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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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Black Box Modelling and Simulating the Dynamic Indoor Relative Humidity of a Laboratory Using Autoregressive–moving-average (ARMA) Model

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IOP Conference Series: Materials Science and engineering 2020年第1期884卷

作者： S F M Hussein M K Mohd Fitri Alif A O Al Rabeei A Faruq S M Zulkapli M S Noorazizi S A Zaki S S Abdullah Biologically Inspired System and Technology (Bio-iST) iKohza Department of Electronic Systems Engineering (ESE) Malaysia-Japan International Institute of Technology (MJIIT) UniversitiTeknologi Malaysia (UTM) Kuala Lumpur Jalan Sultan Yahya Petra (Jalan Semarak) 54100 Kuala Lumpur MALAYSIA. Department of Control and Mechatronics Engineering (CMED) School of Electrical Engineering Faculty of Engineering UniversitiTeknologi Malaysia (UTM) Johor Bahru 81310 Johor DarulTakzim MALAYSIA. Department of Electrical Engineering Faculty of Engineering Universitas Muhammadiyah Malang. Jl. Raya Tlogomas 246 65144 Malang INDONESIA. Engineering Department Razak Faculty of Technology and Informatics UniversitiTeknologi Malaysia (UTM) Kuala Lumpur Jalan Sultan Yahya Petra (Jalan Semarak) 54100 Kuala Lumpur MALAYSIA. Wind Engineering for (Urban Artificial Man-made) Environment Laboratory Department of Mechanical Precision Engineering (MPE) Malaysia-Japan International Institute of Technology (MJIIT) UniversitiTeknologi Malaysia (UTM) Kuala Lumpur Jalan Sultan Yahya Petra (Jalan Semarak) 54100 Kuala Lumpur MALAYSIA.

Modelling and simulation of the dynamic indoor relative humidity behaviour of a building is essential to test any proposed thermal comfort controller and strategy in the building. Like other plants, the dynamic indoor relative humidity behaviour of a building can be developed based on the white box model, black box model, and grey box model. This research focuses on the usage of autoregressive–moving-average (ARMA) model, a type of black box model to represent the dynamic indoor relative humidity behaviour of Industrial Instrumentation Laboratory at Malaysia-Japan International Institute of Technology (MJIIT), UniversitiTeknologi Malaysia (UTM) Kuala Lumpur and uses the real recorded data from the laboratory and minimal knowledge regarding the physical characteristics of the humidity behaviour in the laboratory. The performance of the ARMA model developed in this research is compared with the real recorded data from the laboratory. Result obtained shows that the ARMA model is enough for modelling and simulating the dynamic indoor relative humidity behaviour of the laboratory

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EDITOR'S CLIPBOARD: RELIABILITY, MAINTAINABILITY, AVAILABILITY ‐ THE REAL QUESTION

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Naval Engineers Journal 1983年第5期95卷 76-82页

作者： Richardson, James C. Berman, Paul I. Capt. James C. Richardson Jr. a surface warfare officer was graduated from the U.S. Naval Academy U.S. Naval Postgraduate School and the American University. With proven subspecialities in Material Management and Computer Systems Technology he has served as Commanding Officer USS Hepburn (FF-IOSS) Program Manager of the Mk 86 Gun Fire Control System at the Naval Sea Systems Command and is currently Commanding Officer of the Navy Regional Data Automation Center Washington D. C. Paul Berman is manager of Product Support Engineering for Lockheed Electronics Company Plain field New Jersey. His department is responsible for logistics planning and analysk supply support field engineering training and technical documentation in support of the division as products. His 30 years of experience in product support include preparation of logistics plans engineering data technical publications and training materials. He is also an adjunct instructor at Rutgers University. Mr. Berman received a BA from Queens College in 1951 and an MA from Hunter College in 1957. He attended the U.S. Army Signal Corps radar school and was a field radio and radar repairman during the Korean War. He is currently a member of the Society of Logistics Engineers and the National Management Association.

