• Journal of Internet Computing and Services
    ISSN 2287 - 1136 (Online) / ISSN 1598 - 0170 (Print)
    https://jics.or.kr/
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A Study on the Evaluation of Classification Performance by Capacity of Explosive Components using Convolution Neural Network (CNN)


Chang-Hyeon Lee, Sung-Yoon Cho, Ki-Won Kwon, Tae-Ho Im, Journal of Internet Computing and Services, Vol. 23, No. 4, pp. 11-19, Aug. 2022
10.7472/jksii.2022.23.4.11, Full Text:  HTML
Keywords: Deep Learning, explosive classification, abnormality detection system

Abstract

This paper is a study to evaluate the performance when classifying explosive components by capacity using a convolutional neural network (CNN). Among the existing explosive classification methods, the IMS steam detector method determines the presence or absence of an explosive only when the explosive concentration exceeds the threshold set by the user. The IMS steam detector has a problem of determining that even if an explosive exists, the explosive does not exist in an amount that does not exceed the threshold. Therefore, it is necessary to detect the explosive component even when the concentration of the explosive component does not exceed the threshold. Accordingly, in this paper, after imaging explosive time series data with the Gramian Angular Field (GAF) algorithm, it is possible to determine whether there are explosive components and the amount of explosive components even when the concentration of explosive components does not exceed a threshold.


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Cite this article
[APA Style]
Lee, C., Cho, S., Kwon, K., & Im, T. (2022). A Study on the Evaluation of Classification Performance by Capacity of Explosive Components using Convolution Neural Network (CNN). Journal of Internet Computing and Services, 23(4), 11-19. DOI: 10.7472/jksii.2022.23.4.11.

[IEEE Style]
C. Lee, S. Cho, K. Kwon, T. Im, "A Study on the Evaluation of Classification Performance by Capacity of Explosive Components using Convolution Neural Network (CNN)," Journal of Internet Computing and Services, vol. 23, no. 4, pp. 11-19, 2022. DOI: 10.7472/jksii.2022.23.4.11.

[ACM Style]
Chang-Hyeon Lee, Sung-Yoon Cho, Ki-Won Kwon, and Tae-Ho Im. 2022. A Study on the Evaluation of Classification Performance by Capacity of Explosive Components using Convolution Neural Network (CNN). Journal of Internet Computing and Services, 23, 4, (2022), 11-19. DOI: 10.7472/jksii.2022.23.4.11.