Machine Learning and Data Mining in Aerospace Technology

Aboul Ella Hassanien

ISBN 10: 3030202119 / ISBN 13: 9783030202118

Published by Springer International Publishing, 2019

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9783030202118

Bibliographic Details

• Title: Machine Learning and Data Mining in Aerospace Technology
• Publisher: Springer International Publishing
• Publication Date: 2019
• Language: English
• ISBN 10: 3030202119
• ISBN 13: 9783030202118
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 19D millimeters 241 millimeters width by 160 millimeters height by 19 millimeters depth
• Weight: 530 grams

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.