A Preliminary study on feasibility radar cross-section of foreign object debris for size classification
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Universiti Malaysia Perlis (UniMAP)
2022
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my.unimap-749602022-12-16T01:21:41Z A Preliminary study on feasibility radar cross-section of foreign object debris for size classification P. N., Ja’afar S. M., Idrus S., Ambran A., Hamzah N., Zulkifli N. A., Hamid A., Kanno Shibagaki, N. Kashima, K. T., Kawanishi sevia@utm.my Artificial neural network Backpropagation Classification FOD dataset Foreign object debris Link to publisher's homepage at http://ijneam.unimap.edu.my In this paper, a preliminary evaluation study is conducted, which aiming to investigate the radar cross-section (RCS) value that is capable to be used as an input parameter for Artificial Neural network (ANN) backpropagation for foreign object debris (FOD) size classification. The experimental work procedure for dataset acquisition is described. The FOD simulator is used as the FOD target which is made of metal cylinder shape with nine various dimensions and its RCS is defined by using Maxwell’s equation. The location varying backscattered electromagnetic field from each target is measured for RCS calibration purposes. It is found that by using the received signal from radar, which is the RCS of the target and its locations, it can be utilized as input parameters of backpropagation algorithms. The ANN classification application is to define its size by the ranges; small (-30.99 to -21 dBsm), medium (-20.99 to -11 dBsm), and large (-10.99 to 0 dBsm). The interference signal getting from measurement (22.46 to 25.2 dBsm) exhibited good reflectivity behavior. The acquired input showed to be useful for ANN for FOD size classification. 2022-04-14T05:03:54Z 2022-04-14T05:03:54Z 2021-12 Article International Journal of Nanoelectronics and Materials, vol.14(Special Issue), 2021, pages 165-173 1985-5761 (Printed) 1997-4434 (Online) http://dspace.unimap.edu.my:80/xmlui/handle/123456789/74960 http://ijneam.unimap.edu.my en Universiti Malaysia Perlis (UniMAP) |
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Artificial neural network Backpropagation Classification FOD dataset Foreign object debris |
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Artificial neural network Backpropagation Classification FOD dataset Foreign object debris P. N., Ja’afar S. M., Idrus S., Ambran A., Hamzah N., Zulkifli N. A., Hamid A., Kanno Shibagaki, N. Kashima, K. T., Kawanishi A Preliminary study on feasibility radar cross-section of foreign object debris for size classification |
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Link to publisher's homepage at http://ijneam.unimap.edu.my |
| author2 |
sevia@utm.my |
| author_facet |
sevia@utm.my P. N., Ja’afar S. M., Idrus S., Ambran A., Hamzah N., Zulkifli N. A., Hamid A., Kanno Shibagaki, N. Kashima, K. T., Kawanishi |
| format |
Article |
| author |
P. N., Ja’afar S. M., Idrus S., Ambran A., Hamzah N., Zulkifli N. A., Hamid A., Kanno Shibagaki, N. Kashima, K. T., Kawanishi |
| author_sort |
P. N., Ja’afar |
| title |
A Preliminary study on feasibility radar cross-section of foreign object debris for size classification |
| title_short |
A Preliminary study on feasibility radar cross-section of foreign object debris for size classification |
| title_full |
A Preliminary study on feasibility radar cross-section of foreign object debris for size classification |
| title_fullStr |
A Preliminary study on feasibility radar cross-section of foreign object debris for size classification |
| title_full_unstemmed |
A Preliminary study on feasibility radar cross-section of foreign object debris for size classification |
| title_sort |
preliminary study on feasibility radar cross-section of foreign object debris for size classification |
| publisher |
Universiti Malaysia Perlis (UniMAP) |
| publishDate |
2022 |
| url |
http://dspace.unimap.edu.my:80/xmlui/handle/123456789/74960 |
| _version_ |
1753972984238833664 |