Zobrazeno 1 - 9
of 9
pro vyhledávání: '"R Lily Hu"'
Autor:
R Lily Hu, Damien Pierce, Yusef Shafi, Anudhyan Boral, Vladimir Anisimov, Sella Nevo, Yi-fan Chen
Publikováno v:
The International Journal of High Performance Computing Applications. 36:510-523
Recent advancements in hardware accelerators such as Tensor Processing Units (TPUs) speed up computation time relative to Central Processing Units (CPUs) not only for machine learning but, as demonstrated here, also for scientific modeling and comput
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f6d0512bb50ce31ace14d47e9d13f481
https://doi.org/10.1177/10943420221102873
https://doi.org/10.1177/10943420221102873
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 60:1-13
Predicting wildfire spread is critical for land management and disaster preparedness. To this end, we present `Next Day Wildfire Spread,' a curated, large-scale, multivariate data set of historical wildfires aggregating nearly a decade of remote-sens
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::30ed45fc8db83d5b21c28f20bcfffe11
https://doi.org/10.1109/tgrs.2022.3192974
https://doi.org/10.1109/tgrs.2022.3192974
Autor:
Kevin Leahy, R. Lily Hu, Ioannis C. Konstantakopoulos, Costas J. Spanos, Alice M. Agogino, Dominic T. J. O’Sullivan
Publikováno v:
International Journal of Prognostics and Health Management, Vol 9, Iss 1 (2018)
Unscheduled or reactive maintenance on wind turbines due to component failure incurs significant downtime and, in turn, loss of revenue. To this end, it is important to be able to perform maintenance before it’s needed. To date, a strong effort ha
Externí odkaz:
https://doaj.org/article/9f489ed8b2c0425f9a1f861923c19d7f
Publikováno v:
IEEE Transactions on Big Data. 6:638-651
We describe the background and an analytical framework for a mathematical optimization model for home energy management systems (HEMS) to manage electricity demand on the smart grid by efficiently shifting electricity loads of households from peak ti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::52c00f1736e40cc3e0e0240c78933d54
https://doi.org/10.1109/tbdata.2016.2639528
https://doi.org/10.1109/tbdata.2016.2639528
Autor:
Dominic T. J. O'Sullivan, Costas J. Spanos, Ioannis C. Konstantakopoulos, R. Lily Hu, Kevin Leahy, Alice M. Agogino
Publikováno v:
International Journal of Prognostics and Health Management, Vol 9, Iss 1 (2018)
Unscheduled or reactive maintenance on wind turbines due to component failure incurs significant downtime and, in turn, loss of revenue. To this end, it is important to be able to performmaintenance before it’s needed. To date, a strong effort has
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::74fd48dd77820fea5109396641793501
https://doi.org/10.36001/ijphm.2018.v9i1.2692
https://doi.org/10.36001/ijphm.2018.v9i1.2692
Autor:
David M. Auslander, Costas J. Spanos, R. Lily Hu, Ioannis C. Konstantakopoulos, Kevin Leahy, Alice M. Agogino
Publikováno v:
ICMLA
Maximising electricity production from wind requires improvement of wind turbine reliability. Component failures result in unscheduled or reactive maintenance on turbines which incurs significant downtime and, in turn, increases production cost, ulti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e426f9c2f7b1e80503603a18b3fe1d64
https://doi.org/10.1109/icmla.2016.0056
https://doi.org/10.1109/icmla.2016.0056
Publikováno v:
ICPHM
Unscheduled or reactive maintenance on wind turbines due to component failures incurs significant downtime and, in turn, loss of revenue. To this end, it is important to be able to perform maintenance before it's needed. By continuously monitoring tu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d6705789cffea8bbd3cd0d000df0a0b4
https://doi.org/10.1109/icphm.2016.7542860
https://doi.org/10.1109/icphm.2016.7542860
Publikováno v:
Volume 1B: 35th Computers and Information in Engineering Conference.
To detect degradation in energy efficiency of a chiller in a chiller plant, a multivariate Gaussian mixture model is applied. This classification technique was selected to take advantage of an expected correlation between measurable state variables a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c9157a9025253df7d3ba41c44e9a7b5e
https://doi.org/10.1115/detc2015-47000
https://doi.org/10.1115/detc2015-47000
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