Browsing NORCE vitenarkiv by Author "Bianchi, Filippo Maria"
Now showing items 1-8 of 8
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Condition Monitoring System for Internal Blowout Prevention (IBOP) in Top Drive Assembly System using Discrete Event Systems and Deep Learning Approaches
Noori, Nadia Saad; Waag, Tor Inge; Bianchi, Filippo Maria (Lecture, 2020) -
Deep learning for graphs
Bacciu, Davide; Bianchi, Filippo Maria; Paassen, Benjamin; Alippi, Cesare (Chapter, 2018)Deep learning for graphs encompasses all those neural models endowed with multiple layers of computation operating on data represented as graphs. The most common building blocks of these models are graph encoding layers, ... -
Detecting and Interpreting Faults in Vulnerable Power Grids With Machine Learning
Eikeland, Odin Foldvik; Holmstrand, Inga Setså; Bakkejord, Sigurd; Chiesa, Matteo; Bianchi, Filippo Maria (Journal article; Peer reviewed, 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 ... -
Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling
Bianchi, Filippo Maria; Grattarola, Daniele; Livi, Lorenzo; Alippi, Cesare (Peer reviewed; Journal article, 2020)Abstract—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. ... -
Pyramidal Graph Echo State Networks
Bianchi, Filippo Maria; Gallicchio, Claudio; Micheli, Alessio (Chapter, 2018) -
Short-Term Load Forecasting with Missing Data using Dilated Recurrent Attention Networks
Choi, Changkyu; Bianchi, Filippo Maria; Kampffmeyer, Michael; Jenssen, Robert (Peer reviewed; Journal article, 2020)Forecasting the dynamics of time-varying systems is essential to maintaining the sustainability of the systems. Recent studies have discovered that Recurrent Neural Networks(RNN) applied in the forecasting tasks outperform ... -
Spectral Clustering with Graph Neural Networks for Graph Pooling
Bianchi, Filippo Maria; Grattarola, Daniele; Alippi, Cesare (Peer reviewed; Journal article, 2020)Spectral clustering (SC) is a popular clustering technique to find strongly connected communities on a graph. SC can be used in Graph Neural Networks (GNNs) to implement pooling operations that aggregate nodes belonging ... -
Uncovering Contributing Factors to Interruptions in the Power Grid: An Arctic Case
Eikeland, Odin Foldvik; Bianchi, Filippo Maria; Holmestrand, Inga Setså; Bakkejord, Sigurd; Santos, Sergio; Chiesa, Matteo (Peer reviewed; Journal article, 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 ...