DE GIORGI A.

Data-driven fault diagnosis is a promising approach for the early detection and isolation of malfunctions in power generation plants deploying solid oxide fuel cells (SOFCs). Despite the supervised classifier used in a data-driven system is trained by samples gathered under one specific design-point operating condition, during real operation...

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Fault detection and isolation (FDI) systems represent a crucial element for the commercial diffusion of SOFC-based power generation plants. The physical quantities measured in the plant during the functioning feed a statistical classifier, in view of detecting and identifying possible faults. The classifier training is performed off ...

In this paper, the problem of the spatial-spectral classification of very high-resolution optical images is addressed using a kernel- A nd region-based approach. A novel method based on integrating region-based or object-based information into a kernel machine is developed. A Gaussian process model is used to characterize each segment in a...

The monitoring of the recovery phase in the aftermath of an emergency scenario is tackled in this paper in terms of a change-detection perspective and through the integration of multisensor, multisource, and contextual information associated with high resolution optical and SAR data. The method makes use of the Markov random field theory to...

The aim of this paper is to address the monitoring of the recovery phase in the aftermath of Hurricane Matthew (28 September–10 October 2016) in the town of Jérémie, southwestern Haiti. This is accomplished via a novel change detection method that has been formulated, in a data fusion perspective, in terms of multitemporal supervised...