• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.4
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• pp.655-662
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• 2011

SAR(Synthetic Aperture Radar) has the ability to generate high resolution images regardless of a weather condition(e.g. visibility good or poor and day or night, etc.). SAR is considered as one of the most important powers and needs for the future since it has been utilized in a number of important military fields such as early warning, urban defense, missile guidance system, etc. Additionally there are many civilian demands and applications in aviation, traffic control, earth and space explorations, weather forecast etc. This days, the ability to acquire and analyze information is needed to cope with the urgency of global politics and international changes. In this paper, technical survey and development review for SAR systems are investigated to derive the core and immature technologies of domestic defense industry.

• Journal of Applied Reliability
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• v.17 no.3
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• pp.256-263
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• 2017

Purpose: This article provides the efficiency test results of conducting depot maintenance. Method: ANOVA (analysis of variance) and ANCOVA (analysis of covariance) tests are conducted using data obtained from Defense Logistics Integrated Information System (DELIIS). Results: The result shows that although the effect of the depot maintenance is significant, some parts do not follow the overall effect of the depot maintenance. In order to solve the problem we provide some suggestions to be improved. Conclusion: For improving the combat readiness condition of K-1 main battle tank, the maintenance policies and regulation must be revised for the parts of the weakness. In addition, the collection way of the operation and maintenance data must be redesigned.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.566-576
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• 2018

As the weapon systems of the Korean Army possess massive firepower and multiple functions, the improvement of their quality through reliability prediction is becoming increasingly important. Currently, the reliability prediction of the weapon systems of the Korean Army is a difficult process, because it is conducted by naively calculating the reliability of their constituent parts. Recently, as various studies using accumulated data are undertaken across various industries, the defense industry is also attempting to analyze the Dark Data which have been accumulated but not yet used. Therefore, it is necessary to apply Post-Logistics Support (PLS) data in order to improve the reliability of the weapon systems and, for this purpose, the Korean Army needs to conduct diverse studies. Especially, the PLS data in the defense industry is very useful for reliability prediction, because the data on the defects reported after the development of the weapon systems are accumulated in this phase. This study examines the existing reliability prediction method conducted using the component parts and proposes a new reliability prediction method using PLS data. This framework can ultimately contribute to improve the prediction accuracy and quality of the weapon systems.