• Journal of the Korea Institute of Military Science and Technology
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• v.18 no.5
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• pp.582-593
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• 2015

For military training and course-of-action analysis, OneSAF Int'l version being used in ROK Army has a limited capability to simulate NBC(nuclear, biological, and chemical) damages. For high-fidelity NBC combat simulation, it is required to visualize NBC contamination dispersion in consideration of weather conditions and terrain characteristics. However, OneSAF itself handling interaction among thousands of combat entities cannot carry out a simulation of NBC contamination dispersion because it brings about an excess burden. To resolve this problem, this research aims to design simulation federation for analysis on NBC operational effects. After examining design consideration to connect OneSAF and a NBC contamination dispersion model, we design a federation architecture that facilitates the interaction between OneSAF and a NBC contamination dispersion model. Afterwards, we implement a federation interface to share simulation data by publish-subscribe pattern and to translate them into the proprietary format for each model. We prove the possibility of federation between both models, as showing that dispersion of NBC contaminated cloud and changes in concentration are reflected in OneSAF-based engagement simulation.

• Journal of the Korea Society for Simulation
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• v.33 no.3
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• pp.13-26
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• 2024

The CBRN(Chemical, Biological, Radiological, and Nuclear) hazard prediction model is a system that supports commanders in making better decisions by creating contamination distribution and damage prediction areas based on the weapons used, terrain, and weather information in the events of biochemical and radiological accidents. NBC_RAMS(Nuclear, Biological and Chemical Reporting And Modeling S/W System) developed by ADD (Agency for Defense Development) is used not only supporting for decision making plan for various military operations and exercises but also for post analyzing CBRN related events. With the NBC_RAMS's core engine, we introduced a CBR hazard assessment scenario analysis system that can generate contaminant distribution prediction results reflecting various CBR scenarios, and described how to apply it in specific purposes in terms of input information, meteorological data, land data with land coverage and DEM, and building data with pologon form. As a practical use case, a technology development case is addressed that tracks the origin location of contaminant source with artificial intelligence and a technology that selects the optimal location of a CBR detection sensor with score data by analyzing large amounts of data generated using the CBRN scenario analysis system. Through this system, it is possible to generate AI-specialized CBRN related to training and analysis data and support planning of operation and exercise by predicting battle field.