Electromagnetic and thermal homogenisation of an electrical machine slot

In this paper we propose an original technique based on the finite element method to couple electromagnetic and thermal homogenisation of multiturn windings. The model accurately accounts for skin and proximity effects considering the temperature dependence of electrical resistivity. We validate the approach by modelling a reference electrical machine open slot with representative boundary conditions. The case study refers to a particular wire shape and winding periodic configuration but the method can be applied to any symmetrical wire shape. The homogenisation allows us to efficiently evaluate the hot- spot temperature within the slot. The solution provided by the homogenised model proves to be very accurate over a large range of frequencies, when compared to the results using a fine model where all the conductors are physically reproduced

Reconstruction of Bandlimited Functions from Unsigned Samples

We consider the recovery of real-valued bandlimited functions from the absolute values of their samples, possibly spaced nonuniformly. We show that such a reconstruction is always possible if the function is sampled at more than twice its Nyquist rate, and may not necessarily be possible if the samples are taken at less than twice the Nyquist rate. In the case of uniform samples, we also describe an FFT-based algorithm to perform the reconstruction. We prove that it converges exponentially rapidly in the number of samples used and examine its numerical behavior on some test cases

Audio Source Separation with Discriminative Scattering Networks

In this report we describe an ongoing line of research for solving single-channel source separation problems. Many monaural signal decomposition techniques proposed in the literature operate on a feature space consisting of a time-frequency representation of the input data. A challenge faced by these approaches is to effectively exploit the temporal dependencies of the signals at scales larger than the duration of a time-frame. In this work we propose to tackle this problem by modeling the signals using a time-frequency representation with multiple temporal resolutions. The proposed representation consists of a pyramid of wavelet scattering operators, which generalizes Constant Q Transforms (CQT) with extra layers of convolution and complex modulus. We first show that learning standard models with this multi-resolution setting improves source separation results over fixed-resolution methods. As study case, we use Non-Negative Matrix Factorizations (NMF) that has been widely considered in many audio application. Then, we investigate the inclusion of the proposed multi-resolution setting into a discriminative training regime. We discuss several alternatives using different deep neural network architectures

Sensitivity of Modern Lighting Technologies to Rapid Voltage Changes

Rapid Voltage Changes (RVCs) are one of the Power Quality disturbances that are recently receiving a lot of attention from the point of view of international standards. However, although they can cause or contribute to flicker, IEC 61000-4-15 only addresses periodic amplitude fluctuations and more effort is needed to regulate the occurrence of RVCs, according to their effect on flicker perceptibility. Alongside, flicker perception is challenged by the integration of modern lighting technology, whose response is different from the traditional incandescent lamp. This paper studies the connection between the increasing importance of RVCs and the evolution of illumination technologies. Sensitivity of modern lighting technologies to RVCs is studied by measuring flicker with a high precision light flickermeter. A large set of modern lamps is tested and the relationship between RVCs parameters and flicker perceptibility is analyzed

Recuperación e identificación de variedades de vid en Aragón

1 copia .pdf del Póster original de los Autores.- 1 Tabl.- 4 Fots. Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)A partir de la década de los setenta diferentes circunstancias provocaron que muchas de las zonas más productivas arrancaran las vides antiguas y apostaran por plantaciones de variedades foráneas o distintas de las tradicionales. Conscientes de la pérdida de biodiversidad, desde la Unidad de Tecnología Vegetal (Gobierno de Aragón) se viene prospectando en toda la geografía aragonesa y recopilando accesiones de vid, especialmente en viñas antiguas y a punto de desaparecer. El año 1992 se formó el Banco de Germoplasma de Vid de Aragón (Dep. de Agricultura, Ganadería y Medio Ambiente) que conserva más de 700 accesiones (algunas caracterizadas molecularmente, Buhner-Zaharieva et al., 2010) .Este trabajo ha sido financiado por el INIA (RF2012-00027-C05-02) y el Gobierno de Aragón (A44)Peer reviewe

Novel Calibration systems for the dynamic and steady-state testing of digital instrument transformers

