基于1D-CNN-AdaBoost及电阻层析成像的两相流流型辨识

doi:10.3969/j.issn.1000-1158.2022.12.16

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Abstract Based on the vertical tube gas-liquid two-phase flow measurement data collected by the electrical resistance tomography (ERT) system, the one-dimensional convolutional neural network (1D-CNN) and AdaBoost (Adaptive Boosting) are combined to construct the 1D-CNN-AdaBoost algorithm, a study on the flow pattern identification of gas-liquid two-phase flow has been carried out. The algorithm uses five 1D-CNNs as weak classifiers, trained on experimental data samples, and combined with AdaBoost to form the final strong classifier. Comparing 1D-CNN-AdaBoost algorithm with BP neural network, support vector machine and decision tree algorithm, the results show that the recognition accuracy of 1D-CNN-AdaBoost algorithm is higher than other algorithms, and the average recognition accuracy can reach 97%.

Key words： metrology flow pattern identification electrical capacitance tomography convolutional neural network adaptive boosting

Received: 15 June 2021 Published: 28 December 2022

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TB937

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	Articles by authors
	ZHANG Li-feng
	XIAO Kai
	HUA Hui-chun

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ZHANG Li-feng,XIAO Kai,HUA Hui-chun. Identification of Two-phase Flow Pattern Based on 1D-CNN-AdaBoost and Electrical Resistance Tomography[J]. Acta Metrologica Sinica, 2022, 43(12): 1622-1626.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2022.12.16 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2022/V43/I12/1622

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