• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.6
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• pp.564-573
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• 2012

The objective of this study is to determine the effect for the sound quality according to the noise source and to build the sound quality index of the laundry noise. In order to compare laundry noise among the influence of noise sources, we made virtual laundry noises by synthesizing an actual laundry noise and each noise source such as a dropping noise, water noise, motor noise and circulation pump noise. We conducted a listening test by customers using virtual laundry noises. As a result of listening test, we found that the dropping noise has a decisive effect on the sound quality of the laundry noise. We conducted the multi regression analysis of sound quality for the laundry noise using the statistical data processing. It is verified to the reliability of the multi regression index by comparison with listening results and index results of other actual laundry noises. This study is expected to provide a guide line for improvement of the laundry noise.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.481-481
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• 2000

We investigated the structure-activity relationships on use of multi-layer neural networks. The relationships are techniques required in developments of medicines. Since many kinds of observations might be adopted on the techniques, we discussed some points between the observations and the properties of multi-layer neural networks. In the structure-activity relationships, an important property is not that standard deviations are nearly equal to zero for observed physiological activity, but prediction ability for unknown medicines. Since we adopted non-linear approximation, the function to represent the activity can be defined by observations; therefore, we believe that the standard deviations have not significance. The function was examined by "leave-one-out" method, which was originally introduced for the multi-regression analysis. In the linear approximation, the examination is significance, however, we believe that the method is inappropriate in case of nonlinear fitting as neural networks; therefore, we derived a new index fer the relationships from the differential of information propagation in the neural network. By using the index, we discussed physiological activity of an anti-cancer medicine, Mitomycine derivatives. The neuro-computing suggests that there is no direction to extend the anti-cancer activity of Mitomycine, which is close to the trend of anticancer developing.

• 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.

• Industrial Engineering and Management Systems
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• v.1 no.1
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• pp.73-78
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• 2002

In this paper, we propose a constrained optimization model for conjoint analysis (a preference decomposition technique) to improve parameter estimation by restricting the relative importance of the attributes to an extent as decided by the respondents. Quite simply, respondents are asked to provide some pairwise attribute comparisons that are then incorporated as additional constraints in a linear programming model that estimates the partial preference values. This data collection method is typical in the analytic hierarchy process. Results of a simulation study show the new model can improve the predictive accuracy in partial value estimation by ordinal east squares (OLS) regression.

• Journal of the Korean Society of Clothing and Textiles
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• v.30 no.1 s.149
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• pp.83-93
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• 2006

The purpose of this study is to analyze the relationship among subjective textures, sensibilities and objective handle of knit fabrics and to provide useful information in planning and designing knit fabrics. We made 20 plain knit fabrics, as specimens, with a combination of 5 kinds of wool/rayon fiber contents and 4 kinds of stitch loop length. For the subjective evaluation, we used 29 questions of subjective textures and sensibilities and employed statistical analysis tools such as factor, Pearson's correlation analysis. An objective handle was measured by Kawabata evaluation system and HV and THV was calculated by KN-402-KT and KN-301-winter. The analysis of a Pearson's correlation with objective properties and handles and structural properties of knit fabrics demonstrated a highly linear relationship. Especially, wool/rayon contents and WT of tensile properties and loop stitch length and G of shear properties showed a correlation coefficient over 0.9. But a relationship of objective properties and subjective textures and sensibilities was non-linear and a linear multi-regression analysis showed that a objective handle had a lower prediction power in the area of subjective textures and sensibilities.

• Korean Journal of Child Studies
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• v.28 no.5
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• pp.253-267
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• 2007

This study examined the relationships between multiple intelligences as cognitive factors and affective factors of learning motivation and academic self-concept. The data were collected from 276 4th grade elementary school students and analyzed by correlation, multi-variate analysis, and step-wise multiple regression. Results were that (1) multiple intelligences, learning motivation, and academic self-concept had statistically significant correlations among themselves. Multi-variate analysis showed that intra-personal intelligence explained 58.6% of the linear combination of learning motivation and academic self-concept. (2) Intra-personal intelligence explained 29% to 58% of learning motivation and its sub-factors of achievement motivation, internal locus of control, self-efficacy, and self-regulation. (3) Intra-personal intelligence, logical-mathematical intelligence, musical intelligence, and inter-personal intelligence were explanatory variables for academic self-concept and its sub-factors.

