Simulation of impulse response for indoor visible light communications using 3D CAD models

n this article, a tool for simulating the channel impulse response for indoor visible light communications using 3D computer-aided design (CAD) models is presented. The simulation tool is based on a previous Monte Carlo ray-tracing algorithm for indoor infrared channel estimation, but including wavelength response evaluation. The 3D scene, or the simulation environment, can be defined using any CAD software in which the user specifies, in addition to the setting geometry, the reflection characteristics of the surface materials as well as the structures of the emitters and receivers involved in the simulation. Also, in an effort to improve the computational efficiency, two optimizations are proposed. The first one consists of dividing the setting into cubic regions of equal size, which offers a calculation improvement of approximately 50% compared to not dividing the 3D scene into sub-regions. The second one involves the parallelization of the simulation algorithm, which provides a computational speed-up proportional to the number of processors used.

Simulation of thermal performance of glazing in architecture using scale models = Simulación del comportamiento térmico de los acristalamientos en la arquitectura mediante modelos a escala

El propósito de esta tesis es estudiar la aproximación a los fenómenos de transporte térmico en edificación acristalada a través de sus réplicas a escala. La tarea central de esta tesis es, por lo tanto, la comparación del comportamiento térmico de modelos a escala con el correspondiente comportamiento térmico del prototipo a escala real. Los datos principales de comparación entre modelo y prototipo serán las temperaturas. En el primer capítulo del Estado del Arte de esta tesis se hará un recorrido histórico por los usos de los modelos a escala desde la antigüedad hasta nuestro días. Dentro de éste, en el Estado de la Técnica, se expondrán los beneficios que tiene su empleo y las dificultades que conllevan. A continuación, en el Estado de la Investigación de los modelos a escala, se analizarán artículos científicos y tesis. Precisamente, nos centraremos en aquellos modelos a escala que son funcionales. Los modelos a escala funcionales son modelos a escala que replican, además, una o algunas de las funciones de sus prototipos. Los modelos a escala pueden estar distorsionados o no. Los modelos a escala distorsionados son aquellos con cambios intencionados en las dimensiones o en las características constructivas para la obtención de una respuesta específica por ejemplo, replicar el comportamiento térmico. Los modelos a escala sin distorsión, o no distorsionados, son aquellos que mantienen, en la medida de lo posible, las proporciones dimensionales y características constructivas de sus prototipos de referencia. Estos modelos a escala funcionales y no distorsionados son especialmente útiles para los arquitectos ya que permiten a la vez ser empleados como elementos funcionales de análisis y como elementos de toma de decisiones en el diseño constructivo. A pesar de su versatilidad, en general, se observará que se han utilizado muy poco estos modelos a escala funcionales sin distorsión para el estudio del comportamiento térmico de la edificación. Posteriormente, se expondrán las teorías para el análisis de los datos térmicos recogidos de los modelos a escala y su aplicabilidad a los correspondientes prototipos a escala real. Se explicarán los experimentos llevados a cabo, tanto en laboratorio como a intemperie. Se han realizado experimentos con modelos sencillos cúbicos a diferentes escalas y sometidos a las mismas condiciones ambientales. De estos modelos sencillos hemos dado el salto a un modelo reducido de una edificación acristalada relativamente sencilla. Los experimentos consisten en ensayos simultáneos a intemperie del prototipo a escala real y su