• 중국학논총
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• no.63
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• pp.29-45
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• 2019

现代汉语普通话中单元音'o'在不同韵母中的音值有差别, 但对此专家议论纷纷, 对学汉语的学生, 教汉语的老师是个难点之一. 本论文对'o'的音值和与'o'结合的二合复元韵母, 三合复元韵母进行考察, 寻找不同韵母中的'o'的正确的音值. 本论文把韩国教材里的关于'o'的说明进行分析, 再对中国学者的解释进行对照检查. 分析调查结果说明, 'o'是不同韵母中的音值不一样, 单元音'o'的实际音值是[o]和[ɔ]之间, 'ou'和'iou'的'o'的实际音值可以说是[o]或[ə]. 对于'ou', 'iou', 'uo'的'o'的音值, 有的韩国学者主张跟韩国语中[오]比较接近, 还有的学者主张跟韩国语中[어]更接近. 本论文还对怎样教好韵母'o'的发音提出方案.

• Journal of information and communication convergence engineering
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• v.16 no.2
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• pp.114-124
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• 2018

This paper illustrates a vision-based vehicle positioning method at an intersection to support the C-ITS. It removes the minor shadow that causes the merging problem by simply eliminating the fractional parts of a quotient image. In order to separate the occlusion, it firstly performs the distance transform to analyze the contents of the single foreground object to find seeds, each of which represents one vehicle. Then, it applies the watershed to find the natural border of two cars. In addition, a general vehicle model and the corresponding space estimation method are proposed. For performance evaluation, the corresponding ground truth data are read and compared with the vision-based detected data. In addition, two criteria, IOU and DEER, are defined to measure the accuracy of the extracted data. The evaluation result shows that the average value of IOU is 0.65 with the hit ratio of 97%. It also shows that the average value of DEER is 0.0467, which means the positioning error is 32.7 centimeters.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.474-475
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• 2021

The accuracy of real-time video analysis system based on 3D skeleton data highly depends on the quality of data. This study proposes a methodology to distinguish noise in 3D skeleton frames using Intersection Over Union (IOU) method. IOU is metric that tells how similar two rectangles (i.e., boxes). Simply, the method decides a frame as noise or not by comparing the frame with a set of valid frames. Our proposed method distinguished noise in 3D skeleton frames with the accuracy of 99%. According to the result, our proposed method can be used to track noise in 3D skeleton frames.

• Journal of Korean Neurosurgical Society
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• v.66 no.1
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• pp.53-62
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• 2023

Objective : Deep learning is a machine learning approach based on artificial neural network training, and object detection algorithm using deep learning is used as the most powerful tool in image analysis. We analyzed and evaluated the diagnostic performance of a deep learning algorithm to identify skull fractures in plain radiographic images and investigated its clinical applicability. Methods : A total of 2026 plain radiographic images of the skull (fracture, 991; normal, 1035) were obtained from 741 patients. The RetinaNet architecture was used as a deep learning model. Precision, recall, and average precision were measured to evaluate the deep learning algorithm's diagnostic performance. Results : In ResNet-152, the average precision for intersection over union (IOU) 0.1, 0.3, and 0.5, were 0.7240, 0.6698, and 0.3687, respectively. When the intersection over union (IOU) and confidence threshold were 0.1, the precision was 0.7292, and the recall was 0.7650. When the IOU threshold was 0.1, and the confidence threshold was 0.6, the true and false rates were 82.9% and 17.1%, respectively. There were significant differences in the true/false and false-positive/false-negative ratios between the anterior-posterior, towne, and both lateral views (p=0.032 and p=0.003). Objects detected in false positives had vascular grooves and suture lines. In false negatives, the detection performance of the diastatic fractures, fractures crossing the suture line, and fractures around the vascular grooves and orbit was poor. Conclusion : The object detection algorithm applied with deep learning is expected to be a valuable tool in diagnosing skull fractures.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.5
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• pp.764-772
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• 2022

The shipping and port industries have traditionally been male-centered, and although the scope of entry for female mariners is increasing, the proportion of female workers is still low. However, research on career barriers and career decision levels in this industry has not yet been conducted. This study can explain the dif iculties in career development experienced by women in this industry and comprehensively explain the socio-cultural context or environmental factors to which the individual belongs in order to improve it. The purpose of this study was to derive career barrier factors and investigate how they affect career decision levels among female students enrolled in M University's Maritime College. The career barriers perceived by female students at Maritime College were derived from gender discrimination (GD), career undecided and lack of preparation (IOU), work-family conflict (WFC), lack of individual characteristics (LPQ), and lower-than-expected job prospects (LOE). As a result of analyzing how the derived career barrier factors af ect the career decision level, it was found that IOU had a significant negative effect on the career decision level. GD, WFC, LPQ, and LOE did not have a significant effect on career decision level. The study conclusions can be used as important data for career guidance and counseling for female maritime college women who want to overcome career barriers and improve their career decision-making levels.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.3
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• pp.119-126
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• 2023

