首页|Findings from China University of Petroleum (East China) Broaden Understanding of Support Vector Machines (Sand Particle Charac- terization and Identification In Annular Multiphase Flow Using an Intelligent Method)
Findings from China University of Petroleum (East China) Broaden Understanding of Support Vector Machines (Sand Particle Charac- terization and Identification In Annular Multiphase Flow Using an Intelligent Method)
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Fresh data on Machine Learning - Support Vector Machines are presented in a new report. According to news reporting out of Qingdao, People's Republic of China, by NewsRx editors, research stated, "The intelligent recognition and monitoring of sand particles in annular multiphase flow are of paramount importance for the safe production of high-yield gas wells. In this study, an experiment based on a uniaxial vibration method was initially designed to collect collision response signals between sand particles and the pipe wall." Financial supporters for this research include National Natural Science Foundation of China (NSFC), National Natural Science Foundation of China (NSFC), Natural Science Foundation of Shandong Province, Natural Science Basic Research Program of Shaanxi. Our news journalists obtained a quote from the research from the China University of Petroleum (East China), "Utilizing wavelet packet analysis, the identification and classification of sand-carrying signals in the liquid film and gas core regions were first achieved. The results indicate that the excitation frequency range for sand-carrying signals impacting the pipe wall in the liquid film region was 19.2-38.4 kHz, while in the gas core region, it was 38.4-51.2 kHz. Finally, convolutional neural network (CNN) models, support vector machine (SVM) models, and CNN-SVM models were constructed to characterize and identify sand particles in annular multiphase flow. The results show that the CNN-SVM model improved the accuracy of sand-carrying data recognition by 2.0% compared to CNN and by 5.6% compared to SVM for gas core region data, and by 1.8% compared to CNN and by 8.6% compared to SVM for liquid film region data."
QingdaoPeople's Republic of ChinaAsiaMachine LearningSupport Vector MachinesChina University of Petroleum (East China)
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