Performance comparison of data preprocessing methods for trade-space exploration with AI model: case study of satellite anomalies detection
Satellites are critical components of modern infrastructure, supporting countless applications in communication, navigation, and observation. However, ensuring their functionality and safety within complex space environments can be challenging. The satellite experiences the highest loss in the s...
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| Main Authors: | , , , |
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| Format: | Proceeding Paper |
| Language: | English |
| Published: |
IEEE
2024
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/114532/7/114532_Performance%20comparison%20of%20data.pdf http://irep.iium.edu.my/114532/ https://ieeexplore.ieee.org/document/10675571 |
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| Summary: | Satellites are critical components of modern
infrastructure, supporting countless applications in
communication, navigation, and observation. However,
ensuring their functionality and safety within complex space
environments can be challenging. The satellite experiences the highest loss in the space industry caused by anomalies.
Hence, it needs early detection so that the loss can be avoided
immediately. With the advancement of technology, satellite
anomalies diagnosis and detection can be done with trade-space exploration (TSE) and Artificial Intelligence (AI) models based on satellite data. The problem is that in satellite data
preprocessing step, the data can be too large and sometimes
there are some missing values encountered which leads to
outliers. To mitigate these problems, efficient data
preprocessing is needed so that the accuracy can be leveraged
and requires only minimal computation resources. This paper
presents the examination of the data preprocessing performance
from the combination of both data cleansing and data
normalization methods. Elimination, Imputation, Feature of
Missing and Imperative Imputation methods are involved in
data cleansing. While for the data normalization presented, Min Max, Z-Score using Standard Scalar, Robust Scaling, Vector Normalization and Power Transformation methods are used. As for the AI model classification, it is using Support Vector Machines (SVMs). The test was conducted using data from Satellite Database and Space Market Analysis (Seradata)
consisting of approximately 4,455 data. The result shows that
the accuracy of the Elimination and the Power Transformation
normalization is the highest in training accuracy with 60%.
While the Elimination and the Min Max or the Z-Score methods
are the top in the testing accuracy with 60%. |
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