| Nome |
# |
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A Multidisciplinary Approach for the Development of Smart Distribution Networks, file e2f56ed7-ef7f-3eaf-e053-3a05fe0a5d97
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783
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14 MeV calibration of JET neutron detectors-phase 1: Calibration and characterization of the neutron source, file e2f56ed7-b69b-3eaf-e053-3a05fe0a5d97
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596
|
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Overview of progress in European medium sized tokamaks towards an integrated plasma-edge/wall solution, file e2f56ed6-aea5-3eaf-e053-3a05fe0a5d97
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263
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Automatic disruption classification in JET with the ITER-like wall, file e2f56ed5-47bd-3eaf-e053-3a05fe0a5d97
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180
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NILM techniques applied to a real-time monitoring system of the electricity consumption, file e2f56eda-5b73-3eaf-e053-3a05fe0a5d97
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132
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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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127
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Forecasting-Aided Monitoring for the Distribution System State Estimation, file e2f56ed9-20d1-3eaf-e053-3a05fe0a5d97
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81
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A real time bolometer tomographic reconstruction algorithm in nuclear fusion reactors, file e2f56eda-886e-3eaf-e053-3a05fe0a5d97
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80
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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
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A machine learning approach based on generative topographic mapping for disruption prevention and avoidance at JET, file e2f56eda-9fb8-3eaf-e053-3a05fe0a5d97
|
66
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Inter-machine plasma perturbation studies in EU-DEMO relevant scenarios: lessons learnt for EM forces prediction during VDEs, file e2f56eda-6018-3eaf-e053-3a05fe0a5d97
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53
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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
|
49
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Performance Comparison of Machine Learning Disruption Predictors at JET, file 8ae0b3e3-7f58-4ad6-a578-a646e342231d
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43
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CNN disruption predictor at JET: Early versus late data fusion approach, file 0d1bc3fd-5193-406d-a292-db8b663ed85a
|
35
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Disruption prediction with artificial intelligence techniques in tokamak plasmas, file dfb21f61-5432-4bc0-9040-7b5177a1c0a9
|
26
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Overview of physics studies on ASDEX Upgrade, file c847a5ab-6ae3-4cd8-883c-296f09dd620a
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25
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Progress from ASDEX Upgrade experiments in preparing the physics basis of ITER operation and DEMO scenario development, file 9af11f61-8988-421b-8c2d-7176baac9691
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23
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Overview of JET results for optimising ITER operation, file 9c2b95c1-a478-44bd-ab7d-7cbf11a90846
|
16
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Enhanced performance in fusion plasmas through turbulence suppression by megaelectronvolt ions, file d0466aa4-5f05-4783-853d-d1df06c70ede
|
15
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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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14
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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
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13
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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
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11
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Physics Informed Neural Networks towards the real-time calculation of heat fluxes at W7-X, file f650425c-3ec8-4d0d-872e-793fd7dd54a4
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10
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An improved model for the accurate calculation of parallel heat fluxes at the JET bulk tungsten outer divertor, file baed4a31-9bc6-4264-bd79-480aff577174
|
8
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null, file e2f56ed3-c158-3eaf-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
|
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Tools for Image Analysis and First Wall Protection at W7-X, file 9c8ee301-f49d-42d4-b5bd-355abcf7f867
|
5
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Overview of manifold learning techniques for the investigation of disruptions on JET, file e2f56ed3-9fae-3eaf-e053-3a05fe0a5d97
|
5
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A tool to support the construction of reliable disruption databases, file e2f56ed7-1d6f-3eaf-e053-3a05fe0a5d97
|
5
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Global scaling of the heat transport in fusion plasmas, file d3f71bc7-902e-4d07-b612-ff85966c5414
|
4
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|
null, file e2f56ed3-ac70-3eaf-e053-3a05fe0a5d97
|
3
|
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Ultrasonic testing of masonry structures by features extraction and Self-Organising Maps, file e2f56ed4-b941-3eaf-e053-3a05fe0a5d97
|
3
|
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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
|
|
Axisymmetric global Alfvén eigenmodes within the ellipticity-induced frequency gap in the Joint European Torus, file ff3e3b4a-3a66-4af4-aaaa-fb268f3f861c
|
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
|
|
Integrated modelling of H-mode pedestal and confinement in JET-ILW, file cf83785b-c850-414f-afa1-b4e5c7edb658
|
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
|
|
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
|
|
Full-orbit and drift calculations of fusion product losses due to explosive fishbones on JET, file 2ee92d72-fa20-4f12-b1e2-a2e474108664
|
1
|
|
STRIKE Heat Flux Reconstruction by Using Neural Networks: Application to the Experimental Results, file 3f7dde53-ef80-4ad6-a34b-f06046ff9efc
|
1
|
|
Review of recent experimental and modeling advances in the understanding of lower hybrid current drive in ITER-relevant regimes, file b532b1e4-f66d-486c-9747-d94461f14a70
|
1
|
|
Electron acceleration in a JET disruption simulation, file c83c4db9-988d-49ca-9595-2fa1a6baed4f
|
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
|
|
null, file e2f56ed3-a91f-3eaf-e053-3a05fe0a5d97
|
1
|
|
null, file e2f56ed3-abec-3eaf-e053-3a05fe0a5d97
|
1
|
|
null, file e2f56ed3-b5a5-3eaf-e053-3a05fe0a5d97
|
1
|
|
null, file e2f56ed3-bc91-3eaf-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
|
|
null, file e2f56ed3-c812-3eaf-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
|
|
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
|
|
Neural network based prediction of heat flux profiles on STRIKE, file e2f56ed8-9b04-3eaf-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
|
|
Equilibrium reconstruction at JET using Stokes model for polarimetry, file ee83cc96-9571-4e3d-a54d-5ead02236b43
|
1
|
| Totale |
2.895 |