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손현철(HyeonCheol Son),김성영(SungYoung Kim) 한국정보기술학회 2021 Proceedings of KIIT Conference Vol.2021 No.11
태양광 패널의 불량은 그 특성으로 인해 열을 발산하여 높은 온도를 가진다. 따라서 열화상 카메라로 패널의 열을 촬영하고 열화상 영상으로 태양광 불량을 분류하는 네트워크를 제안한다. 열화상 영상은 그 자체로 특징을 나타내기 때문에 Residual block을 이용해 입력데이터의 Identity를 보존하는 과정을 포함한다. 네트워크의 분류 정확도는 57.14%로 향후 데이터의 많은 수집을 통해 정확도 향상을 기대해 볼 수 있다. The defect of the solar panel has a high temperature by dissipating heat due to the characteristics of the defect. Therefore, photographing heat of a panel with thermal imaging camera and a network that classifying solar defects into thermal imaging images is proposed. Since thermal images themselves represent characteristics, they include the precess of preserving the identity of input data using Residual block. The proposed network classification accuracy is 57.14%, which can be expected to improve accuracy through a large collection of data in the future.
골격 좌표 벡터 및 LSTM 모델을 이용한 넘어짐 검출
시종욱(Jongwook Si),손현철(Hyeoncheol Son),김다슬(Daseul Kim),김문년(Moonnyeon Kim),정지연(Jiyeon Jeong),김규리(Gyuree Kim),김영형(Younghyung Kim),김성영(Sungyoung Kim) 한국정보기술학회 2020 한국정보기술학회논문지 Vol.18 No.12
In recent years, many countries around the world have faced the problems of aging, single-person households, and the increasing number of elderly people who live alone. These problems have led to the need for research to improve the quality of life for the elder and single-person households. In this paper, the method of detecting falls was proposed with a focus on the aspect of safety among the methods of improving the quality of life of single-person households. First, we extract key points of human skeletons based on the existing method, vectorize them to represent correlation between them, and input them into LSTM to determine whether falls have occurred. We try to enhance the performance by using only representative feature points, not all feature points. Five reference datasets were used to evaluate the performance of proposed system and performed well in most datasets.
2차사고 방지를 위한 차량 간 커뮤니케이션 시스템 구현
조국한(Guk-Han Jo),조현우(Hyun-Woo Cho),박윤보(Yun-Bo Park),손현철(Hyeoncheol Son),송영준(Young-Joon Song) 한국정보기술학회 2021 한국정보기술학회논문지 Vol.19 No.6
In this paper, we implement a vehicle-to-vehicle communication system to reduce secondary accidents in vehicles using YOLO and GPS based on a cloud environment. When an accident occurs, the client recognizes the accident through YOLO and transmits the event video data related to the accident to the cloud server. The server calculates the distance of each subscribed client based on the GPS coordinates from the accident location. In addition, clients close to the accident point are grouped, and the accident data is shared within the same group members. In this way, this paper implements a system that can apply the theoretically researched technologies to actual vehicles by comprehensively utilizing technologies such as image recognition, GPS, Cloud Server and IoT networking, as well as the newly proposed grouping technique.