| dc.contributor.author | Qiang Fang | |
| dc.contributor.author | Xavier. Maldague | |
| dc.contributor.other | Computer Vision and Systems Laboratory, Department of Electrical and Computer Engineering, Université Laval, av de la Médecine, Québec, QC 1065, Canada | |
| dc.contributor.other | Computer Vision and Systems Laboratory, Department of Electrical and Computer Engineering, Université Laval, av de la Médecine, Québec, QC 1065, Canada | |
| dc.date.accessioned | 2025-10-09T05:32:54Z | |
| dc.date.available | 2025-10-09T05:32:54Z | |
| dc.date.issued | 01-04-2021 | |
| dc.identifier.uri | https://www.mdpi.com/2076-3417/11/8/3451 | |
| dc.identifier.uri | http://digilib.fisipol.ugm.ac.id/repo/handle/15717717/41153 | |
| dc.description.abstract | The authors wish to make the following corrections to this paper [...] | |
| dc.language.iso | EN | |
| dc.publisher | MDPI AG | |
| dc.subject.lcc | Technology | |
| dc.title | Addendum: Fang, Q.; Maldague, X. A Method of Defect Depth Estimation for Simulated Infrared Thermography Data with Deep Learning. <i>Appl. Sci.</i> 2020, <i>10</i>, 6819 | |
| dc.type | Article | |
| dc.description.keywords | n/a | |
| dc.description.doi | 10.3390/app11083451 | |
| dc.title.journal | Applied Sciences | |
| dc.identifier.e-issn | 2076-3417 | |
| dc.identifier.oai | oai:doaj.org/journal:0fb5e788ebb04514a1231793306f7ce9 | |
| dc.journal.info | Volume 11, Issue 8 | |
