Davide Roverso
Davide Roverso
eSmart Systems
Verified email at
Cited by
Cited by
Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning
R Jenssen, D Roverso
International Journal of Electrical Power & Energy Systems 99, 107-120, 2018
Intelligent monitoring and inspection of power line components powered by UAVs and deep learning
R Jenssen, D Roverso
IEEE Power and energy technology systems journal 6 (1), 11-21, 2019
A methodology for security classification applied to smart grid infrastructures
M Shrestha, C Johansen, J Noll, D Roverso
International Journal of Critical Infrastructure Protection 28, 100342, 2020
Plant diagnostics by transient classification: The aladdin approach
D Roverso
International Journal of Intelligent Systems 17 (8), 767-790, 2002
Multivariate temporal classification by windowed wavelet decomposition and recurrent neural networks
D Roverso
3rd ANS international topical meeting on nuclear plant instrumentation …, 2000
Soft computing tools for transient classification
D Roverso
Information Sciences 127 (3-4), 137-156, 2000
LS-Net: Fast single-shot line-segment detector
VN Nguyen, R Jenssen, D Roverso
Machine Vision and Applications 32 (1), 12, 2021
Sen: A novel feature normalization dissimilarity measure for prototypical few-shot learning networks
VN Nguyen, S Løkse, K Wickstrøm, M Kampffmeyer, D Roverso, ...
Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020
An extended classifiability index for feature selection in nuclear transients
E Zio, P Baraldi, D Roverso
Annals of Nuclear Energy 32 (15), 1632-1649, 2005
Technical condition assessment and remaining useful life estimation of choke valves subject to erosion
BH Nystad, G Gola, JE Hulsund, D Roverso
Annual conference of the PHM Society 2 (1), 2010
Fault diagnosis with the aladdin transient classifier
D Roverso
System Diagnosis and Prognosis: Security and Condition Monitoring Issues III …, 2003
A randomized model ensemble approach for reconstructing signals from faulty sensors
P Baraldi, G Gola, E Zio, D Roverso, M Hoffmann
Expert Systems with Applications 38 (8), 9211-9224, 2011
Ensemble methods for process monitoring in oil and gas industry operations
D Sui, R Nybø, G Gola, D Roverso, M Hoffmann
Journal of Natural Gas Science and Engineering 3 (6), 748-753, 2011
Solutions for plant-wide on-line calibration monitoring
D Roverso, M Hoffmann, E Zio, P Baraldi, G Gola
ESREL 2007, European Safety and Reliability Conference, 827-832, 2007
Abstracting background knowledge for concept learning
A Giordana, D Roverso, L Saitta
Machine Learning—EWSL-91: European Working Session on Learning Porto …, 1991
Genetic algorithms for signal grouping in sensor validation: a comparison of the filter and wrapper approaches
P Baraldi, E Zio, G Gola, D Roverso, M Hoffmann
Proceedings of the Institution of Mechanical Engineers, Part O: Journal of …, 2008
Integrating cross-correlation techniques and neural networks for feedwater flow measurement
D Ruan, D Roverso, PF Fantoni, JI Sanabrias, JA Carrasco, L Fernandez
Progress in Nuclear Energy 43 (1-4), 267-274, 2003
Abstracting concepts with inverse resolution
A Giordana, L Saitta, D Roverso
Machine Learning Proceedings 1991, 142-146, 1991
Neural ensembles for event identification
D Roverso
IFAC Proceedings Volumes 33 (11), 469-474, 2000
Two novel procedures for aggregating randomized model ensemble outcomes for robust signal reconstruction in nuclear power plants monitoring systems
P Baraldi, E Zio, G Gola, D Roverso, M Hoffmann
Annals of Nuclear Energy 38 (2-3), 212-220, 2011
The system can't perform the operation now. Try again later.
Articles 1–20