Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Jiahao Xia"'
Autor:
Hannah Younes, Clinton Andrews, Robert B. Noland, Jiahao Xia, Song Wen, Wenwen Zhang, Dimitri Metaxas, Leigh Ann Von Hagen, Jie Gong
Publikováno v:
Journal of Urban Mobility, Vol 5, Iss , Pp 100071- (2024)
We analyze the effect of a bicycle lane on traffic speeds. Computer vision techniques are used to detect and classify the speed and trajectory of over 9,000 motor-vehicles at an intersection that was part of a pilot demonstration in which a bicycle l
Externí odkaz:
https://doaj.org/article/0f3b4d9db09e41f8ad18829cb662ab89
Publikováno v:
Applied Sciences, Vol 14, Iss 11, p 4559 (2024)
This paper presents an efficient integrated multidisciplinary design optimization method for shaping a high-pressure cooling turbine blade in aero engines. This approach utilizes a novel regression/interpolation combination surrogate model (R/ICSM),
Externí odkaz:
https://doaj.org/article/8a8aa188ae0347faaafe47a10e2e65be
Publikováno v:
Journal of Imaging, Vol 10, Iss 3, p 52 (2024)
In this paper, a Segment Anything Model (SAM)-based pedestrian infrastructure segmentation workflow is designed and optimized, which is capable of efficiently processing multi-sourced geospatial data, including LiDAR data and satellite imagery data.
Externí odkaz:
https://doaj.org/article/54726caa84bf47699d502a80679bb812
Publikováno v:
International Journal of Digital Earth, Vol 12, Iss 1, Pp 62-77 (2019)
China has experienced rapid and massive urban expansion in recent decades. To address the opportunities and challenges posed by urbanization, it is important to investigate this process. Although many studies have characterized China’s urbanization
Externí odkaz:
https://doaj.org/article/0d4965337d1e4b3590914a71dd4b3008
Head Pose Estimation in the Wild Assisted by Facial Landmarks Based on Convolutional Neural Networks
Publikováno v:
IEEE Access, Vol 7, Pp 48470-48483 (2019)
Convolutional neural networks (CNNs) exhibit excellent performance on the head pose estimation problem under controllable conditions, but their generalization ability in the wild needs to be improved. To address this issue, we propose an approach inv
Externí odkaz:
https://doaj.org/article/9d18afa1229445728473b9826ce8c874
Publikováno v:
Frontiers in Neurorobotics, Vol 14 (2020)
Due to the complex visual environment and incomplete display of parking slots on around-view images, vision-based parking slot detection is a major challenge. Previous studies in this field mostly use the existing models to solve the problem, the ste
Externí odkaz:
https://doaj.org/article/1a5d61f7379f46aba4ab7d9161d9fac0
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. :1-12
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c8459956b6e5a362e29b06e402f3e999
https://doi.org/10.1109/tnnls.2022.3228102
https://doi.org/10.1109/tnnls.2022.3228102
Publikováno v:
BCP Business & Management. 35:202-209
Investors often use gold and Bitcoin to reduce the risk of financial investments by combining them due to their low correlation and different dynamic correlation. Therefore, it is essential to study how a combination of the two can be invested to max
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ba5fbf8b686e01af8d7555eada61fe4c
https://doi.org/10.54691/bcpbm.v35i.3294
https://doi.org/10.54691/bcpbm.v35i.3294
Publikováno v:
Peer-to-Peer Networking and Applications. 15:1398-1416
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::604c68bc6407ca1f39beed882d3cbebd
https://doi.org/10.1007/s12083-022-01309-4
https://doi.org/10.1007/s12083-022-01309-4
Publikováno v:
Nanoscale. 14:9925-9931
In single molecule studies, the ergodic hypothesis is inherently assumed, which states that the time average of a physical quantity of a single member of an ensemble is the same as the average of the same quantity on the whole ensemble at a given tim
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::233594a1994694ea1856b627f0fd0230
https://doi.org/10.1039/d2nr02614g
https://doi.org/10.1039/d2nr02614g