Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Carlos Puerto-Santana"'
Autor:
Carlos Puerto-Santana, Concha Bielza, Javier Diaz-Rozo, Guillem Ramirez-Gargallo, Filippo Mantovani, Gaizka Virumbrales, Jesus Labarta, Pedro Larranaga
Publikováno v:
UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
IEEE Internet of Things Journal, ISSN 2327-4662, 2022-10, Vol. 9, No. 20
Universitat Politècnica de Catalunya (UPC)
IEEE Internet of Things Journal, ISSN 2327-4662, 2022-10, Vol. 9, No. 20
The degradation of critical components inside large industrial assets, such as ball-bearings, has a negative impact on production facilities, reducing the availability of assets due to an unexpectedly high failure rate. Machine learning- based monito
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::70b2b609ba44ac8aa230859a2ae344fa
https://doi.org/10.1109/jiot.2022.3173064
https://doi.org/10.1109/jiot.2022.3173064
Publikováno v:
16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021) | 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021) | 22-24 Sep 2021 | Bilbao, España
16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021) ISBN: 9783030878689
16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021) ISBN: 9783030878689
Much attention has been given to supervised feature subset selection methodologies in data streams. However, less attention has been given to data streams produced by sensors in industrial environments, where labels are difficult to obtain. Feature s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2e1f183d1018626bbccf828e0c66cba3
https://oa.upm.es/74471/
https://oa.upm.es/74471/
Autor:
Javier Diaz-Rozo, Concha Bielza, Pedro Larrañaga, Carlos Puerto-Santana, David Atienza, Alberto Ogbechie
Publikováno v:
Industrial Applications of Machine Learning ISBN: 9781351128384
Industrial Applications of Machine Learning
Industrial Applications of Machine Learning
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ae107a23e730fd0d2dd027e858203386
https://doi.org/10.1201/9781351128384-1
https://doi.org/10.1201/9781351128384-1
Autor:
Pedro Larrañaga, David Atienza, Javier Diaz-Rozo, Alberto Ogbechie, Carlos Puerto-Santana, Concha Bielza
Publikováno v:
Industrial Applications of Machine Learning ISBN: 9781351128384
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::583691098c4a4bb56a13c4893013f334
https://doi.org/10.1201/9781351128384-2
https://doi.org/10.1201/9781351128384-2
Autor:
Carlos Puerto-Santana, David Atienza, Pedro Larrañaga, Javier Diaz-Rozo, Alberto Ogbechie, Concha Bielza
Publikováno v:
Industrial Applications of Machine Learning ISBN: 9781351128384
Industrial Applications of Machine Learning
Industrial Applications of Machine Learning
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::85b40ba8d6d40807bc8f1669e42f781d
https://doi.org/10.1201/9781351128384-5
https://doi.org/10.1201/9781351128384-5
Autor:
Concha Bielza, David Atienza, Pedro Larrañaga, Carlos Puerto-Santana, Alberto Ogbechie, Javier Diaz-Rozo
Publikováno v:
Industrial Applications of Machine Learning ISBN: 9781351128384
Industrial Applications of Machine Learning
Industrial Applications of Machine Learning
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eb46a1702114bec6337b7204d1ff6901
https://doi.org/10.1201/9781351128384-4
https://doi.org/10.1201/9781351128384-4
Autor:
Javier Diaz-Rozo, Pedro Larrañaga, Carlos Puerto-Santana, Alberto Ogbechie, David Atienza, Concha Bielza
Publikováno v:
Industrial Applications of Machine Learning ISBN: 9781351128384
Industrial Applications of Machine Learning
Industrial Applications of Machine Learning
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::be8eddfc9872046d1242df949553a107
https://doi.org/10.1201/9781351128384-3
https://doi.org/10.1201/9781351128384-3
Autor:
Alberto Ogbechie, Concha Bielza, David Atienza, Carlos Puerto-Santana, Pedro Larrañaga, Javier Diaz-Rozo
Publikováno v:
Industrial Applications of Machine Learning ISBN: 9781351128384
Industrial Applications of Machine Learning
Industrial Applications of Machine Learning
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f1d60a84b8484f6dbeab64e387968278
https://doi.org/10.1201/9781351128384-6
https://doi.org/10.1201/9781351128384-6
Publikováno v:
Advances in Artificial Intelligence | 18th Conference of the Spanish Association for Artificial Intelligence (CAEPIA 2018) | 23-26 Oct 2018 | Granada, España
Archivo Digital UPM
Universidad Politécnica de Madrid
Advances in Artificial Intelligence ISBN: 9783030003739
CAEPIA
Archivo Digital UPM
Universidad Politécnica de Madrid
Advances in Artificial Intelligence ISBN: 9783030003739
CAEPIA
Hidden Markov models have been successfully applied to model signals and dynamic data. However, when dealing with many variables, traditional hidden Markov models do not take into account asymmetric dependencies, leading to models with overfitting an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b4e70d1fe582e1e613a4ada537e3ef35
https://oa.upm.es/54653/
https://oa.upm.es/54653/