modelo reducido del Taller de Prototipos de la Escuela Técnica Superior de Arquitectura de Madrid (ETSAM). Para el análisis de los datos experimentales hemos aplicado las teorías conocidas, tanto comparaciones directas como el empleo del análisis dimensional. Finalmente, las simulaciones nos permiten comparaciones flexibles con los datos experimentales, por ese motivo, hemos utilizado tanto programas comerciales como un algoritmo de simulación desarrollado ad hoc para esta investigación. Finalmente, exponemos la discusión y las conclusiones de esta investigación. Abstract The purpose of this thesis is to study the approximation to phenomena of heat transfer in glazed buildings through their scale replicas. The central task of this thesis is, therefore, the comparison of the thermal performance of scale models without distortion with the corresponding thermal performance of their full-scale prototypes. Indoor air temperatures of the scale model and the corresponding prototype are the data to be compared. In the first chapter on the State of the Art, it will be shown a broad vision, consisting of a historic review of uses of scale models, from antiquity to our days. In the section State of the Technique, the benefits and difficulties associated with their implementation are presented. Additionally, in the section State of the Research, current scientific papers and theses on scale models are reviewed. Specifically, we focus on functional scale models. Functional scale models are scale models that replicate, additionally, one or some of the functions of their corresponding prototypes. Scale models can be distorted or not. Scale models with distortion are considered scale models with intentional changes, on one hand, in dimensions scaled unevenly and, on the other hand, in constructive characteristics or materials, in order to get a specific performance for instance, a specific thermal performance. Consequently, scale models without distortion, or undistorted scale models scaled evenly, are those replicating, to the extent possible, without distortion, the dimensional proportions and constructive configurations of their prototypes of reference. These undistorted and functional scale models are especially useful for architects because they can be used, simultaneously, as functional elements of analysis and as decision-making elements during the design. Although they are versatile, in general, it is remarkable that these types of models are used very little for the study of the thermal performance of buildings. Subsequently, the theories related to the analysis of the experimental thermal data collected from the scale models and their applicability to the corresponding full-scale prototypes, will be explained. Thereafter, the experiments in laboratory and at outdoor conditions are detailed. Firstly, experiments carried out with simple cube models at different scales are explained. The prototype larger in size and the corresponding undistorted scale model have been subjected to same environmental conditions in every experimental test. Secondly, a step forward is taken carrying out some simultaneous experimental tests of an undistorted scale model, replica of a relatively simple lightweight and glazed building construction. This experiment consists of monitoring the undistorted scale model of the prototype workshop located in the School of Architecture (ETSAM) of the Technical University of Madrid (UPM). For the analysis of experimental data, known related theories and resources are applied, such as, direct comparisons, statistical analyses, Dimensional Analysis and last, but not least important, simulations. Simulations allow us, specifically, flexible comparisons with experimental data. Here, apart the use of the simulation software EnergyPlus, a simulation algorithm is developed ad hoc for this research. Finally, the discussion and conclusions of this research are exposed.

