• Defense and Technology
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• no.2 s.26
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• pp.20-30
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• 1981

• Defense and Technology
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• no.7 s.7
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• pp.60-64
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• 1979

• Defense and Technology
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• no.7 s.77
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• pp.38-45
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• 1985

• Defense and Technology
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• no.9 s.91
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• pp.28-33
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• 1986

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.9
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• pp.4349-4358
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• 2012

ASRP(Ammunition Stockpile Reliability Program) Data is stored and operated in the field of evaluating the ammunition is not only the only field data but also the ammunition performance-oriented data can determine objectively the power of the artillery. However, ASRP has been used as a yardstick to judge the status of ammunitions stockpiled in the field. On the other hand re-evaluation of the accumulated data and in-depth research have not been carried out. A Study on the Effective Management of Artillery Ammunition using ASRP data suggests how to utilize the ASRP data to analyze and manage existing artillery forces whose focus is centered on increasing the performance of artillery ammunitions through setting the test intervals of deployed stockpiled ammunitions, forecasting the shelf-life of ammunitions, and analyzing the effectiveness of the military strength through modelling and simulation.

• Journal of the Korea Society for Simulation
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• v.19 no.2
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• pp.63-72
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• 2010

In the future warfare, the importance of the counter-fire operation is increasing. The counter-fire operation is divided into offensive counter-fire operation and defensive counter-fire operation. Reviewing the researches done so far, the detection asset of offensive counter-fire operation called UAV(Unmanned Aerial Vehicle) and its operational effectiveness analysis is continually progressing. However, the analysis of the detection asset of defensive counterfire called Target Acquisition Radar(TAR) and its quantitative operational effectiveness are not studied yet. Therefore, in this paper, we studied operational effectiveness of TAR using C2 Theory & MANA Simulation model, and showed clear quantitative analysis results by comparing both cases of using TAR and not using TAR.

• Journal of the military operations research society of Korea
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• v.34 no.1
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• pp.1-11
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• 2008

A methodology for the improvement of simulation based training system for the artillery is proposed in this paper. The complex nonlinear relationship inherent among parameters in artillery firing is difficult to model and analyze. By introducing neural network based simulation, accurate representation of artillery firing is made possible. The artillery training system can greatly benefit from the improved prediction. Neural networks learning is conducted using the conjugate gradient algorithm. The evaluation of the proposed methodology is performed through simulation. Prediction errors of both regression analysis model and neural networks model are analyzed. Implementation of neural networks to training system enables more realistic training, improved combat power and reduced budget.

• Journal of the Korea Society for Simulation
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• v.28 no.1
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• pp.11-21
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• 2019

Modeling and simulation(M&S) method are used to quantify the weapon effectiveness. The weapon effectiveness of artillery shells was also partially studied, but there was a lack of research on the effects of the choice of charge. Therefore, this paper presents an artillery shell's EFD(Expected Fractional Damage) calculation model based on the charge and identifies differences in the weapon effectiveness of 3D building targets according to the selection of the charge. First, the input data of the calculation model was collected and a required number of shoots was calculated to achieve the desired effects using the proposed model. Finally, a paired sample t-test was conducted to verify the proposed model.

• Defense and Technology
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• no.6 s.42
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• pp.29-38
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• 1982

• Defense and Technology
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• no.6 s.54
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• pp.47-51
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• 1983