• Journal of the Korean Geotechnical Society
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• v.35 no.6
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• pp.17-26
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• 2019

In this study, the statistical characteristics of the resistance bias factors were analyzed using a high-quality field load test database, and the total resistance bias factors were estimated considering the soil uncertainty and construction errors for the application of the limit state design of aggregate pier foundation. The MLR model by Bong and Kim (2017), which has a higher prediction performance than the previous models was used for estimating the resistance bias factors, and its suitability was evaluated. The chi-square goodness of fit test was performed to estimate the probability distribution of the resistance bias factors, and the normal distribution was found to be most suitable. The total variability in the nominal resistance was estimated including the uncertainty of undrained shear strength and construction errors that can occur during the aggregate pier construction. Finally, the probability distribution of the total resistance bias factors is shown to follow a log-normal distribution. The parameters of the probability distribution according to the coefficient of variation of total resistance bias factors were estimated by Monte Carlo simulation, and their regression equations were proposed for simple application.

• Journal of the Korea Convergence Society
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• v.12 no.5
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• pp.313-326
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• 2021

This study was to introduce ecological momentary assessment and show how to apply it to real-world research. As preliminary study for sustainable development, the result explained growth model using senior's longitudinal data and suitability of multi-level model in EMA data with regression analysis. The total variance of dependent variable was determined through a base model with only intercept and approximately 47% of total variance was caused by individual differences and 53% by time point differences. Second model was used to verified that each individual has a different effect on the senior vitality and effect on time was not significant. This is because it is the result of a preliminary stage where treatment is not involved and there is no significant change in process of collecting EMA data without external intervention. Third model that add gender as an independent variable showed significant change in both time and gender. Finally compared the PRD for each model and found models that without gender variables fit the data more effectively. This suggests that studies dealing with longitudinal data such as EMA data should adopt multi-level model that can measure individual characteristics, taking into account respondents' time and context.

• Tunnel and Underground Space
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• v.32 no.6
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• pp.502-517
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• 2022

As the use of TBM increases, research has recently increased to to analyze TBM data with machine learning techniques to predict the exchange cycle of disc cutters, and predict the advance rate of TBM. In this study, a regression prediction of disc cutte wear of slurry shield TBM site was made by combining machine learning based on the machine data and the geotechnical data obtained during the excavation. The data were divided into 7:3 for training and testing the prediction of disc cutter wear, and the hyper-parameters are optimized by cross-validated grid-search over a parameter grid. As a result, gradient boosting based on the ensemble model showed good performance with a determination coefficient of 0.852 and a root-mean-square-error of 3.111 and especially excellent results in fit times along with learning performance. Based on the results, it is judged that the suitability of the prediction model using data including mechanical data and geotechnical information is high. In addition, research is needed to increase the diversity of ground conditions and the amount of disc cutter data.

• Journal of the Korea Society of Computer and Information
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• v.14 no.2
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• pp.59-68
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• 2009

This paper aims to explore the factors of learner's flow and to develop and validate a scale to measure the flow in Massive Multiplayer Online Role Playing Game(MMORPG) for education. First of all, potential factors were drawn through literature review. The potential stage comprises 6 factors(learner's psychological characteristics, learner's skill, importance of game, environment for learner, instructional design, and instructional environment) and 16 subfactors. With total 48 items developed. a survey was carried out among 293 elementary learners who had been participating in a commercial MMORPG for English skills to measure their flow in the MMORPG by utilizing the potential scale. Using the responses collected from 288 respondents, exploratory factor analysis, reliability analysis, and confirmatory factor analysis were performed. The expository factor analysis showed that items within each sub-factors could be bound into one factor. That is, the variables evaluating learner's flow were divided into six factors(learner's psychological characteristics, learner's skill, importance of game, environment for learner, instructional design, and instructional environment). And these factors were interpreted consisting of 16 sub-ones. Reliability estimates indicated that the evaluation tool had good internal consistency. The confirmatory factor analysis did confirm the model suggested by the expository factor analysis. Over fit measures(CFI, NFI, NNFI) showed the good suitability of the model. Findings of this study confirmed the validity and reliability of the scale to measure learner's flow in MMORPG.

• Journal of Korean Library and Information Science Society
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• v.53 no.1
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• pp.231-263
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• 2022

The understanding and capability to utilize artificial intelligence (AI) incorporated technology has become a required basic skillset for the people living in today's information age, and various members of the university have also increasingly become aware of the need for AI education. Amidst such shifting societal demands, both domestic and international university libraries have recognized the users' need for educational content centered on AI, but a user-centered service that aims to provide personalized recommendations of digital AI educational content is yet to become available. It is critical while the demand for AI education amongst university students is progressively growing that university libraries acquire a clear understanding of user intention towards an AI educational content recommender system and the potential factors contributing to its success. This study intended to ascertain the factors affecting acceptance of such system, using the Extended Technology Acceptance Model with added variables - innovativeness, self-efficacy, social influence, system quality and task-technology fit - in addition to perceived usefulness, perceived ease of use, and intention to use. Quantitative research was conducted via online research surveys for university students, and quantitative research was conducted through written interviews of university librarians. Results show that all groups, regardless of gender, year, or major, have the intention to use the AI-related Educational Content Recommendation System, with the task suitability factor being the most dominant variant to affect use intention. University librarians have also expressed agreement about the necessity of the recommendation system, and presented budget and content quality issues as realistic restrictions of the aforementioned system.