Privacy-preserving for assembly deviation prediction in a machine learning model of hydraulic equipment under value chain collaboration.
- DOI
- 10.1038/s41598-022-14835-1
- Published
- 2022 Jun 24
- Container
- Scientific reports
- Publisher
- Not recorded
- Open access
- yes
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limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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Cite this work
BibTeX
@article{allodium:10.1038/s41598-022-14835-1,
title = {Privacy-preserving for assembly deviation prediction in a machine learning model of hydraulic equipment under value chain collaboration.},
author = {Qiu H and Feng Y and Hong Z and Li K and Tan J},
year = {2022},
journal = {Scientific reports},
doi = {10.1038/s41598-022-14835-1},
url = {https://doi.org/10.1038/s41598-022-14835-1}
}RIS
TY - JOUR TI - Privacy-preserving for assembly deviation prediction in a machine learning model of hydraulic equipment under value chain collaboration. AU - Qiu H AU - Feng Y AU - Hong Z AU - Li K AU - Tan J PY - 2022 JO - Scientific reports DO - 10.1038/s41598-022-14835-1 UR - https://doi.org/10.1038/s41598-022-14835-1 ER -
APA
H, Q., Y, F., Z, H., K, L., & J, T. (2022). Privacy-preserving for assembly deviation prediction in a machine learning model of hydraulic equipment under value chain collaboration.. Scientific reports. https://doi.org/10.1038/s41598-022-14835-1
Source records
- pubmed · retrieved 2026-09-25T19:37:10.366Z