• Title/Summary/Keyword: complex manifold

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Exhaust Flow Characteristics of Catalytic Converter Adapted to Exhaust Manifold (배기매니폴드 직접부착 촉매장치의 배기 유동특성)

  • Park, Young-Cheol;Lee, Chang-Sik
    • Transactions of the Korean Society of Mechanical Engineers B
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    • v.27 no.7
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    • pp.837-844
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    • 2003
  • The exhaust gas flow in the inlet collector of close coupled catalyst(CCC) adapted to the exhaust manifold is very complex flow because the exhaust gas is a pulsation flow with several port flow. The distribution of gas flow and temperature in inlet collector effect to the efficiency of catalytic converter. In this study, it measures temperatures on several point in inlet collector with two kind of inlet collector volume. And it analyzes with CFD to exhaust manifold and close coupled catalyst for temperature and flow. Comparing to measured and analyzed result, it find increasing of collector volume effects to catalyst temperature distribution and uniformity of catalytic converter

On characterizations of real hypersurfaces of type B in a complex hyperbolic space

  • Ahn, Seong-Soo;Suh, Young-Jin
    • Journal of the Korean Mathematical Society
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    • v.32 no.3
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    • pp.471-482
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    • 1995
  • A complex n-dimensional Kaehlerian manifold of constant holomorphic sectional curvature c is called a comples space form, which is denoted by $M_n(c)$. A complete and simply connected complex space form consists of a complex projective space $P_nC$, a complex Euclidean space $C^n$ or a complex hyperbolic space $H_nC$, according as c > 0, c = 0 or c < 0. The induced almost contact metric structure of a real hypersurface M of $M_n(c)$ is denoted by $(\phi, \zeta, \eta, g)$.

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On real hypersurfaces of a complex hyperbolic space

  • Kang, Eun-Hee;Ki, U-Hang
    • Bulletin of the Korean Mathematical Society
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    • v.34 no.2
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    • pp.173-184
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    • 1997
  • An n-dimensional complex space form $M_n(c)$ is a Kaehlerian manifold of constant holomorphic sectional curvature c. As is well known, complete and simply connected complex space forms are a complex projective space $P_n C$, a complex Euclidean space $C_n$ or a complex hyperbolic space $H_n C$ according as c > 0, c = 0 or c < 0.

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Totally real submanifolds with parallel mean curvature vector in a complex space form

  • Ki, U-Hang;Kim, Byung-Hak;Kim, He-Jin
    • Journal of the Korean Mathematical Society
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    • v.32 no.4
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    • pp.835-848
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    • 1995
  • Let $M_n$(c) be an n-dimensional complete and simply connected Kahlerian manifold of constant holomorphic sectional curvature c, which is called a complex space form. Then according to c > 0, c = 0 or c < 0 it is a complex projective space $P_nC$, a complex Euclidean space $C^n$ or a complex hyperbolic space $H_nC$.

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View-Invariant Body Pose Estimation based on Biased Manifold Learning (편향된 다양체 학습 기반 시점 변화에 강인한 인체 포즈 추정)

  • Hur, Dong-Cheol;Lee, Seong-Whan
    • Journal of KIISE:Software and Applications
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    • v.36 no.11
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    • pp.960-966
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    • 2009
  • A manifold is used to represent a relationship between high-dimensional data samples in low-dimensional space. In human pose estimation, it is created in low-dimensional space for processing image and 3D body configuration data. Manifold learning is to build a manifold. But it is vulnerable to silhouette variations. Such silhouette variations are occurred due to view-change, person-change, distance-change, and noises. Representing silhouette variations in a single manifold is impossible. In this paper, we focus a silhouette variation problem occurred by view-change. In previous view invariant pose estimation methods based on manifold learning, there were two ways. One is modeling manifolds for all view points. The other is to extract view factors from mapping functions. But these methods do not support one by one mapping for silhouettes and corresponding body configurations because of unsupervised learning. Modeling manifold and extracting view factors are very complex. So we propose a method based on triple manifolds. These are view manifold, pose manifold, and body configuration manifold. In order to build manifolds, we employ biased manifold learning. After building manifolds, we learn mapping functions among spaces (2D image space, pose manifold space, view manifold space, body configuration manifold space, 3D body configuration space). In our experiments, we could estimate various body poses from 24 view points.