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Proceedings Paper

Bayesian belief network modeling of direct numerically simulated imagery variables for sub-surface structure diagnostics
Author(s): Nicholas V. Scott; Tian-Jian Hsu
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Paper Abstract

Naïve Bayesian belief network modeling is applied to direct numerically simulated imagery of oscillatory sedimentladen flow to illustrate the feasibility of creating a system model which captures the statistical interrelationship of the surface layer sediment concentration, pressure, and vertical velocity eddy scales with the sub-surface Reynolds stress. From a prognostic reasoning viewpoint, preliminary model results suggest that large sediment concentration eddy scales may result from the application of large positive Reynolds stress. However, from a diagnostic reasoning viewpoint, initial results suggest that robustly inferring sub-surface boundary layer stress from surface sediment concentration eddy scales may be a difficult task. The model formulism used allows for the ability to statistically characterize flow structure at depth from observations taken across a surface boundary layer, making the results relevant to image analysis at the airsea interfacial boundary layer in large-scale coastal and riverine systems.

Paper Details

Date Published: 30 April 2018
PDF: 15 pages
Proc. SPIE 10649, Pattern Recognition and Tracking XXIX, 1064912 (30 April 2018); doi: 10.1117/12.2301255
Show Author Affiliations
Nicholas V. Scott, Riverside Research Institute (United States)
Tian-Jian Hsu, Univ. of Delaware (United States)


Published in SPIE Proceedings Vol. 10649:
Pattern Recognition and Tracking XXIX
Mohammad S. Alam, Editor(s)

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