• The Journal of the Korea Contents Association
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• v.10 no.3
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• pp.404-412
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• 2010

The purpose of this study is to suggest the basic data for lowering cyber university students' dropout rate and fostering continuous learning environment through understanding that cyber university student's private variance, an education institute variance and social variance have the impact on a student's determining dropout. For this, we selected students in A cyber university and carried out surveys for 500 students from April first to May 31st, 2009 using convenience sampling. We excluded answers whose results are considered to be insufficient or overlapped among answers of 336 students and used 304 answers in this study. We carried out logistics regression analysis using SPSS for Winow 15.0 for data analysis. First, it proved that individual interest variance affects the dropout. Second, it turned out that educational institute's environment variance has impact on the dropout. Third, it proved that social environment factor affects the dropout. Fourth, only individual variance among individual, an educational institute and social variance has meaningful impact on the dropout in terms of statistics.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.2
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• pp.49-70
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• 2022

Sea Surface Temperature (SST), one of the ocean features, has a significant impact on climate, marine ecosystem and human activities. Therefore, SST prediction has been always an important issue. Recently, deep learning has drawn much attentions, since it can predict SST by training past SST patterns. Compared to the numerical simulations, deep learning model is highly efficient, since it can estimate nonlinear relationships between input data. With the recent development of Graphics Processing Unit (GPU) in computer, large amounts of data can be calculated repeatedly and rapidly. In this study, Short-term SST will be predicted through Convolutional Neural Network (CNN)-based U-Net that can handle spatiotemporal data concurrently and overcome the drawbacks of previously existing deep learning-based models. The SST prediction performance depends on the seasonal and interannual SST variabilities around the southern coast of Korea. The predicted SST has a wide range of variance during spring and summer, while it has small range of variance during fall and winter. A wide range of variance also has a significant correlation with the change of the Pacific Decadal Oscillation (PDO) index. These results are found to be affected by the intensity of the seasonal and PDO-related interannual SST fronts and their intensity variations along the southern Korean seas. This study implies that the SST prediction performance using the developed deep learning model can be significantly varied by seasonal and interannual variabilities in SST.

• Human Ecology Research
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• v.53 no.2
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• pp.219-228
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• 2015

This study evaluated the content of Korean vocabulary learning applications with a focus on tablet PC applications. We analyzed 51 Korean vocabulary learning applications. The instruments in this study were developed based on Yoo et al. (2012)' Vocabulary Learning Game Application Evaluation Criteria and Hyun et al. (2013)' Educational Application Evaluation Criteria. Data were analyzed using a t-test and one-way analysis of variance. The main results are as follows. First, each criteria's score was fairly good; the ease of use had the highest scores and the amusement had the lowest scores. Second, there was a significant difference in the interaction by vocabulary teaching approach. Applications based on a whole language-teaching method had higher scores than applications based on a phonics instructional teaching method inducing more operation and with immediate feedback. Third, there was significant difference in the sum of score and each criteria of developmental appropriateness, educational values, amusement, function and interaction by type of learning. Applications of combining type had higher scores in every criteria except for ease of use than applications of description type. These findings provide a preliminary evidence that the systematic Korean vocabulary learning application facilitates preschoolers' vocabulary learning.

• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.4
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• pp.145-156
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• 2015

Incorporation of individual learning and forgetting behaviors within worker-task assignment models produces a mixed integer nonlinear program (MINLP) problem, which is difficult to solve as a NP hard due to its nonlinearity in the objective function. Previous studies commonly assume homogeneity among workers in workforce scheduling that takes account of learning and forgetting characteristics. This paper expands previous researches by considering heterogeneous individual learning/forgetting, and investigates the impact of worker heterogeneity in initial expertise, steady-state productivity, learning and forgetting on system performance to assist manager's decision-making in worker-task assignments without tackling complex MINLP models. In order to understand the performance implications of workforce heterogeneity, this paper examines analytically how heterogeneity in each of the four parameters of the exponential learning and forgetting (L/F) model affects system performance in three cases : consecutive assignments with no break, n breaks of s-length each, and total b break-periods occurred over T periods. The study presents the direction of change in worker performance under different assignment schedules as the variance in initial expertise, steady-state productivity, learning or forgetting increases. Thus, it implies whether having more heterogenous workforce in terms of each of four parameters in the L/F model is desired or not in different schedules from the perspective of system productivity measurement.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.4_2
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• pp.645-653
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• 2022

Recently, deep reinforcement learning has been applied to a variety of industries, such as games, robotics, autonomous vehicles, and data cooling systems. An algorithm called reinforcement learning allows for automated asset allocation without the requirement for ongoing monitoring. It is free to choose its own policies. The purpose of this paper is to carry out an empirical analysis of the performance of asset allocation strategies. Among the strategies considered were the conventional Mean- Variance Optimization (MVO) and the Proximal Policy Optimization (PPO). According to the findings, the PPO outperformed both its benchmark index and the MVO. This paper demonstrates how dynamic asset allocation can benefit from the development of a reinforcement learning algorithm.

