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송정훈,박종진,Song, Jung-Hoon,Park, Jong-Jin 대한기계학회 1996 大韓機械學會論文集A Vol.20 No.11
Compared to a full three dimensional FEM, the Slab-FEM hybrid method reduces the required computation time distinctly and it can be applied to the analysis of a shape rolling process. However, the method is somewhat approximate and predictions by the method contain certain inaccuracies. In the present investigation a parameter called T-factor was introduced to compensate the inaccuracies of the method and proper values of the parameter were estimated for different widths of bars and reduction ratios. Then, the method was applied to analyze cold and hot rollings of rectangular bars and predicted results were compared to those of experiments. Nonuniform distributions of temperature in the bars were predicted by utilizing the temperature equation obtained for a semi-infinite solid under radiation and convection boundary conditions. It was found out that accuracies of spread and roll separating force predictions could be enhanced by using proper values of the T-factor.
송정훈 ( Jung Hoon Song ),허진국 ( Jin Gook Huh ),김유선 ( You Sun Kim ),이진호 ( Jin Ho Lee ),장원철 ( Won Cheol Jang ),옥경선 ( Kyung Sun Ok ),류수형 ( Soo Hyung Ryu ),이정환 ( Jung Hwan Lee ),문정섭 ( Jeong Seop Moon ) 대한장연구학회 2008 Intestinal Research Vol.6 No.2
Background/Aims: Although colonic diverticular disease is less common in Koreans than in Western people, its incidence has been on the increase in Korea. We investigated the clinical characteristics and related complications of colonic diverticular disease in Koreans. Methods: We retrospectively reviewed the medical records of 9,006 patients who underwent colonoscopy at the Seoul Paik hospital between July 2002 and January 2008. Results: Of the 9,006 patients, there were 654 cases (7.3%) of colonic diverticulosis (472 men, 182 women). The mean age of the patients was 54.6±12.0 years. The right colon was involved in 535 cases, the left colon was involved in 86 cases and both the left and right colon was involved in 33 cases. Among the patients, a single diverticulum was seen in 253 cases and two or more diverticuli were seen in 401 cases. Related complications were diverticulitis (11 cases, 1.7%) and diverticular bleeding (3 cases, 0.5%). Conclusions: The incidence of colonic diverticular disease in Korea shows an increasing trend. Colonic diverticular lesions are frequently found in the right colon. (Intest Res 2008;6:110-115)
영상에서 convolutional denoising autoencoder 모형을 이용한 영상복원
송정훈(Jung Hun Song),김정희(Jeong Hee Kim),임동훈(Dong Hoon Lim) 한국데이터정보과학회 2020 한국데이터정보과학회지 Vol.31 No.1
디지털 영상은 획득, 전송, 처리하는 과정에서 다양한 잡음 (noise)에 의해 훼손되어 이에 따른 영상 복원 (image restoration)의 필요성이 대두되고 있다. 지금까지 영상에서 잡음을 제거하는 방법은 특정분포 하에서 설계된 고유한 필터를 사용하였는데 이 경우 분포의 특성을 만족하지 않는 경우 성능이 현저히 떨어지는 영향이 있다. 본 논문에서는 딥러닝의 convolutional denoising autoencoder (CDAE) 모형을 이용하여 잡음을 제거하고자 한다. CDAE 모형은 CNN (convolutional neural network) 모형과 DAE (denoising autoencoder) 모형의 결합 형태로서 영상의 잡음 분포에 관계없이 적용 가능한 방법이다. 본 논문에서 제안된 CDAE 모형을 평가하기 위해 다양한 잡음 즉, 가우시안 잡음 (gaussian noise), 임펄스 잡음 (impulse noise) 그리고 스펙클 잡음 (speckle noise) 에 의해 훼손된 영상을 고려하였으며, 성능실험결과, CDAE 모형은 기존의 CNN 모형 및 전통적인 필터 즉, Mean 필터, Median 필터 그리고 Lee 필터 보다 좋은 복원 영상을 낳았고 또한, PSNR (peak signal-to-noise ratio)와 MAE (mean absolute error) 면에서 좋은 수치를 보였다. Digital images have been compromised by various noise in the process of acquisition, transmission and processing, resulting in the need for image restoration. Until now, methods of removing noise from images have used unique filters designed under certain distributions, which tend to be significantly less effective if the characteristics of the distribution are not met. In this paper, we are going to use the convolutional denoising autoencoder (CDAE) model of deep learning to eliminate noise. The CDAE model is a combination of the CNN (convolutional neural network) model and the DAE (denoising autoencoder) model, which is an applicable method regardless of the noise distribution of images. In order to evaluate the CDAE model proposed in this paper, we considered images damaged by various noises, Gaussian noise, impulse noise and speckle noise. We compared our CDAE model with CNN and traditional filters such as Mean filter, Median filter and Lee filter. Experimental results on several images show that the CDAE model yields significantly superior image quality and better PSNR (peak signal-to- noise ratio) and MAE (mean absolute error).