• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.1076-1084
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• 2019

In this paper, we introduced a system that extracts metadata by recognizing characters and objects in media using deep learning technology. In the field of broadcasting, multimedia contents such as video, audio, image, and text have been converted to digital contents for a long time, but the unconverted resources still remain vast. Building media archives requires a lot of manual work, which is time consuming and costly. Therefore, by implementing a deep learning-based metadata generation system, it is possible to save time and cost in constructing media archives. The whole system consists of four elements: training data generation module, object recognition module, character recognition module, and API server. The deep learning network module and the face recognition module are implemented to recognize characters and objects from the media and describe them as metadata. The training data generation module was designed separately to facilitate the construction of data for training neural network, and the functions of face recognition and object recognition were configured as an API server. We trained the two neural-networks using 1500 persons and 80 kinds of object data and confirmed that the accuracy is 98% in the character test data and 42% in the object data.

• Journal of the Korea Computer Graphics Society
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• v.25 no.3
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• pp.123-131
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• 2019

In this study, we modified CT images of femoral head in consideration of anatomically meaningful structure, proposing the method to augment the training data of convolution Neural network for segmentation of femur mesh model. First, the femur mesh model is obtained from the CT image. Then divide the mesh model into meaningful parts by using cluster analysis on geometric characteristic of mesh surface. Finally, transform the segments by using an appropriate mesh deformation algorithm, then create new CT images by warping CT images accordingly. Deep learning models using the data enhancement methods of this study show better image division performance compared to data augmentation methods which have been commonly used, such as geometric conversion or color conversion.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.4
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• pp.35-45
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• 2018

Users are sharing many of contents such as text, image, video, and so on in SNS. There are various information as like as personal interesting, opinion, and relationship in social media contents. Therefore, many of recommendation systems or search systems are being developed through analysis of social media contents. In order to extract subject-related topics of social context being collected from social media channels in developing those system, it is necessary to develop ontologies for semantic analysis. However, it is difficult to develop formal ontology because social media contents have the characteristics of non-formal data. Therefore, we develop a social topic system based on semantic and subject correlation. First of all, an extracting system of social topic based on semantic relationship analyzes semantic correlation and then extracts topics expressing semantic information of corresponding social context. Because the possibility of developing formal ontology expressing fully semantic information of various areas is limited, we develop a self-extensible architecture of ontology for semantic correlation. And then, a classifier of social contents and feed back classifies equivalent subject's social contents and feedbacks for extracting social topics according semantic correlation. The result of analyzing social contents and feedbacks extracts subject keyword, and index by measuring the degree of association based on social topic's semantic correlation. Deep Learning is applied into the process of indexing for improving accuracy and performance of mapping analysis of subject's extracting and semantic correlation. We expect that proposed system provides customized contents for users as well as optimized searching results because of analyzing semantic and subject correlation.

• The Journal of Korean Academic Society of Nursing Education
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• v.27 no.1
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• pp.59-67
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• 2021

Purpose: The purpose of this study is to examine the effectiveness of Problem-based Learning (PBL) in an interdisciplinary college class. This class was run under the theme of 'Nurse Social Content Creators' (NSCC) in the Korean Nurses Association (KNA)'s industry-university collaborative project designed to promote a positive image of nurses among the public. Methods: Study 1 examined changes in perception about nurses among the PBL participants before and after the program. A one-group pre-post test experimental design was applied, and the data were analyzed using a Wilcoxon signed-rank test. Study 2 identified differences of perceptions of nurses between people who had observed the PBL final presentation and people who had not. A post-test-only with nonequivalent group experimental design was used, and the data were analyzed using a Mann-Whitney U test. Results: Study 1 revealed a significant increase of positive perceptions towards nurses. Study 2 revealed a significant difference between the PBL presentation audience group and the control group. Students who had observed the PBL program showed more positive perceptions of nurses than students who had not. Conclusion: This research is an important study with high practicality in the area of media studies as well as in nursing. The PBL teaching method was proven to be effective in enhancing perceptions of nurses.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1377-1393
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• 2023

Social media is used for various purposes including entertainment, communication, information search, and voicing their thoughts and concerns about a service, product, or issue. The social media data can be used for information mining and getting insights from it. The World Health Organization has listed COVID-19 as a global epidemic since 2020. People from every aspect of life as well as the entire health system have been severely impacted by this pandemic. Even now, after almost three years of the pandemic declaration, the fear caused by the COVID-19 virus leading to higher depression, stress, and anxiety levels has not been fully overcome. This has also triggered numerous kinds of discussions covering various aspects of the pandemic on the social media platforms. Among these aspects is the part focused on vaccines developed by different countries, their features and the advantages and disadvantages associated with each vaccine. Social media users often share their thoughts about vaccinations and vaccines. This data can be used to determine the popularity levels of vaccines, which can provide the producers with some insight for future decision making about their product. In this article, we used Twitter data for the vaccine popularity detection. We gathered data by scraping tweets about various vaccines from different countries. After that, various machine learning and deep learning models, i.e., naive bayes, decision tree, support vector machines, k-nearest neighbor, and deep neural network are used for sentiment analysis to determine the popularity of each vaccine. The results of experiments show that the proposed deep neural network model outperforms the other models by achieving 97.87% accuracy.

