• Journal of Korean Association for Spatial Structures
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• v.21 no.2
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• pp.89-97
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• 2021

This paper describes an adaptive hybrid evolutionary firefly algorithm for a topology optimization of truss structures. The truss topology optimization problems begins with a ground structure which is composed of all possible nodes and members. The optimization process aims to find the optimum layout of the truss members. The hybrid metaheuristics are then used to minimize the objective functions subjected to static or dynamic constraints. Several numerical examples are examined for the validity of the present method. The performance results are compared with those of other metaheuristic algorithms.

• Korean Journal of Comparative Education
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• v.18 no.4
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• pp.141-162
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• 2008

Many e-learning programs in Korea use lecture as a main instructional method. A meta-analysis study reports that e-learning programs using lectures were the most ineffective. In addition, many researchers in the field of distance education contend that the active participation of learners is the key to the success of e-learning. These imply why we can easily find many people who don't have good impression about e-learning. The quality of e-learning depends on the application of appropriate pedagogy. This study aims to present the implications for the improvement of e-learning programs in the Republic of Korea by analyzing the psychological characteristics reflected on the e-learning programs in the U. S. that have been improved through design research for a long time. The result shows that the e-learning programs in the U. S. have five major psychological aspects, i.e., reflective thinking, collaborative interaction, knowledge construction, situated action, and utilizing multiple representations. Consequently, this study suggests that e-learning programs in the Republic of Korea need to reflect learning principles such as learning by doing, situated learning, collaborative learning, learning with multiple representations in order to improve the quality.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.346-357
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• 2019

The purpose of this study was to examine the effects of flipped learning and explore the learners' experiences. Data were collected from 64 students who participated in flipped learning for 7 weeks at N university. The results were as follows. First, after applying flipped learning, learners feel more comfortable learning together and prefer collaborative learning. Second, flipped learning had no significant effects on learner's overall metacognition, but it had positive effects on the awareness and cognitive strategies. Third, flipped learning had no significant effects on academic self-efficacy, but it positively affected the task difficulty preference and confidence of learners who had a lower level of collaborative tendencies. Fourth, flipped learning had no significant effects on SDL ability, but it positively affected the learning plan of learners who had a higher level of collaborative tendencies. Fifth, learners' class satisfaction of flipped learning was generally very high. We suggested a policy, instructional design and strategies for effective implementation of flipped learning.

• Journal of Advanced Navigation Technology
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• v.13 no.3
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• pp.426-431
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• 2009

Recently, the application of E-learning is changing the method that is able to process the learning to learner more efficiently and conveniently. For this purpose, the application research of U-learning that is able to support the learning using mobile device such as PDA, NetBook, Tablet PC and so on is actively processing. But lots of U-learning framework is only considering the change to fit the exist learning contents the mobile device without SCORM that is able to support to make and process the learning contents by regular forms. In this paper, we proposed the learning contents interconnection of U-learning framework considering SCORM. For this purpose, we have to construct the learning by learning object and asset within SCORM. And this method can support learning information that was reconstructed it by learning contents to fit the mobile device as used the mobile device meta-data.

• Journal of the Korea Convergence Society
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• v.9 no.11
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• pp.23-28
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• 2018

Recent approaches to Relation Extraction methods mostly tend to be limited to mention level relation extractions. These types of methods, while featuring high performances, can only extract relations limited to a single sentence or so. The inability to extract these kinds of data is a terrible amount of information loss. To tackle this problem this paper presents an Augmented External Memory Neural Network model to enable Global Relation Extraction. the proposed model's Global relation extraction is done by first gathering and analyzing the mention level relation extraction by the Augmented External Memory. Additionally the proposed model shows high level of performances in korean due to the fact it can take the often omitted subjects and objectives into consideration.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.11a
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• pp.187-190
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• 2022

