• Annual Conference of KIPS
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• 2021.11a
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• pp.887-890
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• 2021

최근 AI 를 활용한 의료 진단 솔루션 시장이 크게 성장함에 따라 의료 인공지능 기술에 대한 대학 교육에 대한 수요가 증가하고 있지만, 개인정보 유출의 위험성 등으로 인하여 의료 데이터를 대학 교육에 활용하기 어려운 실정이다. 본 논문에서는 실제 의료 데이터 대신 생성적 적대 신경망(GAN)으로 합성된 흉부 X-ray 영상을 활용한 의료 인공지능 교육 모델의 사례를 제시한다. 프로메디우스(주)에 의해 제공받은 흉부 X-ray 합성영상을 사용하여, VGG-16 모델을 훈련하고 성능을 검증 및 평가하며 미세조정을 통해 성능을 개선하는 교육 모델을 구성하였다. 또한 교육모델이 의료 인공지능에 대한 학생들의 이해력 향상에 기여한 효과를 정량적으로 평가하였다.

• 한국정보교육학회:학술대회논문집
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• 2006.01a
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• pp.63-68
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• 2006

초등학교 정보통신기술교육은 정보 사회를 살아가는 학생들에게 학습과 일상 생활에서 필요한 최소한의 컴퓨터 활용 능력을 길러 중학교에서 보다 깊이 있게 다루어질 학습과제를 해결하기 위한 기초 기능을 다지는데 목표가 있다. 그런데 정보통신기술교육을 할 때, 여러 가지 요인으로 인하여 학생들의 개인별 ICT 활용능력 차이가 매우 크게 나타나 있는 실정이다. 따라서 기존의 전통적인 일제식 수업으로는 교수-학습을 하는데 많은 어려움이 있으므로 초등학생의 ICT 활용 능력과 개인차를 고려한 정보통신기술교육의 수준별 교육과정 모델을 개발하고, 개발된 교육과정을 수준별 수업모형에 적용하는 방안을 연구하였다.

• Convergence Security Journal
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• v.5 no.4
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• pp.33-40
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• 2005

As e-learning studying is activated, learner's requirement increased. Therefore, need correct e-learning model augmented requirement of learner and new ubiquitous surrounding. In this treatise when, proposed to supplement studying contents relationship conversion service and cooperation studying service function to LMS that analyze existing e-learning model's limitation for ubiquitous environment e-learning model that can study regardless of, ubiquitously some contents and do based on SCORM ubiquitous-network and next generation sensor technology etc. Learning form conversion service senses a learner's surrounding situations and recognize his/her body condition using smart sensor technology and provides the learner with contents in the optimal form. Using sensing projects like Orestia and SOB, users can more effective collaborative learning service.

• The Journal of Korean Association of Computer Education
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• v.19 no.4
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• pp.21-31
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• 2016

As the science and technology advances, the side effects of information and communications technology use become a social issue and the social demands to strengthen the informatics ethics in the curriculum of elementary and secondary schools becomes great. The contents of informatics ethics education is included in the current curriculum for elementary and secondary school, but they are embedded in multiple subjects without the holistic guidelines for the informatics ethics curriculum. Therefore it can have duplications of the contents or missing among subjects as those subjects are elective in the secondary school. In this study, we proposed a holistic curriculum for informatics ethics education and developed the education program based on subject integrated curriculum model and the cyber internet ethics education model to solve problems of informatics ethics education. The program was practised for 291 students of $10^{th}$ graders for 5 months and effectiveness was proven by the high attainment of ethics understanding. This subject integrated education program is proposed to solve the structural problems of informatics ethics education in 2015 curriculum.

• Journal of the Korea Convergence Society
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• v.9 no.3
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• pp.73-78
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• 2018

Modern society has evolved to such an extent that computing technology has become an integral part of various fields, creating new and superior value to society. Education on computer literacy, including the ability to design and build software, is now becoming a universal education that must be acquired by everyone, regardless of the field of study. Many universities are imparting software education to students to improve their problem-solving ability, including to students who are not majoring in computers. However, software education contains courses that are meant for computer majors and many students encounter difficulty in learning the grammar of programming language. To solve this problem, this paper analyzes the research outcomes of the existing software education model and proposes a Python-based software education model for students who are not majoring in computer science. Along with a Python-based software education model, this paper proposed a curriculum that can be applied during one semester, including learning procedures, and teaching strategies. This curriculum was applied to a liberal arts class and a meaningful result was derived. If the proposed software education model is applied, the students will be interested in the computer literacy class and improve their computational thinking and problem-solving ability.

