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At the preprocessing phase we offer a new approach of stochastic feature selection.
However, the world is facing the challenge of variable renewable energy outputs due to a stochastic feature of the energy sources.
In Fig. 6, the ELM doesn't perform well due to the strong stochastic feature of wind and the weak relationship between wind farm output and wind speed.
Broadly speaking, they can be divided into three major types, namely, energy detection, stochastic feature detection, and matched filter detection, where different types require different amounts of prior knowledge about the PU signal.
Vehicle headway distribution models are widely used in traffic engineering fields, since they reflect the fundamental uncertainty in drivers' car-following maneuvers and meanwhile provide a concise way to describe the stochastic feature of traffic flows.
Finally, sawtooth features in retraction curves are observed only infrequently (no more than 10% of retraction curves display features[46]) thus we may not have collected a sufficient number of retraction curves to observe this stochastic feature.
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Examples of both unsupervised and supervised training are deliberated for distinguishing the set of most relevant stochastic features.
Moreover, the most important stochastic features are captured at a reduced computational cost compared to the LAR method.
The random fields are found to reproduce all stochastic features of the uncertain core material in a proper manner.
Though the data are simulated, they are verified to contain similar stochastic features of actual wind farms and reflect the geographic diversity of wind power outputs.
Firstly, when the considered input set comprises a single type of stochastic features, that is, having the same principle of generation.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com