Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Leslie Ching Ow Tiong"'
Autor:
Leslie Ching Ow Tiong, Hyuk Jun Yoo, Nayeon Kim, Chansoo Kim, Kwan-Young Lee, Sang Soo Han, Donghun Kim
Publikováno v:
npj Computational Materials, Vol 10, Iss 1, Pp 1-12 (2024)
Abstract Although robot-based automation in chemistry laboratories can accelerate the material development process, surveillance-free environments may lead to dangerous accidents primarily due to machine control errors. Object detection techniques ca
Externí odkaz:
https://doaj.org/article/2f5bca8b94604842bf23a01c7f5a72a6
Publikováno v:
IEEE Access, Vol 12, Pp 106056-106069 (2024)
The rapid expansion of online learning has ushered in new educational opportunities, but concurrently introduced challenges in preserving academic integrity during assessments. This transition accentuates the need to address the heightened risks of e
Externí odkaz:
https://doaj.org/article/b7a8ed921ad044df81c7e435d4dc0b5b
Publikováno v:
npj Computational Materials, Vol 6, Iss 1, Pp 1-11 (2020)
Abstract The robust and automated determination of crystal symmetry is of utmost importance in material characterization and analysis. Recent studies have shown that deep learning (DL) methods can effectively reveal the correlations between X-ray or
Externí odkaz:
https://doaj.org/article/0d364a8f4eba4550813da789ef5c508a
Publikováno v:
Applied Sciences, Vol 9, Iss 13, p 2709 (2019)
Periocular recognition remains challenging for deployments in the unconstrained environments. Therefore, this paper proposes an RGB-OCLBCP dual-stream convolutional neural network, which accepts an RGB ocular image and a colour-based texture descript
Externí odkaz:
https://doaj.org/article/5abff36f73194435a830a12b9a16906b
Publikováno v:
IEEE Signal Processing Letters. :1-5
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::857e82f157fabcce3f9a079f77524398
https://doi.org/10.1109/lsp.2023.3256320
https://doi.org/10.1109/lsp.2023.3256320
Publikováno v:
Computer Vision – ACCV 2022 ISBN: 9783031263187
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e08ffeabb797d385b528b12e247540f6
https://doi.org/10.1007/978-3-031-26319-4_13
https://doi.org/10.1007/978-3-031-26319-4_13
Publikováno v:
The Journal of Physical Chemistry Letters. 12:8376-8383
We report a deep learning (DL) model that predicts various material properties while accepting directly accessible inputs from routine experimental platforms: chemical compositions and diffraction data, which can be obtained from the X-ray or electro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::39a3045fde1906b8ddd5aaf31c6733c4
https://doi.org/10.1021/acs.jpclett.1c02305
https://doi.org/10.1021/acs.jpclett.1c02305
Publikováno v:
npj Computational Materials, Vol 6, Iss 1, Pp 1-11 (2020)
The robust and automated determination of crystal symmetry is of utmost importance in material characterization and analysis. Recent studies have shown that deep learning (DL) methods can effectively reveal the correlations between X-ray or electron-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ab377c73e241c9b28a36b772f8ab45ca
https://doaj.org/article/0d364a8f4eba4550813da789ef5c508a
https://doaj.org/article/0d364a8f4eba4550813da789ef5c508a
Publikováno v:
2022 the 5th International Conference on Machine Vision and Applications (ICMVA).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::75be7f0969055efbcc7ae26c73fe320f
https://doi.org/10.1145/3523111.3523112
https://doi.org/10.1145/3523111.3523112
Publikováno v:
Multimedia Tools and Applications. 78:22743-22772
Although there is an abundance of current research on facial recognition, it still faces significant challenges that are related to variations in factors such as aging, poses, occlusions, resolution, and appearances. In this paper, we propose a Multi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0ed97ec4e15e02e3d3f7b4f3592890e0
https://doi.org/10.1007/s11042-019-7618-0
https://doi.org/10.1007/s11042-019-7618-0