• Journal of Korea Water Resources Association
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• v.53 no.1
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• pp.1-13
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• 2020

The most common approach for computing engineering flow problems at high Reynolds number is still the Reynolds-averaged Navier-Stokes (RANS) computations based on turbulence models with wall functions. The recently developed generalized wall functions blending between the wall-limiting viscous and the outer logarithmic relations ensure a smooth transition of flow quantities across two regions. The performances and convergence properties of widely used turbulence models with wall functions that are applicable for turbulence kinetic energy (TKE), turbulent and specific dissipation rates, and eddy viscosity are presented through a series of near wall flow simulations. The present results show that RNG k-𝜖 model should be carefully applied with small tolerance to get the stable solution when the first grid lies in the buffer layer. The standard k-𝜖 and RNG k-𝜖 models are not sensitive to the selection of wall functions for both TKE and eddy viscosity, while the k-ω SST model should be applied together with kL-wall function for TKE and nutUB-wall functions for eddy viscosity to ensure accurate and stable boundary conditions. The applications to a backward-facing step flow at Re=155,000 reveal that the reattachment length is reasonably well predicted on appropriately refined mesh by all turbulence models, except the standard k-𝜖 model which about 13% underestimates the reattachment length regardless of the grid refinement.

• Journal of Korea Multimedia Society
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• v.13 no.3
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• pp.430-437
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• 2010

It is essential for living donor liver transplantation that surgeon must understand the hepatic vessel structure to improve the success rate of operation. In this paper, we extract the liver boundary without other surrounding structures such as heart, stomach, and spleen using the contrast enhanced MDCT liver image sequence. After that, we extract the major hepatic veins (left, middle, right hepatic vein) with morphological filter after review the basic structure of hepatic vessel which reside in segmented liver image region. The purpose of this study is provide the overall status of transplantation operation with size estimation of resection part which is dissected along with the middle hepatic vein. The method of liver extraction is as follows: firstly, we get rid of background and muscle layer with gray level distribution ratio from sampling process. secondly, the coincident images match with unit mesh image are unified with resulted image using the corse coordinate of liver and body. thirdly, we extract the final liver image after expanding and region filling. Using the segmented liver images, we extract the hepatic vessels with morphological filter and reversed the major hepatic vessels only with a results of ascending order of vessel size. The 3D reconstructed views of hepatic vessel are generated after applying the interpolation to provide the smooth view. These 3D view are used to estimate the dissection line after identify the middle hepatic vein. Finally, the volume of resection region is calculated and we can identify the possibility of successful transplantation operation.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.5
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• pp.404-411
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• 2009

We introduce in this paper a new method for smooth foldover-free warping of images, based on the vector field deformation technique proposed by Von Funck et al. It allows users to specify the constraints in two different ways: positional constraints to constrain the position of a point in the image and gradient constraints to constrain the orientation and scaling of some parts of the image. From the user-specified constraints, it computes in the image domain a C1-continuous velocity vector field, along which each pixel progressively moves from its original position to the target. The target positions of the pixels are obtained by solving a set of partial derivative equations with the 4th order Runge-Kutta method. We show how our method can be useful for texture mapping with hard constraints. We start with an unconstrained planar embedding of a target mesh using a previously known method (Least Squares Conformal Map). Then, in order to obtain a texture map that satisfies the given constraints, we use the proposed warping method to align the features of the texture image with those on the unconstrained embedding. Compared to previous work, our method generates a smoother texture mapping, offers higher level of control for defining the constraints, and is simpler to implement.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.10
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• pp.520-529
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• 2005

A feature-based 3D modeling algorithm is presented in this paper. Since conventional methods use depth-based techniques, they need much time for the image matching to extract depth information. Even feature-based methods have less computation load than that of depth-based ones, the calculation of modeling error about whole pixels within a triangle is needed in feature-based algorithms. It also increase the computation time. Therefore, the proposed algorithm consists of three phases, which are an initial 3D model generation, model evaluation, and model refinement phases, in order to acquire an efficient 3D model. Intensity gradients and incremental Delaunay triangulation are used in the Initial model generation. In this phase, a morphological edge operator is adopted for a fast edge filtering, and the incremental Delaunay triangulation is modified to decrease the computation time by avoiding the calculation errors of whole pixels and selecting a vertex at the near of the centroid within the previous triangle. After the model generation, sparse vertices are matched, then the faces are evaluated with the size, approximation error, and disparity fluctuation of the face in evaluation stage. Thereafter, the faces which have a large error are selectively refined into smaller faces. Experimental results showed that the proposed algorithm could acquire an adaptive model with less modeling errors for both smooth and abrupt areas and could remarkably reduce the model acquisition time.

• Culinary science and hospitality research
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• v.22 no.3
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• pp.11-21
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• 2016

This study investigated the quality characteristics and retarding retrogradation of sponge cakes made with red yeast rice (RYR) flour. RYR (Monascus nuruk) is known to help digestion, smooth blood flow, and have anti-cancer, anti-microbial, and inhibitory effects against biosynthesis of cholesterol and blood pressure. This studys aim' was to find the optimal proportion of RYR flour in sponge cake. RYR sponge cakes were prepared with various levels (0, 5, 10, 15 and 20%) replacement of wheat flour and were designated as the control (without RYR), RYR5, RYR10, RYR15 and RYR20 respectively. Specific gravity was the lowest in RYR15 at 0.57, and the baking loss rate was not significantly different among the samples (p<0.05). The dough yield was the highest in RYR15 at 96.61. The moisture contents was highest in order, control, RYR5, and RYR15 at 28.67%, 28.18%, and 26.82% respectively. The L-value of crust tended to increase according to the level of RYR, but the L-value of crumb decreased in accorddance with the the content of RYR. The a-value of crust also decreased according to the level of RYR, although the a-value of crumb increased in response to higher levels of RYR. The b-value tended to decrease with increases of RYR (p<0.05). RYR5 exhibited the highest pH at 8.63, compared with RYR15 (8.57). The hardness, which was measured after cooling for 1 hour, was the lowest in RYR15 at $163.33g/cm^2$ and the springiness was not different significantly (p<0.05). Cohesiveness was the highest in RYR10 at 133.06%. The chewiness was the highest in RYR10 at $391.63g{\cdot}cm$ and lowest in RYR15 ast $169.62g{\cdot}cm$. Avrami equation showed that RYR15 and RYR20 had the lowest Avrami exponent (n) at 0.0664 and 0.4983 respectively. Time constant (1/k) was the highest in RYR15 at 200.00. Sensory evaluation revealed that RYR15 was the highest in color (5.50), flavor (4.95), sweetness (4.90), chewiness (4.75), and overall acceptability (4.60).