• Journal of Convergence for Information Technology
• /
• v.8 no.6
• /
• pp.281-285
• /
• 2018

The purpose of this research is to make a Flipped-MOOC model which can be applied and practiced in the college course after analyzing the characteristics and cases of MOOC and Flipped learning. For this, this study implemented the following tasks. First, this study analyzed the management and class types of MOOC and flipped learning through literature research. Secondly, flipped learning was applied in the course for a semester and the strong point and weak point of the course was analyzed and the alternative was suggested. Thirdly, the core ideas and strategies of Flipped-MOOC model was deducted for enhancing the participation and interaction of the students in the course which uses the MOOC content and applies flipped learning, and the instructional strategies and direction for the effective management in the real educational field was suggested. As a result, Flipped-MOOC model is expected to contribute for the educational revolution, change and quality improvement, and it is expected that Flipped-MOOC model might contribute to the lifelong education and educational competitiveness.

• The Journal of the Convergence on Culture Technology
• /
• v.4 no.2
• /
• pp.197-204
• /
• 2018

One of the hottest issues in our 21st century is AI. Just as the automation of manual labor has been achieved through the Industrial Revolution in the agricultural society, the intelligence information society has come through the SW Revolution in the information society. With the advent of Google 'Alpha Go', the computer has learned and predicted its own machine learning, and now the time has come for the computer to surpass the human, even to the world of Baduk, in other words, the computer. Machine learning ML (machine learning) is a field of artificial intelligence. Machine learning ML (machine learning) is a field of artificial intelligence, which means that AI technology is developed to allow the computer to learn by itself. The time has come when computers are beyond human beings. Many companies use machine learning, for example, to keep learning images on Facebook, and then telling them who they are. We also used a neural network to build an efficient energy usage model for Google's data center optimization. As another example, Microsoft's real-time interpretation model is a more sophisticated translation model as the language-related input data increases through translation learning. As machine learning has been increasingly used in many fields, we have to jump into the AI industry to move forward in our 21st century society.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.10 no.1
• /
• pp.55-67
• /
• 2015

This study examines innovation resistance to smart learning, an emerging innovative technology for startups and corporate ventures in the education market. The study explores whether the relative advantage, compatibility and complexity of an innovation, attitudes toward existing learning method(s), and perceived self-efficacy significantly affect innovation resistance. Additionally, the effects of such innovation resistance on future use and the moderating effect according to demographic characteristics are examined. The results of the analysis using a structural equation model showed that all the factors considered (except relative advantage) affects innovation resistance, innovation resistance significantly affects intention to use.

• Smart Media Journal
• /
• v.11 no.3
• /
• pp.74-89
• /
• 2022

Due to the unique recipes and processes of each company, not only differences among the results of dyeing textile materials exist but they are also difficult to predict. This study attempted to develop a color prediction model based on deep learning to optimize color realization in the dyeing process. For this purpose, deep learning-based models such as multilayer perceptron, CNN and LSTM models were selected. Three forecasting models were trained by collecting a total of 376 data sets. The three predictive models were compared and analyzed using the cross-validation method. The mean of the CMC (2:1) color difference for the prediction results of the LSTM model was found to be the best.

• Annual Conference on Human and Language Technology
• /
• 2018.10a
• /
• pp.584-586
• /
• 2018

사이버 공격 기법이 다양해지고 지능화됨에 따라 침해사고 발생이 증가하고 있으며, 그에 따른 피해도 확산되고 있다. 이에 따라 보안 기업들은 다양한 침해사고를 파악하고 빠르게 대처하기 위하여 위협정보를 정리한 인텔리전스 리포트를 배포하고 있다. 하지만 인텔리전스 리포트의 형식이 정형화되어 있지 않고 점점 증가하고 있어, 인텔리전스 리포트를 수작업을 통해 분류하기 힘들다는 문제점이 있다. 이와 같은 문제를 해결하기 위해 본 논문에서는 개체명 인식 시스템을 활용하여 비정형 인텔리전스 리포트에서 위협정보를 자동으로 탐지하고 추출할 수 있는 모델을 제안한다.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2024.01a
• /
• pp.1-4
• /
• 2024

