891 research outputs found
A strongly interacting gas of two-electron fermions at an orbital Feshbach resonance
We report on the experimental observation of a strongly interacting gas of
ultracold two-electron fermions with orbital degree of freedom and magnetically
tunable interactions. This realization has been enabled by the demonstration of
a novel kind of Feshbach resonance occurring in the scattering of two 173Yb
atoms in different nuclear and electronic states. The strongly interacting
regime at resonance is evidenced by the observation of anisotropic hydrodynamic
expansion of the two-orbital Fermi gas. These results pave the way towards the
realization of new quantum states of matter with strongly correlated fermions
with orbital degree of freedom.Comment: 5 pages, 4 figure
Manipulation of ultracold atomic mixtures using microwave techniques
We used microwave radiation to evaporatively cool a mixture of of 133Cs and
87Rb atoms in a magnetic trap. A mixture composed of an equal number (around
10^4) of Rb and Cs atoms in their doubly polarized states at ultracold
temperatures was prepared. We also used microwaves to selectively evaporate
atoms in different Zeeman states.Comment: 9 pages, 6 figure
A real time bolometer tomographic reconstruction algorithm in nuclear fusion reactors
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
Forecasting-Aided Monitoring for the Distribution System State Estimation
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
Machine learning approach for operational phases identification in H-mode Density Limit disruptions
CNN disruption predictor at JET: Early versus late data fusion approach
This work focuses on the development of a data driven model, based on Convolutional Neural Networks (CNNs), for the real-time detection of disruptive events at JET. The predictor exploits the ability of CNNs in learning relevant spatiotemporal information straight from 1D plasma profiles, avoiding hand-engineered feature extraction procedures. In this paper, the radiation profiles from both the bolometer horizontal and vertical cameras have been considered amongst the predictor inputs, with the aim of discriminating between the core radiation due to impurity accumulations and the outboard radiation phenomena. Moreover, an innovative predictor architecture is proposed, where two separate CNNs are trained to focus on events with different timescales, that is, the destabilization of radiation, electron density and temperature profiles, and the mode-locking and current profile variations. The outputs of the two CNNs are combined with a logic OR function to provide the disruption alarm trigger. The advantages of this data fusion approach impact on the predictor performance, with a very limited number of false alarms (only 1 in the considered test set), and on the model output interpretability as the two different branches are triggered by different types of events
Automatic disruption classification in JET with the ITER-like wall
The new full-metal ITER-like wall at JET was found to have a deep impact on the physics of
disruptions at JET. In order to develop disruption classification, the 10D operational space of
JET with the new ITER-like wall has been explored using the generative topographic mapping
method. The 2D map has been exploited to develop an automatic disruption classification of
several disruption classes manually identified. In particular, all the non-intentional disruptions
have been considered, that occurred in JET from 2011 to 2013 with the new wall. A statistical
analysis of the plasma parameters describing the operational spaces of JET with carbon wall
and JET ITER-like wall has been performed and some physical considerations have been
made on the difference between these two operational spaces and the disruption classes which
can be identified. The performance of the JET- ITER-like wall classifier is tested in realtime
in conjunction with a disruption predictor presently operating at JET with good results.
Moreover, to validate and analyse the results, another reference classifier has been developed,
based on the k-nearest neighbour technique. Finally, in order to verify the reliability of the
performed classification, a conformal predictor based on non-conformity measures has been
developed
AC-induced superfluidity
We argue that a system of ultracold bosonic atoms in a tilted optical lattice
can become superfluid in response to resonant AC forcing. Among others, this
allows one to prepare a Bose-Einstein condensate in a state associated with a
negative effective mass. Our reasoning is backed by both exact numerical
simulations for systems consisting of few particles, and by a theoretical
approach based on Floquet-Fock states.Comment: Accepted for publication in Europhysics letters, 6 pages, 4 figures,
Changes in v2: reference 7 replaced by a more recent on
Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy
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
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