• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.1
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• pp.9-15
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• 2001

브러쉬 없는 직류전동기의 위치제어를 위한 퍼지가변구조제어기를 설계한다. 특히 본 논문에서는 기존의 퍼지제어 기법에서 얻을 수 있는 특징으로부터 하나의 전건부 변수만을 가지는 간단한 퍼지논리제어기의 설계를 기술한다. 가변구조제어는 시스템의 파라메터 변화나 외란에 둔감한 특성을 갖는다. 하지만 리칭페이스에서는 문제가 된다. 이를 개선하기 위하여 본 논문에서는 지수항을 추가한 비선형 슬라이딩면을 구성한다. 그리고 나서 비선형 슬라이딩 면과 슬라이딩 면의 변화율을 입력으로 하는 퍼지 제어기를 설계한다. 이러한 2-입력 퍼지가변구조제어기의 제어 규칙표로부터 하나의 전건부 변수만을 가지는 단일 입력 퍼지가변구조제어기를 설계한다. 이들 제어기의 성능을 입증하기 위하여 시뮬레이션과 실험을 수행한다.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.166-169
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• 2004

In this paper, computational analysis and experiments of subwoofer for car audio speaker system were performed and discussed to analyze acoustical phenomena for subwoofer. Ported enclosure system with subwoofer were manufactured and provided for test and simulation purposes. Subwoofer with single voice coil and double voice coil were identified by linear and nonlinear parameter identification method for loudspeaker parameters. For high power inputs to subwoofer, sound pressure levels were compared according to input powers with linear and nonlinear loudspeaker models. For subwoofer system with high power nonlinear speaker model was showed to be adequate to describe the behaviour of loudspeaker.

• The Korean Journal of Applied Statistics
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• v.31 no.4
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• pp.463-473
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• 2018

Variable selection algorithms for linear regression models of large data are considered. Many algorithms are proposed focusing on the speed and the robustness of algorithms. Among them variance inflation factor (VIF) regression is fast and accurate due to the use of a streamwise regression approach. But a VIF regression is susceptible to outliers because it estimates a model by a least-square method. A robust criterion using a weighted estimator has been proposed for the robustness of algorithm; in addition, a robust VIF regression has also been proposed for the same purpose. In this article a fast and robust variable selection method is suggested via a VIF regression with detecting and removing potential outliers. A simulation study and an analysis of a dataset are conducted to compare the suggested method with other methods.

• Journal of Korean Society of Transportation
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• v.18 no.1
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• pp.61-73
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• 2000

The equilibrium network design problem can be formulated as a mathematical Program with variational inequality constraints. We know this problem may have may multiple local solutions due to its inherent characteristics - Nonlinear Objective function and Nonlinear, Nonconvex constraints. Hence, it is difficult to solve for a globally optimal solution. In this paper, we propose a genetic algorithm to obtain a globa1 optimum among many local optima. A Proposed a1gorithm is compared with 4 different solution algorithms for 1 small test network and 1 real-size network. The results of some computational testing are reported.

• The Journal of the Korea Contents Association
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• v.15 no.1
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• pp.475-481
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• 2015

We use polynomial regression instead of linear regression if there is a nonlinear relation between a dependent variable and independent variables in a regression analysis. The performance of polynomial regression, however, may deteriorate because of the correlation caused by the power terms of independent variables. We present a polynomial regression model for the numerical inversion of PGF and show that polynomial regression results in the deterioration of the estimation of the coefficients. We apply principal components regression to the polynomial regression model and show that principal components regression dramatically improves the performance of the parameter estimation.

• The Korean Journal of Applied Statistics
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• v.21 no.3
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• pp.441-453
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• 2008

We analyzed Korean professional basketball and baseball players salary under the assumption that it depends on the personal records and contribution to the team in the previous year. We extensively used data visualization tools to check the relationship among the variables, to find outliers and to do model diagnostics. We used multiple linear regression and regression tree to fit the model and used cross-validation to find an optimal model. We check the relationship between variables carefully and chose a set of variables for the stepwise regression instead of using all variables. We found that points per game, number of assists, number of free throw successes, career are important variables for the basketball players. For the baseball pitchers, career, number of strike-outs per 9 innings, ERA, number of homeruns are important variables. For the baseball hitters, career, number of hits, FA are important variables.

