One dimensional exciton luminescence induced by extended defects in nonpolar (Al,Ga)N/GaN quantum wells

In this study, we present the optical properties of nonpolar GaN/(Al,Ga)N single quantum wells (QWs) grown on either a- or m-plane GaN templates for Al contents set below 15%. In order to reduce the density of extended defects, the templates have been processed using the epitaxial lateral overgrowth technique. As expected for polarization-free heterostructures, the larger the QW width for a given Al content, the narrower the QW emission line. In structures with an Al content set to 5 or 10%, we also observe emission from excitons bound to the intersection of I1-type basal plane stacking faults (BSFs) with the QW. Similarly to what is seen in bulk material, the temperature dependence of BSF-bound QW exciton luminescence reveals intra-BSF localization. A qualitative model evidences the large spatial extension of the wavefunction of these BSF-bound QW excitons, making them extremely sensitive to potential fluctuations located in and away from BSF. Finally, polarization-dependent measurements show a strong emission anisotropy for BSF-bound QW excitons, which is related to their one-dimensional character and that confirms that the intersection between a BSF and a GaN/(Al,Ga)N QW can be described as a quantum wire.

Numerical analysis of axisymmetric shells by one-dimensional continuum elements suitable for high frequency excitations

Axisymmetric shells are analyzed by means of one-dimensional continuum elements by using the analogy between the bending of shells and the bending of beams on elastic foundation. The mathematical model is formulated in the frequency domain. Because the solution of the governing equations of vibration of beams are exact, the spatial discretization only depends on geometrical or material considerations. For some kind of situations, for example, for high frequency excitations, this approach may be more convenient than other conventional ones such as the finite element method.

Analytical solution to the one-dimensional non-uniform absorption of solar radiation in uncoated and coated single glass panes

The analytical solution to the one-dimensional absorption–conduction heat transfer problem inside a single glass pane is presented, which correctly takes into account all the relevant physical phenomena: the appearance of multiple reflections, the spectral distribution of solar radiation, the spectral dependence of optical properties, the presence of possible coatings, the non-uniform nature of radiation absorption, and the diffusion of heat by conduction across the glass pane. Additionally to the well established and known direct absorptance αe, the derived solution introduces a new spectral quantity called direct absorptance moment βe, that indicates where in the glass pane is the absorption of radiation actually taking place. The theoretical and numerical comparison of the derived solution with existing approximate thermal models for the absorption–conduction problem reveals that the latter ones work best for low-absorbing uncoated single glass panes, something not necessarily fulfilled by modern glazings.

One-dimensional self-similar solution of the dynamics of axisymmetric slender liquid bridges

In this paper the dynamics of axisymmetric, slender, viscous liquid bridges having volume close to the cylindrical one, and subjected to a small gravitational field parallel to the axis of the liquid bridge, is considered within the context of one-dimensional theories. Although the dynamics of liquid bridges has been treated through a numerical analysis in the inviscid case, numerical methods become inappropriate to study configurations close to the static stability limit because the evolution time, and thence the computing time, increases excessively. To avoid this difficulty, the problem of the evolution of these liquid bridges has been attacked through a nonlinear analysis based on the singular perturbation method and, whenever possible, the results obtained are compared with the numerical ones.

One-dimensional linear analysis of the compound jet

The stability of an infinitely long compound liquid column is analysed by using a one-dimensional inviscid slice model. Results obtained from this one-dimensional linear analysis are applicable to the study of compound capillary jets, which are used in the ink-jet printing technique. Stability limits and the breaking regimes of such fluid configurations are established, and, whenever possible, theoretical results are compared with experimental ones.

One-dimensional linear analysis of the liquid injection or removal in a liquid bridge

The filling-withdrawal process of a long liquid bridge is analyzed using a one-dimensional linearized model for the dynamics of the liquid column. To carry out this study, a well-known standard operational method (Laplace transform) has been used, and time variation of both liquid velocity field and interface shape are obtained.

The Proper Generalized Decomposition With Application In Problems Of Dynamics

In this paper we address the new reduction method called Proper Generalized Decomposition (PGD) which is a discretization technique based on the use of separated representation of the unknown fields, specially well suited for solving multidimensional parametric equations. In this case, it is applied to the solution of dynamics problems. We will focus on the dynamic analysis of an one-dimensional rod with a unit harmonic load of frequency (ω) applied at a point of interest. In what follows, we will present the application of the methodology PGD to the problem in order to approximate the displacement field as the sum of the separated functions. We will consider as new variables of the problem, parameters models associated with the characteristic of the materials, in addition to the frequency. Finally, the quality of the results will be assessed based on an example.