• Computers and Concrete
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• v.32 no.6
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• pp.607-613
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• 2023

The objective of this work is to determine the compressive strength of geopolymer concrete utilizing four distinct machine learning approaches. These techniques are known as gradient boosting machine (GBM), generalized linear model (GLM), extremely randomized trees (XRT), and deep learning (DL). Experimentation is performed to collect the data that is then utilized for training the models. Compressive strength is the response variable, whereas curing days, curing temperature, silica fume, and nanosilica concentration are the different input parameters that are taken into consideration. Several kinds of errors, including root mean square error (RMSE), coefficient of correlation (CC), variance account for (VAF), RMSE to observation's standard deviation ratio (RSR), and Nash-Sutcliffe effectiveness (NSE), were computed to determine the effectiveness of each algorithm. It was observed that, among all the models that were investigated, the GBM is the surrogate model that can predict the compressive strength of the geopolymer concrete with the highest degree of precision.

• Communications for Statistical Applications and Methods
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• v.31 no.5
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• pp.585-599
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• 2024

Options pricing remains a critical aspect of finance, dominated by traditional models such as Black-Scholes and binomial tree. However, as market dynamics become more complex, numerical methods such as Monte Carlo simulation are accommodating uncertainty and offering promising alternatives. In this paper, we examine how effective different options pricing methods, from traditional models to machine learning algorithms, are at predicting KOSPI200 option prices and maximizing investment returns. Using a dataset of 2023, we compare the performance of models over different time frames and highlight the strengths and limitations of each model. In particular, we find that machine learning models are not as good at predicting prices as traditional models but are adept at identifying undervalued options and producing significant returns. Our findings challenge existing assumptions about the relationship between forecast accuracy and investment profitability and highlight the potential of advanced methods in exploring dynamic financial environments.

• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.347-353
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• 2022

In the restaurant industry, start-ups are active due to high demand from consumers and low entry barriers. However, the restaurant industry has a high closure rate, and in the case of franchises, there is a large deviation in sales within the same brand. Thus, research is needed to prevent the closure of food franchises. Therefore, this study examines the factors affecting franchise sales and uses machine learning techniques to predict the success and failure of franchises. Various factors that affect franchise sales are extracted by using Point of Sale (PoS) data of food franchise and public data in Gangnam-gu, Seoul. And for more valid variable selection, multicollinearity is removed by using Variance Inflation Factor (VIF). Finally, classification models are used to predict the success and failure of food franchise stores. Through this method, we propose success and failure prediction model for food franchise stores with the accuracy of 0.92.

• Korean Management Science Review
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• v.26 no.2
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• pp.71-89
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• 2009

The purpose of this research is to identify the factors affecting user acceptance of e-learning site. To more precisely explain an individual's behavior of accepting e-learning site, Perceived Interactivity is divided into four components; User Control, Responsiveness, Personalization and Connectedness. This research investigates the causal relationship among four components and basic factors of TAM. This research uses structural equation modeling (SEM) to confirm the validity and analyzes the causal relationship of the suggested model. The results indicates strong support for the validity of proposed model with 54.8% of the variance in behavioral intention to e-learning site. The result finds that all the basic casuality of TAM are significant and most components of Perceived Interactivity are significant. However the path Connectedness to Perceived Ease of Use and User Control to Perceived Playfulness is not significant. Among components of Perceived Interactivity, Personalization is the strongest antecedent of TAM. Perceived Usefulness is the strongest antecedent of behavioral intention of e-learning site.

• Journal of Korean Critical Care Nursing
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• v.7 no.2
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• pp.1-13
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• 2014

Purpose: The purpose of this study was to determine the difference in reported discharge learning needs between nurses and liver transplantation (LT) patients. Methods: The participants of this study were 40 patients discharged after LT at P University Hospital in Y City and 42 nurses in intensive care units and the ward. The data were collected for two months from December 1, 2012, to January 31, 2013, and were analyzed using descriptive statistics, Student's t-test and analysis of variance (ANOVA). Results: Patients earning a low income (p=.041), having no experience of hospitalization after LT (p=.023), and receiving information about LT from nurses (p=.003) indicated higher discharge learning needs. Among the items evaluated regarding discharge learning needs, "rejection symptoms or signs" were regarded to be more important by nurses than LT patients (p=.038). However, "management of other diseases after LT" (p=.003), "risk of recurrence" (p=.001), "food choices" (p<.001), "obesity prevention" (p=.020), "amount of exercise" (p=.007), and "ways to receive financial help"(p=.033), were thought to be more important by LT patients than nurses. Conclusion: There exist differences between LT patients and nurses with respect to their perceptions of LT discharge learning needs. Therefore, an individualized education program reflecting patients' conditions and learning needs rather than providing information uniformly needs to be developed.