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역학 일 기반 근피로도 피팅 모델을 이용한 수영 시 유속의 영향 고찰
정진성(Jinsung Jung),고창훈(Changhun Ko),김종휘(Jonghwi Kim),김성용(Sung Yong Kim),김정(Jung Kim),김진환(Jinwhan Kim),박수경(Sukyung Park) 대한기계학회 2020 대한기계학회 춘추학술대회 Vol.2020 No.12
In this study, a mechanical work-based muscle fatigue fitting model is proposed through the correlation between muscle fatigue and the mechanical work of the joint model, and through the proposed model, I would like to confirm the results of previous studies that swimming in an open-water environment has a greater change in energy cost than swimming in a swimming pool environment. The proposed model showed an overall NRMS Error of about 13% and a R square value of about 0.5, and compared with the results of linear regression analysis, the NRMS Error of about 6% low and a R square value of about 0.3 high. In the proposed muscle fatigue fitting model, it was shown that the fatigue in the case of the external force due to the flow velocity increased faster than that in the case where the external force term due to the flow velocity was not present, and that the fatigue was about 0.75% faster after 2 minutes. From this, compared to the linear regression analysis, the proposed model is more suitable for modeling muscle fatigue. And the results of previous study was shown that fatigue is faster during open-water swimming.
인공지능 기술에 기반한 자율운항선박의 새로운 상황인식 시스템의 설계와 초기 결과
최현택(Hyun-Taek Choi),박정홍(Jeonghong Park),최진우(Jinwoo Choi),강민주(Minju Kang),이영준(Yeongjun Lee),정종대(Jongdae Jung),김종휘(Jonghwi Kim),권혁준(Hyeokjun Kweon),김진환(Jinwhan Kim),윤국진(Kuk-Jin Yoon),김한근(Hanguen Kim),박상 제어로봇시스템학회 2021 제어·로봇·시스템학회 논문지 Vol.27 No.8
With recent advancements in artificial intelligence technologies, the academia and industry have heightened performance expectations when designing autonomous ships, and major ship building companies and governments have been conducting large-scale research projects around the world. This paper proposes a novel design concept, and presents the key features and preliminary results of a situational awareness system for autonomous ships, named the iSAS (Intelligent Situational Awareness System), and is being developed as part of the Korea Autonomous Surface Ships (KASS) project launched in April 2020. The iSAS comprises deep-learning algorithms for detecting marine objects using camera, radar, and LiDAR (Light Detection and Ranging), a probabilistic-based data association and tracking algorithm and a new collision risk evaluation method. Because the iSAS estimates motions of all and each objects along with their semantic information, it could not be said as a simple replacement of what the captain does. By sequentially installing the iSAS on a small test ship and a demonstration ship during the project, we will simultaneously perform algorithm development and field verification to achieve reliability in the real environment. The iSAS can be used not only for autonomous ships but also for manned ships to enhance safety and reduce costs in the near future.