• Journal of the Korea Society for Simulation
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• v.19 no.4
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• pp.291-299
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• 2010

As recent advances in science, technology and performance requirements of the weapons system are getting highly diversified and complex, the performance requirements also get stringent and strict. Moreover, the weapons system should be intimately connected with other systems such as watchdog system, command and control system, C4I system, etc. However, a tremendous amount of time, cost and risk being spent to acquire new weapons system, and not being diminished compared to the rapid pace of its development speed. Defense Modeling and Simulation(M&S) comes into the spotlight as an alternative to overcoming these difficulties as well as constraints. In this paper, we propose the development process of virtual test framework based on the synthetic environment as a tool to analyze the effectiveness of the weapons system of underwater engagement model. To prove the proposed concept, we develop the test-bed of virtual test using Delta3D simulation engine, which is open source S/W. We also design the High Level Architecture and Real-time Infrastructure(HLA/RTI) based Federation for the interoperation with heterogeneous simulators. The significance of the study entails (1)the rapid and easy development of simulation tools that are customized for the Korean Theater of War; (2)the federation of environmental entities and the moving equations of the combat entities to manifest a realistic simulation.

• Journal of the Korea Society for Simulation
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• v.26 no.2
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• pp.59-65
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• 2017

Along with the development of M&S, modeling research utilizing AI technology is attracting attention because of the fact that the needs of fields including human decision making such as defense M&S are increased. Obviously AI is a way to solve complex problems. However, AI did not consider logical time such as input time and processing time required by M&S. Therefore, in this paper we proposed a "time-based expert system" which redesigned the representative AI technology rule-based expert system. It consists of a rule structure "IF-THEN-AFTER" and an inference engine, takes logical time into consideration. We also tried logical analysis using a simple example. As a result of the analysis, the proposal Time-based Expert System proved that the result changes according to the input time point and inference time.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.214-224
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• 2024

Sonobuoys are disposable devices that utilize sound waves for information gathering, detecting engine noises, and capturing various acoustic characteristics. They play a crucial role in accurately detecting underwater targets, making them effective detection systems in anti-submarine warfare. Existing sonobuoy deployment methods in multistatic systems often rely on fixed patterns or heuristic-based rules, lacking efficiency in terms of the number of sonobuoys deployed and operational time due to the unpredictable mobility of the underwater targets. Thus, this paper proposes an optimal sonobuoy placement strategy for Unmanned Aerial Vehicles (UAVs) to overcome the limitations of conventional sonobuoy deployment methods. The proposed approach utilizes reinforcement learning in a simulation-based experimental environment that considers the movements of the underwater targets. The Unity ML-Agents framework is employed, and the Proximal Policy Optimization (PPO) algorithm is utilized for UAV learning in a virtual operational environment with real-time interactions. The reward function is designed to consider the number of sonobuoys deployed and the cost associated with sound sources and receivers, enabling effective learning. The proposed reinforcement learning-based deployment strategy compared to the conventional sonobuoy deployment methods in the same experimental environment demonstrates superior performance in terms of detection success rate, deployed sonobuoy count, and operational time.