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Corrigendum to 'Transition in air pollution, disease burden and health cost in China: A comparative study of long-term and short-term exposure' [Environmental Pollution 277 (2021) Start page 4-End page 4/116770]

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Environmental pollution (Barking, Essex : 1987) 2023年 329卷 121714页

作者： Jun Liu Hao Yin Xiao Tang Tong Zhu Qiang Zhang Zhu Liu XiaoLong Tang HongHong Yi Department of Environmental Engineering School of Energy and Environmental Engineering University of Science and Technology Beijing Beijing 100083 PR China Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants University of Science and Technology Beijing Beijing 100083 PR China Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System Science Tsinghua University Beijing 100084 PR China. Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System Science Tsinghua University Beijing 100084 PR China School of Population and Public Health The University of British Columbia Vancouver BC Canada Energy and Resources Group University of California Berkeley CA USA. State Key Laboratory of Atmospheric Boundary Physics and Atmospheric Chemistry Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 PR China. State Key Joint Laboratory of Environmental Simulation and Pollution Control College of Environmental Sciences and Engineering Peking University Beijing 100871 PR China. Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants University of Science and Technology Beijing Beijing 100083 PR China. Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants University of Science and Technology Beijing Beijing 100083 PR China. Electronic address: yihonghong@***.

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States 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 Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Corrigendum to "Intravenous immunoglobulin alleviates Japanese encephalitis virus-induced peripheral neuropathy by inhibiting the ASM/ceramide pathway" [Int. Immunopharmacol. 133 (2024) 112083]

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International immunopharmacology 2024年 134卷 112270页

作者： Na Zhang Guowei Wang Liping Yang Jinyuan Zhang YanPing Yuan Lijun Ma Huanyu Wang Zhenhai Wang School of Clinical Medicine Ningxia Medical University Yinchuan Ningxia China Neurology Center General Hospital of Ningxia Medical University Yinchuan Ningxia China. School of Clinical Medicine Ningxia Medical University Yinchuan Ningxia China. Department of Human Anatomy Histology and Embryology School of Basic Medicine Ningxia Medical University Yinchuan Ningxia China. National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases National Institute for Viral Disease Control and Prevention Chinese Center for Disease Control and Prevention Beijing China. Electronic address: wanghy@***. Neurology Center General Hospital of Ningxia Medical University Yinchuan Ningxia China Institute of Medical Sciences General Hospital of Ningxia Medical University Diagnosis and Treatment Engineering Technology Research Center of Nervous System Diseases of Ningxia Hui Autonomous Region Yinchuan China. Electronic address: wangzhenhai1968@***.

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THE SHIPBOARD FACILITIES MAINTENANCE DEMONSTRATION STUDY

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Naval Engineers Journal 1978年第2期90卷 91-102页

作者： SCHWARTZ, MELVIN A. CORDER, JAMES L. Mr. Meivin A. Schwartzgraduated from Hunter College in 1959 and received his MS degree in Psychology in 1962 from the City College of New York. He has been a Behavioral Scientist in industry and government since 1960 working on command and control systems and simulations for the Strategic Air Command Job Corps Information Management System Permissive Action Link System Helicopter Attack System Riverine Warfare Systems Manpower Simulation Modeling and Facility Maintenance and Manpower Utilization. He has been employed at the Navy Personnel Research and Development Center formerly the Navy Personnel Research Laboratory since 1967 and has presented papers and participated in numerous international and national symposia. At the present time Mr. Schwartz is the principal investigator of the Facilities Maintenance Demonstration Study being conducted at David W. Taylor Naval Ship Research and Development Center. Mr. James L. Cordergraduated from the U. S. Naval Academy in 1958 and received his MS degree in Engineering Science in 1964 from The George Washington University. He served aboard a Destroyer and instructed in the Engineering Department U. S. Naval Academy until 1964. Since then he has been employed at the David W. Taylor Naval Ship Research and Development Center where he was the Program Manager for the Shipboard Manning and Automation Program until September 1977 and currently is assigned to DTNSRDC Annapolis Md. Mr. Corder who received a Letter of Commendation from the Assistant Secretary of the Navy (R&D) for his work performed while assigned on temporary additional duty at the Navy Research and Development Unit Vietnam in 1968 has been a member of ASNE since February 1965.

