Feature Extraction Method for Gas/Liquid Two-Phase Flow Based on Wavelets Transform

2006 ◽  
Author(s):  
Meng-meng Wu ◽  
Feng Dong ◽  
Guo-hua Qi
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Two Phase Flow ◽  
Phase Flow ◽  
Two Phase ◽  
Feature Extraction Method ◽  
Wavelets Transform

Improving the structure of two-phase flow meter using feature extraction and GMDH neural network

2020 ◽  
Vol 171 ◽  
pp. 108725 ◽  
Author(s):  
Mohammad Amir Sattari ◽  
Gholam Hossein Roshani ◽  
Robert Hanus
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Two Phase Flow ◽  
Phase Flow ◽  
Flow Meter ◽  
Two Phase

scholarly journals Optimization of X-ray Tube Voltage to Improve the Precision of Two Phase Flow Meters Used in Petroleum Industry

2021 ◽  
Vol 13 (24) ◽  
pp. 13622
Author(s):  
Abdullah K. Alanazi ◽  
Seyed Mehdi Alizadeh ◽  
Karina Shamilyevna Nurgalieva ◽  
John William Grimaldo Guerrero ◽  
Hala M. Abo-Dief ◽  
...  
Keyword(s):  
Two Phase Flow ◽  
Detection System ◽  
Petroleum Industry ◽  
Flow Characteristics ◽  
Absolute Error ◽  
Phase Flow ◽  
Tube Voltage ◽  
Two Phase ◽  
X Ray ◽  
Feature Extraction Method

To the best knowledge of the authors, in all the former studies, a fixed value of X-ray tube voltage has been used for investigating gas–liquid two-phase flow characteristics, while the energy of emitted X-ray radiations that depends on the tube voltage can significantly affect the measurement precision of the system. The purpose of present study is to find the optimum tube voltage to increase the accuracy and efficiency of an intelligent X-ray radiation-based two-phase flow meter. The detection system consists of an industrial X-ray tube and one detector located on either side of a steel pipe. Tube voltages in the range of 125–300 kV with a step of 25 kV were investigated. For each tube voltage, different gas volume percentages (GVPs) in the range of 10–90% with a step of 5% were modeled. A feature extraction method was performed on the output signals of the detector in every case, and the obtained matrixes were applied to the designed radial basis function neural networks (RBFNNs). The desired output of the networks was GVP. The precision of the networks in every voltage and every number of neurons in the hidden layer were obtained. The results showed that 225 kV tube voltage is the optimum voltage for this purpose. The obtained mean absolute error (MAE) for this case is less than 0.05, which demonstrates the very high precision of the metering system with an optimum X-ray tube voltage.

scholarly journals Feature Extraction of Solid Air Two Phase Flow CT Image with 3D Wavelets

2001 ◽  
Vol 21 (2Supplement) ◽  
pp. 7-8
Author(s):  
Masahiro TAKEI ◽  
Hui LI ◽  
Mitsuaki OCHI ◽  
Yoshifuru SAITO ◽  
Kiyoshi HORII
Keyword(s):  
Feature Extraction ◽  
Two Phase Flow ◽  
Phase Flow ◽  
Ct Image ◽  
Two Phase

scholarly journals Feature extraction and identification of gas–liquid two-phase flow based on fractal theory

2020 ◽  
pp. 1-8
Author(s):  
Chunling Fan ◽  
Zhongcheng Li ◽  
Qihua Fan ◽  
Jiangfan Qin ◽  
Miaomiao Liu
Keyword(s):  
Feature Extraction ◽  
Two Phase Flow ◽  
Fractal Theory ◽  
Phase Flow ◽  
Two Phase
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