• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.501-517
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• 2020

Three-dimensional numerical simulations were performed to study the effects of flow direction and flow velocity on the flow regime behind a curved pipe represented by a curved circular cylinder. The cylinder is based on a previous study and consists of a quarter segment of a ring and a horizontal part at the end of the ring. The cylinder was rotated in the computational domain to examine five incident flow angles of 0-180° with 45° intervals at Reynolds numbers of 100 and 500. The detailed wake topologies represented by λ2 criterion were captured using a Large Eddy Simulation (LES). The curved cylinder leads to different flow regimes along the span, which shows the three-dimensionality of the wake field. At a Reynolds number of 100, the shedding was suppressed after flow angle of 135°, and oblique flow was observed at 90°. At a Reynolds number of 500, vortex dislocation was detected at 90° and 135°. These observations are in good agreement with the three-dimensionality of the wake field that arose due to the curved shape.

• Communications for Statistical Applications and Methods
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• v.14 no.3
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• pp.687-698
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• 2007

A normal mixture model with which dependence between classes is incorporated is proposed in order to detect differentially expressed genes. Gene clustering approaches suffer from the high dimensional column of microarray expression data matrix which leads to the over-fit problem. Various methods are proposed to solve the problem. In this paper, use of simple averaging data within each class is proposed to overcome the various problems due to high dimensionality when the normal mixture model is fitted. Some experiments through simulated data set and real data set show its availability in actuality.

• The Journal of Korea Robotics Society
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• v.13 no.2
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• pp.113-120
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• 2018

To fly a drone or unmanned aerial vechicle(UAV) safely, its pilot needs to maintain high situation awareness of its flight space. One of the important ways to improve the flight space awareness is to integrate both the global and the local navigation map a drone provides. However, the drone pilot often has to use the inconsistent reference frames or perspectives between the two maps. In specific, the global navigation map tends to display space information in the third-person perspective, whereas the local map tends to use the first-person perspective through the drone camera. This inconsistent perspective problem makes the pilot use mental rotation to align the different perspectives. In addition, integrating different dimensionalities (2D vs. 3D) of the two maps may aggravate the pilot's cognitive load of mental rotation. Therefore, this study aims to investigate the relation between perspective difference ($0^{\circ}$, $90^{\circ}$, $180^{\circ}$, $270^{\circ}$) and the map dimensionality matches (3D-3D vs. 3D-2D) to improve the way of integrating the two maps. The results show that the pilot's flight space awareness improves when the perspective differences are smaller and also when the dimensionalities between the two maps are matched.

• Communications for Statistical Applications and Methods
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• v.3 no.1
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• pp.11-19
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• 1996

Canonical correlation analysis is a multivariate technique for identifying and quantifying the statistical relationship between two sets of variables. Like most multivariate techniques, the main objective of canonical correlation analysis is to reduce the dimensionality of the dataset. It would be particularly useful if high dimensional data can be represented in a low dimensional space. In this study, we will construct statistical graphs for paired sets of multivariate data. Specifically, plots of the observations as well as the variables are proposed. We discuss the geometric interpretation and goodness-of-fit of the proposed plots. We also provide a numerical example.

• Proceedings of the Materials Research Society of Korea Conference
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• 1998.08a
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• pp.71.3-71
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• 1998

• Journal of the Korea Computer Graphics Society
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• v.18 no.3
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• pp.9-16
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• 2012

In this paper, we present a new method for reconstructing detailed facial expression from roughly captured data with a small number of markers. Because of the difference in the required capture resolution between the full-body capture and the facial expression capture, they hardly have been performed simultaneously. However, for generating natural animation, a simultaneous capture for body and face is essential. For this purpose, we provide a method for capturing the detailed facial expression only with a small number of markers. Our basic idea is to build a database for the facial expressions and apply the principal component analysis for reducing the dimensionality. The dimensionality reduction enables us to estimate the full data from a part of the data. We justify our method by applying it to dynamic scenes to show the viability of the method.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.3
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• pp.59-66
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• 2016

