Results 261 to 270 of about 140,191 (290)

Comparison of Advanced Machine Learning Tools for Disruption Prediction and Disruption Studies

IEEE Transactions on Plasma Science, 2013
Machine learning tools have been used since a long time ago to study disruptions and to predict their occurrence. On the other hand, the challenges posed by the quality and quantities of the data available remain substantial. In this paper, methods to optimize the training data set and the potential of kernels-based advanced machine learning tools are ...
Michal Odstrcil, Andrea Murari, Jan Mlynar   +2 more
openaire   +1 more source

Feature extraction for improved disruption prediction analysis at JET

Review of Scientific Instruments, 2008
Disruptions are major instabilities and remain one of the main problems in tokomaks. Using Joint European Torus database, a disruption predictor is developed by computational methods including supervised learning techniques. The main objectives of the work are to develop accurate automatic classifiers, to test their performances, and to determine how ...
Rattá G A, Vega J, Murari A, Johnson M
openaire   +3 more sources

Adaptive Learning for Disruption Prediction

2018
Accurate prediction of catastrophic events is becoming an important area of investigation in many research fields. In Tokamaks, detecting disruptions with sufficient anticipation time is a prerequisite to undertaking any remedial strategy, either for mitigation or for avoidance.
GELFUSA Michela, MURARI Andrea, LUNGARONI Michele, PELUSO Emmanuele, GAUDIO Pasqualino, VEGA Jesus, DORMIDO-CANTO Sebastian   +6 more
openaire   +1 more source

Disruption Prediction on JET during the ILW Experimental Campaigns

Fusion Science and Technology, 2016
The Advanced Predictor of Disruptions (APODIS) has been working in the JET real-time network since the beginning of the ITER-like wall (ILW) campaigns. APODIS is a data-driven system based on a multilayer structure of Support Vector Machines (SVM) classifiers.
Moreno R, Vega J, DormidoCanto S, Pereira A, Murari A, Contributors JET   +5 more
openaire   +2 more sources

Predicting the future of disruptive technologies: The method of alternative histories

Business Horizons, 2019
Abstract With digital technologies shaping competition in many industries, predicting the future of potentially disruptive technologies becomes an essential task of business leaders concerned with the survival and success of their organizations. Looking into the future of disruptive technologies requires a philosophical stance and a practical method ...
openaire   +2 more sources

Predicting Adoption Disruption

Social Work, 1988
Richard P. Barth, Marianne Berry, Rogers Yoshikami, Regina K. Goodfield, Mary Lou Carson   +4 more
openaire   +1 more source

Identification and Prediction of Disruptions in Airline Networks

SSRN Electronic Journal, 2021
Max Li, Karthik Gopalakrishnan, Xiyitao Zhu, Aritro Nandi, Hamsa Balakrishnan, Lavanya Marla   +5 more
openaire   +1 more source

A machine-learning approach for disruption prediction

2016
A machine-learning approach for disruption prediction.
Pau A., Aledda R., Cannas B., Fanni A., Sias G., Mele D., Pisano G., Murari A., Coda S., Baruzzo M., de Vries P. C., Drewelow P., Gerasimov S., Joffrin E., Nunes I., Pautasso G., Rimini F., Tsalas M., JET contributors, the ASDEX Upgrade Team, the EUROfusion MST1 Team   +20 more
openaire   +1 more source

Real-time disruption prediction in the plasma control system of HL-2A based on deep learning

Fusion Engineering and Design, 2022
Zongyu Yang, Xianming Song, Yonggao Li   +2 more
exaly  

More disruption predicted

Nursing Standard, 1988
openaire   +1 more source