Open Access
| Issue |
MATEC Web Conf.
Volume 255, 2019
Engineering Application of Artificial Intelligence Conference 2018 (EAAIC 2018)
|
|
|---|---|---|
| Article Number | 05001 | |
| Number of page(s) | 4 | |
| Section | Deep Learning and Big Data Analytic | |
| DOI | https://doi.org/10.1051/matecconf/201925505001 | |
| Published online | 16 January 2019 | |
- E Muljadi, CP Butterfield, Pitch-controlled variable- speed wind turbine generation, IEEE Trans Ind Appl, 37 (2001) 240–24. [CrossRef] [Google Scholar]
- W. Olsen, E. Walker, Experimental evidence for modifying the current physical model for ice accretion on aircraft surfaces, Third International Workshop on Atmospheric Icing of Structures, Vancouver, Canada, 1986. [Google Scholar]
- M.C. Homola, M.S. Virk, P.J. Nicklasson, Performance losses due to ice accretion for a 5 MW wind turbine, Wind Energy, 15 (2012) 379–389. [CrossRef] [Google Scholar]
- CY Huang, YE Lin, Experimental Study of Wind Turbine Blade Icing Detection System, Instrument Technique& Sensor, 6 (2014) 86–92. [Google Scholar]
- Siqin Tao, Tao Zhang, Jun Yang, Bearing fault diagnosis method based on stacked autoencoder and softmax regression, IEEE, Hangzhou, China, September 2015. [Google Scholar]
- Shao Hai dong, Jiang Hongkai, Zhao Huiwei, A novel deep auto-encoder feature learning method for rotating machinery fault diagnosis, Mechanical System and Signal Processinbg, 95(2017) 187–204. [CrossRef] [Google Scholar]
- O. Chen, D. Simig, G. Weisz, Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction, ICANN 2011: Artificial Neural Networks and Machine Learning - ICANN, (2011) 52–59. [Google Scholar]
- Jason Brownlee, Feature Importance and Feature Selection With XGBoost in Python. 23 September. 2018 <https://machinelearningmastery.com/feature-importance-and-feature-selection-with-xgboost-in-python>. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.