Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Kaixuan Liang"'
Publikováno v:
Engineering Reports, Vol 6, Iss 12, Pp n/a-n/a (2024)
Abstract The visualization method for mechanics knowledge in civil engineering courses is very important, which can help students learn abstract mechanics. Combined with the structural analysis software, this paper develops a visualization teaching a
Externí odkaz:
https://doaj.org/article/9c820d461f834a09b6259ee634d23396
Publikováno v:
Shuiwen dizhi gongcheng dizhi, Vol 51, Iss 2, Pp 58-65 (2024)
Perchlorate (\begin{document}${\mathrm{ClO}}_4^- $\end{document}) is a new persistent pollutant with small molecular weight, rapid diffusion, and high stability. It is widely found in the environment, especially in the areas where fireworks are produ
Externí odkaz:
https://doaj.org/article/c4a05dbac3b84d25b806824cdfbfd715
Publikováno v:
BioResources, Vol 18, Iss 4, Pp 7124-7142 (2023)
As a typical orthotropic material, the mechanical properties of wood in the parallel and perpendicular-to-grain directions are very different. Based on mathematical orthogonality, mechanical force balance condition, and energy comparability, the defo
Externí odkaz:
https://doaj.org/article/f7378be324b34170ac4c79b8f0b2f35b
Publikováno v:
IEEE Transactions on Industrial Informatics. 18:7198-7207
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::79d76dc300d36e222bd784b1b6a50469
https://doi.org/10.1109/tii.2021.3133938
https://doi.org/10.1109/tii.2021.3133938
Publikováno v:
IEEE Transactions on Industrial Electronics. 69:6267-6277
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f8c4d96fbbc5a9851e9f0c59148cd947
https://doi.org/10.1109/tie.2021.3090713
https://doi.org/10.1109/tie.2021.3090713
Publikováno v:
Neural Networks. 145:331-341
Driven by industrial big data and intelligent manufacturing, deep learning approaches have flourished and yielded impressive achievements in the community of machine fault diagnosis. Nevertheless, current diagnosis models trained on a specific datase
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a8a14dbcad08352d14f5551e17c24062
https://doi.org/10.1016/j.neunet.2021.11.003
https://doi.org/10.1016/j.neunet.2021.11.003
Publikováno v:
Materials; Volume 15; Issue 18; Pages: 6391
Identifying relatively weak areas is of great significance for improving the seismic reliability of structures. In this paper, a modal strain energy decomposition method is proposed, which can realize the decoupling of the comprehensive modal strain
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6df1ccb08ce71642974f3ab81f745c86
https://pubmed.ncbi.nlm.nih.gov/36143703
https://pubmed.ncbi.nlm.nih.gov/36143703
Publikováno v:
Journal of Intelligent Manufacturing. 33:2207-2222
Behind the brilliance of the deep diagnosis models, the issue of distribution discrepancy between source training data and target test data is being gradually concerned for catering to more practical and urgent diagnostic requirements. Consequently,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0950eb89e3c52a0c677295d8f3e36543
https://doi.org/10.1007/s10845-021-01777-0
https://doi.org/10.1007/s10845-021-01777-0
Publikováno v:
IEEE Sensors Journal. 20:12252-12261
Envelope demodulation based on vibration data is widely used for the fault detection of rolling element bearing, which yet largely relies on the high signal to noise ratio of signal. In practical scenarios, because of the existence of various interfe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3070fe106da777997b5dbef51153d5c2
https://doi.org/10.1109/jsen.2020.2999107
https://doi.org/10.1109/jsen.2020.2999107
Publikováno v:
IEEE Transactions on Industrial Electronics. 67:7982-7993
This article proposes a novel three-stage condition assessment scheme of rotating machinery using internal encoder data rather than traditional external vibration data. In this article, periodical group sparse derivatives (PGSD)-based signal denoisin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::07dbc22ae83f9f45e201c69e0f98ff23
https://doi.org/10.1109/tie.2019.2941172
https://doi.org/10.1109/tie.2019.2941172