• Annual Conference of KIPS
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• 2013.05a
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• pp.429-430
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• 2013

유비쿼터스 기술의 발전과 더불어 스마트 교육과 관련된 연구가 활발히 진행되고 있다. 교육 분야에서의 최신 IT 기술의 도입의 첫 단추는 도서관 시스템 이였다. 이용자와 도서관의 자료를 효율적으로 관리하기 위하여 많은 기관에서 RFID(Radio Frequency Identification) 소자를 기반으로 관련 RFID 리더 장비와 데이터 관리 장비를 구축하였다. 이러한 기술을 기반으로 유비쿼터스 기술을 학생들이 가장 많은 시간을 보내는 교실의 환경에 적용하여 스마트 교육 시스템으로 구축하는 것은 교육의 첨단화와 연계되어 연구되고 있다. 본 연구에서는 기존의 자원을 활용하여 네트워크 환경에서의 자원 배분을 원활히 이뤄내는 P2P 기술과 스마트 교육 시스템의 연계점을 연구하고자 한다. 기존 기술의 적용은 안정성과 신뢰성 측면에서 유용하며 새로운 구조의 제안에 있어서 빠른 적용이 가능한 모델로 전개될 수 있다.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.51-57
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• 2021

Quite a few elementary school teachers began to utilize AI technology in order to provide students with customized, intelligent information services in recent years. However, learning principles of AI may be as important as utilizing AI in everyday life because understanding principles of AI can empower them to buildup adaptability to changes in highly technological world. In the paper, 'Linear Regression Algorithm' is selected for teaching AI-based prediction system to solve real world problems suitable for elementary students. A simulation program written in Scratch was developed so that students can find a solution of linear regression model using the program. The paper shows that students have learned analyzing data as well as comparing the accuracy of the prediction model. Also, they have shown the ability to solve real world problems by finding suitable prediction models.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.277-279
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• 2021

There is a problem that learning of a prediction model is not well performed depending on the characteristics of each particular matter concentration. To solve this problem, it is necessary to design a prediction model for low concentration and high concentration separately. Therefore, a classification model is needed to classify the concentration of particular matter into low and high concentrations. This paper proposes a classification model to classify low and high concentrations based on the concentration of particular matter. DNN was used as the classification model algorithm, and the classification model was designed by applying the optimal parameters after searching for hyper parameters. As for the result of evaluating the performance of the model, 97.54% of the low concentration classification was measured. And in the case of high concentration classification, 85.51% was measured.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.633-634
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• 2020

본 논문에서는 소외계층 초등정보영재를 위한 온라인 머신러닝 교육 프로그램을 개발하였다. 교육 프로그램은 초등정보영재 전문가가 개발하였으며, 인공지능 교육 전문가가 검증하였다. 교육 프로그램은 15차시로 구성하였으며, 인공지능에 대한 이해, 데이터 수집 및 표현, 모델 선택, 훈련, 평가, 실생활 사례 제작, 예측으로 내용을 구성하였다. 교육 프로그램에서 학습 모형은 이재호와 홍창의(2009)의 문제 중심형 e-PBL 학습 모형을 본 연구에 맞게 수정하여 활용하였다. 향후 연구에서는 개발한 교육 프로그램을 소외계층 초등 정보영재에 적용하고, 교육 프로그램을 통한 소외계층 초등정보 영재의 변화를 분석하고자 한다.

• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.368-376
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• 2021

Artificial intelligence clouds help to efficiently develop the autonomous things integrating artificial intelligence technologies and control technologies by sharing the learned models and providing the execution environments. The existing autonomous things development technologies only take into account for the accuracy of artificial intelligence models at the cost of the increment of the complexity of the models including the raise up of the number of the hidden layers and the kernels, and they consequently require a large amount of computation. Since resource-constrained computing environments, could not provide sufficient computing resources for the complex models, they make the autonomous things violate time criticality. In this paper, we propose a digital twin software development framework that selects artificial intelligence models optimized for the computing environments. The proposed framework uses a load estimation DNN model to select the optimal model for the specific computing environments by predicting the load of the artificial intelligence models with digital twin data so that the proposed framework develops the control software. The proposed load estimation DNN model shows up to 20% of error rate compared to the formula-based load estimation scheme by means of the representative CNN models based experiments.