Machine Learning Algorithms Applied to Telemetry Data of SCD-2 Brazilian Satellite

被引：0

作者：

Tavares, Isabela ^{[1
]}

Oliveira, Junia Maisa ^{[2
]}

Teixeira, Andre Ferreira ^{[3
]}

Pereira, Marconi de Arruda ^{[1
]}

Kakitani, Marcos Tomio ^{[1
]}

Nogueira, Jose Marcos ^{[2
]}

机构：

[1] Univ Fed Sao Joao del Rei, Ouro Branco, Brazil

[2] Univ Fed Minas Gerais, Belo Horizonte, MG, Brazil

[3] Univ Fed Santa Catarina, Florianopolis, SC, Brazil

来源：

PROCEEDINGS OF THE 2022 LATIN AMERICA NETWORKING CONFERENCE, LANC 2022 | 2022年

关键词：

machine learning; satellite; telemetry data; supervised learning; anomaly detection; SCD2;

D O I：

10.1145/3545250.3560847

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

With the advancement of information technologies, big data and data storage and processing capacity, there is an increase in studies on machine learning in different contexts, especially in the spatial context. Specifically talking about artificial satellites, machine learning algorithms can be applied for different purposes, for example to identify the satellite's operating conditions and to predict undesirable situations. The work calculated the performance of six supervised machine learning algorithms in the analysis of telemetry data from the Brazilian satellite SCD2. Five experiments were performed for each supervised machine learning algorithm. To evaluate the algorithms, the following metrics were used: mean squared error (RMSE), coefficient of determination (R2) and mean absolute error (MAE). The Support Vector Machine (SVM) and Bagging Regressor (BR) algorithms obtained better results in the evaluation.

引用

页码：50 / 57

页数：8

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