首页|South Dakota School of Mines and Technology Researcher Focuses on Machine Learni ng (Automated Crack Detection in 2D Hexagonal Boron Nitride Coatings Using Machi ne Learning)
South Dakota School of Mines and Technology Researcher Focuses on Machine Learni ng (Automated Crack Detection in 2D Hexagonal Boron Nitride Coatings Using Machi ne Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on artificial intelligence is now available. According to news reporting from Rapid City, South Dakota, by NewsRx journalists, research stated, "Characterizing defects in 2D materials, s uch as cracks in chemical vapor deposited (CVD)-grown hexagonal boron nitride (h BN), is essential for evaluating material quality and reliability." Financial supporters for this research include National Science Foundation (Nsf) Rii Fec Awards; Nsf Cbet Award; National Institute of General Medical Sciences of The National Institutes of Health. The news journalists obtained a quote from the research from South Dakota School of Mines and Technology: "Traditional characterization methods are often time-c onsuming and subjective and can be hindered by the limited optical contrast of h BN. To address this, we utilized a YOLOv8n deep learning model for automated cra ck detection in transferred CVD-grown hBN films, using MATLAB's Image Labeler an d Supervisely for meticulous annotation and training. The model demonstrates pro mising crack-detection capabilities, accurately identifying cracks of varying si zes and complexities, with loss curve analysis revealing progressive learning."
South Dakota School of Mines and Technol ogyRapid CitySouth DakotaUnited StatesNorth and Central AmericaBoronBoron NitrideChemicalsCyborgsEmerging TechnologiesMachine Learning
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