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Titlebook: Reliability and Statistics in Transportation and Communication; Selected Papers from Igor Kabashkin,Irina Yatskiv,Olegas Prentkovskis Confe

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發(fā)表于 2025-3-21 19:19:16 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Reliability and Statistics in Transportation and Communication
副標(biāo)題Selected Papers from
編輯Igor Kabashkin,Irina Yatskiv,Olegas Prentkovskis
視頻videohttp://file.papertrans.cn/827/826394/826394.mp4
概述Covers methods for modeling and control of complex engineering systems.Describes multidisciplinary approaches for reliability and dependability analysis.Gives a special emphasis to digitalization of a
叢書名稱Lecture Notes in Networks and Systems
圖書封面Titlebook: Reliability and Statistics in Transportation and Communication; Selected Papers from Igor Kabashkin,Irina Yatskiv,Olegas Prentkovskis Confe
描述.This book reports on cutting-edge theories and methods for analyzing complex systems, such as transportation and communication networks and discusses multi-disciplinary approaches to dependability problems encountered when dealing with complex systems in practice. The book presents the most relevant findings discussed at the 23rd International Multidisciplinary Conference on Reliability and Statistics in Transportation and Communication (RelStat 2023), which took place as a hybrid event on October 19 – 21, 2023,? in/from Riga, Latvia. It spans a broad spectrum of advanced theories and methods, giving a special emphasis to the digitalization of transport systems, as well as smart, artificial intelligence, and digital twins applications..
出版日期Conference proceedings 2024
關(guān)鍵詞Decision Support System with AI; Autonomous Transportation Systems; Digitalization of Transportation; I
版次1
doihttps://doi.org/10.1007/978-3-031-53598-7
isbn_softcover978-3-031-53597-0
isbn_ebook978-3-031-53598-7Series ISSN 2367-3370 Series E-ISSN 2367-3389
issn_series 2367-3370
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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investigations of different equivalence notions between logic programs play a fundamental role for understanding modularity and optimization. While strong equivalence between two programs holds if they can be faithfully replaced by each other in any context (facts and rules), uniform equivalence am
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Marzieh Afsari,Nicola Ippolito,Lory Michelle Bresciani Mistrice,Guido Gentile investigations of different equivalence notions between logic programs play a fundamental role for understanding modularity and optimization. While strong equivalence between two programs holds if they can be faithfully replaced by each other in any context (facts and rules), uniform equivalence am
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Christopher Meyer,Laima Gerlitz,Gunnar Prause investigations of different equivalence notions between logic programs play a fundamental role for understanding modularity and optimization. While strong equivalence between two programs holds if they can be faithfully replaced by each other in any context (facts and rules), uniform equivalence am
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Decision Processes (MDP) provide such a framework, but can only model uncertainty that can be expressed as probabilities. Possibilistic counterparts of MDPs allow to model imprecise beliefs, yet they cannot accurately represent probabilistic sources of uncertainty and they lack the efficient online
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Zokhid Rakhimov,Nadezhda Lvova,Svetlana Saksonova,Neli Abramishvilita is exiguous. While most of the previous papers work in the supervised setting, we study the more challenging case of positive-unlabeled transfer learning, where few positive labeled instances are available for both the source and the target domains. Specifically, we focus on the link prediction t
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