• Journal of Fashion Business
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• v.25 no.2
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• pp.18-33
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• 2021

This study sought to understand the clothing consumption values of male consumers and analyze the attributes of online fashion multi-brand stores. The study examined the effect of clothing consumption values of male consumers on the perceived attributes of online fashion multi-brand stores and use intention. The study also aimed to investigate whether online information search moderated the relationship between the perceived attributes of online fashion multi-brand stores and use intention. An online survey of male consumers in their 20s and 30s was conducted, and a total of 338 responses were analyzed. The SPSS 24.0 program was used to perform frequency analysis, factor analysis, reliability analysis, regression analysis, and stepwise-regression analysis. The results are as follows. First, fashionableness out of the four attributes of online fashion multi-brand stores (fashionableness, entertainment, variety, and scarcity) influenced the practical and conspicuous values of clothing consumption values. Entertainment had a significant effect on all clothing consumption values. Variety had a significant impact on practical and conspicuous values and the scarcity factor influenced epistemic and conspicuous values. Second, while entertainment, variety, and scarcity influenced sharing intention, fashion, entertainment, and variety influenced purchase intention. Third, online information search moderated the relationship between the perceived attributes of online fashion multi-brand stores and sharing intention but not purchase intention.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.05a
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• pp.93-96
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• 2008

This study was performed for merge characteristic of Hybrid Rocket with multi-port. PE(Poly Ethylene) is fuel with 4 and 5 port grain and GOX(Gas Oxygen) is oxidizer. This study according to number of ports The multi-port grain merge with other grains during a combustion, then Hybrid Rocket performance is changed by change of a combustion area.

• Journal of Information Technology Applications and Management
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• v.17 no.3
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• pp.135-150
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• 2010

Interactivity has been referred to as an important element promoting students' active participation in virtual classes. Assuming that interactivity cannot be defined by a single dimension, this study proposes multi-dimensional interactivity. Multi-dimensional interactivity includes all types of interactivity in e-learning. This study explored multi-dimensional interactivity which affects learners' satisfaction with e-learning. Data were collected from 132 students who had attended e-learning courses and the relationship between multi-dimensional interactivity and learners' satisfaction levels were tested through regression analysis. The result of this study showed that mechanical, reactive, and creative interactivity were positively related to learners' satisfaction. However, social interactivity seemed not to be related to learners' satisfaction. This study provides new insights on interactivity and verifies the importance of the multi-dimensional interactivity. The result of this study is expected to provide practical implications for interactivity strategies in e-learning.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.13 no.9
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• pp.868-877
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• 2001

This study is to present the typical energy consumption criteria and $CO_2$ exhaust rate in multi-family housing complex by analyzing the energy consumption characteristics. The contents and methodology of this study are as follows; -Examining the documents of maintenance accounts, investigate the cost and its items expended by the annual maintenance in multi-family housing complex. -Survey each consumption of energy sources, maintenance area, location of multi-family housing complex, heating type, and so forth. -After classifying with heating type of multi-family housing complex investigated, Scrutinize the energy consumption by each source. -Analyze the characteristics of energy consumption and $CO_2$ exhaust through multiple regression analyses of maintenance property. -Suggest the typical energy consumption criteria (Mcal/$m^2$.year, Mcal/house.year) and $CO_2$ exhaust rate (kg-c/$m^2$.year, Kg-c/house.year) in multi-family housing complex. the results will come into basic data for estimating energy consumption in multi-family housing complex according to maintenance characteristics.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.18 no.2_spc
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• pp.247-260
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• 2020

A multi-regression model was developed to estimate the decommissioning cost for Kori unit 1 using foreign nuclear power plant (NPP) decommissioning cost data. First, the decommissioning cost data were collected for 13 boiling water reactors and 16 pressurized water reactors and converted into the values as of November 2019. Then, for the regression model, the decommissioning cost was chosen as the dependent variable, and two variables were selected as independent variables: a contamination factor that was designed to reflect the operational characteristics of the decommissioned NPP and the decommissioning period. A statistical package in the R language was used to derive the regression model. Finally, the regression model was applied to estimate the decommissioning cost for Kori unit 1. The estimated decommissioning cost for Kori unit 1 was 663.40~928.32 million US dollars (782,812~1,095,418 million Korean won).

