• The Journal of the Acoustical Society of Korea
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• v.27 no.1E
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• pp.25-29
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• 2008

The projection approximation subspace tracking (PAST) is one of the attractive subspace tracking algorithms, because it estimatesthe signal subspace adaptively and continuously. Furthermore, the computational complexity is relatively low. However, the algorithm still has room for improvement in the subspace estimation accuracy. In this paper, we propose a new algorithm to improve the subspace estimation accuracy using a normally ordered input vector and a reversely ordered input vector simultaneously.

• Journal of the Korean Data and Information Science Society
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• v.3 no.1
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• pp.33-46
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• 1992

The set of eigenvectors of the second moment matrix and the mean vector are the measures of orientation for a distribution supported on the unit sphere. Bootstrap confidence cone for the eigenvector is constructed and the consistency of this method is discussed. The performance of our bootstrap cone for the eigenvector is compared with that of the asymptotic confidence cones for two measures under the parametric assumptions for the underlying distributions and that of the bootstrap cone for the mean vector by Monte Carlo simulation.

• Architectural research
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• v.13 no.1
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• pp.17-24
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• 2011

Isogeometric solution of Poisson equation is provided. NURBS (NonUniform B-spline Surface) is introduced to express both geometry of structure and unknown field of governing equation. The terms of stiffness matrix and load vector are consistently derived with very accurate geometric definition. The validity of the isogeometric formulation is demonstrated by using two numerical examples such as square plate and L-shape plate. From numerical results, the present solutions have a good agreement with analytical and finite element (FE) solutions with the use of a few cells in isogeometric analysis.

• Journal of the Chungcheong Mathematical Society
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• v.5 no.1
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• pp.87-97
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• 1992

Sequential procedure with ${\beta}$-protection for the mean vector ${\mu}(\theta)$ of a p(> 1)-variate multivariate distribution $P_{\theta}$, ${\theta}{\in}{\Theta}$, with covariance matrix ${\sum}(\theta)$ is considered when the only nuisance parameters is ${\sum}(\theta)$. We obtain a confidence set for ${\mu}(\theta)$ with coverage probability condition and ${\beta}$-protection at ${\mu}-{\delta}(\mu)$ for some imprecision function ${\delta}:\mathbb{R}^p{\rightarrow}\mathbb{R}^p$.

• Proceedings of the KSR Conference
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• 2000.05a
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• pp.78-85
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• 2000

Computer algorithms for the loadflow of the DC traction power supply system are examined. Algorithms to solve the nodal equation are reviewed and the two iterative methods to solve the nonlinear nature of the loadflow are analyzed and tested, which are so called conductance matrix method and current vector iterative mettled. The result of the analysis tells that the current vector iterative method makes faster convergency and needs less computing time, and it is verified by the test running of the programs based on each of the iterative methods.

• Kyungpook Mathematical Journal
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• v.61 no.2
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• pp.383-393
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• 2021

In this paper, numerical techniques are presented for solving initial value problems of fractional differential equations with variable coefficients. The method is derived by applying a Taylor vector approximation. Moreover, the operational matrix of fractional integration of a Taylor vector is provided in order to transform the continuous equations into a system of algebraic equations. Furthermore, numerical examples demonstrate that this method is applicable and accurate.

• Fisheries and Aquatic Sciences
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• v.4 no.1
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• pp.1-9
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• 2001

This paper describes a procedure extracting feature vector of a target cell more precisely in the case of identifying specified cell. The classification of object type is based on feature vector such as area, complexity, centroid, rotation angle, effective diameter, perimeter, width and height of the object So, the feature vector plays very important role in classifying objects. Because the feature vectors is affected by noises and holes, it is necessary to remove noises contaminated in original image to get feature vector extraction exactly. In this paper, we propose the following method to do to get feature vector extraction exactly. First, by Otsu's optimal threshold selection method and morphological filters such as cleaning, filling and opening filters, we separate objects from background an get rid of isolated particles. After the labeling step by 4-adjacent neighborhood, the labeled image is filtered by the area filter. From this area-filtered image, feature vector such as area, complexity, centroid, rotation angle, effective diameter, the perimeter based on chain code and the width and height based on rotation matrix are extracted. To prove the effectiveness, the proposed method is applied for yeast Zygosaccharomyces rouxn. It is also shown that the experimental results from the proposed method is more efficient in measuring feature vectors than from only Otsu's optimal threshold detection method.

• Journal of Korea Multimedia Society
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• v.13 no.10
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• pp.1487-1493
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• 2010

H.264 processes more detailed and more motion information for the compression efficiency. However, motion vector of H.264 takes more portion than previous standards such as MPEG-1/2/4 do, so that it is needed to consider motion vectors. This paper proposes the new algorithm with leaf mode in order to compress the motion vector efficiently. The proposed algorithm concentrates modes' distribution with leaf mode and carries out the compression of motion vector with less modes. The proposed algorithm adopted in current $4{\times}4$ motion vector matrix also can be extend to $8{\times}8$. The experiments shows that the proposed algorithm reduces up to 12.68% at header and 9.7% at resultant bitstream.

• Journal of the Korean Statistical Society
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• v.31 no.2
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• pp.237-250
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• 2002

Motivated by Cook's (1986) assessment of local influence by investigating the curvature of a surface associated with the overall discrepancy measure, this paper extends this idea to the linear regression model with AR(p) disturbances. Diagnostic for the linear regression models with AR(p) disturbances are discussed when simultaneous perturbations of the response vector are allowed. For the derived criterion, numerical studies demonstrate routine application of this work.

• Bulletin of the Korean Mathematical Society
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• v.54 no.2
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• pp.667-677
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• 2017

Let $F{\subseteq}{\mathbb{P}}^3$ be a smooth surface of degree $3{\leq}d{\leq}9$ whose equation can be expressed as either the determinant of a $d{\times}d$ matrix of linear forms, or the pfaffian of a $(2d){\times}(2d)$ matrix of linear forms. In this paper we show that F supports families of dimension p of pairwise non-isomorphic, indecomposable, Ulrich bundles for arbitrary large p.