• 한국국방경영분석학회지
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• 제33권1호
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• pp.117-133
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• 2007

무장정확도는 무기체계 획득의 기본 지표가 되는 성능으로 이에 대한 정확한 개념 정립과 시험평가 방법의 설정이 요구된다. 기존의 항공무장의 정확도 시험평가 방법은 무장의 탄착형태를 정확하게 고려하지 않았다. 본 연구는 확률 및 통계기법을 이용하여 최적 CEP 계산법과 각 시험평가 단계별 평가방안을 제시하였다. 먼저, 알려진 여러 무장정확도 계산법 중 항공무장의 탄착형태를 반영하는 최적 CEP 계산법으로 록히드마틴 CEP법과 존슨 CEP법을 추천하며, 시험평가 단계별 최적의 시험평가 방법을 제시하였다. 본 연구결과는 항공무장의 정확도 평가에 유용하게 사용될 수 있을 것이다.

• 한국항공우주학회지
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• 제35권8호
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• pp.741-746
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• 2007

본 논문에서는 공대지 무장의 무장투하정확도에 대한 해석기법을 제시하였다. 항공기의 전투효과도(Combat Effectiveness)를 평가하는데 있어 중요한 요인인 공격능력(Lethality)은 공대지 무장투하정확도 개선능력에 좌우된다. 항공기 초기 설계단계부터 최종 검증단계 까지 무장투하정확도에 영향을 미치는 요소들을 기술하였으며 각 요소들을 무장투하정확도에 반영하는 기법과 정량적으로 평가하는 방안을 제시하였다. 무장투하정확도 분석은 Bias error를 영으로 가정하고 Random error에 의한 투하오차만을 분석 대상으로 하였다.

• 한국시뮬레이션학회논문지
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• 제20권3호
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• pp.59-67
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• 2011

본 논문에서는 오류 검출을 위해 소프트웨어 오류 추정 기법을 활용하여 항공무장 시스템인 공대지 사격의 오류 요인을 분석하기 위한 모델을 제시하고 모델을 기반으로 오류 요인 영향을 분석하였다. 선행 연구에서는 공대지 무기체계의 정확도에 영향을 미치는 오류 요소를 분석하고 결함위치추정 기법인 FBEL(Factor-based Error Localization) 기법을 제안하였으며 실사격 자료에 적용하여 오류 요소를 분석하였다. 그러나 다양한 요인 중 하나의 요인인 정확도를 고정하여 적용하므로 다양한 사격 정확도 변화에 따른 사격 오류 발생 요소와의 상관관계는 밝히지 못하였다. 본 연구에서는 사격 오류 요소의 상관관계 분석을 통하여 보다 세밀한 오류 분석 모델을 제시하고 시간과 비용이 제한적이거나 테스트 케이스가 적은 상태에서 오류 발생 가능 요소를 추정하는 방법을 제안한다. 제안된 방법은 실사격 자료를 이용한 시뮬레이션을 통해 그 효용성을 입증하였으며, 무기체계 공대지 시험평가에서 어떤 요소들이 정확도에 영향을 미치는 지를 분석하는데 성공적으로 활용될 수 있음을 보였다.

• 한국군사과학기술학회지
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• 제13권4호
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• pp.551-560
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• 2010

In this paper, we suggest a localization method of factors affecting the accuracy of Air-to-Ground weapon delivery. The proposed method, called FBEL(Factor-Based Error Localization), is based on the fault localization technique widely utilized in the realm of software engineering field. FBEL localizes the major factors affecting the performance of weapon delivery. To analyze the effectiveness and the applicability of FBEL, we applied FBEL to real firing data and got the major factors caused the errors. We expect that the method could contribute to improve the quality of weapon delivery system. We also expect that it may aid improvement of pilot capability greatly, if it is applied to pilot firing training.

• 한국신뢰성학회지:신뢰성응용연구
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• 제17권4호
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• pp.296-304
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• 2017

Purpose: Recent weapon systems in defense have increased the complexity and importance of software when developing multifunctional equipment. In this study, we analyze the accuracy of the proposed software reliability model when applied to weapon systems. Methods: Determine the similarity between software reliability analysis results (prediction/estimation) utilizing data from developing weapon systems and system failures data during operation of weapon systems. Results: In case of a software reliability prediction model, the predicted failure rate was higher than the actual failure rate, and the estimation model was consistent with actual failure history data. Conclusion: The software prediction model needs to adjust the variables that are appropriate for the domestic weapon system environment. As the reliability of software is increasingly important in the defense industry, continuous efforts are needed to ensure accurate reliability analysis in the development of weapon systems.

