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전해명,노재규,Chon, Haemyung,Noh, Jackyou 대한임베디드공학회 2022 대한임베디드공학회논문지 Vol.17 No.5
According to statistics conducted by the Korea Coast Guard, the number of accidents on small boats under 5 tons is increasing every year. This is because only a small number of people are on board. The previously developed maritime distress and safety systems are not well distributed because passengers must be equipped with additional remote equipment. The purpose of this study is to develop a distress awareness system that recognizes man over-board situations in real time. This study aims to present the part of the passenger tracking system among the small ship's distress awareness situational system that can generate passenger's location information in real time using deep learning based object detection and tracking technologies. The system consisted of the following steps. 1) the passenger location information is generated in the form of Bounding box using its detection model (YOLOv3). 2) Based on the Bounding box data, Deep SORT predicts the Bounding box's position in the next frame of the image with Kalman filter. 3) When the actual Bounding Box is created within the range predicted by Kalman-filter, Deep SORT repeats the process of recognizing it as the same object. 4) If the Bounding box deviates the ship's area or an error occurs in the number of tracking occupant, the system is decided the distress situation and issues an alert. This study is expected to complement the problems of existing technologies and ensure the safety of individuals aboard small boats.
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전해명,김욱,허훈,이준현,원종만,Jeon, Hae-Myung,Kim, Wook,Hur, Hoon,Lee, Joon-Hyun,Won, Jong-Man 대한위암학회 2004 대한위암학회지 Vol.4 No.4
Purpose: Recently, because of the increasing numbers of early gastric cancer patients and improvements in their survivals, greater attention has been directed towards the quality of life and nutritional status of gastric cancer patients after surgery. However, conventional reconstructions, Billroth- I, -II (B-I and B-II) or Roux-en-Y, have proven to have certain limitations, such as a small reservoir, and a malabsorption for iron, fat, calcium, and carotene. To overcome these limitations, we used a jejunal pouch interposition(JPI) after a distal gastrectomy not only to substitute for the small reservoir but also to maintain a physiologic pathway for ingested foods. Materials and Methods: A total of 196 gastric cancer patients who underwent a distal gastrectomy between March 2001 and February 2004 were divided into 3 groups: JPI group (n=100), B-I group (n=29), and B-II group (n=67). We assessed the patient's nutritional status, gastric emptying time, and gastrofiberscopic findings. Results: The percents of body weight loss at 6 months, 1 year, and 2 years postoperatively in the JPI group ($5.14\%,\;3.01\%,\;2.37\%$) were significantly less than those of the conventional B-I ($8.41\%,\;6.69\%,\;5.90\%$) and B-II groups ($7.50\%,\;7.65\%,\;5.86\%$) (P=0.011, 0.000, 0.013). The laboratory findings showed no significant differences between the 3 groups, except for a higher total protein level in the JPI group after 6 months postoperatively. Especially, stage I and II cancers in the JPI group showed much higher total protein levels after 1 year postoperatively. The gastric emptying times in the $\^{99m}$Tc- semisolid scans at 6 months, 1 year, and 2 years postoperatively were 102.5, 83.1, and 58.1 minutes in the JPI group, 95.5, 92.0, and 58.5 minutes in the B-I group, and 53.9, 69.1, and 50.2 minutes in the B-II group, respectively. Also, the symptomatic gastric stasis detected with a gastrofiberscope during the early postoperative period (6 months) was gradually improved. Conclusion: From a nutritional aspect, a jejunal pouch interposition after a distal gastrectomy could be an alternative reconstruction method, especially in stage I and II gastric cancer patients, in spite of the longer operation time and the probable delayed gastric emptying.
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다시점 영상 집합을 활용한 선체 블록 분류를 위한 CNN 모델 성능 비교 연구
전해명(Haemyung Chon),노재규(Jackyou Noh) 대한조선학회 2020 대한조선학회 논문집 Vol.57 No.3
It is important to identify the location of ship hull blocks with exact block identification number when scheduling the shipbuilding process. The wrong information on the location and identification number of some hull block can cause low productivity by spending time to find where the exact hull block is. In order to solve this problem, it is necessary to equip the system to track the location of the blocks and to identify the identification numbers of the blocks automatically. There were a lot of researches of location tracking system for the hull blocks on the stockyard. However there has been no research to identify the hull blocks on the stockyard. This study compares the performance of 5 Convolutional Neural Network (CNN) models with multi-view image set on the classification of the hull blocks to identify the blocks on the stockyard. The CNN models are open algorithms of ImageNet Large-Scale Visual Recognition Competition (ILSVRC). Four scaled hull block models are used to acquire the images of ship hull blocks. Learning and transfer learning of the CNN models with original training data and augmented data of the original training data were done. 20 tests and predictions in consideration of five CNN models and four cases of training conditions are performed. In order to compare the classification performance of the CNN models, accuracy and average F1-Score from confusion matrix are adopted as the performance measures. As a result of the comparison, Resnet-152v2 model shows the highest accuracy and average F1-Score with full block prediction image set and with cropped block prediction image set.
ScienceON
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