| Nome |
# |
|
14 MeV calibration of JET neutron detectors-phase 1: Calibration and characterization of the neutron source, file e2f56ed7-b69b-3eaf-e053-3a05fe0a5d97
|
593
|
|
Overview of progress in European medium sized tokamaks towards an integrated plasma-edge/wall solution, file e2f56ed6-aea5-3eaf-e053-3a05fe0a5d97
|
262
|
|
Recurrence Plots for Dynamic Analysis of Type-I ELMs at JET With a Carbon Wall, file e2f56ed8-2d63-3eaf-e053-3a05fe0a5d97
|
254
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Selection of features based on electric power quantities for non-intrusive load monitoring, file e2f56ed9-c89e-3eaf-e053-3a05fe0a5d97
|
240
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Automatic disruption classification in JET with the ITER-like wall, file e2f56ed5-47bd-3eaf-e053-3a05fe0a5d97
|
178
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Optimization of a power line communication system to manage electric vehicle charging stations in a smart grid, file e2f56ed8-69bf-3eaf-e053-3a05fe0a5d97
|
162
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NILM techniques applied to a real-time monitoring system of the electricity consumption, file e2f56eda-5b73-3eaf-e053-3a05fe0a5d97
|
131
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|
Dependence on plasma shape and plasma fueling for small edge-localized mode regimes in TCV and ASDEX Upgrade, file e2f56ed9-709d-3eaf-e053-3a05fe0a5d97
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125
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Forecasting-Aided Monitoring for the Distribution System State Estimation, file e2f56ed9-20d1-3eaf-e053-3a05fe0a5d97
|
80
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|
A real time bolometer tomographic reconstruction algorithm in nuclear fusion reactors, file e2f56eda-886e-3eaf-e053-3a05fe0a5d97
|
78
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|
Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy, file e2f56ed9-6761-3eaf-e053-3a05fe0a5d97
|
77
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Overview of the JET results in support to ITER, file e2f56ed7-3845-3eaf-e053-3a05fe0a5d97
|
72
|
|
A machine learning approach based on generative topographic mapping for disruption prevention and avoidance at JET, file e2f56eda-9fb8-3eaf-e053-3a05fe0a5d97
|
62
|
|
Inter-machine plasma perturbation studies in EU-DEMO relevant scenarios: lessons learnt for EM forces prediction during VDEs, file e2f56eda-6018-3eaf-e053-3a05fe0a5d97
|
52
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|
Disruption prediction at JET through Deep Convolutional Neural Networks using spatiotemporal information from plasma profiles, file e2f56eda-5e4f-3eaf-e053-3a05fe0a5d97
|
46
|
|
A Closed Form Selected Mapping Algorithm for PAPR Reduction in OFDM Multicarrier Transmission, file d703abc9-2c41-4e6a-818b-00080c904ff9
|
43
|
|
Performance Comparison of Machine Learning Disruption Predictors at JET, file 8ae0b3e3-7f58-4ad6-a578-a646e342231d
|
37
|
|
CNN disruption predictor at JET: Early versus late data fusion approach, file 0d1bc3fd-5193-406d-a292-db8b663ed85a
|
32
|
|
Disruption prediction with artificial intelligence techniques in tokamak plasmas, file dfb21f61-5432-4bc0-9040-7b5177a1c0a9
|
25
|
|
Decision trees for early prediction of inadequate immune response to coronavirus infections: a pilot study on COVID-19, file f18bfd47-c9fd-44d2-9c43-bac6f10abe9b
|
17
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Enhanced performance in fusion plasmas through turbulence suppression by megaelectronvolt ions, file d0466aa4-5f05-4783-853d-d1df06c70ede
|
14
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|
Overview of JET results for optimising ITER operation, file 9c2b95c1-a478-44bd-ab7d-7cbf11a90846
|
13
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Overview of the JET preparation for deuterium–tritium operation with the ITER like-wall, file 26f5a586-6725-4a08-9244-aa8a93fc07a9
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12
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Experimental confirmation of efficient island divertor operation and successful neoclassical transport optimization in Wendelstein 7-X, file e1941219-25a9-4692-941e-848eadf5e9bc
