• Journal of Korean Society on Water Environment
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• v.30 no.1
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• pp.8-15
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• 2014

In this study, regression equation was analyzed to estimate non-point source (NPS) pollutant loads in orchard area. Many factors affecting the runoff of NPS pollutant as precipitation, storm duration time, antecedent dry weather period, total runoff density, average storm intensity and average runoff intensity were used as independent variables, NPS pollutant was used as a dependent variable to estimate multiple regression equation. Based on the real measurement data from 2008 to 2012, we performed correlation analysis among the environmental variables related to the rainfall NPS pollutant runoff. Significance test was confirmed that T-P ($R^2=0.89$) and BOD ($R^2=0.79$) showed the highest similarity with the estimated regression equations according to the NPS pollutant followed by SS and T-N with good similarity ($R^2$ >0.5). In the case of regression equation to estimate the NPS pollutant loads, regression equations of multiplied independent variables by exponential function and the logarithmic function model represented optimum with the experimented value.

• Tribology and Lubricants
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• v.39 no.3
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• pp.94-101
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• 2023

Automotive wheel bearings are a critical component of vehicles that support their weight and facilitate rotation. Life and stiffness are significant performance characteristics of wheel bearings. Designing wheel bearings involves finding optimal design variables that satisfy both performances. CO₂ emission reduction and fuel efficiency regulations attribute to the recent increase in design requirements for lightweight and compact automotive parts while maintaining performance. However, achieving a design that maintains performance while reducing weight poses challenges, as performance and weight are generally inversely proportional. In this study, we perform design optimization of automotive wheel bearings considering life and stiffness. We develop a program that calculates the basic rated life and modified rated life based on international standards for evaluating the life of wheel bearings. We develop a regression equation using regression analysis to address the time-consuming stiffness analysis during repetitive analysis. We perform ANOVA and main effect analyses to understand the statistical characteristics of the developed regression equation. Furthermore, we verify its reliability by comparing the predicted and test results. We perform design optimization using the developed life prediction program, stiffness regression equation and weight regression equation. We select bearing specifications and geometry as design variables, weight as the cost function, and life and stiffness as constraints. Through design optimization, we investigate the influence of design variables on the cost function and constraints by comparing the initial and optimal design values.

• Journal of the Korean Society of Safety
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• v.35 no.5
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• pp.15-21
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• 2020

Quantitative risk assessment (QRA) is used as a legal or voluntary safety management tool for the hazardous material industry and the utilization of the method is gradually increasing. Therefore, a leak frequency analysis based on reliable generic data is a critical element in the evolution of QRA and safety technologies. The aim of this paper is to derive the leak frequency function that can be applied more flexibly in QRA based on OGP report with high reliability and global utilization. For the purpose, we first reviewed the data on the 16 equipments included in the OGP report and selected the predictors. And then we found good equations to fit the OGP data using non-linear regression analysis. The various expectation functions were applied to search for suitable parameter to serve as a meaningful reference in the future. The results of this analysis show that the best fitting parameter is found in the form of DNV function and connection function in natural logarithm. In conclusion, the average percentage error between the fitted and the original value is very small as 3 %, so the derived prediction function can be applicable in the quantitative frequency analysis. This study is to contribute to expand the applicability of QRA and advance safety engineering as providing the generic equations for practical leak frequency analysis.

