• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.63
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• pp.79-87
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• 2001

To make cyber universities competitive, the level of the learning effect for students should be improved. The objective of this study is to analyse the factors influencing the learning effect of cyber universities. For this purpose, questionnaire survey has been used. For the statistical analysis, multivariate linear regression were utilized using the SAS program. According to the results in this study, the variables that influence toward the learning effect are the experience of computer, the systematic and comprehensive lecture note, and the quickly responses to questions. These variables can be utilized in improving the educational service quality of cyber universities.

• Journal of the East Asian Society of Dietary Life
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• v.21 no.4
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• pp.609-624
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• 2011

The purpose of this study was to examine the physical environmental factors in coffee shops which determine customer brand loyalty, and to investigate the mediated effects of customer satisfaction and emotional responses on the causal relationship between the physical environmental factors and brand loyalty. A sample of 400 coffee shop customers was collected from Seoul and Gyeonggi in March, 2011 through a self-administered questionnaire. 351 of 400 subjects were used for validity and reliability analysis. 12 outliers were removed from the analysis, and 339 subjects were used to derive the results. Multiple linear regression and stepwise regression were conducted after the construct validity and reliability. The results can be summarized as follows: (1) Physical environmental factors in coffee shops consists of 5 dimensions such as facility aesthetics, cleanliness, ambiance, layout, and internet environment. (2) Facility aesthetics, ambiance, and internet environment had an influence on brand loyalty. (3) The effects of cleanliness and layout on brand loyalty, were not significant on multivariate analysis. However, the relationship between cleanliness and brand loyalty was mediated by emotional responses and also the relationship between layout and brand loyalty was mediated by customer satisfaction. (4) The mediating effects of customer satisfaction were higher than those of emotional responses.

• Quality Improvement in Health Care
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• v.24 no.1
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• pp.40-52
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• 2018

Purpose: This study aimed to identify the factors influencing on patients' participation in their treatment decision making, and influences of patients' experience on their health status. Methods: Data from the 2015 Korea National Health and Nutrition Examination Survey were used for the analysis. Multivariate logistic regression analysis was conducted to identify the factors influencing on patients' participation in their treatment decision making. The influences of patients' experience on their health status were analyzed using multiple linear regression analysis. Results: Of the 4,497 respondents, 3,698 (82.2%) respondents mostly participated in their treatment decision making. Those who experienced enough visit duration, physicians' explanation easy enough to understand, or more opportunities to ask were more likely to participate in their treatment decision making. After controlling for their sociodemographic factors and health status, those who had better experience during the outpatient visits were more likely to have better self-rated health or quality of life. Conclusion: To improve patients' health outcomes and satisfaction of health care uses, it is necessary to provide better experiences and expand the opportunities for participation in treatment decision making during their hospital visits.

• Proceedings of the KSRS Conference
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• v.1
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• pp.94-97
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• 2006

Timber stand age information of timber in industrialized plantation forest is generally collected by field surveying which is labor-intensive, time-consuming, and very costly. It is also inconsistent in analyses perspective. As an alternative, The objective of this research is to present a practical solution for estimating timber age of loblolly pine plantation using Landsat thematic mapper (TM) images, shuttle radar topography mission (SRTM), and national elevation dataset (NED). A multivariate regression model was developed based upon satellite image-based information (i.e.normalized difference vegetation index (NDVI), tasseled cap (TC) transformation, and derived tree heights). A residual studentized technique was applied to remove potential outliers. After that, a refined age estimation model with a correlation coefficient R-square of 84.6% was obtained. Finally, the feasibility test of estimated model was performed by comparing estimated and measured stand ages of timber plantations using test datasets of plantation stands (2,032 stands). The result shows that the proposed method of this study can estimate loblolly pine stand age within an error of $2{\sim}3$ years in an effective and consistent way in terms of time and cost.

• Journal of Environmental Science International
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• v.15 no.9
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• pp.887-895
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• 2006

Through this research of the analysis on the frequency flood discharges regarding basin property factors, a linear regression system was introduced, and as a result, the item with the highest correlation with the frequency flood discharges from Nakdong river basin is the basin area, and the second highest is the average width of basin and the river length. The following results were obtained after looking at the multi correlation between the flood discharge and the collected basin property factors using the data from the established river maintenance master plan of the one hundred twenty-five rivers in the Nakdong river basin. The result of analysis on multivariate correlation between the flood discharges and the most basic data in determining the flood discharges as basin area, river length, basin slope, river slope, average width of basin, shape factor and probability precipitation showed more than 0.9 of correlation in terms of the multi correlation coefficient and more than 0.85 for the determination coefficient. The model which induced a regression system through multi correlation analysis using basin property factors is concluded to be a good reference in estimating the design flood discharge of unmeasured basin.

