عنوان

Machine learning and data mining in aerospace technology /

شابک

3030202127

شابک

9783030202125

شابک اشتباه

3030202119

شابک اشتباه

9783030202118

عنوان اصلي

Machine learning and data mining in aerospace technology /

نام عام مواد

[Book]

نام نخستين پديدآور

Aboul Ella Hassanien, Ashraf Darwish, Hesham El-Askary, editors.

محل نشرو پخش و غیره

Cham :

نام ناشر، پخش کننده و غيره

Springer,

تاریخ نشرو بخش و غیره

[2020]

نام خاص و کميت اثر

1 online resource (viii, 232 pages) :

ساير جزييات

illustrations (some color)

عنوان فروست

Studies in computational intelligence ;

مشخصه جلد

volume 836

متن يادداشت

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 -- 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 -- Data analytics using satellite remote sensing in healthcare applications -- Design, Implementation, and Testing of Unpacking System for Telemetry Data of Artificial Satellites: Case Study: EGYSAT1 -- Multiscale Satellite Image Classification using Deep Learning Approach -- Security approaches in machine learning for satellite communication -- Machine learning techniques for IoT intrusions detection in aerospace cyber physical systems.

بدون عنوان

0

متن يادداشت

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.

منبع سفارش / آدرس اشتراک

Springer Nature

شماره انبار

com.springer.onix.9783030202125

عنوان

Machine learning and data mining in aerospace technology.

شماره استاندارد بين المللي کتاب و موسيقي

9783030202118

موضوع مستند نشده

Data mining.

موضوع مستند نشده

Machine learning.

موضوع مستند نشده

Data mining.

موضوع مستند نشده

Machine learning.

شماره

ويراست

شماره رده

نشانه اثر

مستند نام اشخاص تاييد نشده

Darwish, Ashraf.

مستند نام اشخاص تاييد نشده

El-Askary, Hesham.

مستند نام اشخاص تاييد نشده

Hassanien, Aboul Ella.

تاريخ عمليات

20200823091329.0

قواعد فهرست نويسي ( بخش توصيفي )

pn

نام الکترونيکي

نوع ماده

[Book]

تكميل شده

Y