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1. Decision trees to evaluate the risk of developing multiple sclerosis

Author

Manuela Pasella, Fabio Pisano, Barbara Cannas, Alessandra Fanni, Eleonora Cocco, Jessica Frau, Francesco Lai, Stefano Mocci, Roberto Littera, and Sabrina Rita Giglio

Subjects
decision trees, human leukocyte antigen, immunogenetic risk markers, likelihood of multiple sclerosis development, machine learning, multiple classifier, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

IntroductionMultiple sclerosis (MS) is a persistent neurological condition impacting the central nervous system (CNS). The precise cause of multiple sclerosis is still uncertain; however, it is thought to arise from a blend of genetic and environmental factors. MS diagnosis includes assessing medical history, conducting neurological exams, performing magnetic resonance imaging (MRI) scans, and analyzing cerebrospinal fluid. While there is currently no cure for MS, numerous treatments exist to address symptoms, decelerate disease progression, and enhance the quality of life for individuals with MS.MethodsThis paper introduces a novel machine learning (ML) algorithm utilizing decision trees to address a key objective: creating a predictive tool for assessing the likelihood of MS development. It achieves this by combining prevalent demographic risk factors, specifically gender, with crucial immunogenetic risk markers, such as the alleles responsible for human leukocyte antigen (HLA) class I molecules and the killer immunoglobulin-like receptors (KIR) genes responsible for natural killer lymphocyte receptors.ResultsThe study included 619 healthy controls and 299 patients affected by MS, all of whom originated from Sardinia. The gender feature has been disregarded due to its substantial bias in influencing the classification outcomes. By solely considering immunogenetic risk markers, the algorithm demonstrates an ability to accurately identify 73.24% of MS patients and 66.07% of individuals without the disease.DiscussionGiven its notable performance, this system has the potential to support clinicians in monitoring the relatives of MS patients and identifying individuals who are at an increased risk of developing the disease.

Published
2023
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2. Decision trees for early prediction of inadequate immune response to coronavirus infections: a pilot study on COVID-19

Author

Fabio Pisano, Barbara Cannas, Alessandra Fanni, Manuela Pasella, Beatrice Canetto, Sabrina Rita Giglio, Stefano Mocci, Luchino Chessa, Andrea Perra, and Roberto Littera

Subjects
artificial intelligence, COVID-19, decision trees, disease severity, immunogenetic background, SARS-CoV-2, Medicine (General), R5-920
Abstract

IntroductionFew artificial intelligence models exist to predict severe forms of COVID-19. Most rely on post-infection laboratory data, hindering early treatment for high-risk individuals.MethodsThis study developed a machine learning model to predict inherent risk of severe symptoms after contracting SARS-CoV-2. Using a Decision Tree trained on 153 Alpha variant patients, demographic, clinical and immunogenetic markers were considered. Model performance was assessed on Alpha and Delta variant datasets. Key risk factors included age, gender, absence of KIR2DS2 gene (alone or with HLA-C C1 group alleles), presence of 14-bp polymorphism in HLA-G gene, presence of KIR2DS5 gene, and presence of KIR telomeric region A/A.ResultsThe model achieved 83.01% accuracy for Alpha variant and 78.57% for Delta variant, with True Positive Rates of 80.82 and 77.78%, and True Negative Rates of 85.00% and 79.17%, respectively. The model showed high sensitivity in identifying individuals at risk.DiscussionThe present study demonstrates the potential of AI algorithms, combined with demographic, epidemiologic, and immunogenetic data, in identifying individuals at high risk of severe COVID-19 and facilitating early treatment. Further studies are required for routine clinical integration.

Published
2023
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3. A self-organised partition of the high dimensional plasma parameter space for plasma disruption prediction

Author

Enrico Aymerich, Alessandra Fanni, Fabio Pisano, Giuliana Sias, Barbara Cannas, JET Contributors, and WPTE Team

Subjects
self-organized map, disruption prediction and avoidance, interpretable machine learning, JET, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

This paper introduces a disruption predictor constructed through a fully unsupervised two-dimensional mapping of the high-dimensional JET operational space. The primary strength of this disruption predictor lies in its inherent self-organization capability. Diverging from both supervised disruption predictors and earlier approaches suggested by the same authors, which were based on unsupervised models such as Self-Organizing or Generative Topographic Maps, this predictor eliminates the need for labeling data of disruption terminated pulses during training. In prior methods, labels were indeed required post-mapping to inform the model about the presence or absence of disruption precursors at each time instant during the disrupted discharges. In contrast, our approach in this study involves no labeling of data from disruption-terminated experiments. The Self-Organizing Map, operating without any a priori information, adeptly identifies the regions characterizing the pre-disruptive phase. Moreover, SOM discovers non-trivial relationships and captures the complicated interplay of device diagnostics on the internal plasma states from the experimental data. The provided model is highly interpretable; it allows the visualization of high-dimensional data and facilitates easy interrogation of the model to understand the reasons behind its correlations. Hence, utilizing SOMs across various devices can prove invaluable in extracting rules and identifying common patterns, thereby facilitating extrapolation to ITER of the knowledge acquired from existing tokamaks.

Published
2024
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4. Performance Comparison of Machine Learning Disruption Predictors at JET

Author

Enrico Aymerich, Barbara Cannas, Fabio Pisano, Giuliana Sias, Carlo Sozzi, Chris Stuart, Pedro Carvalho, Alessandra Fanni, and the JET Contributors

Subjects
tokamak, disruption prediction, machine learning, deep learning, data analysis, plasma diagnostics, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
Abstract

Reliable disruption prediction (DP) and disruption mitigation systems are considered unavoidable during international thermonuclear experimental reactor (ITER) operations and in the view of the next fusion reactors such as the DEMOnstration Power Plant (DEMO) and China Fusion Engineering Test Reactor (CFETR). In the last two decades, a great number of DP systems have been developed using data-driven methods. The performance of the DP models has been improved over the years both for a more appropriate choice of diagnostics and input features and for the availability of increasingly powerful data-driven modelling techniques. However, a direct comparison among the proposals has not yet been conducted. Such a comparison is mandatory, at least for the same device, to learn lessons from all these efforts and finally choose the best set of diagnostic signals and the best modelling approach. A first effort towards this goal is made in this paper, where different DP models will be compared using the same performance indices and the same device. In particular, the performance of a conventional Multilayer Perceptron Neural Network (MLP-NN) model is compared with those of two more sophisticated models, based on Generative Topographic Mapping (GTM) and Convolutional Neural Networks (CNN), on the same real time diagnostic signals from several experiments at the JET tokamak. The most common performance indices have been used to compare the different DP models and the results are deeply discussed. The comparison confirms the soundness of all the investigated machine learning approaches and the chosen diagnostics, enables us to highlight the pros and cons of each model, and helps to consciously choose the approach that best matches with the plasma protection needs.

Published
2023
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5. A Closed Form Selected Mapping Algorithm for PAPR Reduction in OFDM Multicarrier Transmission

Author

Sara Carcangiu, Alessandra Fanni, and Augusto Montisci

Subjects
power lines communication, peak-to-average-power ratio, selected mapping, analytical solution, Technology
Abstract

Nowadays, the demand for communication multi-carriers’ channels, where the sub-channels are made mutually independent by using orthogonal frequency division multiplexing (OFDM), is widespread both for wireless and wired communication systems. Even if OFDM is a spectrally efficient modulation scheme, due to the allowed number of subcarriers, high data rate, and good coverage, the transmitted signal can present high peak values in the time domain, due to inverse fast Fourier transform operations. This gives rise to high peak-to-average power ratio (PAPR) with respect to single carrier systems. These peaks can saturate the transmitting amplifiers, modifying the shape of the OFDM symbol and affecting its information content, and they give rise to electromagnetic compatibility issues for the surrounding electric devices. In this paper, a closed form PAPR reduction algorithm is proposed, which belongs to selected mapping (SLM) methods. These methods consist in shifting the phases of the components to minimize the amplitude of the peaks. The determination of the optimal set of phase shifts is a very complex problem; therefore, the SLM approaches proposed in literature all resort to iterative algorithms. Moreover, as this calculation must be performed online, both the computational cost and the effect on the bit rate (BR) cannot be established a priori. The proposed analytic algorithm finds the optimal phase shifts of an approximated formulation of the PAPR. Simulation results outperform unprocessed conventional OFDM transmission by several dBs. Moreover, the complementary cumulative distribution function (CCDF) shows that, in most of the packets, the proposed algorithm reduces the PAPR if compared with randomly selected phase shifts. For example, with a number of shifted phases U=8, the CCFD curves corresponding to the analytical and random methods intersect at a probability value equal to 10−2, which means that in 99% of cases the former method reduces the PAPR more than the latter one. This is also confirmed by the value of the gain, which, at the same number of shifted phases and at the probability value equal to 10−1, changes from 2.09 dB for the analytical to 1.68 dB for the random SLM.

Published
2022
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6. Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy

Author

Fabio Pisano, Giuliana Sias, Alessandra Fanni, Barbara Cannas, António Dourado, Barbara Pisano, and Cesar A. Teixeira

Subjects
Electronic computers. Computer science, QA75.5-76.95
Abstract

The Nocturnal Frontal Lobe Epilepsy (NFLE) is a form of epilepsy in which seizures occur predominantly during sleep. In other forms of epilepsy, the commonly used clinical approach mainly involves manual inspection of encephalography (EEG) signals, a laborious and time-consuming process which often requires the contribution of more than one experienced neurologist. In the last decades, numerous approaches to automate this detection have been proposed and, more recently, machine learning has shown very promising performance. In this paper, an original Convolutional Neural Network (CNN) architecture is proposed to develop patient-specific seizure detection models for three patients affected by NFLE. The performances, in terms of accuracy, sensitivity, and specificity, exceed by several percentage points those in the most recent literature. The capability of the patient-specific models has been also tested to compare the obtained seizure onset times with those provided by the neurologists, with encouraging results. Moreover, the same CNN architecture has been used to develop a cross-patient seizure detection system, resorting to the transfer-learning paradigm. Starting from a patient-specific model, few data from a new patient are enough to customize his model. This contribution aims to alleviate the task of neurologists, who may have a robust indication to corroborate their clinical conclusions.

Published
2020
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7. A Real Time Bolometer Tomographic Reconstruction Algorithm in Nuclear Fusion Reactors

Author

Augusto Montisci, Sara Carcangiu, Giuliana Sias, Barbara Cannas, and Alessandra Fanni

Subjects
numerical techniques, mathematical modeling, nuclear fusion, tokamaks, bolometer, plasma tomography, Mathematics, QA1-939
Abstract

In tokamak nuclear fusion reactors, one of the main issues is to know the total emission of radiation, which is mandatory to understand the plasma physics and is very useful to monitor and control the plasma evolution. This radiation can be measured by means of a bolometer system that consists in a certain number of elements sensitive to the integral of the radiation along straight lines crossing the plasma. By placing the sensors in such a way to have families of crossing lines, sophisticated tomographic inversion algorithms allow to reconstruct the radiation tomography in the 2D poloidal cross-section of the plasma. In tokamaks, the number of projection cameras is often quite limited resulting in an inversion mathematic problem very ill conditioned so that, usually, it is solved by means of a grid-based, iterative constrained optimization procedure, whose convergence time is not suitable for the real time requirements. In this paper, to illustrate the method, an assumption not valid in general is made on the correlation among the grid elements, based on the statistical distribution of the radiation emissivity over a set of tomographic reconstructions, performed off-line. Then, a regularization procedure is carried out, which merge highly correlated grid elements providing a squared coefficients matrix with an enough low condition number. This matrix, which is inverted offline once for all, can be multiplied by the actual bolometer measures returning the tomographic reconstruction, with calculations suitable for real time application. The proposed algorithm is applied, in this paper, to a synthetic case study.

Published
2021
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8. Selection of Features Based on Electric Power Quantities for Non-Intrusive Load Monitoring

Author

Barbara Cannas, Sara Carcangiu, Daniele Carta, Alessandra Fanni, and Carlo Muscas

Subjects
non-intrusive load monitoring, nonlinear devices, feature selection, machine learning, power definitions, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
Abstract

Non-intrusive load monitoring (NILM) is a process of determining the operating states and the energy consumption of single electric devices using a single energy meter providing aggregate load measurements. Due to the large spread of power electronic-based and nonlinear devices connected to the network, the time signals of both voltage and current are typically non-sinusoidal. The effectiveness of a NILM algorithm strongly depends on determining a set of discriminative features. In this paper, voltage and current signals were combined to define, according to the definitions provided in Standard IEEE 1459, different power quantities, that can be used to distinguish different types of appliance. Multi-layer perceptron (MLP) classifiers were trained to solve the appliance detection problem as a multi-class event classification problem, varying the electric features in input. This allowed to select an optimal set of features guarantying good classification performance in identifying typical electric loads.

Published
2021
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9. Optimization of a Power Line Communication System to Manage Electric Vehicle Charging Stations in a Smart Grid

Author

Sara Carcangiu, Alessandra Fanni, and Augusto Montisci

Subjects
power line communication (PLC), energy storage management, vehicle to grid (V2G), smart grid, multi-objective optimization, Technology
Abstract

In this paper, a procedure is proposed to design a power line communication (PLC) system to perform the digital transmission in a distributed energy storage system consisting of fleets of electric cars. PLC uses existing power cables or wires as data communication multicarrier channels. For each vehicle, the information to be transmitted can be, for example: the models of the batteries, the level of the charge state, and the schedule of charging/discharging. Orthogonal frequency division multiplexing modulation (OFDM) is used for the bit loading, whose parameters are optimized to find the best compromise between the communication conflicting objectives of minimizing the signal power, maximizing the bit rate, and minimizing the bit error rate. The off-line design is modeled as a multi-objective optimization problem, whose solution supplies a set of Pareto optimal solutions. At the same time, as many charging stations share part of the transmission line, the optimization problem includes also the assignment of the sub-carriers to the single charging stations. Each connection between the control node and a charging station has its own frequency response and is affected by a noise spectrum. In this paper, a procedure is presented, called Chimera, which allows one to solve the multi-objective optimization problem with respect to a unique frequency response, representing the whole set of lines connecting each charging station with the central node. Among the provided Pareto solutions, the designer will make the final decision based on the control system requirements and/or the hardware constraints.

Published
2019
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23. Tools for Image Analysis and First Wall Protection at W7-X

Author

Giuliana Sias, Barbara Cannas, Yu Gao, Alessandra Fanni, G. A. Wurden, V. Moncada, Aleix Puig Sitjes, P. Drewelow, Holger Niemann, Wendelstein X Team, M. Jakubowski, Fabio Pisano, and W7-X Team, Max Planck Institute for Plasma Physics, Max Planck Society

Subjects
Nuclear and High Energy Physics, business.industry, Computer science, Mechanical Engineering, Image processing, 7. Clean energy, 01 natural sciences, Monitoring and control, 010305 fluids & plasmas, law.invention, Image (mathematics), Nuclear Energy and Engineering, law, 0103 physical sciences, Thermography, General Materials Science, Computer vision, Artificial intelligence, 010306 general physics, business, Stellarator, Civil and Structural Engineering
Abstract

The Wendelstein 7-X (W7-X) is the most advanced operating stellarator in fusion research. The monitoring and control of the heat loads on the plasma-facing components (PFCs) will be critical during...

Published
2020

24. Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy

Author

B. Pisano, Fabio Pisano, Cesar Teixeira, Alessandra Fanni, Giuliana Sias, Barbara Cannas, and António Dourado

Subjects
medicine.medical_specialty, Neurology, Article Subject, General Computer Science, Computer science, Speech recognition, 02 engineering and technology, Electroencephalography, Convolutional neural network, Nocturnal frontal lobe epilepsy, 03 medical and health sciences, Epilepsy, Seizure onset, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, medicine, Network architecture, Multidisciplinary, medicine.diagnostic_test, QA75.5-76.95, medicine.disease, Seizure detection, Electronic computers. Computer science, 020201 artificial intelligence & image processing, Transfer of learning, 030217 neurology & neurosurgery
Abstract

The Nocturnal Frontal Lobe Epilepsy (NFLE) is a form of epilepsy in which seizures occur predominantly during sleep. In other forms of epilepsy, the commonly used clinical approach mainly involves manual inspection of encephalography (EEG) signals, a laborious and time-consuming process which often requires the contribution of more than one experienced neurologist. In the last decades, numerous approaches to automate this detection have been proposed and, more recently, machine learning has shown very promising performance. In this paper, an original Convolutional Neural Network (CNN) architecture is proposed to develop patient-specific seizure detection models for three patients affected by NFLE. The performances, in terms of accuracy, sensitivity, and specificity, exceed by several percentage points those in the most recent literature. The capability of the patient-specific models has been also tested to compare the obtained seizure onset times with those provided by the neurologists, with encouraging results. Moreover, the same CNN architecture has been used to develop a cross-patient seizure detection system, resorting to the transfer-learning paradigm. Starting from a patient-specific model, few data from a new patient are enough to customize his model. This contribution aims to alleviate the task of neurologists, who may have a robust indication to corroborate their clinical conclusions.

Published
2020

25. Inter-machine plasma perturbation studies in EU-DEMO relevant scenarios: lessons learnt for EM forces prediction during VDEs

Author

Giuliana Sias, Simone Minucci, Massimiliano Lacquaniti, Riccardo Lombroni, Alessandra Fanni, Giuseppe Calabrò, Barbara Cannas, Fabio Pisano, Mattia Siccinio, Giuseppe Ramogida, Francesco Giorgetti, Pierluigi Fanelli, and Francesco Maviglia

Subjects
Nuclear and High Energy Physics, Condensed Matter Physics
Abstract

To support the deployment of DEMO wall protection strategy, the development of comprehensive analyses is essential to understand the implications of transient perturbations on the plasma shape control and on the vertical stability, some of the most critical aspects to be considered in elongated plasmas. Therefore, the design activities of the DEMO limiter structures need the deep understanding of the effects induced by transient plasma perturbations coupled with one of the most severe load conditions occurring in tokamaks, the Vertical Displacement Event (VDE). Since Electromagnetic (EM) loads during VDE phases are among the DEMO limiters design drivers, this study focuses on predictive simulations of the final plasma position and of EM loads following a VDE. For this purpose, a multi-tokamaks study supported by the construction of an inter-machine database containing experimental transient plasma perturbations and VDEs from JET and ASDEX Upgrade (AUG) has been carried out. It aims at the characterization of some transient plasma perturbations that may lead to high control efforts by the vertical stability system in terms of variations of the plasma internal parameters and vertical displacements. Consequently, such experimental transient plasma perturbations have been properly scaled to DEMO reference geometries with different magnetic configurations, to be simulated in terms of plasma dynamical behaviour by means of MAXFEA code. Finally, initial predictive EM loads on DEMO limiter structures will be discussed in the case of VDEs following plasma perturbations.

Published
2022

26. Integrated design strategy for EU-DEMO first wall protection from plasma transients

Author

Francesco Maviglia, Christian Bachmann, Gianfranco Federici, Thomas Franke, Mattia Siccinio, Raffaele Albanese, Roberto Ambrosino, Wayne Arter, Roberto Bonifetto, Giuseppe Calabrò, Riccardo De Luca, Luigi E. Di Grazia, Emiliano Fable, Pierluigi Fanelli, Alessandra Fanni, Mehdi Firdaouss, Jonathan Gerardin, Riccardo Lombroni, Massimiliano Mattei, Matteo Moscheni, William Morris, Gabriella Pautasso, Sergey Pestchanyi, Giuseppe Ramogida, Maria Lorena Richiusa, Giuliana Sias, Fabio Subba, Fabio Villone, Jeong-Ha You, Zsolt Vizvary, Maviglia, F., Bachmann, C., Federici, G., Franke, T., Siccinio, M., Albanese, R., Ambrosino, R., Arter, W., Bonifetto, R., Calabro, G., De Luca, R., Grazia, L. E. D., Fable, E., Fanelli, P., Fanni, A., Firdaouss, M., Gerardin, J., Lombroni, R., Mattei, M., Moscheni, M., Morris, W., Pautasso, G., Pestchanyi, S., Ramogida, G., Richiusa, M. L., Sias, G., Subba, F., Villone, F., You, J. -H., and Vizvary, Z.

Subjects
Discrete limiters, Nuclear Energy and Engineering, Electromagnetic simulation, Mechanical Engineering, Electromagnetic simulations, Discrete limiter, General Materials Science, Plasma transients, DEMO, First wall load, Plasma scenario optimization, Civil and Structural Engineering
Abstract

This work presents an overview of the integrated strategy developed, as part of the DEMO Key Design Integration Issue 1 (KDII1), to protect the EU-DEMO first wall (FW) from planned and unplanned plasma transients by employing discrete limiters. The present Breeding Blanket (BB) FW design, which aims at minimizing the loss of neutrons while travelling to the breeding zone, is able to withstand steady state heat fluxes up to ≈1-1.5 MW/m2 [1], which is not sufficient to guarantee its integrity for most plasma-FW direct contact. This is different from ITER, which has a FW designed for peak heat loads up to 4.6 MW/m2 [2], and it does not have the DEMO BB breeding related requirement. A series of documents was compiled in the DEMO Pre-Conceptual Design Phase, in support of the KDII1. The work presented here was presented at the 2020 DEMO Gate 1 (G1) review, and collects also the comments of the panel and the relative additional studies triggered by them. The design process, presented in this paper was adopted to systematically evaluate the impact of design changes, or new physics inputs, on the FW protection strategy and integration issues. It includes compiling the list of transients, and performing the relative plasma simulations, the design of discrete limiters and the evaluation of their capability to reduce the heat flux density on the FW, and finally a preliminary analysis of the heat loads effects on the Plasma Facing Components (PFC). All these aspects, together with preliminary limiter design, where considered since the beginning, in an integrated way.

