• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.374-382
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• 2020

The purpose of this study is to improve the learning speed of an ammunition stockpile reliability classification artificial neural network model by proposing a normalization method that reduces the number of input variables based on the characteristic of Ammunition Stockpile Reliability Program (ASRP) data without loss of classification performance. Ammunition's performance requirements are specified in the Korea Defense Specification (KDS) and Ammunition Stockpile reliability Test Procedure (ASTP). Based on the characteristic of the ASRP data, input variables can be normalized to estimate the lot percent nonconforming or failure rate. To maintain the unitary hypercube condition of the input variables, min-max normalization method is also used. Area Under the ROC Curve (AUC) of general min-max normalization and proposed 2-step normalization is over 0.95 and speed-up for marching learning based on ASRP field data is improved 1.74 ~ 1.99 times depending on the numbers of training data and of hidden layer's node.

• Journal of the Korea Institute of Military Science and Technology
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• v.8 no.4 s.23
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• pp.32-40
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• 2005

The ASRP(Ammunition Stockpile Reliability Program) is a overall evaluation system of ammunitions to improve the combat strength, having a short history. The storage lifetime of ammunitions, more than 20 years since being produced, differs according to storage circumstance and period. And it shows the different reliability for munitions. Because most ammunitions in military have been stored for a long time, these ammunitions need to be evaluated to maintain Serviceability, Reliability, Safety and Performance for the use of military. In this paper, it suggested the need to develop the techniques for ourselves, to make more laboratories for surveillance and testing and to extend research funds & concerns.