Deep Learning Segmentation of Polycrystalline Superconductors with Different Compositions
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- Nishiya Yoshiki
- Tokyo University of Agriculture and Technology
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- Hosokawa Takahiro
- Tokyo University of Agriculture and Technology
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- Hirabayashi Yu
- Tokyo University of Agriculture and Technology
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- Iga Haruka
- Tokyo University of Agriculture and Technology
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- Tokuta Shinnosuke
- Tokyo University of Agriculture and Technology
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- Shimada Yusuke
- Kyushu University
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- Yamamoto Akiyasu
- Tokyo University of Agriculture and Technology
Bibliographic Information
- Other Title
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- 深層学習による多結晶型超伝導体の学習外の試料に対する相解析
Description
<p>Image analysis to identify the phases from microstructural images is an important issue for understanding the mechanism associated with the microstructures of functional polycrystalline materials. In this study, the segmentation ability of the deep learning model and the effect of data augmentation were investigated when applied to ceramic superconducting materials with different compositions than the trained materials.</p>
Journal
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- IEEJ Transactions on Fundamentals and Materials
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IEEJ Transactions on Fundamentals and Materials 144 (9), 373-376, 2024-09-01
The Institute of Electrical Engineers of Japan
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Details 詳細情報について
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- CRID
- 1390019900048934400
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- ISSN
- 13475533
- 03854205
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- Text Lang
- ja
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- Data Source
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- JaLC
- Crossref
- OpenAIRE
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- Abstract License Flag
- Disallowed