Published
2022

28. Towards an automatic filament detector with a Faster R-CNN on MAST-U

Author

Alessandra Fanni, Fulvio Militello, Giuliana Sias, T. Farley, Nick Walkden, Sara Carcangiu, Augusto Montisci, Barbara Cannas, and Fabio Pisano

Subjects
Toroid, Computer science, business.industry, Mechanical Engineering, Deep learning, Detector, Cognitive neuroscience of visual object recognition, Plasma, 01 natural sciences, 010305 fluids & plasmas, Visible camera, Protein filament, Nuclear Energy and Engineering, 0103 physical sciences, General Materials Science, Computer vision, Artificial intelligence, 010306 general physics, business, Image object, Civil and Structural Engineering
Abstract

In the present magnetically confined plasmas, the prediction of particle loading on material surfaces is a primary concern in view of the protection of plasma facing components for next step devices. Thus, an understanding of filament dynamics is needed. In this context, this work aims to develop an automatic detector for filaments arising in the MAST-U plasma. The identification of the filaments has been done starting from 2D images acquired with a fast visible camera. Therefore, it can be faced as an image object recognition problem. Currently, the object recognition is a key output of deep learning and machine learning algorithms. In this paper, a database of several thousands of images generated by a synthetic diagnostic, which reproduces the statistical properties of experimental filaments in terms of position, size and intensity has been used. The synthetic images are pre-processed by mapping them onto the toroidal midplane of the machine. Then a Faster R-CNN is customized to the problem of identifying the filaments. In particular, in order to enhance the performance of the detector, a suitable definition of the target-boxes defining the filament positions and sizes is adopted with good results.

Published
2019

29. Application of self-organizing map to identify nocturnal epileptic seizures

Author

B. Pisano, António Dourado, Alessandra Fanni, and Cesar Teixeira

Subjects
Self-organizing map, 0209 industrial biotechnology, medicine.diagnostic_test, business.industry, Computer science, Pattern recognition, 02 engineering and technology, Electroencephalography, medicine.disease, Nocturnal frontal lobe epilepsy, Eeg recording, Epilepsy, 020901 industrial engineering & automation, Brain state, Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, medicine, 020201 artificial intelligence & image processing, Artificial intelligence, Sensitivity (control systems), business, Software
Abstract

An approach to find the best patient-specific seizure detection system for nocturnal frontal lobe epilepsy (NFLE) is proposed. Data of several patients affected by NFLE, extracted from the European Epilepsy Database, have been used for this study. As every patient possesses different physiological characteristics, several simulations were performed in order to find the best features to be extracted from the best number of electroencephalogram (EEG) channels and to be inputted to two-dimensional self-organizing maps (SOM). The proposed method allows the definition of maps capable of presenting meaningful information on the actual brain state, revealing the mapping potential of clustering the data coming from seizure and non-seizure patterns; moreover, it also suggests to use SOM for early seizure detection. In fact, the temporal sequence of the patterns in an EEG recording can be projected on the SOM, obtaining a trajectory that describes the dynamics of the brain state as captured by the EEG. The analysis of this trajectory allows one to detect the seizure event, highlighting the possibility to promote therapies aimed at rapid and targeted disarming the seizures. A further contribution of the paper is to propose a methodology to be followed to customize the prediction model to a new patient. The performances of the proposed approach, in terms of sensitivity and specificity, have been compared with those of other well-established detection models proving it is a good alternative to early and accurately detect nocturnal frontal lobe epilepsy seizures.

Published
2019

30. A Real Time Bolometer Tomographic Reconstruction Algorithm in Nuclear Fusion Reactors

Author

Alessandra Fanni, Augusto Montisci, Giuliana Sias, Sara Carcangiu, and Barbara Cannas

Subjects
Tokamak, Computer science, General Mathematics, 01 natural sciences, 010305 fluids & plasmas, law.invention, bolometer, law, 0103 physical sciences, Computer Science (miscellaneous), Emissivity, QA1-939, Projection (set theory), Engineering (miscellaneous), Condition number, nuclear fusion, 010302 applied physics, plasma tomography, Tomographic reconstruction, Bolometer, Constrained optimization, mathematical modeling, numerical techniques, Tomography, tokamaks, Algorithm, Mathematics
Abstract

In tokamak nuclear fusion reactors, one of the main issues is to know the total emission of radiation, which is mandatory to understand the plasma physics and is very useful to monitor and control the plasma evolution. This radiation can be measured by means of a bolometer system that consists in a certain number of elements sensitive to the integral of the radiation along straight lines crossing the plasma. By placing the sensors in such a way to have families of crossing lines, sophisticated tomographic inversion algorithms allow to reconstruct the radiation tomography in the 2D poloidal cross-section of the plasma. In tokamaks, the number of projection cameras is often quite limited resulting in an inversion mathematic problem very ill conditioned so that, usually, it is solved by means of a grid-based, iterative constrained optimization procedure, whose convergence time is not suitable for the real time requirements. In this paper, to illustrate the method, an assumption not valid in general is made on the correlation among the grid elements, based on the statistical distribution of the radiation emissivity over a set of tomographic reconstructions, performed off-line. Then, a regularization procedure is carried out, which merge highly correlated grid elements providing a squared coefficients matrix with an enough low condition number. This matrix, which is inverted offline once for all, can be multiplied by the actual bolometer measures returning the tomographic reconstruction, with calculations suitable for real time application. The proposed algorithm is applied, in this paper, to a synthetic case study.

Published
2021

31. Selection of Features Based on Electric Power Quantities for Non-Intrusive Load Monitoring

Author

Carlo Muscas, Sara Carcangiu, Daniele Carta, Barbara Cannas, and Alessandra Fanni

Subjects
Computer science, 020209 energy, Feature selection, 02 engineering and technology, lcsh:Technology, lcsh:Chemistry, non-intrusive load monitoring, feature selection, Electricity meter, power definitions, 0202 electrical engineering, electronic engineering, information engineering, Electronic engineering, General Materials Science, Instrumentation, lcsh:QH301-705.5, Fluid Flow and Transfer Processes, lcsh:T, Process Chemistry and Technology, 020208 electrical & electronic engineering, General Engineering, Process (computing), Energy consumption, Perceptron, lcsh:QC1-999, Computer Science Applications, Power (physics), machine learning, lcsh:Biology (General), lcsh:QD1-999, lcsh:TA1-2040, Electric power, nonlinear devices, lcsh:Engineering (General). Civil engineering (General), lcsh:Physics, Voltage
Abstract

Non-intrusive load monitoring (NILM) is a process of determining the operating states and the energy consumption of single electric devices using a single energy meter providing aggregate load measurements. Due to the large spread of power electronic-based and nonlinear devices connected to the network, the time signals of both voltage and current are typically non-sinusoidal. The effectiveness of a NILM algorithm strongly depends on determining a set of discriminative features. In this paper, voltage and current signals were combined to define, according to the definitions provided in Standard IEEE 1459, different power quantities, that can be used to distinguish different types of appliance. Multi-layer perceptron (MLP) classifiers were trained to solve the appliance detection problem as a multi-class event classification problem, varying the electric features in input. This allowed to select an optimal set of features guarantying good classification performance in identifying typical electric loads.

Published
2021
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32. Learning control coil currents from heat-flux images using convolutional neural networks at Wendelstein 7-X

Author

Yu Gao, Fabio Pisano, Aleix Puig Sitjes, Wendelstein X Team, Barbara Cannas, Holger Niemann, Giuliana Sias, M. W. Jakubowski, Alessandra Fanni, and W7-X Team, Max Planck Institute for Plasma Physics, Max Planck Society

Subjects
Physics, Nuclear Energy and Engineering, Heat flux, Electromagnetic coil, 0103 physical sciences, Wendelstein 7-X, 010306 general physics, Condensed Matter Physics, Topology, 01 natural sciences, Convolutional neural network, 010305 fluids & plasmas
Abstract

An important goal of Wendelstein 7-X, the most advanced operating fusion experiment of the stellarator line, is to demonstrate the ability of stellarators to perform steady-state discharges. In this respect, the monitoring and control of the heat loads on the plasma facing components, especially of the strike-lines in the ten island divertors, will be critical during next operation phase OP2. In this paper, it is shown that deep convolutional neural networks are able to learn the relationship between the heat-flux images, obtained by the analysis of thermographic data, and the applied control coil currents in standard magnetic configuration experiments. This study is carried out in view of understanding and modeling the relationship between the heat-flux distribution in the divertor strike-lines and the actuators influencing them.

Published
2021

34. Integrated design strategy for EU-DEMO first wall protection from plasma transients Fusion Engineering and Design 177 (2022) 113067

Author

Francesco Maviglia, Christian Bachmann, Gianfranco Federici, Thomas Franke, Mattia Siccinio, Raffaele Albanese, Roberto Ambrosino, Wayne Arter, Roberto Bonifetto, Giuseppe Calabrò, Riccardo De Luca, Luigi E. Di Grazia, Emiliano Fable, Pierluigi Fanelli, Alessandra Fanni, Mehdi Firdaouss, Jonathan Gerardin, Riccardo Lombroni, Massimiliano Mattei, Matteo Moscheni, William Morris, Gabriella Pautasso, Sergey Pestchanyi, Giuseppe Ramogida, Maria Lorena Richiusa, Giuliana Sias, Fabio Subba, Fabio Villone, Jeong-Ha You, and Zsolt Vizvary

Subjects
Nuclear Energy and Engineering, Mechanical Engineering, General Materials Science, Civil and Structural Engineering
Published
2022

35. A Closed Form Selected Mapping Algorithm for PAPR Reduction in OFDM Multicarrier Transmission

Author

Augusto Montisci, Sara Carcangiu, and Alessandra Fanni

Subjects
Control and Optimization, Renewable Energy, Sustainability and the Environment, Energy Engineering and Power Technology, Building and Construction, power lines communication, peak-to-average-power ratio, selected mapping, analytical solution, Electrical and Electronic Engineering, Engineering (miscellaneous), Energy (miscellaneous)
Abstract

Nowadays, the demand for communication multi-carriers’ channels, where the sub-channels are made mutually independent by using orthogonal frequency division multiplexing (OFDM), is widespread both for wireless and wired communication systems. Even if OFDM is a spectrally efficient modulation scheme, due to the allowed number of subcarriers, high data rate, and good coverage, the transmitted signal can present high peak values in the time domain, due to inverse fast Fourier transform operations. This gives rise to high peak-to-average power ratio (PAPR) with respect to single carrier systems. These peaks can saturate the transmitting amplifiers, modifying the shape of the OFDM symbol and affecting its information content, and they give rise to electromagnetic compatibility issues for the surrounding electric devices. In this paper, a closed form PAPR reduction algorithm is proposed, which belongs to selected mapping (SLM) methods. These methods consist in shifting the phases of the components to minimize the amplitude of the peaks. The determination of the optimal set of phase shifts is a very complex problem; therefore, the SLM approaches proposed in literature all resort to iterative algorithms. Moreover, as this calculation must be performed online, both the computational cost and the effect on the bit rate (BR) cannot be established a priori. The proposed analytic algorithm finds the optimal phase shifts of an approximated formulation of the PAPR. Simulation results outperform unprocessed conventional OFDM transmission by several dBs. Moreover, the complementary cumulative distribution function (CCDF) shows that, in most of the packets, the proposed algorithm reduces the PAPR if compared with randomly selected phase shifts. For example, with a number of shifted phases U=8, the CCFD curves corresponding to the analytical and random methods intersect at a probability value equal to 10−2, which means that in 99% of cases the former method reduces the PAPR more than the latter one. This is also confirmed by the value of the gain, which, at the same number of shifted phases and at the probability value equal to 10−1, changes from 2.09 dB for the analytical to 1.68 dB for the random SLM.

Published
2022

36. Forecasting-Aided Monitoring for the Distribution System State Estimation

Author

Giuliana Sias, Alessandra Fanni, Sara Sulis, Sara Carcangiu, and Paolo Attilio Pegoraro

Subjects
Estimation, Multidisciplinary, General Computer Science, Artificial neural network, Article Subject, Computer science, 020209 energy, 020208 electrical & electronic engineering, Estimator, 02 engineering and technology, QA75.5-76.95, AC power, Distribution system, Control theory, Electronic computers. Computer science, 0202 electrical engineering, electronic engineering, information engineering, Distribution grid, State (computer science), Uncertainty analysis
Abstract

In this paper, an innovative approach based on an artificial neural network (ANN) load forecasting model to improve the distribution system state estimation accuracy is proposed. High-quality pseudomeasurements are produced by a neural model fed with both exogenous and historical load information and applied in a realistic measurement scenario. Aggregated active and reactive powers of small or medium enterprises and residential loads are simultaneously predicted by a one-step ahead forecast. The correlation between the forecasted real and reactive power errors is duly kept into account in the definition of the estimator together with the uncertainty of the overall measurement chain. The beneficial effects of the ANN-based pseudomeasurements on the quality of the state estimation are demonstrated by simulations carried out on a small medium-voltage distribution grid.

Published
2020

37. Multiobjective tabu search algorithm for the optimal design of a thermo-acoustic magneto-hydro-dynamic electric generator

Author

Augusto Montisci, Sara Carcangiu, Alessandra Fanni, and Renato Forcinetti

Subjects
010302 applied physics, Optimal design, Computer science, Mechanical Engineering, Electric generator, Condensed Matter Physics, 01 natural sciences, Tabu search, 010305 fluids & plasmas, Electronic, Optical and Magnetic Materials, law.invention, Mechanics of Materials, Control theory, law, 0103 physical sciences, Electrical and Electronic Engineering, Magneto
Published
2018

38. Non-Intrusive Loads Monitoring Techniques for House Energy Management

Author

Luca Fresi, Mirco Marceddu, Paolo Porcu, Barbara Cannas, Giuliana Sias, Alessandra Fanni, Sara Carcangiu, Carlo Muscas, and Beatrice Canetto

Subjects
Smart grid, Power demand, Computer science, business.industry, Energy management, Real-time computing, Monitoring system, AC power, Electrical devices, Internet of Things, business
Abstract

This paper presents the first results of a study, carried out in the framework of an industrial collaboration project, whose aim was the development of a Non-Intrusive Load Monitoring system of electrical device activities to be implemented in an IoT platform for Smart Grid. A load disaggregation algorithm has been developed which implements original strategies to deal with some limits of the methods proposed in the literature.

Published
2019

39. Optimization of a Power Line Communication System to Manage Electric Vehicle Charging Stations in a Smart Grid

Author

Augusto Montisci, Sara Carcangiu, and Alessandra Fanni

Subjects
vehicle to grid (V2G), Control and Optimization, business.product_category, Computer science, 020209 energy, Energy Engineering and Power Technology, 02 engineering and technology, lcsh:Technology, Charging station, Electric vehicle, 0202 electrical engineering, electronic engineering, information engineering, power line communication (PLC), Electrical and Electronic Engineering, smart grid, Engineering (miscellaneous), Renewable Energy, Sustainability and the Environment, business.industry, lcsh:T, Node (networking), Electrical engineering, 020206 networking & telecommunications, Power-line communication, Smart grid, energy storage management, multi-objective optimization, Distributed generation, business, Energy (miscellaneous), Data transmission
Abstract

In this paper, a procedure is proposed to design a power line communication (PLC) system to perform the digital transmission in a distributed energy storage system consisting of fleets of electric cars. PLC uses existing power cables or wires as data communication multicarrier channels. For each vehicle, the information to be transmitted can be, for example: the models of the batteries, the level of the charge state, and the schedule of charging/discharging. Orthogonal frequency division multiplexing modulation (OFDM) is used for the bit loading, whose parameters are optimized to find the best compromise between the communication conflicting objectives of minimizing the signal power, maximizing the bit rate, and minimizing the bit error rate. The off-line design is modeled as a multi-objective optimization problem, whose solution supplies a set of Pareto optimal solutions. At the same time, as many charging stations share part of the transmission line, the optimization problem includes also the assignment of the sub-carriers to the single charging stations. Each connection between the control node and a charging station has its own frequency response and is affected by a noise spectrum. In this paper, a procedure is presented, called Chimera, which allows one to solve the multi-objective optimization problem with respect to a unique frequency response, representing the whole set of lines connecting each charging station with the central node. Among the provided Pareto solutions, the designer will make the final decision based on the control system requirements and/or the hardware constraints.

