Changhee Han, Jong-Hwan Kim, Jinho Cha, Jongkwan Lee, Yunyoung Jung, Jinseon Park, Youngtaek Kim, Youngchan Kim, Jeeseung Ha, Kanguk Lee, Yoonsung Kim, Sungwan Bang, Journal of Internet Computing and Services, Vol. 23, No. 1, pp. 77-86, Feb. 2022
10.7472/jksii.2022.23.1.77, Full Text:
Keywords: Command Control, Surveillance, Artificial intelligence, C4I, Machine Learning & Training, Radar

Abstract

The Republic of Korea is a country in which coastal surveillance is an imperative national task as it is surrounded by seas on three sides under the confrontation between South and North Korea. However, due to Defense Reform 2.0, the number of R/D (Radar) operating personnel has decreased, and the period of service has also been shortened. Moreover, there is always a possibility that a human error will occur. This paper presents specific guidelines for developing an AI learning model for the intelligent coastal surveillance system. We present a three-step strategy to realize the guidelines. The first stage is a typical stage of building an AI learning model, including data collection, storage, filtering, purification, and data transformation. In the second stage, R/D signal analysis is first performed. Subsequently, AI learning model development for classifying real and false images, coastal area analysis, and vulnerable area/time analysis are performed. In the final stage, validation, visualization, and demonstration of the AI learning model are performed. Through this research, the first achievement of making the existing weapon system intelligent by applying the application of AI technology was achieved.

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