Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Yuanrong Xu"'
Autor:
Yuanrong Xu
Publikováno v:
Journal of Population Research. 40
Publikováno v:
IEEE Transactions on Information Forensics and Security. 17:226-236
Publikováno v:
IEEE Transactions on Instrumentation and Measurement. 71:1-13
Publikováno v:
IEEE Transactions on Cybernetics. 52:444-458
With the increased model size of convolutional neural networks (CNNs), overfitting has become the main bottleneck to further improve the performance of networks. Currently, the weighting regularization methods have been proposed to address the overfi
Publikováno v:
IEEE Transactions on Systems, Man, and Cybernetics: Systems. 51:5721-5731
Pore-based fingerprint recognition has been researched for decades. Many algorithms have been proposed to improve the recognition accuracy of the system. However, the accuracies are always improved at the cost of speed. This article proposes a novel
Publikováno v:
2022 5th International Conference on Pattern Recognition and Artificial Intelligence (PRAI).
Publikováno v:
Applied Engineering in Agriculture. 37:1045-1054
HighlightsHA-RF disinfestation treatment had better pre-drying effect of rough rice than that of HA treatmentHighest moisture loss (8.7%) was obtained for samples with highest IMC (39.6% d.b.)Initial moisture content (IMC) correlated negatively with
Publikováno v:
Food and Bioprocess Technology. 13:419-429
Carrot cubes were firstly blanched by radio frequency (RF) heating and then pre-dried by ultrasound-assisted osmotic dehydration (UOD). Hot air–assisted radio frequency (HA-RF) was applied as a final-stage drying method for pre-dried carrot cubes i
Publikováno v:
Pattern Recognition and Computer Vision ISBN: 9783031189098
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::adba4f0db2d980c36089268614dd47cd
https://doi.org/10.1007/978-3-031-18910-4_42
https://doi.org/10.1007/978-3-031-18910-4_42
Publikováno v:
Neural Computing and Applications. 32:10633-10644
Currently, in order to deploy the convolutional neural networks (CNNs) on the mobile devices and address the over-fitting problem caused by the less abundant datasets, reducing the redundancy of parameters is the main target to construct the mobile C