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익상편 절제술 후 익상편의 크기 지표에 따른 평균 각막 굴절력 변화량 예측
남기태,엄영섭,임재원,강수연,김효명,송종석,Ki Tae Nam,MD,Young Sub Eom,MD,Jay Won Rhim,MD,Su Yeon Kang,MD,Hyo Myung Kim,MD,PhD,Jong Suk Song,MD,PhD 대한안과학회 2014 대한안과학회지 Vol.55 No.11
Purpose: To assess the changes in mean corneal refractive power (ΔK) following pterygium surgery and to predict ΔK in cases of combined cataract and pterygium surgery. Methods: Thirty-seven eyes of unilateral pterygium patients who underwent pterygium surgery were analyzed retrospectively with at least more than 1 month of follow-up. Preoperative and postoperative 1 month corneal refractive power was measured using auto-keratometer (RK-F1, Canon, Tokyo, Japan). Pterygium horizontal extension, width, and area were measured and correlation with ΔK before and after surgery analyzed. We also compared ΔK of the contralateral normal eye. Results: The mean corneal refractive (Km) power measured before and 1 month after surgery was 43.30 ± 1.66 D and 44.07 ± 1.42 D, respectively. The Km significantly increased at 4 weeks after surgery (<EM>p </EM>< 0.001). However, postoperative Km was not significantly different when compared with the contralateral normal eye (43.86 ± 1.34 D; p = 0.59). All parameters of pterygium size including horizontal extension, width, and area were positively correlated with the mean ΔK. Among parameters, horizontal extension was best correlated with mean ΔK (<EM>p</EM> < 0.001). The mean ΔK with horizontal extension was predicted using linear regression (2.5 mm to 1 D, 4.0 mm to 1.8 D). Conclusions: We recommend contralateral corneal refractive power or prediction of corneal refractive power using linear regression with pterygium horizontal extension for determining intraocular lens power in cases of combined cataract and pterygium surgery. J Korean Ophthalmol Soc 2014;55(11):1613-1617
이미지 인식을 통한 AI 기반 소방 시설 설계 기술 개발에 관한 연구
남기태,서기준,최두찬 한국재난정보학회 2022 한국재난정보학회 논문집 Vol.18 No.4
Purpose: Currently, in the case of domestic fire fighting facility design, it is difficult to secure high-quality manpower due to low design costs and overheated competition between companies, so there is a limit to improving the fire safety performance of buildings. Accordingly, AI-based firefighting design solutions were studied to solve these problems and secure leading fire engineering technologies. Method: Through AutoCAD, which is widely used in existing fire fighting design, the procedures required for basic design and implementation design were processed, and AI technology was utilized through the YOLO v4 object recognition deep learning model. Result: Through the design process for fire fighting facilities, the facility was determined and the drawing design automation was carried out. In addition, by learning images of doors and pillars, artificial intelligence recognized the part and implemented the function of selecting boundary areas and installing piping and fire fighting facilities. Conclusion: Based on artificial intelligence technology, it was confirmed that human and material resources could be reduced when creating basic and implementation design drawings for building fire protection facilities, and technology was secured in artificial intelligence-based fire fighting design through prior technology development. 연구목적: 현재 국내 소방시설설계의 경우 낮은 설계단가와 업체 간 과열 경쟁으로 고급 인력에 대한 확보가 어려워 건축물의 화재안전성능을 향상시키는데 한계가 있다. 이에 이러한 문제를 해소하고 선도적인 소방엔지니어링 기술을 확보하기 위해 AI 기반 소방설계솔루션을 연구하였다. 연구방법: 기존 소방설계에 많이 사용되는 AutoCAD를 통해 기본 설계 및 실시 설계에 필요한 절차를 프로세스화 하고 YOLO v4 객체 인식 딥러닝 모델을 통해 AI기술을 활용하였다. 연구결과: 소방시설에 대한 설계프로세스를 통해 설비의 결정과 도면 설계 자동화를 진행하였다. 또한 문 및 기둥에 대한 이미지를 학습시켜 인공지능이 해당 부분을 인식하여 경계구역 선정, 배관 및 소방시설을 설치하는 기능을 구현하였다. 결론: 인공지능 기술을 기반으로 건축물 화재방호 설비에 대한 기본 및 실시 설계 도면 작성 시 인적 및 물적 자원을 저감시킬 수 있을 것으로 확인되었으며 선행적인 기술 개발을 통해 인공지능 기반 소방설계에 기술력을 확보하였다.