Published
2019

40. Dependence on plasma shape and plasma fueling for small edge-localized mode regimes in TCV and ASDEX Upgrade

Author

V. Piergotti, F. Pesamosca, Bogdan Hnat, A. Sperduti, A. Krivska, J. Vicente, Panagiotis Tolias, Emanuele Poli, Matthias Hoelzl, Benedikt Geiger, A. Jardin, J. Ayllon-Guerola, G. Apruzzese, T. Lunt, J. Galdon-Quiroga, Riccardo Maggiora, M. Tardocchi, M. Koubiti, T. Jonsson, Bruce Lipschultz, P. Innocente, A. Gude, I Miron, M. G. Dunne, G. F. Harrer, A. Moro, A. Iantchenko, K. Galazka, P. Poloskei, K. Bogar, Roberto Ambrosino, G. Ferr, Vladimir E. Moiseenko, Istvan Cziegler, L. Guimarais, S. Vartanian, B. Erds, G. Pucella, V. Bobkov, James Buchanan, Raffaele Albanese, Harry M. Meyer, D. Boeyaert, G. F. Matthews, Eva Macusova, V. S. Marchenko, R. Zagórski, J. Buermans, A. Fil, W. Zhang, Giuseppe Gorini, B. Tal, D. Zaloga, Hugo Bufferand, A. Romano, L. Colas, J. Zebrowski, M. Weiland, L. Barrera-Orte, Matjaž Panjan, A.J. Thornton, E. Wolfrum, Miglena Dimitrova, R. M. McDermott, R. Lombroni, O. Tudisco, F. Reimold, E. R. Solano, X. Feng, Petra Bilkova, M. Groth, E. Alessi, D. S. Gahle, Olivier Février, I. Voitsekhovitch, Matthew Carr, A. Bock, O. Vasilovici, C. Ham, Lorenzo Figini, Guglielmo Rubinacci, Peter Lang, Pierre Manas, S. Costea, A. Kirk, F. Causa, J. Adamek, Vu N. M. T., M. Cavedon, O. Grover, Geert Verdoolaege, M. Spolaore, L. Sanchis-Sanchez, P. Bohm, P. V. Kazantzidis, Sarah Newton, M. Tomes, M.-L. Mayoral, J. R. Harrison, C. Mazzotta, H. Reimerdes, Jorge Morales, D. Brunetti, J. Gonzalez-Martin, Tomas Markovic, S. S. Henderson, D. Ricci, J. Juul Rasmussen, F. Janky, S. Saarelma, Z. Popovic, C. Tsironis, J. J. Rasmussen, S. K. Hansen, Sandra C. Chapman, Volker Naulin, H. Arnichand, Roberto Paccagnella, M. Faitsch, Anders Nielsen, M. Kong, V. Igochine, C. Piron, C. Bowman, Jorge Ferreira, D. Sytnykov, K. G. McClements, Olivier Sauter, Ondrej Ficker, Matthias Wiesenberger, T. Ravensbergen, C. Reux, Irena Ivanova-Stanik, Dirk Reiser, M. Bernert, M. Vallar, J-M Moret, M. Gruca, D. I. Refy, P. Cano Megias, Benoit Labit, M. Schubert, Giuliana Sias, O. Bogar, P. J. Mc Carthy, I. Faust, Gergely Papp, F. Matos, J. Garcia, C. Marini, E. L. Sorokovoy, Dimitri Voltolina, George Wilkie, J. M. Santos, R. R. Sheeba, Vladimir Weinzettl, Sergei Kasilov, J. Cerovsky, Matteo Agostini, G. Tardini, Laurie Porte, F. Dolizy, L. Gil, Matthias Komm, A. Dal Molin, B. Sieglin, Roch Kwiatkowski, M. C. C Messmer, Toke Koldborg Jensen, Vinodh Bandaru, Ben F. McMillan, Alessandra Fanni, Daniele Carnevale, Shimpei Futatani, D. P. Coster, V. Korovin, S. E. Sharapov, Patrik Ollus, J. Gath, A. Czarnecka, D. Gallart, M. Peterka, P. Vallejos Olivares, Jernej Kovacic, Nicolas Fedorczak, Silvio Ceccuzzi, L. Piron, J. Rosato, G. Kocsis, Stefan Kragh Nielsen, M. Garcia-Mu oz, Radomir Panek, S. F. Smith, Paolo Bettini, A. Mariani, R. Dejarnac, Lorenzo Frassinetti, D. Douai, L. Garzotti, H. J. Sun, C.K. Tsui, N. den Harder, John Elmerdahl Olsen, F. Bombarda, M. Francesco, Piero Martin, D. Hogeweij, P. Blanchard, F. Bouquey, Gabor Por, Luca Boncagni, Carlo Sozzi, Martin Hron, P. A. Schneider, V. P. Loschiavo, David Terranova, D. Aguiam, D. Choi, M. Gobbin, D. Iglesias, M. Reich, G. Avdeeva, A. Gallo, O. Biletskyi, M. Aradi, F. Liu, M. Griener, Antti Snicker, L. Kripner, Jérôme Bucalossi, L. Hesslow, Nick Walkden, M. Rodriguez-Ramos, T. C. Blanken, Cristian Galperti, F. Jaulmes, G. Calabr, G.A. Rattá, W. Bin, S. Garavaglia, V. Plyusnin, Andreas Frank Martitsch, A. Zisis, Rita Lorenzini, Duccio Testa, M. Passeri, Ola Embréus, N. Krawczyk, K. Särkimäki, Davide Galassi, D. Samaddar, M. Oberkofler, E. Seliunin, D. Brida, P. Buratti, F. Nabais, J. Ongena, J. Likonen, Yann Camenen, M. J. Mantsinen, F. Carpanese, S. Wiesen, P. Piovesan, Mirko Salewski, J. Hawke, Florian Laggner, R. Bilato, M. Wischmeier, L. Pigatto, G. I. Pokol, G. Giruzzi, Jens Madsen, D. Gadariya, L. Stipani, Christian Theiler, J. Stober, Michael Barnes, Timothy Goodman, R. D. Nem, J. J. Dominguez-Palacios Duran, F. Militello, Y. Kulyk, D. J. Cruz Zabala, A. Drenik, P. Manz, M. Scheffer, V. Pericoli Radolfini, B. Tilia, John Omotani, B. Vanovac, B. S. Schneider, E. Fable, Jakub Urban, T. Gyergyek, A. N. Karpushov, M. Farnik, Jakub Seidl, Christopher G. Albert, Antoine Merle, A. Cathey, D. A. Ryan, Sergio Galeani, R. Scannell, A. Havranek, G. de Carolis, C. Soria-Hoyo, S. Gibson, D. Carralero, D. Meshcheriakov, Morten Stejner, B. P. Duval, Francesco Cordella, Mitja Kelemen, Svetlana V. Ratynskaia, Stefano Coda, L. Calacci, C. Cianfarani, Faa Federico Felici, A. C. A. Figueiredo, L. Panaccione, E. Viezzer, Fabio Villone, Daniele Milanesio, Winfried Kernbichler, Mario Sassano, A. Teplukhina, S. Zoletnik, L. Laguardia, P. Molina Cabrera, Taina Kurki-Suonio, D. Micheletti, P. Zanca, Daniel Dunai, S. Feng, J. Decker, Stylianos Varoutis, Lorella Carraro, M. Wensing, Gustavo Granucci, Artur Palha, A. Kappatou, J. Garcia-Lopez, Felix I. Parra, Ye. O. Kazakov, S. Brezinsek, Didier Mazon, A. Lahtinen, I. Paradela Perez, P. Chmielewski, L. Giacomelli, Alessandro Pau, Gianluca Spizzo, R. Delogu, R. J. Akers, H. De Oliveira, Petr Vondracek, F. P. Orsitto, J. Hobirk, L. Xiang, A. Burckhart, B. Maljaars, V. Petrzilka, Ocleto D'Arcangelo, P. David, D. Grekov, Tamás Szepesi, Y. Andr be, P. Hacek, M. Toscano-Jimenez, T. Pütterich, L. Cordaro, V. Nikolaeva, F. Orain, M. Rabinski, C. Ionita-Schrittwieser, T. Tala, Maria Ester Puiatti, A. Casolari, T. Happel, Pär Strand, Benjamin Daniel Dudson, P. Mantica, Z. Huang, D. Colette, G. Ciraolo, Jan Mlynar, W. Suttrop, C. Meineri, J. Horacek, Seppo Sipilä, M. Gospodarczyk, S. Mastrostefano, Jesús Vega, Antti Hakola, Kevin Verhaegh, Roman Schrittwieser, C. Marchetto, M. Willensdorfer, Jari Varje, D. C. van Vugt, J. Faustin, Mathias Hoppe, M. Dreval, A. Perek, C. Angioni, Laure Vermare, U. A. Sheikh, J. F. Rivero-Rodriguez, G. Rubino, S.N. Reznik, Tsv K Popov, S. Nowak, A. S. Jacobsen, J. R. Martin Solis, David Moulton, Heinz Isliker, K. Wu, Anna Salmi, F. Nespoli, S. Elmore, O. Kudlacek, A. Kallenbach, Rok Zaplotnik, D. L. Keeling, L. Giannone, M. Maraschek, Carlos B. da Silva, F. Hitzler, M. Valovic, M. W. Jakubowski, L. Gabellieri, Jozef Varju, Marco Cecconello, M. Valisa, Vlado Menkovski, Gábor Cseh, E. Thoren, T. Eich, R. Coelho, F. Bagnato, Matteo Zuin, Alexander Kendl, G. Rocchi, G. Pautasso, D. Naydenkova, R. O. Pavlichenko, M. Fontana, Lionello Marrelli, Tommaso Bolzonella, Nicola Vianello, Pascale Hennequin, R. Ochoukov, Tom Wauters, Christian Hopf, Ch. Fuchs, E. Giovannozzi, Fulvio Auriemma, Roberto Maurizio, Stefan Buller, Massimo Nocente, K. Krieger, G. Grenfell, N. Rispoli, R. Dux, Barbara Cannas, Laboratoire de Physique des Plasmas (LPP), Université Paris-Saclay-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-École polytechnique (X)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Labit, B., Eich, T., Harrer, G. F., Wolfrum, E., Bernert, M., Dunne, M. G., Frassinetti, L., Hennequin, P., Maurizio, R., Merle, A., Meyer, H., Saarelma, S., Sheikh, U., Adamek, J., Agostini, M., Aguiam, D., Akers, R., Albanese, R., Albert, C., Alessi, E., Ambrosino, R., Andr be, Y., Angioni, C., Apruzzese, G., Aradi, M., Arnichand, H., Auriemma, F., Avdeeva, G., Ayllon-Guerola, J. M., Bagnato, F., Bandaru, V. K., Barnes, M., Barrera-Orte, L., Bettini, P., Bilato, R., Biletskyi, O., Bilkova, P., Bin, W., Blanchard, P., Blanken, T., Bobkov, V., Bock, A., Boeyaert, D., Bogar, K., Bogar, O., Bohm, P., Bolzonella, T., Bombarda, F., Boncagni, L., Bouquey, F., Bowman, C., Brezinsek, S., Brida, D., Brunetti, D., Bucalossi, J., Buchanan, J., Buermans, J., Bufferand, H., Buller, S., Buratti, P., Burckhart, A., Calabr, G., Calacci, L., Camenen, Y., Cannas, B., Cano Megias, P., Carnevale, D., Carpanese, F., Carr, M., Carralero, D., Carraro, L., Casolari, A., Cathey, A., Causa, F., Cavedon, M., Cecconello, M., Ceccuzzi, S., Cerovsky, J., Chapman, S., Chmielewski, P., Choi, D., Cianfarani, C., Ciraolo, G., Coda, S., Coelho, R., Colas, L., Colette, D., Cordaro, L., Cordella, F., Costea, S., Coster, D., Cruz Zabala, D. J., Cseh, G., Czarnecka, A., Cziegler, I., D'Arcangelo, O., Dal Molin, A., David, P., De Carolis, G., De Oliveira, H., Decker, J., Dejarnac, R., Delogu, R., Den Harder, N., Dimitrova, M., Dolizy, F., Dominguez-Palacios Duran, J. J., Douai, D., Drenik, A., Dreval, M., Dudson, B., Dunai, D., Duval, B. P., Dux, R., Elmore, S., Embreus, O., Erds, B., Fable, E., Faitsch, M., Fanni, A., Farnik, M., Faust, I., Faustin, J., Fedorczak, N., Felici, F., Feng, S., Feng, X., Ferreira, J., Ferr, G., Fevrier, O., Ficker, O., Figini, L., Figueiredo, A., Fil, A., Fontana, M., Francesco, M., Fuchs, C., Futatani, S., Gabellieri, L., Gadariya, D., Gahle, D., Galassi, D., Galazka, K., Galdon-Quiroga, J., Galeani, S., Gallart, D., Gallo, A., Galperti, C., Garavaglia, S., Garcia, J., Garcia-Lopez, J., Garcia-Mu oz, M., Garzotti, L., Gath, J., Geiger, B., Giacomelli, L., Giannone, L., Gibson, S., Gil, L., Giovannozzi, E., Giruzzi, G., Gobbin, M., Gonzalez-Martin, J., Goodman, T. P., Gorini, G., Gospodarczyk, M., Granucci, G., Grekov, D., Grenfell, G., Griener, M., Groth, M., Grover, O., Gruca, M., Gude, A., Guimarais, L., Gyergyek, T., Hacek, P., Hakola, A., Ham, C., Happel, T., Harrison, J., Havranek, A., Hawke, J., Henderson, S., Hesslow, L., Hitzler, F., Hnat, B., Hobirk, J., Hoelzl, M., Hogeweij, D., Hopf, C., Hoppe, M., Horacek, J., Hron, M., Huang, Z., Iantchenko, A., Iglesias, D., Igochine, V., Innocente, P., Ionita-Schrittwieser, C., Isliker, H., Ivanova-Stanik, I., Jacobsen, A., Jakubowski, M., Janky, F., Jardin, A., Jaulmes, F., Jensen, T., Jonsson, T., Kallenbach, A., Kappatou, A., Karpushov, A., Kasilov, S., Kazakov, Y., Kazantzidis, P. V., Keeling, D., Kelemen, M., Kendl, A., Kernbichler, W., Kirk, A., Kocsis, G., Komm, M., Kong, M., Korovin, V., Koubiti, M., Kovacic, J., Krawczyk, N., Krieger, K., Kripner, L., Krivska, A., Kudlacek, O., Kulyk, Y., Kurki-Suonio, T., Kwiatkowski, R., Laggner, F., Laguardia, L., Lahtinen, A., Lang, P., Likonen, J., Lipschultz, B., Liu, F., Lombroni, R., Lorenzini, R., Loschiavo, V. P., Lunt, T., Macusova, E., Madsen, J., Maggiora, R., Maljaars, B., Manas, P., Mantica, P., Mantsinen, M. J., Manz, P., Maraschek, M., Marchenko, V., Marchetto, C., Mariani, A., Marini, C., Markovic, T., Marrelli, L., Martin, P., Martin Solis, J. R., Martitsch, A., Mastrostefano, S., Matos, F., Matthews, G., Mayoral, M. -L., Mazon, D., Mazzotta, C., Mc Carthy, P., Mcclements, K., Mcdermott, R., Mcmillan, B., Meineri, C., Menkovski, V., Meshcheriakov, D., Messmer, M., Micheletti, D., Milanesio, D., Militello, F., Miron, I. G., Mlynar, J., Moiseenko, V., Molina Cabrera, P. A., Morales, J., Moret, J. -M., Moro, A., Moulton, D., Nabais, F., Naulin, V., Naydenkova, D., Nem, R. D., Nespoli, F., Newton, S., Nielsen, A. H., Nielsen, S. K., Nikolaeva, V., Nocente, M., Nowak, S., Oberkofler, M., Ochoukov, R., Ollus, P., Olsen, J., Omotani, J., Ongena, J., Orain, F., Orsitto, F. P., Paccagnella, R., Palha, A., Panaccione, L., Panek, R., Panjan, M., Papp, G., Paradela Perez, I., Parra, F., Passeri, M., Pau, A., Pautasso, G., Pavlichenko, R., Perek, A., Pericoli Radolfini, V., Pesamosca, F., Peterka, M., Petrzilka, V., Piergotti, V., Pigatto, L., Piovesan, P., Piron, C., Piron, L., Plyusnin, V., Pokol, G., Poli, E., Poloskei, P., Popov, T., Popovic, Z., Por, G., Porte, L., Pucella, G., Puiatti, M. E., Putterich, T., Rabinski, M., Juul Rasmussen, J., Rasmussen, J., Ratta, G. A., Ratynskaia, S., Ravensbergen, T., Refy, D., Reich, M., Reimerdes, H., Reimold, F., Reiser, D., Reux, C., Reznik, S., Ricci, D., Rispoli, N., Rivero-Rodriguez, J. F., Rocchi, G., Rodriguez-Ramos, M., Romano, A., Rosato, J., Rubinacci, G., Rubino, G., Ryan, D. A., Salewski, M., Salmi, A., Samaddar, D., Sanchis-Sanchez, L., Santos, J., Sarkimaki, K., Sassano, M., Sauter, O., Scannell, R., Scheffer, M., Schneider, B. S., Schneider, P., Schrittwieser, R., Schubert, M., Seidl, J., Seliunin, E., Sharapov, S., Sheeba, R. R., Sias, G., Sieglin, B., Silva, C., Sipila, S., Smith, S., Snicker, A., Solano, E. R., Hansen, S. K., Soria-Hoyo, C., Sorokovoy, E., Sozzi, C., Sperduti, A., Spizzo, G., Spolaore, M., Stejner, M., Stipani, L., Stober, J., Strand, P., Sun, H., Suttrop, W., Sytnykov, D., Szepesi, T., Tal, B., Tala, T., Tardini, G., Tardocchi, M., Teplukhina, A., Terranova, D., Testa, D., Theiler, C., Thoren, E., Thornton, A., Tilia, B., Tolias, P., Tomes, M., Toscano-Jimenez, M., Tsironis, C., Tsui, C., Tudisco, O., Urban, J., Valisa, M., Vallar, M., Vallejos Olivares, P., Valovic, M., Van Vugt, D., Vanovac, B., Varje, J., Varju, J., Varoutis, S., Vartanian, S., Vasilovici, O., Vega, J., Verdoolaege, G., Verhaegh, K., Vermare, L., Vianello, N., Vicente, J., Viezzer, E., Villone, F., Voitsekhovitch, I., Voltolina, D., Vondracek, P., Vu, N. M. T., Walkden, N., Wauters, T., Weiland, M., Weinzettl, V., Wensing, M., Wiesen, S., Wiesenberger, M., Wilkie, G., Willensdorfer, M., Wischmeier, M., Wu, K., Xiang, L., Zagorski, R., Zaloga, D., Zanca, P., Zaplotnik, R., Zebrowski, J., Zhang, W., Zisis, A., Zoletnik, S., Zuin, M., Swiss Federal Institute of Technology Lausanne, Max-Planck-Institut für Plasmaphysik, Vienna University of Technology, KTH Royal Institute of Technology, Université Paris-Saclay, JET, Czech Academy of Sciences, National Research Council of Italy, University of Lisbon, University of Naples Federico II, Graz University of Technology, University of Naples Parthenope, Agenzia nazionale per le nuove tecnologie, l'energia e lo sviluppo economico sostenibile, Danmarks Tekniske Universitet, University of Seville, University of Oxford, EUROfusion Programme Management Unit, National Science Center Kharkov Institute of Physics and Technology, Eindhoven University of Technology, Forschungszentrum Jülich, CEA, University of York, Royal Military Academy, Chalmers University of Technology, Tuscia University, Università di Roma Tor Vergata, CNRS, University of Cagliari, CIEMAT, Uppsala University, University of Warwick, Soltan Institute for Nuclear Studies, University of Innsbruck, Hungarian Academy of Sciences, Budapest University of Technology and Economics, Durham University, BarcelonaTech, University of Strathclyde, Barcelona Supercomputing Center, University of Milan - Bicocca, Karlsruhe Institute of Technology, Fusion and Plasma Physics, J. Stefan Institute, VTT Technical Research Centre of Finland, Dutch Institute for Fundamental Energy Research, Aristotle University of Thessaloniki, National Technical University of Athens, National Centre for Nuclear Research, University of Helsinki, Université Côte d'Azur, Polytechnic University of Turin, NASU - Institute of Nuclear Research, University of Cassino and Southern Lazio, University College Cork, National Institute for Laser, Plasma and Radiation Physics, Department of Applied Physics, Sofia University St. Kliment Ohridski, Ghent University, Aalto-yliopisto, Aalto University, Labit, B, Eich, T, Harrer, G, Wolfrum, E, Bernert, M, Dunne, M, Frassinetti, L, Hennequin, P, Maurizio, R, Merle, A, Meyer, H, Saarelma, S, Sheikh, U, Adamek, J, Agostini, M, Aguiam, D, Akers, R, Albanese, R, Albert, C, Alessi, E, Ambrosino, R, Andr be, Y, Angioni, C, Apruzzese, G, Aradi, M, Arnichand, H, Auriemma, F, Avdeeva, G, Ayllon-Guerola, J, Bagnato, F, Bandaru, V, Barnes, M, Barrera-Orte, L, Bettini, P, Bilato, R, Biletskyi, O, Bilkova, P, Bin, W, Blanchard, P, Blanken, T, Bobkov, V, Bock, A, Boeyaert, D, Bogar, K, Bogar, O, Bohm, P, Bolzonella, T, Bombarda, F, Boncagni, L, Bouquey, F, Bowman, C, Brezinsek, S, Brida, D, Brunetti, D, Bucalossi, J, Buchanan, J, Buermans, J, Bufferand, H, Buller, S, Buratti, P, Burckhart, A, Calabr, G, Calacci, L, Camenen, Y, Cannas, B, Cano Megias, P, Carnevale, D, Carpanese, F, Carr, M, Carralero, D, Carraro, L, Casolari, A, Cathey, A, Causa, F, Cavedon, M, Cecconello, M, Ceccuzzi, S, Cerovsky, J, Chapman, S, Chmielewski, P, Choi, D, Cianfarani, C, Ciraolo, G, Coda, S, Coelho, R, Colas, L, Colette, D, Cordaro, L, Cordella, F, Costea, S, Coster, D, Cruz Zabala, D, Cseh, G, Czarnecka, A, Cziegler, I, D'Arcangelo, O, Dal Molin, A, David, P, De Carolis, G, De Oliveira, H, Decker, J, Dejarnac, R, Delogu, R, Den Harder, N, Dimitrova, M, Dolizy, F, Dominguez-Palacios Duran, J, Douai, D, Drenik, A, Dreval, M, Dudson, B, Dunai, D, Duval, B, Dux, R, Elmore, S, Embreus, O, Erds, B, Fable, E, Faitsch, M, Fanni, A, Farnik, M, Faust, I, Faustin, J, Fedorczak, N, Felici, F, Feng, S, Feng, X, Ferreira, J, Ferr, G, Fevrier, O, Ficker, O, Figini, L, Figueiredo, A, Fil, A, Fontana, M, Francesco, M, Fuchs, C, Futatani, S, Gabellieri, L, Gadariya, D, Gahle, D, Galassi, D, Galazka, K, Galdon-Quiroga, J, Galeani, S, Gallart, D, Gallo, A, Galperti, C, Garavaglia, S, Garcia, J, Garcia-Lopez, J, Garcia-Mu oz, M, Garzotti, L, Gath, J, Geiger, B, Giacomelli, L, Giannone, L, Gibson, S, Gil, L, Giovannozzi, E, Giruzzi, G, Gobbin, M, Gonzalez-Martin, J, Goodman, T, Gorini, G, Gospodarczyk, M, Granucci, G, Grekov, D, Grenfell, G, Griener, M, Groth, M, Grover, O, Gruca, M, Gude, A, Guimarais, L, Gyergyek, T, Hacek, P, Hakola, A, Ham, C, Happel, T, Harrison, J, Havranek, A, Hawke, J, Henderson, S, Hesslow, L, Hitzler, F, Hnat, B, Hobirk, J, Hoelzl, M, Hogeweij, D, Hopf, C, Hoppe, M, Horacek, J, Hron, M, Huang, Z, Iantchenko, A, Iglesias, D, Igochine, V, Innocente, P, Ionita-Schrittwieser, C, Isliker, H, Ivanova-Stanik, I, Jacobsen, A, Jakubowski, M, Janky, F, Jardin, A, Jaulmes, F, Jensen, T, Jonsson, T, Kallenbach, A, Kappatou, A, Karpushov, A, Kasilov, S, Kazakov, Y, Kazantzidis, P, Keeling, D, Kelemen, M, Kendl, A, Kernbichler, W, Kirk, A, Kocsis, G, Komm, M, Kong, M, Korovin, V, Koubiti, M, Kovacic, J, Krawczyk, N, Krieger, K, Kripner, L, Krivska, A, Kudlacek, O, Kulyk, Y, Kurki-Suonio, T, Kwiatkowski, R, Laggner, F, Laguardia, L, Lahtinen, A, Lang, P, Likonen, J, Lipschultz, B, Liu, F, Lombroni, R, Lorenzini, R, Loschiavo, V, Lunt, T, Macusova, E, Madsen, J, Maggiora, R, Maljaars, B, Manas, P, Mantica, P, Mantsinen, M, Manz, P, Maraschek, M, Marchenko, V, Marchetto, C, Mariani, A, Marini, C, Markovic, T, Marrelli, L, Martin, P, Martin Solis, J, Martitsch, A, Mastrostefano, S, Matos, F, Matthews, G, Mayoral, M, Mazon, D, Mazzotta, C, Mc Carthy, P, Mcclements, K, Mcdermott, R, Mcmillan, B, Meineri, C, Menkovski, V, Meshcheriakov, D, Messmer, M, Micheletti, D, Milanesio, D, Militello, F, Miron, I, Mlynar, J, Moiseenko, V, Molina Cabrera, P, Morales, J, Moret, J, Moro, A, Moulton, D, Nabais, F, Naulin, V, Naydenkova, D, Nem, R, Nespoli, F, Newton, S, Nielsen, A, Nielsen, S, Nikolaeva, V, Nocente, M, Nowak, S, Oberkofler, M, Ochoukov, R, Ollus, P, Olsen, J, Omotani, J, Ongena, J, Orain, F, Orsitto, F, Paccagnella, R, Palha, A, Panaccione, L, Panek, R, Panjan, M, Papp, G, Paradela Perez, I, Parra, F, Passeri, M, Pau, A, Pautasso, G, Pavlichenko, R, Perek, A, Pericoli Radolfini, V, Pesamosca, F, Peterka, M, Petrzilka, V, Piergotti, V, Pigatto, L, Piovesan, P, Piron, C, Piron, L, Plyusnin, V, Pokol, G, Poli, E, Poloskei, P, Popov, T, Popovic, Z, Por, G, Porte, L, Pucella, G, Puiatti, M, Putterich, T, Rabinski, M, Juul Rasmussen, J, Rasmussen, J, Ratta, G, Ratynskaia, S, Ravensbergen, T, Refy, D, Reich, M, Reimerdes, H, Reimold, F, Reiser, D, Reux, C, Reznik, S, Ricci, D, Rispoli, N, Rivero-Rodriguez, J, Rocchi, G, Rodriguez-Ramos, M, Romano, A, Rosato, J, Rubinacci, G, Rubino, G, Ryan, D, Salewski, M, Salmi, A, Samaddar, D, Sanchis-Sanchez, L, Santos, J, Sarkimaki, K, Sassano, M, Sauter, O, Scannell, R, Scheffer, M, Schneider, B, Schneider, P, Schrittwieser, R, Schubert, M, Seidl, J, Seliunin, E, Sharapov, S, Sheeba, R, Sias, G, Sieglin, B, Silva, C, Sipila, S, Smith, S, Snicker, A, Solano, E, Hansen, S, Soria-Hoyo, C, Sorokovoy, E, Sozzi, C, Sperduti, A, Spizzo, G, Spolaore, M, Stejner, M, Stipani, L, Stober, J, Strand, P, Sun, H, Suttrop, W, Sytnykov, D, Szepesi, T, Tal, B, Tala, T, Tardini, G, Tardocchi, M, Teplukhina, A, Terranova, D, Testa, D, Theiler, C, Thoren, E, Thornton, A, Tilia, B, Tolias, P, Tomes, M, Toscano-Jimenez, M, Tsironis, C, Tsui, C, Tudisco, O, Urban, J, Valisa, M, Vallar, M, Vallejos Olivares, P, Valovic, M, Van Vugt, D, Vanovac, B, Varje, J, Varju, J, Varoutis, S, Vartanian, S, Vasilovici, O, Vega, J, Verdoolaege, G, Verhaegh, K, Vermare, L, Vianello, N, Vicente, J, Viezzer, E, Villone, F, Voitsekhovitch, I, Voltolina, D, Vondracek, P, Vu, N, Walkden, N, Wauters, T, Weiland, M, Weinzettl, V, Wensing, M, Wiesen, S, Wiesenberger, M, Wilkie, G, Willensdorfer, M, Wischmeier, M, Wu, K, Xiang, L, Zagorski, R, Zaloga, D, Zanca, P, Zaplotnik, R, Zebrowski, J, Zhang, W, Zisis, A, Zoletnik, S, Zuin, M, Universitat Politècnica de Catalunya. Departament de Física, Universitat Politècnica de Catalunya. ANT - Advanced Nuclear Technologies Research Group, Control Systems Technology, Science and Technology of Nuclear Fusion, Data Mining, Sensorics for fusion reactors, and Magneto-Hydro-Dynamic Stability of Fusion Plasmas

