Zobrazeno 1 - 10
of 139
pro vyhledávání: '"LI ZhiNong"'
Autor:
LI ZhiNong, WU WeiXiao
Publikováno v:
Jixie qiangdu, Vol 41, Pp 840-844 (2019)
The empirical ridgelet transform has the ability of direction selectivity and adaptive decomposition. 2 DPCA can directly use the original image toconstruct the covariance matrix. Combined with the advantages of Empirical ridgelet transform and 2 DPC
Externí odkaz:
https://doaj.org/article/e1d0a4e9bb984b30ba11e015992eb329
Publikováno v:
Jixie qiangdu, Vol 39, Pp 239-246 (2017)
In this paper,the slant crack is used as the object of study,nonlinear output frequency response function（ NOFRF） is introduced to fault diagnosis of rotor system with slant crack. The NOFRF values of slant crack changing with different crack ang
Externí odkaz:
https://doaj.org/article/09551840f4ad40e8b983bc27a88b77a0
Publikováno v:
Jixie qiangdu, Vol 38, Pp 1-5 (2016)
Grouplet transform is a new directional wavelet. This wavelet can be transformed at any time and space,and adaptively change the basis according to image texture. Therefore Grouplet transform has a good ability of sparse representation.Here,Grouplet
Externí odkaz:
https://doaj.org/article/e0d8239b7a5a44d4a86ed0b80a3f2e11
Publikováno v:
Jixie qiangdu, Vol 37, Pp 393-397 (2015)
Based on the deficiency in the traditional nonlinear blind separation method of mechanical fault sources,i. e. the separation matrix parameter and nonlinear mixing parameter in the nonlinear blind source separation are usually optimized separately,wh
Externí odkaz:
https://doaj.org/article/76f433039ae04b5ba504917b30d5f9b5
Publikováno v:
In Mechanical Systems and Signal Processing 15 January 2025 223
Publikováno v:
In Renewable Energy 15 February 2025 240
A fully automatic bearing fault diagnosis method based on an improved polar coordinate image texture
Publikováno v:
In Mechanical Systems and Signal Processing 1 February 2025 224
Publikováno v:
In Applied Soft Computing September 2024 163
Publikováno v:
MATEC Web of Conferences, Vol 39, p 02004 (2016)
The useful information extracted from fracture images is the most fundamental problem of quantitative analysis and intelligent diagnosis of metal fracture. The pattern recognition or classification is the critical issue of failure analysis of metal f
Externí odkaz:
https://doaj.org/article/f163453d09c84c46b860821c2cdf4118
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.