The estimation of truncation error by tau-estimation revisited

The aim of this paper was to accurately estimate the local truncation error of partial differential equations, that are numerically solved using a finite difference or finite volume approach on structured and unstructured meshes. In this work, we approximated the local truncation error using the @t-estimation procedure, which aims to compare the residuals on a sequence of grids with different spacing. First, we focused the analysis on one-dimensional scalar linear and non-linear test cases to examine the accuracy of the estimation of the truncation error for both finite difference and finite volume approaches on different grid topologies. Then, we extended the analysis to two-dimensional problems: first on linear and non-linear scalar equations and finally on the Euler equations. We demonstrated that this approach yields a highly accurate estimation of the truncation error if some conditions are fulfilled. These conditions are related to the accuracy of the restriction operators, the choice of the boundary conditions, the distortion of the grids and the magnitude of the iteration error.

Mixing Snapshots and Fast Time Integration of PDEs

A local proper orthogonal decomposition (POD) plus Galerkin projection method was recently developed to accelerate time dependent numerical solvers of PDEs. This method is based on the combined use of a numerical code (NC) and a Galerkin sys- tem (GS) in a sequence of interspersed time intervals, INC and IGS, respectively. POD is performed on some sets of snapshots calculated by the numerical solver in the INC inter- vals. The governing equations are Galerkin projected onto the most energetic POD modes and the resulting GS is time integrated in the next IGS interval. The major computa- tional e®ort is associated with the snapshots calculation in the ¯rst INC interval, where the POD manifold needs to be completely constructed (it is only updated in subsequent INC intervals, which can thus be quite small). As the POD manifold depends only weakly on the particular values of the parameters of the problem, a suitable library can be con- structed adapting the snapshots calculated in other runs to drastically reduce the size of the ¯rst INC interval and thus the involved computational cost. The strategy is success- fully tested in (i) the one-dimensional complex Ginzburg-Landau equation, including the case in which it exhibits transient chaos, and (ii) the two-dimensional unsteady lid-driven cavity problem

Contribution to the design of waveguide-fed compound-slot arrays by means of equivalent circuit modelling