• Journal of muscle and joint health
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• v.20 no.3
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• pp.225-234
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• 2013

Purpose: This purpose of study was to investigate the relationships among functional state, self-efficacy, and life satisfaction in the elderly with decreased visual acuity. Methods: The subjects were 162 elderly people from the G university hospital. Functional state was measured by Late-Life Function and Disability Instrument (LLFDI) and Minimum Data Set-Home Care version 2.0 (MDS HC 2.0). Self-efficacy and Life satisfaction were measured by the tool of Rho & Lee (2011) and Yoon (2007). Data were analyzed using t-test, ANOVA, Pearson's Correlation Coefficient, and logistic regression. Results: The daily life function was significantly associated with self-efficacy and vision decrease. The regression model with these two variables explained 35.6% of the variance of daily life function. IADL was significantly associated with vision decrease, age, gender, and self-efficacy. The regression model with the three variables explained 52.9% of the variance of IADL. Life satisfaction is significantly associated with self-efficacy, daily life function, vision decrease and IADL. The last regression model with the four variables explained 51.8% of the variance of life satisfaction. Conclusion: The levels of functional state, self-efficacy and life satisfaction in the elderly with decreased visual acuity were low. Self-efficacy was an important factor that influences on the functional state and life satisfaction. Therefore, nursing interventions that can enhance the self-efficacy are required in order to increase the functional state and life satisfaction in the elderly with decreased visual acuity.

• The Journal of the Korea Contents Association
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• v.14 no.2
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• pp.319-329
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• 2014

Purpose: The purpose of this study was to investigate the effect of adolescent's family function, impulsive behavior, stress on the bullying types. Method: Data were collected from 627 adolescents and analyzed using descriptive statistics, t-test, Pearson correlation coefficients and stepwise multiple regression with the SPSS 18.0. Results: The bullying types (injurer and victim) correlates with family function, impulsive behavior and stress. Stepwise multiple regression analysis showed emotional reactivity, non-planning impulsiveness, friends related stress, experience of drinking (yes), experience of parent depression problem (yes), explained 34.1% of the total variance in bully injurer. Stepwise multiple regression analysis showed communication, motor impulsiveness, friends related stress, gender (male), grade (junior high school), explained 30.9% of the total variance in bully victim. Conclusion: The results of this study are expected to be used as basic data in providing a better understanding of adolescents' bullying, in preventing bullying and in developing an intervention program.

• Journal of Environmental Science International
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• v.28 no.3
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• pp.357-369
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• 2019

In this study, the water quality components (pH, BOD, COD, TOC, SS, DO, TP) and the water quality, observed for 10 years (2008~2017) in the five tributaries of the Nakdong River with the highest flow rates, were analyzed. Monthly levels of the water quality components were estimated and regression functions were used to quantitatively explain the changes in the BOD and COD components, with respect to the TOC components. The results of analyzing the water quality levels in terms of the living environmental standards show that the lowest water quality was observed midstream (ST-3) and the highest water quality was observed upstream (ST-1 and ST-2). The regression function was estimated to be a linear function in all five tributaries, and the goodness of fit of the function was high upstream (ST-2), midstream (ST-3), and downstream (ST-4). According to the regression analysis using the observation data from 2008~2017, we found that the consumption of dissolved oxygen increased with an increase in organic matter in the major tributaries of the Nakdong River.

• Annals of Occupational and Environmental Medicine
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• v.35
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• pp.23.1-23.14
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• 2023

Background: Exposure to heavy metals is a public health concern worldwide. Previous studies on the association between heavy metal exposure and neurobehavioral functions in children have focused on single exposures and clinical manifestations. However, the present study evaluated the effects of heavy metal complex exposure on subclinical neurobehavioral function using a Korean Computerized Neurobehavior Test (KCNT). Methods: Urinary mercury, lead, cadmium analyses as well as symbol digit substitution (SDS) and choice reaction time (CRT) tests of the KCNT were conducted in children aged between 10 and 12 years. Reaction time and urinary heavy metal levels were analyzed using partial correlation, linear regression, Bayesian kernel machine regression (BKMR), the weighted quantile sum (WQS) regression and quantile G-computation analysis. Results: Participants of 203 SDS tests and 198 CRT tests were analyzed, excluding poor cooperation and inappropriate urine sample. Partial correlation analysis revealed no association between neurobehavioral function and exposure to individual heavy metals. The result of multiple linear regression shows significant positive association between urinary lead, mercury, and CRT. BMKR, WQS regression and quantile G-computation analysis showed a statistically significant positive association between complex urinary heavy metal concentrations, especially lead and mercury, and reaction time. Conclusions: Assuming complex exposures, urinary heavy metal concentrations showed a statistically significant positive association with CRT. These results suggest that heavy metal complex exposure during childhood should be evaluated and managed strictly.

