Zobrazeno 1 - 10
of 138
pro vyhledávání: '"Filippo Maria Bianchi"'
Publikováno v:
IEEE Access, Vol 11, Pp 145989-146002 (2023)
Some applications of deep learning require not only to provide accurate results but also to quantify the amount of confidence in their prediction. The management of an electric power grid is one of these cases: to avoid risky scenarios, decision-make
Externí odkaz:
https://doaj.org/article/6b41dd2a703244a89bc210a28acf20a0
Publikováno v:
Energy Conversion and Management: X, Vol 15, Iss , Pp 100239- (2022)
The energy market relies on forecasting capabilities of both demand and power generation that need to be kept in dynamic balance. Nowadays, contracts and auctions of renewable energy in a liberalized electricity market heavily rely on forecasting fut
Externí odkaz:
https://doaj.org/article/3333fc44fb4d4718a8c0a0e866047586
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 75-82 (2021)
Knowledge about frequency and location of snow avalanche activity is essential for forecasting and mapping of snow avalanche hazard. Traditional field monitoring of avalanche activity has limitations, especially when surveying large and remote areas.
Externí odkaz:
https://doaj.org/article/b05622fd7eb5419393fa91097e1bacae
Autor:
Odin Foldvik Eikeland, Inga Setsa Holmstrand, Sigurd Bakkejord, Matteo Chiesa, Filippo Maria Bianchi
Publikováno v:
IEEE Access, Vol 9, Pp 150686-150699 (2021)
Unscheduled power disturbances cause severe consequences both for customers and grid operators. To defend against such events, it is necessary to identify the causes of interruptions in the power distribution network. In this work, we focus on the po
Externí odkaz:
https://doaj.org/article/e6cf18fd246c4185a14693093cd6ecef
Publikováno v:
PLoS ONE, Vol 17, Iss 11, p e0277244 (2022)
Otoliths (ear-stones) in the inner ears of vertebrates containing visible year zones are used extensively to determine fish age. Analysis of otoliths is a time-consuming and difficult task that requires the education of human experts. Human age estim
Externí odkaz:
https://doaj.org/article/160f87df41124ccc9bea66ac757b98d1
Autor:
Odin Foldvik Eikeland, Filippo Maria Bianchi, Inga Setså Holmstrand, Sigurd Bakkejord, Sergio Santos, Matteo Chiesa
Publikováno v:
Energies, Vol 15, Iss 1, p 305 (2022)
Electric failures are a problem for customers and grid operators. Identifying causes and localizing the source of failures in the grid is critical. Here, we focus on a specific power grid in the Arctic region of Northern Norway. First, we collected d
Externí odkaz:
https://doaj.org/article/1a422542aaa84b4393b6d9055fc86b22
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems
—In graph neural networks (GNNs), pooling operators compute local summaries of input graphs to capture their global properties, and they are fundamental for building deep GNNs that learn hierarchical representations. In this work, we propose the No
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::606534095e1e3cee07e363d5fd434c5c
https://doi.org/10.1109/tnnls.2020.3044146
https://doi.org/10.1109/tnnls.2020.3044146
Autor:
Odin Foldvik Eikeland, Filippo Maria Bianchi, Harry Apostoleris, Morten Hansen, Yu-Cheng Chiou, Matteo Chiesa
Publikováno v:
Energies, Vol 14, Iss 4, p 798 (2021)
Forecasting energy demand within a distribution network is essential for developing strategies to manage and optimize available energy resources and the associated infrastructure. In this study, we consider remote communities in the Arctic located at
Externí odkaz:
https://doaj.org/article/50ab5a4f6e1840baaf4e442c3debce04
Autor:
Jakob Grahn, Filippo Maria Bianchi
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 60:1-12
In this paper, we explore the possibility of detecting polar lows in C-band SAR images by means of deep learning. Specifically, we introduce a novel dataset consisting of Sentinel-1 images divided into two classes, representing the presence and absen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c2d9d56da143e7fa12f1a4b36533001c
https://doi.org/10.1109/tgrs.2022.3204886
https://doi.org/10.1109/tgrs.2022.3204886
Publikováno v:
Remote Sensing, Vol 12, Iss 14, p 2260 (2020)
We propose a deep-learning framework to detect and categorize oil spills in synthetic aperture radar (SAR) images at a large scale. Through a carefully designed neural network model for image segmentation trained on an extensive dataset, we obtain st
Externí odkaz:
https://doaj.org/article/f859193fcc3c49cdbff469e46e37a3c4