Results 321 to 330 of about 3,880,536 (344)

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

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

Mapping of the ASDEX Upgrade operational space for disruption prediction

2011 IEEE/NPSS 24th Symposium on Fusion Engineering, 2011
The mapping of the n-dimensional plasma parameter space of ASDEX Upgrade (AUG) has been performed using a 2-D self-organizing map (SOM), which reveals the map potentiality in data visualization. The proposed approach allows us the definition of simple displays capable of presenting meaningful information on the actual state of the plasma, but it also ...
ALEDDA, RAFFAELE, CANNAS, BARBARA, FANNI, ALESSANDRA, SIAS, GIULIANA, Pautasso G.   +4 more
openaire   +3 more sources

Improvements in Disruption Prediction at ASDEX Upgrade [PDF]

, 2014
In large-scale Tokamaks disruptions have the potential to create serious damage to the facility. Hence disruptions must be avoided, but, when a disruption is unavoidable minimizing its severity is mandatory. A reliable detection of a disruptive event is required to trigger proper mitigation actions.
SIAS, GIULIANA, Aledda R, Cannas B, FANNI, ALESSANDRA, Murari A, PAU, ALESSANDRO, ASDEX Upgrade Team   +6 more
openaire  

Statistically predicting disruptive short-time undervoltage condition

Conference Record of 1993 Forty-Fifth Annual Conference of Electrical Engineering Problems in the Rubber and Plastics Industries, 2002
Power abnormalities originating within the utility system and transferred to the commercial user of electricity are discussed. The magnitude, type and duration of these power quality aberrations are quite diverse. The authors define the different types of disturbances.
openaire   +1 more source

Recent progress on the control and mitigation of runaway electrons and disruption prediction in the HL-2A and J-TEXT tokamaks

Reviews of Modern Plasma Physics, 2023
Y. P. Zhang, R. Tong, Z. Yang, Z. Chen, D. Hu, Y. Dong, J. Zhang, Y. Zhu, H. B. Xu, W. Zhong   +9 more
semanticscholar   +1 more source

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

DisruptionBench and Complimentary New Models: Two Advancements in Machine Learning Driven Disruption Prediction

Journal of fusion energy
L. Spangher, Matteo Bonotto, William Arnold, Dhruva Chayapathy, Tommaso Gallingani, Alexander Spangher, Francesco Cannarile, Daniele Bigoni, Eliana de Marchi, Christina Rea   +9 more
semanticscholar   +1 more source

Can We Predict Disruptive School Behavior?

Children & Schools, 2004
M. K. Eamon, S. J. Altshuler
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