• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2000.11a
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• pp.232-236
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• 2000

Multi-regression statistical analyses were applied for the water quality data of shallow alluvial ground water (n = 47) collected from the Youngdeok area, in order to quantitatively generalize the natural (non-anthropogenic) causes of regional water quality variation. Seven samples having the high contamination index ( $C_{a}$ > 3) reflect the striong effects by anthropogenic activity. Most of the alluvial groundwaters have acquired their quality primarily due to the dissolution of carbonate minerals. The results of multi-regression analysis show that chlorine is mainly derived from seawater effect. Sulfur isotopic compositions of dissolved sulfur and the S $O_4$/Cl ratio also enable us to discriminate the samples (n = 18) which are affected by atmospheric input of marine aerosol (sea-spray) and also by mixing between freshwater and seawater. Hydrogen and oxygen isotope data of the samples collected lie close to the local meteoric water line obtained from nearby Pohang city but has lower slope (5.45) on the $\delta$D-$^{18}$ O plot, indicating that alluvial groundwater was recharged from infiltrated meteoric water which has undergone some degree of kinetic evaporation. The estimated initial isotopic composition of the recharged water ($\delta$D = -74.8$^{0}$ /$_{00}$, $\delta$$^{18}$ O = -10.8$^{[-1000]}$ /$_{[-1000]}$ ) suggests that the alluvial ground water recharge largely occurs during summer storm events.s.s.

• Journal of the Korean Applied Science and Technology
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• v.37 no.6
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• pp.1605-1618
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• 2020

The purpose of this study was to investigate the effect of beauty healthcare behaviors of middle-aged women on physical esteem and psychological well-being. The subjects of this study were middle-aged women in Seoul and Gyeonggi area, and surveyed for 6 weeks starting from October 26, 2020, and 334 words were used for analysis. The analysis method used the SPSS 26.0 program to perform frequency analysis, factor analysis, and reliability analysis. To find out the correlation between beauty healthcare, physical esteem, and psychological well-being for middle-aged women, correlation analysis and multi-regression analysis were performed. the statistical significance level was set to p<.05. It was confirmed that beauty healthcare behavior had a statistically significant effect on physical esteem and psychological well-being.

• Proceedings of the KSRS Conference
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• v.1
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• pp.512-515
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• 2006

Forest stand height and volume are important indicators for management purpose as well as for the environmental analysis. Shuttle Radar Topography Mission (SRTM) is backscattered over forest canopy and DSM can be acquired from such scattering characteristic, while National Elevation Dataset (NED) provides bare earth elevation data. The difference between SRTM and NED is estimated as tree height, and it is correlated with forest parameters, it is correlated with forest parameters, including average DBH, Trees per acre, net BF per acre, and total Net MBF. Especially, among them, net Board Foot(BF) per acre is the index that well represents forest volume. The Project site was Douglas-fir dominating plantation area in the western Washington an the northern Oregon in the U.S. This study shows a relationship of high correlation between the forest parameters and the product from SRTM, NED, and ETM+. This research performs multi regression analysis and regression tree algorithm, and can get more improved relationship between several parameters.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.427-431
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• 2006

Forest stand height and volume are important indicators for management purpose as well as for the environmental analysis. Shuttle Radar Topography Mission (SRTM) is backscattered over forest canopy and DSM can be acquired from such scattering characteristic, while National Elevation Dataset (NED) provides bare earth elevation data. The difference between SRTM and NED is estimated as tree height, and it is correlated with forest parameters, it is correlated with forest parameters, including average DBH, Trees per acre, net BF per acre, and total Net MBF. Especially, among them, net Board Foot(BF) per acre is the index that well represents forest volume. The Project site was Douglas-fir dominating plantation area in the western Washington an the northern Oregon in the U.S. This study shows a relationship of high correlation between the forest parameters and the product from SRTM, NED, and ETM+. This research performs multi regression analysis and regression tree algorithm, and can get more improved relationship between several parameters.

