| dc.contributor.author | Tang, Haiyang | |
| dc.contributor.author | Ren, Jinghao | |
| dc.contributor.author | Wu, Binrui | |
| dc.contributor.author | Dang, Jiachen | |
| dc.contributor.author | Peng, Bo | |
| dc.contributor.author | Yi, Xian | |
| dc.contributor.author | Liu, Ruidi | |
| dc.date.accessioned | 2026-08-20T21:15:40Z | |
| dc.date.available | 2026-08-20T21:15:40Z | |
| dc.date.copyright | 2026/04/19 | |
| dc.date.issued | 2026 | |
| dc.identifier.other | JAEEEZ.ASENG-6756.pdf | |
| dc.identifier.uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4314196 | |
| dc.description.abstract | AbstractAircraft icing poses a serious threat to flight safety, and accurately identifying
the three-dimensional (3D) pore structures within dynamic ice is crucial for informing
anti-icing and deicing strategies. Although micro-nano computed tomography (...Practical ApplicationsAircraft icing is a major hazard to flight safety, and understanding the three-dimensional
(3D) pore structures in dynamic ice is key to better anti-icing and deicing. While
micro-nano computed tomography (CT) can take images of ice’... | |
| dc.publisher | American Society of Civil Engineers | |
| dc.title | StructGNN-UNet: Research on Automatic 3D Segmentation of Aircraft Ice Pores Based on Graph Neural Networks | |
| dc.type | Journal Article | |
| dc.journal.volume | 39 | |
| dc.journal.issue | 4 | |
| dc.journal.title | Journal of Aerospace Engineering | |
| dc.identifier.doi | 10.1061/JAEEEZ.ASENG-6756 | |
| dc.journal.fristpage | 04026019-1 | |
| dc.journal.lastpage | 04026019-16 | |
| dc.page | 16 | |
| dc.tree | Journal of Aerospace Engineering:;2026:;Volume ( 039 ):;issue: 004 | |
| dc.contenttype | Fulltext | |