• Journal of Applied Reliability
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• v.18 no.1
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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.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.09a
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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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• v.16 no.2
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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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• v.18 no.3
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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.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.03a
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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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.9
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• pp.1041-1050
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• 2011

An elasto-plastic finite element method using the theory of strain gradient plasticity is proposed to evaluate the size dependency of structural plasticity that occurs when the configuration size decreases to micron scale. For this method, we suggest a low-order plane and three-dimensional displacement-based elements, eliminating the need for a high order, many degrees of freedom, a mixed element, or super elements, which have been considered necessary in previous researches. The proposed method can be performed in the framework of nonlinear incremental analysis in which plastic strains are calculated and averaged at nodes. These strains are then interpolated and differentiated for gradient calculation. We adopted a strain-gradient-hardening constitutive equation from the Taylor dislocation model, which requires the plastic strain gradient. The developed finite elements are tested numerically on the basis of typical size-effect problems such as micro-bending, micro-torsion, and micro-voids. With respect to the strain gradient plasticity, i.e., the size effects, the results obtained by using the proposed method, which are simple in their calculation, are in good agreement with the experimental results cited in previously published papers.