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Sylabus and Matrials Design for Vocational Student and Its Dissemination Using Website

Journal of Physics: Conference Series 2018年第1期1114卷

作者： Astro Julida Harahap Erianto Ongko Hotler Manurung Dian Kurnia H Hartono Dahlan Abdullah Cut Ita Erliana Hardianto Djanggih Kundharu Saddhono Dian Rianita Muhammad Ikhsan Setiawan Yuliyanto Budi Setiawan Rosida Tiurma Manurung S Sriadhi Mangasi Sinurat Aisyah Siregar Rapat Piter Soni Hutauruk Nico Benjamain Sibarani Department of Informatics Akademi Teknologi Industri Immanuel Medan Indonesia Department of Computer Systems Universitas Pancabudi Medan Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Department of Law Universitas Tompotika Luwuk Banggai Indonesia Graduate Program Universitas Sebelas Maret Surakarta Indonesia Department of Administration University of Lancang Kuning Pekanbaru Indonesia Department of Civil Engineering Narotama University Surabaya Indonesia Study Program of Communication Studies Universitas Semarang Indonesia Graduate Program in Scientific Psychology Universitas Kristen Maranatha Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia STIE Bina Karya Tebing Tinggi Indonesia

The learners' needs are an important factor in designing syllabus and materials design, this research deals with the syllabus and material design based on the professional's needs. It is expected that the syllabus and materials designes are communicatively applicable to the professional academy. Descriptive method is applied in this research. The sample of this research is 30 students of ATII Immanuel Academy Medan. They were selected by random sampling to get the data, the questioners were administered to students. the questioners consisted of 54 items and the semi structured interview consisted of 5 questions, the finding indicated that learners' needs were focused on reading and speaking skills. With reference appropriately and proportionally derived for students of the Professional Academy. Further on the basis of the syllabus, materials are designed in which the skills of using language become a priority. The results of this research will be disseminated using the website

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Guest editorial: The 2016 capstone design conference

Guest editorial: The 2016 capstone design conference

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作者： Brackin, Patsy Howe, Susannah Rogers, Peter Stanfill, R. Keith Beyerlein, Steven Kanai, Junichi Vallino, Jim Palo, Scott Department of Mechanical Engineering Rose-Hulman Institute of Technology 5500 Wabash Avenue CM 165 Terre HauteIN47803 United States Picker Engineering Program Smith College 151 Ford Hall NorthamptonMA01063 United States Department of Engineering Education Ohio State University 3070 Smith Laboratory 174 W. 18th Avenue ColumbusOH43210 United States Engineering Innovation Institute University of Florida P.O. Box 116000 GainesvilleFL32611-6000 United States Department of Mechanical Engineering University of Idaho 875 Perimeter Drive MoscowID83844-0902 United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States Department of Software Engineering Rochester Institute of Technology 134 Lomb Memorial Drive RochesterNY14623-5608 United States Department of Aerospace Engineering Sciences University of Colorado 429 UCB BoulderCO80309 United States

The goal of the 2016 Capstone Design Conference held in Columbus, OH was to build upon the success of previous conferences (2007 and 2010 in Boulder, CO, 2012 in Champaign, IL, and 2014 in Columbus, OH) and expand the community of educators, students, and industry members engaged in discussing, analyzing, and improving capstone design education. Sessions at the 2016 Capstone Design Conference were designed for vibrant sharing of ideas and experiences across the capstone community via interactive panel sessions, poster session socials, hands-on workshops, and other formal and informal networking activities. This editorial discusses conference planning, structure, and feedback. Technical papers that follow in this issue document scholarship surrounding noteworthy capstone course innovations. Most of these began as four page peer-reviewed papers included in the conference proceedings. © 2017 TEMPUS Publications.