Los arrays de ranuras son sistemas de antennas conocidos desde los años 40, principalmente destinados a formar parte de sistemas rádar de navíos de combate y grandes estaciones terrenas donde el tamaño y el peso no eran altamente restrictivos. Con el paso de los años y debido sobre todo a importantes avances en materiales y métodos de fabricación, el rango de aplicaciones de este tipo de sistemas radiantes creció en gran medida. Desde nuevas tecnologías biomédicas, sistemas anticolisión en automóviles y navegación en aviones, enlaces de comunicaciones de alta tasa binaria y corta distancia e incluso sistemas embarcados en satélites para la transmisión de señal de televisión. Dentro de esta familia de antennas, existen dos grupos que destacan por ser los más utilizados: las antennas de placas paralelas con las ranuras distribuidas de forma circular o espiral y las agrupaciones de arrays lineales construidos sobre guia de onda. Continuando con las tareas de investigación desarrolladas durante los últimos años en el Instituto de Tecnología de Tokyo y en el Grupo de Radiación de la Universidad Politécnica de Madrid, la totalidad de esta tesis se centra en este último grupo, aunque como se verá se separa en gran medida de las técnicas de diseño y metodologías convencionales. Los arrays de ranuras rectas y paralelas al eje de la guía rectangular que las alimenta son, sin ninguna duda, los modelos más empleados debido a la fiabilidad que presentan a altas frecuencias, su capacidad para gestionar grandes cantidades de potencia y la sencillez de su diseño y fabricación. Sin embargo, también presentan desventajas como estrecho ancho de banda en pérdidas de retorno y rápida degradación del diagrama de radiación con la frecuencia. Éstas son debidas a la naturaleza resonante de sus elementos radiantes: al perder la resonancia, el sistema global se desajusta y sus prestaciones degeneran. En arrays bidimensionales de slots rectos, el campo eléctrico queda polarizado sobre el plano transversal a las ranuras, correspondiéndose con el plano de altos lóbulos secundarios. Esta tesis tiene como objetivo el desarrollo de un método sistemático de diseño de arrays de ranuras inclinadas y desplazadas del centro (en lo sucesivo “ranuras compuestas”), definido en 1971 como uno de los desafíos a superar dentro del mundo del diseño de antennas. La técnica empleada se basa en el Método de los Momentos, la Teoría de Circuitos y la Teoría de Conexión Aleatoria de Matrices de Dispersión. Al tratarse de un método circuital, la primera parte de la tesis se corresponde con el estudio de la aplicabilidad de las redes equivalentes fundamentales, su capacidad para recrear fenómenos físicos de la ranura, las limitaciones y ventajas que presentan para caracterizar las diferentes configuraciones de slot compuesto. Se profundiza en las diferencias entre las redes en T y en ! y se condiciona la selección de una u otra dependiendo del tipo de elemento radiante. Una vez seleccionado el tipo de red a emplear en el diseño del sistema, se ha desarrollado un algoritmo de cascadeo progresivo desde el puerto alimentador hacia el cortocircuito que termina el modelo. Este algoritmo es independiente del número de elementos, la frecuencia central de funcionamiento, del ángulo de inclinación de las ranuras y de la red equivalente seleccionada (en T o en !). Se basa en definir el diseño del array como un Problema de Satisfacción de Condiciones (en inglés, Constraint Satisfaction Problem) que se resuelve por un método de Búsqueda en Retroceso (Backtracking algorithm). Como resultado devuelve un circuito equivalente del array completo adaptado a su entrada y cuyos elementos consumen una potencia acorde a una distribución de amplitud dada para el array. En toda agrupación de antennas, el acoplo mutuo entre elementos a través del campo radiado representa uno de los principales problemas para el ingeniero y sus efectos perjudican a las prestaciones globales del sistema, tanto en adaptación como en capacidad de radiación. El empleo de circuito equivalente se descartó por la dificultad que suponía la caracterización de estos efectos y su inclusión en la etapa de diseño. En esta tesis doctoral el acoplo también se ha modelado como una red equivalente cuyos elementos son transformadores ideales y admitancias, conectada al conjunto de redes equivalentes que representa el array. Al comparar los resultados estimados en términos de pérdidas de retorno y radiación con aquellos obtenidos a partir de programas comerciales populares como CST Microwave Studio se confirma la validez del método aquí propuesto, el primer método de diseño sistemático de arrays de ranuras compuestos alimentados por guía de onda rectangular. Al tratarse de ranuras no resonantes, el ancho de banda en pérdidas de retorno es mucho mas amplio que el que presentan arrays de slots rectos. Para arrays bidimensionales, el ángulo de inclinación puede ajustarse de manera que el campo quede polarizado en los planos de bajos lóbulos secundarios. Además de simulaciones se han diseñado, construido y medido dos prototipos centrados en la frecuencia de 12GHz, de seis y diez elementos. Las medidas de pérdidas de retorno y diagrama de