Subjects
Nuclear and High Energy Physics, Settore ING-INF/04, Work package, grassy ELM, ballooning modes, Nuclear physics, 01 natural sciences, Flattening, Ballooning, 010305 fluids & plasmas, grassy ELMs, separatrix density, ASDEX Upgrade, [PHYS.PHYS.PHYS-PLASM-PH]Physics [physics]/Physics [physics]/Plasma Physics [physics.plasm-ph], 0103 physical sciences, 010306 general physics, Edge-localized mode, QC, H-mode, plasma triangularity, type-II ELMs, Physics, Física [Àrees temàtiques de la UPC], type-II ELM, Plasma, Condensed Matter Physics, Null (physics), Shear (sheet metal), Física nuclear, Atomic physics, ballooning mode
Abstract

Within the EUROfusion MST1 work package, a series of experiments has been conducted on AUG and TCV devices to disentangle the role of plasma fueling and plasma shape for the onset of small ELM regimes. On both devices, small ELM regimes with high confinement are achieved if and only if two conditions are fulfilled at the same time. Firstly, the plasma density at the separatrix must be large enough (), leading to a pressure profile flattening at the separatrix, which stabilizes type-I ELMs. Secondly, the magnetic configuration has to be close to a double null (DN), leading to a reduction of the magnetic shear in the extreme vicinity of the separatrix. As a consequence, its stabilizing effect on ballooning modes is weakened. Peer Reviewed Article escrit per 365 autors/autores: Labit, B.; Eich, T.; Harrer, G. F.; Wolfrum, E.; Bernert, M.; Dunne, M. G.; Frassinetti, L.; Hennequin, P.; Maurizio, R.; Merle, A.; Meyer, H.; Saarelma, S.; Sheikh, U.; Adamek, J.; Agostini, M.; Aguiam, D.; Akers, R.; Albanese, R.; Albert, C.; Alessi, E.; Ambrosino, R.; Andr be, Y.; Angioni, C.; Apruzzese, G.; Aradi, M.; Arnichand, H.; Auriemma, F.; Avdeeva, G.; Ayllon-Guerola, J. M.; Bagnato, F.; Bandaru, V. K.; Barnes, M.; Barrera-Orte, L.; Bettini, P.; Bilato, R.; Biletskyi, O.; Bilkova, P.; Bin, W.; Blanchard, P.; Blanken, T.; Bobkov, V.; Bock, A.; Boeyaert, D.; Bogar, K.; Bogar, O.; Bohm, P.; Bolzonella, T.; Bombarda, F.; Boncagni, L.; Bouquey, F.; Bowman, C.; Brezinsek, S.; Brida, D.; Brunetti, D.; Bucalossi, J.; Buchanan, J.; Buermans, J.; Bufferand, H.; Buller, S.; Buratti, P.; Burckhart, A.; Calabr, G.; Calacci, L.; Camenen, Y.; Cannas, B.; Cano Megías, P.; Carnevale, D.; Carpanese, F.; Carr, M.; Carralero, D.; Carraro, L.; Casolari, A.; Cathey, A.; Causa, F.; Cavedon, M.; Cecconello, M.; Ceccuzzi, S.; Cerovsky, J.; Chapman, S.; Chmielewski, P.; Choi, D.; Cianfarani, C.; Ciraolo, G.; Coda, S.; Coelho, R.; Colas, L.; Colette, D.; Cordaro, L.; Cordella, F.; Costea, S.; Coster, D.; Cruz Zabala, D. J.; Cseh, G.; Czarnecka, A.; Cziegler, I.; D’Arcangelo, O.; Dal Molin, A.; David, P.; De Carolis, G.; De Oliveira, H.; Decker, J.; Dejarnac, R.; Delogu, R.; Den Harder, N.; Dimitrova, M.; Dolizy, F.; Domínguez-Palacios Durán, J. J.; Douai, D.; Drenik, A.; Dreval, M.; Dudson, B.; Dunai, D.; Duval, B. P.; Dux, R.; Elmore, S.; Embréus, O.; Erds, B.; Fable, E.; Faitsch, M.; Fanni, A.; Farnik, M.; Faust, I.; Faustin, J.; Fedorczak, N.; Felici, F.; Feng, S.; Feng, X.; Ferreira, J.; Ferr, G.; Février, O.; Ficker, O.; Figini, L.; Figueiredo, A.; Fil, A.; Fontana, M.; Francesco, M.; Fuchs, C.; Futatani, S.; Gabellieri, L.; Gadariya, D.; Gahle, D.; Galassi, D.; Gałązka, K.; Galdon-Quiroga, J.; Galeani, S.; Gallart, D.; Gallo, A.; Galperti, C.; Garavaglia, S.; Garcia, J.; Garcia-Lopez, J.; Garcia-Mu oz, M.; Garzotti, L.; Gath, J.; Geiger, B.; Giacomelli, L.; Giannone, L.; Gibson, S.; Gil, L.; Giovannozzi, E.; Giruzzi, G.; Gobbin, M.; Gonzalez-Martin, J.; Goodman, T. P.; Gorini, G.; Gospodarczyk, M.; Granucci, G.; Grekov, D. 1; Grenfell, G.; Griener, M.; Groth, M.; Grover, O.; Gruca, M.; Gude, A.; Guimarais, L.; Gyergyek, T.; Hacek, P.; Hakola, A.; Ham, C.; Happel, T.; Harrison, J.; Havranek, A.; Hawke, J.; Henderson, S.; Hesslow, L.; Hitzler, F.; Hnat, B.; Hobirk, J.; Hoelzl, M.; Hogeweij, D.; Hopf, C.; Hoppe, M.; Horacek, J.; Hron, M.; Huang, Z.; Iantchenko, A.; Iglesias, D.; Igochine, V.; Innocente, P.; Ionita-Schrittwieser, C.; Isliker, H.; Ivanova-Stanik, I.; Jacobsen, A.; Jakubowski, M.; Janky, F.; Jardin, A.; Jaulmes, F.; Jensen, T.; Jonsson, T.; Kallenbach, A.; Kappatou, A.; Karpushov, A.; Kasilov, S.; Kazakov, Y.; Kazantzidis, P. V.; Keeling, D.; Kelemen, M.; Kendl, A.; Kernbichler, W.; Kirk, A.; Kocsis, G.; Komm, M.; Kong, M.; Korovin, V.; Koubiti, M.; Kovacic, J.; Krawczyk, N.; Krieger, K.; Kripner, L.; Křivská, A.; Kudlacek, O.; Kulyk, Y.; Kurki-Suonio, T.; Kwiatkowski, R.; Laggner, F.; Laguardia, L.; Lahtinen, A.; Lang, P.; Likonen, J.; Lipschultz, B.; Liu, F.; Lombroni, R.; Lorenzini, R.; Loschiavo, V. P.; Lunt, T.; MacUsova, E.; Madsen, J.; Maggiora, R.; Maljaars, B.; Manas, P.; Mantica, P.; Mantsinen, M. J.; Manz, P.; Maraschek, M.; Marchenko, V.; Marchetto, C.; Mariani, A.; Marini, C.; Markovic, T.; Marrelli, L.; Martin, P.; Martín Solís, J. R.; Martitsch, A.; Mastrostefano, S.; Matos, F.; Matthews, G.; Mayoral, M.-L.; Mazon, D.; Mazzotta, C.; Mc Carthy, P.; McClements, K.; McDermott, R.; McMillan, B.; Meineri, C.; Menkovski, V.; Meshcheriakov, D.; Messmer, M.; Micheletti, D.; Milanesio, D.; Militello, F.; Miron, I. G.; Mlynar, J.; Moiseenko, V.; Molina Cabrera, P. A.; Morales, J.; Moret, J.-M.; Moro, A.; Moulton, D.; Nabais, F.; Naulin, V.; Naydenkova, D.; Nem, R. D.; Nespoli, F.; Newton, S.; Nielsen, A. H.; Nielsen, S. K.; Nikolaeva, V.; Nocente, M.; Nowak, S.; Oberkofler, M.; Ochoukov, R.; Ollus, P.; Olsen, J.; Omotani, J.; Ongena, J.; Orain, F.; Orsitto, F. P.; Paccagnella, R.; Palha, A.; Panaccione, L.; Panek, R.; Panjan, M.; Papp, G.; Paradela Perez, I.; Parra, F.; Passeri, M.; Pau, A.; Pautasso, G.; Pavlichenko, R.; Perek, A.; Pericoli Radolfini, V.; Pesamosca, F.; Peterka, M.; Petrzilka, V.; Piergotti, V.; Pigatto, L.; Piovesan, P.; Piron, C.; Piron, L.; Plyusnin, V.; Pokol, G.; Poli, E.; Pölöskei, P.; Popov, T.; Popovic, Z.; Pór, G.; Porte, L.; Pucella, G.; Puiatti, M. E.; Pütterich, T.; Rabinski, M.; Juul Rasmussen, J.; Rasmussen, J.; Rattá, G. A.; Ratynskaia, S.; Ravensbergen, T.; Réfy, D.; Reich, M.; Reimerdes, H.; Reimold, F.; Reiser, D.; Reux, C.; Reznik, S.; Ricci, D.; Rispoli, N.; Rivero-Rodriguez, J. F.; Rocchi, G.; Rodriguez-Ramos, M.; Romano, A.; Rosato, J.; Rubinacci, G.; Rubino, G.; Ryan, D. A.; Salewski, M.; Salmi, A.; Samaddar, D.; Sanchis-Sanchez, L.; Santos, J.; Särkimäki, K.; Sassano, M.; Sauter, O.; Scannell, R.; Scheffer, M.; Schneider, B. S.; Schneider, P.; Schrittwieser, R.; Schubert, M.; Seidl, J.; Seliunin, E.; Sharapov, S.; Sheeba, R. R.; Sias, G.; Sieglin, B.; Silva, C.; Sipilä, S.; Smith, S.; Snicker, A.; Solano, E. R.; Hansen, S. K.; Soria-Hoyo, C.; Sorokovoy, E.; Sozzi, C.; Sperduti, A.; Spizzo, G.; Spolaore, M.; Stejner, M.; Stipani, L.; Stober, J.; Strand, P.; Sun, H.; Suttrop, W.; Sytnykov, D.; Szepesi, T.; Tál, B.; Tala, T.; Tardini, G.; Tardocchi, M.; Teplukhina, A.; Terranova, D.; Testa, D.; Theiler, C.; Thorén, E.; Thornton, A.; Tilia, B.; Tolias, P.; Tomes, M.; Toscano-Jimenez, M.; Tsironis, C.; Tsui, C.; Tudisco, O.; Urban, J.; Valisa, M.; Vallar, M.; Vallejos Olivares, P.; Valovic, M.; Van Vugt, D.; Vanovac, B.; Varje, J.; Varju, J.; Varoutis, S. 1; Vartanian, S.; Vasilovici, O.; Vega, J.; Verdoolaege, G.; Verhaegh, K.; Vermare, L.; Vianello, N.; Vicente, J.; Viezzer, E.; Villone, F.; Voitsekhovitch, I.; Voltolina, D.; Vondracek, P.; Vu, N. M. T.; Walkden, N.; Wauters, T.; Weiland, M.; Weinzettl, V.; Wensing, M.; Wiesen, S.; Wiesenberger, M.; Wilkie, G.; Willensdorfer, M.; Wischmeier, M.; Wu, K.; Xiang, L.; Zagorski, R.; Zaloga, D.; Zanca, P.; Zaplotnik, R.; Zebrowski, J.; Zhang, W.; Zisis, A.; Zoletnik, S.; Zuin, M.

Published
2019

41. Disruption Prediction Approaches Using Machine Learning Tools in Tokamaks

Author

Sara Carcangiu, Barbara Cannas, Alessandra Fanni, A. Murari, Alessandro Pau, Jet Efda contributors, and Giuliana Sias

Subjects
Schedule, Computer science, Complex system, 02 engineering and technology, Machine learning, computer.software_genre, 01 natural sciences, 7. Clean energy, 010305 fluids & plasmas, Predictive models, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Training, Artificial neural network, business.industry, Dimensionality reduction, Nonlinear dimensionality reduction, Root cause, Visualization, Springs, 13. Climate action, Discharges (electric), 020201 artificial intelligence & image processing, Artificial intelligence, business, Energy source, computer, Tokamaks, Neural networks
Abstract

Nuclear fusion is one of the best options to achieve a virtually limitless energy source in the future. However, sustaining burning plasma reactions is very challenging because disruptive events cause the loss of plasma confinement and damages to the tokamak machine. Thus, reproducing a nuclear fusion reaction on Earth represents an actual scientific and technical challenge. Next generation machines must be able to predict, mitigate or avoid the disruptions but the present understanding of the phenomenon has not yet gone so far as to provide a physical model describing their root cause. On the contrary, a large quantity of experimental disruption data exists. For these reason, in the last decades, machine learning techniques have been applied to design alarm systems both for mitigating or to actively avoid approaching disruptions. More recently, the disruption predictor concept has been evolving toward a more complex system, able to detect the proximity of the plasma state to the boundaries of the operational space free from disruptions in order to schedule avoidance strategies rather than mitigation actions. This system should also be able to determine which type of disruption is about to occur in order to efficiently take suitable disruption avoidance actions. This paper reports an overview of machine learning algorithms as tools for disruption prediction and classification at JET, the world's largest operational plasma physics experiment, located at Culham Centre for Fusion Energy in Oxfordshire, UK. Both traditional neural network models for disruption prediction and manifold learning approaches as a tool for plasma operational space mapping and visualization, disruption prediction and classification, are described.

Published
2019
Full Text
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42. Convolutional Neural Networks for the Identification of Filaments from Fast Visual Imaging Cameras in Tokamak Reactors

Author

Alessandra Fanni, Sara Carcangiu, Fulvio Militello, Barbara Cannas, Giuliana Sias, Fabio Pisano, Nick Walkden, Ivan Lupelli, and Augusto Montisci

Subjects
Toroid, Tokamak, business.industry, Computer science, Deep learning, Pattern recognition, 01 natural sciences, Convolutional neural network, 010305 fluids & plasmas, law.invention, Identification (information), law, Position (vector), 0103 physical sciences, Nuclear fusion, Probability distribution, Artificial intelligence, 010306 general physics, business
Abstract

The paper proposes a region-based deep learning convolutional neural network to detect objects within images able to identify the filamentary plasma structures that arise in the boundary region of the plasma in toroidal nuclear fusion reactors. The images required to train and test the neural model have been synthetically generated from statistical distributions, which reproduce the statistical properties in terms of position and intensity of experimental filaments. The recently proposed Faster Region-based Convolutional Network algorithm has been customized to the problem of identifying the filaments both in location and size with the associated score. The results demonstrate the suitability of the deep learning approach for the filaments detection.

Published
2018

43. Magnetic materials characterization by Tabu Search optimization

Author

Ermanno Cardelli, Alessandra Fanni, M. Caboni, Augusto Montisci, Sara Carcangiu, and Antonio Faba

Subjects
010302 applied physics, Computer science, Computation, Scalar (mathematics), System identification, 02 engineering and technology, 021001 nanoscience & nanotechnology, 01 natural sciences, Tabu search, Magnetization, Error function, Magnet, 0103 physical sciences, 0210 nano-technology, Metaheuristic, Algorithm
Abstract

A Tabu Search metaheuristic procedure has been developed to solve the Moving Vector Hysteron Model identification problem. An analytic modified Preisach model is used to describe the hysteretic behavior of a magnetic material in case of scalar magnetization. Using the Tabu Search optimization algorithm it is possible to explore the model parameters space in a smart way identifying the corresponding values that minimize an error function while limiting the computation effort. The search has been performed for different magnetization values and the best fitting functions have been derived. Model validation is performed with experimental data.

Published
2017

44. Bit loading optimization for smart grid energy storage management

Author

Fabrizio Giulio Luca Pilo, Alessandra Fanni, Gianni Celli, Sara Carcangiu, Augusto Montisci, and Michele Garau

Subjects
Schedule, Engineering, Electric power transmission, Smart grid, business.industry, Distributed generation, Computer data storage, Bit error rate, Electronic engineering, business, Multi-objective optimization, Energy storage
Abstract

In this paper a procedure for the optimization of the bit loading in a PLC system implementing OFDM modulation is presented. The optimization strategy aims to find the best compromise between the conflicting objectives of minimal Signal Power, maximal Bit Rate, and minimal Bit Error Rate. The optimization is performed off line by means of a Multi-Objective approach. A set of Pareto solutions is determined, among which the designer has to take the final choice on the basis of the custom requirements. The approach is used to optimize the communication within a distributed energy storage system, which exploits an available electrical car fleet. The power lines supplying the charging points are used for the communication, while the information concerns, for each car, the identification code, the charge state, the schedule of charging/discharging. A unique OFDM modulation is used to dialogue with the whole cars fleet, so that it has to be optimized taking into account the frequency response of any involved power lines.

Published
2017

45. Application of self organizing map to identify nocturnal epileptic seizures

Author

Cesar Teixeira, António Dourado, B. Pisano, and Alessandra Fanni

Subjects
Self-organizing map, medicine.diagnostic_test, Computer science, business.industry, Feature extraction, Pattern recognition, 02 engineering and technology, Electroencephalography, Nocturnal, medicine.disease, 03 medical and health sciences, Epilepsy, 0302 clinical medicine, Brain state, 0202 electrical engineering, electronic engineering, information engineering, medicine, Trajectory, 020201 artificial intelligence & image processing, Artificial intelligence, Cluster analysis, business, 030217 neurology & neurosurgery
Abstract

A patient-specific seizure detection system for Nocturnal Frontal Lobe Epilepsy (NFLE) is proposed. Data of several patients affected by NFLE, extracted from the EPILEPSIAE database, have been used for this study. As every patient possesses different physiological characteristics, several simulations were performed in order to find the best features to be extracted from electroencephalogram (EEG) signals and to be inputted to 2-dimensional Self Organizing Maps (SOM). The proposed approach allows us the definition of simple displays capable of presenting meaningful information on the actual state of the neural activities, revealing the mapping potential of clustering the data coming from seizure and non-seizure epochs; moreover, it also suggests to use SOM as seizure early detectors. In fact, the temporal sequence of the samples in an EEG recording can be projected on the SOM, obtaining a trajectory that describes the dynamics of the brain state as captured by the EEG. The analysis of the trajectory can provide information on an eventual impending seizure event. The work shows the capability of the system to early and accurately detect Nocturnal Frontal Lobe Epilepsy seizure reaching a mean value of 77.23% and 88.94% for sensitivity and specificity respectively, and highlights the possibility to promote therapies aimed at rapid and targeted treatment of seizures.

Published
2017

46. Electric capacitance tomography for nondestructive testing of standing trees

Author

Alessandra Fanni, Augusto Montisci, and Sara Carcangiu

Subjects
010302 applied physics, Engineering, Artificial neural network, Basis (linear algebra), business.industry, Computer Science::Neural and Evolutionary Computation, 020206 networking & telecommunications, 02 engineering and technology, Inverse problem, 01 natural sciences, Capacitance, Computer Science Applications, Set (abstract data type), Position (vector), Modeling and Simulation, Nondestructive testing, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Electric Capacitance, Electrical and Electronic Engineering, business, Algorithm
Abstract

In this paper, an innovative Artificial Neural Network (ANN)-based approach to solve the bi-dimensional Electric Capacitance Tomography inverse problem is proposed in order to assess the health state of standing trees. Using a set of examples, an ANN is trained to solve the direct problem, ie, to associate the properties of the tree trunk affected by defects to a set of capacitance values. Then, the trained ANN is inverted in order to obtain the defect position and dimensions on the basis of measured capacitance values. The potentiality of the method is presented making reference to several target examples.