关键词： Teaching

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Citizen science and the United Nations Sustainable Development Goals (vol 2, pg 922, 2019)

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NATURE SUSTAINABILITY 2019年第11期2卷 1063-1063页

作者： Fritz, Steffen See, Linda Carlson, Tyler Haklay, Mordechai (Muki) Oliver, Jessie L. Fraisl, Dilek Mondardini, Rosy Brocklehurst, Martin Shanley, Lea A. Schade, Sven Wehn, Uta Abrate, Tommaso Anstee, Janet Arnold, Stephan Billot, Matthew Campbell, Jillian Espey, Jessica Gold, Margaret Hager, Gerid He, Shan Hepburn, Libby Hsu, Angel Long, Deborah Maso, Joan McCallum, Ian Muniafu, Maina Moorthy, Inian Obersteiner, Michael Parker, Alison J. Weisspflug, Maike West, Sarah Ecosystem Services and Management Program International Institute for Applied Systems Analysis Laxenburg Austria School of Resource and Environmental Management Simon Fraser University Burnaby Canada University College London London UK School of Electrical Engineering and Computer Science Queensland University of Technology Brisbane Australia Australian Citizen Science Association c/o Faculty of Science Partner Engagement and Outreach University of Sydney Sydney Australia University of Natural Resources and Life Sciences Vienna Austria Competence Center Citizen Science ETH Zurich and University of Zurich Zurich Switzerland Citizen Science Global Partnership Washington DC USA European Citizen Science Association Museum für Naturkunde Berlin Berlin Germany Nelson Institute University of Wisconsin at Madison Madison USA European Commission Joint Research Center Ispra Italy IHE Delft Institute for Water Education Delft The Netherlands World Meteorological Organization Geneva Switzerland Commonwealth Scientific and Industrial Research Organisation Canberra Australia Federal Statistical Office – Destatis Wiesbaden Germany UN Environment Programme Nairobi Kenya United Nations Sustainable Development Solutions Network New York USA CitizenScience.Asia Hong Kong China Yale-NUS College Singapore Singapore University of Dundee Dundee UK Scottish Environment LINK Perth UK Grumets Research Group CREAF Universitat Autònoma de Barcelona Barcelona Spain African Citizen Science Association Nairobi Kenya Woodrow Wilson Center Washington DC USA Museum für Naturkunde Berlin Berlin Germany Stockholm Environment Institute University of York York UK

Traditional data sources are not sufficient for measuring the United Nations Sustainable Development Goals. New and non-traditional sources of data are required. Citizen science is an emerging example of a non-traditional data source that is already making a contribution. In this Perspective, we present a roadmap that outlines how citizen science can be integrated into the formal Sustainable Development Goals reporting mechanisms. Success will require leadership from the United Nations, innovation from National Statistical Offices and focus from the citizen-science community to identify the indicators for which citizen science can make a real contribution.

关键词： Planning

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The Brain Tumor Segmentation (BraTS) Challenge: Local Synthesis of Healthy Brain Tissue via Inpainting

