• Steel and Composite Structures
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• v.26 no.2
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• pp.171-182
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• 2018

This paper presents an isogeometric discretization of Kirchhoff-Love thin shells using truncated hierarchical B-splines (THB-splines). It is demonstrated that the underlying basis functions are ideally appropriate for adaptive refinement of the so-called thin shell structures in the framework of isogeometric analysis. The proposed approach provides sufficient flexibility for refining basis functions independent of their order. The main advantage of local THB-spline evaluation is that it provides higher degree analysis on tight meshes of arbitrary geometry which makes it well suited for discretizing the Kirchhoff-Love shell formulation. Numerical results show the versatility and high accuracy of the present method. This study is a part of the efforts by the authors to bridge the gap between CAD and CAE.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1645-1651
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• 2016

A lot of data, particularly in the medical field, contain variables that have a measurement error such as blood pressure and body mass index. On the other hand, recently smoothing methods are often used to solve a complex scientific problem. In this paper, we study a Bayesian curve-fitting under functional measurement error model. Especially, we extend our previous model by incorporating covariates free of measurement error. In this paper, we consider penalized splines for non-linear pattern. We employ a hierarchical Bayesian framework based on Markov Chain Monte Carlo methodology for fitting the model and estimating parameters. For application we use the data from the fifth wave (2012) of the Korea National Health and Nutrition Examination Survey data, a national population-based data. To examine the convergence of MCMC sampling, potential scale reduction factors are used and we also confirm a model selection criteria to check the performance.

• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.749-754
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• 2015

This article presents Bayesian approach to regression splines with knots on a grid of equally spaced sample quantiles of the independent variables under functional measurement error model.We consider small area model by using penalized splines of non-linear pattern. Specifically, in a basis functions of the regression spline, we use radial basis functions. To fit the model and estimate parameters we suggest a hierarchical Bayesian framework using Markov Chain Monte Carlo methodology. Furthermore, we illustrate the method in an application data. We check the convergence by a potential scale reduction factor and we use the posterior predictive p-value and the mean logarithmic conditional predictive ordinate to compar models.