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Machine Learning and Data Mining in Aerospace Technology

  • Book
  • © 2020

Overview

Editors:
  1. Aboul Ella Hassanien

    1. Faculty of Computers and Artificial Intelligence, Information Technology Department, Cairo University, Cairo, Egypt

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  2. Ashraf Darwish

    1. Faculty of Science, Helwan University, Cairo, Egypt

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  3. Hesham El-Askary

    1. Center of Excellence in Earth Systems Modeling and Observations, Schmid College of Science and Technology, Chapman University, Orange, USA

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  • Explores the main concepts, algorithms, and techniques of machine learning and data mining for aerospace technology
  • Provides essential information on data mining and machine learning for satellite monitoring
  • Presents an experimental implementation of telemetry data processing to identify hidden events using various data mining techniques

Part of the book series: Studies in Computational Intelligence (SCI, volume 836)

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Table of contents (11 chapters)

  1. Front Matter

    Pages i-viii
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  2. Health Monitoring of Artificial Satellites

    1. Front Matter

      Pages 1-1
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    2. Tensor-Based Anomaly Detection for Satellite Telemetry Data

      • Alaa H. Ramadan, Aboul Ella Hassanien, Hesham A. Hefny, Lamiaa F. Ibrahim
      Pages 3-16

    3. Machine Learning in Satellites Monitoring and Risk Challenges

      • Khaled Alielden
      Pages 17-38

    4. Formalization, Prediction and Recognition of Expert Evaluations of Telemetric Data of Artificial Satellites Based on Type-II Fuzzy Sets

      • Olga M. Poleshchuk
      Pages 39-64

    5. Intelligent Health Monitoring Systems for Space Missions Based on Data Mining Techniques

      • Sara Abdelghafar, Ashraf Darwish, Aboul Ella Hassanien
      Pages 65-78

    6. Design, Implementation, and Validation of Satellite Simulator and Data Packets Analysis

      • Kadry Ali Ezzat, Lamia Nabil Mahdy, Aboul Ella Hassanien, Ashraf Darwish
      Pages 79-103

  3. Telemetry Data Analytics and Applications

    1. Front Matter

      Pages 105-105
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    2. Crop Yield Estimation Using Decision Trees and Random Forest Machine Learning Algorithms on Data from Terra (EOS AM-1) & Aqua (EOS PM-1) Satellite Data

      • Roheet Bhatnagar, Ganesh Borpatra Gohain
      Pages 107-124

    3. Data Analytics Using Satellite Remote Sensing in Healthcare Applications

      • Kamaljit I. Lakhtaria, Sailesh S. Iyer
      Pages 125-146

    4. Design, Implementation, and Testing of Unpacking System for Telemetry Data of Artificial Satellites: Case Study: EGYSAT1

      • Sara Abdelghafar, Ahmed Salama, Mohamed Yahia Edries, Ashraf Darwish, Aboul Ella Hassanien
      Pages 147-163

    5. Multiscale Satellite Image Classification Using Deep Learning Approach

      • Noureldin Laban, Bassam Abdellatif, Hala M. Ebied, Howida A. Shedeed, Mohamed F. Tolba
      Pages 165-186

  4. Security Issues in Telemetry Data

    1. Front Matter

      Pages 187-187
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    2. Security Approaches in Machine Learning for Satellite Communication

      • Mamata Rath, Sushruta Mishra
      Pages 189-204

    3. Machine Learning Techniques for IoT Intrusions Detection in Aerospace Cyber-Physical Systems

      • Yassine Maleh
      Pages 205-232
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Keywords

About this book

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.

Editors and Affiliations

  • Faculty of Computers and Artificial Intelligence, Information Technology Department, Cairo University, Cairo, Egypt

    Aboul Ella Hassanien

  • Faculty of Science, Helwan University, Cairo, Egypt

    Ashraf Darwish

  • Center of Excellence in Earth Systems Modeling and Observations, Schmid College of Science and Technology, Chapman University, Orange, USA

    Hesham El-Askary

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