국내 화강암의 점하중강도와 일축압축강도간의 상관분석

우익 ( Ik Woo ) 대한지질공학회 2014 지질공학 Vol.24 No.1

이 연구에서는 국내에 분포하는 화강암에 대한 점하중강도(Is(50)) 와 일축압축강도(UCS) 사이의 상관관계를 선형회귀분석을 통하여 구하였다. 이를 위하여 암석시료의 물리적 물성에 근거하여 화강암 시료를 재분류하여 세 경우에 대한 회귀분석을 수행하였다. 첫째로, 풍화등급에 따른 원점회귀분석을 수행하여 풍화에 따른 상관관계를 구하였으나 풍화등급별 시료의 개수가 부족하여 만족할만한 신뢰수준의 회귀분석결과를 얻지 못하였지만, 풍화가 진행될수록 회귀직선의 기울기가 급해지는 경향을 파악할 수 있었다. 두 번째, 전체 화강암에 대한 회귀분석을 수행하기 위하여 단순선형회귀분석과 신뢰도 및 정확도를 향상시키기 위하여 부트스트랩 리샘플링법을 이용한 단순선형회귀분석을 통하여 두 강도간의 상관관계를 구하였다. 세 번째로는 유사한 물리적 물성을 지닌 화강암 시료들의 평균강도에 대한 선형회귀분석을 수행하여 상관관계를 구하였다. 이 방법들을 사용하여 구한 회귀직선방정식의 기울기는 14 내외의 값을 보이고 작은 편차를 지니고 있으며 국내 화강암에 대하여 수행한 기존 연구와 유사한 값을 보이고 있었다. 그러나 16에서 43의 범위를 지닌 y-축 절편은 큰 편차를 보이기 있었기 때문에, 점 하중강도로 일축압축강도를 추정할 때에는 이러한 회귀방정식의 오차범위를 고려하여야 할 것으로 판단된다. This study presents the results of a regression analysis of the point-load strength (Is(50)) and the uniaxial compressive strength (UCS) of granites in Korea. The regression was carried out for three cases using the least-squares method, reclassifying the granite samples based on their physical properties. The first regression analysis through the origin according to the weathering grade did not give a result with a sufficient degree of confidence, due to the small number of samples. However, the general trend of the correlation between UCS and Is(50) according to weathering grade shows that the slope of the linear regression for weathered granite is steeper than that for fresh granite. The second analysis was a simple linear regression for all the granite samples using the least-squares method as well as a linear regression using the bootstrap resampling method in order to increase the confidence level and the accuracy of the regression results. The third regression considered the average strength of granite groups reclassified according to physical properties. These linear regression analyses yielded linear regression equations with slopes of 14 and small standard deviations being similar to values reported in previous studies on Korean granites, but whose intercept values range from 16 to 43 and have a larger standard deviation than those of the present study. In conclusion, it would be advisable to estimate UCS from Is(50), considering the error range derived from the deviation of the regression equations.

Fuzzy Regression Analysis using Trapezoidal Fuzzy Numbers

Ilyas Idrisovich Ismagilov,Ghena Alsaied 대한산업공학회 2020 Industrial Engineeering & Management Systems Vol.19 No.4

As a widely used method, regression analysis plays an increasingly important role in creating statistical models and making forecasts in the field of economics and finance. The use of traditional regression for modeling socio-economic processes is not sufficiently substantiated in some situations. Currently, a new direction is being actively developed, associated with fuzzy regression analysis and its application as an alternative to classical methods for modeling economic phenomena. Fuzzy regression methods are based on the theory of fuzzy sets. A number of methods and their modifications are proposed for constructing fuzzy regression models, but most of them use triangular fuzzy symmetric numbers. In this paper, we propose a new method for constructing linear fuzzy regression using trapezoidal fuzzy numbers. The method is based on dividing the sample using a regression model which is estimated by using the ordinary least squares. Two fuzzy regressions using triangular numbers are estimated from the formed samples, on the basis of which a fuzzy model with trapezoidal fuzzy numbers is constructed. Basing on the proposed method, a linear fuzzy model of the gross regional product as an indicator of the economic development of the Republic of Tatarstan of Russia is constructed depending on a number of factors. A comparative assessment of the quality of fuzzy regression models using triangular and trapezoidal numbers was performed.

