Momentum-Based Adversarial Attacks and Multi-Level Denoising Defenses in Deep Learning-Based Wind Power Forecasting.
- DOI
- 10.3390/s26072073
- Published
- 2026 Mar 26
- Container
- Sensors (Basel, Switzerland)
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3390/s26072073,
title = {Momentum-Based Adversarial Attacks and Multi-Level Denoising Defenses in Deep Learning-Based Wind Power Forecasting.},
author = {Min Y and Jiang C and Yang K and Wen X and Chen K},
year = {2026},
journal = {Sensors (Basel, Switzerland)},
doi = {10.3390/s26072073},
url = {https://doi.org/10.3390/s26072073}
}RIS
TY - JOUR TI - Momentum-Based Adversarial Attacks and Multi-Level Denoising Defenses in Deep Learning-Based Wind Power Forecasting. AU - Min Y AU - Jiang C AU - Yang K AU - Wen X AU - Chen K PY - 2026 JO - Sensors (Basel, Switzerland) DO - 10.3390/s26072073 UR - https://doi.org/10.3390/s26072073 ER -
APA
Y, M., C, J., K, Y., X, W., & K, C. (2026). Momentum-Based Adversarial Attacks and Multi-Level Denoising Defenses in Deep Learning-Based Wind Power Forecasting.. Sensors (Basel, Switzerland). https://doi.org/10.3390/s26072073
Source records
- pubmed · retrieved 2026-09-26T22:29:20.321Z