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Donghyeon Lee,Donghyeon Han,Seyeop Jung,Nyunjong Lee,Suzuki Ippei,Takahashi Yukiko,Sanghoon Kim 한국자기학회 2021 한국자기학회 학술연구발표회 논문개요집 Vol.31 No.1
A granular magnetic thin film is a well-known material system for achieving an ultra-high density recording media. Such nano-scale segregation of magnetic elements with excellent thermal stability can be made in the thin film. This material system typically consists of small grains in a few nanometers scale which is isolated by few nanometer-thick boundary. FePt-C is the most popular material for the hard disk drive industry. In general, L1<sub>0</sub>-structured FePt grains surrounded by C are placed on an MgO layer. Grain size in the film varies in a range between 7 nm~20 nm. Magnetic easy axis of each grain is also deviated from the vertical direction to the plane. In this presentation, we suggest the Stoner-Wohlfarth model with the standard deviation of the magnetic easy axis to understand the magnetization switching behavior of the FePt-C thin film. From our model, we can quantify a degree of deviation of the magnetic easy axis in the FePt-C granular film.
아군 충돌 회피를 위한 강화학습 기반 지대공 유도 미사일 제어 방안
한동윤(DongYoon Han),이동현(DongHyeon Lee),백민석(MinSeok Baek),반재필(Jaepil Ban) 한국정보기술학회 2022 Proceedings of KIIT Conference Vol.2022 No.12
본 논문은 강화학습 제어를 적용한 지대공 유도 미사일에 관한 연구이다. 목표물과 유도미사일 사이에 아군 비행체가 통과하는 경우 강화학습을 통해 제어기를 학습하여 아군을 회피함과 동시에 표적을 타격하도록 하였다. 시뮬레이션 결과를 통해 강화학습을 통해 학습된 제어기가 적용된 미사일이 효과적으로 목적을 수행하는 것을 확인 하였다. This study proposes a reinforcement learning-based control method for surface-to-air guided missiles. In the proposed method, reinforcement learning is used to learn a control algorithm that hit a target and simultaneously avoids an ally in case the ally passes between the target and missile. Simulation results show the proposed reinforcement learning-based control method achieves the given objective effectively.