• 대한수학회보
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• 제31권2호
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• pp.215-236
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• 1994

The purpose of the present paper is to study generic submanifolds of a Sasakian space form with nonvanishing parallel mean curvature vector field such that the shape operator in the direction of the mean curvature vector field commutes with the structure tensor field induced on the submanifold. In .cint. 1 we state general formulas on generic submanifolds of a Sasakian manifold, especially those of a Sasakian space form. .cint.2 is devoted to the study a generic submanifold of a Sasakian manifold, which is not tangent to the structure vector. In .cint.3 we investigate generic submanifolds, not tangent to the structure vector, of a Sasakian space form with nonvanishing parallel mean curvature vactor field. In .cint.4 we discuss generic submanifolds tangent to the structure vector of a Sasakian space form and compute the restricted Laplacian for the shape operator in the direction of the mean curvature vector field. As a applications of these, in the last .cint.5 we prove our main results.

• 대한수학회지
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• 제44권3호
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• pp.647-659
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• 2007

Relationships between the Ricci curvature and the squared mean curvature and between the shape operator associated with the mean curvature vector and the sectional curvature function for slant submanifolds of an S-space-form are proved, particularizing them to invariant and anti-invariant submanifolds tangent to the structure vector fields.

• 한국통신학회논문지
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• 제24권9B호
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• pp.1731-1741
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• 1999

칼라 영상은 센서 잡음이나 채널 전송 에러에 의해 생기는 잡음에 의해 자주 오염되어진다. 이러한 칼라 잡음을 제거하기 위해 벡터 미디안, 벡터 $\alpha$-trimmed 평균 필터 등 여러 형태의 필터들이 개발되어져 왔다. 본 논문에서 제안된 클러스터 필터는 잡음에 오염된 환경 하에서 강건한 소속함수 값을 얻을 수 있는 가능적 c-mean 클러스터링 방법을 이용하였다. 또한, 본 논문에서는 혼합된 잡음에서 우수한 벡터 $\alpha$-trimmed 평균 필터를 개선하여, 원도우내의 화소중 중심에 위치한 화소에는 더 가중치를 부여하여 가중화 된 평균 필터링을 수행하는 가중화 벡터$\alpha$-trimmed 평균 필터를 제안하였다. 본 논문에서는 칼라 잡음이 발생한 영상에서 제안된 필터들의 성능을 평가하기 위해 칼라 잡음 발생기를 구현하였으며, 실험 결과는 NCD 척도 및 관측자의 시각에 의해 평가되었다. 실험 결과 제안된 퍼지 클러스터 필터는 NCD 관점에서 기존의 필터들에 비해 혼합된 잡음에서 우수한 성능을 보였고, 제안된 가중화된 벡터 $\alpha$-trimmed 평균 필터는 벡터 $\alpha$-trimmed 평균 필터에 비해 어떠한 잡음 하에서도 양호한 결과를 보였다.

• 품질경영학회지
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• 제29권1호
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• pp.1-10
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• 2001

We consider the problem of detecting special variations in multivariate $T^2$-control chart when two or more multivariate outliers are present. Since a multivariate outlier may reflect slippage in mean, variance, or correlation, it can distort the sample mean vector and sample covariance matrix. Damaged sample mean vector and sample covariance matrix have difficulty in examining special variations clearly, An alternative to detection outliers or special variations is to use robust estimators of mean vector and covariance matrix that are less sensitive to extreme observations than are the standard estimators $\bar{x}$ and $\textbf{S}$. We applied popular minimum volume ellipsoid(MVE) and minimum covariance determinant(MCD) method to estimate mean vector and covariance matrix and compared its results with standard $T^2$-control chart using simulated multivariate data with outliers. We found that the modified $T^2$-control chart based on the above robust methods were more effective in detecting special variations clearly than the standard $T^2$-control chart.

