Machine Learning and Data Mining in Aerospace Technology (Paperback, 1st ed. 2020)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Studies in Computational Intelligence, 836
Release date:	August 2020
First published:	2020
Editors:	Aboul Ella Hassanien • Ashraf Darwish • Hesham El-Askary
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	232
Edition:	1st ed. 2020
ISBN-13:	978-3-03-020214-9
Categories:	Books > Professional & Technical > Transport technology > Aerospace & aviation technology > General Books > Computing & IT > Applications of computing > Artificial intelligence > General Promotions
LSN:	3-03-020214-3
Barcode:	9783030202149

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