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Using observed data, ensemble members are assimilated to production data and then we can get reasonable uncertainty ranges for future performances from multiple models.
Moreover, since in this approach the POD modes are used to observe rather than span the true system dynamics, the computation is much less sensitive than POD-Galerkin to values of the system parameters (e.g., the Reynolds number) and the particular simulation data ensemble used to obtain the POD basis functions.
During node aggregation, the data ensemble process also takes place.
Data ensemble also takes place while aggregating the nodes.
The data have associated errors which are used to generate a perturbed data ensemble.
It offers an energy-efficient routing in WSN based on the effective data ensemble and optimal clustering.
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In the presence of limited amounts of labeled data, ensemble-based semi-supervised approaches can successfully leverage the unlabeled data to enhance supervised ensembles learned from highly imbalanced data distributions.
To determine the molecular details of the structures giving rise to the PRE NMR data, ensembles of structures compatible with the PRE-derived distances were determined using replica-averaged restrained MD (PRE-RMD) simulations.
We present a multi-data ensemble Kalman filter-based history matching framework for the simultaneous incorporation of various reservoir data such as seismic, electromagnetics, gravimetry and InSAR for best possible characterization of the reservoir formation.
Experiment has been performed at 1-degree spatial resolution generating 1 TB data per ensemble member totaling 20 TB for 20 ensembles.
Then, a novel frequency ratio data mining ensemble model (FREM) was introduced and evaluated.
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