Published
2017

47. Overview of the JET results in support to ITER

Author

Alfredo Pironti, J. Simpson-Hutchinson, Sean Conroy, J. Uljanovs, D. Middleton-Gear, G. Possnert, C. Angioni, R. McAdams, Nicholas Watkins, E. Fortuna-Zalesna, A. Garcia-Carrasco, K. Gałązka, D. Nodwell, Pasquale Gaudio, R.A. Pitts, Svetlana V. Ratynskaia, Seppo Koivuranta, O. J. Kwon, C. Boyd, A. Boboc, M. Reinhart, Igor Lengar, Jarrod Leddy, Hiroyasu Utoh, J. H. Ahn, A. Stevens, J. Lönnroth, U. Kruezi, C. Guillemaut, N. Fonnesu, W. Studholme, Marek Rubel, P. Cahyna, O. McCormack, A. S. Jacobsen, D. Mazon, Gunta Kizane, N. Ashikawa, William Tang, J. Goff, F. Nespoli, Thomas Giegerich, G. Petravich, Angela Busse, Corneliu Porosnicu, M. Bigi, M. Wheatley, Christopher N. Bowman, J. Zacks, Ivan Calvo, U. Losada, H. Weisen, B. Bauvir, Stanislas Pamela, Sylvain Brémond, M.F. Stamp, Scott W. McIntosh, A. Rakha, S. Glöggler, V. Braic, C. Bottereau, S. Murphy, S. Knott, Luigi Fortuna, P. Bunting, N. Vora, S. D. Scott, A. Lazaros, R. Dejarnac, P. Buratti, H.R. Strauss, Gabriele Croci, M. Nocente, A. Hollingsworth, S. Reynolds, D. J. Wilson, D. D. Brown, T.C. Luce, S. Zoletnik, E. Nilsson, L. Laguardia, O. Marchuk, F.P. Orsitto, E. Cecil, V. Huber, J. B. Girardo, Stylianos Varoutis, M. D. Axton, Hyun-Tae Kim, E. Safi, Ch. Day, S. Arshad, J. Rzadkiewicz, P. Prior, A. Meigs, S. Esquembri, P. Gohil, K. Purahoo, Torbjörn Hellsten, N. Tipton, R. Guirlet, E. Joffrin, V. Aldred, Calin Besliu, M. Valentinuzzi, G. T. Jones, J. Edwards, Giuseppe Ambrosino, Laurent Marot, N. Lam, F. Crisanti, G. Verona Rinati, R. Marshal, Michael L. Brown, D. Frigione, D. Chandra, Michaele Freisinger, R. Olney, Jari Varje, S. Whetham, F. Parra Diaz, M. R. Hough, P. Dinca, F. Salzedas, A. Goodyear, R. Gowland, J. A. Wilson, J. Horacek, D. King, K. Flinders, I. R. Merrigan, M. Ghate, R. Michling, F. Saint-Laurent, G. Kocsis, D. Van Eester, C. Young, R. O. Dendy, A. Meakins, N. Pace, C. L. Hunter, D. Alegre, S. Foster, V. Riccardo, M. Bulman, C. Jeong, Marek Szawlowski, B. D. Whitehead, Vasily Kiptily, James Harrison, Hiroshi Tojo, G. T. A. Huijsmans, J. W. Coenen, X. Litaudon, Justin Williams, C. Hidalgo, S. Lesnoj, I.E. Day, A. W. Morris, R. Mooney, Yann Corre, S. Brezinsek, B. Gonçalves, M. Kresina, D. Coombs, F. Köchl, J. L. Gardarein, W. Davis, Aqsa Shabbir, Kanti M. Aggarwal, L. Colas, A. B. Kukushkin, Seppo Sipilä, Elisabeth Rachlew, Leena Aho-Mantila, O. G. Pompilian, E. Viezzer, Shane Cooper, Fabio Villone, P. Blanchard, Patrick Tamain, P. Camp, T. Szabolics, C. Luna, Kalle Heinola, H. G. Esser, V. Bobkov, James Buchanan, Andrew West, Hajime Urano, Roberta Lima Gomes, J.P. Coad, Th. Pütterich, A. Sinha, S. Hollis, R. D. Wood, G. D. Ewart, F. S. Griph, T. Kobuchi, X. Lefebvre, S. Warder, A.J. Thornton, S. Peschanyi, B. Graham, Giuseppe Telesca, M. Kempenaars, J. Bernardo, M. Hughes, Eva Belonohy, S. Schmuck, Kai Nordlund, T. J. Smith, P. Hertout, K. D. Lawson, M. Brix, Matthew Sibbald, Grégoire Hornung, C. Tame, Matthew Carr, S. Wray, P. T. Doyle, A. Somers, Giuseppe Chitarin, D. C. Campling, Mitul Abhangi, I. Jepu, David A. Wood, J. Miettunen, A. Sopplesa, Raffaele Fresa, S. Saarelma, M. Bacharis, J. Pozzi, P. Vallejos Olivares, Teddy Craciunescu, Raffaele Albanese, S. Knipe, Jason P. Byrne, A. C. C. Sips, S. Hazel, V. Kazantzidis, G. Stankūnas, A. Kundu, J. Mailloux, C. Guerard, Pramit Dutta, J. E. Boom, Eduardo Alves, P. Grazier, Saskia Mordijck, V.S. Neverov, Kazuo Hoshino, A. P. Vadgama, P. D. Brennan, P. Innocente, Piergiorgio Sonato, M. Irishkin, M. Berry, D. W. Robson, Dieter Leichtle, Fabio Pisano, P. McCullen, T. M. Huddleston, Kensaku Kamiya, D. Pacella, Tommy Ahlgren, A. Kirschner, B. Magesh, A. Ash, J. Mlynář, C. Castaldo, C. Marchetto, D. L. Hillis, M. Incelli, B. Viola, R. J. Robins, E. Andersson Sundén, G. Ramogida, Matthew Reinke, Gerd Meisl, Yannis Kominis, R. Proudfoot, C. Noble, N. J. Conway, V. P. Lo Schiavo, Jorge Luis Rodriguez, Hugo Bufferand, C. H. A. Hogben, B. Evans, R. Sartori, H. Greuner, M. G. Dunne, K. Schöpf, M. I. K. Santala, E. Giovannozzi, A. E. Shevelev, C. Gil, P. Boulting, P. Sagar, A.E. Shumack, P. A. Coates, C. Ayres, R. Prakash, C. Giroud, M. Parsons, J. C. Giacalone, S. Meshchaninov, A. Peackoc, G. De Temmerman, A.C.A. Figueiredo, D. Gallart, P. Santa, Sergey Popovichev, Ivan Lupelli, M. Valovic, Thomas Johnson, Y. Martynova, M. Rack, Olivier Sauter, J. Garcia, P. Siren, I. Balboa, S. Lee, Hans Nordman, R. Roccella, M. Faitsch, Julien Hillairet, Patrick J. McCarthy, C. Reux, Irena Ivanova-Stanik, V. Coccorese, Ye. O. Kazakov, R. El-Jorf, C. Hamlyn-Harris, Matthias Weiszflog, C. F. Maggi, Panagiotis Tolias, N. C. Hawkes, E. Clark, Bruno Santos, B. Sieglin, R. Rodionov, Roch Kwiatkowski, P. Denner, C. Woodley, Hugh Summers, Francesco Pizzo, G. Pucella, D. Croft, F. Di Maio, M. Tomes, D. Molina, A. Fernades, L. Amicucci, Marco Cecconello, A. Bisoffi, Z. Ul-Abidin, J. Wilkinson, H. Maier, S. Rowe, M. Beckers, P.J. Knight, E. Pajuste, Choong-Seock Chang, K. Deakin, M. Enachescu, A. Cobalt, D. Tskhakaya Jun, Michela Gelfusa, Rémy Nouailletas, R. Ragona, N. Bonanomi, D. A. Homfray, K. Riddle, Yann Camenen, J. D. Thomas, R.P. Doerner, Timothy P. Robinson, Y. Miyoshi, Ph. Jacquet, H. T. Lambertz, D. Pulley, A. Bécoulet, E. Tholerus, O. Bogar, M. Peterka, R. Crowe, C. Sommariva, A. R. Talbot, N. K. Butler, N. Reid, R. Zagórski, Gerald Pintsuk, Juri Romazanov, Andre Neto, G. L. Ravera, Paolo Arena, A. Manning, F. Durodié, Maryna Chernyshova, D. Karkinsky, Štefan Matejčík, J. P. Thomas, A. Wilson, L. Joita, R. Naish, P. Strand, M. Balden, M. Kaufman, T. Powell, V. Schmidt, D. Barnes, José Vicente, S. Doswon, Daniel F. Valcarcel, Claudia Corradino, R. Warren, Annette M. Hynes, J. D. Strachan, A. M. Messiaen, M. Kovari, O. Omolayo, D. M. Witts, R. C. Felton, C. Fleming, C. A. Marren, Patrick Maget, J. Galdon-Quiroga, H. R. Koslowski, Bruce Lipschultz, Ana Elisa Bauer de Camargo Silva, J. Waterhouse, R. J. Dumont, M. Schneider, Sara Moradi, K. J. Nicholls, M. Beldishevski, Benedikt Geiger, A. Jardin, A. Ekedahl, A. Lyssoivan, C. Waldon, Davide Galassi, F. Jaulmes, A. Kirk, Yannick Marandet, F. Hasenbeck, Gabor Szepesi, R. C. Pereira, J. Juul Rasmussen, Nobuyuki Aiba, Michelle E. Walker, Gábor Cseh, Scott W. Mosher, R. Bastow, A. Di Siena, E. Lazzaro, M. Curuia, C. D. Challis, Z. Ghani, J. Deane, João M. C. Sousa, Henrik Sjöstrand, T. O'Gorman, H. R. Wilson, P. Devynck, M. Price, C. A. Thompson, Daniele Marocco, A. Cullen, M. Clark, M. Lennholm, D. Carralero, N. Balshaw, Roland Sabot, I. Stepanov, N. Petrella, Filippo Sartori, L. W. Packer, P. Thomas, M. Lungu, A. V. Krasilnikov, R. Young, Jonathan Graves, J. C. Hillesheim, Mǎdǎlina Vlad, Duccio Testa, Pierre Dumortier, Paulo Carvalho, M. Gosk, Yong-Su Na, M. Buckley, Carlos A. Silva, V. Fuchs, K. Vasava, P. A. Tigwell, B. Wakeling, M. Medland, M. Bellinger, K. Gal, Petter Ström, E. Veshchev, F. Nabais, A. Wynn, L. Lauro Taroni, B. Beckett, L. Gil, M. Towndrow, Brian Grierson, Harry M. Meyer, V. Philipps, A. de Castro, D. Kinna, D. Conka, Göran Ericsson, L. Piron, J. Hawkins, D. Cooper, Kenneth Hammond, V.V. Parail, Cristian Ruset, G.J. van Rooij, M. N. A. Beurskens, N. Fawlk, G. Evison, M. Van De Mortel, N. Marcenko, B. Slade, Th. Franke, Simone Peruzzo, N. den Harder, D. Baião, A. Martin de Aguilera, Frederic Imbeaux, Carlo Sozzi, J.L. de Pablos, J. Svensson, A. Withycombe, Ane Lasa, H. Sheikh, V.A. Yavorskij, Nick Walkden, E. Lerche, C. S. Gibson, Roberto Zanino, Y. Peysson, David Hatch, B. Bazylev, E. de la Cal, S. Hacquin, T. D. V. Haupt, S. A. Silburn, T.T.C. Jones, Maria Teresa Porfiri, Walid Helou, S. E. Sharapov, M. Zerbini, Ken W Bell, Marco Marinelli, Kyriakos Hizanidis, J. M. Fontdecaba, N. Teplova, K. K. Kirov, S. Vartanian, W. W. Pires de Sa, T. C. Hender, J. K. Blackburn, I. Monakhov, H. Patten, P. A. Simmons, Y. Austin, J. Regana, Stefano Coda, Amanda J. Page, D. Fuller, António J.N. Batista, A. Horton, P. Heesterman, S. Cramp, J. Hobirk, F. Clairet, A. Burckhart, M. Allinson, Larry R. Baylor, W. Leysen, D. B. Gin, P. Nielsen, A. Kantor, Yueqiang Liu, A.V. Stephen, Jose Ramon Martin-Solis, P. Mantica, B. C. Regan, Aleksander Drenik, A. Lukin, L. Thorne, G. Nemtsev, J. Denis, M. E. Graham, D. Rigamonti, W. Van Renterghem, M. Tardocchi, M. Koubiti, A. Malaquias, M. Tsalas, A. Cufar, Giuseppe Prestopino, D. Kogut, N. Pomaro, J. Keep, Jochen Linke, Shimpei Futatani, Boris Breizman, A. Sirinelli, M. Chandler, M. Fortune, F. Degli Agostini, I. Jenkins, T. Spelzini, G. Calabrò, O. N. Kent, A. Lunniss, Etienne Hodille, Z. Vizvary, Volker Naulin, T. Eich, F. Mink, A. Alkseev, P. W. Haydon, Massimo Angelone, Norberto Catarino, J. Lapins, Roberto Pasqualotto, R. Lawless, T. Schlummer, F. Bonelli, M. Wischmeier, Stéphane Devaux, G. Saibene, Dirk Reiser, Y. R. Martin, H. Bergsåker, Jon Godwin, Alessia Santucci, C. Lane, Justyna Grzonka, Ph. Mertens, Claudio Verona, David Moulton, E. Delabie, Anna Salmi, P. G. Smith, T. Bolzonella, Silvio Ceccuzzi, Ulrich Fischer, G. Liu, M. A. Henderson, M. Marinucci, T. Suzuki, Jakub Bielecki, João Figueiredo, M. Afzal, J. Cane, Robert Hager, Luciano Bertalot, M. Firdaouss, G. Tvalashvili, D. Hepple, D. Esteve, M. De Bock, Y. Baranov, R. D'Inca, G. De Tommasi, Ch. Linsmeier, T. Nicolas, I. J. Pearson, P. Finburg, Ireneusz Książek, S. Talebzadeh, A. Czarnecka, A. Botrugno, M. Gethins, Bohdan Bieg, R. Baughan, I. Borodkina, B. Kos, A. Muraro, T. Vasilopoulou, G. Hermon, S.J. Wukitch, Jari Likonen, D. P. Coster, Guglielmo Rubinacci, I. H. Coffey, Justine M. Kent, S. E. Dorling, J. Dankowski, Geert Verdoolaege, Daisuke Nishijima, R. Clarkson, E. R. Solano, M. Stephen, A. Lescinskis, P. Staniec, Karl Schmid, M. Mayer, Peter Lang, T. Franklin, M.I. Williams, C. G. Elsmore, F. Maviglia, C. Di Troia, C. Penot, A. Zarins, Pierre Manas, D. F. Gear, Yu Gao, Philipp Drews, E. Letellier, A. S. Thompson, L. Forsythe, I. Zychor, E. Khilkevich, A. Manzanares, T. Nakano, Paulo Rodrigues, J. Edmond, Sebastián Dormido-Canto, R. Dux, C. Appelbee, L. Moser, Angelo Cenedese, D. Fagan, N. Richardson, Giuseppe Gorini, V. Rohde, R. Paprok, João P. S. Bizarro, P. Aleynikov, M. Sertoli, Ł. Świderski, Simone Palazzo, O. W. Davies, D. Douai, N. Macdonald, M. Baruzzo, J. López-Razola, M. Lungaroni, D. Clatworthy, R. Bravanec, J. Lovell, Ambrogio Fasoli, S.-P. Pehkonen, M. E. Puiatti, P. Papp, G. Bodnar, V. Aslanyan, A. Weckmann, K. A. Taylor, R. Henriques, I. T. Chapman, Ewa Pawelec, Miles M. Turner, Steven J. Meitner, M. Bernert, Ph. Maquet, R. C. Meadows, A. Shaw, N. Vianello, L. Barrera Orte, Tomas Markovic, A. Fil, A. S. Couchman, Inessa Bolshakova, J. Fyvie, Konstantina Mergia, J. Gallagher, R.V. Budny, Frank Leipold, C. J. Rapson, R. C. Lobel, Gennady V. Miloshevsky, K.-D. Zastrow, Ph. Duckworth, Gianluca Rubino, G. Withenshaw, S. Maruyama, S. P. Hallworth Cook, M. Newman, Jérôme Bucalossi, P. Drewelow, Nuno Cruz, D. Iglesias, I. Nedzelski, T. Donne, P. Leichuer, R. Cesario, M. D. J. Bright, T. Boyce, N. Imazawa, Per Petersson, R. King, A. Loving, L. Garzotti, Jorge Ferreira, G. Corrigan, D. Sandiford, B. Tal, P. Puglia, Daniel Tegnered, J. Karhunen, James S. Wright, Tom Wauters, J. McKehon, K. Rathod, Olivier Février, Alessandro Formisano, Petra Bilkova, M. Groth, Ricardo Magnus Osorio Galvao, F. Medina, S. Collins, H. J. Boyer, Elena Bruno, Horacio Fernandes, M. J. Stead, R. Paccagnella, J. Kaniewski, Ion E. Stamatelatos, F. Causa, M. F. F. Nave, A. Patel, D. C. McDonald, L. Moreira, Mariano Ruiz, K. Dylst, Raymond A. Shaw, A. Brett, Jane Johnston, P. P. Pereira Puglia, J. Ongena, N. A. Benterman, V. N. Amosov, Christian Grisolia, J. Simpson, C. Perez von Thun, Jan Weiland, P. Tonner, F. Belli, T. Odupitan, T. Dittmar, Edmund Highcock, Taina Kurki-Suonio, I. Uytdenhouwen, Estelle Gauthier, M. Oberkofler, B. Alper, Iris D. Young, S. Soare, Yuji Hatano, D. Reece, D. Borodin, M. Moneti, W. Yanling, S. Mianowski, K. Fenton, Stephen J. Bailey, R. Coelho, Sandra C. Chapman, E. Łaszyńska, A. R. Field, F.J. Martínez, Anders Nielsen, M. Smithies, M. J. Mantsinen, A. J. Capel, N. D. Smith, A. Pires dos Reis, M.-L. Mayoral, T. Loarer, P. Carman, N. Grazier, S. Breton, J. M. A. Bradshaw, Alexandre C. Pereira, Fulvio Auriemma, Fulvio Militello, Barbara Cannas, D. Ulyatt, A. Kappatou, P. Blatchford, R. Scannell, B. I. Oswuigwe, Darren Price, Robert E. Grove, D. Guard, M. Leyland, G. Stubbs, J. W. Banks, V.V. Plyusnin, M. S. J. Rainford, Andrea Murari, Sanjeev Ranjan, A. Huber, V. Krasilnikov, C. Bower, H. Leggate, S. Abduallev, P. Tsavalas, G. Giruzzi, K. Maczewa, Colin Roach, P. Beaumont, R. P. Johnson, Anna Widdowson, L. A. Kogan, A. Baron Wiechec, Markus Airila, J. Morris, Robert Skilton, Katarzyna Słabkowska, M. A. Barnard, Jean-Paul Booth, Alessandro Pau, R. Price, R. Bament, M. Tokitani, I. Turner, T. Vu, P. Huynh, S.N. Gerasimov, D. I. Refy, Yunfeng Liang, Anders Hjalmarsson, S. Dalley, Roberto Ambrosino, O. Hemming, T. R. Blackman, Y. Zhou, Vasile Zoita, P. Vincenzi, A. Loarte, C. Rayner, Martin Imrisek, M. Tripsky, C. Mazzotta, A. Uccello, V. Basiuk, Lide Yao, V. Goloborod'ko, S. Villari, B. P. Duval, N. Bulmer, W. Zhang, L. Hackett, D. N. Borba, M. Halitovs, Mario Pillon, H. Arnichand, Alberto Alfier, A. Lawson, A. Masiello, T. Makkonen, A. Vitins, D. Rendell, D. Paton, L. Avotina, A. Krivska, M. Maslov, Richard Verhoeven, Marc Goniche, A. Broslawski, Marica Rebai, E. de la Luna, E. Militello-Asp, V. Cocilovo, L. Carraro, Michael Fitzgerald, Bernardo B. Carvalho, D. Young, C.G. Lowry, F. J. Casson, L.-G. Eriksson, T. M. Biewer, B. Esposito, F.G. Rimini, J. Fessey, G. Kaveney, S. Hall, Robin Barnsley, Michael Lehnen, N. Bekris, L. F. Ruchko, P. Batistoni, E. Alessi, M. G. O'Mullane, D. S. Darrow, C. N. Grundy, N. Hayter, Ivo S. Carvalho, M. Brombin, Enrico Zilli, M. Valisa, M. Reich, S. Panja, C. Gurl, Charles Harrington, Emmanuele Peluso, M. Porton, Michael Walsh, D. Falie, A. Reed, Jacob Eriksson, P. Macheta, J. M. Faustin, S. Cortes, S. Fietz, P. Piovesan, D. Ciric, Eric Nardon, R. Neu, Bojiang Ding, G.A. Rattá, F. Reimold, R. Craven, M. Cox, J. Orszagh, Aaro Järvinen, A. S. Thrysøe, A. Shepherd, I. Ďuran, Andrew M. Edwards, A. Kinch, J. Beal, M. Gherendi, Martin Köppen, D. Samaddar, P. Dalgliesh, I. Vinyar, J. Jansons, Nengchao Wang, J. Wu, John Wright, S. Wiesen, C. King, Alessandra Fanni, L. D. Horton, N. Krawczyk, J. Buch, K. Krieger, Václav Petržílka, D. Schworer, C. Watts, T. Keenan, Andrea Malizia, B. D. Stevens, P. Trimble, C. P. Lungu, V. Prajapati, Marco Ariola, C. Wellstood, S. Gilligan, Mirko Salewski, Michael Barnes, Florin Spineanu, H. Doerk, C. Kennedy, S. Jachmich, J. Caumont, Isabel L. Nunes, A. Petre, A. Kallenbach, M. Anghel, B. Lomanowski, Marco Riva, M. Romanelli, G. De Masi, T. May-Smith, T. Xu, A. Goussarov, S. Romanelli, M. Okabayashi, A. Baker, R. Salmon, T. Tala, Nicolas Fedorczak, S. Lanthaler, Giuliana Sias, J. Risner, Clarisse Bourdelle, M. E. Manso, Fabio Moro, R. Lucock, M. Bassan, M. T. Ogawa, V. Thompson, A. M. Whitehead, S. D. A. Reyes Cortes, Igor Bykov, Gennady Sergienko, E. Stefanikova, Mattia Frasca, H. Dabirikhah, Lorenzo Frassinetti, N. Dzysiuk, D. L. Keeling, Juan Manuel López, M. Turnyanskiy, Daniel Dunai, David Taylor, Arturo Buscarino, Carolina Björkas, A. Baciero, S. Meigh, M. Garcia-Munoz, Massimiliano Mattei, M. Hill, Gwyndaf Evans, S. Minucci, Xiang Gao, A. V. Chankin, Francesco Romanelli, A. Lahtinen, L. Giacomelli, A. Owen, Jesús Vega, Jonathan Citrin, Antti Hakola, Petr Vondracek, Sehyun Kwak, P. Abreu, L. Meneses, S. S. Medley, G. Gervasini, Surya K. Pathak, Kristel Crombé, M. Cleverly, H.S. Kim, C. Stan-Sion, Nobuyuki Asakura, E. Wang, A. Cardinali, L. Fazendeiro, R. Cavazzana, P. J. Lomas, J. Hawes, G. Stables, Silvia Spagnolo, S. P. Hotchin, N. R. Green, Slawomir Jednorog, Ewa Kowalska-Strzęciwilk, A. Martin, Linwei Li, Rajnikant Makwana, Richard Goulding, I. Voitsekhovitch, M. Bowden, I. Kodeli, Peter Hawkins, S. S. Henderson, Ondrej Ficker, Carl Hellesen, D. Yadikin, Fabio Subba, Luka Snoj, Anthony Laing, N. Ben Ayed, Mario Cavinato, M. Goodliffe, C. Clements, D. Kenny, Axel Klix, S. Gee, R. J. E. Smith, P. de Vries, L. Fittill, Min-Gu Yoo, S. Menmuir, K. Cave-Ayland, S. Potzel, D. Grist, K. Blackman, S. A. Robinson, Rodney Walker, David Pfefferlé, W. Broeckx, D. Harting, S. G. J. Tyrrell, F. Binda, L. Horvath, Davide Flammini, P. V. Edappala, Raul Moreno, G. M. D. Hogeweij, P. Card, A. Hagar, Ion Tiseanu, Rita Lorenzini, L. Appel, Jet Contributors, J. Flanagan, C. Paz Soldan, U. Samm, Otto Asunta, F. Eriksson, C. Taliercio, F. S. Zaitsev, G. F. Matthews, Tuomas Koskela, P. J. Howarth, D. Terranova, M. Skiba, Amanda Hubbard, R. Otin, K. G. McClements, M. Park, R. McKean, C. Christopher Klepper, I. Karnowska, Peter J. Pool, G. Ciraolo, Jennifer M. Lehmann, Institut de Mécanique des Fluides et des Solides (IMFS), Université Louis Pasteur - Strasbourg I-Centre National de la Recherche Scientifique (CNRS), VTT Technical Research Centre of Finland (VTT), Association EURATOM-TEKES, Association EURATOM-TEKES, Helsinki University of Technology, Finland, Assoc. Euratom-ENEA-CREATE, Universita Mediterranea of Reggio Calabria [Reggio Calabria], EURATOM/CCFE Fusion Association, Culham Science Centre [Abingdon], Instituto Tecnológico e Nuclear (ITN), ITN, University of Naples Federico II = Università degli studi di Napoli Federico II, Max-Planck-Institut für Plasmaphysik [Garching] (IPP), Università degli studi di Catania = University of Catania (Unict), National Institute for Fusion Science (NIFS), Laboratoire de Physique Nucléaire et de Hautes Énergies (LPNHE), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), ITER organization (ITER), Karlsruhe Institute of Technology (KIT), Institut de Chimie des Substances Naturelles (ICSN), Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche sur la Fusion par confinement Magnétique (IRFM), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), European Fusion Development Agreement [Garching bei München] ( EFDA-CSU), Institut d'ophtalmologie Hédi-Rais de Tunis, Service Cardiologie [CHU Toulouse], Pôle Cardiovasculaire et Métabolique [CHU Toulouse], Centre Hospitalier Universitaire de Toulouse (CHU Toulouse)-Centre Hospitalier Universitaire de Toulouse (CHU Toulouse), H. Niewodniczanski Institute of Nuclear Physics, Polska Akademia Nauk = Polish Academy of Sciences (PAN), Laboratoire de recherche en Hydrodynamique, Énergétique et Environnement Atmosphérique (LHEEA), École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS), Euratom/UKAEA Fusion Assoc., Magnetic Sensor laboratory [Lviv] (MSL), National Polytechnic University of Lviv (LPNU), The National Research Nuclear University MEPhI (Moscow Engineering Physics Institute) [Moscow, Russia], Institute of Energy and Climate Research - Plasma Physics (IEK-4), Forschungszentrum Jülich GmbH | Centre de recherche de Juliers, Helmholtz-Gemeinschaft = Helmholtz Association-Helmholtz-Gemeinschaft = Helmholtz Association, Institute for Problems of Material Science, National Academy of Sciences of Ukraine (NASU), Institute of Plasma Physics [Praha], Czech Academy of Sciences [Prague] (CAS), Physique des interactions ioniques et moléculaires (PIIM), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Département Méthodes et Modèles Mathématiques pour l'Industrie (3MI-ENSMSE), École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Centre G2I, Department of Hydraulics, Transportations and Roads, Laboratoire de microbiologie et génétique moléculaires - UMR5100 (LMGM), Centre de Biologie Intégrative (CBI), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Metallurgical & Materials Engineering Department (MS 388), University of Nevada [Reno], AUTRES, Institute of Plasma Physics and Laser Microfusion [Warsaw] (IPPLM), Culham Centre for Fusion Energy (CCFE), Astrophysics Research Centre [Belfast] (ARC), Queen's University [Belfast] (QUB), Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information (CEA-LETI), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), School of Mathematics [Cardiff], Cardiff University, Associazone EURATOM ENEA sulla Fusione, EURATOM, Laboratoire de physique des plasmas de l'ERM, Laboratorium voor plasmafysica van de KMS (LPP ERM KMS), Ecole Royale Militaire / Koninklijke Militaire School (ERM KMS), Paul-Drude-Institut für Festkörperelektronik (PDI), Institut für Physik, University of Basel (Unibas), Dutch Institute for Fundamental Energy Research [Nieuwegein] (DIFFER), Dutch Institute for Fundamental Energy Research [Eindhoven] (DIFFER), Institut Jean Lamour (IJL), Institut de Chimie du CNRS (INC)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), CEA Cadarache, Dipartimento di Energia [Milano], Politecnico di Milano [Milan] (POLIMI), Laboratoire de Mécanique, Modélisation et Procédés Propres (M2P2), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Lille économie management - UMR 9221 (LEM), Université d'Artois (UA)-Université catholique de Lille (UCL)-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Euratom research and training programme 633053, Institut de Mécanique des Fluides et des Solides ( IMFS ), Université Louis Pasteur - Strasbourg I-Centre National de la Recherche Scientifique ( CNRS ), VTT Technical Research Centre of Finland ( VTT ), Univ. Mediterranea RC, Culham Science Centre, Instituto Tecnológico e Nuclear ( ITN ), Università degli studi di Napoli Federico II, Max-Planck-Institut für Plasmaphysik [Garching] ( IPP ), Università degli studi di Catania [Catania], National Institute for Fusion Science, National Institutes of Natural Sciences, Laboratoire de Physique Nucléaire et de Hautes Énergies ( LPNHE ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut National de Physique Nucléaire et de Physique des Particules du CNRS ( IN2P3 ) -Université Paris Diderot - Paris 7 ( UPD7 ) -Centre National de la Recherche Scientifique ( CNRS ), School of Geography, Earth and Environmental Sciences, ITER Organization, Karlsruhe Institute of Technology ( KIT ), Laboratoire de Nanotechnologie et d'Instrumentation Optique ( LNIO ), Institut Charles Delaunay ( ICD ), Université de Technologie de Troyes ( UTT ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Technologie de Troyes ( UTT ) -Centre National de la Recherche Scientifique ( CNRS ), Institut de Chimie des Substances Naturelles ( ICSN ), Centre National de la Recherche Scientifique ( CNRS ), Institut de Recherche sur la Fusion par confinement Magnétique ( IRFM ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ), European Fusion Development Agreement [Garching bei München] ( EFDA-CSU ), Service de cardiologie [Toulouse], Université Paul Sabatier - Toulouse 3 ( UPS ) -CHU Toulouse [Toulouse]-Hôpital de Rangueil, ITER [St. Paul-lez-Durance], ITER, Polska Akademia Nauk ( PAN ), Laboratoire de recherche en Hydrodynamique, Énergétique et Environnement Atmosphérique ( LHEEA ), École Centrale de Nantes ( ECN ) -Centre National de la Recherche Scientifique ( CNRS ), MSL, Lviv Polytechnic National University ( MSL ), Lviv Polytechnic National University, Centre d'études et de recherches appliquées à la gestion ( CERAG ), Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Centre National de la Recherche Scientifique ( CNRS ), Institute of Energy and Climate Research - Plasma Physics ( IEK-4 ), Forschungszentrum Jülich GmbH, National Academy of Sciences of Ukraine ( NASU ), Lille - Economie et Management ( LEM ), Université catholique de Lille ( UCL ) -Université de Lille-Centre National de la Recherche Scientifique ( CNRS ), Czech Academy of Sciences [Prague] ( ASCR ), Physique des interactions ioniques et moléculaires ( PIIM ), Aix Marseille Université ( AMU ) -Centre National de la Recherche Scientifique ( CNRS ), Département Méthodes et Modèles Mathématiques pour l'Industrie ( 3MI-ENSMSE ), École des Mines de Saint-Étienne ( Mines Saint-Étienne MSE ), Institut Mines-Télécom [Paris]-Institut Mines-Télécom [Paris]-Centre G2I, Laboratoire de microbiologie et génétique moléculaires ( LMGM ), Université Paul Sabatier - Toulouse 3 ( UPS ) -Centre National de la Recherche Scientifique ( CNRS ), University of Nevada, Institute of Plasma Physics and Laser Microfusion [Warsaw] ( IPPLM ), UCL Department of Space and Climate Physics, University College of London [London] ( UCL ), Astrophysics Research Centre [Belfast] ( ARC ), Queen's University [Belfast] ( QUB ), Laboratoire d'Electronique et des Technologies de l'Information ( CEA-LETI ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Grenoble Alpes [Saint Martin d'Hères], Cardiff School of Mathematics, Laboratoire de physique des plasmas de l'ERM, Laboratorium voor plasmafysica van de KMS ( LPP ERM KMS ), Ecole Royale Militaire / Koninklijke Militaire School ( ERM KMS ), Paul-Drude-Institut für Festkörperelektronik, University of Basel ( Unibas ), Dutch Institute for Fundamental Energy Research [Nieuwegein] ( DIFFER ), Dutch Institute for Fundamental Energy Research [Eindhoven] ( DIFFER ), Institut Jean Lamour ( IJL ), Centre National de la Recherche Scientifique ( CNRS ) -Université de Lorraine ( UL ), Dipartimento di Energia, Politecnico di Milano [Milan], Max Planck Institute for Plasma Physics, Laboratoire de Mécanique, Modélisation et Procédés Propres ( M2P2 ), Aix Marseille Université ( AMU ) -Ecole Centrale de Marseille ( ECM ) -Centre National de la Recherche Scientifique ( CNRS ), Universitat Politècnica de Catalunya. Departament de Física, Universitat Politècnica de Catalunya. ANT - Advanced Nuclear Technologies Research Group, JET Contributors, Litaudon, X, Abduallev, S, Abhangi, M, Abreu, P, Afzal, M, Aggarwal, K, Ahlgren, T, Ahn, J, Aho Mantila, L, Aiba, N, Airila, M, Albanese, R, Aldred, V, Alegre, D, Alessi, E, Aleynikov, P, Alfier, A, Alkseev, A, Allinson, M, Alper, B, Alves, E, Ambrosino, G, Ambrosino, R, Amicucci, L, Amosov, V, Andersson Sundén, E, Angelone, M, Anghel, M, Angioni, C, Appel, L, Appelbee, C, Arena, P, Ariola, M, Arnichand, H, Arshad, S, Ash, A, Ashikawa, N, Aslanyan, V, Asunta, O, Auriemma, F, Austin, Y, Avotina, L, Axton, M, Ayres, C, Bacharis, M, Baciero, A, Baiã¡o, D, Bailey, S, Baker, A, Balboa, I, Balden, M, Balshaw, N, Bament, R, Banks, J, Baranov, Y, Barnard, M, Barnes, D, Barnes, M, Barnsley, R, Baron Wiechec, A, Barrera Orte, L, Baruzzo, M, Basiuk, V, Bassan, M, Bastow, R, Batista, A, Batistoni, P, Baughan, R, Bauvir, B, Baylor, L, Bazylev, B, Beal, J, Beaumont, P, Beckers, M, Beckett, B, Becoulet, A, Bekris, N, Beldishevski, M, Bell, K, Belli, F, Bellinger, M, Belonohy, Ã, Ben Ayed, N, Benterman, N, Bergsã¥ker, H, Bernardo, J, Bernert, M, Berry, M, Bertalot, L, Besliu, C, Beurskens, M, Bieg, B, Bielecki, J, Biewer, T, Bigi, M, Bã­lkovã¡, P, Binda, F, Bisoffi, A, Bizarro, J, Bjã¶rkas, C, Blackburn, J, Blackman, K, Blackman, T, Blanchard, P, Blatchford, P, Bobkov, V, Boboc, A, Bodnã¡r, G, Bogar, O, Bolshakova, I, Bolzonella, T, Bonanomi, N, Bonelli, F, Boom, J, Booth, J, Borba, D, Borodin, D, Borodkina, I, Botrugno, A, Bottereau, C, Boulting, P, Bourdelle, C, Bowden, M, Bower, C, Bowman, C, Boyce, T, Boyd, C, Boyer, H, Bradshaw, J, Braic, V, Bravanec, R, Breizman, B, Bremond, S, Brennan, P, Breton, S, Brett, A, Brezinsek, S, Bright, M, Brix, M, Broeckx, W, Brombin, M, Broså‚awski, A, Brown, D, Brown, M, Bruno, E, Bucalossi, J, Buch, J, Buchanan, J, Buckley, M, Budny, R, Bufferand, H, Bulman, M, Bulmer, N, Bunting, P, Buratti, P, Burckhart, A, Buscarino, A, Busse, A, Butler, N, Bykov, I, Byrne, J, Cahyna, P, Calabrã², G, Calvo, I, Camenen, Y, Camp, P, Campling, D, Cane, J, Cannas, B, Capel, A, Card, P, Cardinali, A, Carman, P, Carr, M, Carralero, D, Carraro, L, Carvalho, B, Carvalho, I, Carvalho, P, Casson, F, Castaldo, C, Catarino, N, Caumont, J, Causa, F, Cavazzana, R, Cave Ayland, K, Cavinato, M, Cecconello, M, Ceccuzzi, S, Cecil, E, Cenedese, A, Cesario, R, Challis, C, Chandler, M, Chandra, D, Chang, C, Chankin, A, Chapman, I, Chapman, S, Chernyshova, M, Chitarin, G, Ciraolo, G, Ciric, D, Citrin, J, Clairet, F, Clark, E, Clark, M, Clarkson, R, Clatworthy, D, Clements, C, Cleverly, M, Coad, J, Coates, P, Cobalt, A, Coccorese, V, Cocilovo, V, Coda, S, Coelho, R, Coenen, J, Coffey, I, Colas, L, Collins, S, Conka, D, Conroy, S, Conway, N, Coombs, D, Cooper, D, Cooper, S, Corradino, C, Corre, Y, Corrigan, G, Cortes, S, Coster, D, Couchman, A, Cox, M, Craciunescu, T, Cramp, S, Craven, R, Crisanti, F, Croci, G, Croft, D, Crombã©, K, Crowe, R, Cruz, N, Cseh, G, Cufar, A, Cullen, A, Curuia, M, Czarnecka, A, Dabirikhah, H, Dalgliesh, P, Dalley, S, Dankowski, J, Darrow, D, Davies, O, Davis, W, Day, C, Day, I, De Bock, M, De Castro, A, De La Cal, E, De La Luna, E, De Masi, G, De Pablos, J, De Temmerman, G, De Tommasi, G, De Vries, P, Deakin, K, Deane, J, Degli Agostini, F, Dejarnac, R, Delabie, E, Den Harder, N, Dendy, R, Denis, J, Denner, P, Devaux, S, Devynck, P, Di Maio, F, Di Siena, A, Di Troia, C, Dinca, P, D'Inca, R, Ding, B, Dittmar, T, Doerk, H, Doerner, R, Donnã©, T, Dorling, S, Dormido Canto, S, Doswon, S, Douai, D, Doyle, P, Drenik, A, Drewelow, P, Drews, P, Duckworth, P, Dumont, R, Dumortier, P, Dunai, D, Dunne, M, Äžuran, I, Durodiã©, F, Dutta, P, Duval, B, Dux, R, Dylst, K, Dzysiuk, N, Edappala, P, Edmond, J, Edwards, A, Edwards, J, Eich, T, Ekedahl, A, El Jorf, R, Elsmore, C, Enachescu, M, Ericsson, G, Eriksson, F, Eriksson, J, Eriksson, L, Esposito, B, Esquembri, S, Esser, H, Esteve, D, Evans, B, Evans, G, Evison, G, Ewart, G, Fagan, D, Faitsch, M, Falie, D, Fanni, A, Fasoli, A, Faustin, J, Fawlk, N, Fazendeiro, L, Fedorczak, N, Felton, R, Fenton, K, Fernades, A, Fernandes, H, Ferreira, J, Fessey, J, Fã©vrier, O, Ficker, O, Field, A, Fietz, S, Figueiredo, A, Figueiredo, J, Fil, A, Finburg, P, Firdaouss, M, Fischer, U, Fittill, L, Fitzgerald, M, Flammini, D, Flanagan, J, Fleming, C, Flinders, K, Fonnesu, N, Fontdecaba, J, Formisano, A, Forsythe, L, Fortuna, L, Fortuna Zalesna, E, Fortune, M, Foster, S, Franke, T, Franklin, T, Frasca, M, Frassinetti, L, Freisinger, M, Fresa, R, Frigione, D, Fuchs, V, Fuller, D, Futatani, S, Fyvie, J, Gã¡l, K, Galassi, D, Gaå‚azka, K, Galdon Quiroga, J, Gallagher, J, Gallart, D, Galvã¡o, R, Gao, X, Gao, Y, Garcia, J, Garcia Carrasco, A, García Muñoz, M, Gardarein, J, Garzotti, L, Gaudio, P, Gauthier, E, Gear, D, Gee, S, Geiger, B, Gelfusa, M, Gerasimov, S, Gervasini, G, Gethins, M, Ghani, Z, Ghate, M, Gherendi, M, Giacalone, J, Giacomelli, L, Gibson, C, Giegerich, T, Gil, C, Gil, L, Gilligan, S, Gin, D, Giovannozzi, E, Girardo, J, Giroud, C, Giruzzi, G, Glã¶ggler, S, Godwin, J, Goff, J, Gohil, P, Goloborod'Ko, V, Gomes, R, Goncalves, B, Goniche, M, Goodliffe, M, Goodyear, A, Gorini, G, Gosk, M, Goulding, R, Goussarov, A, Gowland, R, Graham, B, Graham, M, Graves, J, Grazier, N, Grazier, P, Green, N, Greuner, H, Grierson, B, Griph, F, Grisolia, C, Grist, D, Groth, M, Grove, R, Grundy, C, Grzonka, J, Guard, D, Guã©rard, C, Guillemaut, C, Guirlet, R, Gurl, C, Utoh, H, Hackett, L, Hacquin, S, Hagar, A, Hager, R, Hakola, A, Halitovs, M, Hall, S, Hallworth Cook, S, Hamlyn Harris, C, Hammond, K, Harrington, C, Harrison, J, Harting, D, Hasenbeck, F, Hatano, Y, Hatch, D, Haupt, T, Hawes, J, Hawkes, N, Hawkins, J, Hawkins, P, Haydon, P, Hayter, N, Hazel, S, Heesterman, P, Heinola, K, Hellesen, C, Hellsten, T, Helou, W, Hemming, O, Hender, T, Henderson, M, Henderson, S, Henriques, R, Hepple, D, Hermon, G, Hertout, P, Hidalgo, C, Highcock, E, Hill, M, Hillairet, J, Hillesheim, J, Hillis, D, Hizanidis, K, Hjalmarsson, A, Hobirk, J, Hodille, E, Hogben, C, Hogeweij, G, Hollingsworth, A, Hollis, S, Homfray, D, Horã¡ä ek, J, Hornung, G, Horton, A, Horton, L, Horvath, L, Hotchin, S, Hough, M, Howarth, P, Hubbard, A, Huber, A, Huber, V, Huddleston, T, Hughes, M, Huijsmans, G, Hunter, C, Huynh, P, Hynes, A, Iglesias, D, Imazawa, N, Imbeaux, F, Imrã­å¡ek, M, Incelli, M, Innocente, P, Irishkin, M, Ivanova Stanik, I, Jachmich, S, Jacobsen, A, Jacquet, P, Jansons, J, Jardin, A, Jã¤rvinen, A, Jaulmes, F, Jednorã³g, S, Jenkins, I, Jeong, C, Jepu, I, Joffrin, E, Johnson, R, Johnson, T, Johnston, J, Joita, L, Jones, G, Jones, T, Hoshino, K, Kallenbach, A, Kamiya, K, Kaniewski, J, Kantor, A, Kappatou, A, Karhunen, J, Karkinsky, D, Karnowska, I, Kaufman, M, Kaveney, G, Kazakov, Y, Kazantzidis, V, Keeling, D, Keenan, T, Keep, J, Kempenaars, M, Kennedy, C, Kenny, D, Kent, J, Kent, O, Khilkevich, E, Kim, H, Kinch, A, King, C, King, D, King, R, Kinna, D, Kiptily, V, Kirk, A, Kirov, K, Kirschner, A, Kizane, G, Klepper, C, Klix, A, Knight, P, Knipe, S, Knott, S, Kobuchi, T, Kã¶chl, F, Kocsis, G, Kodeli, I, Kogan, L, Kogut, D, Koivuranta, S, Kominis, Y, Kã¶ppen, M, Kos, B, Koskela, T, Koslowski, H, Koubiti, M, Kovari, M, Kowalska StrzÈ©ciwilk, E, Krasilnikov, A, Krasilnikov, V, Krawczyk, N, Kresina, M, Krieger, K, Krivska, A, Kruezi, U, Ksiaå¼ek, I, Kukushkin, A, Kundu, A, Kurki Suonio, T, Kwak, S, Kwiatkowski, R, Kwon, O, Laguardia, L, Lahtinen, A, Laing, A, Lam, N, Lambertz, H, Lane, C, Lang, P, Lanthaler, S, Lapins, J, Lasa, A, Last, J, Å aszyå„ska, E, Lawless, R, Lawson, A, Lawson, K, Lazaros, A, Lazzaro, E, Leddy, J, Lee, S, Lefebvre, X, Leggate, H, Lehmann, J, Lehnen, M, Leichtle, D, Leichuer, P, Leipold, F, Lengar, I, Lennholm, M, Lerche, E, Lescinskis, A, Lesnoj, S, Letellier, E, Leyland, M, Leysen, W, Li, L, Liang, Y, Likonen, J, Linke, J, Linsmeier, C, Lipschultz, B, Liu, G, Liu, Y, Lo Schiavo, V, Loarer, T, Loarte, A, Lobel, R, Lomanowski, B, Lomas, P, Lã¶nnroth, J, Lã³pez, J, López Razola, J, Lorenzini, R, Losada, U, Lovell, J, Loving, A, Lowry, C, Luce, T, Lucock, R, Lukin, A, Luna, C, Lungaroni, M, Lungu, C, Lungu, M, Lunniss, A, Lupelli, I, Lyssoivan, A, Macdonald, N, Macheta, P, Maczewa, K, Magesh, B, Maget, P, Maggi, C, Maier, H, Mailloux, J, Makkonen, T, Makwana, R, Malaquias, A, Malizia, A, Manas, P, Manning, A, Manso, M, Mantica, P, Mantsinen, M, Manzanares, A, Maquet, P, Marandet, Y, Marcenko, N, Marchetto, C, Marchuk, O, Marinelli, M, Marinucci, M, Markoviä , T, Marocco, D, Marot, L, Marren, C, Marshal, R, Martin, A, Martin, Y, Martín De Aguilera, A, Martã­nez, F, Martín Solís, J, Martynova, Y, Maruyama, S, Masiello, A, Maslov, M, Matejcik, S, Mattei, M, Matthews, G, Maviglia, F, Mayer, M, Mayoral, M, May Smith, T, Mazon, D, Mazzotta, C, Mcadams, R, Mccarthy, P, Mcclements, K, Mccormack, O, Mccullen, P, Mcdonald, D, Mcintosh, S, Mckean, R, Mckehon, J, Meadows, R, Meakins, A, Medina, F, Medland, M, Medley, S, Meigh, S, Meigs, A, Meisl, G, Meitner, S, Meneses, L, Menmuir, S, Mergia, K, Merrigan, I, Mertens, P, Meshchaninov, S, Messiaen, A, Meyer, H, Mianowski, S, Michling, R, Middleton Gear, D, Miettunen, J, Militello, F, Militello Asp, E, Miloshevsky, G, Mink, F, Minucci, S, Miyoshi, Y, Mlynã¡å™, J, Molina, D, Monakhov, I, Moneti, M, Mooney, R, Moradi, S, Mordijck, S, Moreira, L, Moreno, R, Moro, F, Morris, A, Morris, J, Moser, L, Mosher, S, Moulton, D, Murari, A, Muraro, A, Murphy, S, Asakura, N, Na, Y, Nabais, F, Naish, R, Nakano, T, Nardon, E, Naulin, V, Nave, M, Nedzelski, I, Nemtsev, G, Nespoli, F, Neto, A, Neu, R, Neverov, V, Newman, M, Nicholls, K, Nicolas, T, Nielsen, A, Nielsen, P, Nilsson, E, Nishijima, D, Noble, C, Nocente, M, Nodwell, D, Nordlund, K, Nordman, H, Nouailletas, R, Nunes, I, Oberkofler, M, Odupitan, T, Ogawa, M, O'Gorman, T, Okabayashi, M, Olney, R, Omolayo, O, O'Mullane, M, Ongena, J, Orsitto, F, Orszagh, J, Oswuigwe, B, Otin, R, Owen, A, Paccagnella, R, Pace, N, Pacella, D, 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J., Pehkonen, S. -P., Peluso, E., Penot, C., Pereira, A., Pereira, R., Pereira Puglia, P. P., Perez von Thun, C., Peruzzo, S., Peschanyi, S., Peterka, M., Petersson, P., Petravich, G., Petre, A., Petrella, N., Petržilka, V., Peysson, Y., Pfefferlé, D., Philipps, V., Pillon, M., Pintsuk, G., Piovesan, P., Pires dos Reis, A., Piron, L., Pironti, A., Pisano, F., Pitts, R., Pizzo, F., Plyusnin, V., Pomaro, N., Pompilian, O. G., Pool, P. J., Popovichev, S., Porfiri, M. T., Porosnicu, C., Porton, M., Possnert, G., Potzel, S., Powell, T., Pozzi, J., Prajapati, V., Prakash, R., Prestopino, G., Price, D., Price, M., Price, R., Prior, P., Proudfoot, R., Pucella, G., Puglia, P., Puiatti, M. E., Pulley, D., Purahoo, K., Pütterich, Th., Rachlew, E., Rack, M., Ragona, R., Rainford, M. S. J., Rakha, A., Ramogida, G., Ranjan, S., Rapson, C. J., Rasmussen, J. 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K., Santos, B., Santucci, A., Sartori, F., Sartori, R., Sauter, O., Scannell, R., Schlummer, T., Schmid, K., Schmidt, V., Schmuck, S., Schneider, M., Schöpf, K., Schwörer, D., Scott, S. D., Sergienko, G., Sertoli, M., Shabbir, A., Sharapov, S. E., Shaw, A., Shaw, R., Sheikh, H., Shepherd, A., Shevelev, A., Shumack, A., Sias, G., Sibbald, M., Sieglin, B., Silburn, S., Silva, A., Silva, C., Simmons, P. A., Simpson, J., Simpson-Hutchinson, J., Sinha, A., Sipilä, S. K., Sips, A. C. C., Sirén, P., Sirinelli, A., Sjöstrand, H., Skiba, M., Skilton, R., Slabkowska, K., Slade, B., Smith, N., Smith, P. G., Smith, R., Smith, T. J., Smithies, M., Snoj, L., Soare, S., Solano, E. R., Somers, A., Sommariva, C., Sonato, P., Sopplesa, A., Sousa, J., Sozzi, C., Spagnolo, S., Spelzini, T., Spineanu, F., Stables, G., Stamatelatos, I., Stamp, M. F., Staniec, P., Stankūnas, G., Stan-Sion, C., Stead, M. J., Stefanikova, E., Stepanov, I., Stephen, A. V., Stephen, M., Stevens, A., Stevens, B. D., Strachan, J., Strand, P., Strauss, H. R., Ström, P., Stubbs, G., Studholme, W., Subba, F., Summers, H. P., Svensson, J., Świderski, Ł., Szabolics, T., Szawlowski, M., Szepesi, G., Suzuki, T. T., Tál, B., Tala, T., Talbot, A. R., Talebzadeh, S., Taliercio, C., Tamain, P., Tame, C., Tang, W., Tardocchi, M., Taroni, L., Taylor, D., Taylor, K. A., Tegnered, D., Telesca, G., Teplova, N., Terranova, D., Testa, D., Tholerus, E., Thomas, J., Thomas, J. D., Thomas, P., Thompson, A., Thompson, C. -A., Thompson, V. K., Thorne, L., Thornton, A., Thrysøe, A. S., Tigwell, P. A., Tipton, N., Tiseanu, I., Tojo, H., Tokitani, M., Tolias, P., Tomeš, M., Tonner, P., Towndrow, M., Trimble, P., Tripsky, M., Tsalas, M., Tsavalas, P., Tskhakaya jun, D., Turner, I., Turner, M. M., Turnyanskiy, M., Tvalashvili, G., Tyrrell, S. G. J., Uccello, A., Ul-Abidin, Z., Uljanovs, J., Ulyatt, D., Urano, H., Uytdenhouwen, I., Vadgama, A. P., Valcarcel, D., Valentinuzzi, M., Valisa, M., Vallejos Olivares, P., Valovic, M., Van De Mortel, M., Van Eester, D., Van Renterghem, W., van Rooij, G. J., Varje, J., Varoutis, S., Vartanian, S., Vasava, K., Vasilopoulou, T., Vega, J., Verdoolaege, G., Verhoeven, R., Verona, C., Verona Rinati, G., Veshchev, E., Vianello, N., Vicente, J., Viezzer, E., Villari, S., Villone, F., Vincenzi, P., Vinyar, I., Viola, B., Vitins, A., Vizvary, Z., Vlad, M., Voitsekhovitch, I., Vondráček, P., Vora, N., Vu, T., Pires de Sa, W. W., Wakeling, B., Waldon, C. W. F., Walkden, N., Walker, M., Walker, R., Walsh, M., Wang, E., Wang, N., Warder, S., Warren, R. J., Waterhouse, J., Watkins, N. W., Watts, C., Wauters, T., Weckmann, A., Weiland, J., Weisen, H., Weiszflog, M., Wellstood, C., West, A. T., Wheatley, M. R., Whetham, S., Whitehead, A. M., Whitehead, B. D., Widdowson, A. M., Wiesen, S., Wilkinson, J., Williams, J., Williams, M., Wilson, A. R., Wilson, D. J., Wilson, H. R., Wilson, J., Wischmeier, M., Withenshaw, G., Withycombe, A., Witts, D. M., Wood, D., Wood, R., Woodley, C., Wray, S., Wright, J., Wright, J. C., Wu, J., Wukitch, S., Wynn, A., Xu, T., Yadikin, D., Yanling, W., Yao, L., Yavorskij, V., Yoo, M. G., Young, C., Young, D., Young, I. D., Young, R., Zacks, J., Zagorski, R., Zaitsev, F. S., Zanino, R., Zarins, A., Zastrow, K. D., Zerbini, M., Zhang, W., Zhou, Y., Zilli, E., Zoita, V., Zoletnik, S., Zychor, I., Andersson Sundén, E., Baiã¡o, D., Belonohy, Ã. ., Bergsã¥ker, H., Bã­lkovã¡, P., Bjã¶rkas, C., Bodnã¡r, G., Broså awski, A., Calabrã², G., Crombã©, K., De Castro, A., De La Cal, E., De La Luna, E., De Pablos, J. L., De Vries, P., Den Harder, N., D'Inca, R., Donnã©, T., Duckworth, P. h., Ä uran, I., Durodiã©, F., Eich, T. h., Fã©vrier, O., Gã¡l, K., Gaå azka, K., Galvã¡o, R., García-Muñoz, M., Gardarein, J. -. L., Glã¶ggler, S., Goloborod'Ko, V., Goncalves, B., Guã©rard, C., Horã¡ä ek, J., Imrã­å¡ek, M., Jã¤rvinen, A., Jednorã³g, S., Kã¶chl, F., Kã¶ppen, M., Kowalska-StrzÈ©ciwilk, E., Ksiaå¼ek, I., Å aszyå ska, E., Linsmeier, C. h., Lã¶nnroth, J., Lã³pez, J. M., López-Razola, J., Maquet, P. h., Markoviä , T., Martín De Aguilera, A., Martã­nez, F. J., Martín-Solís, J. R., Mertens, P. h., Mlynã¡å , J., O'Gorman, T., O'Mullane, M., Pehkonen, S. -. P., Perez Von Thun, C., Petrå¾ilka, V., Pfefferlã©, D., Pires Dos Reis, A., Pã¼tterich, T. h., Rattã¡, G., Rã©fy, D., Regaã±a, J., Schã¶pf, K., Schwã¶rer, D., Sipilã¤, S. K., Sirã©n, P., Sjã¶strand, H., Stankå«nas, G., Strã¶m, P., Å widerski, Å. ., Tã¡l, B., Thompson, C. -. A., Thrysã¸e, A. S., Tomeå¡, M., Tskhakaya Jun, D., Van Rooij, G. J., Vondrã¡ä ek, P., Pires De Sa, W. W., Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Pierre et Marie Curie - Paris 6 (UPMC), Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC), Hôpital de Rangueil, CHU Toulouse [Toulouse]-CHU Toulouse [Toulouse], Laboratoire de microbiologie et génétique moléculaires (LMGM), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Université de Lorraine (UL)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Dipartimento di Energia [Milano] (DENG), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Research Centre Julich (FZJ), Institute for Plasma Research, Instituto Superior Tecnico Lisboa, Queen's University Belfast, University of Helsinki, CEA, Department of Applied Physics, School services, SCI, National Institutes for Quantum and Radiological Science and Technology, VTT, University of Naples Federico II, Universidad Nacional de Educacion a Distancia, CNR, Russian Research Centre Kurchatov Institute, Universita degli Studi di Napoli Parthenope, Ente Per Le Nuove Tecnologie L'energia e l'ambiente, Troitsk Institute for Innovation and Fusion Research, Uppsala University, National Institute for Cryogenics and Isotopic Technology, Max-Planck-Institut fur Plasmaphysik, University of Catania, Fusion for Energy Joint Undertaking, National Institutes of Natural Sciences - National Institute for Fusion Science, Massachusetts Institute of Technology, University of Latvia, Imperial College London, CIEMAT, University of Oxford, EUROfusion Programme Management Unit, Oak Ridge National Laboratory, Karlsruhe Institute of Technology KIT, University of York, Royal Institute of Technology, Maritime University of Szczecin, H. Niewodniczanski Institute of Nuclear Physics of the Polish Academy of Sciences, Czech Academy of Sciences, University of Trento, Ecole Polytechnique Federale de Lausanne (EPFL), Wigner Research Centre for Physics, Comenius University, University of Milan - Bicocca, National Institute for Optoelectronics, Fourth State Research, University of Texas at Austin, Belgian Nuclear Research Center, National Centre for Nuclear Research (NCBJ), Princeton University, CNRS, University of Cagliari, University of Warwick, Soltan Institute for Nuclear Studies, FOM Institute DIFFER, National Institute for Laser, Plasma and Radiation Physics, Ghent University, J. Stefan Institute, Universite de Lorraine, CAS - Institute of Plasma Physics, University of California at San Diego, Koninklijke Militaire School - Ecole Royale Militaire, Horia Hulubei National Institute of Physics and Nuclear Engineering, Chalmers University of Technology, School services, ELEC, Department of Signal Processing and Acoustics, Automaatio- ja systeemitekniik, Universidad Politecnica de Madrid, Second University of Naples, Warsaw University of Technology, Universita della Basilicata, Barcelona Supercomp. Center, Universidad de Sevilla, Centro Brasileiro de Pesquisas Fisicas, Department of Electrical Engineering and Automation, Sähkötekniikan laitos, University of Rome Tor Vergata, RAS - Ioffe Physico Technical Institute, General Atomics, University of Innsbruck, Fusion and Plasma Physics, University of Toyama, University of Strathclyde, National Technical University of Athens, Universita della Tuscia, Technical University of Denmark, Korea Advanced Institute of Science and Technology, Seoul National University, University College Cork, Vienna University of Technology, University of Opole, Daegu University, National Fusion Research Institute, Dublin City University, Universidad Politécnica de Madrid, PELIN LLC, Arizona State University, Universidad Complutense, University of Basel, Universidad Carlos III de Madrid, Consorzio CREATE, Demokritos National Centre for Scientific Research, Purdue University, Universite Libre de Bruxelles, School Services, ARTS, Department of Design, University of California Office of the President, Universidade de Sao Paulo, School Services, BIZ, Department of Information and Service Management, Lithuanian Energy Institute, HRS Fusion, Politecnico di Torino, University of Cassino, University of Electronic Science and Technology of China, Department of Electronics and Nanoengineering, Aalto-yliopisto, Aalto University, and Faculdade de Engenharia

