• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.10
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• pp.1549-1554
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• 2010

Recently, plastic screws have replaced metal screws because of the former's light weight, thermal and electrical insulating properties, and anticorrosion characteristics. Plastic screws are usually produced by injection molding, which involves material shrinkage during the solidification of the polymer. This shrinkage results in the degeneration of the dimensional accuracy. In the present study, the effect of injection-molding conditions on the dimensional accuracy of plastic screws was investigated through a numerical simulation of injection molding; on the basis of this simulation, we could determine the mold-design parameters. The design of experiment was applied in accordance with the numerical analysis in order to optimize the injection-molding conditions with a view to improving the dimensional accuracy of the precision plastic screw.

• Journal of the Korean Wood Science and Technology
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• v.30 no.1
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• pp.40-47
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• 2002

This study was carried out to investigate the dimensional stability of three softwoods (Pinus densiflora S. et Z., Pinus koraiensis S. et Z., Larix kaempferi C_ARR.) and two hardwoods(Quercus mongolica F., Quercus variabilis B_L.) treated with polyethylene glycol(PEG). Existence of PEG in wood was examined by an X-ray diffraction method. Shrinkage of the sample woods decreased with increasing concentration of PEG. The dimensional stability of five species had improved highly in thirty percent solutions of PEG with molecular weight 1000~4000, and the dimension of PEG-treated softwoods was more stable than that of PEG-treated hardwoods. Weight gain and bulking effect of the woods treated with PEG increased with increasing concentration of PEG. X-ray diffractograms of Pinus koraiensis wood treated with PEG showed two peaks derived from PEG crystal at 2𝜃 = 19° and 2𝜃 = 23°. However, the diffractograms of Quercus mongolica wood did not present any peaks caused by PEG crystal. From the result of X-ray diffraction, it was revealed that the content of PEG in Pinus koraiensis wood was higher than that in Quercus mongolica wood.

• Journal of the Korean Wood Science and Technology
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• v.24 no.4
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• pp.32-39
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• 1996

The overall purpose of this study was to investigate the dimensional changes of melamine sheet laminated medium density fiberboard(MDF) floorings by sub-heating system(Ondol). This study was also conducted to improve the properties of melamine sheet laminated MDF floorings. The effects of density, resin content, manufacturing speed of MDF and types of melamine sheet on dimensional and weight changes of floorings were investigated. The results were as followings. 1. Dimensional and weight change of melamine sheet laminated MDF flooring by heating decreased with decreasing the density of MDF. 2. Dimensional and weight change of melamine sheet laminated MDF flooring by heating decreased with increasing the resin content of MDF. 3. Dimensional and weight change of melamine sheet laminated MDF flooring by heating decreased with decreasing the manufacturing speed of MDF. 4. Dimensional change of melamine sheet laminated MDF flooring in width direction by heating was doubled than that in machine direction. 5. Dimensional change and curling of high pressure melamine laminate(HPM) laminated MDF flooring by heating was less than those of low pressure melamine laminate(LPL) flooring. 6. Weight loss of melamine sheet laminated MDF flooring by heating has linear relationship with shrinkage.

• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.747-758
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• 2013

The covariance matrix is important in multivariate statistical analysis and a sample covariance matrix is used as an estimator of the covariance matrix. High dimensional data has a larger dimension than the sample size; therefore, the sample covariance matrix may not be suitable since it is known to perform poorly and event not invertible. A number of covariance matrix estimators have been recently proposed with three different approaches of shrinkage, thresholding, and modified Cholesky decomposition. We compare the performance of these newly proposed estimators in various situations.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.881-891
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• 2014

Graphical models can be used as an intuitive tool for modeling a complex stochastic system with a large number of variables related each other because the conditional independence between random variables can be visualized as a network. Graphical least absolute shrinkage and selection operator (LASSO) is considered to be effective in avoiding overfitting in the estimation of Gaussian graphical models for high dimensional data. In this paper, we consider the model selection problem in graphical LASSO. Particularly, we compare various model selection criteria via simulations and analyze a real financial data set.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.24 no.3
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• pp.426-434
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• 2000

