical at each time step. Copyright © 2017, The Authors. All rights reserved.

关键词： Kalman filters

The Liver Tumor Segmentation Benchmark (LiTS)

学校读者我要写书评

暂无评论

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

Correction to: Synchronization of coupled neural networks with infinite-time distributed delays via quantized intermittent pinning control

学校读者我要写书评

暂无评论

Nonlinear Dynamics 2018年第3期94卷 2305-2305页

作者： Yang, Xinsong Feng, Yuming Yiu, Ka Fai Cedric Song, Qiang Alsaadi, Fuad E. School of Mathematical Sciences Chongqing Normal University Chongqing China Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education Chongqing China Department of Applied Mathematics Hong Kong Polytechnic University Hong Kong China College of Electrical Engineering Henan University of Technology Zhengzhou China Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia

The third author’s name was incomplete and incorrect in the original publication.

关键词：