• 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.

• 한국시뮬레이션학회논문지
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• 제26권3호
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• pp.35-46
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• 2017

기지/창 계획정비와 불시 결함발생에 따른 항공기 불가동 최소화를 위해 장착엔진에 추가로 보유하는 엔진이 예비엔진으로 항공기의 적정 가용도 달성을 통한 군사력 유지에 핵심적인 중요성을 지닌다. 본 연구의 목적은 다양한 군용항공기의 유형을 고려한 예비엔진/모듈의 최적 소요를 산출하는 방법을 제시하는 것이다. 먼저, 이 분야의 대표적 접근법인 METRIC과 메타모형에 대하여 각각의 개념, 특징 및 제한사항을 고찰하고 본 연구에서의 접근 방향을 제시하였다. 다음으로, 다양한 군용항공기에 대한 검토를 수행하고 이들을 총 5가지 유형으로 분류하였다. 유형별 계획, 비계획 정비에 대한 상세 분석을 기반으로 관련된 변수와 파라메터들을 도출하였다. 본 문제의 복잡성으로 인해 수식을 이용한 최적해의 도출이 불가능하며, 기지/야전/창 등의 정비로직을 분석한 후 ARENA 기반의 시뮬레이션과 OptQuest를 이용하여 최적소요를 산출하였다. 개발된 시뮬레이션 모델이 일련의 사례들을 통하여 최적해를 효율적으로 도출할 수 있음을 보였다.