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Related publications (28)

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

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

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 ...

2019

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, Roberto Pérez, 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, Stefan Mihaila

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

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IOP Publishing Ltd

Samuel Lanthaler

Li Shuai

Pedro Camilo de Oliveira e Silva

Alberto Hernando de Castro

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