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A Method of Interoperating Heterogeneous Simulation Middleware for L-V-C Combined Environment
Kunryun Cho, Giseop No, Sihyun Jung, Nopphon Keerativoranan, Chongkwon Kim
Simulation is used these days to verify the hypothesis or the new technology. In particular, National Defense Modeling & Simulation (M&S) is used to predict wartime situation and conduct the military training. National Defense M&S can be divided into three parts, live simulation, virtual simulation, and constructive simulation. Live simulation is based on the real environment, which allows more realistic sumulation; however, it has decreased budget efficiency, but reduced depictions of reality. In contrast, virtual and constructive simulations which are based on the virtual environment, have increased budget efficiency, but reduced depictions of reality. Thus, if the three parts of the M&S are combined to make the L-V-C combined environment, the disadvantages of each simulation can be complemented to increases the quality of the simulation. In this paper, a method of interworking heterogeneous simulation middeware for L-V-C combined environment is proposed, and the test results of interworking between Data Distribution Service (DDS) and High Level Architecture (HLA) are shown.
Designing an Algorithm for the Priority Deciding and Recommending of the Logistic Support with Stationary Distribution
Giseop Noh, Sihyun Jeong, Chong-Kwon Kim, Hayoung Oh
One of the important roles used to ensure victory in a war is to maximize the overall military forces and to make sure that the capability of the military forces can be sustained as much as possible. Although several researchers have proposed various possible methodologies for logistics support, no research trials have been undertaken to investigate logistics support that considers all relevant elements of such. Unlike previous in trials that consider and analyze the system fault ratio as the main methodology, we propose an approach that simultaneously decides and recommends logistic priority by reflecting and combining item costs, transportation, fault-ratio, and system complexity. Also, we designed an algorithm that can recommend optimized logistics support priority using stationary distribution.
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