• Ocean Systems Engineering
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• v.12 no.1
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• pp.63-86
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• 2022

A refined projection-based purely Lagrangian meshfree method is presented towards reliable numerical analysis of fluid flow interactions with saturated/unsaturated porous media of uniform/spatially-varying porosities. The governing equations are reformulated on the basis of two-phase mixture theory with incorporation of volume fraction. These principal equations of mixture are discretized in the context of Incompressible SPH (Smoothed Particle Hydrodynamics) method. Associated with the consideration of governing equations of mixture, a new term arises in the source term of PPE (Poisson Pressure Equation), resulting in modified source term. The linear and nonlinear force terms are included in momentum equation to represent the resistance from porous media. Volume increase of fluid particles are taken into consideration on account of the presence of porous media, and hence multi-resolution ISPH framework is also incorporated. The stability and accuracy of the proposed method are thoroughly examined by reproducing several numerical examples including the interactions between fluid flow and saturated/unsaturated porous media of uniform/spatially-varying porosities. The method shows continuous pressure field, smooth variations of particle volumes and regular distributions of particles at the interface between fluid and porous media.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1326-1328
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• 2003

An elliptical basis function (EBF) network is proposed in this study for the classification of remotely sensed images. Though similar in structure, the EBF network differs from the well-known radial basis function (RBF) network by incorporating full covariance matrices and uses the expectation-maximization (EM) algorithm to estimate the basis functions. Since remotely sensed data often take on mixture -density distributions in the feature space, the proposed network not only possesses the advantage of the RBF mechanism but also utilizes the EM algorithm to compute the maximum likelihood estimates of the mean vectors and covariance matrices of a Gaussian mixture distribution in the training phase. Experimental results show that the EM-based EBF network is faster in training, more accurate, and simpler in structure.

• 한국가시화정보학회:학술대회논문집
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• 2004.12a
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• pp.65-74
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• 2004

Measurement of concentration distributions of suspended particles in a micro-channel is out of the most crucial necessities in the area of Lab-on-a-chip to be used for various bio-chemical applications. One most feasible way to measure the concentration field in the micro-channel is using micro-LIF(Laser Induced Fluorescence) method. However, an accurate concentration field at a given cross plane in a micro-channel has not been successfully achieved so far due to various limitations in the light illumination and fluorescence signal detection. The present study demonstrates a novel method to provide an ultra thin laser sheet beam having five(5) microns thickness by use of a micro focus laser line generator. The laser sheet beam illuminates an exact plane of concentration measurement field to increase the signal to noise ratio and considerably reduce the depth uncertainty. Nile Blue A was used as fluorescent dye for the present LIF measurement. The enhancement of the fluorescent intensity signals was performed by a solvent mixture of water $(95\%)$ and ethanol (EtOH)/methanol (MeOH) $(5\%)$ mixture. To reduce the rms errors resulted from the CCD electronic noise and other sources, an expansion of grid size was attempted from $1\times1\;to\;3\times3\;or\;5\times5$ pixel data windows and the pertinent signal-to-noise level has been noticeably increased accordingly.

• 한국연소학회:학술대회논문집
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• 2002.11a
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• pp.123-128
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• 2002

The synergistic effect of ethylene/propane and ethylene/methane mixtures on soot formation is studied experimentally with a concentric co-flow burner. The integrated soot volume fractions, laser light scattering signal and PAH concentrations are measured for different fuel supply configurations. The synergistic effect in ethylene/propane diffusion flames is found to be affected not only by the composition of mixture but also by the way of mixing. Comparing to the homogeneously mixed ethylene/propane case, the increase of soot formation is observed when propane is supplied through the inner nozzle, while the decrease is observed when propane is supplied through the outer nozzle. However, the measured PAH concentration distributions are inconsistent with the current view of the synergistic effect of ethylene./propane mixture on soot formation. Virtually no synergistic effect is observed in ethylene-methane flames regardless of the fuel supply configuration, which suggests the important role of $C_3$ species produced during the propane pyrolysis process for the synergistic effect.