|
11
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Observations and modelling of ion cyclotron emission observed in JET plasmas using a sub-harmonic arc detection system during ion cyclotron resonance heating, file 8bb28982-ef29-4783-ab38-98a74d72a47d
|
10
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Optimal Design of an Inductive MHD Electric Generator, file 259d15ee-5d7d-4cca-8135-1993ccb575f2
|
9
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null, file e2f56ed3-c158-3eaf-e053-3a05fe0a5d97
|
7
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Multiobjective tabu search algorithm for the optimal design of a thermo-acoustic magneto-hydro-dynamic electric generator, file e2f56ed7-e7ef-3eaf-e053-3a05fe0a5d97
|
7
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|
Operational Space Mapping at ASDEX Upgrade using Clustering Techniques, file e2f56ed3-c330-3eaf-e053-3a05fe0a5d97
|
6
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|
Convolutional neural networks for the identification of filaments from fast visual imaging cameras in tokamak reactors, file e2f56ed7-b2ff-3eaf-e053-3a05fe0a5d97
|
6
|
|
Overview of manifold learning techniques for the investigation of disruptions on JET, file e2f56ed3-9fae-3eaf-e053-3a05fe0a5d97
|
5
|
|
A tool to support the construction of reliable disruption databases, file e2f56ed7-1d6f-3eaf-e053-3a05fe0a5d97
|
5
|
|
Physics Informed Neural Networks towards the real-time calculation of heat fluxes at W7-X, file f650425c-3ec8-4d0d-872e-793fd7dd54a4
|
5
|
|
Global scaling of the heat transport in fusion plasmas, file d3f71bc7-902e-4d07-b612-ff85966c5414
|
4
|
|
Decision trees to evaluate the risk of developing multiple sclerosis, file 43795037-e08d-455c-8bc3-67e839449d78
|
3
|
|
null, file e2f56ed3-ac70-3eaf-e053-3a05fe0a5d97
|
3
|
|
Automatic disruption classification at JET: comparison of different pattern recognition techniques, file e2f56ed3-b1fd-3eaf-e053-3a05fe0a5d97
|
3
|
|
Ultrasonic testing of masonry structures by features extraction and Self-Organising Maps, file e2f56ed4-b941-3eaf-e053-3a05fe0a5d97
|
3
|
|
Improvements in disruption prediction at ASDEX Upgrade, file e2f56ed5-34c2-3eaf-e053-3a05fe0a5d97
|
3
|
|
Autosomal dominant nocturnal frontal lobe epilepsy seizure characterization through wavelet transform of eeg records and self organizing maps, file e2f56ed6-4d35-3eaf-e053-3a05fe0a5d97
|
3
|
|
A First Analysis of JET Plasma Profile-Based Indicators for Disruption Prediction and Avoidance, file e2f56ed7-e7ee-3eaf-e053-3a05fe0a5d97
|
3
|
|
Application of self organizing map to identify nocturnal epileptic seizures, file e2f56ed7-e7f3-3eaf-e053-3a05fe0a5d97
|
3
|
|
Comparison of runaway electron generation parameters in small, medium-sized and large tokamaks - A survey of experiments in COMPASS, TCV, ASDEX-Upgrade and JET, file 4c21044c-2103-47f4-9999-3c729f966d5a
|
2
|
|
An adaptive disruption predictor based on FDI approach for next generation Tokamaks, file e2f56ed3-8bea-3eaf-e053-3a05fe0a5d97
|
2
|
|
Improvements in Disruption Prediction at ASDEX Upgrade, file e2f56ed3-8c6b-3eaf-e053-3a05fe0a5d97
|
2
|
|
Dynamic behaviour of type I Edge Localized Modes in the JET, file e2f56ed3-c5bb-3eaf-e053-3a05fe0a5d97
|
2
|
|
Ultrasonic testing of masonry structures by features extraction and self organising maps, file e2f56ed4-c959-3eaf-e053-3a05fe0a5d97
|
2
|
|
Multivariate statistical models for disruption prediction at ASDEX Upgrade, file e2f56ed4-dd84-3eaf-e053-3a05fe0a5d97
|
2
|
|
Nonlinear dynamic analysis of D α signals for type I edge localized modes characterization on JET with a carbon wall, file e2f56ed7-123e-3eaf-e053-3a05fe0a5d97
|
2
|
|
Exploratory Statistical Analysis of Time Intervals between Type I Edge Localized
Modes in the JET Tokamak with the Carbon Wall, file e2f56ed3-8bde-3eaf-e053-3a05fe0a5d97
|
1
|
|
null, file e2f56ed3-9c48-3eaf-e053-3a05fe0a5d97
|
1
|
|
null, file e2f56ed3-9ca6-3eaf-e053-3a05fe0a5d97
|
1
|
|
Mapping of the ASDEX upgrade operational space for disruption prediction, file e2f56ed3-9ebb-3eaf-e053-3a05fe0a5d97
|
1
|
|
