• The Journal of the Korea Contents Association
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• v.20 no.1
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• pp.581-592
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• 2020

This research aims to explore the effect of participation in 'peer tutoring(learning tutoring)' program designed for low achiever students, and to provide an explanation for the improvement of related extracurricular activity. For this, firstly, the study analyzed differences between goal attainment group and non-attainment group in self-directed learning readiness, learning presence and learning transfer. Secondly, the relationships between three variables were analyzed. Based on an online survey of 154 low achievers participating in learning tutoring, two research questions were examined using t-test, correlation and hierarchical multiple regression analyses. Our findings show that firstly, the academic achievement after participating in tutoring improved more than before. Secondly, there were differences in three variables by gender and grades. Also, there were differences in three variables between two groups. Finally, there was a high positive correlation between three variables, and 71% of learning transfer was explained by self-directed learning readiness and learning presence. Based on these findings, the practical implications are discussed regarding the improvement of tutoring program.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.3
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• pp.55-65
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• 2019

Although crack on concrete exists from its early formation, crack requires attention as it affects stiffness of structure and can lead demolition of structure as it grows. Detecting cracks on concrete is needed to take action prior to performance degradation of structure, and deep learning can be utilized for it. In this study, transfer learning, one of the deep learning techniques, was used to detect the crack, as the amount of crack's image data was limited. Pre-trained Inception-v3 was applied as a base model for the transfer learning. Web scrapping was utilized to fetch images of concrete wall with or without crack from web. In the recognition of crack, image post-process including changing size or removing color were applied. In the visualization of crack, source images divided into 30px, 50px or 100px size were used as input data, and different numbers of input data per category were applied for each case. With the results of visualized crack image, false positive and false negative errors were examined. Highest accuracy for the recognizing crack was achieved when the source images were adjusted into 224px size under gray-scale. In visualization, the result using 50 data per category under 100px interval size showed the smallest error. With regard to the false positive error, the best result was obtained using 400 data per category, and regarding to the false negative error, the case using 50 data per category showed the best result.

• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.375-383
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• 2020

The study was to examine the mediating effect of self-efficacy in learning in the relationship between the learning satisfaction and motivation to transfer of nursing students who received the psychiatric nursing convergence simulation practicum using standardized patients. Participants were 144 third grade nursing students. Data were analyzed descriptive statistics, t-test, one-way ANOVA, Pearson's correlation coefficient analysis, and multiple regression following the Baron and Kenny's method and Sobel test for mediation. There were significant correlations between learning satisfaction and self-efficacy in learning(r=.686, p<.001), learning satisfaction and motivation to transfer(r=.633, p<.001) and self-efficacy in learning and motivation to transfer(r=.804, p<.001). Self-efficacy in learning showed partial mediating effects in the relationship between learning satisfaction and motivation to transfer(Z=7.63, p<.001). To increase the motivation to transfer, strategies to enhance the self-efficacy of nursing students are required.

• Journal of Digital Convergence
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• v.13 no.11
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• pp.119-132
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• 2015

This study empirically examined which factors facilitate or disturb the learning and practical knowledge transfer in consulting and which factors have most powerful influence on the learning and transfer of consulting knowledge. Analysing 160 data collected from FTA origin managers in export companies, the study findings show the ambiguity(-), complexity(+), consulting competences(+), intervention design and delivery(+), self-efficacy(+) and government subsidies(+) significantly affected on Client's learning, while consultant's expertise(+), consulting involvement(+), transfer culture(+) significantly affected on consulting knowledge transfer, respectively. It showed that consulting competence and causal ambiguity have an greater influence on learning while consultant's expertise has a greater influence on consulting knowledge transfer, respectively. The findings implicate that consulting success depends on rather consultant's factors(consultant's expertise and consulting competence) than client's input factors. To succeed in consulting project, it is important that the consultants effectively develop and apply consulting methods & tools as shared interfaces between consultant and client.

• The Journal of Korean Institute for Practical Engineering Education
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• v.1 no.1
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• pp.7-12
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• 2009

The worldwide spread of the internet has made it an important topic in our everyday lives. It has not only changed ordinary lives, but also the whole spectrum of modern society. Thus, it is necessary to understand the characteristic changes in learners as well as social demand in this drastic transformation period and to modify the goal and methods of learning to nurture future intellects. To achieve this, there have been recent attempts to invent new learning methods involving Web 2.0, which focuses on the user. Out of the various aspects of Web 2.0, the blog is expected to invoke learner-oriented education and active discussions between learners, in that the individual manages the blog autonomously and there is much interaction between users. This study will construct a learning system using the blog, apply it on real learners, and analyze its effect on the learners' scholastic achievements, interests, and learning transfer.

