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Fig. 3 Map view of regional resistivity model for depth slices a 6 km, b 10 km, c 15 km, d 20 km.
First, we train a CNN model for depth images by teaching the network to reproduce the mid-level semantic representation learned from RGB images for which there are paired images.
It is mathematically similar to a mixture model for depth [24].
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Although numerous studies have demonstrated the applicability of a constant scaling of the Battjes and Janssen (1978) dissipation model for depth-induced breaking, recent studies have shown its inability to sufficiently reproduce wave dissipation over complex field cases.
Once adaptive neighborhoods are found, one must find some modeling for depth channel before utilizing this structural information.
Lower branch: Deep CNN-based global-context modelling for depth saliency detection with a superpixel-centred window padded with the mean pixel value of the depth training dataset.
The method requires planar surface to be imaged from various poses and presents a new depth distortion model for the depth sensor.
Niu et al. [20] explored stereo saliency by analyzing the characteristics of stereo vision and proposed a depth saliency model for a depth map that would expand the 2D saliency model for stereo saliency analysis.
Carex secta, in contrast, showed greatest biomass at intermediate depths, and the best model for its depth response was a logistic hormesis model, for both above- and below-ground tissue.
Overall, there is relatively good resolution of both the V p and V p /V s models for depths of 2 17 km.
The proposed artificial neural networks (ANN) model for rutting depth has shown good agreement with experimental results.
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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