@inproceedings{38c6547ae4ae413cbac07f63df3cc33a,
title = "Wet gas metering using a venturi-meter and Support Vector Machines",
abstract = "A new approach to the measurement of wet gas flows is introduced in this paper. Support Vector Machine (SVM) was employed in wet gas metering. Typical features were extracted from the signals obtained by a throat-extended Venturi meter. The features and the corresponding flow rates (targets) were used to train the SVM model. The trained model was then used to predict the flow rates of wet gas. Experimental results suggest that this method provides a solution that is much better than the empirical formulas. The average prediction error of this method is smaller than that of the empirical formulas by about 50\%. This method is also proved to be better than the technique using a venturi-meter and neural network.",
keywords = "Principal component analysis (PCA), Support vector machines (SVM), Venturi-meter, Wet gas metering",
author = "Lijun Xu and Shaliang Tang",
year = "2009",
doi = "10.1109/IMTC.2009.5168628",
language = "英语",
isbn = "9781424433537",
series = "2009 IEEE Intrumentation and Measurement Technology Conference, I2MTC 2009",
publisher = "IEEE Computer Society",
pages = "1152--1156",
booktitle = "2009 IEEE Intrumentation and Measurement Technology Conference, I2MTC 2009",
address = "美国",
note = "2009 IEEE Intrumentation and Measurement Technology Conference, I2MTC 2009 ; Conference date: 05-05-2009 Through 07-05-2009",
}