• Steel and Composite Structures
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• v.28 no.5
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• pp.541-557
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• 2018

This paper presents the influence of carbon nanotubes (CNTs) waviness and aspect ratio on the vibrational behavior of functionally graded nanocomposite sandwich annular sector plates resting on two-parameter elastic foundations. The carbon nanotube-reinforced (CNTR) sandwich plate has smooth variation of CNT fraction along the thickness direction. The distributions of CNTs are considered functionally graded (FG) or uniform along the thickness and their mechanical properties are estimated by an extended rule of mixture. In this study, the classical theory concerning the mechanical efficiency of a matrix embedding finite length fibers has been modified by introducing the tube-to-tube random contact, which explicitly accounts for the progressive reduction of the tubes' effective aspect ratio as the filler content increases. Effects of CNT distribution, volume fraction, aspect ratio and waviness, and also effects of Pasternak's elastic foundation coefficients, sandwich plate thickness, face sheets thickness and plate aspect ratio are investigated on the free vibration of the sandwich plates with wavy CNT-reinforced face sheets. The study is carried out based on three-dimensional theory of elasticity and in contrary to two-dimensional theories, such as classical, the first- and the higher-order shear deformation plate theories, this approach does not neglect transverse normal deformations. The sandwich annular sector plate is assumed to be simply supported in the radial edges while any arbitrary boundary conditions are applied to the other two circular edges including simply supported, clamped and free.

• Journal of Microbiology and Biotechnology
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• v.12 no.3
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• pp.449-456
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• 2002

The objective of this study was to optimize medium composition of initial pH, tryptone, glucose, yeast extract, and mineral mixture for production of bacteriocin by Lactobacillus acidophilus ATCC 4356, using response surface methodology. A response surface approach including new statistical and plotting methods was employed for design and analysis of the experiment. An interiorly augmented central composite design was used as an experimental design. A normal-distribution log-link generalized linear model based on a subset fourth-order polynomial ($R^2$=0.94, Mean Error Deviance=0.0065) was used as an analysis model. This model was statistically superior to the full second-order polynomial-based generalized linear model ($R^2$=0.80, Mean Error Deviance=0.0140). Nonlinear programming determined the optimum composition of the medium as initial pH 6.35, typtone $1.21\%$, glucose $0.9\%$, yeast extract $0.65\%$, and mineral mixture $1.17\%$. A validation experiment confirmed that the optimized medium was comparable to the MRS medium in bacteriocin production, having the advantage of economy and practicality.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.607-617
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• 2003

This paper offers a practical comparison of efficiency between local likelihood approach and conventional kernel approach in density estimation. The local likelihood estimation procedure maximizes a kernel smoothed log-likelihood function with respect to a polynomial approximation of the log likelihood function. We use two types of data driven bandwidths for each method and compare the mean integrated squares for several densities. Numerical results reveal that local log-linear approach with simple plug-in bandwidth shows better performance comparing to the standard kernel approach in heavy tailed distribution. For normal mixture density cases, standard kernel estimator with the bandwidth in Sheather and Jones(1991) dominates the others in moderately large sample size.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.381-384
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• 2000

This paper proposes a method of estimating the lifetime distribution at use condition for constant stress accelerated lift tests when extrinsic failure mode as well as intrinsic one exists. A mixture of two log-normal distributions is introduced to describe these failure modes and it is assumed that a linear relation exists between the location parameter and stress. An estimation procedure using the expectation and maximization algorithm is proposed and a numerical example is given.

• Journal of environmental and Sanitary engineering
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• v.12 no.1
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• pp.15-23
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• 1997

The anionic exchange resins with the Dowex-1 and Amberlite CG-400 form were transformed into resin of sulfate and acetate acid form, respectively. The uranyl complex ions with SO$_{4}$$^{2-}$ and CH$_{3}$COO$^{-}$ were adsorbed on the anion exchange resion mentioned above, and these complex ions were eluted as mixture eluents of 0.7M HNO$_{3}$ - 0.5M NH$_{4}$NO$_{3}$ by anion exchange chromatography. The optimum adsorption conditions of uranyl anion complex ions adsorbed on the upper of the resin colmun were 1.5-2.0 ml/min of flow rates at pH 2.0 and adsorptive power of uranyl complex ion of sulfuric acid type were nearly consistent with the Caussion normal distribution curve, whereas the elution state of UO$_{2}$(Ac)$_{2}$$^{4-}$ with acetic acid type was departed. The weighing form obtained from resin of sulfuric acid and aceric acid type was U$_{3}$O$_{8}$ whose recovery was 91.7%. The possibility of recovering uranium from the monazite sulfate solution using a strong base anion resin, Amberlite CG-400(sulfate form), was successfully recovered more than 90%.

