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김승필 ( Kim Seungpil ),신명수 ( Shin Myoungsu ),손동성 ( Sohn Dong-seong ),권혁주 ( Kwon Hyukjoo ) 한국구조물진단유지관리공학회 2019 한국구조물진단유지관리공학회 학술발표대회 논문집 Vol.23 No.1
This study examined the safety of nuclear spent-fuel (NSF) transport casks against accidental puncture events. Finite element analyses were conducted according to the accident conditions in NUREG-1536 and RG 7.8, which specify 1-m parallel and vertical drops on a rigid rod that has 15-cm diameter and 20-cm height. We focused on the safety evaluation of the cask body, baskets, and canister based on ASME BPVC Section III, and evaluated whether these elements would undergo excessive fracture or puncture.
김종범 ( Kim Jongbum ),김승필 ( Kim Seungpil ) 공군사관학교 2019 空士論文集 Vol.70 No.-
본 연구는 인공위성 자세제어를 위한 교육용 시뮬레이터를 개발하는 것을 목표로 한다. 이를 위해 센서 및 구동기 관련 기초연구를 진행하였다. 그리고 좌표계 정의, 인공위성 강체 모델링, 지구 및 우주환경 모델링을 수행하였다. 또한 다양한 자세 결정 및 제어 알고리즘을 연구하였다. 각속도 감쇠 모드, 안전 모드, 그리고 임무모드 등 다양한 모드에 대한 자세제어 시뮬레이션을 수행하는 시뮬레이터를 개발하였다. 본 연구를 통해 개발된 시뮬레이터는 실제 개발되는 인공위성 자세제어 검증은 물론 생도 교육에 활용될 수 있을 것이다. The study aims to develop an educational simulator for satellite attitude control. To do this, basic research on sensors and actuator was conducted. And coordinate system definition, satellite rigid body modeling, and earth and space environment modeling were carried out. In addition, various attitude determination and control algorithm were studied. Simulator has been developed to perform simulations for various modes such as detumbling mode, safety mode, and mission mode. The simulator developed through this study will be used for cadet education as well as attitude control verification of actual satellites.
심층학습 기반의 자동 객체 추적 및 핸디 모션 제어 드론 시스템 구현 및 검증
김영수,이준범,이찬영,전혜리,김승필,Kim, Youngsoo,Lee, Junbeom,Lee, Chanyoung,Jeon, Hyeri,Kim, Seungpil 대한임베디드공학회 2021 대한임베디드공학회논문지 Vol.16 No.5
In this paper, we implemented a deep learning-based automatic object tracking and handy motion control drone system and analyzed the performance of the proposed system. The drone system automatically detects and tracks targets by analyzing images obtained from the drone's camera using deep learning algorithms, consisting of the YOLO, the MobileNet, and the deepSORT. Such deep learning-based detection and tracking algorithms have both higher target detection accuracy and processing speed than the conventional color-based algorithm, the CAMShift. In addition, in order to facilitate the drone control by hand from the ground control station, we classified handy motions and generated flight control commands through motion recognition using the YOLO algorithm. It was confirmed that such a deep learning-based target tracking and drone handy motion control system stably track the target and can easily control the drone.