GACV for partially linear support vector regression

심주용,석경하 한국데이터정보과학회 2013 한국데이터정보과학회지 Vol.24 No.2

Partially linear regression is capable of providing more complete description of the linear and nonlinear relationships among random variables. In support vector regression (SVR) the hyper-parameters are known to affect the performance of regression. In this paper we propose an iterative reweighted least squares (IRWLS) procedure to solve the quadratic problem of partially linear support vector regression with a modified loss function, which enables us to use the generalized approximate cross validation function to select the hyper-parameters. Experimental results are then presented which illustrate the performance of the partially linear SVR using IRWLS procedure.

GACV for partially linear support vector regression

Shim, Jooyong,Seok, Kyungha The Korean Data and Information Science Society 2013 한국데이터정보과학회지 Vol.24 No.2

Partially linear regression is capable of providing more complete description of the linear and nonlinear relationships among random variables. In support vector regression (SVR) the hyper-parameters are known to affect the performance of regression. In this paper we propose an iterative reweighted least squares (IRWLS) procedure to solve the quadratic problem of partially linear support vector regression with a modified loss function, which enables us to use the generalized approximate cross validation function to select the hyper-parameters. Experimental results are then presented which illustrate the performance of the partially linear SVR using IRWLS procedure.

상관성과 단순선형회귀분석

박선일,오태호 한국임상수의학회 2010 한국임상수의학회지 Vol.27 No.4

Correlation is a technique used to measure the strength or the degree of closeness of the linear association between two quantitative variables. Common misuses of this technique are highlighted. Linear regression is a technique used to identify a relationship between two continuous variables in mathematical equations, which could be used for comparison or estimation purposes. Specifically, regression analysis can provide answers for questions such as how much does one variable change for a given change in the other, how accurately can the value of one variable be predicted from the knowledge of the other. Regression does not give any indication of how good the association is while correlation provides a measure of how well a least-squares regression line fits the given set of data. The better the correlation, the closer the data points are to the regression line. In this tutorial article, the process of obtaining a linear regression relationship for a given set of bivariate data was described. The least square method to obtain the line which minimizes the total error between the data points and the regression line was employed and illustrated. The coefficient of determination,the ratio of the explained variation of the values of the independent variable to total variation, was described. Finally,the process of calculating confidence and prediction interval was reviewed and demonstrated.

전력수요예측을 위한 다양한 퍼지 최소자승 선형회귀 모델

송경빈,Song, Kyung-Bin 한국조명전기설비학회 2007 조명·전기설비학회논문지 Vol.21 No.7

전력수요예측은 전력계통의 운용을 위해 필수적이다. 따라서 다양한 방법이 제시되어 왔으며, 특히 특수일의 수요예측은 평일과 구분되며, 부하 패턴을 축출하기에 충분한 자료 확보가 어려워 예측 오차가 크게 나타난다. 본 논문에서는 특수일의 부하예측 정확도를 개선하기 위해 퍼지 최소자승 선형회귀 모델을 분석한다. 4종류의 퍼지 최소자승 선형회귀 모델에 대해 분석과 사례연구를 통하여 가장 정확한 모델을 제시한다. The load forecasting has been an important part of power system Accordingly, it has been proposed various methods for the load forecasting. The load patterns of the special days is quite different than those of ordinary weekdays. It is difficult to accurately forecast the load of special days due to the insufficiency of the load patterns compared with ordinary weekdays, so we have proposed fuzzy least squares linear regression algorithm for the load forecasting. In this paper we proposed four models for fuzzy least squares linear regression. It is separated by coefficients of fuzzy least squares linear regression equation. we compared model of H1 with H4 and prove it H4 has accurately forecast better than H1.

