http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
리튬 이온 배터리의 냉각 채널에서 유동 방향이 열전달 특성에 미치는 영향
허인석,최주혁,한태희,이수룡 한국기계기술학회 2020 한국기계기술학회지 Vol.22 No.6
The effect of flow direction on heat transfer in water cooling channel of lithium-ion battery is numerically investigated. Battery Design StudioⓇ software is used for modeling electro-chemical heat generation in the battery and the conjugated heat transfer is analyzed with the commercial package STAR-CCM+. The result shows that the maximum temperature and temperature difference of battery with Type 1 are the lowest because the heat transfer in the entrance region near the electrode is enhanced. As the inlet velocity is increased, the maximum temperature and temperature difference of battery decreases but the pressure loss increases. The pressure loss in Type 2 channel is the lowest due to the shortest channel length, while the pressure loss with Type 3 or 4 channel is the highest because of the longest channel length. Considering heat transfer performance and pressure loss, Type 1 is the best cooling channel.
Nd:YAG 레이저빔의 펄스 파형에 따른 Al 3003 합금의 용접에 관한 연구
허인석,김도훈 한국레이저가공학회 2000 학술발표대회 Vol.2000 No.2
파장이 짧고 높은 흡수율을 가진 Nd:YAG레이저를 이용하여 Al 3003합금 H18의 맞대기 용접을 수행하였다. 또한 실험계획법을 통해 최적조건을 도출하였다. 최적조건은 (Scanning Speed : 7mm/sec, Frequncy : 31Hz, Pulse Width는 첫 번째 파형이 0.3ms, 두 번째 파형이 1.0ms, 세 번째 파형이 1.4ms, 네 번째 파형이 0ms, Peak Power는 첫 번째 파형이 63%, 두 번째 파형이 38%, 세 번째 파형이 32%, 네 번째 파형이 0%)이다.
착용형 로봇의 선행제어를 위한 하지 관절운동의 동작분할 패턴 분석
허인석,김상호 대한인간공학회 2023 大韓人間工學會誌 Vol.42 No.6
Objective: This study analyzes the angular variation patterns of clustered 3D skeletal data through unsupervised learning to characterize lower extremity joint motion activities and propose a motion segmentation system. Background: For the commercialization of wearable robots, it is important to recognize and react to the user's intentions in time so that they can move appropriately according to the operator's joint movements. To improve human intent prediction performance, computers should apply motion segmentation techniques to efficiently learn human behavior. Method: Angular data for the back, hip, knee, and ankle joints are extracted from 3D skeletal data using a kinect sensor for 6 major lower extremity working activities of 4 male subjects. It compresses high-dimensional data through a CNN-based autoencoder and analyzes joint movement patterns between clusters by performing KMeans Clustering. Results: Unsupervised learning on motion patterns showed that there is a clear pattern between clusters for the 6 major working activities. the motion segmentation of the lower extremity joints is classified into clusters with patterns of back (3), hip (4), knee (3), and ankle (3). Conclusion: The combination of clusters provides a simple representation of the 6 major working activities and can be utilized as an approach to represent more complex activities. Application: Beyond simply classifying motion patterns, it is expected to be used in the development process of algorithms for motion prediction.