Zobrazeno 1 - 10
of 54
pro vyhledávání: '"Xudong Jian"'
Publikováno v:
Data-Centric Engineering, Vol 3 (2022)
The dimension of models derived on the basis of data is commonly restricted by the number of observations, or in the context of monitored systems, sensing nodes. This is particularly true for structural systems, which are typically high-dimensional i
Externí odkaz:
https://doaj.org/article/1d9538089d3747aa85fb88867ea8f5e2
Publikováno v:
Structural Control and Health Monitoring. 29
Publikováno v:
Structural Control and Health Monitoring. 29
Publikováno v:
Heritage Science, Vol 12, Iss 1, Pp 1-14 (2024)
Abstract This study compares the surface patina of ancient tin rich bronze with pure hydrothermally synthesized SnO2 nanoparticles using various analytical techniques, including metallographic microscopy, scanning electron microscopy, transmission el
Externí odkaz:
https://doaj.org/article/5789d9dc8a62485ca28eecec0dc88308
Publikováno v:
Structural Control and Health Monitoring. 29
Publikováno v:
Journal of International Medical Research, Vol 52 (2024)
Objective This study aimed to investigate the role of RAB32 in glioblastomas and its molecular mechanisms that regulate gliomas. Methods The expression and prognostic value of RAB32 were evaluated using western blotting and the Gene Expression Profil
Externí odkaz:
https://doaj.org/article/8ad97223ef8a4885b0ec3fe2600bed06
Autor:
Xudong JIAN
Complicated traffic scenarios, including random change of vehicles’ speed and lane, as well as the simultaneous presence of multiple vehicles on bridge, are main obstacles that prevents bridge weigh-in-motion (BWIM) technique from reliable and accu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b5cde87a8597a4c7280b9d5c7119dad9
https://engrxiv.org/anfmt
https://engrxiv.org/anfmt
Publikováno v:
Mechanical Systems and Signal Processing. 182:109607
Publikováno v:
Structural Control and Health Monitoring. 28
Publikováno v:
Journal of Sensors, Vol 2019 (2019)
Collecting the information of traffic load, especially heavy trucks, is crucial for bridge statistical analysis, safety evaluation, and maintenance strategies. This paper presents a traffic sensing methodology that combines a deep learning based comp