• Tribology and Lubricants
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• v.33 no.6
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• pp.303-308
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• 2017

Most automobile tire companies have not yet considered the geometric information of a road at the design stage of a tire because the topographical characterization of a road surface is very difficult owing to its vastness and randomness. A road surface shows variable surface roughness values according to magnification, and thus, the contact force between the road and tire significantly fluctuates with respect to the scale. In this study, we make an attempt to define a representative value for surface topographical information at multi-scale levels. To represent surface topography, we use a statistical method called power spectral density (PSD). We use the fast Fourier transform (FFT) and PSD to analyze the height profiles of a random surface. The FFT and PSD of a surface help in obtaining a fractal dimension, which is a representative value of surface topography at all length scales. We develop three surfaces with different fractal dimensions. We use finite element analysis (FEA) to observe the contact forces between a tire and the road surfaces with three different fractal dimensions. The results from FEA reveal that an increase in the fractal dimension decreases the contact length between the tire and road surfaces. On the contrary, the average contact force increases. This result indicates that designing and manufacturing a tire considering the fractal dimension of a road makes safe driving possible, owing to the improvement in service life and braking performance of the tire.

• Journal of the Korean Society of Clothing and Textiles
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• v.22 no.3
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• pp.417-427
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• 1998

Price, one of the marketing 4p's, is a key decision variable affecting market share and the profitability of individual products. For consumers, since price is almost always known to and can be compared, it is one of the most important criteria when they make a purchase decision making. With the consumers' increasing consciousness for price due to economic recession, and the saturation of domestic apparel market, it is expected that the effect of price on consumers' decision making would be greater than ever. This study, the first in two part series, focuses on testing the validity of dimensions of clothing price using Lichtenstein et. at. (1993)'s suggestion. In addition, the effect of demographic variables on the perception of each price dimension was investigated. The subjects were 264 college students living in Seoul, Korea. The data were collected by self -administered questionnaires and analyzed by t-test, ANOVA, regression analysis and Lisrel confirmatory factor analysis. The result supported Lichtenstein et. al. (1993)'s suggestion. That is, consumers' perception of clothing price is not mini-dimensional, but has six dimensions: sale proneness, price mavenism, value consciousness, price consciousness, price -quality schema and prestige sensitivity. Demographic variables partially effect on the consumers' perception of each clothing price dimension. The level of monthly pocket money, however, has influence on all price dimensions. Based on these results, marketing implications for apparel manufacturers were suggested.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.12 no.1
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• pp.107-116
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• 2001

In this thesis, characteristics of compact PIFA are analyzed for operation in dual frequencies (dual-PIFA) as variety of parameters. The antenna is composed of two patches. These are operated in high frequency and low frequency. The dimension of experiential antenna is fixed for attaching at the handset. The variable parameters are dimension of small patch, length of shorting strip and dimension of folded conductor plate, the frequencies are 900 MHz and 1800 MHz. The compact antenna is implemented with 2-layer type, electric field intensities and radiation patterns are simulated. In order to analyze characteristics of a performance as variety of parameters, FDTD method is used.

• Communications for Statistical Applications and Methods
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• v.23 no.3
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• pp.259-268
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• 2016

High-dimensional survival data with large numbers of predictors has become more common. The analysis of such data can be facilitated if the dimensions of predictors are adequately reduced. Recent studies show that a method called sliced inverse regression (SIR) is an effective dimension reduction tool in high-dimensional survival regression. However, it faces incapability in implementation due to a double categorization procedure. This problem can be overcome in the right-censoring type by transforming the observed survival time and censoring status into a single variable. This provides more flexibility in the categorization, so the applicability of SIR can be enhanced. Numerical studies show that the proposed transforming approach is equally good to (or even better) than the usual SIR application in both balanced and highly-unbalanced censoring status. The real data example also confirms its practical usefulness, so the proposed approach should be an effective and valuable addition to usual statistical practitioners.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.357-360
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• 2005

In this paper we consider the well-known semiparametric proportional hazards (PH) models for survival analysis. These models are usually used with few covariates and many observations (subjects). But, for a typical setting of gene expression data from DNA microarray, we need to consider the case where the number of covariates p exceeds the number of samples n. For a given vector of response values which are times to event (death or censored times) and p gene expressions (covariates), we address the issue of how to reduce the dimension by selecting the significant genes. This approach enable us to estimate the survival curve when n < < p. In our approach, rather than fixing the number of selected genes, we will assign a prior distribution to this number. The approach creates additional flexibility by allowing the imposition of constraints, such as bounding the dimension via a prior, which in effect works as a penalty. To implement our methodology, we use a Markov Chain Monte Carlo (MCMC) method. We demonstrate the use of the methodology to diffuse large B-cell lymphoma (DLBCL) complementary DNA(cDNA) data.

