• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2000년도 추계학술대회 논문집
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• pp.207-210
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• 2000

Singular points are those at which the determinant of a Jacobian matrix is zero. A parallel manipulator gains mostly an extra DOF at the singular points, where it can not be properly controlled. In this study, singular points of a cubic parallel manipulator are illustrated by obtaining the determinant of a Jacobian matrix mathematically, and the singular points of the manipulator are found to be three separate planes in a 3D space. The dependency among links for each singular point is determined by applying linear algebra. Also, the singular points and workspace of the cubic parallel manipulator are plotted to check if the workspace contain singular points.

• 대한수학회보
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• 제48권2호
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• pp.261-275
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• 2011

We give characterizations of the differentiability points and the non-differentiability points of the Riesz-N$\'{a}$gy-Tak$\'{a}$cs(RNT) singulr function using the distribution sets in the unit interval. Using characterizations, we show that the Hausdorff dimension of the non-differentiability points of the RNT singular function is greater than 0 and the packing dimension of the infinite derivative points of the RNT singular function is less than 1. Further the RNT singular function is nowhere differentiable in the sense of topological magnitude, which leads to that the packing dimension of the non-differentiability points of the RNT singular function is 1. Finally we show that our characterizations generalize a recent result from the ($\tau$, $\tau$ - 1)-expansion associated with the RNT singular function adding a new result for a sufficient condition for the non-differentiability points.

• 한국통신학회논문지
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• 제22권5호
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• pp.928-938
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• 1997

We propose an algorithm for extracting singular points of fingerprint image using directional information. First, we extract the candidates of singular points using Poincare index in two(lower and higher) resolutional directional images. Then we remove the false singular points using smoothing technique from lower resolutional directional image. And finally we select the singular points in higher resolution corresponding to those in lower resolution. The possible missing points in lower resolution are found by computing Poincare index algong the proposed small curve. And the reliable points are selected from analysis around them. We also propose a method for segmentation of fingerprint as preprocessing step to enhance the computational speed and the performance of system.

• 대한기계학회논문집
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• 제19권11호
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• pp.2971-2980
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• 1995

A numerical study of fluid flow in driven cavity was carried out using singular finite element method. The driven cavity problem is known to have infinite velocity gradients as well as dual velocity conditions at the singular points. To overcome such difficulties, a finite element method with singular shape functions was used and a special technique was employed to allow multiple values of velocities at the singular points. Application of singular elements in the driven cavity problem has a significant influence on the stability of solution. It was found the singular elements gave a stable solution, especially, for the pressure distribution of the entire flow field by keeping up a large pressure at the singular points. In the existing solutions of driven cavity problem, most efforts were focused on the study of streamlines and vorticities, and pressure were seldom mentioned. In this study, however, more attention was given to the pressure distribution. Computations showed that pressure decreased very rapidly as the distance from the singular point increased. Also, the pressure distribution along the vertical walls showed a smoother transition with singular elements compared to those of conventional method. At the singular point toward the flow direction showed more pressure increase compared with the other side as Reynolds number increased.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 합동 추계학술대회 논문집 정보 및 제어부문
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• pp.321-324
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• 2002

In this paper, we introduce a new approach to fingerprint classification based on both singular points and gabor features. We find singular points of fingerprint image by using squared direction field and Poincare index. Then, the input fingerprint image can be classified into one of 5 classes using the number of singular points and their location. However, it is often impossible to classify the fingerprint image because the numbers and the position of the singular points are not correct due to noise. In this case Gabor features are extracted from unclassified images using Gator filter and they are classified by using k-NN classifier. This method has been tested on the NIST-4 database. The experimental results show that the proposed method is reliable.

• 대한수학회지
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• 제40권5호
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• pp.901-920
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• 2003

The notion of infinitely near singular points, classical in the case of plane curves, has been generalized to higher dimensions in my earlier articles ([5], [6], [7]). There, some basic techniques were developed, notably the three technical theorems which were Differentiation Theorem, Numerical Exponent Theorem and Ambient Reduction Theorem [7]. In this paper, using those results, we will prove the Finite Presentation Theorem, which the auther believes is the first of the most important milestones in the general theory of infinitely near singular points. The presentation is in terms of a finitely generated graded algebra which describes the total aggregate of the trees of infinitely near singular points. The totality is a priori very complex and intricate, including all possible successions of permissible blowing-ups toward the reduction of singularities. The theorem will be proven for singular data on an ambient algebraic shceme, regular and of finite type over any perfect field of any characteristics. Very interesting but not yet apparent connections are expected with many such works as ([1], [8]).

• 대한기계학회논문집B
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• 제23권11호
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• pp.1426-1433
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• 1999

Topological and kinematical studies of the singular points found in flows around a surface-mounted cube in a channel are presented. Numerical simulation was carried out using high-resolution grid systems. Singular points(saddles and nodes) were found around the cube, which satisfy the topological rules suggested by Hunt et al. As the Reynolds number increases, the structure of vortices around the cube becomes complex and the number of singular points increases. Nevertheless, the rule governing the numbers of singular points is still valid. This confirms that our simulation is correct from topological and kinematical point of view, and enables one to infer complex flow patterns in our simulation.

• 한국멀티미디어학회논문지
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• 제8권6호
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• pp.761-767
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• 2005

The personal identification procedure through the fingerprints is divided as the classification process by the type of the fingerprints and the matching process to confirm oneself. Many existing researches for the classification and the matching of the fingerprint depend on the number of the minutiae of the fingerprints and the flow patterns by their direction information. In this paper, we focus on extracting the singular points by using the flow patterns of the direction information from identification. The extracted singular points are utilized as a standard point for the matching process by connecting with the extracted information from the singular point embodied. The orthogonal coordinates which is generated by the axises of the standard point can increase the accuracy of the fingerprints matching because of minimizing the effects on the location changes of the fingerprint images.

• 대한수학회보
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• 제54권4호
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• pp.1173-1183
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• 2017

We give some sufficient conditions for the null and infinite derivatives of the $Riesz-N{\acute{a}}gy-Tak{\acute{a}}cs$ (RNT) singular function. Using these conditions, we show that the Hausdorff dimension of the set of the infinite derivative points of the RNT singular function coincides with its packing dimension which is positive and less than 1 while the Hausdorff dimension of the non-differentiability set of the RNT singular function does not coincide with its packing dimension 1.

• Journal of the Korean Data and Information Science Society
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• 제18권4호
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• pp.1135-1143
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• 2007

The singular value decomposition and the spectral decomposition are the useful methods in the area of matrix computation for multivariate techniques such as principal component analysis and multidimensional scaling. These techniques aim to find a simpler geometric structure for the data points. The singular value decomposition and the spectral decomposition are the methods being used in these techniques for this purpose. In this paper, the singular value decomposition and the spectral decomposition are compared.