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이주형(Ju Hyung Lee),이호영(Ho Young Lee),김현수(Hyeon Su Kim),김인기(In Ki Kim),이중기(Choong Ki Lee),손경락(Kyung Rak Sohj),문세광(Sac Kwang Moon) 대한소화기학회 1996 대한소화기학회지 Vol.28 No.1
Multiple lymphomatous polyposis(MLP) of the gastrointestinal tract is a rare form of gastrointes- tinal lymphoma. Clinical, histopathologic, and immunohistochemical findings are somewhat different from those of primary gastrointestinal lymphoma. Therefore, it is important to recognize this rare form of gastrointestinal lymphoma for its prognostic and therapeutic implications. We report a case of MLP of descending colon, sigmoid colon and rectum in 56 years old man who has a soft tissue mass in right middle abdomen with a review of literature. He was treated with 6 cycles of CHOP regimen resulting in good clinical remission. (Korean J Gastroenterol 1996; 28:118 - 123)
Efficient Large Dataset Construction using Image Smoothing and Image Size Reduction
Jaemin HWANG,Sac LEE,Hyunwoo LEE,Seyun PARK,Jiyoung LIM 한국인공지능학회 2023 인공지능연구 (KJAI) Vol.11 No.1
With the continuous growth in the amount of data collected and analyzed, deep learning has become increasingly popular for extracting meaningful insights from various fields. However, hardware limitations pose a challenge for achieving meaningful results with limited data. To address this challenge, this paper proposes an algorithm that leverages the characteristics of convolutional neural networks (CNNs) to reduce the size of image datasets by 20% through smoothing and shrinking the size of images using color elements. The proposed algorithm reduces the learning time and, as a result, the computational load on hardware. The experiments conducted in this study show that the proposed method achieves effective learning with similar or slightly higher accuracy than the original dataset while reducing computational and time costs. This color-centric dataset construction method using image smoothing techniques can lead to more efficient learning on CNNs. This method can be applied in various applications, such as image classification and recognition, and can contribute to more efficient and cost-effective deep learning. This paper presents a promising approach to reducing the computational load and time costs associated with deep learning and provides meaningful results with limited data, enabling them to apply deep learning to a broader range of applications.
Real-time Image Scanning System for Detecting Tunnel Cracks Using Linescan Cameras
Dong Hyun Jeong,Young Rin Kim,I-Sac Cho,Eun Ju Kim,Kang Moon Lee,Kwang Won Jin,Chang Geun Song 한국멀티미디어학회 2007 멀티미디어학회논문지 Vol.10 No.6
In this paper, real-time image scanning system using linescan cameras is designed. The system is specially designed to diagnose and analyse the conditions of tunnels such as crack widths through the captured images. The system consists of two major parts, the image acquisition system and the image merging system. To save scanned image data into storage media in real-time, the image acquisition system has been designed with two different control and management modules. The control modules are in charge of controlling the hardware device and the management modules handle system resources so that the scanned images are safely saved to the magnetic storage devices. The system can be mounted to various kinds of vehicles. After taking images, the image merging system generates extended images by combining saved images. Several tests are conducted in laboratory as well as in the field. In the laboratory simulation, both systems are tested several times and upgraded. In the field-testing, the image acquisition system is mounted to a specially designed vehicle and images of the interior surface of the tunnel are captured. The system is successfully tested in a real tunnel with a vehicle at the speed of 20 ㎞/h. The captured images of the tunnel condition including cracks are vivid enough for an expert to diagnose the state of the tunnel using images instead of seeing through his/her eyes.
Real-time Image Scanning System for Detecting Tunnel Cracks Using Linescan Cameras
Jeong, Dong-Hyun,Kim, Young-Rin,Cho, I-Sac,Kim, Eun-Ju,Lee, Kang-Moon,Jin, Kwang-Won,Song, Chang-Geun Korea Multimedia Society 2007 멀티미디어학회논문지 Vol.10 No.6
In this paper, real-time image scanning system using linescan cameras is designed. The system is specially designed to diagnose and analyse the conditions of tunnels such as crack widths through the captured images. The system consists of two major parts, the image acquisition system and the image merging system. To save scanned image data into storage media in real-time, the image acquisition system has been designed with two different control and management modules. The control modules are in charge of controlling the hardware device and the management modules handle system resources so that the scanned images are safely saved to the magnetic storage devices. The system can be mounted to various kinds of vehicles. After taking images, the image merging system generates extended images by combining saved images. Several tests are conducted in laboratory as well as in the field. In the laboratory simulation, both systems are tested several times and upgraded. In the field-testing, the image acquisition system is mounted to a specially designed vehicle and images of the interior surface of the tunnel are captured. The system is successfully tested in a real tunnel with a vehicle at the speed of 20 km/h. The captured images of the tunnel condition including cracks are vivid enough for an expert to diagnose the state of the tunnel using images instead of seeing through his/her eyes.