• 한국신뢰성학회지:신뢰성응용연구
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• 제18권1호
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• pp.33-39
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• 2018

Purpose: Accelerated degradation tests can be effective in assessing product reliability when degradation leading to failure can be observed. This article proposes an accelerated degradation test model for highly reliable solid state drives (SSDs). Methods: We suggest a nonlinear mixed-effects (NLME) model to degradation data for SSDs. A Monte Carlo simulation is used to estimate lifetime distribution in accelerated degradation testing data. This simulation is performed by generating random samples from the assumed NLME model. Conclusion: We apply the proposed method to degradation data collected from SSDs. The derived power model is shown to be much better at fitting the degradation data than other existing models. Finally, the Monte Carlo simulation based on the NLME model provides reasonable results in lifetime estimation.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2010년도 추계 학술발표회
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• pp.416-422
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• 2010

Estimating condition of geotechnical structures are difficult because of nonlinear time dependency and seasonal effects. Measuring data of structure failure is highly variable in time and space, and a unique approach cannot be defined to model structure movements. Characteristics of movements are obtained by using a statistical method called Principal Component Analysis(PCA). The PCA is a non-parametric method to separate unknown, statistically uncorrelated source processes from observed mixed processes. Instead, since the "best" mathematical relationship is estimated for given data sets of the input and output measured from target systems. As a consequence, this method is advantageous in modeling systems whose geomechanical properties are unknown or difficult to be measured.

• The Korean Journal of Physiology and Pharmacology
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• 제16권2호
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• pp.97-106
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• 2012

The pharmacokinetics/pharmacodynamics analysis software NONMEM$^{(R)}$ output provides model parameter estimates and associated standard errors. However, the standard error of empirical Bayes estimates of inter-subject variability is not available. A simple and direct method for estimating standard error of the empirical Bayes estimates of inter-subject variability using the NONMEM$^{(R)}$ VI internal matrix POSTV is developed and applied to several pharmacokinetic models using intensively or sparsely sampled data for demonstration and to evaluate performance. The computed standard error is in general similar to the results from other post-processing methods and the degree of difference, if any, depends on the employed estimation options.

• The Korean Journal of Physiology and Pharmacology
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• 제18권3호
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• pp.217-223
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• 2014

Plasma ionized calcium ($Ca^{2+}$) concentrations are tightly regulated in the body and maintained within a narrow range; thus it is challenging to quantify calcium absorption under normal physiologic conditions. This study aimed to develop a mechanistic model for the parathyroid hormone (PTH) response after calcium intake and indirectly compare the difference in oral calcium absorption from PTH responses. PTH and $Ca^{2+}$ concentrations were collected from 24 subjects from a clinical trial performed to evaluate the safety and calcium absorption of Geumjin Thermal Water in comparison with calcium carbonate tablets in healthy subjects. Indirect response models (NONMEM Ver. 7.2.0) were fitted to observed $Ca^{2+}$ and PTH data, respectively, in a manner that absorbed but unobserved $Ca^{2+}$ inhibits the secretion of PTH. Without notable changes in $Ca^{2+}$ levels, PTH responses were modeled and used as a marker for the extent of calcium absorption.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2010년도 춘계 학술발표회
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• pp.1358-1365
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• 2010

Estimating condition of slope is difficult because of nonlinear time dependency and seasonal effects, which affect the displacements. Displacements and displacement patterns of landslides are highly variable in time and space, and a unique approach cannot be defined to model landslide movements. Characteristics of movements are obtained by using a statistical method called Principal Component Analysis(PCA). The PCA is a non-parametric method to separate unknown, statistically uncorrelated source processes from observed mixed processes. In the non-parametric approaches, no physical assumptions of target systems are required. Instead, since the "best" mathematical relationship is estimated for given data sets of the input and output measured from target systems. As a consequence, non-parametric approaches are advantageous in modeling systems whose geomechanical properties are unknown or difficult to be measured. Non-parametric approaches are consequently more flexible in modeling than parametric approaches. This method is expected to be a useful tool for the slope management of and alarm systems.

• 대한기계학회논문집A
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• 제35권9호
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• pp.1041-1050
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• 2011

미크론 단위의 크기를 갖는 구조물의 소성변형에서 나타나는 길이 효과를 고려하여 유한요소 해석을 하기 위하여 변형률 구배 소성이론을 이용하는 탄소성 유한요소 모델링 및 해석법을 제안하였다. 기존의 연구에서 주로 고차, 고자유도 및 혼합요소, 초 요소 등을 필요로 하였던 것에 비하여 본 논문에서는 이들을 배제하는 변위법 저차 평면 요소 및 삼차원 요소를 도입하였다. 이는 비선형 증분 해석의 프레임워크에서 계산된 소성 변형률의 절점 평균값으로 보간하여 적분점에서의 변형률 구배를 구하고 테일러 전위 모델에 의한 변형률 경화 구성방정식을 적용하므로서 가능하였다. 제안된 방법론은 선형 삼각 및 사각요소, 선형 사면체, 육면체 요소에 대해 적용되었으며 마이크로 굽힘, 마이크로 비틀림, 마이크로 기공과 같은 대표적인 길이 스케일 문제를 통하여 수치적으로 검증하였다. 본 논문에서 제안한 방법은 계산이 매우 쉬우면서도 실험값들과 비교해 볼 때, 변형률 구배 소성이론 즉, 길이 효과를 잘 나타내어 주었다.