Importancia de la humedad de la madera de las barricas de roble en su permeabilidad al oxígeno

El oxígeno es un elemento que puede interactuar con el vino y cambiar su composición físico-química, en ocasiones para mejorar las propiedades del vino y en otras ocasiones puede tener efectos negativos. En este trabajo se pretende dar a conocer todos los factores que hacen que la elección del roble como material para construir las barricas no sea casual. También se pretende comprender el modelo líquido-madera-gas y todas las interacciones que ocurren en esa interfase como el movimiento de transferencia de humedad y evaporación hacia el exterior o la entrada de compuestos extraíbles y oxígeno hacia el interior de la barrica. Se sabe que la permeabilidad del oxígeno es del orden de 20 a 100 veces menor cuando la madera está saturada de humedad, de ahí la importancia de conocer la cinética de impregnación del vino en la barrica y su evaporación al exterior como base para calcular la cantidad de oxígeno que después puede llegar a entrar en la barrica.Departamento de Ingeniería Agrícola y ForestalMáster en Calidad, Desarrollo e Innovación de Alimento

Modelling and PID Control of a Rotary Dryer

This paper describes the modelling and the PID control of a drying process. The plant uses a co-current rotary dryer to evaporate moisture of a waste product generated by olive-oil mills, called alpeorujo or two phase cake. The paper shows the development of a model based upon first principles combined with experimental results. A control strategy has been tested under simulation based on PID controllers for the main loops in this process

One Relator Quotients of Graph Products

In this paper, we generalise Magnus' Freiheitssatz and solution to the word problem for one-relator groups by considering one relator quotients of certain classes of right-angled Artin groups and graph products of locally indicable polycyclic groups

Geometric Rényi divergence: A comparative measure with applications to atomic densities

An alternative one-parameter measure of divergence is proposed, quantifying the discrepancy among general probability densities. Its main mathematical properties include (i) comparison among an arbitrary number of functions, (ii) the possibility of assigning different weights to each function according to its relevance on the comparative procedure, and (iii) ability to modify the relative contribution of different regions within the domain. Applications to the study of atomic density functions, in both conjugated spaces, show the versatility and universality of this divergence

A Speech Recognizer based on Multiclass SVMs with HMM-Guided Segmentation

Automatic Speech Recognition (ASR) is essentially a problem of pattern classification, however, the time dimension of the speech signal has prevented to pose ASR as a simple static classification problem. Support Vector Machine (SVM) classifiers could provide an appropriate solution, since they are very well adapted to high-dimensional classification problems. Nevertheless, the use of SVMs for ASR is by no means straightforward, mainly because SVM classifiers require an input of fixed-dimension. In this paper we study the use of a HMM-based segmentation as a mean to get the fixed-dimension input vectors required by SVMs, in a problem of isolated-digit recognition. Different configurations for all the parameters involved have been tested. Also, we deal with the problem of multi-class classification (as SVMs are initially binary classifers), studying two of the most popular approaches: 1-vs-all and 1-vs-1

Fisher-Shannon analysis of ionization processes and isoelectronic series

The Fisher-Shannon plane which embodies the Fisher information measure in conjunction with the Shannon entropy is tested in its ability to quantify and compare the informational behavior of the process of atomic ionization. We report the variation of such an information measure and its constituents for a comprehensive set of neutral atoms, and their isoelectronic series including the mononegative ions, using the numerical data generated on 320 atomic systems in position, momentum, and product spaces at the Hartree-Fock level. It is found that the Fisher-Shannon plane clearly reveals shell-filling patterns across the periodic table. Compared to position space, a significantly higher resolution is exhibited in momentum space. Characteristic features in the Fisher-Shannon plane accompanying the ionization process are identified, and the physical reasons for the observed patterns are described

Fisher-Shannon plane and statistical complexity of atoms

Using the Hartree-Fock non-relativistic wave functions in the position and momentum spaces, the statistical measure of complexity C, due to López-Ruiz, Mancini, and Calbet for the neutral atoms as well as their monopositive and mononegative ions with atomic number Z=1-54 are reported. In C, given by the product of exponential power Shannon entropy and the average density, the latter is then replaced by the Fisher measure to obtain the Fisher-Shannon plane. Our numerical results suggest that in overall the Fisher-Shannon plane reproduces the trends given by C, with significantly enhanced sensitivity in the position, momentum and the product spaces in all neutral atoms and ions considered

SVMs for Automatic Speech Recognition: a Survey

Hidden Markov Models (HMMs) are, undoubtedly, the most employed core technique for Automatic Speech Recognition (ASR). Nevertheless, we are still far from achieving high-performance ASR systems. Some alternative approaches, most of them based on Artificial Neural Networks (ANNs), were proposed during the late eighties and early nineties. Some of them tackled the ASR problem using predictive ANNs, while others proposed hybrid HMM/ANN systems. However, despite some achievements, nowadays, the preponderance of Markov Models is a fact. During the last decade, however, a new tool appeared in the field of machine learning that has proved to be able to cope with hard classification problems in several fields of application: the Support Vector Machines (SVMs). The SVMs are effective discriminative classifiers with several outstanding characteristics, namely: their solution is that with maximum margin; they are capable to deal with samples of a very higher dimensionality; and their convergence to the minimum of the associated cost function is guaranteed. These characteristics have made SVMs very popular and successful. In this chapter we discuss their strengths and weakness in the ASR context and make a review of the current state-of-the-art techniques. We organize the contributions in two parts: isolated-word recognition and continuous speech recognition. Within the first part we review several techniques to produce the fixed-dimension vectors needed for original SVMs. Afterwards we explore more sophisticated techniques based on the use of kernels capable to deal with sequences of different length. Among them is the DTAK kernel, simple and effective, which rescues an old technique of speech recognition: Dynamic Time Warping (DTW). Within the second part, we describe some recent approaches to tackle more complex tasks like connected digit recognition or continuous speech recognition using SVMs. Finally we draw some conclusions and outline several ongoing lines of research

Relativistic global and local divergences in hydrogenic systems: A study in position and momentum spaces

Relativistic effects in one-particle densities of hydrogenic systems are quantified by means of global and local density functionals: the Jensen-Shannon and the Jensen-Fisher divergences, respectively. The Schrödinger and Dirac radial densities are compared, providing complementary results in position and momentum spaces. While the electron cloud gets compressed towards the origin in the Dirac case, the momentum density spreads out over its domain, and the raising of minima in position space does not occur in the momentum space. Regarding the dependence on the nuclear charge and the state quantum numbers for all divergences here considered, as well as their mutual interconnection, accurate powerlike laws y˜Cxa are found systematically. The parameters {C,a} defining the respective dependences are extremely sensitive to the closeness of the system to the ground and/or the circular state. Particularly interesting are the analyses of (i) the plane subtended by the Jensen-Shannon and Jensen-Fisher divergences, in a given space (position or momentum), and (ii) either of the above two divergences in the position-momentum plane. These kinds of results show the complementary role of global and local divergences and that of both conjugate spaces