• Proceedings of the Korea Society for Simulation Conference
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• 2003.06a
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• pp.129-134
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• 2003

In this paper, we determine optimal reduction in the lead time and setup cost for some stochastic inventory models. And we propose more general model that allow the backorder rate as a control variable. We first assume that the lead time demand follows a normal distribution. And we assume that the backorder rate is dependent on the length of lead time through the amount of shortages. The stochastic models analyzed in this paper are the classical continuous and periodic review policy models with a mixture of backorders and lost sales. For each of these models, we provide a sufficient conditions for the uniqueness of the optimal operating policy. We also develop algorithms for solving these models and provide illustrative numerical examples.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.435-442
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• 2017

Consider the problem of estimating a $p{\times}1$ mean vector ${\theta}$ (p ${\geq}$ 3) under the quadratic loss from multi-variate normal population. We find a James-Stein type estimator which shrinks towards the projection vectors when the underlying distribution is that of a variance mixture of normals. In this case, the norm ${\parallel}{\theta}-K{\theta}{\parallel}$ is known where K is a projection vector with rank(K) = q. The class of this type estimator is quite general to include the class of the estimators proposed by Merchand and Giri (1993). We can derive the class and obtain the optimal type estimator. Also, this research can be applied to the simple and multiple regression model in the case of rank(K) ${\geq}2$.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.75-77
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• 2003

The spatial distribution of snow cover area is a crucial input to models of hydrology and climate in alpine and other seasonally snow covered areas.The objective in our study is to develop a rapidly automatic and high accuracy snow cover mapping algorithm applicable for the Tibetan Plateau which is the most sensitive about climatic change. Monitoring regional snow extent reqires higher temoral frequency-moderate spatial resolution imagery.Our algorithm is based AVHRR and MODIS data and will provide long-term fraction snow cover area map.We present here a technique is based on the multiple endmembers approach and by taking advantages of current approaches, we developed a technique for automatic selection of local reference spectral endmembers.

• Korean Journal of Remote Sensing
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• v.1 no.1
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• pp.5-27
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• 1985

A hill-sliding technique was devised to extract Gaussian clusters from the multivariate probability density estimates of sample data for the first step of iterative unsupervised classification. The underlying assumption in this approach was that each cluster possessed a unimodal normal distribution. The key idea was that a clustering function proposed could distinguish elements of a cluster under formation from the rest in the feature space. Initial clusters were extracted one by one according to the hill-sliding tactics. A dimensionless cluster compactness parameter was proposed as a universal measure of cluster goodness and used satisfactorily in test runs with Landsat multispectral scanner (MSS) data. The normalized divergence, defined by the cluster divergence divided by the entropy of the entire sample data, was utilized as a general separability measure between clusters. An overall clustering objective function was set forth in terms of cluster covariance matrices, from which the cluster compactness measure could be deduced. Minimal improvement of initial data partitioning was evaluated by this objective function in eliminating scattered sparse data points. The hill-sliding clustering technique developed herein has the potential applicability to decomposition of any multivariate mixture distribution into a number of unimodal distributions when an appropriate diatribution function to the data set is employed.

• Fashion & Textile Research Journal
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• v.7 no.5
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• pp.494-500
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• 2005

This study classifies somatotype from exact measurements according to the women somatotype with different drop value and isfunctional and has high body fitness. This study was preceded in the following process. Took 147 women in their 20's measurement directly and grasped the general body characteristic, and classified somatotype according to distribution of drop. Drop value is classified into Drop I, Drop II, and Drop III. Drop I is the difference value between bust line and waist line measurement. Drop II is the difference value between hip line and waist line measurement. Drop III is the difference value between hip line and bust line measurement. Suggested distribution of somatotype based on drop istribution. Classified somatotype into 4 types according to the mixture of combination of Drop I, II, and III. Comprehended the characteristics of somatotype based on basic statistical data. Type 1, M type, displays 40 % distribution that is most high distribution rate among four types and it is normal type which is similar to average value in size in the this study. Type 2, A type, displays 32.4% and it's a lower half of their body developed type with a flat busted and hippy. Type 3, X type, displays 19% and it's a healthy type with tall height, slender waist and curves of bust and hip lines. Type 4, H type, displays 8.6% and it's a biggest type in size which is busty, full hipped and nearly has no curves of body line.