Automatic Detection of Local Obstacles in a Long Length RE-123 Coated Conductor by Deep Learning Based Image Classification in Reel-to-Reel Magnetic Microscopy-Reference-Cited by-同舟云学术

Automatic Detection of Local Obstacles in a Long Length RE-123 Coated Conductor by Deep Learning Based Image Classification in Reel-to-Reel Magnetic Microscopy

Published:2022-09 Issue:6 Volume:32 Page:1-4
ISSN:1051-8223
Container-title:IEEE Transactions on Applied Superconductivity
language:
Short-container-title:IEEE Trans. Appl. Supercond.

Author:

Somjaijaroen N.¹^ORCID,Kiss T.¹^ORCID,Imamura K.¹,Higashikawa K.¹^ORCID

Affiliation:

1. Department of Electrical Engineering, Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan

Funder

Japan Society for the Promotion of Science

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Condensed Matter Physics,Electronic, Optical and Magnetic Materials

Link

http://xplorestaging.ieee.org/ielx7/77/9712164/09729409.pdf?arnumber=9729409

Reference15 articles.

1. Predicting new superconductors and their critical temperatures using machine learning

2. Image processing techniques for classification of linear welding defects

3. PowerAI vision guide,2021

4. Visualizing Transport Properties in IBAD Based YBCO Coated Conductors by Multiple Analysis Techniques

5. Deep GoogLeNet Features for Visual Object Tracking

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