The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
권호 표지
DOI QR코드

DOI QR Code

A study on estimating rifle ammunition RSR based on truncated Weibull model

우측중도절단된 와이블 분포를 이용한 소총 탄약 소요보급률 추정 연구

  • Park, Jaeshin (Department of Mathematics, Korea Military Academy) ;
  • Bang, Sungwan (Department of Mathematics, Korea Military Academy)
  • Received : 2018.09.14
  • Accepted : 2018.12.12
  • Published : 2019.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

Ammunition is an integral element of a weapon systems and in calculating fighting strength. The Korea Army utilizes the basic load (B/L) concept to supply ammunition smoothly. The required supply rate (RSR) is the basis of a B/L that is estimated from real combat data that includes a troop's mission and operation terrain. The current RSR is based on Korean War data and the sample mean has some problems in applications to modern combat. Therefore, this study used Korea Combat Training Center (KCTC) data that is similar to real combat to estimate rifle ammunition RSR. We used a quantile of truncated Weibull distribution to estimate rifle ammunition RSR considering that rifle ammunition consumption data in KCTC is truncated. As a result, we obtained a rifle ammunition RSR which covers most ammunition consumption by reflecting the individual consumption of rifle ammunition.

무기체계의 구성요소 중 하나인 탄약은 전투력 발휘에서 필수적이다. 원활한 탄약의 공급과 전투력 발휘를 위하여 군은 탄약의 기본휴대량을 개인별 임무와 화기에따라 정하여 활용중이다. 탄약의 기본휴대량 산출에 핵심이 되는 탄약의 소요보급률은 부대의 작전환경과 임무등을 고려한 실제 전투 자료에 기반하여 산출이 되어져야한다. 한국전쟁의 자료를 통해 얻어진 기존의 소요보급률은 현대의 전투 양상을 반영하지 못한다는 한계가 있어, 본 연구에서는 실전과 가장 유사한 훈련인 육군 과학화 훈련단(Korea Combat Training Center; KCTC)의 소총 탄약 소모량 자료를 통하여 소총 탄약의 소요보급률을 추정하였다. KCTC 소총 소모량 자료가 중도 절단되었던 점을 고려하여 우측 중도절단된 와이블 분포를 추정하고 상위백분위수로 탄약의 소요보급률을 추정하였다. 그 결과 개인별 탄약 소모량을 반영하여 대부분의 전투원들이 추가 보급없이 전투를 수행할 수 있는 소요보급률을 추정할 수 있었다.

Keywords

GCGHDE_2019_v32n1_129_f0001.png 이미지

Figure 3.1. Histograms for rifle ammunition consumption in defense and offense operations. Figures (a) and (b) show histograms for rifle ammunition consumption in defense and offense operations, respectively.

GCGHDE_2019_v32n1_129_f0002.png 이미지

Figure 4.1. Estimating density of rifle ammunition consumption. Figures (a) and (b) show histograms for 31 drills’rifle ammunition consumption in defense and offense operations, respectively. Red lines represent the estimatedtruncated Weibull distribution for rifle ammunition consumption.

Table 2.1. Current basic load calculation formula

GCGHDE_2019_v32n1_129_t0001.png 이미지

Table 4.1. P -values of Kolmogorov-Smirnov test results under Weibull and normal distributions

GCGHDE_2019_v32n1_129_t0002.png 이미지

Table 4.2. Estimating required supply rate (RSR)

GCGHDE_2019_v32n1_129_t0003.png 이미지

Table 4.3. Summary statistics of 95% quantiles and 99% quantiles for 31 drills data

GCGHDE_2019_v32n1_129_t0004.png 이미지

References

  1. Army Headquarters (1987). Doctrine Literature 333.2 A Study on Ammunition basic Load, Army Headquarters.
  2. Army Headquarters (2002). Field Manual 42-6 Ammunition Service, Army Headquarters.
  3. Choi, M. J. and Lee, J. H. (2016). A binomial CUSUM chart for monitoring type I right-censored Weibull lifetimes, The Korean Journal of Applied Statistics, 29, 823-833. https://doi.org/10.5351/KJAS.2016.29.5.823
  4. Han, S. W. and Lee, J. H. (2017). A generalized likelihood ratio chart for monitoring type I right-censored Weibull lifetimes, The Korean Journal of Applied Statistics, 30, 647-663. https://doi.org/10.5351/KJAS.2017.30.5.647
  5. Kim, J. O., Cho, H. J., and Kim, G. G. (2015). Analysis of survivability for combatants during offensive operations at the tactical level, The Korean Journal of Applied Statistics, 28, 921-932. https://doi.org/10.5351/KJAS.2015.28.5.921
  6. Kim, J. O., Bang, S. W., and Kwon, O. J. (2017). Analysis of scientific military training data using zero-inflated and hurdle regression, Journal of the Korean Data & Information Science Society, 28, 1511-1520. https://doi.org/10.7465/jkdi.2017.28.6.1511
  7. Lee, J. Y., Kim, S. S., and Kim, Y. C. (2017). A study on application of augmented reality and virtual reality for combat training center. In Proceedings of Symposium of the Korean Institute of communications and Information, 439-440.
  8. Park, J. W., Lee, J. Y., and Park, J. K. (2008). A study on computation of optimal basic load of rifle and firearm using Arena, Journal of the Military Operations Research Society of Korea, 34, 1-26.
  9. Ryu, H. J., Ko, H. H., Kim, J. H., and Kim, S. S. (2008). Enhancement of artillery simulation training system using by neural network, Journal of the Military Operations Research Society of Korea, 34, 1-11.
  10. Shim, H. G. and Kim, S. K. (2008). A study on forecasting the operational continuous ability in battalion defensive operations using artificial neural network, Journal of Intelligence and Information Systems, 14, 25-39.
  11. Wingo, D. R. (1988). Methods for fitting the right-truncated Weibull distribution to life?test and survival Data, Biometrical Journal, 30, 545-551. https://doi.org/10.1002/bimj.4710300505