Subjects
Technology, fusion, Física [Ciências exactas e naturais], Tokamak, Nuclear engineering, DIAGNOSTICS, 01 natural sciences, ILW, 010305 fluids & plasmas, law.invention, Ilw, [SPI.MECA.MEFL]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Fluids mechanics [physics.class-ph], Plasma, H-Mode Plasmas, law, ITER, Disruption Prediction, COLLISIONALITY, EDGE LOCALIZED MODES, Diagnostics, Operation, JET, plasma, Nuclear and High Energy Physics, Condensed Matter Physics, Physics, Jet (fluid), JET, plasma, fusion, ITER, Divertor, Settore FIS/01 - Fisica Sperimentale, Fusion, Plasma and Space Physics, DENSITY PEAKING, Carbon Wall, H-MODE PLASMAS, [ SPI.MECA.MEFL ] Engineering Sciences [physics]/Mechanics [physics.med-ph]/Fluids mechanics [physics.class-ph], Density Peaking, Neutron transport, Facing Components, Collisionality, 114 Physical sciences, Física, Física, Nuclear physics, Physical sciences [Natural sciences], Fusion, plasma och rymdfysik, Pedestal, 0103 physical sciences, Nuclear fusion, ddc:530, Neutron, 010306 general physics, Fusion, Physics, Physical sciences, Nuclear and High Energy Physic, Edge Localized Modes, QC717, Física [Àrees temàtiques de la UPC], Reactors de fusió, Física, FACING COMPONENTS, Fusion reactors, Jet, CARBON WALL, DISRUPTION PREDICTION, OPERATION, ddc:600
Abstract

