Machine Learning and Data Mining in Aerospace Technology

Hassanien, Aboul Ella (Edited by)/ Darwish, Ashraf (Edited by)/ El-Askary, Hesham (Edited by)

ISBN 10: 3030202119 / ISBN 13: 9783030202118

Published by Springer, 2019

About this Item

232 pages. 9.25x6.25x0.50 inches. In Stock.

Seller Inventory # x-3030202119

Bibliographic Details

• Title: Machine Learning and Data Mining in Aerospace Technology
• Publisher: Springer
• Publication Date: 2019
• Language: English
• ISBN 10: 3030202119
• ISBN 13: 9783030202118
• Binding: Hardcover
• Condition: Brand New
• Weight: 0.54 kilograms

About this title

Synopsis

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.