Measured multi-source semi-supervised working condition recognition based on curvelet pooling and attention mechanism learning.

Yang S, Zhou B, Wang Y, Liu W

DOI: 10.1038/s41598-025-23732-2
Published: 2025 Nov 14
Container: Scientific reports
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1038/s41598-025-23732-2,
  title = {Measured multi-source semi-supervised working condition recognition based on curvelet pooling and attention mechanism learning.},
  author = {Yang S and Zhou B and Wang Y and Liu W},
  year = {2025},
  journal = {Scientific reports},
  doi = {10.1038/s41598-025-23732-2},
  url = {https://doi.org/10.1038/s41598-025-23732-2}
}

RIS

TY  - JOUR
TI  - Measured multi-source semi-supervised working condition recognition based on curvelet pooling and attention mechanism learning.
AU  - Yang S
AU  - Zhou B
AU  - Wang Y
AU  - Liu W
PY  - 2025
JO  - Scientific reports
DO  - 10.1038/s41598-025-23732-2
UR  - https://doi.org/10.1038/s41598-025-23732-2
ER  -

APA

S, Y., B, Z., Y, W., & W, L. (2025). Measured multi-source semi-supervised working condition recognition based on curvelet pooling and attention mechanism learning.. Scientific reports. https://doi.org/10.1038/s41598-025-23732-2

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pubmed · retrieved 2026-11-10T11:48:01.727Z