Ojeong Kwon
Korea Advanced Institute of Science and Technology · 工学
研究室紹介
Professor Ojeong Kwon's research lab specializes in advanced decision-making systems for military operations, with a focus on optimizing weapon-target allocation, unmanned aerial systems (drones), and urban warfare modeling. The lab integrates mathematical programming, reinforcement learning, and agent-based simulation to develop intelligent, autonomous systems capable of operating in complex environments. Key research directions include cost-efficient resource allocation, the enhancement of drone autonomy through AI-driven perception, and the theoretical modeling of urban combat dynamics using fractal geometry. The lab emphasizes practical applicability through computational experiments and real-world simulation frameworks.
Research Overview
Research Output Trend
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Selected Papers
5Abstract In this paper, we consider a new weapon‐target allocation problem with the objective of minimizing the overall firing cost. The problem is formulated as a nonlinear integer programming model, but it can be transformed into a linear integer programming model. We present a branch‐and‐price algorithm for the problem employing the disaggregated formulation, which has exponentially many columns denoting the feasible allocations of weapon systems to each target. A greedy‐style heuristic is us
Drones have become integral in modern warfare, and their evolution towards greater autonomy is inevitable. This study explores the trajectory towards intelligent, minimally human-dependent military drones, detailing the necessary technological advancements. We simulate drone reconnaissance operations to identify and analyze emergent challenges. This study delves into various technologies critical for enhancing drone intelligence, with a focus on reinforcement learning based on object detection,
In this paper, we consider a new weapon–target allocation problem with the objective of minimizing the overall firing cost. The problem is formulated as a nonlinear integer programming model. We applied Lagrangian relaxation and a branch-and-bound method to the problem after transforming the nonlinear constraints into linear ones. An efficient primal heuristic is developed to find a feasible solution to the problem to facilitate the procedure. In the branch-and-bound method, three different bran
With rapid urbanization, the importance of urban warfare is increasing, and it is also required to reflect the characteristics of cities in wargame models. However, in the military's wargame models, the urbanization factor was calculated and used without theoretical basis. In this study, we investigate techniques for estimating the urbanization factor using Fractal dimension theory. The urbanization factor we propose can suggest a logical and valid representative value when used in conjunction w