• The Korean Journal of Applied Statistics
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• v.37 no.1
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• pp.1-12
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• 2024

In many real-world data, multiple response variables are often dependent on the same set of explanatory variables. In particular, if several response variables are correlated with each other, simultaneous estimation considering the correlation between response variables might be more effective way than individual analysis by each response variable. In this multivariate regression analysis, least distance estimator (LDE) can estimate the regression coefficients simultaneously to minimize the distance between each training data and the estimates in a multidimensional Euclidean space. It provides a robustness for the outliers as well. In this paper, we examine the least distance estimation method in multivariate linear regression analysis, and furthermore, we present the penalized least distance estimator (PLDE) for efficient variable selection. The LDE technique applied with the adaptive group LASSO penalty term (AGLDE) is proposed in this study which can reflect the correlation between response variables in the model and can efficiently select variables according to the importance of explanatory variables. The validity of the proposed method was confirmed through simulations and real data analysis.

• Korean Journal of Optics and Photonics
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• v.27 no.6
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• pp.223-228
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• 2016

The birefringence of distributed, uniaxially anisotropic molecules like liquid crystals is calculated as the degree of ordering is varied. The relation between the normalized birefringence ${\Delta}n_{rel}$ and the orientational order parameter S is investigated. The distribution function, which enables one to monitor the degree of ordering of liquid crystals including randomly distributed ones, is introduced. Using this distribution function, a series of distributed liquid crystals with order parameters ranging from 0 to 1 are generated, and ${\Delta}n_{rel}$ and S of the correspondingly distributed liquid crystals are calculated. Based on the calculated data, it is revealed that ${\Delta}n_{rel}$ and S satisfy the quasi-linear relation of $S=(1+a){\Delta}n_{rel}-a{\Delta}n^2_{rel}$, where a can be approximated as $n_o{\frac{{\Delta}n}{4}}$. The anisotropy of molecular polarizability is also calculated, using the birefringence, and separately following Vuks' method and Neugebauer's method, and it is shown that the relations between S and the molecular-polarizability anisotropy are also quasi-linear.

• Journal of Korea Water Resources Association
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• v.52 no.9
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• pp.589-601
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• 2019

Various applications of radar rainfall data have been actively employed in the field of hydro-meteorology. Since radar rainfall is estimated by using predefined reflectivity-rainfall intensity relationships, they may not have sufficient reproducibility of observations. In this study, a generalized linear model is introduced to better capture the Z-R relationship in the context of bias correction within a Bayesian regression framework. The bias-corrected radar rainfall with the generalized linear model is more accurate than the widely used mean field bias correction method. In addition, we analyzed variability of the bias correction parameters under various geomorphological conditions such as the height of the weather station and the separation distance from the radar. The identified relationship is finally used to derive a regionalized formula which can provide bias correction factors over the entire watershed. It can be concluded that the bias correction parameters and regionalized method obtained from this study could be useful in the field of radar hydrology.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.5
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• pp.502-508
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• 2012

Quantitative ultrasound technologies for osteoporosis diagnosis measure ultrasonic parameters such as speed of sound(SOS) and normalized broadband ultrasound attenuation(nBUA) in the calcaneus (heel bone). In the present study, the dependences of SOS and nBUA on bone mineral density in the proximal femur with high risk of fracture were investigated by using 20 trabecular bone samples extracted from bovine femurs. SOS and nBUA in the femoral trabecular bone samples were measured by using a transverse transmission method with one matched pair of ultrasonic transducers with a center frequency of 1.0 MHz. SOS and nBUA measured in the 20 trabecular bone samples exhibited high Pearson's correlation coefficients (r) of r = 0.83 and 0.72 with apparent bone density, respectively. The multiple regression analysis with SOS and nBUA as independent variables and apparent bone density as a dependent variable showed that the correlation coefficient r = 0.85 of the multiple linear regression model was higher than those of the simple linear regression model with either parameter SOS or nBUA as an independent variable. These high linear correlations between the ultrasonic parameters and the bone density suggest that the ultrasonic parameters measured in the femur can be useful for predicting the femoral bone mineral density.