• 한국군사과학기술학회지
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• 제22권6호
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• pp.806-814
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• 2019

One of the future weapon systems is the individual smart weapon which has a structure mounted on the forearm of soldiers. The structure may cause injuries or affect the accuracy of fire due to its impact on joints when shooting. This paper proposes human-impact interaction modeling and a verification methodology in order to estimate the impact of fire applied to the forearm. For this purpose, a human musculoskeletal model was constructed and the joints' behavior in various shooting positions was simulated. In order to verify the simulation results, an impact testing device substituting the smart weapon was made and the experiment was performed on a real human body. This paper compares the simulation results performed under various impact conditions and the experimental values in terms of accuracy and introduces methods to complement them. The results of the study are expected to be a basis for a reliable human-impact interaction modeling, and smart individual weapon development.

• 융합보안논문지
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• 제11권3호
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• pp.11-17
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• 2011

무기체계의 획득은 획득과정 초기단계에서 시험평가 자료의 원천으로 모델링 및 시뮬레이션의 사용이 적극적으로 권장되고 있다. 이와 같이 무기체계의 획득에 있어서 신뢰성은 가장 중요한 부분 이라할 수 있고, 이를 위해 M&S를 적용하여 시험평가의 신뢰성과 정확성을 제공할 수 있으며, 시험에 소요되는 시간과 비용의 절감, 사전 예측 및 사후 검증등이 가능한 자료를 제공할 수 있다. 따라서 본 연구에서는 M&S의 이론적인 고찰과 무기체계의 획득을 위한 M&S 활용의 중요성, 획득모델의 특징, 무기체계 획득을 위한 활용실태 등을 고찰하여 효과적인 무기체계 획득을 위한 M&S의 활용방안을 찾고자 한다.

• 한국군사과학기술학회지
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• 제23권4호
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• pp.337-345
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• 2020

The Weapon-Target Assignment(WTA) problem can be formulated as an optimization problem that minimize the threat of targets. Existing methods consider the trade-off between optimality and execution time to meet the various mission objectives. We propose a multi-agent reinforcement learning algorithm for WTA based on mean field game to solve the problem in real-time with nearly optimal accuracy. Mean field game is a recent method introduced to relieve the curse of dimensionality in multi-agent learning algorithm. In addition, previous reinforcement learning models for WTA generally do not consider weapon interference, which may be critical in real world operations. Therefore, we modify the reward function to discourage the crossing of weapon trajectories. The feasibility of the proposed method was verified through simulation of a WTA problem with multiple targets in realtime and the proposed algorithm can assign the weapons to all targets without crossing trajectories of weapons.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2012년도 추계학술대회
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• pp.128-130
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• 2012

One of the hot issues on GNSS might be that China declared to broadcast the signal of the new Global Navigation Satellite System called Beidou-Compass in December 2011. The multi-GNSS systems with the existing GPS and GLONASS consist of more than 100 GNSS satellites and transmit their signals in near future. Many benefits are expected in accuracy, availability, integrity and increasing anti-jam performance. In this presentation, we have mainly investigated the latest issue for multi- GNSS and discussed spectrum analysis as well as the accuracy improvement issue. The use of the modern weapon system based on satellite navigation information was also briefly investigated in warfare.

• Journal of Information Processing Systems
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• 제19권5호
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• pp.673-687
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• 2023

Convolutional neural network-based deep learning technology is the most commonly used in image identification, but it requires large-scale data for training. Therefore, application in specific fields in which data acquisition is limited, such as in the military, may be challenging. In particular, the identification of ground weapon systems is a very important mission, and high identification accuracy is required. Accordingly, various studies have been conducted to achieve high performance using small-scale data. Among them, the ensemble method, which achieves excellent performance through the prediction average of the pre-trained models, is the most representative method; however, it requires considerable time and effort to find the optimal combination of ensemble models. In addition, there is a performance limitation in the prediction results obtained by using an ensemble method. Furthermore, it is difficult to obtain the ensemble effect using models with imbalanced classification accuracies. In this paper, we propose a transfer learning-based feature fusion technique for heterogeneous models that extracts and fuses features of pre-trained heterogeneous models and finally, fine-tunes hyperparameters of the fully connected layer to improve the classification accuracy. The experimental results of this study indicate that it is possible to overcome the limitations of the existing ensemble methods by improving the classification accuracy through feature fusion between heterogeneous models based on transfer learning.