• Journal of Ecology and Environment
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• v.33 no.2
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• pp.165-173
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• 2010

Soil respiration ($R_S$) is a critical component of the annual carbon balance of forests, but few studies thus far have attempted to evaluate empirical regression models in $R_S$. The principal objectives of this study were to evaluate the relationship between $R_S$ rates and soil temperature (ST) and soil water content (SWC) in soil from a cool-temperate deciduous broad-leaved forest, and to evaluate empirical regression models for the prediction of $R_S$ using ST and SWC. We have been measuring $R_S$, using an open-flow gas-exchange system with an infrared gas analyzer during the snowfree season from 1999 to 2001 at the Takayama Forest, Japan. To evaluate the empirical regression models used for the prediction of $R_S$, we compared a simple exponential regression (flux = $ae^{bt}$Eq. [1]) and two polynomial multiple-regression models (flux = $ae^{bt}{\times}({\theta}{\nu}-c){\times}(d-{\theta}{\nu})^f:$ Eq. [2] and flux = $ae^{bt}{\times}(1-(1-({\theta}{\nu}/c))^2)$: Eq. [3]) that included two variables (ST: t and SWC: ${\theta}{\nu}$) and that utilized hourly data for $R_S$. In general, daily mean $R_S$ rates were positively well-correlated with ST, but no significant correlations were observed with any significant frequency between the ST and $R_S$ rates on periods of a day based on the hourly $R_S$ data. Eq. (2) has many more site-specific parameters than Eq. (3) and resulted in some significant underestimation. The empirical regression, Eq. (3) was best explained by temporal variations, as it provided a more unbiased fit to the data compared to Eq. (2). The Eq. (3) (ST $\times$ SWC function) also increased the predictive ability as compared to Eq. (1) (only ST exponential function), increasing the $R^2$ from 0.71 to 0.78.

• Journal of Broadcast Engineering
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• v.26 no.2
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• pp.184-196
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• 2021

This paper proposes statistics adaptive linear regression-based object size prediction method for object detection. YOLOv2 and YOLOv3, which are typical deep learning-based object detection algorithms, designed the last layer of a network using statistics adaptive exponential regression model to predict the size of objects. However, an exponential regression model can propagate a high derivative of a loss function into all parameters in a network because of the property of an exponential function. We propose statistics adaptive linear regression layer to ease the gradient exploding problem of the exponential regression model. The proposed statistics adaptive linear regression model is used in the last layer of the network to predict the size of objects with statistics estimated from training dataset. We newly designed the network based on the YOLOv3tiny and it shows the higher performance compared to YOLOv3 tiny on the UFPR-ALPR dataset.

• 한국신재생에너지학회:학술대회논문집
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• 2010.11a
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• pp.65.1-65.1
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• 2010

Thermal performance tests of solar thermal collectors include determination of coefficient parameters in an efficiency equation. The parameters can be estimated using regression method to minimize an objective function as sum of differences between measured efficiency data and regressed efficiency equation. However, this conventional approach doesn't consider measurement uncertainties. In this presentation, a method to determine regression parameters in the efficiency equation and uncertainties of the parameters is described with mainly mathematical expressions based on literature reviews. In the method, parameters in the equation for collector efficiency can be determined using regression analysis with a weighting factor in the objective function. The weighting factor can be uncertainties of the differences between measured and fitted efficiencies. To evaluate the approach, performance estimation of a solar collector using the efficiency equation with uncertainties is compared to the result using the conventional efficiency equation by a simulated way for a case in one of previous studies.