Sarcopenia is not well known enough to be classified as a disease in 2021 in Korea, but it is recognized as a social problem in developed countries that have entered an aging society. The diagnosis of sarcopenia follows the international standard guidelines presented by the European Working Group for Sarcopenia in Older People (EWGSOP) and the d Asian Working Group for Sarcopenia (AWGS). Recently, it is recommended to evaluate muscle function by using physical performance evaluation, walking speed measurement, and standing test in addition to absolute muscle mass as a diagnostic method. As a representative method for measuring muscle mass, the body composition analysis method using DEXA has been formally implemented in clinical practice. In addition, various studies for measuring muscle mass using abdominal images of MRI or CT are being actively conducted. In this paper, we develop an AI image segmentation model based on abdominal images of CT with a relatively short imaging time for the diagnosis of sarcopenia and describe the multicenter validation. We developed an artificial intelligence model using U-Net that can automatically segment muscle, subcutaneous fat, and visceral fat by selecting the L3 region from the CT image. Also, to evaluate the performance of the model, internal verification was performed by calculating the intersection over union (IOU) of the partitioned area, and the results of external verification using data from other hospitals are shown. Based on the verification results, we tried to review and supplement the problems and solutions.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.1
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• pp.65-70
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• 2023

In this paper, we used a deep learning model to detect and segment flame and smoke in real time from fires. To this end, well known U-NET was used to separate and divide the flame and smoke of the fire using multi-class. As a result of learning using the proposed technique, the values of loss error and accuracy are very good at 0.0486 and 0.97996, respectively. The IOU value used in object detection is also very good at 0.849. As a result of predicting fire images that were not used for learning using the learned model, the flame and smoke of fire are well detected and segmented, and smoke color were well distinguished. Proposed method can be used to build fire prediction and detection system.

• The Korean journal of internal medicine
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• v.39 no.4
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• pp.555-562
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• 2024

This study reviews the recent progress of artificial intelligence for colonoscopy from detection to diagnosis. The source of data was 27 original studies in PubMed. The search terms were "colonoscopy" (title) and "deep learning" (abstract). The eligibility criteria were: (1) the dependent variable of gastrointestinal disease; (2) the interventions of deep learning for classification, detection and/or segmentation for colonoscopy; (3) the outcomes of accuracy, sensitivity, specificity, area under the curve (AUC), precision, F1, intersection of union (IOU), Dice and/or inference frames per second (FPS); (3) the publication year of 2021 or later; (4) the publication language of English. Based on the results of this study, different deep learning methods would be appropriate for different tasks for colonoscopy, e.g., Efficientnet with neural architecture search (AUC 99.8%) in the case of classification, You Only Look Once with the instance tracking head (F1 96.3%) in the case of detection, and Unet with dense-dilation-residual blocks (Dice 97.3%) in the case of segmentation. Their performance measures reported varied within 74.0-95.0% for accuracy, 60.0-93.0% for sensitivity, 60.0-100.0% for specificity, 71.0-99.8% for the AUC, 70.1-93.3% for precision, 81.0-96.3% for F1, 57.2-89.5% for the IOU, 75.1-97.3% for Dice and 66-182 for FPS. In conclusion, artificial intelligence provides an effective, non-invasive decision support system for colonoscopy from detection to diagnosis.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.05a
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• pp.550-555
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• 2001

최근 Internet의 가장 보편적이고 저렴한 Backbone Network 기술이다. 이러한 Internet의 전달 능력은 그대로 이용하면서 보안이 우수한 가상 사설망(VPN)을 구축하는 기술에 대한 수요가 증가하고 있다. 본 논문에서는 MPLS 기반의 터널링을 이용한 VPN망 구축 방안과 광 경로(Lightpath)를 갖는 Optical VPN에서 고려해야할 사항에 대해 제시하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.57-58
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• 2020

본 논문은 영상분할 기법 중 DeepLab V3+를 적용하여 초음파 영상속에서 특정 장기, 혹은 기관을 발견하고자한다. 그와 동시에 찾아진 Object의 area를 mIOU 기반으로 초음파 영상속에서의 DeepLab V3+의 성능을 확인하고자 한다.