• Journal of Korean society of Dental Hygiene
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• v.22 no.5
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• pp.323-332
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• 2022

Objectives: This study aims to confirm the educational necessity and utilization of VR media. And it was conducted to prepare basic data necessary for the use of VR in various dental hygiene education in the future and the development of innovative practical training courses. Methods: Before and after using VR in oral radiology practice classes, learning interest (4 items), learning commitment (9 items), learning motivation (5 items), educational media preference (4 items), and satisfaction (10 items) were investigated and analyzed. Friedman two way ANOVA by ran a nonparametric analysis corresponding to repeated measures ANOVA was performed. The statistical significance level was 0.05. Results: It was found that there were statistically significant differences in learning interest, learning immersion, and learning motivation according to the type of oral radiology practice education medium (p<0.05). Conclusions: VR is expected that the use of learning media using VR will lead to students' interest, immersion, and learning motivation in class, and that positive learning effects on VR education media can be sufficiently obtained.

• Journal of Digital Contents Society
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• v.19 no.6
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• pp.1197-1205
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• 2018

In this paper, we perform Single Image Super Resolution(SISR) for acquired images of EOTS or IRST from naval combat system. In order to conduct super resolution, we use Generative Adversarial Networks(GANs), which consists of a generative model to create a super-resolution image from the given low-resolution image and a discriminative model to determine whether the generated super-resolution image is qualified as a high-resolution image by adjusting various learning parameters. The learning parameters consist of a crop size of input image, the depth of sub-pixel layer, and the types of training images. Regarding evaluation method, we apply not only general image quality metrics, but feature descriptor methods. As a result, a larger crop size, a deeper sub-pixel layer, and high-resolution training images yield good performance.

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.247-256
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• 2018

The purpose of this study is to examine effects of pre-learning and role learning using video on self-regulated learning of nursing students in fundamental nursing practice education. A nonequivalent control group was designed to conduct a pre-post test for this study. The participants were assigned to the experimental(n=84) or control group(n=76). Data was collected from March to June, 2016. The experimental group received education based on pre-learning and role learning using video for 13 weeks. On the other hand, the control group only received explanation-based education. Data was analyzed using ${\chi}^2-test$, independent t-test, and ANCOVA. There was a significant increase in rehearsal, metacognition, self-efficacy, and help seeking in the experimental group compared to those in the control group. Results of this study indicate that pre-learning and role learning using video were effective in enhancing students' ability in rehearsal, metacognition, self-efficacy, and help seeking sections.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.05c
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• pp.219-222
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• 2002

본 논문에서는 잘못된 인쇄로 인한 문서상의 잡영이 생기거나 문자 훼손이 있는 문서영상을 복원 하고자 한다. 제안하는 방법은 문서영상을 스캐너로 읽어들여 잡영을 제거 한 뒤 훼손된 숫자 영상에 대해서 프로젝션을 이용하여 숫자 열을 낱낱의 숫자로 분할한다. 각각의 숫자에 대해서 크기가 일정하도록 정규화를 시킨 다음, Backpropagation을 이용하여 훼손된 숫자를 학습하였다. 학습시킨 다음 원 영상과 훼손된 영상을 각 픽셀단위로 비교하여 4-방향 마스크를 이용하여 원래의 숫자 영상으로 복원하도록 한다.

• Journal of Broadcast Engineering
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• v.27 no.6
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• pp.897-905
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• 2022

Research for video key frame detection has been actively conducted in the fields of computer vision. Recently with the advances on deep learning techniques, performance of key frame detection has been improved, but the various type of video content and complicated background are still a problem for efficient learning. In this paper, we propose a novel method for key frame detection, witch utilizes contrastive learning and memory bank module. The proposed method trains the feature extracting network based on the difference between neighboring frames and frames from separate videos. Founded on the contrastive learning, the method saves and updates key frames in the memory bank, witch efficiently reduce redundancy from the video. Experimental results on video dataset show the effectiveness of the proposed method for key frame detection.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.11
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• pp.137-145
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• 2014

This paper suggests an LDA-based long distance face recognition algorithm for intelligent surveillance system. The existing face recognition algorithm using single distance face image as training images caused a problem that face recognition rate is decreased with increasing distance. The face recognition algorithm using face images by actual distance as training images showed good performance. However, this also causes user inconvenience as it requires the user to move one to five meters in person to acquire face images for initial user registration. In this paper, proposed method is used for training images by using single distance face image to automatically create face images by various distances. The test result showed that the proposed face recognition technique generated better performance by average 16.3% in short distance and 18.0% in long distance than the technique using the existing single distance face image as training. When it was compared with the technique that used face images by distance as training, the performance fell 4.3% on average at a close distance and remained the same at a long distance.

