Название: Machine Learning and Data Mining in Aerospace Technology
Автор: Hassanien F.E., Darwish A., El-Askary H. (ed.)
Издательство: Springer
Год: 2020
Формат: djvu
Страниц: 236
Размер: 10 mb
Язык: английский

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.

Health Monitoring of Artificial Satellites
Tensor-Based Anomaly Detection for Satellite Telemetry Data
Machine Learning in Satellites Monitoring and Risk Challenges
Formalization, Prediction and Recognition of Expert Evaluations of Telemetric Data of Artificial Satellites Based on Type-II Fuzzy Sets
Intelligent Health Monitoring Systems for Space Missions Based on Data Mining Techniques
Design, Implementation, and Validation of Satellite Simulator and Data Packets Analysis
Telemetry Data Analytics and
Security Issues in Telemetry Data
Security Approaches in Machine Learning for Satellite Communication
Machine Learning Techniques for IoT Intrusions Detection in Aerospace Cyber-Physical Systems.