Machine Learning and Data Mining in Aerospace Technology (Studies in Computational Intelligence, 836, Band 836) [Hardcover] Hassanien, Aboul Ella; Darwish, Ashraf and El-Askary, Hesham

Hassanien, Aboul Ella; Darwish, Ashraf; El-Askary, Hesham

ISBN 10: 3030202119 / ISBN 13: 9783030202118

Edité par Springer, 2019

A propos de cet article

unread, some shelfwear.

N° de réf. du vendeur CE-2302C-ADMIRAL-04-1000XS

Détails bibliographiques

• Titre: Machine Learning and Data Mining in Aerospace Technology (Studies in Computational Intelligence, 836, Band 836) [Hardcover] Hassanien, Aboul Ella; Darwish, Ashraf and El-Askary, Hesham
• Éditeur: Springer
• Date de parution: 2019
• Langue: anglais
• ISBN à 10 chiffres: 3030202119
• ISBN à 13 chiffres: 9783030202118
• Reliure: Hardcover
• Édition: 1. Auflage
• État de l'article: Very Good

A propos de ce titre

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.