scholarly journals Detection of the Stator Inter-Turn Fault Using the Energy Feature of the Wavelet Coefficients Obtained by Continuous Wavelet Transform

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Saba M. Hosseini ◽  
Mostafa Abedi
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Continuous Wavelet ◽  
Wavelet Coefficients ◽  
Energy Feature

BESOV NORMS IN TERMS OF THE CONTINUOUS WAVELET TRANSFORM. APPLICATION TO STRUCTURE FUNCTIONS

1996 ◽  
Vol 06 (05) ◽  
pp. 649-664 ◽  
Author(s):  
VALÉRIE PERRIER ◽  
CLAUDE BASDEVANT
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Besov Spaces ◽  
Inversion Formula ◽  
Structure Functions ◽  
Continuous Wavelet ◽  
Wavelet Coefficients ◽  
Small Scales ◽  
Besov Norms

The continuous wavelet transform is extended to Lp spaces and an inversion formula is demonstrated. From this the Besov spaces can be characterized by the behavior at small scales of the wavelet coefficients. These results apply to the measurement of structure functions.

Preprocessing of Coefficients for Reusable Continuous Wavelet Transform

2014 ◽  
Vol 1040 ◽  
pp. 975-979 ◽  
Author(s):  
Alexander A. Khamukhin ◽  
Alexey A. Khamukhin
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Computing Time ◽  
Continuous Wavelet ◽  
Wavelet Coefficients ◽  
Nonstationary Signal ◽  
Online Detection ◽  
Second Stage ◽  
Time Required ◽  
Two Stages

The division into two stages of the Continuous Wavelet Transform (CWT) computing is proposed. This is expedient in circumstances when CWT is repeated many times, e.g., for online detection of nonstationary signal singularities. It is shown that the preprocessing of wavelet coefficients in the first stage can significantly reduce computing time required in the second stage. The comparative estimation of the runtime reduction in the second stage of CWT calculation is deduced.

Continue Wavelet Transform's Analysis in Zhuji Station's Rainfall Sequence Transformation

2014 ◽  
Vol 574 ◽  
pp. 708-711
Author(s):  
Hui Fang Guo ◽  
Zheng Dong Sun ◽  
Yu Liang Chen
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Transformation Process ◽  
Monthly Rainfall ◽  
Small Scale ◽  
Continuous Wavelet ◽  
Wavelet Coefficients ◽  
Real Component ◽  
Sequence Transformation

In this paper, it uses continuous wavelet transform in analysing zhuji station monthly rainfall. from the transformed wavelet coefficients’ real component, variance and modulus square, we can get the main measure contained in zhuji’s monthly rainfall sequence. By anlysing the transformation process of continuous wavelet transform coefficien’s real part of various scales, we can get zhuji station’s monthly rainfall sequence wet-dry transformation process of various scales. By caculated , we found zhuji station’s monthly rainfall sequence contains 10 month , 171 month and 393 month scale. In large scales, zhuji station’s rainfall is in hemiplegia period from 2013 to 2021. in small scale, zhuji station’s rainfall shifts strongly.

WAVELET CHARACTERIZATIONS OF MULTI-DIRECTIONAL REGULARITY

Fractals ◽  
2012 ◽  
Vol 20 (03n04) ◽  
pp. 245-256 ◽  
Author(s):  
MOURAD BEN SLIMANE
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Hölder Regularity ◽  
Continuous Wavelet ◽  
Wavelet Coefficients ◽  
Several Variables ◽  
Directional Regularity ◽  
Holder Regularity ◽  
Pointwise Regularity

The study of d dimensional traces of functions of m several variables leads to directional behaviors. The purpose of this paper is two-fold. Firstly, we extend the notion of one direction pointwise Hölder regularity introduced by Jaffard to multi-directions. Secondly, we characterize multi-directional pointwise regularity by Triebel anisotropic wavelet coefficients (resp. leaders), and also by Calderón anisotropic continuous wavelet transform.

The continuous wavelet transform with rotations

2005 ◽  
Vol 4 (1) ◽  
pp. 45-55
Author(s):  
Jaime Navarro ◽  
Miguel Angel Alvarez
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Continuous Wavelet

scholarly journals Continuous Wavelet Transform of Schwartz Distributions in DL2′(Rn), n ≥ 1

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1106
Author(s):  
Jagdish N. Pandey
Keyword(s):  
Wavelet Transform ◽  
Function Space ◽  
Continuous Wavelet Transform ◽  
Inversion Formula ◽  
Continuous Wavelet ◽  
The Real ◽  
Schwartz Distributions ◽  
Distributional Sense ◽  
Wavelet Inversion

We define a testing function space DL2(Rn) consisting of a class of C∞ functions defined on Rn, n≥1 whose every derivtive is L2(Rn) integrable and equip it with a topology generated by a separating collection of seminorms {γk}|k|=0∞ on DL2(Rn), where |k|=0,1,2,… and γk(ϕ)=∥ϕ(k)∥2,ϕ∈DL2(Rn). We then extend the continuous wavelet transform to distributions in DL2′(Rn), n≥1 and derive the corresponding wavelet inversion formula interpreting convergence in the weak distributional sense. The kernel of our wavelet transform is defined by an element ψ(x) of DL2(Rn)∩DL1(Rn), n≥1 which, when integrated along each of the real axes X1,X2,…Xn vanishes, but none of its moments ∫Rnxmψ(x)dx is zero; here xm=x1m1x2m2⋯xnmn, dx=dx1dx2⋯dxn and m=(m1,m2,…mn) and each of m1,m2,…mn is ≥1. The set of such wavelets will be denoted by DM(Rn).

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