Intelligent Recognition of Gas-Liquid Two-Phase Flow Based on Optical Image

Shujuan Wang; Haofu Guan; Yuqing Wang; Kanghui Zhang; Yuntao Dai; Shouxu Qiao; Jihong Shen

doi:10.34028/iajit

Article Details

Intelligent Recognition of Gas-Liquid Two-Phase Flow Based on Optical Image

Authors:

Shujuan Wang | Haofu Guan | Yuqing Wang | Kanghui Zhang | Yuntao Dai | Shouxu Qiao | Jihong Shen

Publisher:

Zarqa University, Jordan

Journal:

The International Arab Journal of Information Technology (IAJIT) Vol.20 Issue 4, pp.609-617

Document Type:

Article

Publication Date:

1/2023

Document Language:

English

Document ID (Org No.):

2001389613

pISSN:

2309-4524

DOI:

10.34028/iajit

Keywords:

Abstract:

 Gas-liquid two-phase flow is widely involved in many scientific and technological fields, such as energy, electricity, nuclear energy, aerospace and environmental protection. In some fields, extracting the accurate position of bubbles in space can not only accurately capture the characteristics of bubbles in two-phase flow, but also plays an important role in the subsequent research like bubble tracking. It has got some progresses to use Convolutional Neural Network (CNNs) to identify bubbles in gas-liquid two-phase flow, while accurate pixel segmentation map in the bubble identification problem is more desirable in many areas. In this paper, VGG16-FCN model and U-Net model are utilized to identify bubbles in two-phase flow images from the perspective of semantic segmentation. LabelMe is used to label the images in the experiment, which can remove the noise in the original image. In addition, bubble pixels with low ratio relative to the background affects the loss function value tinily which cause the irrational evaluation for the recognition in traditional semantic segmentation, thus, Dice loss is used as the loss function for training to improve the recognition effect. The research results show that the two deep learning models have strong feature extraction ability and accurately detect the bubble boundary.

Article Details

Intelligent Recognition of Gas-Liquid Two-Phase Flow Based on Optical Image

Authors:

Shujuan Wang | Haofu Guan | Yuqing Wang | Kanghui Zhang | Yuntao Dai | Shouxu Qiao | Jihong Shen

Publisher:

Zarqa University, Jordan

Journal:

The International Arab Journal of Information Technology (IAJIT) Vol.20 Issue 4, pp.609-617

Document Type:

Article

Publication Date:

1/2023

Document Language:

English

Document ID (Org No.):

2001389613

pISSN:

2309-4524

DOI:

10.34028/iajit

Optical image

gas-liquid two-phase flow

machine vision

FCN

U-Net

Issues

Article Details

Intelligent Recognition of Gas-Liquid Two-Phase Flow Based on Optical Image

Authors: Shujuan Wang | Haofu Guan | Yuqing Wang | Kanghui Zhang | Yuntao Dai | Shouxu Qiao | Jihong Shen

Publisher: Zarqa University, Jordan

Journal: The International Arab Journal of Information Technology (IAJIT) Vol.20 Issue 4, pp.609-617

Document Type: Article

Publication Date: 1/2023

Document Language: English

Document ID (Org No.): 2001389613

pISSN: 2309-4524

DOI: 10.34028/iajit

Optical image

gas-liquid two-phase flow

machine vision

FCN

U-Net

Issues

Authors:

Shujuan Wang | Haofu Guan | Yuqing Wang | Kanghui Zhang | Yuntao Dai | Shouxu Qiao | Jihong Shen

Publisher:

Zarqa University, Jordan

Journal:

The International Arab Journal of Information Technology (IAJIT) Vol.20 Issue 4, pp.609-617

Document Type:

Article

Publication Date:

1/2023

Document Language:

English

Document ID (Org No.):

2001389613

pISSN:

2309-4524

DOI:

10.34028/iajit