• Proceedings of the Korean Institute of Interior Design Conference
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• 2008.05a
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• pp.226-231
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• 2008

Indoor space becomes limited by each factor engirdling and defining infinite empty space. Those factors appear as physical ones called wall, floor, and ceiling, and earn existential value as a living space by placing furniture in the space organized in this way. However, it is not easy to find a clear harmony between the space and furniture. Since long ago, a great deal of effort has been put into creating a relational harmony between the space and furniture. This study is to be unfolded by assuming that spatial furniture comes amid these efforts. Namely, furniture does not exist as a separate entity in a space but interacts with the construction; thus, furniture is immersed into construction or construction becomes furniture, which demonstrates that furniture is on the continuum of the composition of the space. This study looks into the characteristics of spatial furniture in contemporary indoor space through relevant cases, and prepares the ground for a creative interpretation of Indoor space.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.709-720
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• 2017

In healthcare and medical research, many important variables have a measurement error such as body mass index and laboratory data. It is also not easy to collect samples of large size because of high cost and long time required to collect the target patient satisfied with inclusion and exclusion criteria. Beside, the demand for solving a complex scientific problem has highly increased so that a semiparametric regression approach could be of substantial value solving this problem. To address the issues of measurement error, small domain and a scientific complexity, we conduct a multivariable Bayesian smoothing under structural measurement error covariate in this article. Specifically we enhance our previous model by incorporating other useful auxiliary covariates free of measurement error. For the regression spline, we use a radial basis functions with fixed knots for the measurement error covariate. We organize a fully Bayesian approach to fit the model and estimate parameters using Markov chain Monte Carlo. Simulation results represent that the method performs well. We illustrate the results using a national survey data for application.