본 논문은 메타버스 등 인공지능 연계 증강/가상현실 부동 중계 플랫폼에서 부동산 영상 기반 매물 소개 시스템 구축에서 사생활 및 개인정보가 영상에 담기게 될 수 있는 위험이 존재하기에 부동산 영상 내의 개인정보 및 민감 정보를 인공지능 기술을 기반으로 검출하여 삭제해주고 복원해주는 인공지능 기술 연구개발을 목표로 하였다. 한국형 부동산 내 민감 object 를 정의하고, 최신 인공지능 딥러닝 기술 기반 민감 object detection 알고리즘을 연구 개발하며, 영상에서 삭제된 부분은 인공지능 기술을 기반으로 물체가 없는 실제 공간영상으로 복원해주는 영상복원 기술도 연구 개발하였다. 한국형 부동산 환경 (영상 촬영 조도, 디스플레이 스타일, 주변 가구 배치 등)에 맞는 인공지능 모델 구축을 위하여, 자체적으로 한국 영상 database 구축 및 Transfer learning for target domain adaptation 을 진행하였다. 제안된 알고리즘은 일반적인 환경에서 98%의 정확도와 challenge 환경에서 (occlusion 빛 반사, 저조도 등) 81%의 정확도를 보였다. 본 기술은 Proptech 분야에서 주목받고 있는 메타버스 기반 온라인 중계 서비스 기술을 활성화하기 위하여 기획되었으며, 특히 메타버스 부동산 중계 플랫폼의 활성화를 위하여 사생활 보호 측면에서 필요한 중요 기술을 인공지능 기술을 활용하여 연구 개발하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.281-283
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• 2021

The representative companies mentioned in the recommendation service in the domestic OTT(Over-the-top media service) market are YouTube and Netflix. YouTube, through various methods, started personalized recommendations in earnest by introducing an algorithm to machine learning that records and uses users' viewing time from 2016. Netflix categorizes users by collecting information such as the user's selected video, viewing time zone, and video viewing device, and groups people with similar viewing patterns into the same group. It records and uses the information collected from the user and the tag information attached to the video. In this paper, we propose a method to improve video media recommendation by automatically generating metadata of video media that was written by hand.

• Journal of Korean Society of Archives and Records Management
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• v.20 no.2
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• pp.67-83
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• 2020

Many local governments in Korea provide online services for people to easily access the audio-visual archives of events occurring in the area. However, the current method of managing these archives of the local governments has several problems in terms of compatibility with other organizations and convenience for searching of the archives because of the lack of standard metadata and the low utilization of image information. To solve these problems, we propose the metadata design and machine learning-based automatic indexing technology for the efficient management of the image archives of local governments in Korea. Moreover, we design metadata items specialized for the image archives of local governments to improve the compatibility and include the elements that can represent the basic information and characteristics of images into the metadata items, enabling efficient management. In addition, the text and objects in images, which include pieces of information that reflect events and categories, are automatically indexed based on the machine learning technology, enhancing users' search convenience. Lastly, we developed the program that automatically extracts text and objects from image archives using the proposed method, and stores the extracted contents and basic information in the metadata items we designed.

• The Journal of Bigdata
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• v.4 no.1
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• pp.99-118
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• 2019

This study used network analysis and clustering techniques to analyze studies on social media participation. As a result of the main path analysis, 37 major studies were extracted and divided into two networks: community-related networks and new media-related. Network analysis and clustering result in four clusters. This study has the academic significance of using academic data to grasp research trends at a macro level and using network analysis and machine learning as a methodology.

• 한국정보교육학회:학술대회논문집
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• 2009.08a
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• pp.189-194
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• 2009

디지털교과서 시범사업은 미래교육의 시발점으로서 다양한 장점 및 교육적 효과를 제공할 수 있다. 한편 디지털교과서 환경에서는 현실세계와 유사한 태블릿 PC 기반의 펜 스케치를 메타포로 사용하고 있으며, 디지털 잉크 기반의 이러닝은 다양한 교육적 장점을 제공할 수 있다. 그러나 현재 보급되고 있는 디지털교과서 콘텐츠는 펜 기반의 메타포를 고려하지 않은 서책형 교과서의 멀티미디어 버전 형태이며 그 결과 태블릿 PC의 본연의 기능을 충분히 활용하지 못하고 있다. 따라서 본 연구에서는 학습자가 자기주도적으로 전자 펜 기반 스케치 입력방식을 통해 문제를 해결하고 학습의 결과물을 직접 확인할 수 있는 스케치 기반의 인터페이스를 설계 및 적용하여 교수자와 학습자간의 활발한 상호작용을 제공하고자 한다. 본 연구의 결과는 온라인 교육이나 전자 책, 지능형 교육 시스템 등 상호작용이 가능한 교육용 콘텐츠 개발에 확대 적용될 수 있을 것으로 기대한다.