• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.05a
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• pp.1443-1448
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• 2006

The reduction of the vehicle interior noise has been the main interest of NVH engineers. The driver's perception on the vehicle noise is affected largely by psychoacoustic characteristic of the noise as well as the SPL. In particular, the HVAC sound among the vehicle interior noise has been reflected sensitively in the side of psychology. Even though the HVAC noise is not louder than overall noise level, it clearly affects subjective perception in the way of making a diver become nervous or annoyed. Therefore, these days a vehicle engineer takes aim at developing sound quality as well as reduction of noise. In this paper, we acquired noises in the HVAC from many vehicles. Through the objective and subjective sound quality evaluation with acquiring noises caused by the vehicle HVAC system, the simple and multiple regression models were obtained for the subjective evaluation 'Pleasant' using the sound quality metrics. The regression procedure also allows you to produce diagnostic statistics to evaluate the regression estimates including appropriation and accuracy. Furthermore, the neural network model were obtained using three inputs(loudness, sharpness and roughness) of the sound quality metrics and one output(subjective 'Pleasant'). And then the models were compared with correlations between sound quality index outputs and hearing test results for 'Pleasant'. As a result of application of the sound quality index, the neural network was verified with the largest correlation of the sound quality index.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1693-1705
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• 2013

To restore old aqueduct in Korea which is a irrigation bridge to supply water in paddy field area, it is needed to estimate approximate costs of restoration because the basic design for estimation of construction costs is often ruled out in current system. In this paper, estimating models of construction costs were developed on the basis of performance data for restoration of RC aqueduct bridges since 2003. The regression analysis (RA) model and case-based reasoning (CBR) model for the estimation of construction costs were developed respectively. Error rate of simple RA model was lower than that of multiple RA model. CBR model using genetic algorithm (GA) has been applied in the estimation of construction costs. In the model three factors like attribute weight, attribute deviation and rank of case similarity were optimized. Especially, error rate of estimated construction costs decreased since limit ranges of the attribute weights were applied. The results showed that error rates between RA model and CBR models were inconsiderable statistically. It is expected that the proposed estimating method of approximate costs of aqueduct restoration will be utilized to support quick decision making in phased rehabilitation project.

• Journal of the Korean Home Economics Association
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• v.26 no.2
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• pp.49-67
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• 1988

The purpose of this study was to present a conceptual model for predicting housing quality. Housing quality was measured in three ways: perceived quality about physical features of houses, perceived level of the quality in comparison with perceived average level I urban area and housing satisfaction. The specific objectives to achieve the purpose were ; 1) to measure the perceived quality about physical features of houses and perceived level of the quality in comparison with the perceived average level I urban 2) to measure the level of housing satisfaction 3) to clarify the causality between the composite variables of housing quality. A final instrument was developed through two stage pilot surveys. The respondents were 1292 homemakers of middle and high economic class in seoul and Daejeon, selected through stratified random sampling technique. Data were collected during March and April, 1986, and analyzed using SPSS and SAS computer packages. The statistics used were frequency, percentage, F-test, Duncan's Multiple Range, x2, Cramer's V, Multiple linear Regression, Path analysis. The major finding were as follows; the variables significantly related to predict the housing quality were found. The simple, composite variables and 3 measures of housing quality were linked using path analysis, thereby a conceptual model predicting housing quality was suggested.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.9
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• pp.351-360
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• 2018

The LWR (Locally Weighted Regression) model, which is traditionally a lazy learning model, is designed to obtain the solution of the prediction according to the input variable, the query point, and it is a kind of the regression equation in the short interval obtained as a result of the learning that gives a higher weight value closer to the query point. We study on an incremental ensemble learning approach for LWR, a form of lazy learning and memory-based learning. The proposed incremental ensemble learning method of LWR is to sequentially generate and integrate LWR models over time using a genetic algorithm to obtain a solution of a specific query point. The weaknesses of existing LWR models are that multiple LWR models can be generated based on the indicator function and data sample selection, and the quality of the predictions can also vary depending on this model. However, no research has been conducted to solve the problem of selection or combination of multiple LWR models. In this study, after generating the initial LWR model according to the indicator function and the sample data set, we iterate evolution learning process to obtain the proper indicator function and assess the LWR models applied to the other sample data sets to overcome the data set bias. We adopt Eager learning method to generate and store LWR model gradually when data is generated for all sections. In order to obtain a prediction solution at a specific point in time, an LWR model is generated based on newly generated data within a predetermined interval and then combined with existing LWR models in a section using a genetic algorithm. The proposed method shows better results than the method of selecting multiple LWR models using the simple average method. The results of this study are compared with the predicted results using multiple regression analysis by applying the real data such as the amount of traffic per hour in a specific area and hourly sales of a resting place of the highway, etc.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.1
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• pp.95.2-95.2
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• 2012

