Juliana Rodrigues Tovar Garbin, Franciéle Marabotti Costa Leite, Cristiano Soares Silva Dell'Antonio, Larissa Soares Dell'Antonio, Ana Paula Brioschi Dos Santos, Luís Carlos Lopes-Júnior
Hospitalizations for coronavirus disease 2019: an analysis of the occurrence waves
Scientific reports
phased array ultrasonic testing paut requires highly trained qualified personnel interpret analyze images takes solid understanding wave propagation physics comprehend generated images inspectors judgment level experience significant impact analysiss outcome addition procedure prone error laborious ai shown effective computer vision variety classification detection tasks regarding paut studies also demonstrated machine learning may able identify defects level accuracy par even superior trained qualified inspectors nonetheless use computer vision paut remains limited primary cause challenge accessing large databases labelled inspections fact considerable amount training data required machine learning easy access sizeable labelled databases mri scans photographs instance case paut inspection results usually confidential project large database generated using mockups commonly used train evaluate inspectors different defects contained mockups used train machine learning model data acquired several different probes centered different frequencies acquisition performed using full matrix capture fmc postprocessing data contained fmc allows compute sectoral scan focal laws result comprehensive database composed hundreds thousands sectoral scans generated fmc acquisitions completeness database facilitated robust training defect detection model paut sectoral scans evaluation model demonstrated ability generalize even defect types never trained furthermore detection performance remained consistent even high noise conditions contrasttonoise ratio cnr low