• Journal of the Society of Naval Architects of Korea
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• v.57 no.5
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• pp.297-304
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• 2020

In this study, the prediction method was reviewed to process a cylindrical plate forming using machine learning as a data-driven approach by roll bending equipment. The calculation of the forming variables was based on the analysis using the mechanical relationship between the material properties and the roll bending machine in the bending process. Then, by applying the finite element analysis method, the accuracy of the deformation prediction model was reviewed, and a large number data set was created to apply to machine learning using the finite element analysis model for deformation prediction. As a result of the application of the machine learning model, it was confirmed that the calculation is slightly higher than the linear regression method. Applicable results were confirmed through the machine learning method.

• Imaging Science in Dentistry
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• v.40 no.2
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• pp.63-68
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• 2010

Purpose : To compare the CT numbers on 3 cone-beam CT (CBCT) images with those on multi-detector CT (MDCT) image using CT phantom and to develop linear regressive equations using CT numbers to material density for all the CT scanner each. Materials and Methods : Mini CT phantom comprised of five 1 inch thick cylindrical models with 1.125 inches diameter of materials with different densities (polyethylene, polystyrene, plastic water, nylon and acrylic) was used. It was scanned in 3 CBCTs (i-CAT, Alphard VEGA, Implagraphy SC) and 1 MDCT (Somatom Emotion). The images were saved as DICOM format and CT numbers were measured using OnDemand 3D. CT numbers obtained from CBCTs and MDCT images were compared and linear regression analysis was performed for the density, $\rho$ ($g/cm^3$), as the dependent variable in terms of the CT numbers obtained from CBCTs and MDCT images. Results : CT numbers on i-CAT and Implagraphy CBCT images were smaller than those on Somatom Emotion MDCT image (p<0.05). Linear relationship on a range of materials used for this study were $\rho$=0.001H+1.07 with $R^2$ value of 0.999 for Somatom Emotion, $\rho$=0.002H+1.09 with $R^2$ value of 0.991 for Alphard VEGA, $\rho$=0.001H+1.43 with $R^2$ value of 0.980 for i-CAT and $\rho$=0.001H+1.30 with $R^2$ value of 0.975 for Implagraphy. Conclusion: CT numbers on i-CAT and Implagraphy CBCT images were not same as those on Somatom Emotion MDCT image. The linear regressive equations to determine the density from the CT numbers with very high correlation coefficient were obtained on three CBCT and MDCT scan.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.2
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• pp.93-101
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• 2019

The objective of this study is to develop a model which can predict the seismic performance of the slope relatively accurately and efficiently by using artificial neural network(ANN) technique. The quantification of such the seismic performance of the slope is not easy task due to the randomness and the uncertainty of the earthquake input and slope model. Under these circumstances, probabilistic seismic fragility analyses of slope have been carried out by several researchers, and a closed-form equation for slope seismic performance was proposed through a multiple linear regression analysis. However, a traditional statistical linear regression analysis has shown a limit that cannot accurately represent the nonlinearistic relationship between the slope of various conditions and seismic performance. In order to overcome these problems, in this study, we attempted to apply the ANN to generate prediction models of the seismic performance of the slope. The validity of the derived model was verified by comparing this with the conventional multi-linear and multi-nonlinear regression models. As a result, the models obtained through the ANN basically showed excellent performance in predicting the seismic performance of the slope, compared to the models obtained by the statistical regression analyses of the previous study.

• Korean Chemical Engineering Research
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• v.46 no.3
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• pp.567-570
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• 2008

The present study investigated the applicability of the minium fludization velocity measuring method using linear regression analysis between the standard deviation of pressure fluctuation and gas velocity in multi-particle sand on a fluidized bed 0.109 in inner diameter. We measured minium fludization velocity according to the standard deviation of particle distribution in Gaussian distribution. The measured value compared with other researchers' equations. The minium fludization velocity derived from the linear regression analysis of the standard deviation of pressure fluctuation and pressure drop inside the bed. We also found that the minium fludization velocity of a multi-particle system using the standard deviation of pressure fluctuation must be measured at freely bubbling region.