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- Ferrari, P., et al.
(författare)
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Performance evaluation of full-cloud and edge-cloud architectures for Industrial IoT anomaly detection based on deep learning
- 2019
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Ingår i: Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728104294 ; , s. 420-425
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Konferensbidrag (refereegranskat)abstract
- One of the most interesting application of data analysis to industry is the real-time detection of anomalies during production. Industrial IoT paradigm includes all the components to realize predictive systems, like the anomaly detection ones. In this case, the goal is to discover patterns, in a given dataset, that do not resemble the 'normal' behavior, to identify faults, malfunctions or the effects of bad maintenance. The use of complex neural networks to implement deep learning algorithm for anomaly detection is very common. The position of the deep learning algorithm is one of the main problem: this kind of algorithm requires both high computational power and data transfer bandwidth, rising serious questions on the system scalability. Data elaboration in the edge domain (i.e. close to the machine) usually reduce data transfer but requires to instantiate expensive physical assets. Cloud computing is usually cheaper but Cloud data transfer is expensive. In this paper a test methodology for the comparison of the two architectures for anomaly detection system is proposed. A real use case is described in order to demonstrate the feasibility. The experimental results show that, by means of the proposed methodology, edge and Cloud solutions implementing deep learning algorithms for industrial applications can be easily evaluated. In details, for the considered use case (with Siemens controller and Microsoft Azure platform) the tradeoff between scalability, communication delay, and bandwidth usage, has been studied. The results show that the full-cloud architecture can outperform the edge-cloud architecture when Cloud computation power is scaled. © 2019 IEEE.
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- Guzzi, G., et al.
(författare)
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Exact hybrid-kinetic equilibria for magnetized plasmas with shearing flows
- 2021
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Ingår i: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 645
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Tidskriftsartikel (refereegranskat)abstract
- Context. Magnetized plasmas characterized by shearing flows are present in many natural contexts, such as the Earth's magnetopause and the solar wind. The collisionless nature of involved plasmas requires a kinetic description. When the width of the shear layer is on the order of ion scales, the hybrid Vlasov-Maxwell approach can be adopted for this purpose.Aims. The aim of this work is to derive explicit forms for stationary configurations of magnetized plasmas with planar shearing flows within the hybrid Vlasov-Maxwell description. Two configurations are considered: the first with a uniform magnetic field obliquely directed with respect to the bulk velocity and the second with a uniform-magnitude variable-direction magnetic field.Methods. We obtained stationary ion distribution functions by combining single-particle constant of motions, which are derived through the study of particle dynamics. Preliminary information about the form of the distribution functions were analytically derived in considering a local approximation for the background electromagnetic field. Then a numerical method was set up to obtain a solution for general profiles.Results. We determined explicit distribution functions that allow us to obtain profiles of density, bulk velocity, temperature, and heat flux. Anisotropy and agyrotropy in the distribution function were also evaluated. The stationarity of the solution during numerical simulations was checked in the uniform oblique magnetic field case.Conclusions. The configurations considered here can be used as models for the Earth's magnetopause in simulations of the Kelvin-Helmholtz instability.
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- Pezzi, O., et al.
(författare)
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Turbulence and particle energization in twisted flux ropes under solar-wind conditions
- 2024
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Ingår i: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 686
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Tidskriftsartikel (refereegranskat)abstract
- Context. The mechanisms regulating the transport and energization of charged particles in space and astrophysical plasmas are still debated. Plasma turbulence is known to be a powerful particle accelerator. Large-scale structures, including flux ropes and plasmoids, may contribute to confining particles and lead to fast particle energization. These structures may also modify the properties of the turbulent, nonlinear transfer across scales. Aims. We aim to investigate how large-scale flux ropes are perturbed and, simultaneously, how they influence the nonlinear transfer of turbulent energy toward smaller scales. We then intend to address how these structures affect particle transport and energization. Methods. We adopted magnetohydrodynamic simulations perturbing a large-scale flux rope in solar-wind conditions and possibly triggering turbulence. Then, we employed test-particle methods to investigate particle transport and energization in the perturbed flux rope. Results. The large-scale helical flux rope inhibits the turbulent cascade toward smaller scales, especially if the amplitude of the initial perturbations is not large (∼5%). In this case, particle transport is inhibited inside the structure. Fast particle acceleration occurs in association with phases of trapped motion within the large-scale flux rope.
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