한국시뮬레이션학회논문지 (Journal of the Korea Society for Simulation)

한국시뮬레이션학회 (The Korea Society for Simulation)
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인공신경망을 적용한 직사화기 무기체계의 살상확률(Pk) 산출방법론 연구

A Study on Pk(Probability of Kill) Calculation Method of the Direct Fire Weapon System using ANN

  • 장영천 ;
  • 한현진 (합동참모본부 분석실험실 전력분석1과) ;
  • 이기택 (합동참모본부 분석실험실 합동M&S체계관리팀) ;
  • 송미진 (합참 분석실험실 탄약효과자료구축팀) ;
  • 이휘영 (합참 분석실험실 탄약효과자료구축팀) ;
  • 김종헌 (합참 분석실험실)
  • 투고 : 2019.01.31
  • 심사 : 2019.03.29
  • 발행 : 2019.03.31
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초록

기존 본 연구팀에서 연구한 직사화기 무기효과 산출방법론은 JMEM(Joint Munitions Effectiveness Manuals : 합동탄약효과편람)내(內) 표적에 대해서만 무기효과자료를 산출 가능하다는 제한 사항을 가지고 있었다. 따라서 본 연구에서는 JMEM 이외의 현실적 표적을 추가하여 무기효과자료를 산출하는 방법을 인공신경망(ANN : Artificial Neural Network)을 적용하여 도출하는 데 중점을 두었다. 즉, 미국 JMEM내(內) 표적의 제원과 효과지수인 취약면적(Av : vulnerable area)을 이용하여 인공신경망의 예측모델을 학습시키고, 학습된 예측모델에 현실적 표적의 제원을 적용하여 취약면적을 계산한다. 최종적으로 본 연구팀이 연구한 기존 직사화기 무기효과 산출방법론에 계산된 취약면적을 적용하여 살상확률(Pk)을 계산하는 방법론을 제시하였다.

Until now it has had the limitation of the target in the US JMEM to calculate the Pk with the existing method by our study. In this study, we focused on deriving a method to calculate the Pk of the actual targets except JMEM targets using ANN. We study the initial predictive model of ANN(Artificial Neural Network) from the targets data of the specification and the vulnerable area in the US JMEM(Joint Munitions Effectiveness Manuals), and calculate the actual targets vulnerable area by using this method. Finally, we propose a method to calculate the Pk by applying those data to the existing method of us.

키워드

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Fig. 1. Concept of JMEM Production(Moon et al., 2015)

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Fig. 2. Process of AMSSA Surrogation

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Fig. 3. Concept of JMEM Production(Moon et al. 2012)

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Fig. 4. Concept of estimation method for probability of kill based on simulation(Choi et al., 2017)

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Fig. 5. Concept of estimation method for probability of kill using ANN

SMROBX_2019_v28n1_99_f0006.png 이미지

Fig. 6. Concept of processor for the predictive model using ANN

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Fig. 7. Concept of processor for calculating the vulnerable area using ANN

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Fig. 8. Concept of calculating the vulnerable area rate(Notional data)

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Fig. 9. Concept of estimation method for Pk using ANN

Table 1. The scope of the actual targets

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Table 2. The result of predictive model using ANN

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Table 3. The target table of the application for the rate of vulnerable area

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Table 4. Weapons and targets for verification

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Table 5. Comparison of the calculation data and the JMEM data at TOW weapon system(Notional data)

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Table 6. Statistics for verification

SMROBX_2019_v28n1_99_t0006.png 이미지

참고문헌

  1. Agency for Defense Development, "The results of technical firing test for the next generation tank ammunition, HEAT-MP, Dajeon Korea, pp. 78-83, 2007.
  2. Chung-Young Kim, Seong-Jin Kang, Seok-Chul Choi and Sang-Young Choi, Military Operations Research Theory and Applications, Dunam, Seoul Korea, pp. 493-498. 2010.
  3. Faulkner, "Adobe Photoshop Cc Classroom in a Book", Prentice Hall, New Jersey U.S., 2015.
  4. Hyung-Kon Moon,Young-Bo Suh, (2012) "Hybrid Method for Ground Weapon Systems Vulnerability Estimation(HYVEN), The 12nd ROK-US Munitions Effectiveness Seminar
  5. Joshua kim(2015) "JMEM Content Alteration Tool(JCAT) Overview", CAA, The 15th ROK-US Munitions Effectiveness Seminar
  6. Korea Institute for Defense Analyses, "A Study on the Constructive Direction of ROK-JWS", Seoul Korea, pp. 13-17, 2015.
  7. Michael Negnevitsky, Introduction of Artificial Intelligence 2nd Ed., AdDDISON_WEDLEY, Australia, pp. 412-416. 2013.
  8. Morris R. Driels, Weaponeering: Conventional Weapon System Effectiveness, AIAA education series, Virginia U.S., p. 298, 387, 2013.
  9. ROK Military Academi, Weapons System Engineering, Bookshill, Seoul Korea, P. 140, 2003.
  10. Sei-Hoon Moon, Jin-woong Chong, (2015) "Target Geometric Model & Vulnerability Data Development", ADD
  11. Youn-Hwan Chun et al. (2016) Defense Modeling and Simulation
  12. Yun Ho Choi, Ki TeaLee, Jai Jeong Pyhun, Young Cheon Jang(2017) "A Study on Pk(Probability of Kill) calculation method of the direct fire weapon system using simulation", The Korea Society for Simulation, Vol. 26, NO. 3, pp. 115-123