Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Daisy H. Green"'
Publikováno v:
IEEE Open Journal of Instrumentation and Measurement, Vol 1, Pp 1-14 (2022)
Electromechanical systems experience both gradual and sudden fault conditions. Power monitoring provides a valuable approach for detecting faults, essentially turning a machine into its own sensor for observing developing and abrupt failures. Machine
Externí odkaz:
https://doaj.org/article/5ba3e98657484dcdaacd0e3a5656fa62
Autor:
Daisy H. Green, Aaron W. Langham, Devin W. Quinn, Thomas C. Krause, Steven R. Shaw, Steven B. Leeb
Publikováno v:
IEEE Access, Vol 10, Pp 117985-117999 (2022)
Nonintrusive identification of the energy consumption of individual loads from an aggregate power stream typically relies on relatively well-defined transient signatures. However, some loads have non-constant power demand that varies with loading con
Externí odkaz:
https://doaj.org/article/ce09955e06d0478a91aef91f5d2de020
Autor:
Thomas Deeter, Daisy H. Green, Stephen Kidwell, Thomas J. Kane, John S. Donnal, Katherine Vasquez, Bartholomew Sievenpiper, Steven B. Leeb
Publikováno v:
IEEE Access, Vol 9, Pp 35633-35645 (2021)
Trends in power system simulation that demand computationally-intensive, physics-based models may impede the acquisition of useful results for applications like condition-based maintenance, electrical plant load analysis (EPLA), and the scheduling an
Externí odkaz:
https://doaj.org/article/decba07be21f46af9cdc23afada06f64
Publikováno v:
IEEE Transactions on Industrial Informatics. 19:2363-2373
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::246b53837dfae927e19bd35f83b78a21
https://doi.org/10.1109/tii.2022.3202798
https://doi.org/10.1109/tii.2022.3202798
Publikováno v:
IEEE Transactions on Instrumentation and Measurement. 71:1-9
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::db7cf8b1aae37bdfd870cdb99277bcc7
https://doi.org/10.1109/tim.2021.3135321
https://doi.org/10.1109/tim.2021.3135321
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. :1-15
Practical machine learning applications for streaming data can involve concept drift (the change in statistical properties of data over time), one-shot or few-shot learning (starting with only one or a few examples for each class), a scarcity of repr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fc01023f9117d1413185bc657e96c86b
https://doi.org/10.1109/tnnls.2022.3184011
https://doi.org/10.1109/tnnls.2022.3184011
Publikováno v:
IEEE Transactions on Instrumentation and Measurement. 71:1-12
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9d11c528065fca06fcc731e683d3c4b1
https://doi.org/10.1109/tim.2022.3193178
https://doi.org/10.1109/tim.2022.3193178
Autor:
Daisy H. Green, Thomas Krause, Lukasz Huchel, Steven B. Leeb, Andrew Moeller, Katherine Camenzind
Publikováno v:
IEEE Transactions on Instrumentation and Measurement. 71:1-10
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a4ec9a77d1ffd10cb5d9b0e30d46afa1
https://doi.org/10.1109/tim.2021.3128709
https://doi.org/10.1109/tim.2021.3128709
Publikováno v:
IEEE Transactions on Instrumentation and Measurement. 71:1-10
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dc162a0df199725b466a57ab63894090
https://doi.org/10.1109/tim.2022.3151146
https://doi.org/10.1109/tim.2022.3151146
Autor:
Joseph W. O'Connell, Daisy H. Green, Lukasz Huchel, Steven B. Leeb, Andrew Moeller, Kahyun Lee, Stephen Kidwell, Brian T. Mills
Publikováno v:
IEEE Sensors Journal. 21:19268-19278
Ventilation systems depend on periodic preventive maintenance. Unfortunately, mission critical systems, exposed to varying levels of particulates and other contaminants, may require unpredictable and highly variable maintenance intervals. Direct sens
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::55bde671790afe8a4ecd94732939d504
https://doi.org/10.1109/jsen.2021.3092184
https://doi.org/10.1109/jsen.2021.3092184