Within the frame of the European project 'Future Grid II-Metrology for the next-generation digital substation instrumentation', several partners developed traceable calibration systems which allow the calibration of conventional or non-conventional instrument transformers (IT) even with a sampled value (digital) output according to IEC 61869-9. Different setups are prepared to allow the calibration with complex test waveforms to emulate steady state, dynamic or temporary events during the assessment of the ITs. The laboratory calibration setups for either current transformers or voltage transformers are briefly described. Several results obtained for different kind of voltage or current transformers are presented

Guest Editorial: Non-Euclidean Machine Learning

Over the past decade, deep learning has had a revolutionary impact on a broad range of fields such as computer vision and image processing, computational photography, medical imaging and speech and language analysis and synthesis etc. Deep learning technologies are estimated to have added billions in business value, created new markets, and transformed entire industrial segments. Most of today’s successful deep learning methods such as Convolutional Neural Networks (CNNs) rely on classical signal processing models that limit their applicability to data with underlying Euclidean grid-like structure, e.g., images or acoustic signals. Yet, many applications deal with non-Euclidean (graph- or manifold-structured) data. For example, in social network analysis the users and their attributes are generally modeled as signals on the vertices of graphs. In biology protein-to-protein interactions are modeled as graphs. In computer vision & graphics 3D objects are modeled as meshes or point clouds. Furthermore, a graph representation is a very natural way to describe interactions between objects or signals. The classical deep learning paradigm on Euclidean domains falls short in providing appropriate tools for such kind of data. Until recently, the lack of deep learning models capable of correctly dealing with non-Euclidean data has been a major obstacle in these fields. This special section addresses the need to bring together leading efforts in non-Euclidean deep learning across all communities. From the papers that the special received twelve were selected for publication. The selected papers can naturally fall in three distinct categories: (a) methodologies that advance machine learning on data that are represented as graphs, (b) methodologies that advance machine learning on manifold-valued data, and (c) applications of machine learning methodologies on non-Euclidean spaces in computer vision and medical imaging. We briefly review the accepted papers in each of the groups

Multisensory 3D saliency for artficial attention systems

In this paper we present proof-of-concept for a novel solution consisting of a short-term 3D memory for artificial attention systems, loosely inspired in perceptual processes believed to be implemented in the human brain. Our solution supports the implementation of multisensory perception and stimulus-driven processes of attention. For this purpose, it provides (1) knowledge persistence with temporal coherence tackling potential salient regions outside the field of view, via a panoramic, log-spherical inference grid; (2) prediction, by using estimates of local 3D velocity to anticipate the effect of scene dynamics; (3) spatial correspondence between volumetric cells potentially occupied by proto-objects and their corresponding multisensory saliency scores. Visual and auditory signals are processed to extract features that are then filtered by a proto-object segmentation module that employs colour and depth as discriminatory traits. We consider as features, apart from the commonly used colour and intensity contrast, colour bias, the presence of faces, scene dynamics and also loud auditory sources. Combining conspicuity maps derived from these features we obtain a 2D saliency map, which is then processed using the probability of occupancy in the scene to construct the final 3D saliency map as an additional layer of the Bayesian Volumetric Map (BVM) inference grid

Electron attachment to valence-excited CO

The possibility of electron attachment to the valence $^{3}\Pi$ state of CO is examined using an {\it ab initio} bound-state multireference configuration interaction approach. The resulting resonance has $^{4}\Sigma^{-}$ symmetry; the higher vibrational levels of this resonance state coincide with, or are nearly coincident with, levels of the parent $a^{3}\Pi$ state. Collisional relaxation to the lowest vibrational levels in hot plasma situations might yield the possibility of a long-lived CO$^-$ state.Comment: Revtex file + postscript file for one figur

Geometric deep learning

The goal of these course notes is to describe the main mathematical ideas behind geometric deep learning and to provide implementation details for several applications in shape analysis and synthesis, computer vision and computer graphics. The text in the course materials is primarily based on previously published work. With these notes we gather and provide a clear picture of the key concepts and techniques that fall under the umbrella of geometric deep learning, and illustrate the applications they enable. We also aim to provide practical implementation details for the methods presented in these works, as well as suggest further readings and extensions of these ideas