scholarly journals Using Artificial Neural Networks to Produce High-Resolution Soil Property Maps

2018 ◽  
Author(s):  
Zhengyong Zhao ◽  
Fan-Rui Meng ◽  
Qi Yang ◽  
Hangyong Zhu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
High Resolution ◽  
Soil Property ◽  
Artificial Neural

scholarly journals Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks

2018 ◽  
Author(s):  
Rishi Rajalingham ◽  
Elias B. Issa ◽  
Pouya Bashivan ◽  
Kohitij Kar ◽  
Kailyn Schmidt ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
High Resolution ◽  
Large Scale ◽  
State Of The Art ◽  
Neural Mechanisms ◽  
Mechanistic Models ◽  
Object Discrimination ◽  
Ann Models ◽  
Artificial Neural

ABSTRACTPrimates—including humans—can typically recognize objects in visual images at a glance even in the face of naturally occurring identity-preserving image transformations (e.g. changes in viewpoint). A primary neuroscience goal is to uncover neuron-level mechanistic models that quantitatively explain this behavior by predicting primate performance for each and every image. Here, we applied this stringent behavioral prediction test to the leading mechanistic models of primate vision (specifically, deep, convolutional, artificial neural networks; ANNs) by directly comparing their behavioral signatures against those of humans and rhesus macaque monkeys. Using high-throughput data collection systems for human and monkey psychophysics, we collected over one million behavioral trials for 2400 images over 276 binary object discrimination tasks. Consistent with previous work, we observed that state-of-the-art deep, feed-forward convolutional ANNs trained for visual categorization (termed DCNNIC models) accurately predicted primate patterns of object-level confusion. However, when we examined behavioral performance for individual images within each object discrimination task, we found that all tested DCNNIC models were significantly non-predictive of primate performance, and that this prediction failure was not accounted for by simple image attributes, nor rescued by simple model modifications. These results show that current DCNNIC models cannot account for the image-level behavioral patterns of primates, and that new ANN models are needed to more precisely capture the neural mechanisms underlying primate object vision. To this end, large-scale, high-resolution primate behavioral benchmarks—such as those obtained here—could serve as direct guides for discovering such models.SIGNIFICANCE STATEMENTRecently, specific feed-forward deep convolutional artificial neural networks (ANNs) models have dramatically advanced our quantitative understanding of the neural mechanisms underlying primate core object recognition. In this work, we tested the limits of those ANNs by systematically comparing the behavioral responses of these models with the behavioral responses of humans and monkeys, at the resolution of individual images. Using these high-resolution metrics, we found that all tested ANN models significantly diverged from primate behavior. Going forward, these high-resolution, large-scale primate behavioral benchmarks could serve as direct guides for discovering better ANN models of the primate visual system.

scholarly journals High resolution non-destructive evaluation of defects using artificial neural networks and wavelets

2012 ◽  
Vol 52 ◽  
pp. 69-75 ◽  
Author(s):  
S.J. Farley ◽  
J.F. Durodola ◽  
N.A. Fellows ◽  
L.H. Hernández-Gómez
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
High Resolution ◽  
Non Destructive Evaluation ◽  
Artificial Neural ◽  
Non Destructive

scholarly journals Prediction of Chromatographic Retention Time in High-Resolution Anti-Doping Screening Data Using Artificial Neural Networks

2013 ◽  
Vol 85 (21) ◽  
pp. 10330-10337 ◽  
Author(s):  
Thomas H. Miller ◽  
Alessandro Musenga ◽  
David A. Cowan ◽  
Leon P. Barron
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
High Resolution ◽  
Retention Time ◽  
Chromatographic Retention ◽  
Artificial Neural

scholarly journals Porosity prediction from X-ray computed tomography logs (RHOB and PEF) using Artificial Neural Networks (ANN)

2020 ◽  
Vol 42 (3) ◽  
pp. 141-149
Author(s):  
Andrés Felipe Ortiz ◽  
Edwar Hernando Herrera ◽  
Nicolás Santos
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
High Resolution ◽  
Porous Space ◽  
Core Analysis ◽  
Detailed Measurement ◽  
X Ray ◽  
X Ray Computed ◽  
Artificial Neural

This work presents a method for rock porosity prediction from the X-ray computed tomography (CT) logs obtained using a double energy approach, bulk density (RHOB) and photoelectric factor (PEF). The proposed method seeks to correlate the known porosity from the Routine Core Analysis (RCAL) with RHOB and PEF high-resolution logs, as the response of these two measurements depends on the volumetric quantity of different rock materials and of the volume of its porous space. Artificial Neural Networks (ANNs) are trained so they can predict porosity from CT logs at a high resolution (0.625 mm). The ANNs validation and regression plots show that porosity predictions are good. High-resolution porosity models linked to CT images could contribute to enhancing the petrophysics model as they allow a more refined identification of intervals of interest due to the detailed measurement.

scholarly journals Assessment of Surface Soil Moisture Using High-Resolution Multi-Spectral Imagery and Artificial Neural Networks

2015 ◽  
Vol 7 (3) ◽  
pp. 2627-2646 ◽  
Author(s):  
Leila Hassan-Esfahani ◽  
Alfonso Torres-Rua ◽  
Austin Jensen ◽  
Mac McKee
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Soil Moisture ◽  
High Resolution ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Artificial Neural
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