• 제목/요약/키워드: invariant

검색결과 2,151건 처리시간 0.031초

Mutual Detectability and System Enlargement of Detection Filters: An Invariant Zero Approach

  • Kim, Yong-Min;Park, Jae-Hong
    • International Journal of Control, Automation, and Systems
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    • 제4권6호
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    • pp.769-774
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    • 2006
  • In this paper, we discuss the problem of non-mutual detectability using the invariant zero. We propose a representation method for excess spaces by linear equation based on the Rosenbrock system matrix. As an alternative to the system enlargement method proposed by White[1], we propose an appropriate form of an enlarged system to make a set of faults mutually detectable by assigning sufficient geometric multiplicity of invariant zeros. We show the equivalence between the two methods and a necessary condition for the system enlargement in terms of the geometric and algebraic multiplicities of invariant zeros.

Shadow and Highlight Invariant Color Models

  • 이자용;강훈
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 2005년도 추계종합학술대회
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    • pp.557-560
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    • 2005
  • The color of objects varies with changes in illuminant color and viewing conditions. As a consequence, color boundaries are influenced by a large variety of imaging variables such as shadows, highlights, illumination, and material changes. Therefore, invariant color models are useful for a large number of applications such as object recognitions, detections, and segmentations. In this paper, we propose invariant color models. These color models are independent of the object geometry, object pose, and illumination. From these color models, color invariant edges are derived. To show the validity of the proposed invariant color models, some examples are given.

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수중 음향 측정을 위한 새로운 임계치 함수에 의한 TI 웨이블렛 잡음제거 기법 (Translation-invariant Wavelet Denoising Method Based on a New Thresholding Function for Underwater Acoustic Measurement)

  • 최재용
    • 한국소음진동공학회논문집
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    • 제16권11호
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    • pp.1149-1157
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    • 2006
  • Donoho et al. suggested a wavelet thresholding denoising method based on discrete wavelet transform. This paper proposes an improved denoising method using a new thresholding function based on translation-invariant wavelet for underwater acoustic measurement. The conventional wavelet thresholding denoising method causes Pseudo-Gibbs phenomena near singularities due to the lack of translation-invariant of the wavelet basis. To suppress Pseudo-Gibbs phenomena, a denoising method combining a new thresholding function based on the translation-invariant wavelet transform is proposed in this paper. The new thresholding function is a modified hard-thresholding to each node according to the discriminated threshold so as to reject unknown external noise and white gaussian noise. The experimental results show that the proposed method can effectively eliminate noise, extract characteristic information of radiated noise signals.

T-STRUCTURE AND THE YAMABE INVARIANT

  • Sung, Chan-Young
    • 대한수학회보
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    • 제49권2호
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    • pp.435-443
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    • 2012
  • The Yamabe invariant is a topological invariant of a smooth closed manifold, which contains information about possible scalar curvature on it. It is well-known that a product manifold $T^m{\times}B$ where $T^m$ is the m-dimensional torus, and B is a closed spin manifold with nonzero $\^{A}$-genus has zero Yamabe invariant. We generalize this to various T-structured manifolds, for example $T^m$-bundles over such B whose transition functions take values in Sp(m, $\mathbb{Z}$) (or Sp(m - 1, $\mathbb{Z}$) ${\oplus}\;{{\pm}1}$ for odd m).

NEW LOOK AT THE CONSTRUCTIONS OF MULTIWAVELET FRAMES

  • Kim, Hong-Oh;Kim, Rae-Young;Lim, Jae-Kun
    • 대한수학회보
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    • 제47권3호
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    • pp.563-573
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    • 2010
  • Using the fiberization technique of a shift-invariant space and the matrix characterization of the decomposition of a shift-invariant space of finite length into an orthogonal sum of singly generated shift-invariant spaces, we show that the main result in [13] can be interpreted as a statement about the length of a shift-invariant space, and give a more natural construction of multiwavelet frames from a frame multiresolution analysis of $L^2(\mathbb{R}^d)$.