UTHM Institutional Repository

Prediction of shielding effectiveness of cement-graphite powder using artificial neural network

Yee, See Khee and Dahlan, Samsul Haimi and Mohd Jenu, Mohd Zarar and Sia , Chee Kiong (2016) Prediction of shielding effectiveness of cement-graphite powder using artificial neural network. Jurnal Teknologi (Sciences & Engineering), 78 (6-2). pp. 33-38. ISSN 21803722

Full text not available from this repository.

Abstract

This paper presents the method to predict the shielding effectiveness of cement powder mixed with different amount of graphite powder. Cement mixed with different percentage of graphite is prepared. Their dielectric constant and loss tangent are measured based on the transmission/reflection technique using APC7 connector. The measured data is fed into Artificial Neural Network (ANN) for training. When the training process is completed the neural network is used to predict the dielectric constant and loss tangent of cement-graphite mixture that contains different amount of graphite. The comparison shows that the trained neural network is very successful to predict the dielectric constant and loss tangent of cement-graphite mixture. The proposed graphical user interface has made the process of shielding effectiveness prediction becomes more user friendly especially for those designers who are not familiar with the analytical calculation of shielding effectiveness and dielectric measurement.

Item Type: Article
Uncontrolled Keywords: Dielectric constant; loss tangent; cement powder; graphite powder
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-5865 Telecommunication. Telegraph.
Divisions: Faculty of Electrical and Electronic Engineering > Department of Communication Engineering
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 13 Aug 2018 03:37
Last Modified: 13 Aug 2018 03:37
URI: http://eprints.uthm.edu.my/id/eprint/8619
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item