The 2014–2016 JET results are reviewed in the light of their significance for optimising the ITER research plan for the active and non-active operation. More than 60 h of plasma operation with ITER first wall materials successfully took place since its installation in 2011. New multi-machine scaling of the type I-ELM divertor energy flux density to ITER is supported by first principle modelling. ITER relevant disruption experiments and first principle modelling are reported with a set of three disruption mitigation valves mimicking the ITER setup. Insights of the L–H power threshold in Deuterium and Hydrogen are given, stressing the importance of the magnetic configurations and the recent measurements of fine-scale structures in the edge radial electric. Dimensionless scans of the core and pedestal confinement provide new information to elucidate the importance of the first wall material on the fusion performance. H-mode plasmas at ITER triangularity (H = 1 at ßN ~ 1.8 and n/nGW ~ 0.6) have been sustained at 2 MA during 5 s. The ITER neutronics codes have been validated on high performance experiments. Prospects for the coming D–T campaign and 14 MeV neutron calibration strategy are reviewed. This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme 2014–2018 under grant agreement No 633053. Peer Reviewed Article signat per 1.173 autors/es: X. Litaudon35, S. Abduallev39, M. Abhangi46, P. Abreu53, M. Afzal7, K.M. Aggarwal29, T. Ahlgren101, J.H. Ahn8, L. Aho-Mantila112, N. Aiba69, M. Airila112, R. Albanese105, V. Aldred7, D. Alegre93, E. Alessi45, P. Aleynikov55, A. Alfier12, A. Alkseev72, M. Allinson7, B. Alper7, E. Alves53, G. Ambrosino105, R. Ambrosino106, L. Amicucci90, V. Amosov88, E. Andersson Sundén22, M. Angelone90, M. Anghel85, C. Angioni62, L. Appel7, C. Appelbee7, P. Arena30, M. Ariola106, H. Arnichand8, S. Arshad41, A. Ash7, N. Ashikawa68, V. Aslanyan64, O. Asunta1, F. Auriemma12, Y. Austin7, L. Avotina103, M.D. Axton7, C. Ayres7, M. Bacharis24, A. Baciero57, D. Baião53, S. Bailey7, A. Baker7, I. Balboa7, M. Balden62, N. Balshaw7, R. Bament7, J.W. Banks7, Y.F. Baranov7, M.A. Barnard7, D. Barnes7, M. Barnes27, R. Barnsley55, A. Baron Wiechec7, L. Barrera Orte34, M. Baruzzo12, V. Basiuk8, M. Bassan55, R. Bastow7, A. Batista53, P. Batistoni90, R. Baughan7, B. Bauvir55, L. Baylor73, B. Bazylev56, J. Beal110, P.S. Beaumont7, M. Beckers39, B. Beckett7, A. Becoulet8, N. Bekris35, M. Beldishevski7, K. Bell7, F. Belli90, M. Bellinger7, É. Belonohy62, N. Ben Ayed7, N.A. Benterman7, H. Bergsåker42, J. Bernardo53, M. Bernert62, M. Berry7, L. Bertalot55, C. Besliu7, M. Beurskens63, B. Bieg61, J. Bielecki47, T. Biewer73, M. Bigi12, P. Bílková50, F. Binda22, A. Bisoffi31, J.P.S. Bizarro53, C. Björkas101, J. Blackburn7, K. Blackman7, T.R. Blackman7, P. Blanchard33, P. Blatchford7, V. Bobkov62, A. Boboc7, G. Bodnár113, O. Bogar18, I. Bolshakova60, T. Bolzonella12, N. Bonanomi97, F. Bonelli56, J. Boom62, J. Booth7, D. Borba35,53, D. Borodin39, I. Borodkina39, A. Botrugno90, C. Bottereau8, P. Boulting7, C. Bourdelle8, M. Bowden7, C. Bower7, C. Bowman110, T. Boyce7, C. Boyd7, H.J. Boyer7, J.M.A. Bradshaw7, V. Braic87, R. Bravanec40, B. Breizman107, S. Bremond8, P.D. Brennan7, S. Breton8, A. Brett7, S. Brezinsek39, M.D.J. Bright7, M. Brix7, W. Broeckx78, M. Brombin12, A. Brosławski65, D.P.D. Brown7, M. Brown7, E. Bruno55, J. Bucalossi8, J. Buch46, J. Buchanan7, M.A. Buckley7, R. Budny76, H. Bufferand8, M. Bulman7, N. Bulmer7, P. Bunting7, P. Buratti90, A. Burckhart62, A. Buscarino30, A. Busse7, N.K. Butler7, I. Bykov42, J. Byrne7, P. Cahyna50, G. Calabrò90, I. Calvo57, Y. Camenen4, P. Camp7, D.C. Campling7, J. Cane7, B. Cannas17, A.J. Capel7, P.J. Card7, A. Cardinali90, P. Carman7, M. Carr7, D. Carralero62, L. Carraro12, B.B. Carvalho53, I. Carvalho53, P. Carvalho53, F.J. Casson7, C. Castaldo90, N. Catarino53, J. Caumont7, F. Causa90, R. Cavazzana12, K. Cave-Ayland7, M. Cavinato12, M. Cecconello22, S. Ceccuzzi90, E. Cecil76, A. Cenedese12, R. Cesario90, C.D. Challis7, M. Chandler7, D. Chandra46, C.S. Chang76, A. Chankin62, I.T. Chapman7, S.C. Chapman28, M. Chernyshova49, G. Chitarin12, G. Ciraolo8, D. Ciric7, J. Citrin38, F. Clairet8, E. Clark7, M. Clark7, R. Clarkson7, D. Clatworthy7, C. Clements7, M. Cleverly7, J.P. Coad7, P.A. Coates7, A. Cobalt7, V. Coccorese105, V. Cocilovo90, S. Coda33, R. Coelho53, J.W. Coenen39, I. Coffey29, L. Colas8, S. Collins7, D. Conka103, S. Conroy22, N. Conway7, D. Coombs7, D. Cooper7, S.R. Cooper7, C. Corradino30, Y. Corre8, G. Corrigan7, S. Cortes53, D. Coster62, A.S. Couchman7, M.P. Cox7, T. Craciunescu86, S. Cramp7, R. Craven7, F. Crisanti90, G. Croci97, D. Croft7, K. Crombé15, R. Crowe7, N. Cruz53, G. Cseh113, A. Cufar81, A. Cullen7, M. Curuia85, A. Czarnecka49, H. Dabirikhah7, P. Dalgliesh7, S. Dalley7, J. Dankowski47, D. Darrow76, O. Davies7, W. Davis55,76, C. Day56, I.E. Day7, M. De Bock55, A. de Castro57, E. de la Cal57, E. de la Luna57, G. De Masi12, J. L. de Pablos57, G. De Temmerman55, G. De Tommasi105, P. de Vries55, K. Deakin7, J. Deane7, F. Degli Agostini12, R. Dejarnac50, E. Delabie73, N. den Harder38, R.O. Dendy7, J. Denis8, P. Denner39, S. Devaux62,104, P. Devynck8, F. Di Maio55, A. Di Siena62, C. Di Troia90, P. Dinca86, R. D’Inca62, B. Ding51, T. Dittmar39, H. Doerk62, R.P. Doerner9, T. Donné34, S.E. Dorling7, S. Dormido-Canto93, S. Doswon7, D. Douai8, P.T. Doyle7, A. Drenik62,81, P. Drewelow63, P. Drews39, Ph. Duckworth55, R. Dumont8, P. Dumortier58, D. Dunai113, M. Dunne62, I. Ďuran50, F. Durodié58, P. Dutta46, B. P. Duval33, R. Dux62, K. Dylst78, N. Dzysiuk22, P.V. Edappala46, J. Edmond7, A.M. Edwards7, J. Edwards7, Th. Eich62, A. Ekedahl8, R. El-Jorf7, C.G. Elsmore7, M. Enachescu84, G. Ericsson22, F. Eriksson16, J. Eriksson22, L.G. 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Trimble7, M. Tripsky58, M. Tsalas38, P. Tsavalas71, D. Tskhakaya jun102, I. Turner7, M.M. Turner32, M. Turnyanskiy34, G. Tvalashvili7, S.G.J. Tyrrell7, A. Uccello45, Z. Ul-Abidin7, J. Uljanovs1, D. Ulyatt7, H. Urano69, I. Uytdenhouwen78, A.P. Vadgama7, D. Valcarcel7, M. Valentinuzzi8, M. Valisa12, P. Vallejos Olivares42, M. Valovic7, M. Van De Mortel7, D. Van Eester58, W. Van Renterghem78, G.J. van Rooij38, J. Varje1, S. Varoutis56, S. Vartanian8, K. Vasava46, T. Vasilopoulou71, J. Vega57, G. Verdoolaege58, R. Verhoeven7, C. Verona95, G. Verona Rinati95, E. Veshchev55, N. Vianello45, J. Vicente53, E. Viezzer62,92, S. Villari90, F. Villone100, P. Vincenzi12, I. Vinyar74, B. Viola90, A. Vitins103, Z. Vizvary7, M. Vlad86, I. Voitsekhovitch34, P. Vondráček50, N. Vora7, T. Vu8, W.W. Pires de Sa52, B. Wakeling7, C.W.F. Waldon7, N. Walkden7, M. Walker7, R. Walker7, M. Walsh55, E. Wang39, N. Wang39, S. Warder7, R.J. Warren7, J. Waterhouse7, N.W. Watkins28, C. Watts55, T. Wauters58, A. Weckmann42, J. Weiland23, H. Weisen33, M. Weiszflog22, C. Wellstood7, A.T. West7, M.R. Wheatley7, S. Whetham7, A.M. Whitehead7, B.D. Whitehead7, A.M. Widdowson7, S. Wiesen39, J. Wilkinson7, J. Williams7, M. Williams7, A.R. Wilson7, D.J. Wilson7, H.R. Wilson110, J. Wilson7, M. Wischmeier62, G. Withenshaw7, A. Withycombe7, D.M. Witts7, D. Wood7, R. Wood7, C. Woodley7, S. Wray7, J. Wright7, J.C. Wright64, J. Wu89, S. Wukitch64, A. Wynn110, T. Xu7, D. Yadikin16, W. Yanling39, L. Yao89, V. Yavorskij102, M.G. Yoo80, C. Young7, D. Young7, I.D. Young7, R. Young7, J. Zacks7, R. Zagorski49, F.S. Zaitsev18, R. Zanino75, A. Zarins103, K.D. Zastrow7, M. Zerbini90, W. Zhang62, Y. Zhou42, E. Zilli12, V. Zoita86, S. Zoletnik113, I. Zychor65 and JET Contributorsa // EUROfusion Consortium JET, Culham Science Centre, Abingdon, OX14 3DB, United Kingdom / 1 Aalto University, PO Box 14100, FIN-00076 Aalto, Finland / 2 Aix Marseille Université, CNRS, Centrale Marseille, M2P2 UMR 7340, 13451, Marseille, France / 3 Aix-Marseille Université, CNRS, IUSTI UMR 7343, 13013 Marseille, France / 4 Aix-Marseille Université, CNRS, PIIM, UMR 7345, 13013 Marseille, France / 5 Arizona State University, Tempe, AZ, United States of America / 6 Barcelona Supercomputing Center, Barcelona, Spain / 7 CCFE, Culham Science Centre, Abingdon, Oxon, OX14 3DB, United Kingdom / 8 CEA, IRFM, F-13108 Saint Paul Lez Durance, France / 9 Center for Energy Research, University of California at San Diego, La Jolla, CA 92093, United States of America / 10 Centro Brasileiro de Pesquisas Fisicas, Rua Xavier Sigaud, 160, Rio de Janeiro CEP 22290-180, Brazil / 11 Consorzio CREATE, Via Claudio 21, 80125 Napoli, Italy / 12 Consorzio RFX, corso Stati Uniti 4, 35127 Padova, Italy / 13 Daegu University, Jillyang, Gyeongsan, Gyeongbuk 712-174, Republic of Korea / 14 Departamento de Física, Universidad Carlos III de Madrid, 28911 Leganés, Madrid, Spain / 15 Department of Applied Physics UG (Ghent University) St-Pietersnieuwstraat 41 B-9000 Ghent, Belgium / 16 Department of Earth and Space Sciences, Chalmers University of Technology, SE-41296 Gothenburg, Sweden / 17 Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi 09123, Cagliari, Italy / 18 Department of Experimental Physics, Faculty of Mathematics, Physics and Informatics Comenius University Mlynska dolina F2, 84248 Bratislava, Slovakia / 19 Department of Materials Science, Warsaw University of Technology, PL-01-152 Warsaw, Poland / 20 Department of Nuclear and Quantum Engineering, KAIST, Daejeon 34141, Korea / 21 Department of Physics and Applied Physics, University of Strathclyde, Glasgow, G4 ONG, United Kingdom / 22 Department of Physics and Astronomy, Uppsala University, SE-75120 Uppsala, Sweden / 23 Department of Physics, Chalmers University of Technology, SE-41296 Gothenburg, Sweden / 24 Department of Physics, Imperial College London, London, SW7 2AZ, United Kingdom / 25 Department of Physics, SCI, KTH, SE-10691 Stockholm, Sweden / 26 Department of Physics, University of Basel, Basel, Switzerland / 27 Department of Physics, University of Oxford, Oxford, OX1 2JD, United Kingdom / 28 Department of Physics, University of Warwick, Coventry, CV4 7AL, United Kingdom / 29 Department of Pure and Applied Physics, Queens University, Belfast, BT7 1NN, United Kingdom / 30 Dipartimento di Ingegneria Elettrica Elettronica e Informatica, Università degli Studi di Catania, 95125 Catania, Italy / 31 Dipartimento di Ingegneria Industriale, University of Trento, Trento, Italy / 32 Dublin City University (DCU), Dublin, Ireland / 33 Ecole Polytechnique Fédérale de Lausanne (EPFL), Swiss Plasma Center (SPC), CH-1015 Lausanne, Switzerland / 34 EUROfusion Programme Management Unit, Boltzmannstr. 2, 85748 Garching, Germany / 35 EUROfusion Programme Management Unit, Culham Science Centre, Culham, OX14 3DB, United Kingdom / 36 European Commission, B-1049 Brussels, Belgium / 37 Fluid and Plasma Dynamics, ULB—Campus Plaine—CP 231 Boulevard du Triomphe, 1050 Bruxelles, Belgium / 38 FOM Institute DIFFER, Eindhoven, Netherlands / 39 Forschungszentrum Jülich GmbH, Institut für Energie- und Klimaforschung—Plasmaphysik, 52425 Jülich, Germany / 40 Fourth State Research, 503 Lockhart Dr, Austin, TX, United States of America / 41 Fusion for Energy Joint Undertaking, Josep Pl. 2, Torres Diagonal Litoral B3, 08019, Barcelona, Spain / 42 Fusion Plasma Physics, EES, KTH, SE-10044 Stockholm, Sweden / 43 General Atomics, PO Box 85608, San Diego, CA 92186-5608, United States of America / 44 HRS Fusion, West Orange, NJ, United States of America / 45 IFP-CNR, via R. Cozzi 53, 20125 Milano, Italy / 46 Institute for Plasma Research, Bhat, Gandhinagar-382 428, Gujarat State, India / 47 Institute of Nuclear Physics, Radzikowskiego 152, 31-342 Kraków, Poland / 48 Institute of Physics, Opole University, Oleska 48, 45-052 Opole, Poland / 49 Institute of Plasma Physics and Laser Microfusion, Hery 23, 01-497 Warsaw, Poland / 50 Institute of Plasma Physics AS CR, Za Slovankou 1782/3, 182 00 Praha 8, Czechia / 51 Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031, People’s Republic of China / 52 Instituto de Física, Universidade de São Paulo, Rua do Matão Travessa R Nr.187 CEP 05508-090 Cidade Universitária, São Paulo, Brasil / 53 Instituto de Plasmas e Fusão Nuclear, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal / 54 Ioffe Physico-Technical Institute, 26 Politekhnicheskaya, St Petersburg 194021, Russian Federation / 55 ITER Organization, Route de Vinon, CS 90 046, 13067 Saint Paul Lez Durance, France / 56 Karlsruhe Institute of Technology, PO Box 3640, D-76021 Karlsruhe, Germany / 57 Laboratorio Nacional de Fusión, CIEMAT, Madrid, Spain / 58 Laboratory for Plasma Physics Koninklijke Militaire School—Ecole Royale Militaire, Renaissancelaan 30 Avenue de la Renaissance B-1000, Brussels, Belgium / 59 Lithuanian energy institute, Breslaujos g. 3, LT-44403, Kaunas, Lithuania / 60 Magnetic Sensor Laboratory, Lviv Polytechnic National University, Lviv, Ukraine / 61 Maritime University of Szczecin, Waly Chrobrego 1-2, 70-500 Szczecin, Poland / 62 Max-Planck-Institut für Plasmaphysik, D-85748 Garching, Germany / 63 Max-Planck-Institut für Plasmaphysik, Teilinsitut Greifswald, D-17491 Greifswald, Germany / 64 MIT Plasma Science and Fusion Centre, Cambridge, MA 02139, United States of America / 65 National Centre for Nuclear Research (NCBJ), 05-400 Otwock-Świerk, Poland / 66 National Fusion Research Institute (NFRI), 169-148 Gwahak-ro, Yuseong-gu, Daejeon 305-806, Republic of Korea / 67 National Institute for Fusion Science, Oroshi, Toki, Gifu 509-5292, Japan / 68 National Institute for Fusion Science, Toki, 509-5292, Japan / 69 National Institutes for Quantum and Radiological Science and Technology, Naka, Ibaraki 311-0193, Japan / 70 National Technical University of Athens, Iroon Politechniou 9, 157 73 Zografou, Athens, Greece / 71 NCSR ‘Demokritos’, 153 10, Agia Paraskevi Attikis, Greece / 72 NRC Kurchatov Institute, 1 Kurchatov Square, Moscow 123182, Russian Federation / 73 Oak Ridge National Laboratory, Oak Ridge, TN 37831-6169, United States of America / 74 PELIN LLC, 27a, Gzhatskaya Ulitsa, Saint Petersburg, 195220, Russian Federation / 75 Politecnico di Torino, Corso Duca degli Abruzzi 24, I-10129 Torino, Italy / 76 Princeton Plasma Physics Laboratory, James Forrestal Campus, Princeton, NJ 08543, United States of America / 77 Purdue University, 610 Purdue Mall, West Lafayette, IN 47907, United States of America / 78 SCK-CEN, Nuclear Research Centre, 2400 Mol, Belgium / 79 Second University of Napoli, Consorzio CREATE, Via Claudio 21, 80125 Napoli, Italy / 80 Seoul National University, Shilim-Dong, Gwanak-Gu, Republic of Korea / 81 Slovenian Fusion Association (SFA), Jozef Stefan Institute, Jamova 39, SI-1000 Ljubljana, Slovenia / 82 Space and Plasma Physics, EES, KTH SE-100 44 Stockholm, Sweden / 83 Technical University of Denmark, Department of Physics, Bldg 309, DK-2800 Kgs Lyngby, Denmark / 84 The ‘Horia Hulubei’ National Institute for Physics and Nuclear Engineering, Magurele-Bucharest, Romania / 85 The National Institute for Cryogenics and Isotopic Technology, Ramnicu Valcea, Romania / 86 The National Institute for Laser, Plasma and Radiation Physics, Magurele-Bucharest, Romania / 87 The National Institute for Optoelectronics, Magurele-Bucharest, Romania / 88 Troitsk Insitute of Innovating and Thermonuclear Research (TRINITI), Troitsk 142190, Moscow Region, Russian Federation / 89 University of Electronic Science and Technology of China, Chengdu, People’s Republic of China / 90 Unità Tecnica Fusione, ENEA C. R. Frascati, via E. Fermi 45, 00044 Frascati (Roma), Italy / 91 Universidad Complutense de Madrid, Madrid, Spain / 92 Universidad de Sevilla, Sevilla, Spain / 93 Universidad Nacional de Educación a Distancia, Madrid, Spain / 94 Universidad Politécnica de Madrid, Grupo I2A2, Madrid, Spain / 95 Università di Roma Tor Vergata, Via del Politecnico 1, Roma, Italy / 96 University College Cork (UCC), Ireland / 97 University Milano-Bicocca, piazza della Scienza 3, 20126 Milano, Italy / 98 University of Basilicata, Consorzio CREATE, Via Claudio 21, 80125 Napoli, Italy / 99 University of California, 1111 Franklin St., Oakland, CA 94607, United States of America / 100 University of Cassino, Consorzio CREATE, Via Claudio 21, 80125 Napoli, Italy / 101 University of Helsinki, PO Box 43, FI-00014 University of Helsinki, Finland / 102 University of Innsbruck, Fusion@Österreichische Akademie der Wissenschaften (ÖAW), Innsbruck, Austria / 103 University of Latvia, 19 Raina Blvd., Riga, LV 1586, Latvia / 104 University of Lorraine, CNRS, UMR7198, YIJL, Nancy, France / 105 University of Napoli ‘Federico II’, Consorzio CREATE, Via Claudio 21, 80125 Napoli, Italy / 106 University of Napoli Parthenope, Consorzio CREATE, Via Claudio 21, 80125 Napoli, Italy / 107 University of Texas at Austin, Institute for Fusion Studies, Austin, TX 78712, United States of America / 108 University of Toyama, Toyama, 930-8555, Japan / 109 University of Tuscia, DEIM, Via del Paradiso 47, 01100 Viterbo, Italy / 110 University of York, Heslington, York YO10 5DD, United Kingdom / 111 Vienna University of Technology, Fusion@Österreichische Akademie der Wissenschaften (ÖAW), Austria / 112 VTT Technical Research Centre of Finland, PO Box 1000, FIN-02044 VTT, Finland / 113 Wigner Research Centre for Physics, PO Box 49, H-1525 Budapest, Hungary

Published
2017

48. Multivariate statistical models for disruption prediction at ASDEX Upgrade

Author

Giuliana Sias, Barbara Cannas, Alessandra Fanni, G. Pautasso, and R. Aledda

Subjects
Computer science, Mechanical Engineering, Prediction system, computer.software_genre, Fault detection and isolation, Set (abstract data type), Nuclear Energy and Engineering, Autoregressive model, ASDEX Upgrade, General Materials Science, Data mining, Aging effect, Multivariate statistical, computer, Civil and Structural Engineering
Abstract

In this paper, a disruption prediction system for ASDEX Upgrade has been proposed that does not require disruption terminated experiments to be implemented. The system consists of a data-based model, which is built using only few input signals coming from successfully terminated pulses. A fault detection and isolation approach has been used, where the prediction is based on the analysis of the residuals of an auto regressive exogenous input model. The prediction performance of the proposed system is encouraging when it is applied to the same set of campaigns used to implement the model. However, the false alarms significantly increase when we tested the system on discharges coming from experimental campaigns temporally far from those used to train the model. This is due to the well know aging effect inherent in the data-based models. The main advantage of the proposed method, with respect to other data-based approaches in literature, is that it does not need data on experiments terminated with a disruption, as it uses a normal operating conditions model. This is a big advantage in the prospective of a prediction system for ITER, where a limited number of disruptions can be allowed.

Published
2013

50. Autosomal dominant nocturnal frontal lobe epilepsy seizure characterization through wavelet transform of eeg records and self organizing maps

Author

Augusto Montisci, Alessandra Fanni, G. Milioli, Giuliana Sias, Barbara Cannas, Fabio Pisano, B. Pisano, and M. Puligheddu

Subjects
Self-organizing map, medicine.diagnostic_test, Computer science, Speech recognition, Feature extraction, Nonlinear dimensionality reduction, Wavelet transform, Autosomal dominant nocturnal frontal lobe epilepsy, Electroencephalography, medicine.disease, 01 natural sciences, 010305 fluids & plasmas, body regions, 03 medical and health sciences, Epilepsy, 0302 clinical medicine, 0103 physical sciences, medicine, psychological phenomena and processes, 030217 neurology & neurosurgery
Abstract

In this paper, a Manifold Learning approach for the automatic detection of Autosomal Dominant Nocturnal Frontal Lobe Epilepsy seizures is presented, with the aim to support neurologists in the labelling efforts. Features extracted from polysomnography signals are used in order to detect and discriminate seizure epochs. This task has been addressed by mapping the electroencephalographic signal epochs in different regions of the features space. The result is a Self Organizing Map, which allows to investigate over not straightforward relations in the complex input space for the characterization of seizures.

Published
2016

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