arXiv

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arXiv 2023年

作者： Kofler, Florian Steinbauer, Felix Meissen, Felix Graf, Robert Ehrlich, Stefan K. Reinke, Annika Oswald, Eva Waldmannstetter, Diana Hoelzl, Florian Horvath, Izabela Turgut, Oezguen Shit, Suprosanna Bukas, Christina Yang, Kaiyuan Paetzold, Johannes C. de da Rosa, Ezequiel Mekki, Isra Vinayahalingam, Shankeeth Kassem, Hasan Zhang, Juexin Chen, Ke Weng, Ying Durrer, Alicia Cattin, Philippe C. Wolleb, Julia Sadique, M.S. Rahman, M.M. Farzana, W. Temtam, A. Iftekharuddin, K.M. Adewole, Maruf Anwar, Syed Muhammad Baid, Ujjwal Janas, Anastasia Kazerooni, Anahita Fathi LaBella, Dominic Bran Li, Hongwei Moawad, Ahmed W. Conte, Gian-Marco Farahani, Keyvan Eddy, James Sheller, Micah Pati, Sarthak Karagyris, Alexandros Aristizabal, Alejandro Bergquist, Timothy Chung, Verena Shinohara, Russell Takeshi Dako, Farouk Wiggins, Walter Reitman, Zachary Wang, Chunhao Liu, Xinyang Jiang, Zhifan Johanson, Elaine Meier, Zeke Familiar, Ariana Davatzikos, Christos Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Colen, Rivka R. Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-André Mahajan, Abhishek Mohan, Suyash Mongan, John Hess, Christopher Cha, Soonmee Villanueva-Meyer, Javier Colak, Errol Crivellaro, Priscila Jakab, Andras Fatade, Abiodun Omidiji, Olubukola Akinola Lagos, Rachel Olatunji, O.O. Khanna, Goldey Kirkpatrick, John Alonso-Basanta, Michelle Rashid, Arif Bornhorst, Miriam Nabavizadeh, Ali Lepore, Natasha Palmer, Joshua Porras, Antonio Albrecht, Jake Anazodo, Udunna Aboian, Mariam Calabrese, Evan Rudie, Jeffrey David Linguraru, Marius George Iglesias, Juan Eugenio Van Leemput, Koen Bakas, Spyridon Wiestler, Benedikt Ezhov, Ivan Piraud, Marie Menze, Bjoern H. Helmholtz AI Germany Technical University of Munich Germany Helmholtz Munich Germany Klinikum Rechts der Isar Munich Germany SETLabs Research GmbH Healthcare Technologies Munich Germany Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Institute of Clinical Neuroimmunology Faculty of Medicine Ludwig Maximilian University Munich Germany University of Zurich Switzerland Helmholtz Research Center Munich Germany Imperial College London United Kingdom Icometrix Leuven Belgium Department of Oral and Maxillofacial Surgery Radboudumc Netherlands Department of Artificial Intelligence Radboud University Netherlands MLCommons University of Nottingham Ningbo China Ningbo315100 China Center for medical Image Analysis and Navigation Department of Biomedical Engineering University of Basel Allschwil Switzerland Old Dominion University NorfolkVA23529 United States Lab Crestview Radiology Lagos Nigeria Children's National Hospital WashingtonDC United States George Washington University WashingtonDC United States 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 Yale University New HavenCT United States Children's Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States Duke University Medical Center Department of Radiation Oncology United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Department of Neuroradiology Technical University of Munich Munich Germany Mercy Catholic Medical Center DarbyPA United States Department of Radiology Mayo Clinic RochesterMN United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 U

A myriad of algorithms for the automatic analysis of brain MR images is available to support clinicians in their decision-making. For brain tumor patients, the image acquisition time series typically starts with an already pathological scan. This poses problems, as many algorithms are designed to analyze healthy brains and provide no guarantee for images featuring lesions. Examples include but are not limited to algorithms for brain anatomy parcellation, tissue segmentation, and brain extraction. To solve this dilemma, we introduce the BraTS inpainting challenge. Here, the participants explore inpainting techniques to synthesize healthy brain scans from lesioned ones. The following manuscript contains the task formulation, dataset, and submission procedure. Later, it will be updated to summarize the findings of the challenge. The challenge is organized as part of the ASNR-BraTS MICCAI challenge. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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A GPU-based implementation for the Gamma multiset rewriting paradigm 16

A GPU-based implementation for the Gamma multiset rewriting ...

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31st Annual ACM Symposium on Applied Computing, SAC 2016

作者： De Almeida, Rubens H. P. Mello, Rui R. Paillard, Gabriel A. L. França, Felipe M. G. Rio de Janeiro RJ Brazil Virtual University Institute Federal University of Ceará Fortaleza CE Brazil Systems Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro RJ Brazil

ISBN: (纸本)9781450337397

This work presents a GPU-based implementation of the Gamma multiset rewriting paradigm. Inspired by the chemical reaction metaphor, Gamma was conceived as a highlevel abstract language for programs specification in a very simple, concise, and naturally parallel way, making clearer the distinction between the problem itself and the underlying implementation issues. We believe that the computational model adopted by Gamma matches naturally with the Graphics Processing Units execution mode, once both concepts consist basically in parallel processing of data. We extended an earlier distributed and parallel implementation of the Gamma language called Gamma-Base, adding support to execution of Gamma programs over the GPUs, and we called this new implementation Gamma-GPU. Practical experiments were conducted in a GPU cluster, which showed us the correctness of the new implementation, besides good speedups comparing to a sequential Gamma implementation, proving the contributions introduced by our work. © 2016 ACM.