Facilities Maintenance (FM), as currently performed by shipboard personnel, requires a considerable expenditure of man—hours and material resources and is not performed efficiently nor effectively. Potential solutions to underlying problems were studied on portions of an operational ship of the FF 1052 Class. The study indicated that: 1) a significant reduction in man—hour expenditure and cost to the Navy is feasible through a systematic innovation program, 2) skill and knowledge of FM Team personnel is improved, and 3) shipboard spaces are cleaner and better maintained with FM innovations. The next phase of the project includes the total ship, and it is planned to commence in July 1978 aboard FF 1052, FFG 7, and LST 1179 Class ships. © 1978 American Society of Naval Engineers

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A Study On Iterative Learning control With Adjustment Of Learning Interval For Monotone Convergence In The Sense Of Sup-Norm

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Asian Journal of control 2008年第1期4卷

作者： Kwang-Hyun Park Zeungnam Bien Division of EE Department of EECS Korea Advanced Institute of Science and Technology 373–1 Kusong-dong Yusong-gu Taejon 305–701 Korea. Zeungname Bien:received the B.S. degree in electronics engineering from Seoul National University Seoul Korea in 1969 and the M.S. and Ph.D. degrees in electrical engineering from the University of Iowa Iowa City Iowa U.S.A. in 1972 and 1975 respectively. During 1976–1977 academic years he taught as assistant professor at the Department of Electrical Engineering University of Iowa. Then Dr. Bien joined Korea Advanced Institute of Science and Technology summer 1977 and is now Professor of Control Engineering at the Department of Electrical Engineering and Computer Science KAIST. Dr. Bien was the president of the Korea Fuzzy Logic and Intelligent Systems Society during 1990–1995 and also the general chair of IFSA World Congress 1993 and for FUZZ-IEEE99 respectively. He is currently co-Editor-in-Chief for International Journal of Fuzzy Systems (IJFS) Associate Editor for IEEE Transactions on Fuzzy Systems and a regional editor for the International Journal of Intelligent Automation and Soft Computing. He has been serving as Vice President for IFSA since 1997 and is now Chief Chairman of Institute of Electronics Engineers of Korea and Director of Humanfriendly Welfare Robot System Research Center. His current research interests include intelligent control methods with emphasis on fuzzy logic systems service robotics and rehabilitation engineering and large-scale industrial control systems. Kwang-Hyun Park:received the B.S. M.S. and Ph.D. degrees in electrical engineering and computer science from KAIST Korea in 1994 19997 and 2001 respectively. He is now a researcher at Human-friendly Welfare Robot System Research Center. His research interests include learning control machine learning human-friendly interfaces and service robotics.

It has been found that some huge overshoot in the sense of sup-norm may be observed when typical iterative learning control (ILC) algorithms are applied to LTI systems, even though monotone convergence in the sense of λ-norm is guaranteed. In this paper, a new ILC algorithm with adjustment of learning interval is proposed to resolve such an undesirable phenomenon, and it is shown that the output error can be monotonically converged to zero in the sense of sup-norm when the proposed ILC algorithm is applied. A numerical example is given to show the effectiveness of the proposed algorithm.

