Zobrazeno 1 - 10
of 67
pro vyhledávání: '"Antonio Guarino"'
Publikováno v:
Energies, Vol 12, Iss 17, p 3377 (2019)
In this paper, the Dual Kalman Filter (DKF) is used for the parametric identification of an RC model of a Polymer Electrolyte Membrane Fuel Cell (FC) stack. The identification is performed for diagnostic purposes, starting from time-domain voltage an
Externí odkaz:
https://doaj.org/article/8907eb2da26a4b568095d410fee28246
Autor:
Antonella Ianni, Antonio Guarino
Publikováno v:
Games, Vol 1, Iss 4, Pp 438-458 (2010)
We study social learning in a large population of agents who only observe the actions taken by their neighbours. Agents have to choose one, out of two, reversible actions, each optimal in one, out of two, unknown states of the world. Each agent choos
Externí odkaz:
https://doaj.org/article/2b49a80bdb23485c82709a5d6e78d19e
Publikováno v:
Journal of Financial Economics. 146:1044-1072
Akademický článek
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Publikováno v:
SSRN Electronic Journal.
Autor:
Giovanni Spagnuolo, Antonio Guarino
Publikováno v:
International Journal of Hydrogen Energy. 46:34854-34866
In this paper, a procedure aimed at the automatic extraction of the features from polymer electrolyte membrane fuel cell impedance spectra is proposed. An artificial neural network that is trained by exploiting the similarity learning concept has bee
Publikováno v:
SSRN Electronic Journal.
Autor:
Antonio Guarino, Damien Chanal, Elodie Pahon, Marie Cecile Pera, Giovanni Spagnuolo, Daniel Hissel, Didier Chamagne, Nadia Yousfi Steiner
Publikováno v:
SSRN Electronic Journal.
Autor:
Antonio Guarino
Il contributo, partendo dall'utilizzazione dell'istituto dei patrimoni destinati nell'ambito della riforma del terzo settore, esamina le ricadute sulla disciplina degli enti religiosi.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3730::41da50db883abf3a938b143c81131827
https://hdl.handle.net/11588/906684
https://hdl.handle.net/11588/906684
In this paper a computationally efficient optimization approach to the parametric identification of a fuel cell equivalent circuit model is presented. It is based on the inverse model and on machine learning regressions. During the training phase, th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d4b1ebd797a196de8d0c462ef2d70417
http://hdl.handle.net/11583/2935155
http://hdl.handle.net/11583/2935155