Oil-in-Water Two-Phase Flow Pattern Identification From Experimental Snapshots Using Convolutional Neural Network

作者: Meng Du , Hongyi Yin , Xiaoyan Chen , Xinqiang Wang

DOI: 10.1109/ACCESS.2018.2888733

关键词:

摘要: … VGG-16 NET In this paper, we also adopt the VGG-16 net [41] to recognize the oil-water two-phase flow patterns. Compared to the architecture of AlexNet, it has more convolution and …

参考文章(36)
Weizhi Wang, Ying Zheng, Zhengkai Li, Kenneth Lee, PIV investigation of oil–mineral interaction for an oil spill application Chemical Engineering Journal. ,vol. 170, pp. 241- 249 ,(2011) , 10.1016/J.CEJ.2011.03.062
Haili ZHOU, Lijun XU, Zhang CAO, Jinhai HU, Xingbin LIU, Image Reconstruction for Invasive ERT in Vertical Oil Well Logging Chinese Journal of Chemical Engineering. ,vol. 20, pp. 319- 328 ,(2012) , 10.1016/S1004-9541(12)60394-2
G. W. Govier, G. A. Sullivan, R. K. Wood, The upward vertical flow of oil-water mixtures Canadian Journal of Chemical Engineering. ,vol. 39, pp. 67- 75 ,(1961) , 10.1002/CJCE.5450390204
Hao Ding, Zhiyao Huang, Zhihuan Song, Yong Yan, Hilbert–Huang transform based signal analysis for the characterization of gas–liquid two-phase flow Flow Measurement and Instrumentation. ,vol. 18, pp. 37- 46 ,(2007) , 10.1016/J.FLOWMEASINST.2006.12.004
Paul V C Hough, Method and means for recognizing complex patterns U.S. Patent 3,069,645. ,(1960)
Y. Lecun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition Proceedings of the IEEE. ,vol. 86, pp. 2278- 2324 ,(1998) , 10.1109/5.726791
Corinna Cortes, Vladimir Vapnik, Support-Vector Networks Machine Learning. ,vol. 20, pp. 273- 297 ,(1995) , 10.1023/A:1022627411411
Ilya Sutskever, Geoffrey E. Hinton, Alex Krizhevsky, ImageNet Classification with Deep Convolutional Neural Networks neural information processing systems. ,vol. 25, pp. 1097- 1105 ,(2012)
Wenhao Huang, Guojie Song, Haikun Hong, Kunqing Xie, Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning IEEE Transactions on Intelligent Transportation Systems. ,vol. 15, pp. 2191- 2201 ,(2014) , 10.1109/TITS.2014.2311123