Development of Gas–Liquid Flow Regimes Identification Using a . . . The problem of classifying gas–liquid two-phase flow regimes from ultrasonic signals is considered A new method, belt-shaped features (BSFs), is proposed for performing feature extraction on the preprocessed data
Development of Gas-Liquid Flow Regimes Identification Using a . . . The problem of classifying gas-liquid two-phase flow regimes from ultrasonic signals is considered A new method, belt-shaped features (BSFs), is proposed for performing feature extraction on the preprocessed data
Development of Gas-Liquid Flow Regimes Identification Using a . . . The problem of classifying gas-liquid two-phase flow regimes from ultrasonic signals is considered A new method, belt-shaped features (BSFs), is proposed for performing feature extraction on the preprocessed data
Gas-liquid flow regimes identification using non-intrusive Doppler . . . The problem of gas-liquid (two-phase) flow regime identification in an S-shaped riser using an ultrasonic sensor and convolutional recurrent neural networks (CRNN) is addressed This research systematically evaluates three different schemes with four CRNN-based classifiers over fourteen experiments
Boyu Kuang - Google Scholar Research fellow in Computer Vision and AI Cranfield University - Cited by 1,285 - computer vision - deep learning - target detection - pattern recognition - artificial
Zeeshan A. Rana - dblp Development of Gas-Liquid Flow Regimes Identification Using a Noninvasive Ultrasonic Sensor, Belt-Shape Features, and Convolutional Neural Network in an S-Shaped Riser IEEE Trans Cybern 53 (1): 3-17 (2023)