Results 241 to 250 of about 2,114,322 (282)

Disruption Prediction With Adaptive Neural Networks for ASDEX Upgrade

Fusion Engineering and Design, 2011
Abstract In this paper, an adaptive neural system has been built to predict the risk of disruption at ASDEX Upgrade. The system contains a Self Organizing Map, which determines the ‘novelty’ of the input of a Multi Layer Perceptron predictor module. The answer of the MLP predictor will be inhibited whenever a novel sample is detected. Furthermore, it
Alessandra Fanni, Giuliana Sias, Barbara Cannas   +2 more
exaly   +4 more sources

Feature Selection for Prediction of Railway Disruption Length

2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2019
Disruptions such as accidents, facility failures and power outage occur frequently during daily railway operation, which can disrupt the railway timetable and cause financial cost in terms of lost patronage. Recent study indicates that skilled dispatchers can estimate the disruption length based on the analysis of related features.
Menglin Fan, Wei Zheng
openaire   +2 more sources

Bid Prediction for Multi-Robot Exploration with Disrupted Communications

2021 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), 2021
Teams of autonomous mobile robots have potential to contribute to surveillance as well as search and rescue operations. Larger and more complex disaster scenarios with high operational tempo, such as when delay may mean further loss of life, may benefit from cooperative teams of many robots working together for efficient search operations ...
Bradley Woosley, Carlos Nieto-Granda, John G. Rogers, Nicholas Fung, Arthur Schang   +4 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

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

Prediction and mitigation of disruptions in ASDEX Upgrade

Journal of Nuclear Materials, 2001
Abstract Disruptions in tokamaks are instabilities events which can damage the machine components. The avoidance and mitigation of these events is desirable in present machines as well as in Next Step devices (such as ITER). A neural network has been developed to predict the occurrence of disruptions caused by edge cooling mechanisms in ASDEX Upgrade.
Pautasso, G., Egorov, S., Tichmann, C., Fuchs, J., Herrmann, A., Maraschek, M., Mast, K., Mertens, V., Perchermeier, J., Windsor, C., Zehetbauer, T., ASDEX Upgrade Team   +11 more
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

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

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