|
Records |
Links |
|
Author |
Samal, D.; Gauquelin, N.; Takamura, Y.; Lobato, I.; Arenholz, E.; Van Aert, S.; Huijben, M.; Zhong, Z.; Verbeeck, J.; Van Tendeloo, G.; Koster, G. |
|
|
Title |
Unusual structural rearrangement and superconductivity in infinite layer cuprate superlattices |
Type |
A1 Journal article |
|
Year |
2023 |
Publication |
Physical review materials |
Abbreviated Journal |
|
|
|
Volume |
7 |
Issue |
5 |
Pages |
054803 |
|
|
Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT) |
|
|
Abstract |
|
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Wos |
001041792100007 |
Publication Date |
2023-05-30 |
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
2475-9953 |
ISBN |
|
Additional Links |
UA library record; WoS full record |
|
|
Impact Factor |
3.4 |
Times cited |
|
Open Access |
OpenAccess |
|
|
Notes |
Air Force Office of Scientific Research; European Office of Aerospace Research and Development, FA8655-10-1-3077 ; Office of Science, DE-AC02-05CH11231 ; National Science Foundation, DMR-1745450 ; Seventh Framework Programme, 278510 ; Bijzonder Onderzoeksfonds UGent; |
Approved |
Most recent IF: 3.4; 2023 IF: NA |
|
|
Call Number |
EMAT @ emat @c:irua:196973 |
Serial |
8790 |
|
Permanent link to this record |
|
|
|
|
Author |
van der Jeught, S.; Muyshondt, P.G.G.; Lobato, I. |
|
|
Title |
Optimized loss function in deep learning profilometry for improved prediction performance |
Type |
A1 Journal article |
|
Year |
2021 |
Publication |
JPhys Photonics |
Abbreviated Journal |
|
|
|
Volume |
3 |
Issue |
2 |
Pages |
024014 |
|
|
Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT) |
|
|
Abstract |
Single-shot structured light profilometry (SLP) aims at reconstructing the 3D height map of an object from a single deformed fringe pattern and has long been the ultimate goal in fringe projection profilometry. Recently, deep learning was introduced into SLP setups to replace the task-specific algorithm of fringe demodulation with a dedicated neural network. Research on deep learning-based profilometry has made considerable progress in a short amount of time due to the rapid development of general neural network strategies and to the transferrable nature of deep learning techniques to a wide array of application fields. The selection of the employed loss function has received very little to no attention in the recently reported deep learning-based SLP setups. In this paper, we demonstrate the significant impact of loss function selection on height map prediction accuracy, we evaluate the performance of a range of commonly used loss functions and we propose a new mixed gradient loss function that yields a higher 3D surface reconstruction accuracy than any previously used loss functions. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Wos |
000641030000001 |
Publication Date |
2021-03-18 |
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
2515-7647 |
ISBN |
|
Additional Links |
UA library record; WoS full record; WoS citing articles |
|
|
Impact Factor |
|
Times cited |
|
Open Access |
OpenAccess |
|
|
Notes |
|
Approved |
Most recent IF: NA |
|
|
Call Number |
UA @ admin @ c:irua:178171 |
Serial |
6797 |
|
Permanent link to this record |