Gas/liquid two-phase flow regime recognition by combining the features of wavelet transform energy with the improved Elman network

Jun Han; Feng Dong; Yaoyuan Xu; Zhiqiang Zhang

doi:10.1117/12.806453

13 October 2008 Gas/liquid two-phase flow regime recognition by combining the features of wavelet transform energy with the improved Elman network

Jun Han, Feng Dong, Yaoyuan Xu, Zhiqiang Zhang

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Proceedings Volume 7127, Seventh International Symposium on Instrumentation and Control Technology: Sensors and Instruments, Computer Simulation, and Artificial Intelligence; 71271V (2008) https://doi.org/10.1117/12.806453
Event: Seventh International Symposium on Instrumentation and Control Technology, 2008, Beijing, China

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.

Citation Download Citation

Jun Han, Feng Dong, Yaoyuan Xu, and Zhiqiang Zhang "Gas/liquid two-phase flow regime recognition by combining the features of wavelet transform energy with the improved Elman network", Proc. SPIE 7127, Seventh International Symposium on Instrumentation and Control Technology: Sensors and Instruments, Computer Simulation, and Artificial Intelligence, 71271V (13 October 2008); https://doi.org/10.1117/12.806453

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KEYWORDS

Wavelets

Wavelet transforms

Neural networks

Feature extraction

Data acquisition

Resistance

Electrodes

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