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Proceedings Paper

Gas/liquid two-phase flow regime recognition by combining the features of wavelet transform energy with the improved Elman network
Author(s): Jun Han; Feng Dong; Yaoyuan Xu; Zhiqiang Zhang
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Paper Abstract

This paper investigates a new method for gas/liquid two-phase flow recognition by combining the features of wavelet energy and the recurrent artificial neural networks. The information of the method that provided by Cross-Sectional Measured Resistance Information (CSMRI) is the measured data in horizontal pipe. The feature vector of wavelet transform energy that can express the essential information of gas/liquid two-phase flow is constructed. The improved recurrent Elman neural network is brought forward to recognize the Gas/Liquid Two-Phase flow regime. The architecture of an Elman neural networks with the context units that memorize the past input feature value can express the time series. The obtained results indicate that the method is suitable to estimate the flow regime and the higher recognizing rate of the flow regime is obtained.

Paper Details

Date Published: 13 October 2008
PDF: 6 pages
Proc. SPIE 7127, Seventh International Symposium on Instrumentation and Control Technology: Sensors and Instruments, Computer Simulation, and Artificial Intelligence, 71271V (13 October 2008); doi: 10.1117/12.806453
Show Author Affiliations
Jun Han, Tianjin Univ. (China)
Capital Normal Univ. (China)
Feng Dong, Tianjin Univ. (China)
Yaoyuan Xu, Tianjin Univ. (China)
Zhiqiang Zhang, Tianjin Univ. (China)


Published in SPIE Proceedings Vol. 7127:
Seventh International Symposium on Instrumentation and Control Technology: Sensors and Instruments, Computer Simulation, and Artificial Intelligence

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