• Journal of the Korea Computer Industry Society
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• v.5 no.2
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• pp.301-310
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• 2004

The Current 7th Curriculum for Computer Education emphasized the class of practice oriented, student oriented. But it is very hard because of many students, poor environments, insufficiency of the teaching model. So ADPM will gives our help. a ADPM based practical class using ebook synchronized with video files give a little student's wating time for answering, much student's learning efficiency, much student's voluntary learning custom, a individualized learning. And this study developed the prototype to support the ADPM. This prototype will make up for the weak points in authoring systems, which they are a wizard type program, capturing video file, synchronizing video files. And it will improve a practical class.

• Journal of KIISE:Software and Applications
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• v.28 no.2
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• pp.132-140
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• 2001

야외에서 인지되는 자연 경치는 다양한 개체, 빛의 산란, 또는 변화를 주는 많은 요소들 때문에 컴퓨터 영상처리에서 인식하기가 쉽지 않다. 본 논문에서는 다층 인지 신경망을 이용하여 도로가 포함된 야외영상에 나타나는 개체들을 인식하는 방법을 연구하였다. 자연 영상을 영역화한 후, 각각의 영역들에 대하여 색상과 기하학적인 특성에 근거하여 특성벡터를 추출하고 이를 신경망에 입력하여 각 영역을 구분하는 2단계의 알고리듬을 제안한다. 먼저 야외 영상들을 개선된 영역 확장법과 병합과정에 의하여 개체별로 영역화하였다. 영역화된 연상은 자연 영상과 함께 영상 데이타베이스에 저장되고, 이 자료들을 이용하여 각 영역의 특성벡터를 계산하였다. 이 특성 벡터를 구성된 신경망의 입력층에 전달하면, 각 영역은 27개의 개체 중의 하나로 출력층에서 인식된다. 제안된 방법은 학습에 사용된 데이타, 학스베 사용되지 않은 새로운 데이타, 그리고 모두 합하여 놓은 데이타의 세가지 데이타 군에서 무작위로 선별하여 인식률을 측정하였다. 학습된 데이타에서는 99.4%까지의 인식률을 보여주었고, 학습되지 않은 데이타에 대해서도 최고 89.1%까지의 인식률을 나타내었다. 제안된 방법은 평균적으로 88.1%~97.9%의 인식률을 보여주어 자연 경치의 인식에 신뢰성이 있는 방법으로 사용될 수 있음을 증명하였다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.6
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• pp.887-892
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• 2019

Many researchers have developed a series of vision-based technologies to monitor construction sites automatically. To achieve high performance of vision-based technologies, it is essential to build a large amount and high quality of training image database (DB). To do that, researchers usually visit construction sites, install cameras at the jobsites, and collect images for training DB. However, such human and site-dependent approach requires a huge amount of time and costs, and it would be difficult to represent a range of characteristics of different construction sites and resources. To address these problems, this paper proposes a framework that automatically constructs a training image DB using web crawling techniques. For the validation, the authors conducted two different experiments with the automatically generated DB: construction work type classification and equipment classification. The results showed that the method could successfully build the training image DB for the two classification problems, and the findings of this study can be used to reduce the time and efforts for developing a vision-based technology on construction sites.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.661-663
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• 2004

본 연구는 웹상에서 학습자들에게 동영상(Flash Animation)학습과 심화학습(Feedback Learning)을 통하여 흥미롭고 자기 주도적으로 학습을 할 수 있도록 하여 홍미를 유발시키고 학습효과를 놓이고자 한다. 전체적으로 자료구조에 대한 기초적이고 전반적인 이론 학습 및 알고리즘 수행과정 실습을 할 수 있도록 하였으며 이해하기 힘든 학습내용을 단순한 텍스트 위주의 설명식 수업에서 탈피하여 자바스크립트 및 플래시 액션 기능을 활용한 코스웨어 상에서의 학습자 상호작용에 기반한 환경을 제공하였다 각 단위별로 기본 학습 밀 동영상 학습, 심화학습, 형성평가로 이루어져 있으며 , 학습화면 구성을 윈도우 운영체제 기본 환경과 유사하게 설정하여 학습에 흥미를 돋우고자 하였다.