• Journal of the Korean Statistical Society
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• v.36 no.1
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• pp.129-148
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• 2007

Frailty estimates from the proportional hazards frailty model often lead us to conjecture the heterogeneity in frailty such that the variance of the frailty varies over different covariate groups (e.g. male group versus female group). For such systematic heterogeneity in frailty, we consider a regression model for the variance components in the proportional hazards frailty model, denoted by the MLFM. However, in many cases, the observed data do not show any statistically significant preference between the homogeneous frailty model and the heterogeneous frailty model. In this paper, we propose a Bayesian model averaging procedure with the reversible jump Markov chain Monte Carlo which selects the appropriate model automatically. The resulting regression coefficient estimate ignores the model uncertainty from the frailty distribution in view of Bayesian model averaging (Hoeting et al., 1999). Finally, the proposed model and the estimation procedure are illustrated through the analysis of the kidney infection data in McGilchrist and Aisbett (1991) and a simulation study is implemented.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.1032-1035
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• 1997

The purpose of this paper is to reduce the number of cutting tests and to predict the main cutting force and the specific cutting energy. By using the SOFM neural network, the most suitable cutting test conditions has been found. As a result, the number of cutting tests has been reduced to one-third. And by using MLP neural network and regression analysis, the main cutting force and specific cutting energy has been predicted. Predicted values of main cutting force and specific cutting energy are well concide with the measured ones.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2003.04a
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• pp.217-220
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• 2003

In the case of the remediation studies, push pull test is a more time and cost effective mettled than multi-well tracer test. It also gives Just as much or more information than the traditionally used methods. But the data analysis for the hydraulic parameters, there have been some defections such as underestimation of dispersivity, requirement for effective porosity, and calculation of recovery of center of mass to estimate linear velocity. In this research, Weibull distribution function is proposed to estimate the center of mass of breakthrough curve for Push pull test. The hydraulic parameter estimation using Weibull function showed more exact values of center of mass than those of exponential regression for field test data.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.2_2
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• pp.315-322
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• 2022

Apartments are being established as a universal housing type because of the perception that they are excellent in preservation of asset values and convenience. In this study, through multiple regression analysis, it is a thesis that explores whether it affects the housing market in Gimhae, a small and medium-sized city in the province, and how the price flow in neighboring cities has an effect. It is possible to examine how macroeconomic variables such as the balloon effect and the lowest interest rate caused by the government's tweezers regulation bring about changes in the housing market of small and medium-sized cities in local regions.

• Earthquakes and Structures
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• v.25 no.6
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• pp.429-442
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• 2023

To study the seismic vulnerability of the composite material structure of adobe and timber, we collected and statistically analysed empirical observation samples of 542,214,937 m² and 467,177 buildings that were significantly impacted during the 179 earthquakes that occurred in mainland China from 1976 to 2010. In multi-intensity regions, combined with numerical analysis and a probability model, a non-linear continuous regression model of the vulnerability, considering the empirical seismic damage area (number of buildings) and the ratio of seismic damage, was established. Moreover, a probability matrix model of the empirical seismic damage mean value was provided. Considering the coupling effect of the annual and seismic fortification factors, an empirical seismic vulnerability curve model was constructed in the multiple-intensity regions. A probability matrix model of the mean vulnerability index (MVI) was proposed, and was validated through the above-mentioned reconnaissance sample data. A matrix model of the MVI of the regions (19 provinces in mainland China) based on the parameter (MVI) was established.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.05a
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• pp.75-80
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• 1999

As current manufacturing processes require high spindle speed and precise machining, increasing accuracy by reducing volumetric errors of the machine itself, particularly thermal errors, is very important. Thermal errors can be estimated by many empirical models, for example, an FEM model, a neural network model, a linear regression model, an engineering judgment model etc. This paper discusses to make a modeling of thermal errors efficiently through backward elimination and fuzzy logic strategy. The model of a thermal error using fuzzy logic strategy overcome limitation of accuracy in the linear regression model or the engineering judgment model. And this model is compared with the engineering judgment model. It is not necessary complex process such like multi-regression analysis of the engineering judgment model. A fuzzy model does not need to know the characteristics of the plant, and the parameters of the model can be mathematically calculated. Like a regression model, this model can be applied to any machine, but it delivers greater accuracy and robustness.