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A machine learning approach to tracking and characterizing planar or near planar fluid flow
journal contribution
posted on 2021-02-22, 11:47 authored by Mahendra Gooroochurn, David Kerr, Kaddour Bouazza-MaroufThis paper presents a framework to segment planar or near-planar fluid flow and uses artificial neural networks to characterize fluid flow by determining the rate of flow and source of the fluid, which can be applied in various areas (e.g., characterizing fluid flow in surface irrigation from aerial pictures, in leakage detection, and in surgical robotics for characterizing blood flow over an operative site). For the latter, the outcome enables to assess bleeding severity and find the source of the bleeding. Based on its importance in assessing injuries and from a medical perspective in directing the course of surgery, fluid flow assessment is deemed to be a desirable addition to a surgical robot's capabilities. The results from tests on fluid flows generated from a test rig show that the proposed methods can contribute to an automated characterization of fluid flow, which in the presence of several fluid flow sources can be achieved by tracking the flows, determining the locations of the sources and their relative severities, with execution times suitable for real-time operation.
History
School
- Mechanical, Electrical and Manufacturing Engineering
Published in
International Journal of Natural Computing ResearchVolume
9Issue
3Pages
76 - 87Publisher
IGI GlobalVersion
- AM (Accepted Manuscript)
Publication date
2020-07-01ISSN
1947-928XeISSN
1947-9298Publisher version
Language
- en
Depositor
Dr Kaddour Bouazza-Marouf. Deposit date: 19 February 2021Usage metrics
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