Machine Learning Based Flow Regime Identification using Ultrasonic Doppler Data and Feature Relevance Determination-Reference-Cited by-同舟云学术

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Machine Learning Based Flow Regime Identification using Ultrasonic Doppler Data and Feature Relevance Determination

Published:2022-06 Issue: Volume:3 Page:100024
ISSN:2772-5081
Container-title:Digital Chemical Engineering
language:en
Short-container-title:Digital Chemical Engineering

Author:

Roxas  II Ricardo,Evangelista Matthew Angelo,Sombillo Jalen Aeron,Nnabuife Somtochukwu Godfrey,Pilario Karl Ezra^ORCID

Publisher

Elsevier BV

Reference38 articles.

1. Non-invasive classification of gas-liquid two-phase horizontal flow regimes using an ultrasonic Doppler sensor and a neural network;Abbagoni;Measurement Science and Technology,2016

2. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI);Adadi;IEEE Access,2018

3. Emerging applications of wavelets: A review;Akansu;Physical Communication,2010

4. A review of cell-scale multiphase flow modeling, including water management, in polymer electrolyte fuel cells;Andersson;Applied Energy,2016

5. An extensive comparative study of cluster validity indices;Arbelaitz;Pattern Recognition,2013

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3. Teaching classical machine learning as a graduate-level course in chemical engineering: An algorithmic approach;Digital Chemical Engineering;2024-06

4. Self-supervised learning-based two-phase flow regime identification using ultrasonic sensors in an S-shape riser;Expert Systems with Applications;2024-02

5. Two-phase flow regime identification using multi-method feature extraction and explainable kernel Fisher discriminant analysis;International Journal of Numerical Methods for Heat & Fluid Flow;2023-12-25

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