• 국제학술발표논문집
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• The 2th International Conference on Construction Engineering and Project Management
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• pp.450-458
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• 2007

This paper presents the results of a statistical analysis and its research findings focusing on the learning aspect in the process of international joint ventures (IJVs). The contents of this paper is derived from a sample of 96 field cases based on a proposed conceptual model of effective learning for international construction joint ventures (ICJVs). The paper presents a brief review on the conceptual model with hypotheses and summarized the key results of statistical analysis including factor and multiple regression analysis for the testing of the validity of the proposed conceptual model and its associated research hypotheses. Among other research findings, the research confirms that ICJVs provides an excellent platform of in-action learning for construction organization and suggests that good outcomes in learning could be reaped by a company who has a clear learning intent from the beginning and subsequently take corresponding learning actions during the full process of the joint venture.

• 디지털산업정보학회논문지
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• 제19권2호
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• pp.1-10
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• 2023

Digital transformation is accelerating in all industries due to COVID-19 and rapidly developing ICT technology. In the field of education, teaching methods that combine various IT devices and software technologies are being applied. The education requires a future learning environment using EduTech such as digital learning tools. We perform a case study on the use of digital learning tools for effective class. In this study, digital learning tools were applied to an university class. The class was held in the second semester of 2022 at A university, with 67 students participating. In our case, QuizN, Mentimeter, and Google Forms were applied as digital learning tools. In order to evaluate our case, a survey was conducted using the Google Questionnaire. From the results of the survey evaluation, more than 85% of all survey questions answered that they were satisfied. From it, digital learning tools were shown to be effective in class operation.

• 융합보안논문지
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• 제22권4호
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• pp.129-133
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• 2022

딥러닝 프레임워크는 현재에도 계속해서 발전되어 가고 있으며, 다양한 프레임워크들이 존재한다. 딥러닝의 대표적인 프레임워크는 TensorFlow, PyTorch, Keras 등이 있다. 딥러님 프레임워크는 이미지 학습을 통해 이미지 분류에서의 최적화 모델을 이용한다. 본 논문에서는 딥러닝 이미지 인식 분야에서 가장 많이 사용하고 있는 TensorFlow와 PyTorch 프레임워크를 활용하여 이미지 학습을 진행하였으며, 이 과정에서 도출한 결과를 비교 분석하여 최적화된 프레임워크을 알 수 있었다.

• 한국IT서비스학회지
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• 제20권5호
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• pp.147-158
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• 2021

The digital transformation and COVID-19 are also causing major changes in teaching-learning methods. The biggest change is the spread of remote training and the emergence of various innovative learning platforms. Distance education has been criticized for not meeting technology trends and field demands..However, the problem of distance education is being solved through a system that supports various interactions and collaborations and supports customized learning paths. The researcher conducted a case study on domestic and foreign learning platforms that provide non-face-to-face ICT education. Based on the case study results, the researcher presented the functional characteristics of a learning platform that effectively supports non-face-to-face learning. In common, these sites faithfully supported the basic functions of the information system. In addition to learning progress check and learning guidance, some innovative learning platforms were providing differentiated functions in practice support, performance management, mentoring, learning data analysis, curation provision, and CDP support. Most learning platforms supported one-way, superficial interaction. If the platform effectively supports a variety of learning experiences and provides an integrated learning experience thanks to the development of IT technology, user satisfaction with the learning platform, intention to continue learning, and achievement will increase.

• 실천공학교육논문지
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• 제12권1호
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• pp.73-83
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• 2020

최근 공교육, 사교육, 평생교육, 직업훈련교육 분야에서 지식전달 위주의 주입식 교육방식에서 학습자들이 능동적으로 지식에 대처할 수 있는 자기주도학습에 대해 중요성이 대두되고 있으며, 이는 사회변화에 따라 요구되는 인재상으로 스스로 자아개념, 자신감, 창의성을 발견하고 계발시키는 학습 방법으로 더욱 중요해지고 있다. 하지만 자기주도적 학습에 대한 개념 및 전략 등 다양한 이론적 지식들이 존재하고 있지만, 실제 자기주도학습 운영계획 또는 학습 분야에 따라 학습자가 원하는 학문 분야의 콘텐츠를 손쉽게 제공받는 시스템에 대해 부족한 상황이다. 따라서 본 논문에서는 학습자가 자기주도적 학습을 위한 다양한 학습 콘텐츠를 제공받기 위해 정보를 획득하고 의미를 정제하여 범주화 할 수 있는 텍스트 마이닝 기법을 활용하여 온라인상에서 학습자가 습득하려고 하는 학문 분야의 다양한 콘텐츠를 제공하는 시스템을 설계하고 활용하는 방안에 대한 연구를 수행하였다.

