Industrial Data Imputation Based on Multiscale Spatiotemporal Information Embedding With Asymmetrical Transformer
- DOI
- 10.1109/tnnls.2025.3527581
- Published
- 2025-08
- Container
- IEEE Transactions on Neural Networks and Learning Systems
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/tnnls.2025.3527581,
title = {Industrial Data Imputation Based on Multiscale Spatiotemporal Information Embedding With Asymmetrical Transformer},
author = {Xing-Yuan Li and Yuan Xu and Qun-Xiong Zhu and Yan-Lin He},
year = {2025},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
doi = {10.1109/tnnls.2025.3527581},
url = {https://doi.org/10.1109/tnnls.2025.3527581}
}RIS
TY - JOUR TI - Industrial Data Imputation Based on Multiscale Spatiotemporal Information Embedding With Asymmetrical Transformer AU - Xing-Yuan Li AU - Yuan Xu AU - Qun-Xiong Zhu AU - Yan-Lin He PY - 2025 JO - IEEE Transactions on Neural Networks and Learning Systems DO - 10.1109/tnnls.2025.3527581 UR - https://doi.org/10.1109/tnnls.2025.3527581 ER -
APA
Li, X., Xu, Y., Zhu, Q., & He, Y. (2025). Industrial Data Imputation Based on Multiscale Spatiotemporal Information Embedding With Asymmetrical Transformer. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/tnnls.2025.3527581
Source records
- crossref · retrieved 2026-11-07T16:10:35.314Z