Deep learning-based automated and universal bubble detection and mask extraction in complex two-phase flows
Abstract While investigating multiphase flows experimentally, the spatiotemporal variation in the interfacial shape between different phases must be measured to analyze the transport phenomena. For this, numerous image processing techniques have been proposed, showing good performance. However, they...
Saved in:
Main Authors: | Yewon Kim, Hyungmin Park |
---|---|
Format: | article |
Language: | EN |
Published: |
Nature Portfolio
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/b4c2274d734e4dd996b87f79e18e4921 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Bubble behavior in horizontal two-phase flow under flow rate fluctuation
by: Jun-ichi TAKANO, et al.
Published: (2014) -
Two-Phase Deep Learning-Based EDoS Detection System
by: Chien-Nguyen Nhu, et al.
Published: (2021) -
Automated Generation of Masked Hardware
by: David Knichel, et al.
Published: (2021) -
Influence of Bubble Deformation on the Signal Characteristics Generated Using an Optical Fiber Gas–liquid Two-Phase Flow Sensor
by: Yu Ma, et al.
Published: (2021) -
Single-bubble EHD behavior into water two-phase flow under electric-field stress and gravitational acceleration using PFM
by: Maryam Aliakbary Mianmahale, et al.
Published: (2021)