• 대한원격탐사학회지
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• 제39권4호
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• pp.371-393
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• 2023

Crack detection in structures plays a vital role in ensuring their safety, durability, and reliability. Traditional crack detection methods sometimes need significant manual inspections, which are laborious, expensive, and prone to error by humans. Deep learning algorithms, which can learn intricate features from large-scale datasets, have emerged as a viable option for automated crack detection recently. This study presents an in-depth review of crack detection methods used till now, like image processing, traditional machine learning, and deep learning methods. Specifically, it will provide a comparative analysis of crack detection methods using deep learning, aiming to provide insights into the advancements, challenges, and future directions in this field. To facilitate comparative analysis, this study surveys publicly available crack detection datasets and benchmarks commonly used in deep learning research. Evaluation metrics employed to check the performance of different models are discussed, with emphasis on accuracy, precision, recall, and F1-score. Moreover, this study provides an in-depth analysis of recent studies and highlights key findings, including state-of-the-art techniques, novel architectures, and innovative approaches to address the shortcomings of the existing methods. Finally, this study provides a summary of the key insights gained from the comparative analysis, highlighting the potential of deep learning in revolutionizing methodologies for crack detection. The findings of this research will serve as a valuable resource for researchers in the field, aiding them in selecting appropriate methods for crack detection and inspiring further advancements in this domain.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2014년도 추계학술대회
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• pp.690-692
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• 2014

기존의 학교현장에서의 단방향 주입식 교육은 학습자의 학습 흥미와 몰입감, 학습능률이 매우 떨어지는 단점을 가지고 있고 집단지성, 협력학습에 한계를 드러내고 있다. 학생들의 자발적인 학습참여를 유도하여 자기주도적인 학습을 통한 학습효과를 높일 수 있는 수단으로 근거리 통신을 활용한 교육콘텐츠 서비스 플랫폼이 요구되고 있다. 본 연구는 IoT(Internet of Things) 기반의 근거리 통신기술인 블루투스를 이용해 교사음성과 교수학습이 전자칠판(Interactive White Board)과 통신하고, NFC 기술을 이용해 학생들의 개인 강의노트 콘텐츠를 재생성하여 빅데이터화 되고 학생 간 공유가 가능해짐으로써 질 좋은 교육 콘텐츠를 생성하는 스마트 스쿨에 걸맞는 교육콘텐츠 제작시스템 기술을 연구하였다.

• 아태비즈니스연구
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• 제11권3호
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• pp.261-271
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• 2020

Purpose - This study was to establish a unified tourism management class for university students based on Gangwon-do. Community based learning(CBL) was applied to provide a tangible and intangible resource of tourism resources the theoretical approaches and the actual experiences of the community. Design/methodology/approach - In order to design a unified tourism management module, this study applied qualitative research and quantitative research methods to collect information on the direction of the module. the study conducted in-depth interviews and then an online survey. Findings - According to the results of the study, the main parts should include necessity of unification, inter-Korean tourism, inter-Korean cooperation, inter-Korean economy, and international relations. Research implications or Originality - The overall composition of the unification tourism management class should be designed as the unification tourism management theory to acquire the subject knowledge, the field trip to the border area for experiential learning, and the assignment of the field study task to understand the community.

• International Journal of Internet, Broadcasting and Communication
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• 제14권4호
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• pp.58-63
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• 2022

Recently, the improvement of computational processing ability due to the rapid development of computing technology has greatly advanced the field of artificial intelligence, and research to apply it in various domains is active. In particular, in the national defense field, attention is paid to intelligent recognition among machine learning techniques, and efforts are being made to develop object identification and monitoring systems using artificial intelligence. To this end, various image processing technologies and object identification algorithms are applied to create a model that can identify friendly and enemy weapon systems and personnel in real-time. In this paper, we conducted image processing and object identification focused on tanks among various weapon systems. We initially conducted processing the tanks' image using a convolutional neural network, a deep learning technique. The feature map was examined and the important characteristics of the tanks crucial for learning were derived. Then, using YOLOv5 Network, a CNN-based object detection network, a model trained by labeling the entire tank and a model trained by labeling only the turret of the tank were created and the results were compared. The model and labeling technique we proposed in this paper can more accurately identify the type of tank and contribute to the intelligent recognition system to be developed in the future.

• Structural Engineering and Mechanics
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• 제87권4호
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• pp.305-315
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• 2023

The idea of the combination of the fractional-order operators with the brain emotional learning-based intelligent controller (BELBIC) is developed for implementation in seismic-excited structures equipped with active mass damper (AMD). For this purpose, a new design framework of the mentioned combination namely fractional-order BEBIC (FOBELBIC) is proposed based on a modified-teaching-learning-based optimization (MTLBO) algorithm. The seismic performance of the proposed controller is then evaluated for a 15-story building equipped with AMD subjected to two far-field and two near-field earthquakes. An optimal BELBIC based on the MTLBO algorithm is also introduced for comparison purposes. In comparison with the structure equipped with a passive tuned mass damper (TMD), an average reduction of 44.7% and 42.8% are obtained in terms of the maximum absolute and RMS top floor displacement for FOBELBIC, while these reductions are obtained as 30.4% and 30.1% for the optimal BELBIC, respectively. Similarly, the optimal FOBELBIC results in an average reduction of 42.6% and 39.4% in terms of the maximum absolute and RMS top floor acceleration, while these reductions are given as 37.9% and 30.5%, for the optimal BELBIC, respectively. Consequently, the superiority of the FOBELBIC over the BELBIC is concluded in the reduction of maximum and RMS seismic responses.