Dario Piga
Dario Piga
Senior Researcher at IDSIA - Dalle Molle Institute for ArtificiaI Intelligence, SUPSI-USI, Lugano
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Citado por
Citado por
Benefits and challenges of using smart meters for advancing residential water demand modeling and management: A review
A Cominola, M Giuliani, D Piga, A Castelletti, AE Rizzoli
Environmental Modelling & Software 72, 198-214, 2015
Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets
F D'Ascenzo, O De Filippo, G Gallone, G Mittone, MA Deriu, ...
The Lancet 397 (10270), 199-207, 2021
A hybrid signature-based iterative disaggregation algorithm for non-intrusive load monitoring
A Cominola, M Giuliani, D Piga, A Castelletti, AE Rizzoli
Applied energy 185, 331-344, 2017
High-altitude wind power generation
L Fagiano, M Milanese, D Piga
IEEE Transactions on Energy Conversion 25 (1), 168-180, 2009
Optimization of airborne wind energy generators
L Fagiano, M Milanese, D Piga
International Journal of robust and nonlinear control 22 (18), 2055-2083, 2012
Performance-oriented model learning for data-driven MPC design
D Piga, M Forgione, S Formentin, A Bemporad
IEEE control systems letters 3 (3), 577-582, 2019
Direct data-driven control of constrained systems
D Piga, S Formentin, A Bemporad
IEEE Transactions on Control Systems Technology 26 (4), 1422-1429, 2017
Sparse optimization for automated energy end use disaggregation
D Piga, A Cominola, M Giuliani, A Castelletti, AE Rizzoli
IEEE Transactions on Control Systems Technology 24 (3), 1044-1051, 2015
Piecewise affine regression via recursive multiple least squares and multicategory discrimination
V Breschi, D Piga, A Bemporad
Automatica 73, 155-162, 2016
Set-membership error-in-variables identification through convex relaxation techniques
V Cerone, D Piga, D Regruto
IEEE Transactions on Automatic Control 57 (2), 517-522, 2011
Direct learning of LPV controllers from data
S Formentin, D Piga, R Tóth, SM Savaresi
Automatica 65, 98-110, 2016
Continuous-time system identification with neural networks: Model structures and fitting criteria
M Forgione, D Piga
European Journal of Control 59, 69-81, 2021
Fitting jump models
A Bemporad, V Breschi, D Piga, SP Boyd
Automatica 96, 11-21, 2018
Human–robot collaboration in sensorless assembly task learning enhanced by uncertainties adaptation via Bayesian Optimization
L Roveda, M Magni, M Cantoni, D Piga, G Bucca
Robotics and Autonomous Systems 136, 103711, 2021
LPV system identification under noise corrupted scheduling and output signal observations
D Piga, P Cox, R Tóth, V Laurain
Automatica 53, 329-338, 2015
An instrumental least squares support vector machine for nonlinear system identification
V Laurain, R Tóth, D Piga, WX Zheng
Automatica 54, 340-347, 2015
Torque vectoring for high-performance electric vehicles: an efficient MPC calibration
A Lucchini, S Formentin, M Corno, D Piga, SM Savaresi
IEEE Control Systems Letters 4 (3), 725-730, 2020
Robot control parameters auto-tuning in trajectory tracking applications
L Roveda, M Forgione, D Piga
Control Engineering Practice 101, 104488, 2020
A machine learning approach for mortality prediction in COVID-19 pneumonia: development and evaluation of the Piacenza score
G Halasz, M Sperti, M Villani, U Michelucci, P Agostoni, A Biagi, L Rossi, ...
Journal of Medical Internet Research 23 (5), e29058, 2021
Set-membership LPV model identification of vehicle lateral dynamics
V Cerone, D Piga, D Regruto
Automatica 47 (8), 1794-1799, 2011
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