This paper presents an empirical evaluation on dimensionality reduction strategies by which dissimilarity-based classifications (DBC) can be implemented efficiently. In DBC, classification is not based on feature measurements of individual objects (a set of attributes), but rather on a suitable dissimilarity measure among the individual objects (pair-wise object comparisons). One problem of DBC is the high dimensionality of the dissimilarity space when a lots of objects are treated. To address this issue, two kinds of solutions have been proposed in the literature: prototype selection (PS)-based methods and dimension reduction (DR)-based methods. In this paper, instead of utilizing the PS-based or DR-based methods, a way of performing DBC in Eigen spaces (ES) is considered and empirically compared. In ES-based DBC, classifications are performed as follows: first, a set of principal eigenvectors is extracted from the training data set using a principal component analysis; second, an Eigen space is expanded using a subset of the extracted and selected Eigen vectors; third, after measuring distances among the projected objects in the Eigen space using $l_p$-norms as the dissimilarity, classification is performed. The experimental results, which are obtained using the nearest neighbor rule with artificial and real-life benchmark data sets, demonstrate that when the dimensionality of the Eigen spaces has been selected appropriately, compared to the PS-based and DR-based methods, the performance of the ES-based DBC can be improved in terms of the classification accuracy.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.319-324
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• 2005

An integrated approach for prostate cancer detection using proteomic data is presented. Due to the high-dimensional feature of proteomic data, the discrete wavelet transform (DWT) is used in the first-stage for data reduction as well as noise removal. After the process of DWT, the dimensionality is reduced from 43,556 to 1,599. Thus, each sample of proteomic data can be represented by 1599 wavelet coefficients. In the second stage, a voting method is used to select a common set of wavelet coefficients for all samples together. This produces a 987-dimension subspace of wavelet coefficients. In the third stage, the Autoassociator algorithm reduces the dimensionality from 987 to 400. Finally, the artificial neural network (ANN) is applied on the 400-dimension space for prostate cancer detection. The integrated approach is examined on 9 categories of 2-class experiments, and also 3- and 4-class experiments. All of the experiments were run 10 times of ten-fold cross-validation (i. e. 10 partitions with 100 runs). For 9 categories of 2-class experiments, the average testing accuracies are between 81% and 96%, and the average testing accuracies of 3- and 4-way classifications are 85% and 84%, respectively. The integrated approach achieves exciting results for the early detection and diagnosis of prostate cancer.

• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.677-685
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• 2015

Disease of human and economic traits of livestocks are affected a lot by gene combination effect rather than a single gene effect. In this study, we used SNPHarvester method that supplement existing method in order to investigate the interaction of these genes. The used genes are SREBPs (g.3270+10274 C>T, g.13544 T>C) and FABP4 (g.2634+1018 A>T, g.2988 A>G, g.3690 G>A, g.3710 G>C, g.3977-325 T>C, g.4221 A>G) that are closely related to the fatty acid composition affecting the meatiness of Korean cattle. The economic traits which are used are oleic acid (C18:1), monounsaturated fatty acid (MUFA), marbling score (MS). First, we have utilized the SNPHarvester method in order to find excellent gene combination, and then used the multifactor dimensionality reduction method in order to identify excellent genotype in gene combination.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1331-1339
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• 2013

The lipoprotein lipase (LPL) gene can be considered a functional candidate gene that regulates fatty acid composition. Oh etc (2013) investigated the relationship between unsaturated fatty acids and five novel SNPs, and had confirmed that three polymorphic SNPs (c.322G>A, c.329A>T and c.1591G>A) were associated with fatty acid composition. We have used generalized linear model for adjusted environmental effects and multifactor dimensionality reduction (MDR) method to identify gene-gene interaction effect of statistical model in general. We applied the MDR method on the identify major interaction effects of exonic single nucleotide polymorphisms (SNPs) in the LPL gene for economic traits in Korean cattles population.