Predicting body movements for person identification under different walking conditions

Nguyen, Duc-Phong,Phan, Cong-Bo,Koo, Seungbum Elsevier 2018 Forensic Science International Vol.290 No.-

<P><B>Abstract</B></P> <P>Human motion during walking provides biometric information which can be utilized to quantify the similarity between two persons or identify a person. The purpose of this study was to develop a method for identifying a person using their walking motion when another walking motion under different conditions is given. This type of situation occurs frequently in forensic gait science. Twenty-eight subjects were asked to walk in a gait laboratory, and the positions of their joints were tracked using a three-dimensional motion capture system. The subjects repeated their walking motion both without a weight and with a tote bag weighing a total of 5% of their body weight in their right hand. The positions of 17 anatomical landmarks during two cycles of a gait trial were generated to form a gait vector. We developed two different linear transformation methods to determine the functional relationship between the normal gait vectors and the tote-bag gait vectors from the collected gait data, one using linear transformations and the other using partial least squares regression. These methods were validated by predicting the tote-bag gait vector given a normal gait vector of a person, accomplished by calculating the Euclidean distance between the predicted vector to the measured tote-bag gait vector of the same person. The mean values of the prediction scores for the two methods were 96.4 and 95.0, respectively. This study demonstrated the potential for identifying a person based on their walking motion, even under different walking conditions.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Contribution to person identification in different walking conditions. </LI> <LI> Predicting human motion from normal to tote bag walking condition using function transformation. </LI> <LI> Human motion 3D coordinate processing with principal component analysis. </LI> <LI> Linear transformation and partial least square regression as function transformation. </LI> </UL> </P>

GACV for partially linear support vector regression

Joo Yong Shim,Kyung Ha Seok 한국데이터정보과학회 2013 한국데이터정보과학회지 Vol.24 No.2

Partially linear regression is capable of providing more complete description of the linear and nonlinear relationships among random variables. In support vector regression (SVR) the hyper-parameters are known to affect the performance of regression , In this paper we propose an iterative reweihted least squares (IRWLS) procedure to solve the quadratic problem of partially linear support vector regression with a modified loss function, which enables us to use the generalized approximate cross validation function to select the hyper-parameters Experimental results are then presented which illustrate the performance of the partially linear SVR using IRWLS procedure.

Robustness of Minimum Disparity Estimators in Linear Regression Models

Pak, Ro-Jin The Korean Statistical Society 1995 Journal of the Korean Statistical Society Vol.24 No.2

This paper deals with the robustness properties of the minimum disparity estimation in linear regression models. The estimators defined as statistical quantities whcih minimize the blended weight Hellinger distance between a weighted kernel density estimator of the residuals and a smoothed model density of the residuals. It is shown that if the weights of the density estimator are appropriately chosen, the estimates of the regression parameters are robust.

Finding explicit solutions for linear regression without correspondences based on rearrangement inequality

Mijin Kim,Hyungu Lee,Hayoung Choi 한국전산응용수학회 2024 Journal of applied mathematics & informatics Vol.42 No.1

A least squares problem without correspondences is expressed as the following optimization: \begin{equation*} \underset{\mathbf{\Pi} \in \mathcal{P}_m,~\bm{x} \in \mathbb{R}^n}{\operatorname{min}} \left\| \mathbf{A} \bm{x} -\mathbf{\Pi} \mathbf{y} \right\|, \end{equation*} where $\mathbf{A} \in \mathbb{R}^{m\times n}$ and $\mathbf{y} \in \mathbb{R}^m$ are given. In general, solving such an optimization problem is highly challenging. In this paper we use the rearrangement inequalities to find the closed form of solutions for certain cases. Moreover, despite the stringent constraints, we successfully tackle the nonlinear least squares problem without correspondences by leveraging rearrangement inequalities.