radiación revelan excelentes resultados, certificando la bondad del método genuino Method of Moments - Forward Matching Procedure desarrollado a lo largo de esta tésis. Abstract The slot antenna arrays are well known systems from the decade of 40s, mainly intended to be part of radar systems of large warships and terrestrial stations where size and weight were not highly restrictive. Over the years, mainly due to significant advances in materials and manufacturing methods, the range of applications of this type of radiating systems grew significantly. From new biomedical technologies, collision avoidance systems in cars and aircraft navigation, short communication links with high bit transfer rate and even embedded systems in satellites for television broadcast. Within this family of antennas, two groups stand out as being the most frequent in the literature: parallel plate antennas with slots placed in a circular or spiral distribution and clusters of waveguide linear arrays. To continue the vast research work carried out during the last decades in the Tokyo Institute of Technology and in the Radiation Group at the Universidad Politécnica de Madrid, this thesis focuses on the latter group, although it represents a technique that drastically breaks with traditional design methodologies. The arrays of slots straight and parallel to the axis of the feeding rectangular waveguide are without a doubt the most used models because of the reliability that they present at high frequencies, its ability to handle large amounts of power and their simplicity of design and manufacturing. However, there also exist disadvantages as narrow bandwidth in return loss and rapid degradation of the radiation pattern with frequency. These are due to the resonant nature of radiating elements: away from the resonance status, the overall system performance and radiation pattern diminish. For two-dimensional arrays of straight slots, the electric field is polarized transverse to the radiators, corresponding to the plane of high side-lobe level. This thesis aims to develop a systematic method of designing arrays of angled and displaced slots (hereinafter "compound slots"), defined in 1971 as one of the challenges to overcome in the world of antenna design. The used technique is based on the Method of Moments, Circuit Theory and the Theory of Scattering Matrices Connection. Being a circuitry-based method, the first part of this dissertation corresponds to the study of the applicability of the basic equivalent networks, their ability to recreate the slot physical phenomena, their limitations and advantages presented to characterize different compound slot configurations. It delves into the differences of T and ! and determines the selection of the most suitable one depending on the type of radiating element. Once the type of network to be used in the system design is selected, a progressive algorithm called Forward Matching Procedure has been developed to connect the proper equivalent networks from the feeder port to shorted ending. This algorithm is independent of the number of elements, the central operating frequency, the angle of inclination of the slots and selected equivalent network (T or ! networks). It is based on the definition of the array design as a Constraint Satisfaction Problem, solved by means of a Backtracking Algorithm. As a result, the method returns an equivalent circuit of the whole array which is matched at its input port and whose elements consume a power according to a given amplitude distribution for the array. In any group of antennas, the mutual coupling between elements through the radiated field represents one of the biggest problems that the engineer faces and its effects are detrimental to the overall performance of the system, both in radiation capabilities and return loss. The employment of an equivalent circuit for the array design was discarded by some authors because of the difficulty involved in the characterization of the coupling effects and their inclusion in the design stage. In this thesis the coupling has also been modeled as an equivalent network whose elements are ideal transformers and admittances connected to the set of equivalent networks that represent the antennas of the array. By comparing the estimated results in terms of return loss and radiation with those obtained from popular commercial software as CST Microwave Studio, the validity of the proposed method is fully confirmed, representing the first method of systematic design of compound-slot arrays fed by rectangular waveguide. Since these slots do not work under the resonant status, the bandwidth in return loss is much wider than the longitudinal-slot arrays. For the case of two-dimensional arrays, the angle of inclination can be adjusted so that the field is polarized at the low side-lobe level plane. Besides the performed full-wave simulations two prototypes of six and ten elements for the X-band have been designed, built and measured, revealing excellent results and agreement with the expected results. These facts certify that the genuine technique Method of Moments - Matching Forward Procedure developed along this thesis is valid and trustable.

