no code implementations • 31 Oct 2023 • Cesare Donati, Martina Mammarella, Fabrizio Dabbene, Carlo Novara, Constantino Lagoa
The aim of this paper is to present a novel physics-based framework for the identification of dynamical systems, in which the physical and structural insights are reflected directly into a backpropagation-based learning algorithm.
no code implementations • 6 Feb 2022 • Martina Mammarella, Cesare Donati, Takumi Shimizu, Masaya Suenaga, Lorenzo Comba, Alessandro Biglia, Kuniaki Uto, Takeshi Hatanaka, Paolo Gay, Fabrizio Dabbene
In this paper, we present an innovative approach where a fleet of unmanned aerial vehicles is exploited to perform remote sensing tasks over an apple orchard for reconstructing a 3D map of the field, formulating the covering control problem to combine the position of a monitoring target and the viewing angle.
no code implementations • 15 Jan 2021 • Martina Mammarella, Victor Mirasierra, Matthias Lorenzen, Teodoro Alamo, Fabrizio Dabbene
In this paper, a sample-based procedure for obtaining simple and computable approximations of chance-constrained sets is proposed.
no code implementations • 13 Apr 2020 • Teodoro Alamo, Daniel G. Reina, Martina Mammarella, Alberto Abella
In an attempt to facilitate the rapid response to the study of the seasonal behaviour of Covid-19, we enumerate the main open resources in terms of weather and climate variables.