• 대한수학회보
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• 제48권2호
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• pp.427-444
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• 2011

In this paper we give some non-existence theorems for Hopf hypersurfaces in the complex two-plane Grassmannian $G_2(\mathbb{C}^{m+2})$ with Lie parallel normal Jacobi operator $\bar{R}_N$ and totally geodesic D and $D^{\bot}$ components of the Reeb flow.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 제40회 하계학술대회
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• pp.223_224
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• 2009

This paper investigates a normal induced voltage according to earth resistivity from distribution lines using calculation method of the normal induced voltage on telecommunication line. The induced voltage according to earth resistivity from distribution lines is verified by vector analysis and EMTP(Electro-Magnetic Transients Program).

• Journal of the Korean Data and Information Science Society
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• 제27권6호
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• pp.1453-1463
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• 2016

가장 선호하는 금융위험 측정 방법은 통계적으로 최대손실금액을 추정하는 VaR (Value at Risk)이다. 포트폴리오를 구성하는 여러 산업에 대한 VaR (Value at Risk)는 분산공분산 행렬과 특정한 포트폴리오가 포함되어 변환된 일변량 위험을 이용하여 추정한다. Hong 등 (2016)은 다변량 분위벡터를 바탕으로 Vector at Risk를 정의하였으며, 특정한 포트폴리오가 설정되면 Vector at Risk 중의 한 점을 최적의 VaR 즉, 대안적인 VaR (AVaR)로 제안하였다. 본 연구에서는 다변량 정규분포에 대하여 AVaR의 특성을 탐색한다. 여러 종류의 분산공분산 행렬과 다양한 포트폴리오 가중값 벡터인 경우의 이변량과 삼변량의 정규분포를 따르는 모의실험 자료와 실증예제를 이용하여 대안적인 최대손실금액인 AVaR을 구하고 VaR과 비교 분석한다. 다변량 분위벡터를 이용한 AVaR는 VaR보다 작게 추정함을 발견하였으며, 이런 특징과 함께 AVaR의 특성을 토론한다.

• 한국CDE학회논문집
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• 제6권3호
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• pp.184-192
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• 2001

In this paper we present a remeshing algorithm for irregular meshes with boundaries. The irregular meshes are approximated by regular meshes where the topological regularity is essential for the multiresolutional analysis of the given meshes. Normal meshes are utilized to reduce the necessary data size at each resolution level of the regularized meshes. The normal mesh uses one scalar value, i.e., normal offset value which is based on the regular rule of a uniform subdivision, while other remeshing schemes use one 3D vector at each vertex. Since the normal offset cannot be properly used for the boundaries of meshes, we use a combined subdivision scheme which resolves a problem of the proposed normal offset method at the boundaries. Finally, we show an example to see the effectiveness of the proposed scheme to reduce the data size of a mesh model.

• Toxicological Research
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• 제19권3호
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• pp.247-257
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• 2003

Medicine is undergoing a revolution in the understanding of the mechanisms through which disease processes develop. The advent of genetics and molecular biology to oncology not only is providing surrogate predictors of therapy response and survival which are forming the basis for selection among established treatment options, but is providing targets for new directions in therapy as well. Molecular modification of somatic cells for the purposes of protecting the normal cells from the toxicity of cancer chemotherapy, for the sensitization of the tumor cells to therapy and use of conditionally replicating viral vector have been new directions of cancer treatment which have reached the clinical arena. Advances in molecular pharmacology and vector design summarized in this paper may provide solutions to some of the existing problems in the technology of gene transfer therapy. Continued basic research into the biological basis of human disease, systemic studies of the application of these discoveries to therapy and the improvement of vector for gene delivery all combined may result in advances in this important field of therapy over the next few years.

• 대한수학회지
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• 제31권3호
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• pp.339-352
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• 1994

A sumbanifold M of a quaternionic Kaehlerian manifold $\tilde{M}^m$ of real dimension 4m is called a generic submanifold if the normal space N(M) of M is always mapped into the tangent space T(M) under the action of the quaternionic Kaehlerian structure tensors of the ambient manifold at the same time.The purpose of the present paper is to study generic submanifold of quaternionic Kaehlerian manifold of constant Q-sectional curvature with nonvanishing parallel mean curvature vector. In section 1, we state general formulas on generic submanifolds of a quaternionic Kaehlerian manifold of constant Q-sectional curvature. Section 2 is devoted to the study generic submanifolds with nonvanishing parallel mean curvature vector and compute the restricted Laplacian for the second fundamental form in the direction of the mean curvature vector. As applications of those results, in section 3, we prove our main theorems. In this paper, the dimension of a manifold will always indicate its real dimension.

• 품질경영학회지
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• 제38권3호
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• pp.354-362
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• 2010

This paper presents a non-linear control chart based on support vector machine regression (SVM-R) to improve the accuracy of fault detection of cyclic signals. The proposed algorithm consists of the following two steps. First, the center line of the control chart is constructed by using SVM-R. Second, we calculate control limits by variances that are estimated by perpendicular and normal line of the center line. For performance evaluation, we apply proposed algorithm to the industrial data of the chemical vapor deposition process which is one of the semiconductor processes. The proposed method has better fault detection performance than other existing method

• Kyungpook Mathematical Journal
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• 제52권1호
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• pp.49-59
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• 2012

In this article, using the example of C. Camci([7]) we reconfirm necessary sufficient condition for a slant curve. Next, we find some necessary and sufficient conditions for a slant curve in a Sasakian 3-manifold to have: (i) a $C$-parallel mean curvature vector field; (ii) a $C$-proper mean curvature vector field (in the normal bundle).

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제3권1호
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• pp.100-104
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• 2003

The SVDD(support vector data description) is one of the most well-known one-class support vector learning methods, in which one tries the strategy of utilizing balls defined on the kernel feature space in order to distinguish a set of normal data from all other possible abnormal objects. The major concern of this paper is to consider the problem of modifying the SVDD into the direction of utilizing ellipsoids instead of balls in order to enable better classification performance. After a brief review about the original SVDD method, this paper establishes a new method utilizing ellipsoids in feature space, and presents a solution in the form of SDP(semi-definite programming) which is an optimization problem based on linear matrix inequalities.

• Journal of the Korean Data and Information Science Society
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• 제28권2호
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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) ${\geq}2$.