Low-dimensional modeling of streaks in a wedge flow boundary layer

This paper is concerned with the low dimensional structure of optimal streaks in a wedge flow boundary layer, which have been recently shown to consist of a unique (up to a constant factor) three-dimensional streamwise evolving mode, known as the most unstable streaky mode. Optimal streaks exhibit a still unexplored/unexploited approximate self-similarity (not associated with the boundary layer self-similarity), namely the streamwise velocity re-scaled with their maximum remains almost independent of both the spanwise wavenumber and the streamwise coordinate; the remaining two velocity components instead do not satisfy this property. The approximate self-similar behavior is analyzed here and exploited to further simplify the description of optimal streaks. In particular, it is shown that streaks can be approximately described in terms of the streamwise evolution of the scalar amplitudes of just three one-dimensional modes, providing the wall normal profiles of the streamwise velocity and two combinations of the cross flow velocity components; the scalar amplitudes obey a singular system of three ordinary differential equations (involving only two degrees of freedom), which approximates well the streamwise evolution of the general streaks.

Order 10 4 speedup in global linear instability analysis using matrix formation

A unified solution framework is presented for one-, two- or three-dimensional complex non-symmetric eigenvalue problems, respectively governing linear modal instability of incompressible fluid flows in rectangular domains having two, one or no homogeneous spatial directions. The solution algorithm is based on subspace iteration in which the spatial discretization matrix is formed, stored and inverted serially. Results delivered by spectral collocation based on the Chebyshev-Gauss-Lobatto (CGL) points and a suite of high-order finite-difference methods comprising the previously employed for this type of work Dispersion-Relation-Preserving (DRP) and Padé finite-difference schemes, as well as the Summationby- parts (SBP) and the new high-order finite-difference scheme of order q (FD-q) have been compared from the point of view of accuracy and efficiency in standard validation cases of temporal local and BiGlobal linear instability. The FD-q method has been found to significantly outperform all other finite difference schemes in solving classic linear local, BiGlobal, and TriGlobal eigenvalue problems, as regards both memory and CPU time requirements. Results shown in the present study disprove the paradigm that spectral methods are superior to finite difference methods in terms of computational cost, at equal accuracy, FD-q spatial discretization delivering a speedup of ð (10 4). Consequently, accurate solutions of the three-dimensional (TriGlobal) eigenvalue problems may be solved on typical desktop computers with modest computational effort.