• Korean Journal of Agricultural Science
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• v.38 no.4
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• pp.761-767
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• 2011

The objective of this study was to predict total bacteria count of pork meats by using the portable electronic nose systems developed throughout two stages of the prototypes. Total bacteria counts were measured for pork meats stored at $4^{\circ}C$ for 21days and compared with the signals of the electronic nose systems. PLS(Partial least square), PCR (Principal component regression), MLR (Multiple linear regression) models were developed for the prediction of total bacteria count of pork meats. The coefficient of determination ($R_p{^2}$) and root mean square error of prediction (RMSEP) for the models were 0.789 and 0.784 log CFU/g with the 1st system for the pork loin, 0.796 and 0.597 log CFU/g with the 2nd system for the pork belly, and 0.661 and 0.576 log CFU/g with the 2nd system for the pork loin respectively. The results show that the developed electronic system has potential to predict total bacteria count of pork meats.

• Journal of Biosystems Engineering
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• v.26 no.5
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• pp.475-480
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• 2001

Sensing soil organic matter is crucial for precision farming and environment friendly agriculture. Near infra-red(NIR) was utilized to measure the soil organic matter. Multivariate calibration methods, including stepwise multiple linear regression(MLR), principal components recession(PCR) and partial least squares regression(PLS), were applied to soil spectral reflectance data to predict the organic matter content. The effect of soil particle size and water content was studied. The range of soil organic matter contents was from 0.5 to 11%. Near infrared (NIR) region from 700 to 2,500nm was applied. For uniform soil particle size, result had good correlation (R$\^$2/ = 0.984, standard error of prediction= 0.596). The effect of soil particle size could be eliminated with 1st order derivative of the NIR signal. However. moist soil had a little lower correlation. R$\^$2/ was 0.95 and standard error of prediction was 0.94% using the PLS method. The results showed the possibility of soil organic matter measurement using NIR reflectance on the field.

• Steel and Composite Structures
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• v.43 no.4
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• pp.501-509
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• 2022

Cracks are common defects in concrete structures. Thus far, crack inspection has been manually performed using the contact inspection method. This manpower-dependent method inevitably increases the cost and work hours. Various non-contact studies have been conducted to overcome such difficulties. However, previous studies have focused on developing a methodology for non-contact inspection or local quantitative detection of crack width or length on concrete surfaces. However, crack depth can affect the safety of concrete structures. In particular, although macrocrack depth is structurally fatal, it is difficult to find it with the existing method. Therefore, an experimental investigation based on non-contact infrared thermography and multivariate machine learning was performed in this study to estimate the hidden macrocrack depth. To consider practical applications for inspection, an experiment was conducted that considered the simulated piloting of an unmanned aerial vehicle equipped with infrared thermography equipment. The crack depths (10-60 mm) were comparatively evaluated using linear regression, gradient boosting, and random forest (AI regression methods).

• The Transactions of the Korea Information Processing Society
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• v.7 no.5
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• pp.1361-1369
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• 2000

For the implementation of 3D Sound Localization system, the binaural filtering by HRTFs is generally employed. But the HRTF filter is of high order and its coefficients for all directions have to be stored, which imposes a rather large memory requirement. To cope with this, research works have centered on obtaining low dimensional HRTF representations without significant loss of information and synthesizing the original HRTF efficiently, by means of feature extraction methods for multivariate dat including PCA. In these researches, conventional linear PCA was applied to the frequency domain HRTF data and using relatively small number of principal components the original HRTFs could be synthesized in approximation. In this paper we applied neural network based nonlinear PCA model (NLPCA) and the nonlinear PLS repression model (NLPLS) for this low dimensional HRTF modeling and analyze the results in comparison with the PCA. The NLPCA that performs projection of data onto the nonlinear surfaces showed the capability of more efficient HRTF feature extraction than linear PCA and the NLPLS regression model that incorporates the direction information in feature extraction yielded more stable results in synthesizing general HRTFs not included in the model training.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1739-1756
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• 2021

Atmospheric nitrogen dioxide (NO₂) is mainly caused by anthropogenic emissions. It contributes to the formation of secondary pollutants and ozone through chemical reactions, and adversely affects human health. Although ground stations to monitor NO₂ concentrations in real time are operated in Korea, they have a limitation that it is difficult to analyze the spatial distribution of NO₂ concentrations, especially over the areas with no stations. Therefore, this study conducted a comparative experiment of spatial interpolation of NO₂ concentrations based on two linear-regression methods(i.e., multi linear regression (MLR), and regression kriging (RK)), and two machine learning approaches (i.e., random forest (RF), and support vector regression (SVR)) for the year of 2020. Four approaches were compared using leave-one-out-cross validation (LOOCV). The daily LOOCV results showed that MLR, RK, and SVR produced the average daily index of agreement (IOA) of 0.57, which was higher than that of RF (0.50). The average daily normalized root mean square error of RK was 0.9483%, which was slightly lower than those of the other models. MLR, RK and SVR showed similar seasonal distribution patterns, and the dynamic range of the resultant NO₂ concentrations from these three models was similar while that from RF was relatively small. The multivariate linear regression approaches are expected to be a promising method for spatial interpolation of ground-level NO₂ concentrations and other parameters in urban areas.