Enhanced CFRP Defect Detection From Highly Undersampled Thermographic Data via Low-Rank Tensor Completion-Based Thermography-Reference-Cited by-同舟云学术

Enhanced CFRP Defect Detection From Highly Undersampled Thermographic Data via Low-Rank Tensor Completion-Based Thermography

Published:2022-12 Issue:12 Volume:18 Page:8641-8653
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Luo Zhitao¹^ORCID,Luo Hao¹,Wang Sheng¹,Chen Fei²,Su Zihao¹,Shen Peng¹^ORCID,Zhang Hui¹^ORCID

Affiliation:

1. Jiangsu Key Laboratory of Design and Manufacturing of Micro-Nano Biomedical Instruments, School of Mechanical Engineering, Southeast University, Nanjing, China

2. School of Science, Nanjing University of Science and Technology, Nanjing, China

Funder

National Natural Science Foundation of China

Key R&D Program of Jiangsu Province of China

Scientific Research Foundation of Graduate School of Southeast University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/9424/9906876/09722967.pdf?arnumber=9722967

Reference50 articles.

1. Low-rank tensor completion via smooth matrix factorization

2. Matrix factorization for low-rank tensor completion using framelet prior

3. Ensemble Joint Sparse Low-Rank Matrix Decomposition for Thermography Diagnosis System

4. Structured iterative alternating sparse matrix decomposition for thermal imaging diagnostic system

5. Sparse Low-Rank Tensor Decomposition for Metal Defect Detection Using Thermographic Imaging Diagnostics

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