• Journal for History of Mathematics
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• v.11 no.2
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• pp.83-90
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• 1998

Observing that for any compact space X, the minimal basically disconnected cover ${\bigwedge}Λ_X$ : ${\bigwedge}Λ_X{\leftrightarro}$ is ${\sigma}Z^#$-irreducible, we will show that the projective objects in the category of compact spaces and ${\sigma}Z^#$-irreducible maps are precisely basically disconnected spaces.

• Bulletin of the Korean Mathematical Society
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• v.37 no.1
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• pp.103-108
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• 2000

Using spectral sequences we calculate the highest nonvanishing index of Tor for modules of finite projective dimension. The result is applied to compute the depth of the highest nonvanishing Tor. This is one of the cases when a problem of Auslander is positive.

• Bulletin of the Korean Mathematical Society
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• v.58 no.4
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• pp.939-958
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• 2021

Some properties of strongly Gorenstein C-projective, C-injective and C-flat modules are studied, mainly considering these properties under change of rings. Specifically, the completions of rings, the localizations and the polynomial rings are considered.

• Bulletin of the Korean Mathematical Society
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• v.60 no.5
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• pp.1295-1298
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• 2023

It is known that the complex projective space ℂℙⁿ admits a spin structure if and only if n is odd. In this paper, we provide another proof that ℂℙ^2m does not admit a spin structure, by using a circle action.

• Bulletin of the Korean Mathematical Society
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• v.60 no.6
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• pp.1705-1714
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• 2023

We explicitly construct the smooth toric Fano variety which is isomorphic to the blow-up of the projective space at torus invariant points in codimension one by anti-flips.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.391-394
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• 2005

In this paper, we present an approach that is able to reconstruct 3 dimensional metric models from un-calibrated images acquired by a freely moved camera system. If nothing is known of the calibration of either camera, nor the arrangement of one camera which respect to the other, then the projective reconstruction will have projective distortion which expressed by an arbitrary projective transformation. The distortion on the reconstruction is removed from projection to metric through self-calibration. The self-calibration requires no information about the camera matrices, or information about the scene geometry. Self-calibration is the process of determining internal camera parameters directly from multiply un-calibrated images. Self-calibration avoids the onerous task of calibrating cameras which needs to use special calibration objects. The root of the method is setting a uniquely fixed conic(absolute quadric) in 3D space. And it can make possible to figure out some way from the images. Once absolute quadric is identified, the metric geometry can be computed. We compared reconstruction image from calibrated images with the result by self-calibration method.

• Journal of Korea Multimedia Society
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• v.15 no.6
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• pp.770-783
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• 2012

In this paper, we propose a novel structure recovery algorithm in the projective space using image feature points. We use normalized image feature coordinates for the numerical stability. To acquire an initial value of the structure and motion, we decompose the scaled measurement matrix using the singular value decomposition. When recovering structure and motion in projective space, we introduce matrix decomposition constraints. In the reconstruction procedure, a nonlinear iterative optimization technique is used. Experimental results showed that the proposed method provides proper accuracy and the error deviation is small.

• Bulletin of the Korean Mathematical Society
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• v.56 no.5
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• pp.1187-1198
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• 2019

Let R be a domain with its field Q of quotients. An R-module M is said to be weak w-projective if $Ext^1_R(M,N)=0$ for all $N{\in}{\mathcal{P}}^{\dagger}_w$, where ${\mathcal{P}}^{\dagger}_w$ denotes the class of GV-torsionfree R-modules N with the property that $Ext^k_R(M,N)=0$ for all w-projective R-modules M and for all integers $k{\geq}1$. In this paper, we define a domain R to be w-Matlis if the weak w-projective dimension of the R-module Q is ${\leq}1$. To characterize w-Matlis domains, we introduce the concept of w-Matlis cotorsion modules and study some basic properties of w-Matlis modules. Using these concepts, we show that R is a w-Matlis domain if and only if $Ext^k_R(Q,D)=0$ for any ${\mathcal{P}}^{\dagger}_w$-divisible R-module D and any integer $k{\geq}1$, if and only if every ${\mathcal{P}}^{\dagger}_w$-divisible module is w-Matlis cotorsion, if and only if w.w-pdRQ/$R{\leq}1$.

• Journal of applied mathematics & informatics
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• v.41 no.5
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• pp.989-999
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• 2023

The aim of this paper is to study the projective curvature tensor field of the Curvature tensor Rⁱ_jkh on a recurrent non Riemannian space admitting recurrent affine motion, which is also decomposable in the form Rⁱ_jkh=Xⁱ Y_jkh, where Xⁱ and Y_jkh are non-null vector and tensor respectively. In this paper we decompose Special Pseudo Projective Curvature Tensor Field. In the sequal of decomposition we established several properties of such decomposed tensor fields. We have considered the curvature tensor field Rⁱ_jkh in a Finsler space equipped with non symmetric connection and we study the decomposition of such field. In a special Pseudo recurrent Finsler Space, if the arbitrary tensor field 𝜓ⁱ_j is assumed to be a covariant constant then, in view of the decomposition rule, 𝜙_kh behaves as a recurrent tensor field. In the last, we have considered the decomposition of curvature tensor fields in Kaehlerian recurrent spaces and have obtained several related theorems.

• The Korean Journal of Applied Statistics
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• v.35 no.5
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• pp.657-666
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• 2022

An informative predictor subspace is useful to estimate the central subspace, when conditions required in usual suffcient dimension reduction methods fail. Recently, for multivariate regression, Ko and Yoo (2022) newly defined a projective-resampling informative predictor subspace, instead of the informative predictor subspace, by the adopting projective-resampling method (Li et al. 2008). The new space is contained in the informative predictor subspace but contains the central subspace. In this paper, a method directly to estimate the informative predictor subspace is proposed, and it is compapred with the method by Ko and Yoo (2022) through theoretical aspects and numerical studies. The numerical studies confirm that the Ko-Yoo method is better in the estimation of the central subspace than the proposed method and is more efficient in sense that the former has less variation in the estimation.