Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Mingtao Ge"'
Publikováno v:
Shock and Vibration, Vol 2018 (2018)
A rolling bearing fault diagnosis method based on ensemble local characteristic-scale decomposition (ELCD) and extreme learning machine (ELM) is proposed. Vibration signals were decomposed using ELCD, and numerous intrinsic scale components (ISCs) we
Externí odkaz:
https://doaj.org/article/6f827c6d610c4feba539954b2484a074
Publikováno v:
Applied Sciences, Vol 9, Iss 10, p 2126 (2019)
Aimed at the problems of high redundancy of trajectory and susceptibility to background interference in traditional dense trajectory behavior recognition methods, a human action recognition method based on foreground trajectory and motion difference
Externí odkaz:
https://doaj.org/article/c99166a915c6416fb46f7656260c0c0c
Publikováno v:
Symmetry, Vol 10, Iss 12, p 730 (2018)
Because of the cyclic symmetric structure of rolling bearings, its vibration signals are regular when the rolling bearing is working in a normal state. But when the rolling bearing fails, whether the outer race fault or the inner race fault, the symm
Externí odkaz:
https://doaj.org/article/fc6f407491aa45c68983ed5f767cf257
Publikováno v:
Entropy, Vol 20, Iss 6, p 455 (2018)
According to the dynamic characteristics of the rolling bearing vibration signal and the distribution characteristics of its noise, a fault identification method based on the adaptive filtering empirical wavelet transform (AFEWT) and kernel density e
Externí odkaz:
https://doaj.org/article/e31cdfb71e3b48659733676065b3c5bd
Publikováno v:
Entropy, Vol 19, Iss 12, p 633 (2017)
This study proposes a novel fault diagnosis method that is based on empirical wavelet transform (EWT) and kernel density estimation classifier (KDEC), which can well diagnose fault type of the rolling element bearings. With the proposed fault diagnos
Externí odkaz:
https://doaj.org/article/025581b9c6e34f0fb7a866349a9aa9f5
Publikováno v:
2022 3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fd7bb2d7d2ad41f7542d4417fed44bb0
https://doi.org/10.1109/icbaie56435.2022.9985814
https://doi.org/10.1109/icbaie56435.2022.9985814
Publikováno v:
Infrared Physics & Technology. 101:13-24
The dim and small target detection in complex background is a challenging problem. Conventional methods may lead to high false alarm rates when targets are extremely dim and almost approximate to background noise. In this paper, a novel method named
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a660e79ff7c0a63e987cf7fe01184b74
https://doi.org/10.1016/j.infrared.2019.05.010
https://doi.org/10.1016/j.infrared.2019.05.010
Publikováno v:
Applied Sciences, Vol 9, Iss 10, p 2126 (2019)
Applied Sciences
Volume 9
Issue 10
Applied Sciences
Volume 9
Issue 10
Aimed at the problems of high redundancy of trajectory and susceptibility to background interference in traditional dense trajectory behavior recognition methods, a human action recognition method based on foreground trajectory and motion difference
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ef5fa2cb147741692b5bc1cf47da65b4
https://www.mdpi.com/2076-3417/9/10/2126
https://www.mdpi.com/2076-3417/9/10/2126
Autor:
Hongzhi Liu, Mingtao Ge
Publikováno v:
Journal of Materials Chemistry A. 4:16714-16722
A novel hybrid nanoporous network was easily fabricated by the one-step Heck reaction of octavinylsilsesquioxane with dibromothiophene. FTIR, solid state CP-MAS-NMR (13C and 29Si), and elemental analysis indicate that a thiophene unit has been succes
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8c1cf6da1e7a4e5878c4311d9b57ee2f
https://doi.org/10.1039/c6ta06656a
https://doi.org/10.1039/c6ta06656a
Publikováno v:
Entropy, Vol 20, Iss 6, p 455 (2018)
Entropy
Volume 20
Issue 6
Entropy
Volume 20
Issue 6
According to the dynamic characteristics of the rolling bearing vibration signal and the distribution characteristics of its noise, a fault identification method based on the adaptive filtering empirical wavelet transform (AFEWT) and kernel density e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::78d18d2d8436d441cd1fffe3f0ad8c26
http://www.mdpi.com/1099-4300/20/6/455
http://www.mdpi.com/1099-4300/20/6/455