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LuckyViewPicks: 기념일을 위한 운세기반 영화 추천
김진환(Jinwhan Kim),이태일(Taeil Lee) 한국HCI학회 2024 한국HCI학회 학술대회 Vol.2024 No.1
본 논문에서는 기념일에 대한 운세와 영화, 두 콘텐츠를 색다른 방식으로 체험할 수 있는 인터랙션 방안을 소개한다. 작품의 테마는 매년 가장 중요하게 여기는 기념일 중 하나인 크리스마스로 선정하였다. 지인들과 크리스마스카드 주고받기는 오래된 아날로그식 관례이자 동심을 유발하는 매체이다. 이를 모티브로 하여 관람객과 디지털 환경의 매개체가 될 실물 카드를 제작하였다. 이후, 웹 베이스의 머신러닝 도구인 티처블 머신을 활용해 이미지 학습, 모델을 생성한 뒤 화면상에서 운세를 확인할 수 있게 하였다. 연결된 두 요소는 데이터셋과 다시 연동되어 788 편의 크리스마스 영화 중 해당하는 운세의 내용과 유사한 장르의 영화 데이터를 매핑, 추천하도록 시각화하였다. 본 작품은 그날의 운세 뽑기와 동시에 영화를 추천받아 마치 크리스마스카드가 영화 티켓의 역할을 하는 듯한 인터랙션 경험을 통해 크리스마스를 한층 즐겁게 보낼 수 있도록 돕는다.
시불변 불균일 스칼라장에서의 항법을 위한 실시간 필터 알고리듬
김진환(Jinwhan Kim) 대한조선학회 2011 대한조선학회 학술대회자료집 Vol.2011 No.6
An algorithm for localization and navigation using a series of point measurements in a time-invariant non-uniform scalar field whose distribution is known in advance is presented in this research. For point measurements, various scalar physical quantities can be considered such as temperature, density, salinity, intensity of light or an electromagnetic field, etc. A spatial distribution of these physical quantities forms a field map which enables map-based navigation for a vehicle. The filter formulation for this field map-based navigation leads to a nonlinear estimation problem. Several nonlinear estimation techniques including the extended Kalman filter and the pariticle filter are considered in this research. In addition, the feasibility of using a Rao-Blackwellized particle filter is examined and its performance is demonstrated through numerical simulations in a synthetic scalar field.
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.
라오-블랙웰라이즈드 입자필터를 이용한 지형참조 수중항법
김태윤(Taeyun Kim),김진환(Jinwhan Kim),최현택(Hyun-Taek Choi) 제어로봇시스템학회 2013 제어·로봇·시스템학회 논문지 Vol.19 No.8
Navigation is a crucial capability for all types of manned or unmanned vehicles. However, vehicle navigation in underwater environments still remains a challenging problem since GPS signals for position fixes are not available in the water. Terrain-referenced underwater navigation is an alternative navigation technique that utilizes geometric information of the subsea terrain to correct drift errors due to dead-reckoning or inertial navigation. Terrain-referenced navigation requires the description of an undulating terrain surface as a mathematical function or table, which often leads to a highly nonlinear estimation problem. Recently, PFs (Particle Filters), which do not require any restrictive assumptions about the system dynamics and uncertainty distributions, have been widely used for nonlinear filtering applications. However, PF has considerable computational requirements which used to limit its applicability to problems of relatively low state dimensions. This study proposes the use of a Rao-Blackwellized particle filter that is computationally more efficient than the standard PF for terrain-referenced underwater navigation involving a moderate number of states, and its performance is compared with that of the extended Kalman filter algorithm. The validity and feasibility of the proposed algorithm is demonstrated through numerical simulations.
홍승조(Seungjo Hong),김진환(Jinwhan Kim) 한국해군과학기술학회 2021 Journal of the KNST Vol.4 No.1
Berthing operation is essential for all types of ships. In particular, tugboats are required to large vessels with limited actuation capabilities at low speed. Tugboat automation is a difficult topic to apply typical control algorithms. Recently, to solve these problems, reinforcement learning, one of the techniques of machine learning, is being implemented. In this research, we solved the problem of tugboats using reinforcement learning method. The tugboats dynamic model is defined as a hybrid system in which continuous and discrete dynamic models interact. And solve this problem with proximal policy optimization(PPO) algorithm which trust region method based policy gradient algorithm. The result is shown through a simulation.
윤석민(Sukmin Yoon),김진환(Jinwhan Kim) 한국해양공학회 2014 韓國海洋工學會誌 Vol.28 No.6
In this study, the problem of trajectory optimization for underwater gliders considering depth constraints is discussed. Typically, underwater gliders are controlled to dive and climb in a saw-tooth pattern at constant gliding angles. This approach is effective and close to optimal for deep water applications. However, the optimal path deviates from the saw-tooth path in shallow water conditions. This study focuses on finding more efficient gliding paths that can minimize the traverse time in the horizontal plane when the water depth is limited. The trajectory optimization problem is formulated into a minimum time control problem with inequality path constraints and hydrodynamic drag effects. A numerical approach based on the pseudo-spectral method is adopted as a solution approach, and the simulation results are presented.