We have compared the geoeffective parameters of halo coronal mass ejections (CMEs) to predict geomagnetic storms. For this we consider 50 front-side full halo CMEs whose asymmetric cone model parameters and earthward direction parameter were available. For each CME we use its projected velocity (Vp), radial velocity (Vr), angle between cone axis and sky plane (${\gamma}$) from the cone model, earthward direction parameter (D), source longitude (L), and magnetic field orientation (M) of the CME source region. We make a simple and multiple linear regression analysis to find out the relationship between CME parameters and Dst index. Major results are as follows. (1) $Vr{\times}{\gamma}$ has a higher correlation coefficient (cc = 0.70) with the Dst index than the others. When we make a multiple regression of Dst and two parameters ($Vr{\times}{\gamma}$, D), the correlation coefficient increases from 0.70 to 0.77. (2) Correlation coefficients between Dst index and $Vr{\times}{\gamma}$ have different values depending on M and L. (3) Super geomagnetic storms (Dst ${\leq}$ -200 nT) only appear in the western and southward events. Our results demonstrate that not only the cone model parameters together with the earthward direction parameter improve the relationship between CME parameters and Dst index but also the source longitude and its magnetic field orientation play a significant role in predicting geomagnetic storms.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.15-16
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• 2006

In-process surface roughness prediction is studied in this research. To implement in-process prediction, spindle displacement is introduced. Machined surface's roughness is assumed to be expressed in terms of spindle displacement. In-process measurement of spindle displacement is conducted using CCDS (cylindrical capacitive displacement sensor). Two prediction models are developed. One is simple linear model between measured surface roughness and values by spindle displacement. The other is multiple regression model including machining parameters like spindle speed, fee rate and radial depth of cut. Relation between machined surface roughness and roughness by spindle displacement are verified.

• The Journal of Economics, Marketing and Management
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• v.10 no.5
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• pp.15-24
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• 2022

Purpose: The purpose of this study is to find a way to improve and overcome the psychological treatment limited to the negative factors dealt with in psychology from a positive psychological point of view. To this end, this study aims to verify whether a positive psychotherapy program can improve happiness, resilience, and post-traumatic growth along with improvement of psychological symptoms such as depression. Research design, data and methodology: To this end, in this study, mean difference analysis was conducted using t-test on 10 participants in the 16th PPT program and 14 in the control group. Also, after setting the main variables, we tried to confirm the effectiveness through simple regression analysis and multiple regression analysis of the causal relationship model. Results: As a result of the independent sample t-test and the paired sample t-test, it was confirmed that the group participating in the PPT program had higher flourish, happiness, resilience, post-traumatic growth, and lower depression. In addition, as a result of regression analysis, it was confirmed that post-traumatic growth had a positive effect, and that depression was a life-threatening factor. Conclusions: Since the PPT program has a positive effect on the participants with relatively negative psychological symptoms, it is necessary to expand it. In addition, it is necessary to introduce various preventive programs such as PPT as well as traditional psychological treatment for negative symptoms such as depression.

• 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 the Korean Institute of Intelligent Systems
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• v.18 no.6
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• pp.876-880
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• 2008

Outliers are the observations which are very larger or smaller than most observations in the given data set. These are shown by some sources. The result of the analysis with outliers may be depended on them. In general, we do data analysis after removing outliers. But, in data mining applications such as fraud detection and intrusion detection, outliers are included in training data because they have crucial information. In regression models, simple and multiple regression models need to eliminate outliers from given training data by standadized and studentized residuals to construct good model. In this paper, we use support vector regression(SVR) based on statistical teaming theory to analyze data with outliers in regression. We verify the improved performance of our work by the experiment using synthetic data sets.

• Journal of Gastric Cancer
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• v.14 no.4
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• pp.259-265
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• 2014

Purpose: Survival analysis of gastric cancer patients requires knowledge about factors that affect survival time. This paper attempted to analyze the survival of patients with incomplete registered data by using imputation methods. Materials and Methods: Three missing data imputation methods, including regression, expectation maximization algorithm, and multiple imputation (MI) using Monte Carlo Markov Chain methods, were applied to the data of cancer patients referred to the cancer institute at Imam Khomeini Hospital in Tehran in 2003 to 2008. The data included demographic variables, survival times, and censored variable of 471 patients with gastric cancer. After using imputation methods to account for missing covariate data, the data were analyzed using a Cox regression model and the results were compared. Results: The mean patient survival time after diagnosis was $49.1{\pm}4.4$ months. In the complete case analysis, which used information from 100 of the 471 patients, very wide and uninformative confidence intervals were obtained for the chemotherapy and surgery hazard ratios (HRs). However, after imputation, the maximum confidence interval widths for the chemotherapy and surgery HRs were 8.470 and 0.806, respectively. The minimum width corresponded with MI. Furthermore, the minimum Bayesian and Akaike information criteria values correlated with MI (-821.236 and -827.866, respectively). Conclusions: Missing value imputation increased the estimate precision and accuracy. In addition, MI yielded better results when compared with the expectation maximization algorithm and regression simple imputation methods.