首页|New Machine Learning Findings from Pennsylvania State University (Penn State) De scribed (Crystal Growth Characterization of Wse 2 Thin Film Using Machine Learni ng)
New Machine Learning Findings from Pennsylvania State University (Penn State) De scribed (Crystal Growth Characterization of Wse 2 Thin Film Using Machine Learni ng)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting out of University Park, Penns ylvania, by NewsRx editors, research stated, "Materials characterization remains a labor-intensive process, with a large amount of expert time required to post- process and analyze micrographs. As a result, machine learning has become an ess ential tool in materials science, including for materials characterization." Financial support for this research came from National Science Foundation (NSF). Our news journalists obtained a quote from the research from Pennsylvania State University (Penn State), "In this study, we perform an in-depth analysis of the prediction of crystal coverage in WSe 2 thin film atomic force microscopy (AFM) height maps with supervised regression and segmentation models. Regression model s were trained from scratch and through transfer learning from a ResNet pretrain ed on ImageNet and MicroNet to predict monolayer crystal coverage. Models traine d from scratch outperformed those using features extracted from pretrained model s, but fine-tuning yielded the best performance, with an impressive 0.99 R 2 val ue on a diverse set of held-out test micrographs. Notably, features extracted fr om MicroNet showed significantly better performance than those from ImageNet, bu t fine-tuning on ImageNet demonstrated the reverse. As the problem is natively a segmentation task, the segmentation models excelled in determining crystal cove rage on image patches. However, when applied to full images rather than patches, the performance of segmentation models degraded considerably, while the regress ors did not, suggesting that regression models may be more robust to scale and d imension changes compared to segmentation models."
University ParkPennsylvaniaUnited St atesNorth and Central AmericaCrystal GrowthCyborgsEmerging TechnologiesHealth and MedicineMachine LearningPennsylvania State University (Penn Stat e)
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