关键词： Graphics processing unit

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Retraction: Sylabus and Matrials Design for Vocational Student and Its Dissemination Using Website (J. Phys.: Conf. Ser. 1114 012023)

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Journal of Physics: Conference Series 2018年第1期1114卷

作者： Astro Julida Harahap Ongko, Erianto Manurung, Hotler Kurnia, Dian Hartono, H Dahlan Abdullah Cut Ita Erliana Saddhono, Kundharu Rianita, Dian Setiawan, Muhammad Ikhsan Yuliyanto, Budi Setiawan Manurung, Rosida Tiurma Sriadhi, S Sinurat, Mangasi Siregar, Aisyah Rapat Piter Soni Hutauruk Sibarani, Nico Benjamain 1 Department of Informatics Akademi Teknologi Industri Immanuel Medan Indonesia Department of Computer Systems Universitas Pancabudi Medan Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Graduate Program Universitas Sebelas Maret Surakarta Indonesia Department of Administration University of Lancang Kuning Pekanbaru Indonesia Department of Law Universitas Tompotika Luwuk Banggai Indonesia Study Program of Communication Studies Universitas Semarang Indonesia Graduate Program in Scientific Psychology Universitas Kristen Maranatha Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia STIE Bina Karya Tebing Tinggi Indonesia

This paper was retracted by IOP Publishing on 12 December 2018. This paper was published due to a technical error and was not intended to be included in this journal.Retraction published: 8 February 2019

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Information Extraction to Improve Link Prediction in Scientific Social Networks

Information Extraction to Improve Link Prediction in Scienti...

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IEEE International Conference on computer Supported Cooperative Work in Design

作者： Victor Stroele Fernanda Campos Crystiam K. Pereira Geraldo Zimbrao Jano M. Souza PGCC - Postgraduate Program in Computer Science UFJF - Federal University of Juiz de Fora COPPE - Systems Engineering and Computer Program UFRJ - Federal University of Rio de Janeiro

ISBN: (纸本)9781509019168

Link Prediction is a classic social networks analysis problem. Knowing in advance future actions in social network can help, for example, agents decision. Link Prediction techniques are based on metrics that have different approaches. In this paper, we model a multi-relational scientific social network to assess the impact of content extraction on topological metrics. Thus, a metric composed of topological and semantic approach is proposed to solve link prediction problem. The results were compared with those presented by Katz metric.

关键词： Information Extraction Link Prediction Scientific Social Network

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Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs

arXiv

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arXiv 2021年

作者： Sanders, Lauren M. Yang, Jason H. Scott, Ryan T. Qutub, Amina Ann Martin, Hector Garcia Berrios, Daniel C. Hastings, Jaden J.A. Rask, Jon Mackintosh, Graham Hoarfrost, Adrienne L. Chalk, Stuart Kalantari, John Khezeli, Kia Antonsen, Erik L. Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Delgado-Aparicio, Guillermo M. Glicksberg, Benjamin S. Greene, Casey S. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Mason, Christopher E. Matar, Mona Mias, George I. Miller, Jack Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Park, Seung-Min Parsons-Wingerter, Patricia Prabhu, R.K. Reynolds, Robert J. Saravia-Butler, Amanda Saria, Suchi Sawyer, Aenor Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik Theriot, Corey A. van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Zitnik, Marinka Costes, Sylvain V. Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Center for Emerging and Re-Emerging Pathogens Department of Microbiology Biochemistry and Molecular Genetics Rutgers New Jersey Medical School NewarkNJ07103 United States KBR Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States AI MATRIX Consortium Department of Biomedical Engineering University of Texas San Antonio and UT Health Sciences San AntonioTX78249 United States Biological Systems and Engineering Division Lawrence Berkeley National Lab. BerkeleyCA94608 United States DOE Agile BioFoundry EmeryvilleCA94608 United States Joint BioEnergy Institute EmeryvilleCA94608 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Office of the Center Director NASA Ames Research Center Moffett Field CA94035 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett Field CA94035 United States Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Center for Individualized Medicine Department of Surgery Department of Quantitative Health Sciences Mayo Clinic RochesterMN55905 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin - Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Data Science Analytics Georgia Institute of Technology Lima15024

