首页|New Machine Learning Study Findings Recently Were Reported by Researchers at Uni versity of Florida (Unsupervised machine learning and cepstral analysis with 4D- STEM for characterizing complex microstructures of metallic alloys)
New Machine Learning Study Findings Recently Were Reported by Researchers at Uni versity of Florida (Unsupervised machine learning and cepstral analysis with 4D- STEM for characterizing complex microstructures of metallic alloys)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in artific ial intelligence. According to news reporting from the University of Florida by NewsRx journalists, research stated, "Four-dimensional scanning transmission ele ctron microscopy, coupled with a wide array of data analytics, has unveiled new insights into complex materials." Financial supporters for this research include National Science Foundation. The news editors obtained a quote from the research from University of Florida: "Here, we introduce a straightforward unsupervised machine learning approach that entails dimensionality reduction and clustering with minimal hyperparameter tu ning to semi-automatically identify unique coexisting structures in metallic all oys. Applying cepstral transformation to the original diffraction dataset improv es this process by effectively isolating phase information from potential signal ambiguity caused by sample tilt and thickness variations, commonly observed in electron diffraction patterns. In a case study of a NiTiHfAl shape memory alloy, conventional scanning transmission electron microscopy imaging struggles to acc urately identify a low-contrast precipitate at lower magnifications, posing chal lenges for microscale analyses. We find that our method efficiently separates mu ltiple coherent structures while using objective means of determining hyperparam eters."
University of FloridaAlloysCyborgsEmerging TechnologiesMachine Learning
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