Coronal fluid-dynamics in laser fusion

The fluid-dynamics of the corona ejected by laser-fusion targets in the direct-drive approach (thermal radiation and atomic physics unimportant) is discussed. A two-fluid model involves inverse bremsstrahlung absorption, refraction, different ion and electron temperatures with energy exchange, different ion and electron velocities and magnetic field generation, and their effect on ion-electron friction and heat flux. Four dimensionless parameters determine coronal regimes for one-dimensional flows under uniform irradiation. One additional parameter is involved in two-dimensional problems,including the stability of one-dimensional flows, and the smoothing of nonuniform driving.

Contributions to Bayesian network learning with applications to neuroscience

Neuronal morphology is a key feature in the study of brain circuits, as it is highly related to information processing and functional identification. Neuronal morphology affects the process of integration of inputs from other neurons and determines the neurons which receive the output of the neurons. Different parts of the neurons can operate semi-independently according to the spatial location of the synaptic connections. As a result, there is considerable interest in the analysis of the microanatomy of nervous cells since it constitutes an excellent tool for better understanding cortical function. However, the morphologies, molecular features and electrophysiological properties of neuronal cells are extremely variable. Except for some special cases, this variability makes it hard to find a set of features that unambiguously define a neuronal type. In addition, there are distinct types of neurons in particular regions of the brain. This morphological variability makes the analysis and modeling of neuronal morphology a challenge. Uncertainty is a key feature in many complex real-world problems. Probability theory provides a framework for modeling and reasoning with uncertainty. Probabilistic graphical models combine statistical theory and graph theory to provide a tool for managing domains with uncertainty. In particular, we focus on Bayesian networks, the most commonly used probabilistic graphical model. In this dissertation, we design new methods for learning Bayesian networks and apply them to the problem of modeling and analyzing morphological data from neurons. The morphology of a neuron can be quantified using a number of measurements, e.g., the length of the dendrites and the axon, the number of bifurcations, the direction of the dendrites and the axon, etc. These measurements can be modeled as discrete or continuous data. The continuous data can be linear (e.g., the length or the width of a dendrite) or directional (e.g., the direction of the axon). These data may follow complex probability distributions and may not fit any known parametric distribution. Modeling this kind of problems using hybrid Bayesian networks with discrete, linear and directional variables poses a number of challenges regarding learning from data, inference, etc. In this dissertation, we propose a method for modeling and simulating basal dendritic trees from pyramidal neurons using Bayesian networks to capture the interactions between the variables in the problem domain. A complete set of variables is measured from the dendrites, and a learning algorithm is applied to find the structure and estimate the parameters of the probability distributions included in the Bayesian networks. Then, a simulation algorithm is used to build the virtual dendrites by sampling values from the Bayesian networks, and a thorough evaluation is performed to show the model’s ability to generate realistic dendrites. In this first approach, the variables are discretized so that discrete Bayesian networks can be learned and simulated. Then, we address the problem of learning hybrid Bayesian networks with different kinds of variables. Mixtures of polynomials have been proposed as a way of representing probability densities in hybrid Bayesian networks. We present a method for learning mixtures of polynomials approximations of one-dimensional, multidimensional and conditional probability densities from data. The method is based on basis spline interpolation, where a density is approximated as a linear combination of basis splines. The proposed algorithms are evaluated using artificial datasets. We also use the proposed methods as a non-parametric density estimation technique in Bayesian network classifiers. Next, we address the problem of including directional data in Bayesian networks. These data have some special properties that rule out the use of classical statistics. Therefore, different distributions and statistics, such as the univariate von Mises and the multivariate von Mises–Fisher distributions, should be used to deal with this kind of information. In particular, we extend the naive Bayes classifier to the case where the conditional probability distributions of the predictive variables given the class follow either of these distributions. We consider the simple scenario, where only directional predictive variables are used, and the hybrid case, where discrete, Gaussian and directional distributions are mixed. The classifier decision functions and their decision surfaces are studied at length. Artificial examples are used to illustrate the behavior of the classifiers. The proposed classifiers are empirically evaluated over real datasets. We also study the problem of interneuron classification. An extensive group of experts is asked to classify a set of neurons according to their most prominent anatomical features. A web application is developed to retrieve the experts’ classifications. We compute agreement measures to analyze the consensus between the experts when classifying the neurons. Using Bayesian networks and clustering algorithms on the resulting data, we investigate the suitability of the anatomical terms and neuron types commonly used in the literature. Additionally, we apply supervised learning approaches to automatically classify interneurons using the values of their morphological measurements. Then, a methodology for building a model which captures the opinions of all the experts is presented. First, one Bayesian network is learned for each expert, and we propose an algorithm for clustering Bayesian networks corresponding to experts with similar behaviors. Then, a Bayesian network which represents the opinions of each group of experts is induced. Finally, a consensus Bayesian multinet which models the opinions of the whole group of experts is built. A thorough analysis of the consensus model identifies different behaviors between the experts when classifying the interneurons in the experiment. A set of characterizing morphological traits for the neuronal types can be defined by performing inference in the Bayesian multinet. These findings are used to validate the model and to gain some insights into neuron morphology. Finally, we study a classification problem where the true class label of the training instances is not known. Instead, a set of class labels is available for each instance. This is inspired by the neuron classification problem, where a group of experts is asked to individually provide a class label for each instance. We propose a novel approach for learning Bayesian networks using count vectors which represent the number of experts who selected each class label for each instance. These Bayesian networks are evaluated using artificial datasets from supervised learning problems. Resumen La morfología neuronal es una característica