• Advances in Computational Design
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• v.7 no.2
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• pp.113-128
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• 2022

Water consumption is strongly affected by numerous factors, such as population, climatic, geographic, and socio-economic factors. Therefore, the implementation of a reliable predictive model of water consumption pattern is challenging task. This study investigates the performance of predictive models based on multi-layer perceptron (MLP), multiple linear regression (MLR), and support vector regression (SVR). To understand the significant factors affecting water consumption, the stepwise regression (SW) procedure is used in MLR to obtain suitable variables. Then, this study also implements three predictive models based on these significant variables (e.g., SWMLR, SWMLP, and SWSVR). Annual data of water consumption in Thailand during 2006 - 2015 were compiled and categorized by provinces and distributors. By comparing the predictive performance of models with all variables, the results demonstrate that the MLP models outperformed the MLR and SVR models. As compared to the models with selected variables, the predictive capability of SWMLP was superior to SWMLR and SWSVR. Therefore, the SWMLP still provided satisfactory results with the minimum number of explanatory variables which in turn reduced the computation time and other resources required while performing the predictive task. It can be concluded that the MLP exhibited the best result and can be utilized as a reliable water demand predictive model for both of all variables and selected variables cases. These findings support important implications and serve as a feasible water consumption predictive model and can be used for water resources management to produce sufficient tap water to meet the demand in each province of Thailand.

• Journal of IKEEE
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• v.20 no.1
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• pp.94-102
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• 2016

This paper proposes a new prediction method to reduce times and labor of repetitive multi-physics simulation. To achieve exact results from the whole simulation processes, complex modeling and huge amounts of time are required. Current multi-physics analysis focuses on the simulation method itself and the simulation environment to reduce times and labor. However this paper proposes an alternative way to reduce simulation times and labor by exploiting machine learning algorithm trained with data set from simulation results. Through comparing each machine learning algorithm, Gaussian Process Regression showed the best performance with under 100 training data and how similar results can be achieved through machine-learning without a complex simulation process. Given trained machine learning algorithm, it's possible to predict the result after changing some features of the simulation model just in a few second. This new method will be helpful to effectively reduce simulation times and labor because it can predict the results before more simulation.

• Journal of Korean society of Dental Hygiene
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• v.19 no.2
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• pp.287-295
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• 2019

Objectives: The purpose of this study was to investigate the factors affecting subjective oral symptoms according to the gender of youth from multi-cultural families in Korea using data from the 14th (2018) Korean Youth Health Behavior Survey. Methods: The independent variables used in this study consisted of gender and sweet drink intake. The dependent variable was experience of subjective oral symptoms. Compensation variables consisted of general characteristics of school type, academic performance, economic status, drinking status, smoking status, and number of tooth brushings day before. The subjects of the study were 835 children of multi-cultural families whose parents were foreigners. All statistical analyses were performed by complex samples cross-tabulation analysis and complex samples logistic regression analysis. Statistical analysis was performed using the PASW statistical package 21.0 (Statistical Packages for Social Science Inc., Chicago, IL, USA). A significance level of 0.05 was used for statistical significance. Results: The composite sample logistic regression analysis showed that there was a statistically significant difference between gender and intake of sweet drinks in experience of subjective oral symptoms. Conclusions: These results suggest that factors influence subjective oral symptoms in Korean multi-cultural adolescents. Therefore, I hope that they will be used as basic data for the introduction and development of a customized oral health education program for improving oral health of multi-cultural adolescents.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.1
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• pp.187-194
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• 1996

In this paper, we propose regression neural networks based on regression trees. We map regression trees into three layered feedforward networks. We put multi feature split functions in the first layer so that the networks have a better chance to get optimal partitions of input space. We suggest two supervised learning algorithms for the network training and test both in single feature split and multifeature split functions. In experiments, the proposed regression neural networks is proved to have the better learning performance than those of the single feature split regression trees and the single feature split regression networks. Furthermore, we shows that the proposed learning schemes have an effect to prune an over-grown tree without degrading the learning performance.

• Journal of Korean Tunnelling and Underground Space Association
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• v.13 no.6
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• pp.531-555
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• 2011

This study aims to derive the statistical models for the estimation of the required specifications of a rock TBM as well as for its cutterhead design suitable for a given rock mass condition. From a series of multi-variate regression analysis of 871 TBM driving data and 51 linear rock cutting test results, the optimum models were newly proposed to consider a variety of rock properties and mechanical cutting conditions. When the derived models were applied to two domestic shield tunnels, their predictions of cutter penetration depth, cutter acting forces and cutter spacing were very close to real TBM driving data, showing their high applicability.