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복강경 담낭 절제술 2,523예 시행 중 개복술로 전환한 111예에 대한 임상적 고찰
방지성(Ji Sung Bang),최유신(Yu Sin Choi),김범규(Beom Gyu Kim),차성재(Sung Jae Cha),지경천(Kyung Choun Chi),이정효(Jung Hyo Lee),장인택(In Taik Chang) 한국간담췌외과학회 2008 한국간담췌외과학회지 Vol.12 No.3
Purpose: While laparoscopic cholecystectomy can be successfully performed in the majority of patients, conversion to open procedure is still necessary in certain cases. The purpose of this study was to identify the discerning factors that helped to predict the need for conversion to open cholecystectomy. Methods: A retrospective review was conducted on the data for 2,523 laparoscopic cholecystectomies performed at Chung-Ang University Hospital between January 2002 and July 2007. Patient sex, age, height, weight, body mass index (BMI), duration of preoperative hospital stay, preoperative physical examination, laboratory data, radiologic findings, and reasons for conversion to open procedure were evaluated. Results: Adhesion was perceived to be the most critical factor for conversion in 56 of 111 total cases (50.5%). Bleeding (22.5%), bile duct injury (11.7%), inflammation (9.0%), and uncertain anatomy (6.3%) followed sequentially in incidence. Factors found to significantly increase the risk of conversion on univariate analysis were patient age >70 years, male sex, previous abdominal operation, preoperative common bile duct stone, tenderness in the right upper quadrant, distended shape of the gallbladder, and pericholecystic fluid collection. On multivariate analysis, the following factors were found to be associated with a higher risk: patient age >70 years (p=0.002), male sex (p=0.012), previous abdominal operation (p<0.0001), and preoperative common bile duct stone (p=0.041). Conclusion: In the case of operations with such discerning factors, surgeons should be more cautious and delicate in all procedures throughout the operative period. Furthermore, to reduce the risk of additional severe complications, surgeons need to decide early on if they will perform a conversion.
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HOG와 인공신경망을 이용한 자동차 모델 인식 시스템 성능 분석
박기완(Ki-Wan Park),방지성(Ji-Sung Bang),김병만(Byeong-Man Kim) 한국산업정보학회 2016 한국산업정보학회논문지 Vol.21 No.5
본 논문에서는 영상처리와 기계학습을 이용하여 자동차를 판별하는 시스템을 제안하고 그 성능을 확인한다. 차량의 앞면을 인식 하도록 하였으며 앞면을 선택한 이유는 제조사, 모델별로 앞면이 다르고 개조가 힘들기 때문이다. 제안하는 방법은 먼저 학습 데이터로부터 HOG특징을 추출하고, 이 특징 데이터에 대해 인공신경망 학습기법을 적용하여 판별 모델을 구축한다. 그리고 사용자가 자동차의 앞면을 찍으면 그 사진에서 특징점을 추출하고 특징점을 학습된 판별 모델을 거쳐 차량의 정보를 표시한다. 실험 결과, 98%의 높은 평균 인식률을 보였다. In this paper, a car model recognition system using image processing and machine learning is proposed and it’s performance is also evaluated. The system recognizes the front of car because the front of car is different for every car model and manufacturer, and difficult to remodel. The proposed method extracts HOG features from training data set, then builds classification model by the HOG features. If user takes photo of the front of car, then HOG features are extracted from the photo image and are used to determine the model of car based on the trained classification model. Experimental results show a high average recognition rate of 98%.
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