A greedy genetic algorithm for continuous variables electromagnetic optimization problems, file e2f56ed3-9fd4-3eaf-e053-3a05fe0a5d97
|
1
|
|
null, file e2f56ed3-a2c2-3eaf-e053-3a05fe0a5d97
|
1
|
|
null, file e2f56ed3-a648-3eaf-e053-3a05fe0a5d97
|
1
|
|
null, file e2f56ed3-a91f-3eaf-e053-3a05fe0a5d97
|
1
|
|
null, file e2f56ed3-abec-3eaf-e053-3a05fe0a5d97
|
1
|
|
Electro-mechanical analysis of the European Superconducting Dipole, file e2f56ed3-aee3-3eaf-e053-3a05fe0a5d97
|
1
|
|
null, file e2f56ed3-b0ee-3eaf-e053-3a05fe0a5d97
|
1
|
|
Performance improvement of a hybrid optimization algorithm for electromagnetic devices design, file e2f56ed3-b136-3eaf-e053-3a05fe0a5d97
|
1
|
|
null, file e2f56ed3-b5a5-3eaf-e053-3a05fe0a5d97
|
1
|
|
Linear programming models for traffic engineering in 100% survivable networks under combined IS-IS/OSPF and MPLS-TE, file e2f56ed3-b62b-3eaf-e053-3a05fe0a5d97
|
1
|
|
null, file e2f56ed3-bc91-3eaf-e053-3a05fe0a5d97
|
1
|
|
null, file e2f56ed3-bd3a-3eaf-e053-3a05fe0a5d97
|
1
|
|
Acoustic Tomography for Non Destructive Testing of Stone Masonry, file e2f56ed3-becf-3eaf-e053-3a05fe0a5d97
|
1
|
|
Acoustic Tomography for Non Destructive Testing of Stone Masonry, file e2f56ed3-bed0-3eaf-e053-3a05fe0a5d97
|
1
|
|
null, file e2f56ed3-c243-3eaf-e053-3a05fe0a5d97
|
1
|
|
Disruption prediction at ASDEX Upgrade using neural networks, file e2f56ed3-c538-3eaf-e053-3a05fe0a5d97
|
1
|
|
Tabu search metaheuristic for designing digital filters, file e2f56ed3-c563-3eaf-e053-3a05fe0a5d97
|
1
|
|
null, file e2f56ed3-c812-3eaf-e053-3a05fe0a5d97
|
1
|
|
Extensive statistical analysis of ELMs on JET with a carbon wall, file e2f56ed3-c932-3eaf-e053-3a05fe0a5d97
|
1
|
|
null, file e2f56ed3-ca19-3eaf-e053-3a05fe0a5d97
|
1
|
|
Geometrical Kernel Machine for Prediction and Novelty Detection of Disruptive Events in TOKAMAK Machines, file e2f56ed3-ca27-3eaf-e053-3a05fe0a5d97
|
1
|
|
Geometrical Kernel Machine for Prediction and Novelty Detection of Disruptive Events in TOKAMAK Machines, file e2f56ed3-ca29-3eaf-e053-3a05fe0a5d97
|
1
|
|
Overview of manifold learning techniques for the investigation of disruptions on JET, file e2f56ed3-e384-3eaf-e053-3a05fe0a5d97
|
1
|
|
Overview of manifold learning techniques for the investigation of disruptions on JET, file e2f56ed3-e385-3eaf-e053-3a05fe0a5d97
|
1
|
|
Overview of manifold learning techniques for the investigation of disruptions on JET, file e2f56ed3-e386-3eaf-e053-3a05fe0a5d97
|
1
|
|
Frequency analysis of ultrasonic signals for non-destructive diagnosis of masonry structures, file e2f56ed4-b587-3eaf-e053-3a05fe0a5d97
|
1
|
|
Ultrasonic testing of masonry structures by features extraction and self organising maps, file e2f56ed4-c95a-3eaf-e053-3a05fe0a5d97
|
1
|
|
Improvements in disruption prediction at ASDEX Upgrade, file e2f56ed5-3f67-3eaf-e053-3a05fe0a5d97
|
1
|
|
A comparative multivariate analysis of disruption classes between JET and AUG, file e2f56ed6-74d4-3eaf-e053-3a05fe0a5d97
|
1
|
|
Moving vector hysteron model identification based on neural network inversion, file e2f56ed6-7c78-3eaf-e053-3a05fe0a5d97
|
1
|
|
null, file e2f56ed6-ae31-3eaf-e053-3a05fe0a5d97
|
1
|
|
Advances in the development of DIS-tool and first analysis on TCV disruptions, file e2f56ed7-acae-3eaf-e053-3a05fe0a5d97
|
1
|
|
Magnetic materials characterization by Tabu Search optimization, file e2f56ed7-e7f1-3eaf-e053-3a05fe0a5d97
|
1
|
|
A locked mode indicator for disruption prediction on JET and ASDEX upgrade, file e2f56ed8-cdde-3eaf-e053-3a05fe0a5d97
|
1
|
|
Towards an automatic filament detector with a Faster R-CNN on MAST-U, file e2f56ed9-4272-3eaf-e053-3a05fe0a5d97
|
1
|
|
Learning control coil currents from heat-flux images using convolutional neural networks at Wendelstein 7-X, file e2f56eda-2686-3eaf-e053-3a05fe0a5d97
|
1
|
|
A machine learning approach based on generative topographic mapping for disruption prevention and avoidance at JET, file e2f56eda-9ef2-3eaf-e053-3a05fe0a5d97
|
1
|
|
Integrated design strategy for EU-DEMO first wall protection from plasma transients, file e6834d39-4a42-44bf-8d1d-aeef13b09484
|
1
|
|
Tools for Image Analysis and First Wall Protection at W7-X, file ffac2540-4e11-4d1e-9088-d37f5303d75e
|
1
|
| Totale |
2.761 |