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Titlebook: Data Analytics in System Engineering; Proceedings of 7th C Radek Silhavy,Petr Silhavy Conference proceedings 2024 The Editor(s) (if applica

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書目名稱Data Analytics in System Engineering
副標題Proceedings of 7th C
編輯Radek Silhavy,Petr Silhavy
視頻videohttp://file.papertrans.cn/263/262715/262715.mp4
概述Presents the latest research on Software Engineering Application in Informatics.Gathers the Proceedings of the 7th Computational Methods in Systems and Software 2023 (CoMeSySo2023) conference.Written
叢書名稱Lecture Notes in Networks and Systems
圖書封面Titlebook: Data Analytics in System Engineering; Proceedings of 7th C Radek Silhavy,Petr Silhavy Conference proceedings 2024 The Editor(s) (if applica
描述These proceedings offer an insightful exploration of integrating data analytics in system engineering. This book highlights the essential role of data in driving innovation, optimizing processes, and solving complex challenges in the field. Targeted at industry professionals, researchers, and enthusiasts, this book serves as a comprehensive resource, providing actionable insights and showcasing transformative applications of data in engineering. It is a must-read for anyone keen on understanding and participating in the ongoing evolution of system engineering in our data-centric world.
出版日期Conference proceedings 2024
關鍵詞Software Engineering; Software Engineeering Applications; CoMeSySo; Computational Methods in Systems an
版次1
doihttps://doi.org/10.1007/978-3-031-53552-9
isbn_softcover978-3-031-53551-2
isbn_ebook978-3-031-53552-9Series 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
The information of publication is updating

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,A New Approach to Eliminating the “Flip” Effect of the Approximating Function Under Conditions of ation values are used only on the interval of the initial value, i.e. the interval where the “flip” effect was observed. The approach used for processing the measurement results makes it possible to significantly reduce the error in estimating the function of the measured process under conditions of a priori uncertainty. #COMESYSO1120.
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