The Experts below are selected from a list of 66 Experts worldwide ranked by ideXlab platform

Michio Yoshiwaki - One of the best experts on this subject based on the ideXlab platform.

  • Relative Derived Dimensions for cotilting modules
    Journal of Algebra, 2017
    Co-Authors: Michio Yoshiwaki
    Abstract:

    Abstract For a Noetherian ring R and a cotilting R-module T of injective Dimension at least 1, we prove that the Derived Dimension of R with respect to the category X T is precisely the injective Dimension of T by applying Auslander–Buchweitz theory and Ghost Lemma. In particular, when R is a commutative Noetherian Cohen–Macaulay local ring with a canonical module ω R and dim ⁡ R ≥ 1 , the Derived Dimension of R with respect to the category of maximal Cohen–Macaulay modules is precisely dim ⁡ R .

  • Relative Derived Dimensions for cotilting modules
    arXiv: Representation Theory, 2016
    Co-Authors: Michio Yoshiwaki
    Abstract:

    For a Noetherian ring $R$ and a cotilting $R$-module $T$ of injective Dimension at least $1$, we prove that the Derived Dimension of $R$ with respect to the category $\mathcal{X}_T$ is precisely the injective Dimension of $T$ by applying Auslander-Buchweitz theory and Ghost Lemma. In particular, when $R$ is a commutative Noetherian local ring with a canonical module $\omega_R$ and $\dim R\ge1$, the Derived Dimension of R with respect to the category of maximal Cohen-Macaulay modules is precisely $\dim R$.

Peter J.t. Verheijen - One of the best experts on this subject based on the ideXlab platform.

  • Influence of noise on power-law scaling functions and an algorithm for Dimension estimations
    Physical Review E, 1997
    Co-Authors: Hans Oltmans, Peter J.t. Verheijen
    Abstract:

    The influence of Gaussian noise on power-law scaling functions of interpoint distances has been investigated. These functions appear in the estimation of the correlation Dimension a of the attractor of a chaotic dynamical system, where the relative number of pairwise distances smaller than r ~correlation integral! theoretically scales as r a . Assuming the noise added to each measurement is independent and the distribution of the distances is governed completely by the power-law scaling rule in the noise-free case, the scaling functions of the perturbed distances have been calculated exactly. By considering the limiting cases for small and large distances, a method is presented to estimate the variance of the added noise and approximations of the scaling functions, which are suitable for data analysis, are Derived. Dimension estimation can be improved by applying a nonlinear fit procedure to histograms of interpoint distances instead of the usual linear regression on log-log plots. @S1063-651X~97!04506-6#

Pu Zhang - One of the best experts on this subject based on the ideXlab platform.

Hans Oltmans - One of the best experts on this subject based on the ideXlab platform.

  • Influence of noise on power-law scaling functions and an algorithm for Dimension estimations
    Physical Review E, 1997
    Co-Authors: Hans Oltmans, Peter J.t. Verheijen
    Abstract:

    The influence of Gaussian noise on power-law scaling functions of interpoint distances has been investigated. These functions appear in the estimation of the correlation Dimension a of the attractor of a chaotic dynamical system, where the relative number of pairwise distances smaller than r ~correlation integral! theoretically scales as r a . Assuming the noise added to each measurement is independent and the distribution of the distances is governed completely by the power-law scaling rule in the noise-free case, the scaling functions of the perturbed distances have been calculated exactly. By considering the limiting cases for small and large distances, a method is presented to estimate the variance of the added noise and approximations of the scaling functions, which are suitable for data analysis, are Derived. Dimension estimation can be improved by applying a nonlinear fit procedure to histograms of interpoint distances instead of the usual linear regression on log-log plots. @S1063-651X~97!04506-6#

M. Rostami - One of the best experts on this subject based on the ideXlab platform.