• 대한수학회지
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• 제55권1호
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• pp.1-16
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• 2018

In this paper, we give characteristic differential equations of a kind of projective vector fields on Finsler manifolds. Using these equations, we prove the vanishing theorem of projective vector fields on any compact Finsler manifold with the negative mean Ricci curvature, which is defined in [10]. This result involves the vanishing theorem of Killing vector fields in the Riemannian case and the work of [1, 14].

• Communications for Statistical Applications and Methods
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• 제30권3호
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• pp.245-258
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• 2023

This study proposes a modification to the objective function of the support vector machine for the linearly non-separable case of a binary classifier y_i ∈ {-1, 1}. The modification takes into account the position of each data item x_i from its corresponding class centroid. The resulting optimization function involves the centroid mean vector, and the spread of data besides the support vectors, which should be minimized by the choice of hyper-plane β. Theoretical assumptions have been tested to derive an optimal separable hyperplane that yields the minimal misclassification rate. The proposed method has been evaluated using simulation studies and real-life COVID-19 patient outcome hospitalization data. Results show that the proposed method performs better than the classical linear SVM classifier as the sample size increases and is preferred in the presence of correlations among predictors as well as among extreme values.

• 동력기계공학회지
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• 제4권4호
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• pp.25-31
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• 2000

This paper represents the vector fields and three dimensional mean velocities in the X-Y plane of cone type swirl gas burner measured by using X-probe from the hot-wire anemometer system. This experiment is carried out at flowrate 350 and $450{\ell}/min$ respectively in the test section of subsonic wind tunnel. The vector plot shows that the maximum axial mean velocity component is focused in the narrow slits distributed radially on the edge of a cone type swirl burner, for that reason, there is some entrainment of ambient air in the outer region of the burner and the rotational flow can be shown in the inner region of the burner because mean velocity W is distributed about twice as large as mean velocity V due to inclined flow velocity ejecting from the swirl vanes of a cone type baffle plate of burner. Moreover, the mean velocities are largely distributed near the outer region of burner within $X/R{\fallingdotseq}1.5$, hence, the turbulent characteristics are anticipated to be distributed largely in the center of this region due to the large inclination of mean velocity and swirl effect.

• Journal of the Korean Statistical Society
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• 제15권2호
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• pp.97-106
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• 1986

Assume that $X_1, X_2, \cdots, X_N$ are iid p-dimensional normal random vectors ($p \geq 3$) with unknown covariance matrix. The problem of estimating multivariate normal mean vector in an empirical Bayes situation is considered. Empirical Bayes estimators, obtained by Bayes treatmetn of the covariance matrix, are presented. It is shown that the estimators are minimax, each of which domainates teh maximum likelihood estimator (MLE), when the loss is nonsingular quadratic loss. We also derive approximate credibility region for the mean vector that takes advantage of the fact that the MLE is not the best estimator.

• 한국지능시스템학회:학술대회논문집
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• 한국지능시스템학회 2007년도 추계학술대회 학술발표 논문집
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• pp.267-271
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• 2007

본 논문은 원본이미지와 은닉이미지의 좋은 압축률과 만족할만한 이미지의 질, 그리고 외부공격에 강인한 이미지은닉의 한 방법으로 특이치 분해와 퍼지 군집화를 이용한 벡터양자화를 이용한 워터마킹 방법을 소개하였다. 실험에서는 은닉된 이미지의 비가시성과 외부공격에 대한 강인성을 증명하였다.

• 대한전자공학회논문지
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• 제25권6호
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• pp.672-679
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• 1988

In this paper, attempts have been made to code images employing a separating mean vector quantizer(SMVQ). Then we analyzed the performance of the SMVQ experimentally as well as analytically. The results of simulation with natural images are presented. But, conclusively the performance of the SMVQ technique is not better than that of the conventional vector quantizer. In this paper, a brief analysis in which we revealed that the performance, based on the mean square error measure, of the SMVQ is not favorable is favorable is discussed.