• Smart Media Journal
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• v.10 no.1
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• pp.55-62
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• 2021

We applied machine learning of semi-supervised learning, transfer learning, and federated learning as examples of AI use cases that can be applied to the three major industries(Automobile industry, Energy industry, and AI/Healthcare industry) of Gwangju Metro-city, and established an ML strategy for AI services for the major industries. Based on the ML strategy of AI service, practical approaches are suggested, the semi-supervised learning approach is used for automobile image recognition technology, and the transfer learning approach is used for diabetic retinopathy detection in the healthcare field. Finally, the case of the federated learning approach is to be used to predict electricity demand. These approaches were tested based on hardware such as single board computer Raspberry Pi, Jaetson Nano, and Intel i-7, and the validity of practical approaches was verified.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.1
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• pp.39-48
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• 2023

Because a railway bridge is designed over a long period of time and covers a large site, it involves various environmental factors and uncertainties. For this reason, design changes often occur, even if the design was thoroughly reviewed in the initial design stage. In particular, design changes of large-scale facilities, such as railway bridges, consume significant time and cost, and it is extremely inefficient to repeat all the procedures each time. In this study, a technique that can improve the efficiency of learning after design change was developed by utilizing the learning result before design change through transfer learning among deep-learning algorithms. For analysis, scenarios were created, and a database was built using a previously developed railway bridge deep-learning-based prediction system. The proposed method results in similar accuracy when learning only 1000 data points in the new domain compared with the 8000 data points used for learning in the old domain before the design change. Moreover, it was confirmed that it has a faster convergence speed.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1000-1011
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• 2021

In recent years, using Deep Learning methods to apply for medical and biomedical image analysis has seen many advancements. In clinical, using Deep Learning-based approaches for cancer image analysis is one of the key applications for cancer detection and treatment. However, the scarcity and shortage of labeling images make the task of cancer detection and analysis difficult to reach high accuracy. In 2015, the Unet model was introduced and gained much attention from researchers in the field. The success of Unet model is the ability to produce high accuracy with very few input images. Since the development of Unet, there are many variants and modifications of Unet related architecture. This paper proposes a new approach of using Unet++ with pretrained EfficientNet as backbone architecture for breast tumor cell nuclei segmentation and uses the multi-organ transfer learning approach to segment nuclei of breast tumor cells. We attempt to experiment and evaluate the performance of the network on the MonuSeg training dataset and Triple Negative Breast Cancer (TNBC) testing dataset, both are Hematoxylin and Eosin (H & E)-stained images. The results have shown that EfficientUnet++ architecture and the multi-organ transfer learning approach had outperformed other techniques and produced notable accuracy for breast tumor cell nuclei segmentation.

• Journal of Information Technology Applications and Management
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• v.28 no.4
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• pp.33-43
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• 2021

While youth unemployment has recorded the lowest level since the global COVID-19 pandemic, SMEs(small and medium sized enterprises) are still struggling to fill vacancies. It is difficult for SMEs to find good candidates as well as for job seekers to find appropriate job offers due to information mismatch. To overcome information mismatch, this study proposes the fine-turning model for bidirectional HR matching based on a pre-learning language model called BERT(Bidirectional Encoder Representations from Transformers). The proposed model is capable to recommend job openings suitable for the applicant, or applicants appropriate for the job through sufficient pre-learning of terms including technical jargons. The results of the experiment demonstrate the superior performance of our model in terms of precision, recall, and f1-score compared to the existing content-based metric learning model. This study provides insights for developing practical models for job recommendations and offers suggestions for future research.

• Asia pacific journal of information systems
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• v.16 no.2
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• pp.85-110
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• 2006

The objective of this research is to identify the process of knowledge transfer and to examine the effect of IT human capability on knowledge transfer in information systems outsourcing. Through a field survey, it is found that clients' IT human capability significantly affects on cooperative learning with vendors and knowledge transfer from vendors to clients. The survey also shows that clients' trust with vendors indirectly enhances the knowledge transfer by increasing the cooperative learning. This study provides a solution of knowledge transfer problem in information systems outsourcing. Also it brings out issues that can be accrued in the outsourcing situation, such as clients' dependency on vendors and knowledge asymmetries developed in favor of the vendors. These issues should be topics for future research on information systems outsourcing.