Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Kailun Deng"'
Autor:
Veronica Marchante Rodriguez, Marzio Grasso, Yifan Zhao, Haochen Liu, Kailun Deng, Andrew Roberts, Gareth James Appleby-Thomas
Publikováno v:
Journal of Composites Science, Vol 6, Iss 10, p 282 (2022)
The present research is aimed at the study of the failure analysis of composite panels impacted orthogonally at a high velocity and with an angle. Woven carbon-fibre panels with and without external Kevlar layers were impacted at different energy lev
Externí odkaz:
https://doaj.org/article/9f50d563dff249af9b48635e843508d9
Publikováno v:
Neural Computing and Applications. 34:21701-21714
Quantitative defect and damage reconstruction play a critical role in industrial quality management. Accurate defect characterisation in Infrared Thermography (IRT), as one of the widely used Non-Destructive Testing (NDT) techniques, always demands a
Autor:
Haochen Liu, Lawrence Tinsley, Kailun Deng, Yizhong Wang, Andrew Starr, Zhenmao Chen, Yifan Zhao
Laser Thermography manifests superior sensitivity and compatibility to detect cracks and small subsurface defects. However, the existing related systems have limitations on either inspection efficiency or unknown directional cracks due to the utiliza
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::48edbf577ea7f5e1f63c89efc86d5de9
https://dspace.lib.cranfield.ac.uk/handle/1826/19483
https://dspace.lib.cranfield.ac.uk/handle/1826/19483
With the increasingly comprehensive utilisation of Carbon Fibre-Reinforced Polymers (CFRP) in modern industry, defects detection and characterisation of these materials have become very important and draw significant research attention. During the pa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::efbb78388cb118f21e695da52cb2b7e2
https://dspace.lib.cranfield.ac.uk/handle/1826/19123
https://dspace.lib.cranfield.ac.uk/handle/1826/19123
This paper proposes a novel framework to characterise the morphological pattern of Barely Visible Impact Damage using machine learning. Initially, a sequence of image processing methods are introduced to extract the damage contour, which is then desc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::02217f9dfc0710b9effa34f2491f3a3d
https://dspace.lib.cranfield.ac.uk/handle/1826/17349
https://dspace.lib.cranfield.ac.uk/handle/1826/17349
Publikováno v:
Talanta. 192:212-219
Nucleus imaging is of great importance for understanding cellular processes of genetic expression, proliferation and growth, etc. Although many nucleic-acid selective dyes for nucleus staining are available, few of them meet multiple standards. Herei
Publikováno v:
SSRN Electronic Journal.
Publikováno v:
Sensors and Actuators B: Chemical. 296:126645
Numerous efforts are contributed to develop fluorescent sensors for qualitative and quantitative detection of cyanide anion (CN−) as it is inevitably utilized in various fields and detrimental to human health and ecosystem. Herein, a new fluorescen