This paper deals with a generalized similarity solution for the one-dimensional solidification of ternary or higher-order multicomponent alloys. The present approach not only retains the existing features of binary systems such as temperature- solute coupling, shrinkage-induced flow, solid-liquid property differences, and finite back diffusion, but also is capable of handling a multicomponent alloy without restrictions on the partition coefficient and microsegregation parameter. For an alloy of N-solute species, governing equations in the mushy region reduce to (N+2) nonlinear ordinary differential equations via similarity transformation, which are to be solved along with the closed-form solutions for the solid and liquid regions. A linearized correction scheme adopted in the solution procedure facilitates to determine the solidus and liquidus positions stably. The result for a sample ternary alloy agrees excellently with the numerical prediction as well as the reported similarity solution. Additional calculations are also presented to show the utility of this study. Finally, it is concluded that the present analysis includes the previous analytical approaches as subsets.

• Journal of the Korean Wood Science and Technology
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• v.36 no.6
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• pp.88-95
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• 2008

The shrinkage of wood members after construction has been a greater and common concern in wooden buildings with the durability. Particularly, the traditional structure applying solid members actively is easily exposed to the shrinkage that caused by the joints, members, and walls. Moreover, even though domestic larch glulam members are widespread recently in the post-beam construction, the shrinkage (swelling) problem is still the critical defect on the wooden structures by the moisture content change in Korea. Various moisture contents were applied for the specimens to survey the dimensional changes for Japanese larch solid and glulam specimens, and the glulam specimens varied in the number of their laminations. Test results showed that glulam specimens with over 3 bonding layers showed good dimension stabilities. Therefore, to solve the shrinkage problem, sufficient drying fitted to the end-use service conditions should be conducted on the solid or glulam members can be applied.

• The Korean Journal of Applied Statistics
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• v.35 no.2
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• pp.285-298
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• 2022

Continuous shrinkage priors, as well as spike and slab priors, have been widely employed for Bayesian inference about sparse regression coefficient vectors or covariance matrices. Continuous shrinkage priors provide computational advantages over spike and slab priors since their model space is substantially smaller. This is especially true in high-dimensional settings. However, variable selection based on continuous shrinkage priors is not straightforward because they do not give exactly zero values. Although few variable selection approaches based on continuous shrinkage priors have been proposed, no substantial comparative investigations of their performance have been conducted. In this paper, We compare two variable selection methods: a credible interval method and the sequential 2-means algorithm (Li and Pati, 2017). Various simulation scenarios are used to demonstrate the practical performances of the methods. We conclude the paper by presenting some observations and conjectures based on the simulation findings.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.9
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• pp.991-997
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• 2011

This paper deals with the basic principles and procedures involved in the steam-chest molding process used for manufacturing expanded polypropylene (EPP) bead foam. Steam-chest molding is an integral process for EPP technology. However, little research has been carried out on the processing conditions for steam-chest molding this process. The characteristics of EPP foam are energy absorption, multiple-impact protection, low weight, structural strength, and durability. In this study, the steam pressure in steam-chest molding was varied to determine the optimum conditions for manufacturing EPP foam. Moreover, annealing was performed after EPP-foam molding to prevent the shrinkage of the steam-molded product. It was possible to verify the mechanism of foam shrinkage by observing the change in weight with time at different annealing temperatures. Moreover, a tensile test and scanning electron microscopy (SEM) analysis were performed to support these experimental results. The dimensional stability of each molded product was investigated at different steam pressures.

• Journal of the Korean Data and Information Science Society
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• v.24 no.5
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• pp.1077-1088
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• 2013

High-dimensional data analysis arises from almost all scientific areas, evolving with development of computing skills, and has encouraged penalized estimations that play important roles in statistical learning. For the past years, various penalized estimations have been developed, and the least absolute shrinkage and selection operator (LASSO) proposed by Tibshirani (1996) has shown outstanding ability, earning the first place on the development of penalized estimation. In this paper, we first introduce a number of recent advances in high-dimensional data analysis using the LASSO. The topics include various statistical problems such as variable selection and grouped or structured variable selection under sparse high-dimensional linear regression models. Several unsupervised learning methods including inverse covariance matrix estimation are presented. In addition, we address further studies on new applications which may establish a guideline on how to use the LASSO for statistical challenges of high-dimensional data analysis.