Estimating the phase volume fraction of multi-phase steel via unsupervised deep learning
Abstract Advanced high strength steel (AHSS) is a steel of multi-phase microstructure that is processed under several conditions to meet the current high-performance requirements from the industry. Deep neural network (DNN) has emerged as a promising tool in materials science for the task of estimat...
Saved in:
Main Authors: | Sung Wook Kim, Seong-Hoon Kang, Se-Jong Kim, Seungchul Lee |
---|---|
Format: | article |
Language: | EN |
Published: |
Nature Portfolio
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/dcbc6a1fd009497aa6d565446bece42c |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Quantum enhanced multiple-phase estimation with multi-mode N00N states
by: Seongjin Hong, et al.
Published: (2021) -
Deep learning for end-to-end kidney cancer diagnosis on multi-phase abdominal computed tomography
by: Kwang-Hyun Uhm, et al.
Published: (2021) -
Unsupervised multi-source domain adaptation with no observable source data.
by: Hyunsik Jeon, et al.
Published: (2021) -
Author Correction: Deep learning for high-resolution and high-sensitivity interferometric phase contrast imaging
by: Seho Lee, et al.
Published: (2021) -
Multi-Dimensional Angle of Arrival Estimation by Circular Phased Adaptive Array Antennas
by: Bassim Sayed Mohammed
Published: (2017)