• Proceedings of the KSME Conference
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• 2003.04a
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• pp.235-240
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• 2003

Methods to predict fatigue crack growth are compared in a quantitative manner for crack growth test data of 2024-T351 aluminum alloy under narrow and wide band random loading. In order to account for the effect of load ratio, crack closure model, Hater's equation and NASGRO's equation have been employed. Load interaction effect under random loading has been considered by crack closure model, Willenborg's model and Wheeler's model. The prediction method using the measured crack opening results provides the best result among the prediction methods discussed for narrow and wide band random loading data.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.10
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• pp.1785-1792
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• 2003

Methods to predict fatigue crack growth are compared in a quantitative manner for crack growth test data of 2024- T351 aluninum alloy under narrow and wide band random loading. In order to account for the effect of load ratio, crack closure model, Hater's equation and NASGRO's equation have been employed. Load interaction effect under random loading has been considered by crack closure model, Willenborg's model and Wheeler's model. The prediction method using the measured crack opening results provides the best result among the prediction methods discussed for narrow and wide band random loading data.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1429-1440
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• 2008

In a meta-analysis combining the results from different clinical trials, it is important to consider the possible heterogeneity in outcomes between trials. Such variations can be regarded as random effects. Thus, random-effect models such as HGLMs (hierarchical generalized linear models) are very useful. In this paper, we propose a HGLM framework for analyzing the binominal response data which may have variations in the odds-ratios between clinical trials. We also present the prediction intervals for random effects which are in practice useful to investigate the heterogeneity of the trial effects. The proposed method is illustrated with a real-data set on 22 trials about respiratory tract infections. We further demonstrate that an appropriate HGLM can be confirmed via model-selection criteria.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.4
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• pp.262-268
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• 2004

This thesis is proposing novel RPWM (Random PWM) techniques that can locate PWM pulse to do random. RPWM techniques to propose locates SVPWM (Space Vector PWM) pulse by number of each random and principle to locate of pulse applies different random function and locate pulse. For propriety verification of proposed techniques, achieve an simulation and experiment that use MATLAB/SIMULINK about proposed RPWM techniques algorithm and IGBT inverter composition that use DSP(TMS320C31). Specially, analyze harmonic spectra of inverter output current when the induction motor speed is more than 10,000 rpm, confirm that RPWM's effect in high speed degree appears. Proposed RPWM techniques propriety prove from reduction effect of harmonic magnitude that corresponds to an integer times of switching frequency.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.801-810
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• 2018

Diffusion is a random process used to model financial and physical phenomena. When we construct statistical models for repeatedly observed diffusion processes, the idea of random effects needs to be considered. In this research, we introduce random parameters for an Ornstein-Uhlenbeck diffusion model and geometric Brownian motion diffusion model. In order to apply the maximum likelihood estimation method, we tried to build likelihoods in closed-forms, by assuming appropriate distributions for random effects. We applied the random effect models to data consisting of Dow Jones Industrial Average indices recorded daily over 27 years from 1991 to 2017.

• Journal of Korean Society for Quality Management
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• v.24 no.1
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• pp.87-95
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• 1996

We construct bootstrap confidence bounds for reliability, R=P(X>Y), where X and Y are independent normal random variables. 1-way random effect models with equal variances are assumed for the populations of X and Y. We compare the accuracy of the proposed bootstrap confidence bounds and classical confidence bound for small samples via Monte Carlo simulation.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.950-953
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• 1999

Spin tunneling giant magnetoresistance effect was studied to utilize in the application of random access memory. Ferromagnetic/Insulator/Ferromagnetic films were sputtered on glass substrates and perpendicular current was applied. Measurements of magneto- resistance of the junction showed 8.6% of MR ratio. Voltage output depends on the magnetization directions of the write line and read line, thus enabling the system to be used as a random access memory

• Journal of the Korean Statistical Society
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• v.28 no.2
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• pp.225-234
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• 1999

We consider the following semi-parametric non-linear mixed effect regression model : y\ulcorner=f($\chi$\ulcorner;$\beta$)+$\sigma$$\mu$($\chi$\ulcorner)+$\sigma$$\varepsilon$\ulcorner,i=1,…,n,y*=f($\chi$;$\beta$)+$\sigma$$\mu$($\chi$) where y'=(y\ulcorner,…,y\ulcorner) is a vector of n observations, y* is an unobserved new random variable of interest, f($\chi$;$\beta$) represents fixed effect of known functional form containing unknown parameter vector $\beta$\ulcorner=($\beta$$_1$,…,$\beta$\ulcorner), $\mu$($\chi$) is a random function of mean zero and the known covariance function r(.,.), $\varepsilon$'=($\varepsilon$$_1$,…,$\varepsilon$\ulcorner) is the set of uncorrelated measurement errors with zero mean and unit variance and $\sigma$ is an unknown dispersion(scale) parameter. On the basis of finite-sample, small-dispersion asymptotic framework, we derive an absolute lower bound for the asymptotic mean squared errors of prediction(AMSEP) of the regular-consistent non-linear predictors of the new random variable of interest y*. Then we construct an optimal predictor of y* which attains the lower bound irrespective of types of distributions of random effect $\mu$(.) and measurement errors $\varepsilon$.

• International Journal of Ocean System Engineering
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• v.4 no.1
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• pp.1-18
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• 2014

Heave plates have been widely used to enhance viscous damping and thus reduces the heave response of Spar platforms. Single heave plate attached to the keel of the Spar has been reported in literature (Tao and Cai 2004). The effect of double heave plates on hydrodynamic response in random waves has been investigated in this study. The influence of relative spacing $L_d/D_d$ ($D_d$-the diameter of the heave plate) on the hydrodynamic response in random waves has been simulated in wave basin experiments and numerical model. The experimental investigation has been carried out using 1:100 scale model of Spar with double heave plates in random waves for different relative spacing and varying wave period. The influence of relative spacing between the heave plates on the motion responses of Spar are evaluated and presented. Numerical investigation has been carried out to investigate effect of relative spacing on hydrodynamic characteristics such as heave added mass and hydrodynamic responses. The measured results were compared with those obtained from numerical simulation and found to be in good agreement. Experimental and numerical simulation shows that the damping coefficient and added mass does not increase for relative spacing of 0.4 and the effect greater than relative spacing on significant heave response is insignificant.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.957-981
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• 2017

A generalized linear mixed model is an extension of a generalized linear model that allows random effect as well as provides flexibility in developing a suitable model when observations are correlated or when there are other underlying phenomena that contribute to resulting variability. We describe maximum likelihood estimation methods for logistic regression models that include random effects - the Laplace approximation, Gauss-Hermite quadrature, adaptive Gauss-Hermite quadrature, and pseudo-likelihood. Applications are provided with social science problems by analyzing the effect of mental health and life satisfaction on volunteer activities from Korean welfare panel data; in addition, we observe that the inclusion of random effects in the model leads to improved analyses with more reasonable inferences.