M. Lungaroni

发表

Advanced signal processing based on support vector regression for lidar applications

J. Vega, Michela Gelfusa, A. Murari, 2015, SPIE Remote Sensing.

A Novel Facility to Investigate Dust Mobilization in Confined Environments with Applications to the Security of the Pharmaceutical Industry

Andrea Malizia, Maria Richetta, A. Murari, 2016 .

Deep neural networks for plasma tomography with applications to JET and COMPASS

S. Lanthaler, R. J. Smith, N. W. Eidietis, 2019, Journal of Instrumentation.

Adaptive learning for disruption prediction in non-stationary conditions

A. Murari, M. Lungaroni, Emmanuele Peluso, 2019, Nuclear Fusion.

Stacking of predictors for the automatic classification of disruption types to optimize the control logic

Riccardo Rossi, Michela Gelfusa, M. Lungaroni, 2020 .

Geodesic Distance on Gaussian Manifolds to Reduce the Statistical Errors in the Investigation of Complex Systems

Michela Gelfusa, M. Lungaroni, Emmanuele Peluso, 2019, Complex..

On the transfer of adaptive predictors between different devices for both mitigation and prevention of disruptions

Pasqualino Gaudio, Michela Gelfusa, M. Lungaroni, 2020, Nuclear Fusion.

First tests of a multi-wavelength mini-DIAL system for the automatic detection of greenhouse gases

M. Lungaroni, Emmanuele Peluso, Pasqualino Gaudio, 2017, Remote Sensing.

Testing the consistency of multimachine databases for physical studies of regression

Michela Gelfusa, M. Lungaroni, M. Gelfusa, 2019, Nuclear Fusion.

Lidar and Dial application for detection and identification: a proposal to improve safety and security

D Di Giovanni, Carlo Bellecci, Michela Gelfusa, 2017 .

On the Use of Entropy to Improve Model Selection Criteria

M. Lungaroni, Emmanuele Peluso, Andrea Murari, 2019, Entropy.

A support vector machine approach to the automatic identification of fluorescence spectra emitted by biological agents

Jesús Vega, M. Lungaroni, Emmanuele Peluso, 2016, Security + Defence.

On determining the prediction limits of mathematical models for time series

Michela Gelfusa, M. Lungaroni, Emmanuele Peluso, 2016 .

Multispectral analysis of biological agents to implement a quick tool for stand-off biological detection

Michela Gelfusa, M. Lungaroni, Pasqualino Gaudio, 2015, SPIE Security + Defence.

On the potential of ruled-based machine learning for disruption prediction on JET

J. Vega, Gonzalo Farias, Michela Gelfusa, 2018 .

A machine learning approach to the identification of chemical substances from lidar measurements

A. Murari, Andrea Malizia, M. Lungaroni, 2017 .

How to assess the efficiency of synchronization experiments in tokamaks

Michela Gelfusa, M. Lungaroni, Emmanuele Peluso, 2016 .

Early Identification of Disruption Paths for Prevention and Avoidance

O. Sauter, A. Murari, S. N. Gerasimov, 2018 .

Application of transfer entropy to causality detection and synchronization experiments in tokamaks

M. Lungaroni, Emmanuele Peluso, Michela Gelfusa, 2016 .

First attempts at measuring widespread smoke with a mobile LIDAR system

J. Vega, A. Murari, M. Lungaroni, 2015 .

New analysis methods to push the boundaries of diagnostic techniques in the environmental sciences

J. Vega, M. Lungaroni, Emmanuele Peluso, 2016 .

Overview of the JET results in support to ITER

S. Lanthaler, Massimiliano Mattei, Alfredo Pironti, 2017, Nuclear Fusion.

Overview of the JET preparation for deuterium–tritium operation with the ITER like-wall

S. Lanthaler, R. J. Smith, P. Arena, 2019, Nuclear Fusion.

On the Determination of the Backscattering Profile with Lidar in Presence of Widespread Smoke

J. Vega, A. Murari, M. Lungaroni, 2016 .