Intrinsic instability of electronic interfaces with strong Rashba coupling

We consider a model for the two-dimensional electron gas formed at the interface of oxide heterostructures, which includes a Rashba spin-orbit coupling proportional to the electric field perpendicular to the interface. Based on the standard mechanism of polarity catastrophe, we assume that the electric field is proportional to the electron density. Under these simple and general assumptions, we show that a phase separation instability occurs for realistic values of the spin-orbit coupling and of the band parameters. This could provide an intrinsic mechanism for the recently observed inhomogeneous phases at the LaAlO_3/SrTiO_3 or LaTiO_3/SrTiO_3 interfaces.Comment: 5 pages, 4 figure

Phase diagrams of voltage-gated oxide interfaces with strong Rashba coupling

We propose a model for the two-dimensional electron gas formed at the interface of oxide heterostructures that includes a Rashba spin-orbit coupling proportional to an electric field oriented perpendicularly to the interface. Taking into account the electron density dependence of this electric field confining the electron gas at the interface, we report the occurrence of a phase separation instability (signaled by a negative compressibility) for realistic values of the spin-orbit coupling and of the electronic band-structure parameters at zero temperature. We extend the analysis to finite temperatures and in the presence of an in-plane magnetic field, thereby obtaining two phase diagrams which exhibit a phase separation dome. By varying the gating potential the phase separation dome may shrink and vanish at zero temperature into a quantum critical point where the charge fluctuates dynamically. Similarly the phase separation may be spoiled by a planar magnetic field even at zero temperature leading to a line of quantum critical points.Comment: 17 pages, 17 figure

Multilayer perceptron neural networks model for meteosat second generation SEVIRI daytime cloud masking

A multilayer perceptron neural network cloud mask for Meteosat Second Generation SEVIRI (Spinning Enhanced Visible and Infrared Imager) images is introduced and evaluated. The model is trained for cloud detection on MSG SEVIRI daytime data. It consists of a multi-layer perceptron with one hidden sigmoid layer, trained with the error back-propagation algorithm. The model is fed by six bands of MSG data (0.6, 0.8, 1.6, 3.9, 6.2 and 10.8 μm) with 10 hidden nodes. The multiple-layer perceptrons lead to a cloud detection accuracy of 88.96%, when trained to map two predefined values that classify cloud and clear sky. The network was further evaluated using sixty MSG images taken at different dates. The network detected not only bright thick clouds but also thin or less bright clouds. The analysis demonstrated the feasibility of using machine learning models of cloud detection in MSG SEVIRI imagery

The combination of band ratioing techniques and neural networks algorithms for MSG SEVIRI and Landsat ETM+ cloud masking

In this paper a new approach from the combination of band ratioing function and MLP Neural Networks technique is proposed to differentiate between clouds and background in Landsat ETM+ and MSG SEVIRI data. First, in order to increase the contrast of the clouds and background, a band ratioing function is applied to each sub-image. Second, the sub-images are segmented by MLP Neural Networks technique. The proposed approach was tested on 40 Landsat ETM+ sub-images of Gulf of Mexico and on 40 MSG SEVIRI sub-images over Italy. The same parameters were used in all tests. For the overall dataset, the average accuracy of 89 % was obtained for Landsat ETM+ images and the average accuracy of 85 % was obtained for MSG SEVIRI images. Our experimental results demonstrate that the proposed approach is robust and effective

Twenty-four hour solar irradiance forecast based on neural networks and numerical weather prediction

In this paper, several models to forecast the hourly solar irradiance with a day in advance using artificial neural network techniques have been developed and analyzed. The forecast irradiance is the one incident on the plane of the modules array of a photovoltaic plant. Pure statistical (ST) models that use only local measured data and model output statistics (MOS) approaches to refine numerical weather prediction data are tested for the University of Rome "Tor Vergata" site. The performance of ST and MOS, together with the persistence model (PM), is compared. The ST models improve the performance of the PM of around 20%. The combination of ST and NWP in the MOS approach gives the best performance, improving the forecast of approximately 39% with respect to the PM

Solar radiation forecast using neural networks for the prediction of grid connected PV plants energy production (DSP project)

The work presented in this paper is part of a project aimed to develop a prototype device (DSP) able to forecast with a day in advance the energy produced by PV plants. The energy forecast is required by the National Authority for the electricity in order to control the high instabilities of the electric grid induced by unpredictable energy sources such as photovoltaic. In the paper several models to forecast the hourly solar irradiance with a day in advance using Artificial Neural Network (ANN) techniques are described. Statistical (ST) models that use only local measured data and Hybrid model (HY) that also use Numerical Weather Prediction (NWP) data are tested for the University of Rome “Tor Vergata” site. The performance of ST, NWP and HY models, together with the Persistence model (PM), are compared. The ST models and the NWP model exhibit similar results improving the performance of the PM of around 20%. Nevertheless different sources of forecast errors between ST and NWP models are identified. The Hybrid models give the better performance, improving the forecast of approximately 39% with respect to the Persistence model