코로나 19(Covide-19)이후 가상과 현실이 융·복합 되어 사회·경제·문학활동과 가치 창출이 가능한 메타버스가 차세대 핵심산업으로 부상하고 있다. 이에 자사 보유 기술, IP(Intellectual Property) 등을 활용하여 메타버스 플랫폼을 구축하고자 하는 기업들이 증가하여 지식재산권을 둔 법적 이슈들이 새롭게 나타나고 있다. 따라서 본 논문에서는 상표권 침해를 보호하기 위하여 딥 러닝 기반 객체 탐지모델인 YOLOv5 모델을 활용한 메타버스 환경에서의 상표권 탐지 시스템을 제안한다.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.16 no.4
• /
• pp.189-196
• /
• 2016

The purpose of this study is to investigate the causal relationships among self-efficacy, perceived usefulness, supervisor and peer support, satisfaction, and transfer intentions in the corporate mobile learning. For this study, the web survey was administered to 302 mobile learning learners of the A domestic corporation in South Korea. Structural equation modeling(SEM) analysis was conducted in order to examine the causal relationships among the variables. The results indicated that first, self-efficacy, perceived usefulness, and supervisor and peer support had positive effects on satisfaction. Second, supervisor and peer support and satisfaction had positive effects on transfer intentions. Third, satisfaction mediated the relationship between self-efficacy and perceived usefulness, while it did partially the relationship between supervisor and peer support and transfer intentions. Based on the result of the research, the study proposes organizational environment with cooperative supervisor and peer support should be made in order to improve the level of learners' transfer intentions. In addition, learning strategies that facilitate learners' self-efficacy and mobile information technology acceptance are needed to develop for enhancing the learners' satisfaction.

• Journal of Convergence for Information Technology
• /
• v.10 no.6
• /
• pp.59-69
• /
• 2020

This study is a qualitative study in which a focus group interview is applied to explore nursing students' perception of blended learning. 21 students in the 4th grade of nursing department were divided into 4 groups to collect data through interviews and content analysis was conducted. As a result of the study, it was categorized into four topics: 'Application and operation that are not thoroughly prepared', 'Loss of direction and departure from learning', 'One-way listening', and 'Convenience'. Students were satisfied with blended learning which is free from time and space constraints and repetitive, but felt inadequacy and unsatisfactoriness about quality of online contents, system, and preparation for applying blended learning. In order to apply blended learning in the future nursing classes, high-quality online content should be developed based on the effective design of online and offline classes considering the curriculum, and a systematic, administrative, financial, and institutional foundation to support online course should be prepared. In addition, a support system should be created to guide students' self-directed learning activities in online classes of blended learning.

• Journal of Korea Technology Innovation Society
• /
• v.21 no.2
• /
• pp.818-837
• /
• 2018

Technological innovation has inherent difficulties, largely due to the uncertainties of technology. Thus, the forecasting methodology to reduce the risk of uncertainty in the innovation process has been presented both in quantitative and qualitative fields. On the other hand, big data and artificial intelligence have attracted great interest recently, and deep learning, which is one of the algorithms of AlphaGo, is showing excellent performance. In this study, deep learning methodology was applied to the prediction of innovation performance. To make the prediction model, we used KIS 2016 data. The input factors were importance of information source and innovation objectives and the output factor was innovation performance index, which was calculated for this study. As a result of the analysis, it can be confirmed that the accuracy of prediction is improved compared with the previous studies. As learning progressed, the degree of freedom of prediction also improved.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2021.07a
• /
• pp.363-366
• /
• 2021

본 연구에서는 콘크리트의 균열을 추출하여 추출한 균열을 분석하고 시각화하여 나타내는 방법을 제안한다. 추출한 균열을 분석하여 길이, 넓이, 평균 폭 등의 주요 지표를 측정하여 균열 부위에 대한 세부 정보를 파악할 수 있게 하였다. 특히 균열 분석 과정에서 기존의 균열 중심부와 에지 간의 직선 최단 거리 계산을 통한 균열 폭 측정 방식이 아닌 내접원 탐색 방식을 적용하여 다각형의 균열에 대한 폭 측정 방식을 제안하고 있다. 또한 분석 결과를 Wavefront 3D OBJ 모델과 CAD 파일로 생성하였고, 이를 웹 브라우저를 통해 입체적으로 볼 수 있도록 시각화 하였다.