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Titlebook: Machine Learning and Data Mining in Aerospace Technology; Aboul Ella Hassanien,Ashraf Darwish,Hesham El-Aska Book 2020 Springer Nature Swi

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發(fā)表于 2025-3-21 17:52:15 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Machine Learning and Data Mining in Aerospace Technology
編輯Aboul Ella Hassanien,Ashraf Darwish,Hesham El-Aska
視頻videohttp://file.papertrans.cn/621/620453/620453.mp4
概述Explores the main concepts, algorithms, and techniques of machine learning and data mining for aerospace technology.Provides essential information on data mining and machine learning for satellite mon
叢書名稱Studies in Computational Intelligence
圖書封面Titlebook: Machine Learning and Data Mining in Aerospace Technology;  Aboul Ella Hassanien,Ashraf Darwish,Hesham El-Aska Book 2020 Springer Nature Swi
描述This book explores the main concepts, algorithms, and techniques of Machine Learning and data mining for aerospace technology. Satellites are the ‘eagle eyes’ that allow us to view massive areas of the Earth simultaneously, and can gather more data, more quickly, than tools on the ground. Consequently, the development of intelligent health monitoring systems for artificial satellites – which can determine satellites’ current status and predict their failure based on telemetry data – is one of the most important current issues in aerospace engineering..This book is divided into three parts, the first of which discusses central problems in the health monitoring of artificial satellites, including tensor-based anomaly detection for satellite telemetry data and machine learning in satellite monitoring, as well as the design, implementation, and validation of satellite simulators. The second part addresses telemetry data analytics and mining problems, while the last part focuses on security issues in telemetry data..
出版日期Book 2020
關(guān)鍵詞Data Mining; Machine Learning; LEO satellites; Telemetry Data Processing; Satellite Monitoring; Aerospace
版次1
doihttps://doi.org/10.1007/978-3-030-20212-5
isbn_softcover978-3-030-20214-9
isbn_ebook978-3-030-20212-5Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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發(fā)表于 2025-3-23 00:53:59 | 只看該作者
Tensor-Based Anomaly Detection for Satellite Telemetry Dataurements values, such as the satellites subsystems measurement, some significant anomalies may stay hidden with these traditional methods. Tensor-based anomaly detection (TAD) applied in a variety set of disciplines over the recent years, although it is not recognized yet as an official category of
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Formalization, Prediction and Recognition of Expert Evaluations of Telemetric Data of Artificial Satoped for a special class interval type-II fuzzy sets, which can simplify the procedures of expert evaluation. The third model is nonlinear and allows predicting expert evaluations of qualitative parameters. The fourth model with interval type-II fuzzy coefficients is developed for prediction numeric
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