• Bulletin of the Korean Mathematical Society
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• v.29 no.1
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• pp.137-143
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• 1992

The purpose of this paper is to provide the affirmative solution of the following conjecture due to Davis and Geramita. Conjecture; Let A=R[T] be a polynomial ring in one variable, where R is a regular local ring of dimension d. Then maximal ideals in A are complete intersection. Geramita has proved that the conjecture is true when R is a regular local ring of dimension 2. Whatwadekar has rpoved that conjecture is true when R is a formal power series ring over a field and also when R is a localization of an affine algebra over an infinite perfect field. Nashier also proved that conjecture is true when R is a local ring of D[ $X_{1}$,.., $X_{d-1}$] at the maximal ideal (.pi., $X_{1}$,.., $X_{d-1}$) where (D,(.pi.)) is a discrete valuation ring with infinite residue field. The methods to establish our results are following from Nashier's method. We divide this paper into three sections. In section 1 we state Theorems without proofs which are used in section 2 and 3. In section 2 we prove some lemmas and propositions which are used in proving our results. In section 3 we prove our main theorem.eorem.rem.

• Communications for Statistical Applications and Methods
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• v.22 no.2
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• pp.173-180
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• 2015

We propose a selection procedure of principal components in principal component regression. Our method selects principal components using variable selection procedures instead of a small subset of major principal components in principal component regression. Our procedure consists of two steps to improve estimation and prediction. First, we reduce the number of principal components using the conventional principal component regression to yield the set of candidate principal components and then select principal components among the candidate set using sparse regression techniques. The performance of our proposals is demonstrated numerically and compared with the typical dimension reduction approaches (including principal component regression and partial least square regression) using synthetic and real datasets.

• Structural Engineering and Mechanics
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• v.13 no.2
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• pp.231-239
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• 2002

The problem of dynamic analysis of truss structures based on probability is studied in this paper. Considering the randomness of both physical parameters (elastic module and mass density) of structural materials and geometric dimension of bars respectively or simultaneously, the stiffness and mass matrixes of the elements and structure have been built. The structure dynamic characteristic based on probability is analyzed, and the expressions of numeral characteristics of inherence frequency random variable are derived from the Rayleigh's quotient. The method of structural dynamic analysis based on probability is developed. Finally, two examples are given.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2733-2746
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• 2018

The two inverse regression estimation methods, SIR and SAVE to estimate the central space are computationally easy and are widely used. However, SIR and SAVE may have poor performance in finite samples and need strong assumptions (linearity and/or constant covariance conditions) on predictors. The two non-parametric estimation methods, MAVE and dMAVE have much better performance for finite samples than SIR and SAVE. MAVE and dMAVE need no strong requirements on predictors or on the response variable. MAVE is focused on estimating the central mean subspace, but dMAVE is to estimate the central space. This paper explores and compares four methods to explain the dimension reduction. Each algorithm of these four methods is reviewed. Empirical study for simulated data shows that MAVE and dMAVE has relatively better performance than SIR and SAVE, regardless of not only different models but also different distributional assumptions of predictors. However, real data example with the binary response demonstrates that SAVE is better than other methods.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.10
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• pp.1574-1582
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• 2004

Binary manipulators have recently received much attention due to hyper-redundancy, light weight, good controllability and high reliability. The precise positioning of the manipulator end-effecter requires the use of many modules, which results in a high-dimensional workspace. When the workspace dimension is large, existing inverse kinematics methods such as the Ebert-Uphoff algorithm may require impractically large memory size in determining the binary positions of all actuators. To overcome this limitation, we propose a new inverse kinematics algorithm: the inverse kinematics problem is formulated as an optimization problem using real-valued design variables, The key procedure in this approach is to transform the integer-variable optimization problem to a real-variable optimization problem and to push the real-valued design variables as closely as possible to the permissible binary values. Since the actual optimization is performed in real-valued design variables, the design sensitivity becomes readily available, and the optimization method becomes extremely efficient. Because the proposed formulation is quite general, other design considerations such as operation power minimization can be easily considered.