Agent-based modelling for cement hydration

The Agent-Based Modelling and simulation (ABM) is a rather new approach for studying complex systems withinteracting autonomous agents that has lately undergone great growth in various fields such as biology, physics, social science, economics and business. Efforts to model and simulate the highly complex cement hydration process have been made over the past 40 years, with the aim of predicting the performance of concrete and designing innovative and enhanced cementitious materials. The ABM presented here - based on previous work - focuses on the early stages of cement hydration by modelling the physical-chemical processes at the particle level. The model considers the cement hydration process as a time and 3D space system, involving multiple diffusing and reacting species of spherical particles. Chemical reactions are simulated by adaptively selecting discrete stochastic simulation for the appropriate reaction, whenever that is necessary. Interactions between particles are also considered. The model has been inspired by reported cellular automata?s approach which provides detailed predictions of cement microstructure at the expense of significant computational difficulty. The ABM approach herein seeks to bring about an optimal balance between accuracy and computational efficiency.

An integral model for natural regeneration of Pinus pinea L. in the Northern Plateau (Spain) = Modelo integral de regeneración natural para Pinus pinea L. en la Meseta Norte (España)

Natural regeneration is an ecological key-process that makes plant persistence possible and, consequently, it constitutes an essential element of sustainable forest management. In this respect, natural regeneration in even-aged stands of Pinus pinea L. located in the Spanish Northern Plateau has not always been successfully achieved despite over a century of pine nut-based management. As a result, natural regeneration has recently become a major concern for forest managers when we are living a moment of rationalization of investment in silviculture. The present dissertation is addressed to provide answers to forest managers on this topic through the development of an integral regeneration multistage model for P. pinea stands in the region. From this model, recommendations for natural regeneration-based silviculture can be derived under present and future climate scenarios. Also, the model structure makes it possible to detect the likely bottlenecks affecting the process. The integral model consists of five submodels corresponding to each of the subprocesses linking the stages involved in natural regeneration (seed production, seed dispersal, seed germination, seed predation and seedling survival). The outputs of the submodels represent the transitional probabilities between these stages as a function of climatic and stand variables, which in turn are representative of the ecological factors driving regeneration. At subprocess level, the findings of this dissertation should be interpreted as follows. The scheduling of the shelterwood system currently conducted over low density stands leads to situations of dispersal limitation since the initial stages of the regeneration period. Concerning predation, predator activity appears to be only limited by the occurrence of severe summer droughts and masting events, the summer resulting in a favourable period for seed survival. Out of this time interval, predators were found to almost totally deplete seed crops. Given that P. pinea dissemination occurs in summer (i.e. the safe period against predation), the likelihood of a seed to not be destroyed is conditional to germination occurrence prior to the intensification of predator activity. However, the optimal conditions for germination seldom take place, restraining emergence to few days during the fall. Thus, the window to reach the seedling stage is narrow. In addition, the seedling survival submodel predicts extremely high seedling mortality rates and therefore only some individuals from large cohorts will be able to persist. These facts, along with the strong climate-mediated masting habit exhibited by P. pinea, reveal that viii the overall probability of establishment is low. Given this background, current management –low final stand densities resulting from intense thinning and strict felling schedules– conditions the occurrence of enough favourable events to achieve natural regeneration during the current rotation time. Stochastic simulation and optimisation computed through the integral model confirm this circumstance, suggesting that more flexible and progressive regeneration fellings should be conducted. From an ecological standpoint, these results inform a reproductive strategy leading to uneven-aged stand structures, in full accordance with the medium shade-tolerant behaviour of the species. As a final remark, stochastic simulations performed under a climate-change scenario show that regeneration in the species will not be strongly hampered in the future. This resilient behaviour highlights the fundamental ecological role played by P. pinea in demanding areas where other tree species fail to persist.

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.

A simple genetic algorithm for calibration of stochastic rock discontinuity networks

Este artículo propone un método para llevar a cabo la calibración de las familias de discontinuidades en macizos rocosos. We present a novel approach for calibration of stochastic discontinuity network parameters based on genetic algorithms (GAs). To validate the approach, examples of application of the method to cases with known parameters of the original Poisson discontinuity network are presented. Parameters of the model are encoded as chromosomes using a binary representation, and such chromosomes evolve as successive generations of a randomly generated initial population, subjected to GA operations of selection, crossover and mutation. Such back-calculated parameters are employed to make assessments about the inference capabilities of the model using different objective functions with different probabilities of crossover and mutation. Results show that the predictive capabilities of GAs significantly depend on the type of objective function considered; and they also show that the calibration capabilities of the genetic algorithm can be acceptable for practical engineering applications, since in most cases they can be expected to provide parameter estimates with relatively small errors for those parameters of the network (such as intensity and mean size of discontinuities) that have the strongest influence on many engineering applications.

An approach to operational modal analysis using the expectation maximization algorithm

This paper presents the Expectation Maximization algorithm (EM) applied to operational modal analysis of structures. The EM algorithm is a general-purpose method for maximum likelihood estimation (MLE) that in this work is used to estimate state space models. As it is well known, the MLE enjoys some optimal properties from a statistical point of view, which make it very attractive in practice. However, the EM algorithm has two main drawbacks: its slow convergence and the dependence of the solution on the initial values used. This paper proposes two different strategies to choose initial values for the EM algorithm when used for operational modal analysis: to begin with the parameters estimated by Stochastic Subspace Identification method (SSI) and to start using random points. The effectiveness of the proposed identification method has been evaluated through numerical simulation and measured vibration data in the context of a benchmark problem. Modal parameters (natural frequencies, damping ratios and mode shapes) of the benchmark structure have been estimated using SSI and the EM algorithm. On the whole, the results show that the application of the EM algorithm starting from the solution given by SSI is very useful to identify the vibration modes of a structure, discarding the spurious modes that appear in high order models and discovering other hidden modes. Similar results are obtained using random starting values, although this strategy allows us to analyze the solution of several starting points what overcome the dependence on the initial values used.