• Journal of the Korean Statistical Society
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• v.26 no.4
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• pp.521-530
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• 1997

The new Laplace autoregressive model of order 2-NLAR92) studied by Dewald and Lewis (1985) is extended to the p-th order model-NLAR(p). A necessary and sufficient condition for the existence of an innovation sequence and a stationary ergodic NLAR(p) model is obtained. It is shown that the distribution of the innovation sequence is given by the probabilistic mixture of independent Laplace distributions and a degenrate distribution.

• Journal of the Korean Society of Safety
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• v.12 no.2
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• pp.22-26
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• 1997

A small scale centrifugal oil separator consisted of two concentric tubes was fabricated for spilt oil recovery. With speed control of the inner tube, its performance of oil separation was investigated. Oil-water mixture is separated by forced vortex motion with the rotating inner tube. Velocity and pressure distributions in the tubes were calculated. Control of rotating speed, which is the most influencing parameter, showed an optimum value 946rpm corresponding to the acceleration of 20g at the inner tube surface. Separation performance was suddenly deteriorated at rotating speed higher than 1200rpm.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.627-641
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• 2016

In this paper, we consider a seemingly unrelated regression (SUR) model and propose a nonparametric Bayesian approach to SUR with a Dirichlet process mixture of normals for modeling an unknown error distribution. Posterior distributions are derived based on the proposed model, and the posterior inference is performed via Markov chain Monte Carlo methods based on the collapsed Gibbs sampler of a Dirichlet process mixture model. We present a simulation study to assess the performance of the model. We also apply the model to precipitation data over South Korea.

• Communications for Statistical Applications and Methods
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• v.28 no.1
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• pp.89-97
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• 2021

Due to boundedness and sum constraint, compositional data are often transformed by logratio transformation and their transformed data are put into traditional binary classification or discriminant analysis. However, it may be problematic to directly apply traditional multivariate approaches to the transformed data because class distributions are not Gaussian and Bayes decision boundary are not polynomial on the transformed space. In this study, we propose to use flexible classification approaches to transformed data for compositional data classification. Empirical studies using synthetic and real examples demonstrate that flexible approaches outperform traditional multivariate classification or discriminant analysis.

• Journal of KIISE:Software and Applications
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• v.32 no.8
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• pp.781-788
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• 2005

LDA (Linear Discriminant Analysis) is a data discrimination technique that seeks transformation to maximize the ratio of the between-class scatter and the within-class scatter While it has been successfully applied to several applications, it has two limitations, both concerning the underfitting problem. First, it fails to discriminate data with complex distributions since all data in each class are assumed to be distributed in the Gaussian manner; and second, it can lose class-wise information, since it produces only one transformation over the entire range of classes. We propose three extensions of LDA to overcome the above problems. The first extension overcomes the first problem by modeling the within-class scatter using a PCA mixture model that can represent more complex distribution. The second extension overcomes the second problem by taking different transformation for each class in order to provide class-wise features. The third extension combines these two modifications by representing each class in terms of the PCA mixture model and taking different transformation for each mixture component. It is shown that all our proposed extensions of LDA outperform LDA concerning classification errors for handwritten digit recognition and alphabet recognition.

• Korean Journal of Remote Sensing
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• v.23 no.6
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• pp.537-546
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• 2007

In hyperspectral remote sensing, linear spectral mixture model is a common procedure decomposing into the components of a mixed pixel and estimating the fraction of each end-member. Although linear spectral mixture model is frequently used in geology and mineral mapping because this model is simple and easy to apply, this model is not always valid in forest and urban area having rather complex structure. This study aims to analyze possible error for applying linear spectral mixture model. For the study, we measured laboratory spectra of mixture sample, having various materials, fractions, distributions. The accuracy of linear mixture model is low with the mixture sample having similar fraction because the multi-scattering between components is maximum. Additionally, this multi-scattering is related to the types, fraction, and distribution of components. Further analysis is necessary to quantify errors from linear spectral mixture model.