한국인터넷정보학회 논문지
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| 한글제목(Korean Title) |
A Methodology of AI Learning Model Construction for Intelligent Coastal Surveillance |
| 영문제목(English Title) |
A Methodology of AI Learning Model Construction for Intelligent Coastal Surveillance |
| 저자(Author) |
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
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| 원문수록처(Citation) |
VOL 23 NO. 01 PP. 0077 ~ 0086 (2022. 02) |
한글내용
(Korean Abstract) |
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영문내용
(English 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. |
| 키워드(Keyword) |
Command Control
Surveillance
Artificial intelligence
C4I
Machine Learning & Training
Radar
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| 파일첨부 |
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