• 한국군사과학기술학회지
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• 제6권4호
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• pp.45-55
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• 2003

Weather conditions have played a vital role in a war. Many historical records reported that the miss use of weather information is the main reason of the lost a war. In this study we demonstrated the possibility of applying the numerical weather prediction system(NWPS) for military operations. The NWPS consists of PC-cluster as a super computer, data assimilation system ingesting many remote sensing observation, and graphic systems. High resolution prediction in NWPS can provide useful weather information such as wind, temperature, sea fog and so on for military operations.

• 한국정보시스템학회지:정보시스템연구
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• 제31권3호
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• pp.177-195
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• 2022

Purpose This paper aims to prepare a full operational readiness by establishing an optimal flight plan considering the weather conditions in order to effectively perform the mission and operation of military aircraft. This paper suggests a flight prediction model and rules by analyzing the correlation between flight implementation and cancellation according to weather conditions by using big data collected from historical flight information of military aircraft supplied by Korean manufacturers and meteorological information from the Korea Meteorological Administration. In addition, by deriving flight rules according to weather information, it was possible to discover an efficient flight schedule establishment method in consideration of weather information. Design/methodology/approach This study is an analytic study using data mining techniques based on flight historical data of 44,558 flights of military aircraft accumulated by the Republic of Korea Air Force for a total of 36 months from January 2013 to December 2015 and meteorological information provided by the Korea Meteorological Administration. Four steps were taken to develop optimal flight prediction models and to derive rules for flight implementation and cancellation. First, a total of 10 independent variables and one dependent variable were used to develop the optimal model for flight implementation according to weather condition. Second, optimal flight prediction models were derived using algorithms such as logistics regression, Adaboost, KNN, Random forest and LightGBM, which are data mining techniques. Third, we collected the opinions of military aircraft pilots who have more than 25 years experience and evaluated importance level about independent variables using Python heatmap to develop flight implementation and cancellation rules according to weather conditions. Finally, the decision tree model was constructed, and the flight rules were derived to see how the weather conditions at each airport affect the implementation and cancellation of the flight. Findings Based on historical flight information of military aircraft and weather information of flight zone. We developed flight prediction model using data mining techniques. As a result of optimal flight prediction model development for each airbase, it was confirmed that the LightGBM algorithm had the best prediction rate in terms of recall rate. Each flight rules were checked according to the weather condition, and it was confirmed that precipitation, humidity, and the total cloud had a significant effect on flight cancellation. Whereas, the effect of visibility was found to be relatively insignificant. When a flight schedule was established, the rules will provide some insight to decide flight training more systematically and effectively.

• 한국군사과학기술학회지
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• 제6권4호
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• pp.147-158
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• 2003

The prediction of wind field is very important fact in the radioactive and chemical warfare. In spite of advanced numerical weather prediction modelling and computing technology, the high resolution prediction of wind field is limited by the very high integration costs. In this study we coupled the mesoscale numerical model and microscale diagnostic numerical model with minimized integration costs. This coupled model has not only the ability of prediction of high resolution wind field including complex building but also microscale pollutant diffusion fields. For military operation this system can help making a practical and cost-effective decision in a battle field.

• 한국시뮬레이션학회논문지
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• 제33권3호
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• pp.13-26
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• 2024

화생방 확산 예측 모델은 전쟁 상황에서 생화학 작용제 및 방사능 물질을 활용한 공격 시 사건 발생 시간, 위치, 작용제 종류 및 투발 수단과 기상정보의 필수 시나리오 정보와 지형 및 건물정보를 바탕으로 피해 예측 정보를 생성하여 보다 나은 지휘관의 결심을 돕는 시스템이다. 국방과학연구소에서 개발한 화생방 보고관리 및 모델링 S/W 시스템(Nuclear, Biological, and Chemical Reporting And Modeling S/W System)은 화생방 사건 분석을 위해 독자적으로 개발된 모델로 여러 군사작전과 훈련 계획 수립을 지원한다. 본 논문에서는 NBC_RAMS의 오염확산 및 피해 예측 핵심 엔진을 사용하여 다양한 화생방 시나리오가 반영된 대용량 오염확산 예측 결과를 생성하고 분석할 수 있는 화생방 오염확산 시나리오 분석 시스템을 소개하고 이 시스템의 시나리오 입력정보 요소인 사건, 기상, 지형 및 건물정보를 상세히 설명하고 이에 대한 활용방안을 기술하였다. 실사용 사례로 화생방 오염확산 시나리오 분석 시스템을 활용하여 생성된 대용량 데이터를 인공지능 기술로 학습하여 오염운의 원점을 추적하는 기술과 화생방 탐지 센서 최적의 위치를 선정하는 기술 개발 사례를 소개하고자 한다. 해당 시스템을 통해 인공지능에 특화된 화생방 상황 분석 자료를 생성할 수 있으며 화생방 야전 상황 예측 및 분석으로 군사작전 지원 등의 다방면으로 활용이 가능할 것으로 기대된다.