• Journal of the Korean Society for Library and Information Science
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• v.12
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• pp.121-147
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• 1985

In the modern communicative age, the standards of the school libraries are the qualitative guarantee on the services of school libraries or school library media programs, as the guidline, the active guide, the policy documentation and criteria for the professional excellence. The standards of SLMP were revised the sixth time by the school library profession(ALA) with the members or agency of NEA in the U.S. There are the first standard was a quantitative; 'the Certain Report'(by A.L.A., 1920) appearing that the school library is the heart of the school, 2nd 1925; turning up the teaching material source and personel, 'School Libraries for today and tomorrow' (by AASL, 1945) incluseing the instructional materials and the 7th educational ideas in the quantitative feature, 'Standards for School Library Programs' (by AASL, 1960) expressing the instructional material center, communicative environment, learning and teaching laboratory, 'Standards for school media programs' (by DAVI & AASL, 1969) implicating the instructional resource, learning and teaching laboratory, the condition precedent of qualitative education for excellence, 'Standards for media programs; District/school (by AASL & AECT, 1975) containing the improving user's educational experience and personal freedom on the use of SLMP's services. Through changing the standards of SLMP in the US, We have known that the main educational idea in the standards are; (1) SLMP is the instructional force and resource for qualitative, excellence education by learning and teaching laboratory, instructional resource, communicative environment (2) SLMP is the actualizing force and resource for user's self-realization by intellectual and personal excellence, individualizing, humanizing and personalizing education.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.07a
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• pp.423-425
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• 2014

스마트기기의 발달과 보급의 확산과 함께 모바일 인터넷 등 통신서비스 환경이 발전함에 따라 이러닝 환경의 고도화가 진행되면서 유비쿼터스러닝, 모바일러닝을 넘어 스마트 디바이스와 이러닝 연관 신기술이 융 복합된 새로운 형태의 교육 시스템인 스마트러닝으로 발전하고 있다. 하지만 현재 다양한 스마트 디바이스 기반의 스마트러닝 서비스를 통해 교육 콘텐츠를 학습자에게 제공하기 위해서는 기존 이러닝 콘텐츠의 구조 개선이 불가피한 상황이며 콘텐츠의 재사용 가능성, 접근성, 상호운용성, 항구성 및 질적 수월성의 향상을 위한 스마트러닝 표준화가 요구되고 있다. 이에 본 논문에서는 기존 이러닝 콘텐츠를 통한 스마트러닝을 구현하기 위한 방안으로 EPUB 3.0 표준을 활용한 스마트러닝 솔루션을 제안하고자 한다.

• Journal of Korea Multimedia Society
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• v.24 no.2
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• pp.208-214
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• 2021

Since face masks in public were mandated during COVID-19, more people have taken temperature checks, with their masks on. The study has developed a contactless thermal camera that accurately measures temperatures of people wearing different kinds of masks, detect people wearing masks wrong, and record the temperature data. The built-in system that identifies people wearing masks wrong is what masks our contactless thermal camera differentiated from other thermal cameras. Also our contactless thermal camera can keep track of the number of mask wearers in different regions and their temperatures. Thus, the analysis of such regional data can significantly contribute to stemming the spread of the virus.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.47-49
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• 2020

본 논문은 유아기 언어 발달 시기에 한글의 기초 단계를 학습하기 위해 기존의 학습지 형태의 한글 교육 선행 학습과는 달리 시각적, 청각적 효과로 몸의 감각을 통해 창의적이고 동적으로 사물을 배우며 이러한 자극으로 정보를 기억하고 축적할 수 있는 한글 학습 교육용 콘텐츠를 개발하고자 한다. 이는 유아의 호기심을 자극할 뿐만 아니라 모바일 플랫폼과의 상호작용을 통해 재미와 즐거움을 키우며 나아가 지식을 얻을 수 있다. 더불어 유아가 한글 학습의 놀이 과정을 통해 창의력을 높이고, 다방면으로 문제를 해결할 수 있는 능력을 키울 뿐만 아니라 학습을 통해 스스로 이끌어가는 자기 주도적 학습 능력을 키울 수 있을 것으로 기대한다.

• Science of Emotion and Sensibility
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• v.14 no.2
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• pp.197-206
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• 2011

The purpose of the study was to suggest automatic recognition of the subject's level of arousal into high arousal and low arousal with neural network SOM learning. The automatic recognition in the level of arousal is composed of three stages. First, it is a stage of ECG measurement and analysis. It measures the subject playing a shooting game with ECG and extracts characteristics for SOM learning. Second, it is a stage of SOM learning. It learns input vectors extracting characteristics. Finally, it is a stage of arousal recognition which recognize the subject's level of arousal when new vectors are input after SOM learning is completed. The study expresses recognition results in the level of arousal and the level of arousal in numerical value and graph when SOM learning results in the level of arousal and new vectors are input. Finally, SOM evaluation was analyzed average 86% by comparing emotion evaluation results of the existing research with automatic recognition results of SOM in order. The study could experience automatic recognition with other levels of arousal by each subject with SOM.