clave en el estudio de los circuitos cerebrales, ya que está altamente relacionada con el procesado de información y con los roles funcionales. La morfología neuronal afecta al proceso de integración de las señales de entrada y determina las neuronas que reciben las salidas de otras neuronas. Las diferentes partes de la neurona pueden operar de forma semi-independiente de acuerdo a la localización espacial de las conexiones sinápticas. Por tanto, existe un interés considerable en el análisis de la microanatomía de las células nerviosas, ya que constituye una excelente herramienta para comprender mejor el funcionamiento de la corteza cerebral. Sin embargo, las propiedades morfológicas, moleculares y electrofisiológicas de las células neuronales son extremadamente variables. Excepto en algunos casos especiales, esta variabilidad morfológica dificulta la definición de un conjunto de características que distingan claramente un tipo neuronal. Además, existen diferentes tipos de neuronas en regiones particulares del cerebro. La variabilidad neuronal hace que el análisis y el modelado de la morfología neuronal sean un importante reto científico. La incertidumbre es una propiedad clave en muchos problemas reales. La teoría de la probabilidad proporciona un marco para modelar y razonar bajo incertidumbre. Los modelos gráficos probabilísticos combinan la teoría estadística y la teoría de grafos con el objetivo de proporcionar una herramienta con la que trabajar bajo incertidumbre. En particular, nos centraremos en las redes bayesianas, el modelo más utilizado dentro de los modelos gráficos probabilísticos. En esta tesis hemos diseñado nuevos métodos para aprender redes bayesianas, inspirados por y aplicados al problema del modelado y análisis de datos morfológicos de neuronas. La morfología de una neurona puede ser cuantificada usando una serie de medidas, por ejemplo, la longitud de las dendritas y el axón, el número de bifurcaciones, la dirección de las dendritas y el axón, etc. Estas medidas pueden ser modeladas como datos continuos o discretos. A su vez, los datos continuos pueden ser lineales (por ejemplo, la longitud o la anchura de una dendrita) o direccionales (por ejemplo, la dirección del axón). Estos datos pueden llegar a seguir distribuciones de probabilidad muy complejas y pueden no ajustarse a ninguna distribución paramétrica conocida. El modelado de este tipo de problemas con redes bayesianas híbridas incluyendo variables discretas, lineales y direccionales presenta una serie de retos en relación al aprendizaje a partir de datos, la inferencia, etc. En esta tesis se propone un método para modelar y simular árboles dendríticos basales de neuronas piramidales usando redes bayesianas para capturar las interacciones entre las variables del problema. Para ello, se mide un amplio conjunto de variables de las dendritas y se aplica un algoritmo de aprendizaje con el que se aprende la estructura y se estiman los parámetros de las distribuciones de probabilidad que constituyen las redes bayesianas. Después, se usa un algoritmo de simulación para construir dendritas virtuales mediante el muestreo de valores de las redes bayesianas. Finalmente, se lleva a cabo una profunda evaluaci ón para verificar la capacidad del modelo a la hora de generar dendritas realistas. En esta primera aproximación, las variables fueron discretizadas para poder aprender y muestrear las redes bayesianas. A continuación, se aborda el problema del aprendizaje de redes bayesianas con diferentes tipos de variables. Las mixturas de polinomios constituyen un método para representar densidades de probabilidad en redes bayesianas híbridas. Presentamos un método para aprender aproximaciones de densidades unidimensionales, multidimensionales y condicionales a partir de datos utilizando mixturas de polinomios. El método se basa en interpolación con splines, que aproxima una densidad como una combinación lineal de splines. Los algoritmos propuestos se evalúan utilizando bases de datos artificiales. Además, las mixturas de polinomios son utilizadas como un método no paramétrico de estimación de densidades para clasificadores basados en redes bayesianas. Después, se estudia el problema de incluir información direccional en redes bayesianas. Este tipo de datos presenta una serie de características especiales que impiden el uso de las técnicas estadísticas clásicas. Por ello, para manejar este tipo de información se deben usar estadísticos y distribuciones de probabilidad específicos, como la distribución univariante von Mises y la distribución multivariante von Mises–Fisher. En concreto, en esta tesis extendemos el clasificador naive Bayes al caso en el que las distribuciones de probabilidad condicionada de las variables predictoras dada la clase siguen alguna de estas distribuciones. Se estudia el caso base, en el que sólo se utilizan variables direccionales, y el caso híbrido, en el que variables discretas, lineales y direccionales aparecen mezcladas. También se estudian los clasificadores desde un punto de vista teórico, derivando sus funciones de decisión y las superficies de decisión asociadas. El comportamiento de los clasificadores se ilustra utilizando bases de datos artificiales. Además, los clasificadores son evaluados empíricamente utilizando bases de datos reales. También se estudia el problema de la clasificación de interneuronas. Desarrollamos una aplicación web que permite a un grupo de expertos clasificar un conjunto de neuronas de acuerdo a sus características morfológicas más destacadas. Se utilizan medidas de concordancia para analizar el consenso entre los expertos a la hora de clasificar las neuronas. Se investiga la idoneidad de los términos anatómicos y de los tipos neuronales utilizados frecuentemente en la literatura a través del análisis de redes bayesianas y la aplicación de algoritmos de clustering. Además, se aplican técnicas de aprendizaje supervisado con el objetivo de clasificar de forma automática las interneuronas a partir de sus valores morfológicos. A continuación, se presenta una metodología para construir un modelo que captura las opiniones de todos los expertos. Primero, se genera una red bayesiana para cada experto y se propone un algoritmo para agrupar las redes bayesianas que se corresponden con expertos con comportamientos similares. Después, se induce una red bayesiana que modela la opinión de cada grupo de expertos. Por último, se construye una multired bayesiana que modela las opiniones del conjunto completo de expertos. El análisis del modelo consensuado permite identificar diferentes comportamientos entre los expertos a la hora de clasificar las neuronas. Además, permite extraer un conjunto de características morfológicas relevantes para cada uno de los tipos neuronales mediante inferencia con la multired bayesiana. Estos descubrimientos se utilizan para validar el modelo y constituyen información relevante acerca de la morfología neuronal. Por último, se estudia un problema de clasificación en el que la etiqueta de clase de los datos de entrenamiento es incierta. En cambio, disponemos de un conjunto de etiquetas para cada instancia. Este problema está inspirado en el problema de la clasificación de neuronas, en el que un grupo de expertos proporciona una etiqueta de clase para cada instancia de manera individual. Se propone un método para aprender redes bayesianas utilizando vectores de cuentas, que representan el número de expertos que seleccionan cada etiqueta de clase para cada instancia. Estas redes bayesianas se evalúan utilizando bases de datos artificiales de problemas de aprendizaje supervisado.