Space biology research aims to understand fundamental effects of spaceflight on organisms, develop foundational knowledge to support deep space exploration, and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life. To advance these aims, the field leverages experiments, platforms, data, and model organisms from both spaceborne and ground-analog studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally autonomous, light, agile, and intelligent to expedite knowledge discovery. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning, and modeling applications which offer key solutions toward these space biology challenges. In the next decade, the synthesis of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modeling and analytics, support maximally autonomous and reproducible experiments, and efficiently manage spaceborne data and metadata, all with the goal to enable life to thrive in deep space. © 2021, CC BY.

关键词： Predictive analytics

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Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and Precision Space Health

arXiv

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arXiv 2021年

作者： Scott, Ryan T. Antonsen, Erik L. Sanders, Lauren M. Hastings, Jaden J.A. Park, Seung-Min Mackintosh, Graham Reynolds, Robert J. Hoarfrost, Adrienne L. Sawyer, Aenor Greene, Casey S. Glicksberg, Benjamin S. Theriot, Corey A. Berrios, Daniel C. Miller, Jack Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Chalk, Stuart Delgado-Aparicio, Guillermo M. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Kalantari, John Khezeli, Kia Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Martin, Hector Garcia Mason, Christopher E. Matar, Mona Mias, George I. Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Parsons-Wingerter, Patricia Prabhu, R.K. Qutub, Amina Ann Rask, Jon Saravia-Butler, Amanda Saria, Suchi Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Yang, Jason H. Zitnik, Marinka Costes, Sylvain V. KBR Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Department of Urology Department of Radiology Stanford University School of Medicine StanfordCA94305 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett FieldCA94035 United States Mortality Research & Consulting Inc. HoustonTX77058 United States Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States UC Space Health Department of Orthopaedic Surgery University of California San Francisco San FranciscoCA94143 United States Center for Health AI Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine Anschutz Medical Campus AuroraCO80045 United States Hasso Plattner Institute for Digital Health at Mount Sinai Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New YorkNY10065 United States Department of Preventive Medicine and Community Health UTMB GalvestonTX77551 United States Human Health and Performance Directorate NASA Johnson Space Center HoustonTX77058 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Data Scienc

Human space exploration beyond low Earth orbit will involve missions of significant distance and duration. To effectively mitigate myriad space health hazards, paradigm shifts in data and space health systems are necessary to enable Earth-independence, rather than Earth-reliance. Promising developments in the fields of artificial intelligence and machine learning for biology and health can address these needs. We propose an appropriately autonomous and intelligent Precision Space Health system that will monitor, aggregate, and assess biomedical statuses;analyze and predict personalized adverse health outcomes;adapt and respond to newly accumulated data;and provide preventive, actionable, and timely insights to individual deep space crew members and iterative decision support to their crew medical officer. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration, on future applications of artificial intelligence in space biology and health. In the next decade, biomonitoring technology, biomarker science, spacecraft hardware, intelligent software, and streamlined data management must mature and be woven together into a Precision Space Health system to enable humanity to thrive in deep space. © 2021, CC BY.

关键词： Earth (planet)

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Soil resistivity study and simulation with finite elements method 16

Soil resistivity study and simulation with finite elements m...

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16th International Conference on Environment and Electrical engineering, EEEIC 2016

作者： Pereira, Thiago Martins De Lima, Rodrigo Alves Gomes, Viviane Margarida Gutembergue, Sostenes Alves, Aylton Jose Calixto, Wesley Pacheco Electrical Mechanical and Computer Engineering School EMC Federal University of Goias UFG Brazil Experimental and Technological Research and Study Group NExT Federal Institute of Goias IFG Brazil Program in Electronic Systems and Automation PGEA University of Brasilia UnB Brazil

ISBN: (纸本)9781509023196

This work purpose is to study the influence of concrete buried structures in grounding systems. Comparison of soil characteristics between dry and rainy seasons and soil electrical behavior was carried out. Simulations in finite element method was performed for tree layer stratified soil and the electrostatic equipotential surfaces were mapped into the region of interest. © 2016 IEEE.

关键词： Finite element method

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