Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Jiabiao Yi"'
Publikováno v:
Energies, Vol 15, Iss 21, p 8059 (2022)
Aiming at the problem of unbalanced data categories of UHV converter valve fault data, a method for UHV converter valve fault detection based on optimization cost-sensitive extreme random forest is proposed. The misclassification cost gain is integra
Externí odkaz:
https://doaj.org/article/ffcd31d450d94d2eb801a811989205da
Publikováno v:
Sensors, Vol 22, Iss 18, p 6763 (2022)
Aiming at the problem of class imbalance in the wind turbine blade bolts operation-monitoring dataset, a fault detection method for wind turbine blade bolts based on Gaussian Mixture Model–Synthetic Minority Oversampling Technique–Gaussian Mixtur
Externí odkaz:
https://doaj.org/article/ac8158a128e944859e6f6b2e09536ea9
Publikováno v:
Frontiers in Energy Research, Vol 9 (2021)
The number of normal samples of wind turbine generators is much larger than the number of fault samples. To solve the problem of imbalanced classification in wind turbine generator fault detection, a cost-sensitive extremely randomized trees (CS-ERT)
Externí odkaz:
https://doaj.org/article/d49bc9dfa9ee41c4bdfa206013d1d186
Publikováno v:
Sensors, Vol 21, Iss 18, p 6215 (2021)
It is difficult to optimize the fault model parameters when Extreme Random Forest is used to detect the electric pitch system fault model of the double-fed wind turbine generator set. Therefore, Extreme Random Forest which was optimized by improved g
Externí odkaz:
https://doaj.org/article/099a6bf7913b4b78a842947a34dd756a
Publikováno v:
Mathematical Methods in the Applied Sciences. 45:6515-6534
This is the first attempt to investigate the effects of the factors related to non-pharmaceutical interventions (NPIs) and the physical condition of the public on virulence evolution of SARS-CoV-2 and the trend of the epidemics of COVID-19 under an a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a26da308419d39a0a03145be951024e1
https://doi.org/10.1002/mma.8184
https://doi.org/10.1002/mma.8184
Publikováno v:
Fractals.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::27b5c05be39bc786e4695bed30f0d68a
https://doi.org/10.1142/s0218348x23401424
https://doi.org/10.1142/s0218348x23401424
Autor:
Mingzhu Tang, Zixin Liang, Dongxu Ji, Jiabiao Yi, Zhonghui Peng, Yujie Huang, Jiachen Wang, Donglin Chen
Publikováno v:
Applied Thermal Engineering. 227:120386
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d75d722cfb80b6b8ad18b948c9e9c9fd
https://doi.org/10.1016/j.applthermaleng.2023.120386
https://doi.org/10.1016/j.applthermaleng.2023.120386
Publikováno v:
Journal of Engineering Mechanics. 148
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c941d38c49489f7c1a452dd99dc80f30
https://doi.org/10.1061/(asce)em.1943-7889.0002060
https://doi.org/10.1061/(asce)em.1943-7889.0002060
Publikováno v:
Frontiers in Energy Research, Vol 9 (2021)
The number of normal samples of wind turbine generators is much larger than the number of fault samples. To solve the problem of imbalanced classification in wind turbine generator fault detection, a cost-sensitive extremely randomized trees (CS-ERT)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eac5fa90f75ee6f4ffbdead446eaf672
https://www.frontiersin.org/articles/10.3389/fenrg.2021.686616/full
https://www.frontiersin.org/articles/10.3389/fenrg.2021.686616/full
Publikováno v:
Sensors (Basel, Switzerland)
Sensors
Volume 21
Issue 18
Sensors, Vol 21, Iss 6215, p 6215 (2021)
Sensors
Volume 21
Issue 18
Sensors, Vol 21, Iss 6215, p 6215 (2021)
It is difficult to optimize the fault model parameters when Extreme Random Forest is used to detect the electric pitch system fault model of the double-fed wind turbine generator set. Therefore, Extreme Random Forest which was optimized by improved g
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fb78be5501a4875f6703fa613eaff031
https://doi.org/10.3390/s21186215
https://doi.org/10.3390/s21186215