首页|Findings in Support Vector Machines Reported from Shanghai Maritime University ( A Rolling Bearing Fault Diagnosis Technique Based On Recurrence Quantification A nalysis and Bayesian Optimization Svm)
Findings in Support Vector Machines Reported from Shanghai Maritime University ( A Rolling Bearing Fault Diagnosis Technique Based On Recurrence Quantification A nalysis and Bayesian Optimization Svm)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Machine Learning - Support Vector Machines. According to news reporting originat ing from Shanghai, People’s Republic of China, by NewsRx correspondents, researc h stated, “A rolling bearing fault diagnosis technique is proposed based on Recu rrence Quantification Analysis (abbreviated as RQA) and Bayesian optimized Suppo rt Vector Machine (abbreviated as RQA-Bayes-SVM). Firstly, analyzing the vibrati on signal with recurrence plot and the nonlinear feature parameters are extracte d with RQA, constructing a feature matrix describing the fault mode and fault de gree comprehensively.”
ShanghaiPeople’s Republic of ChinaAsiaMachine LearningSupport Vector MachinesShanghai Maritime University
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