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It is particularly suitable for modeling deep BHEs which contain varying pipe diameters and materials.
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These possibilities — modeled deep into this century — are detailed in a new assessment of the impact that climate change will have in New York State.
We present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of linear-chain conditional random fields to model deep nested Markov processes.
The results of dynamometer analysis show that mathematical model of side-flow pumping system and its computer programs can successfully model deep rod string performance.
However, despite the advantages of this approach there are still several limitations and constraints that must be recognised in modelling deep-sea organisms at global scales (see below).
For training data, three channel models are considered: flat fading model, deep fading model, and no fading model.
Colloid deposition in a model deep bed filter has been imaged using synchrotron-based X-ray tomography.
At present, deep learning feature representation includes autoencoder, restricted Boltzmann model, deep belief network, convolutional neural network and recurrent neural network, etc.
In addition to conventional machine learning algorithms, based on shallow learning models, deep learning is creating space as an option for big data reduction methods [81].
In [27, 28], Huang et al. proposed to jointly optimize the deep learning models (deep neural networks and recurrent neural networks) with an extra masking layer to enforce a reconstruction constraint.
The model had a R2 of 0.67 and an adjusted R2 of 0.58 (Table 4, model NSRV-CM) shows the significant variables in this multivariate model: deep infection and CVRF.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com