D
Dario Piga
Researcher at Dalle Molle Institute for Artificial Intelligence Research
Publications - 174
Citations - 2462
Dario Piga is an academic researcher from Dalle Molle Institute for Artificial Intelligence Research. The author has contributed to research in topics: Computer science & Optimization problem. The author has an hindex of 21, co-authored 151 publications receiving 1657 citations. Previous affiliations of Dario Piga include IMT Institute for Advanced Studies Lucca & Delft University of Technology.
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Benefits and challenges of using smart meters for advancing residential water demand modeling and management
TL;DR: This manuscript is the first comprehensive review of the literature in this quickly evolving water research domain and contributes a general framework for the classification of residential water demand modeling studies, which allows revising consolidated approaches, describing emerging trends, and identifying potential future developments.
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Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets
Fabrizio D'Ascenzo,Ovidio De Filippo,Guglielmo Gallone,Gianluca Mittone,Marco Agostino Deriu,Mario Iannaccone,Albert Ariza-Solé,Christoph Liebetrau,Sergio Manzano-Fernández,Giorgio Quadri,Tim Kinnaird,Gianluca Campo,José P.S. Henriques,James M. Hughes,Alberto Dominguez-Rodriguez,Marco Aldinucci,Umberto Morbiducci,Giuseppe Patti,Sergio Raposeiras-Roubín,Emad Abu-Assi,Gaetano M. De Ferrari,Francesco Piroli,Andrea Saglietto,Federico Conrotto,Pierluigi Omedè,Antonio Montefusco,Mauro Pennone,Francesco Bruno,Pier Paolo Bocchino,Giacomo Boccuzzi,Enrico Cerrato,Ferdinando Varbella,Michela Sperti,Stephen B. Wilton,Lazar Velicki,Ioanna Xanthopoulou,Angel Cequier,Andrés Íñiguez-Romo,Isabel Muñoz Pousa,María Cespón Fernández,Berenice Caneiro Queija,Rafael Cobas-Paz,Ángel López-Cuenca,Alberto Garay,Pedro Flores Blanco,Andrea Rognoni,Giuseppe Biondi Zoccai,Simone Biscaglia,Iván J. Núñez-Gil,Toshiharu Fujii,Alessandro Durante,Xiantao Song,Tetsuma Kawaji,Dimitrios Alexopoulos,Zenon Huczek,José Ramón González Juanatey,Shaoping Nie,Masa-aki Kawashiri,Iacopo Colonnelli,Barbara Cantalupo,Roberto Esposito,Sergio Leonardi,Walter Grosso Marra,Alaide Chieffo,Umberto Michelucci,Dario Piga,Marta Malavolta,Sebastiano Gili,Marco G. Mennuni,Claudio Montalto,Luigi Oltrona Visconti,Yasir Arfat +71 more
TL;DR: In this article, a machine learning-based risk stratification model was developed to predict all-cause death, recurrent acute myocardial infarction, and major bleeding after acute coronary syndrome (ACS).
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A Hybrid Signature-based Iterative Disaggregation algorithm for Non-Intrusive Load Monitoring
TL;DR: HSID is a hybrid, computationally efficient, algorithm for NILM, based on the combination of Factorial Hidden Markov Models and Iterative Subsequence Dynamic Time Warping, that is able to accurately disaggregate the power consumption measured from a single-point smart meter, thus providing a detailed characterization of the consumers’ behavior in terms of power consumption.
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High-Altitude Wind Power Generation
TL;DR: In this article, the authors present the innovative technology of high-altitude wind power generation, indicated as Kitenergy, which exploits the automatic flight of tethered airfoils (e.g., power kites) to extract energy from wind blowing between 200 and 800 m above the ground.
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Optimization of airborne wind energy generators
TL;DR: In this paper, the authors investigated three important theoretical aspects: the evaluation of the performance achieved by the employed control law, the optimization of the generator operating cycle, and the possibility to generate continuously a constant and maximal power output.