Classification of Aging Grade of Heat-resistant Steel based on Improved Shufflenet Network
Author:
Affiliation:
1. China Special Equipment Inspection & Research Institute,State Key Laboratory of Low-Carbon Thermal Power Generation Technology and Equipment,Beijing,China
Funder
Ministry of Science and Technology
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10603447/10603450/10603619.pdf?arnumber=10603619
Reference14 articles.
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3. Quantitative metallography of deformed grains
4. High Throughput Quantitative Metallography for Complex Microstructures Using Deep Learning: A Case Study in Ultrahigh Carbon Steel
5. Large-area, high-resolution characterisation and classification of damage mechanisms in dual-phase steel using deep learning
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