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Titlebook: Dependable Computing - EDCC 2020 Workshops; AI4RAILS, DREAMS, DS Simona Bernardi,Valeria Vittorini,Paolo Masci Conference proceedings 2020

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发表于 2025-3-21 17:12:26 | 显示全部楼层 |阅读模式
书目名称Dependable Computing - EDCC 2020 Workshops
副标题AI4RAILS, DREAMS, DS
编辑Simona Bernardi,Valeria Vittorini,Paolo Masci
视频video
丛书名称Communications in Computer and Information Science
图书封面Titlebook: Dependable Computing - EDCC 2020 Workshops; AI4RAILS, DREAMS, DS Simona Bernardi,Valeria Vittorini,Paolo Masci Conference proceedings 2020
描述This book constitutes refereed proceedings of the Workshops of the 16th European Dependable Computing Conference, EDCC: ​1st Workshop on Articial Intelligence for Railways, AI4RAILS 2020, 1st Worskhop on Dynamic Risk Management for Autonomous Systems, DREAMS 2020, 2nd Workshop on Dependable Solutions for Intelligent Electricity Distribution Grids, DSOGRI 2020, 12th Workshop on Software Engineering for Resilient Systems, SERENE 2020, held in September 2020. Due to the COVID-19 pandemic the workshops were held virtually. .The 12 full papers and 4 short papers were thoroughly reviewed and selected from 35 submissions. The workshop papers complement the main conference topics by addressing dependability or security issues in specic application domains or by focussing in specialized topics, such as system resilience..
出版日期Conference proceedings 2020
关键词artificial intelligence; computer hardware; computer networks; computer programming; computer systems; co
版次1
doihttps://doi.org/10.1007/978-3-030-58462-7
isbn_softcover978-3-030-58461-0
isbn_ebook978-3-030-58462-7Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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https://doi.org/10.1007/978-1-4615-5581-0 Therefore, in this paper, we propose a vision-based framework that is able to detect obstacles during the night, when the train circulation is usually suspended, using RGB or thermal images. Acquisition cameras and external light sources are placed in the frontal part of a rail drone and a new data
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https://doi.org/10.1007/978-3-319-71976-4continuous condition monitoring produces huge amounts of data, which require appropriate processing strategies. Deep learning has become a promising tool in analyzing large volumes of sensory data. In this work, we demonstrate the potential of artificial intelligence to analyze railway track defects
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