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Publications associées (29)

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Adaptive Laser Welding Control: A Reinforcement Learning Approach

Giulio Masinelli, Silvio Zanoli

Despite extensive research efforts in the field of laser welding, the imperfect repeatability of the weld quality still represents an open topic. Indeed, the inherent complexity of the underlying physical phenomena prevents the implementation of an effecti ...

2020

A machine learning approach based on generative topographic mapping for disruption prevention and avoidance at JET

Alessandro Pau

The need for predictive capabilities greater than 95% with very limited false alarms are demanding requirements for reliable disruption prediction systems in tokamaks such as JET or, in the near future, ITER. The prediction of an upcoming disruption must b ...

IOP Publishing Ltd2019

A challenge of predicting seizure frequency in temporal lobe epilepsy using neuroanatomical features

Chang-Hyun Park

The pathological and clinical characteristics of temporal lobe epilepsy (TLE) tend to be affected by epileptic seizures, specifically represented by seizure lateralization and frequency. Although the lateralization of the epileptogenic zone can be clarifie ...

ELSEVIER IRELAND LTD2019

Adaptive learning for disruption prediction in non-stationary conditions

Yiming Li, Olivier Sauter, Richard Pitts, Ambrogio Fasoli, Basil Duval, Henri Weisen, Jan Horacek, Javier García Hernández, Duccio Testa, Marco Wischmeier, Nicola Vianello, Mikhail Maslov, Patrick Blanchard, Alessandro Pau, Davide Galassi, Federico Alberto Alfredo Felici, Antonio José Pereira de Figueiredo, Cristian Sommariva, Samuel Lanthaler, Yann Camenen, Jonathan Graves, Emiliano Fable, Francesca Maria Poli, Antoine Pierre Emmanuel Alexis Merle, Federico Nespoli, Daniele Brunetti, Anna Teplukhina, Hamish William Patten, Haomin Sun, Alberto Mariani, Michele Marin

For many years, machine learning tools have proved to be very powerful disruption predictors in tokamaks. On the other hand, the vast majority of the techniques deployed assume that the input data is independent and is sampled from exactly the same probabi ...

IOP Publishing Ltd2019

Machine Learning at the Atomic Scale

Michele Ceriotti, Félix Benedito Clément Musil

Statistical learning algorithms are finding more and more applications in science and technology. Atomic-scale modeling is no exception, with machine learning becoming commonplace as a tool to predict energy, forces and properties of molecules and condense ...

SWISS CHEMICAL SOC2019

Citizen Visual Search Engine: Detection and Curation of Urban Objects

Jeffrey Huang, Immanuel Chee Beng Koh

Increasingly, the ubiquity of satellite imagery has made the data analysis and machine learning of large geographical datasets one of the building blocks of visuospatial intelligence. It is the key to discover current (and predict future) cultural, social, ...

SPRINGER INTERNATIONAL PUBLISHING AG2019

A Hybrid Machine Learning Approach for Predictive Maintenance in Smart Factories of the Future

Dimitrios Kyritsis, Gökan May, Sangje Cho, Ioannis Tourkogiorgis

Advanced technologies based on Internet of Things (IOT) are blazing a trail to effective and efficient management of an overall plant. In this context, manufacturing companies require an innovative strategy to survive in a competitive business environment, ...

SPRINGER-VERLAG BERLIN2018

A Predictive Model for User Motivation and Utility Implications of Privacy-Protection Mechanisms in Location Check-Ins

Jean-Pierre Hubaux, Kévin Clément Huguenin, Igor Bilogrevic, Reza Shokri, Italo Ivan Dacosta Petrocelli, Joana Catarina Soares Machado

Location check-ins contain both geographical and semantic information about the visited venues. Semantic information is usually represented by means of tags (e.g., “restaurant”). Such data can reveal some personal information about users beyond what they a ...

2018

On the potential of ruled-based machine learning for disruption prediction on JET

Olivier Sauter, Martinus Adela Maria Gijs, Jonathan Graves, Ambrogio Fasoli, Stefano Coda, Basil Duval, Henri Weisen, Richard Pitts, Yves Martin, Javier García Hernández, Duccio Testa, Miguel Fernández Ruiz, Nicola Vianello, Robin Humphry-Baker, Sun Hee Kim, Federico Nespoli, Patrick Blanchard, Alessandro Pau, David Pfefferlé, Davide Galassi, Jonathan Marc Philippe Faustin, Cristian Sommariva, Hamish William Patten, Samuel Lanthaler, Jan Horacek, Yann Camenen, Bruno Emanuel Ferreira De Sousa Correia, José Pedro Rebelo Ferreira Marques, Mikhail Maslov, Marco Wischmeier, Dalziel Joseph Wilson, Liang Yao, Daniel Scott Alessi, Arnout Lodewijk M Beckers, Ana Francisca Leal Silva Soares, Jonnathan Cesar Hidalgo Acosta, Antonio José Pereira de Figueiredo, Partha Dutta, Pierre-Thomas Paul Brun, Pedro Camilo de Oliveira e Silva, Alberto Hernando de Castro, Julio Rodriguez, Vlad Trifa, Li Shuai, Rebecca Hill

In the last years, it has become apparent that detecting disruptions with sufficient anticipation time is an essential but not exclusive task of predictors. It is also important that the prediction is accompanied by appropriate qualifications of its reliab ...

2018