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3491-3500 trong số 5190 kết quả
DDxNet [electronic resource] : a deep learning model for automatic interpretation of electronic health records, electrocardiograms and electroencephalograms
Tác giả:
Xuất bản: Washington, D.C. : Oak Ridge, Tenn: United States. National Nuclear Security Administration ; Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy , 2020
Bộ sưu tập: Báo, Tạp chí
ddc:  616.12
 
A Bayesian Approach for Estimating Uncertainty in Stochastic Economic Dispatch considering Wind Power Penetration [elect...
Tác giả:
Xuất bản: Washington, D.C. : Oak Ridge, Tenn: United States. National Nuclear Security Administration ; Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy , 2020
Bộ sưu tập: Báo, Tạp chí
ddc:  621.645
 
Diagnostic Models for Wind Turbine Gearbox Components Using SCADA Time Series Data [electronic resource] : Preprint
Tác giả:
Xuất bản: Washington, D.C. : Oak Ridge, Tenn: United States. Dept. of Energy. Office of Energy Efficiency and Renewable Energy ; Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy , 2018
Bộ sưu tập: Báo, Tạp chí
ddc:  621.45
 
Probabilistic Short-Term Wind Forecasting Based on Pinball Loss Optimization [electronic resource] : Preprint
Tác giả:
Xuất bản: Washington, D.C. : Oak Ridge, Tenn: United States. Dept. of Energy. Office of Energy Efficiency and Renewable Energy ; Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy , 2018
Bộ sưu tập: Báo, Tạp chí
ddc:  333.914
 
Implementing Machine Learning in the PCWG Tool [electronic resource]
Tác giả:
Xuất bản: Washington, D.C. : Oak Ridge, Tenn: United States. Dept. of Energy. Office of Energy Efficiency and Renewable Energy ; Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy , 2016
Bộ sưu tập: Báo, Tạp chí
ddc:  621.45
 
Wind turbine gearbox fault prognosis using high-frequency SCADA data [electronic resource]
Tác giả:
Xuất bản: Washington, D.C. : Oak Ridge, Tenn: United States. Dept. of Energy. Office of Energy Efficiency and Renewable Energy ; Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy , 2022
Bộ sưu tập: Báo, Tạp chí
ddc:  621.47
 
Assessing boundary condition and parametric uncertainty in numerical-weather-prediction-modeled, long-term offshore wind speed through machine learning and analog ensemble [electronic resource]
Tác giả:
Xuất bản: Washington, D.C. : Oak Ridge, Tenn: United States. Dept. of Energy. Office of Energy Efficiency and Renewable Energy ; Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy , 2021
Bộ sưu tập: Báo, Tạp chí
ddc:  621.45
 
Proof-of-concept of a reinforcement learning framework for wind farm energy capture maximization in time-varying wind [electronic resource]
Tác giả:
Xuất bản: Washington, D.C. : Oak Ridge, Tenn: United States. Dept. of Energy. Office of Energy Efficiency and Renewable Energy ; Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy , 2021
Bộ sưu tập: Báo, Tạp chí
ddc:  333.8
 
New methods to improve the vertical extrapolation of near-surface offshore wind speeds [electronic resource]
Tác giả:
Xuất bản: Washington, D.C. : Oak Ridge, Tenn: United States. Dept. of Energy. Office of Energy Efficiency and Renewable Energy ; Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy , 2021
Bộ sưu tập: Báo, Tạp chí
ddc:  621.45
 
Utilizing physics-based input features within a machine learning model to predict wind speed forecasting error [electronic resource]
Tác giả:
Xuất bản: Richland, Wash. : Oak Ridge, Tenn: Pacific Northwest National Laboratory (U.S.) ; Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy , 2021
Bộ sưu tập: Báo, Tạp chí
ddc:  333.9
 

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