关键词： Iterative learning control monotone convergence learning interval sup-norm

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Interactive power system data preparation and verification using a pc‐editor

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European Transactions on Electrical Power 1991年第3期1卷 149-152页

作者： Linke, K. Rumpel, D. Dip1.-Ing. Klaus Linke (1939) VDEis the director of the department "load dispatching and network operation" of Vereinigte Elektrizitatswerke Westfalen AG (VEW). DortmundGermany. After the study of electrical engineering at the Technical University of KarlsruhdGermany he started his professional career in an engineering-concern where he was responsible among others for investigations concerning the installation of process-computers in chemical plants. In 1968 he changed to VEW. Here in 1984 he took over his present position in which he is mainly engaged with the tasks of the management of the HV and EHV network and the power system control together with the problems concerning network automation incident to it. (Vereinigte Elektrizitatswerke Westfalen AG Hauptverwaltung Rheinlanddamm 24 W-4600 Dortmund 1 T +49 23 114 38-42 41) Dr.-Ing. Dieter Rumpel (1932) VDE is full professor and since 1978 head of the Power Systems Institute of Duisburg University Germany. He got his DiplAng. degree from the Technical University Munich and his Dr.-Ing. degree from the Technical University Berlin. Before his call to Duisburg he worked at Siemens AG for 23 years. (Universiiit-GHDuisburg Fachbereich Elektrotechnik PF 10 15 03 W-4100 Duisburg T 4 9 20 313 79-34 37)

For computerized power system operation, all objects of the network must be mapped in a data‐model. For this task, the „Grid Data Language” (GDL) does not apply format‐bound source data, but describes the objects using a format‐free source text. To handle such texts, an editing‐ and verification‐system was implemented. This paper reports its application when describing an extra‐high voltage network for use in a training simulator. In that way, the full content of a dispatcher EHV data‐model could be converted to GDL, requiring a work of 15 to 20 man‐days. Copyright © 1991 John Wiley & Sons, Ltd.

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Corrigendum to "Differences in apparent diffusion coefficients between normal brain echo-planar images and turbo spin-echo diffusion-weighted images with distortion correction" [Eur. J. Radiol. 149 (2022) 110202]

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European journal of radiology 2022年 153卷 110371页

作者： Yasuo Takatsu Masafumi Nakamura Hajime Sagawa Yuichi Suzuki Nobuyuki Mori Shunichi Motegi Tosiaki Miyati Department of System Control Engineering Graduate School of Engineering Tokushima Bunri University 1314-1 Shido Sanuki-city Kagawa 769-2193 Japan Department of Radiological Technology Faculty of Health and Welfare Tokushima Bunri University 1314-1 Shido Sanuki-city Kagawa 769-2193 Japan Division of Health Sciences Graduate School of Medical Sciences Kanazawa University 5-11-80 Kodatsuno Kanazawa Ishikawa 920-0942 Japan. Electronic address: ytakatsu@kgw.bunri-u.ac.jp. Division of Health Sciences Graduate School of Medical Sciences Kanazawa University 5-11-80 Kodatsuno Kanazawa Ishikawa 920-0942 Japan Department of Radiology Otsu City Hospital 2-9-9 Motomiya Otsu-city Shiga 520-0804 Japan. Electronic address: nakamura-xx@leto.eonet.ne.jp. Division of Clinical Radiology Service Kyoto University Hospital 54 Kawaharacho Shogoin Sakyo-ku Kyoto 606-8507 Japan. Electronic address: sagawah@kuhp.kyoto-u.ac.jp. Department of Radiology The University of Tokyo Hospital 7-3-1 Hongo Bunkyo-ku Tokyo 113-8655 Japan. Electronic address: suzukiy-rad@h.u-tokyo.ac.jp. Department of Radiology Osaka Red Cross Hospital 5-30 Fudegasaki Tennouji-ku Osaka 543-8555 Japan. Electronic address: nbykmr@osaka-med.jrc.or.jp. Department of Radiological Sciences International University of Health and Welfare 2600-1 Kitakanamaru Ohtawara City Tochigi 324-8501 Japan. Electronic address: s_motegi_rt@yahoo.co.jp. Division of Health Sciences Graduate School of Medical Sciences Kanazawa University 5-11-80 Kodatsuno Kanazawa Ishikawa 920-0942 Japan. Electronic address: ramiyati@mhs.mp.kanazawa-u.ac.jp.

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