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Mobile Application for Emergency Incident Response Management
전찬웅(Chun Woong Jeonu),임명재(Myung Jae Lim) 한국IT마케팅학회 2015 Journal of Information Technology and Management Vol.3 No.1
It has already been demonstrated in many developed countries, there is a correlation between hospital mortality and disability rates, depending on whether the arrival of first aid measures. Especially when emergencies occur, the influence of the patient's life, depending on how fast do the deal in time and precise action of the surrounding people . If an urgent situation actually occurs proposes an application that can inform the exact instructions for this situation specific coping.
Small Tool Image Database and Object Detection Approach for Indoor Construction Site Safety
이강혁,전찬웅,신도형 대한토목학회 2023 KSCE Journal of Civil Engineering Vol.27 No.3
Object detection refers to computer vision technology that identifies the class and location of objects in an image. With the development of deep-learning techniques, object detection has been widely studied in the construction industry and has mainly focused on detecting heavy equipment and workers at outdoor sites. Therefore, an object detection model for small tools in indoor sites, where various accidents frequently occur owing to them, is required to enhance safety. In particular, many high-quality image databases are important for developing object detection approaches for small tools. In this study, 12 small tools commonly used in indoor construction were selected, and a database of 34,738 images of the selected tools was created and used to train the object detection models. YOLOv5x and YOLOv5s are selected as object detection models, for which the mean average precision with the 0.5 intersection-over-union value were 69.1% and 64.4%, respectively, and their frame rates were 58.82 and 142.86 frame per second, respectively. The results demonstrate that the established database is appropriate for the development of object detection for small tools and that object detection can be used for real-time safety management of construction indoor sites.
Variational Autoencoder를 이용한 교량 손상 위치 추정방법
이강혁,정민웅,전찬웅,신도형 대한토목학회 2020 대한토목학회논문집 Vol.40 No.2
Most deep learning (DL) approaches for bridge damage localization based on a structural health monitoring system commonly use supervised learning-based DL models. The supervised learning-based DL model requires the response data obtained from sensors on the bridge and also the label which indicates the damaged state of the bridge. However, it is impractical to accurately obtain the label data in fields, thus, the supervised learning-based DL model has a limitation in that it is not easily applicable in practice. On the other hand, an unsupervised learning-based DL model has the merit of being able to train without label data. Considering this advantage, thisstudy aims to propose and theoretically validate a damage localization approach for bridges using a variational autoencoder, a representative unsupervised learning-based DL network: as a result, this study indicated the feasibility of VAE for damage localization 구조물 건전도 모니터링 시스템을 기반하는 교량 딥러닝 손상 추정 기법들은 대부분 지도학습을 기반으로 하고 있다. 지도학습의 특성상 손상 위치 추정 딥러닝 모델의 학습을 위해 교량의 손상 위치를 나타내는 라벨(Label) 데이터와 이에 따른 교량의 거동 데이터가 필요하다. 하지만 실제현장에서 손상 위치 라벨 데이터를 정확히 얻어내는 것은 매우 어려운 일이므로, 지도학습 기반 딥러닝은 현장 적용성이 떨어진다는 한계가 있다. 반면에, 비지도학습 기반 딥러닝은 이러한 라벨 데이터 없이도 학습이 가능하다는 장점이 있다. 이러한 점에 착안하여 본 연구에서는 비지도 학습의 대표적인 딥러닝 기법인 Variational Autoencoder를 활용한 교량 손상 위치 추정의 방법을 제안하고 검증하였으며, 그 결과, 교량 손상위치 추정을 위한 VAE의 적용 가능성을 보였다.