Zobrazeno 1 - 10
of 3 470
pro vyhledávání: '"Lopes,S"'
Publikováno v:
Sensors 2022, 22, 8500
While many companies worldwide are still striving to adjust to Industry 4.0 principles, the transition to Industry 5.0 is already underway. Under such a paradigm, Cyber-Physical Human-centered Systems (CPHSs) have emerged to leverage operator capabil
Externí odkaz:
http://arxiv.org/abs/2212.06294
Autor:
Spini A, L'Abbate L, Ingrasciotta Y, Pellegrini G, Carollo M, Ientile V, Leoni O, Zanforlini M, Ancona D, Stella P, Cavazzana A, Scapin A, Lopes S, Belleudi V, Trifirò G
Publikováno v:
Clinical Epidemiology, Vol Volume 16, Pp 395-407 (2024)
Andrea Spini,1 Luca L’Abbate,2 Ylenia Ingrasciotta,1 Giorgia Pellegrini,1 Massimo Carollo,1 Valentina Ientile,2 Olivia Leoni,3 Martina Zanforlini,4 Domenica Ancona,5 Paolo Stella,5 Anna Cavazzana,6 Angela Scapin,6 Sara Lopes,7 Valeria Belleudi,7 Gi
Externí odkaz:
https://doaj.org/article/23b659e73fea4ebc89dd1ee1d0a3c25e
Autor:
Dérobert, X., Villain, G., Palma-Lopes, S., Bouvard-Coconet, V., Decitre, J.M., Jabbour, J., Qu, S., Geffard, J.L., Durand, O., Gugole, G., Abraham, O.
Publikováno v:
In NDT and E International December 2024 148
We consider the problem of testing for two Gibbs probabilities $\mu_0$ and $\mu_1$ defined for a dynamical system $(\Omega,T)$. Due to the fact that in general full orbits are not observable or computable, one needs to restrict to subclasses of tests
Externí odkaz:
http://arxiv.org/abs/2112.00670
Publikováno v:
In Neurología October 2024 39(8):658-665
Publikováno v:
In Journal of Molecular Structure 15 September 2024 1312 Part 1
Autor:
Castro-Lopes, S., Oliveira, D.M., Abrão, J.E., Assis, L.K.C.S., Silva, J.F.O., Neves-Araújo, J., Soares, J.M., Rodrigues, A.R., Padrón-Hernández, E.
Publikováno v:
In Nano-Structures & Nano-Objects September 2024 39
Autor:
França, E.L.T., Santos, A.R., Assis, L.K.C.S., Castro-Lopes, S., Oliveira, D.M., Carvalho, A.S., Padrón Hernández, E.
Publikováno v:
In Journal of Magnetism and Magnetic Materials 1 September 2024 605
In this work, we study the class of stochastic process that generalizes the Ornstein-Uhlenbeck processes, hereafter called by \emph{Generalized Ornstein-Uhlenbeck Type Process} and denoted by GOU type process. We consider them driven by the class of
Externí odkaz:
http://arxiv.org/abs/2108.06374
Extracting relevant properties of empirical signals generated by nonlinear, stochastic, and high-dimensional systems is a challenge of complex systems research. Open questions are how to differentiate chaotic signals from stochastic ones, and how to
Externí odkaz:
http://arxiv.org/abs/2107.02860