Vibro-acoustic analysis of spacecraft structures with thin air layers

Esta Tesis presenta un estudio sobre el comportamiento vibroacústico de estructuras espaciales que incluyen capas de aire delgadas, así como sobre su modelización numérica. Las capas de aire pueden constituir un elemento fundamental en estos sistemas, como paneles solares plegados, que se consideran el caso de estudio en este trabajo. Para evaluar la influencia de las capas de aire en la respuesta dinámica del sistema se presenta el uso de modelos unidimensionales. La modelización de estos sistemas se estudia para los rangos de baja y alta frecuencia. En el rango de baja frecuencia se propone un conjunto de estrategias de simulación basadas en técnicas numéricas que se utilizan habitualmente en la industria aeroespacial para facilitar la aplicación de los resultados de la Tesis en los modelos numéricos actuales. Los resultados muestran el importante papel de las capas de aire en la respuesta del sistema. El uso de modelos basados en elementos finitos o de contorno para estos elementos proporciona resultados equivalentes aunque la aplicabilidad de estos últimos puede estar condicionada por la geometría del problema. Se estudia asimismo el uso del Análisis Estadístico de la Energía (SEA) para estos elementos. Una de las estrategias de simulación propuestas, que incluye una formulación energética para el aire que rodea a la estructura, se propone como estimador preliminar de la respuesta del sistema y sus frecuencias propias. Para el rango de alta frecuencia, se estudia la influencia de la definición del propio modelo SEA. Se presenta el uso de técnicas de reducción para determinar una matriz de pérdidas SEA reducida para definiciones incompletas del sistema (si algún elemento que interactúa con el resto no se incluye en el modelo). Esta nueva matriz tiene en cuenta la contribución de las subestructuras que no se consideran parte del modelo y que suelen ignorarse en el procedimiento habitual para reducir el tamaño del mismo. Esta matriz permite también analizar sistemas que incluyen algún componente con problemas de accesibilidad para medir su respuesta. Respecto a la determinación de los factores de pérdidas del sistema, se presenta una metodología que permite abordar casos en los que el método usual, el Método de Inyección de Potencia (PIM), no puede usarse. Se presenta un conjunto de métodos basados en la técnicas de optimización y de actualización de modelos para casos en los que no se puede medir la respuesta de todos los elementos del sistema y también para casos en los que no todos los elementos pueden ser excitados, abarcando un conjunto de casos más amplio que el abordable con el PIM. Para ambos rangos de frecuencia se presentan diferentes casos de análisis: modelos numéricos para validar los métodos propuestos y un panel solar plegado como caso experimental que pone de manifiesto la aplicación práctica de los métodos presentados en la Tesis. ABSTRACT This Thesis presents an study on the vibro-acoustic behaviour of spacecraft structures with thin air layers and their numerical modelling. The air layers can play a key role in these systems as solar wings in folded configuration that constitute the study case for this Thesis. A method based on one-dimensional models is presented to assess the influence of the air layers in the dynamic response of the system. The modelling of such systems is studied for low and high frequency ranges. In the low frequency range a set of modelling strategies are proposed based on numerical techniques used in the industry to facilitate the application of the results in the current numerical models. Results show the active role of the air layers in the system response and their great level of influence. The modelling of these elements by means of Finite Elements (FE) and Boundary Elements (BE) provide equivalent results although the applicability of BE models can be conditioned by the geometry of the problem. The use of Statistical Energy Analysis (SEA) for these systems is also presented. Good results on the system response are found for models involving SEA beyond the usual applicability limit. A simulation strategy, involving energetic formulation for the surrounding fluid is proposed as fast preliminary approach for the system response and the coupled eigenfrequencies. For the high frequency range, the influence of the definition of the SEA model is presented. Reduction techniques are used to determine a Reduced SEA Loss Matrix if the system definition is not complete and some elements, which interact with the rest, are not included. This new matrix takes into account the contribution of the subsystems not considered that are neglected in the usual approach for decreasing the size of the model. It also allows the analysis of systems with accessibility restrictions on some element in order to measure its response. Regarding the determination of the loss factors of a system, a methodology is presented for cases in which the usual Power Injection Method (PIM) can not be applied. A set of methods are presented for cases in which not all the subsystem responses can be measured or not all the subsystems can be excited, as solar wings in folded configuration. These methods, based on error minimising and model updating techniques can be used to calculate the system loss factors in a set of cases wider than the PIM’s. For both frequency ranges, different test problems are analysed: Numerical models are studied to validate the methods proposed; an experimental case consisting in an actual solar wing is studied on both frequency ranges to highlight the industrial application of the new methods presented in the Thesis.