Zobrazeno 1 - 10 of 29 pro vyhledávání: '"Yongyan Hou"'
1
Akademický článek
A Triple Unlocking Mechanism Model Against Forging Signature Attack Based on Multivariate Polynomial Public Key Cryptosystem
Autor: Yongyan Hou, Baiyang Dong, Wenqiang Guo, Xin Wang, Qinkun Xiao
Publikováno v: IEEE Access, Vol 11, Pp 134614-134622 (2023)
Since the original signature model did not account for the possible threat of the forging signature attack, the majority of existing multivariate polynomial public key based signature schemes are at risk of forging signatures by equivalent key attack
Externí odkaz: https://doaj.org/article/58bc457bd3af493ca5ff94f4c4780956
Zobrazit plný text záznamu
2
Akademický článek
Bearing Fault Diagnosis Under Small Data Set Condition: A Bayesian Network Method With Transfer Learning for Parameter Estimation
Autor: Yongyan Hou, Ao Yang, Wenqiang Guo, Enrang Zheng, Qinkun Xiao, Zhigao Guo, Zixuan Huang
Publikováno v: IEEE Access, Vol 10, Pp 35768-35783 (2022)
Bearings are broadly applied in various types of industrial systems. Fault diagnosis, as a promising way for reliability of modern industrial internet of thing applications, has attracted increasing attention from both academia and industry fields. B
Externí odkaz: https://doaj.org/article/d1307744e24e4067b4c720f5782c1ab5
Zobrazit plný text záznamu
3
Akademický článek
Learning Bayesian Network Parameters With Small Data Set: A Parameter Extension under Constraints Method
Autor: Yongyan Hou, Enrang Zheng, Wenqiang Guo, Qinkun Xiao, Ziwei Xu
Publikováno v: IEEE Access, Vol 8, Pp 24979-24989 (2020)
Recent advances have illustrated substantial benefits from learning Bayesian networks (BNs). However, when the available data size is small, the BN parameter learning becomes a key challenge in many intelligent applications. By integrating both sampl
Externí odkaz: https://doaj.org/article/7dfb37d8e38e479990bec2df876012ab
Zobrazit plný text záznamu
6
Learning Bayesian Network Parameters With Small Data Set: A Parameter Extension under Constraints Method
Autor: Enrang Zheng, Xu Ziwei, Qinkun Xiao, Wenqiang Guo, Yongyan Hou
Publikováno v: IEEE Access, Vol 8, Pp 24979-24989 (2020)
Recent advances have illustrated substantial benefits from learning Bayesian networks (BNs). However, when the available data size is small, the BN parameter learning becomes a key challenge in many intelligent applications. By integrating both sampl
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5b92507c1e7d953acb8a0eaf7088639e
https://doi.org/10.1109/access.2020.2971099
Zobrazit plný text záznamu
7
Pain Assessment Using Facial Action Units and Bayesian Network
Autor: Yongyan Hou, Wenqiang Guo, Zixuan Huang, Xu Ziwei, Zhigao Guo, Lingling Mao
Publikováno v: 2021 40th Chinese Control Conference (CCC).
Automatic pain assessment systems based on facial videos are consistently studied due to the demand of robust and cost-effective pain management. In order to improve the assessment accuracy under the dynamic and uncertain pain assessment environment,
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::0202718df673dfe50236df93b6ec0ceb
https://doi.org/10.23919/ccc52363.2021.9550304
Zobrazit plný text záznamu
8
Environment Parameter Rating Evaluation for Smart Museum Based on Improved K-Means Clustering Algorithm
Autor: Wenqiang Guo, Zixuan Huang, Yongyan Hou, Lingling Mao, Qinkun Xiao, Jia Jia
Publikováno v: 2020 Chinese Control And Decision Conference (CCDC).
The cultural heritage is a unique and irreplaceable witness of our past and it is vulnerable to natural disasters and anthropic behaviors. A rating evaluation system for smart museum environment parameter can serve as a reference for decision-making.
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::e3af66a78e916a1cdb14df0fd2d38f0f
https://doi.org/10.1109/ccdc49329.2020.9164662
Zobrazit plný text záznamu
9
A Novel Algorithm for Bayesian Network Parameter Learning with Informative Prior Constraints and Maximum Entropy Model
Autor: Yongyan Hou, Li Ran, Wenqiang Guo, Gao Wenqiang, Xu Cheng, Qinkun Xiao
Publikováno v: 2018 37th Chinese Control Conference (CCC).
A key challenge in many real-world decision support applications is the parameters learning of Bayesian networks (BNs) is, particularly when the available data is small. An effective way to address this challenge is to introduce related domain expert
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::75af76f2065ec7e3175c7f3dae610726
https://doi.org/10.23919/chicc.2018.8483914
Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání

Omezení vyhledávání
Plný text Recenzováno Digitální knihovna AV ČR
Zdroje