Performance comparison of data preprocessing methods for trade-space exploration with AI model: case study of satellite anomalies detection

Mutholib, Abdul and Abdul Rahim, Nadirah and Gunawan, Teddy Surya and Ahmarofi, Ahmad Afif (2024) Performance comparison of data preprocessing methods for trade-space exploration with AI model: case study of satellite anomalies detection. In: 2024 IEEE 10th International Conference on Smart Instrumentation, Measurement and Applications ( ICSIMA), 30-31 July 2024, Bandung, Indonesia.

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Abstract

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%.

Item Type:	Proceeding Paper (Slide Presentation)
Uncontrolled Keywords:	Trade-space exploration, Satellite Anomaly, AI model, Seradata, Data preprocessing
Subjects:	BPC Science and Technology in Islam > BPC175 Islam and engineering. Sustainable engineering. Sustainable building T Technology > T Technology (General) T Technology > T Technology (General) > T55.4 Industrial engineering.Management engineering. T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101 Telecommunication. Including telegraphy, radio, radar, television
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering Kulliyyah of Engineering
Depositing User:	Dr Nadirah Abdul Rahim
Date Deposited:	20 Sep 2024 11:47
Last Modified:	20 Sep 2024 11:47
URI:	http://irep.iium.edu.my/id/eprint/114532

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