Name
Abstract | ||
|---|---|---|
Gas–liquid two-phase flow is of great importance in various industrial processes. How to accurately measure the flow parameters in the gas–liquid two-phase flow remains a challenging problem. In this article, we develop a novel deep learning based soft measure technique to predict the gas void fraction, which is one key parameter in a gas–liquid two-phase flow. We conduct the vertical upward gas–l... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TII.2020.2978944 | IEEE Transactions on Industrial Informatics |
Keywords | DocType | Volume |
Kernel,Convolution,Feature extraction,Brain modeling,Genetic algorithms,Convolutional neural networks | Journal | 17 |
Issue | ISSN | Citations |
9 | 1551-3203 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhongke Gao | 1 | 59 | 8.79 |
| Linhua Hou | 2 | 1 | 1.37 |
| Wei-Dong Dang | 3 | 25 | 3.60 |
| Xinmin Wang | 4 | 21 | 1.94 |
| Xiaolin Hong | 5 | 0 | 1.35 |
| Xiong Yang | 6 | 36 | 4.51 |
| G. Chen | 7 | 183 | 12.90 |