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가상 건설환경에서의 딥러닝 기반 폐색영역 검출을 위한 데이터 베이스 구축
김경수(K. S. Kim),이재인(J. I. Lee),곽석우(S. W. Gwak),강원률(W.R. Kang),신대영(D.Y. Shin),황성호(S. H. Hwang) 유공압건설기계학회 2021 유공압건설기계학회 학술대회논문집 Vol.2021 No.11
In this paper, a method for constructing and verifying datasets used in deep learning technology is proposed to prevent safety accidents in the construction environment. The virtual construction simulator was developed to implement various construction environments, and custom data in the virtual environment was set to diversify the location and parameters of the sensors. A database for real-time deep learning models was built and detected at the pixel level to detect uneven roads and objects in the construction site. When deep learning-based detection is performed, it may interfere with the accurate judgment due to occlusion areas hidden by other objects. Therefore, the database for developing the occlusion area detection algorithm in virtual environments was constructed and verified.
최희윤,이인승,강원경,김한중 大韓成形外科學會 1986 Archives of Plastic Surgery Vol.13 No.2
The initial reaction of a person to any damage to his face is commonly one of acute distress, which may be followed by periods of mouring and prolonged depression. So facial deformities due to traumatic injury are carefully reconstructed with individual considerations. Successful reconstruction of facial deformities is a difficult and intriguing problem, and various surgical procedure have been proved for the repair of facial deformities depending upon their causes, locations, and associated with other lesions. This article deals with 2 cases of traumatic facial deformities; One patient had frontal bone defect, eyelid ectropion, eyebrow loss, saddle nose deformity by traffic accident, and another one had generalized hypertrophic scar on face, eyebrow loss, eyelid ectropion, upper lip deformity by burn which were required various surgical procedure. So we repaired these facial deformities with more reliable, easy accessible procedures with good results.