Zobrazeno 1 - 10
of 579
pro vyhledávání: '"Enrico Macii"'
Publikováno v:
Engineering, Vol 26, Iss , Pp 198-213 (2023)
The rising awareness of environmental issues and the increase of renewable energy sources (RESs) has led to a shift in energy production toward RES, such as photovoltaic (PV) systems, and toward a distributed generation (DG) model of energy productio
Externí odkaz:
https://doaj.org/article/f834cb872093421da3f43cfac0f05e0a
Autor:
Marco Castangia, Angelica Urbanelli, Awet Abraha Girmay, Christian Camarda, Enrico Macii, Edoardo Patti
Publikováno v:
Energy Informatics, Vol 5, Iss S4, Pp 1-16 (2022)
Abstract Nowadays, Non-Intrusive Load Monitoring techniques are sufficiently accurate to provide valuable insights to the end-users and improve their electricity behaviours. Indeed, previous works show that commonly used appliances (fridge, dishwashe
Externí odkaz:
https://doaj.org/article/eac57857399146e99b0696e58419d30e
Publikováno v:
Frontiers in Neuroscience, Vol 16 (2022)
Spiking Neural Networks (SNNs), known for their potential to enable low energy consumption and computational cost, can bring significant advantages to the realm of embedded machine learning for edge applications. However, input coming from standard d
Externí odkaz:
https://doaj.org/article/1886ca102fb144a794d78bc9b72ae584
Autor:
Alessandro Aliberti, Daniele Fucini, Lorenzo Bottaccioli, Enrico Macii, Andrea Acquaviva, Edoardo Patti
Publikováno v:
IEEE Access, Vol 9, Pp 122829-122846 (2021)
Due to the continuous increasing importance of renewable energy sources as an alternative to fossil fuels, to contrast air pollution and global warming, the prediction of Global Horizontal Irradiation (GHI), one of the main parameters determining sol
Externí odkaz:
https://doaj.org/article/22050883418a4545883523405ed71062
Publikováno v:
IEEE Access, Vol 9, Pp 150495-150507 (2021)
In the Demand Side Management (DSM) context, residential customers have the potential for reducing costs and relieving the grid with non-thermostatic appliances. These appliances might be optimally scheduled by a central entity, taking into account u
Externí odkaz:
https://doaj.org/article/6505e0e70dab42aa973e8214a9f6020f
Autor:
Chen Xie, Alessio Burrello, Francesco Daghero, Luca Benini, Andrea Calimera, Enrico Macii, Massimo Poncino, Daniele Jahier Pagliari
Publikováno v:
Sensors, Vol 23, Iss 4, p 2065 (2023)
Hand gesture recognition applications based on surface electromiographic (sEMG) signals can benefit from on-device execution to achieve faster and more predictable response times and higher energy efficiency. However, deploying state-of-the-art deep
Externí odkaz:
https://doaj.org/article/e2afbec0de2642088e76caab77b57ff8
Publikováno v:
Applied Sciences, Vol 12, Iss 15, p 7730 (2022)
The evolution of industrial digitalisation has accelerated in recent years with the availability of hyperconnectivity, low-cost miniaturised electronic components, edge computing, and Internet of Things (IoT) technologies. More generally, with these
Externí odkaz:
https://doaj.org/article/35b93055eb32426880e8629b506c1652
Autor:
Yukai Chen, Donkyu Baek, Jaemin Kim, Santa Di Cataldo, Naehyuck Chang, Enrico Macii, Sara Vinco, Massimo Poncino
Publikováno v:
IEEE Access, Vol 7, Pp 25779-25791 (2019)
Driving range is one of the most critical issues for electric vehicles (EVs): running out of battery charge while driving results in serious inconvenience even comparable to a vehicle breakdown, as an effect of long fuel recharging times and lack of
Externí odkaz:
https://doaj.org/article/0f6cf950b35e4d6ebaaaec3a2f299436
Autor:
Alessandro Aliberti, Irene Pupillo, Stefano Terna, Enrico Macii, Santa Di Cataldo, Edoardo Patti, Andrea Acquaviva
Publikováno v:
IEEE Access, Vol 7, Pp 69311-69325 (2019)
Continuous glucose monitoring systems (CGMSs) allow measuring the blood glycaemic value of a diabetic patient at a high sampling rate, producing a considerable amount of data. These data can be effectively used by machine learning techniques to infer
Externí odkaz:
https://doaj.org/article/eefdf6bd91fa430ea2be08fc89fa736a
Publikováno v:
IEEE Access, Vol 7, Pp 172030-172044 (2019)
In recent years, designers are trying to move part of the computing tasks involved in Internet of Things applications from the cloud to the edge. This imposes increasing performance demands on edge nodes, which usually clash with their limited energy
Externí odkaz:
https://doaj.org/article/6dfa7e73a2de4a5989cdf35abe97c5ee