• International conference on construction engineering and project management
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• 2007.03a
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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.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.2
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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.

• Convergence Security Journal
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• v.22 no.4
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• pp.129-133
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• 2022

Deep learning frameworks are still evolving, and there are various frameworks. Typical deep learning frameworks include TensorFlow, PyTorch, and Keras. The Deepram framework utilizes optimization models in image classification through image learning. In this paper, we use the TensorFlow and PyTorch frameworks, which are most widely used in the deep learning image recognition field, to proceed with image learning, and compare and analyze the results derived in this process to know the optimized framework. was made.

• Journal of Information Technology Services
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• v.20 no.5
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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.

• Journal of Practical Engineering Education
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• v.12 no.1
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• pp.73-83
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• 2020

Recently, the importance of self-directed learning has emerged in the fields of public education, private education, lifelong education, and vocational training education, in which learners can actively cope with knowledge in an infusion-oriented way. However, there are various theoretical knowledge such as concepts and strategies for self-directed learning, but the situation is insufficient for a system where learners can easily receive content in the academic field they want, depending on the actual self-directed learning operation plan or learning area. Therefore, since it is important to provide various learning content in this paper, we utilize text mining techniques to obtain appropriate information and refine and categorize the meaning. On-line, they want to study a system that provides a variety of content in the academic field that learners are trying to acquire.

• Korean Journal of Remote Sensing
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• v.39 no.4
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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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.690-692
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• 2014

Conventional one-way cramming education at schools has the disadvantage of poor student interest in learning, immersion, and learning efficiency as well as a limitation in realizing collective intelligence and collaborative learning. Therefore, an educational content service platform using a near-field communication(NFC) technology is required as a tool for encouraging the voluntary learning participation of students and increasing learning effectiveness through self-directed studying. This study focuses on the development of an educational content production system that creates high-quality education contents suitable for smart schools. In these schools, students and teachers generally communicate through an electronic blackboard using Bluetooth, which is an NFC technology. Further, the lecture notes of individual students are reproduced and collected as big data, which will facilitate the sharing of these notes.

• Asia-Pacific Journal of Business
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• v.11 no.3
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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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• v.14 no.4
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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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• v.87 no.4
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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.