Maximum Likelihood Estimation of Modal Parameters in Structures Using the Expectation Maximization Algorithm

This paper presents a time-domain stochastic system identification method based on maximum likelihood estimation (MLE) with the expectation maximization (EM) algorithm. The effectiveness of this structural identification method is evaluated through numerical simulation in the context of the ASCE benchmark problem on structural health monitoring. The benchmark structure is a four-story, two-bay by two-bay steel-frame scale model structure built in the Earthquake Engineering Research Laboratory at the University of British Columbia, Canada. This paper focuses on Phase I of the analytical benchmark studies. A MATLAB-based finite element analysis code obtained from the IASC-ASCE SHM Task Group web site is used to calculate the dynamic response of the prototype structure. A number of 100 simulations have been made using this MATLAB-based finite element analysis code in order to evaluate the proposed identification method. There are several techniques to realize system identification. In this work, stochastic subspace identification (SSI)method has been used for comparison. SSI identification method is a well known method and computes accurate estimates of the modal parameters. The principles of the SSI identification method has been introduced in the paper and next the proposed MLE with EM algorithm has been explained in detail. The advantages of the proposed structural identification method can be summarized as follows: (i) the method is based on maximum likelihood, that implies minimum variance estimates; (ii) EM is a computational simpler estimation procedure than other optimization algorithms; (iii) estimate more parameters than SSI, and these estimates are accurate. On the contrary, the main disadvantages of the method are: (i) EM algorithm is an iterative procedure and it consumes time until convergence is reached; and (ii) this method needs starting values for the parameters. Modal parameters (eigenfrequencies, damping ratios and mode shapes) of the benchmark structure have been estimated using both the SSI method and the proposed MLE + EM method. The numerical results show that the proposed method identifies eigenfrequencies, damping ratios and mode shapes reasonably well even in the presence of 10% measurement noises. These modal parameters are more accurate than the SSI estimated modal parameters.

A stochastic model updating method for parameter variability quantification based on response surface models and Monte Carlo simulation

Stochastic model updating must be considered for quantifying uncertainties inherently existing in real-world engineering structures. By this means the statistical properties,instead of deterministic values, of structural parameters can be sought indicating the parameter variability. However, the implementation of stochastic model updating is much more complicated than that of deterministic methods particularly in the aspects of theoretical complexity and low computational efficiency. This study attempts to propose a simple and cost-efficient method by decomposing a stochastic updating process into a series of deterministic ones with the aid of response surface models and Monte Carlo simulation. The response surface models are used as surrogates for original FE models in the interest of programming simplification, fast response computation and easy inverse optimization. Monte Carlo simulation is adopted for generating samples from the assumed or measured probability distributions of responses. Each sample corresponds to an individual deterministic inverse process predicting the deterministic values of parameters. Then the parameter means and variances can be statistically estimated based on all the parameter predictions by running all the samples. Meanwhile, the analysis of variance approach is employed for the evaluation of parameter variability significance. The proposed method has been demonstrated firstly on a numerical beam and then a set of nominally identical steel plates tested in the laboratory. It is found that compared with the existing stochastic model updating methods, the proposed method presents similar accuracy while its primary merits consist in its simple implementation and cost efficiency in response computation and inverse optimization.

Operational Modal Analysis using Expectation Maximization Algorithm

This paper presents a time-domain stochastic system identification method based on Maximum Likelihood Estimation and the Expectation Maximization algorithm. The effectiveness of this structural identification method is evaluated through numerical simulation in the context of the ASCE benchmark problem on structural health monitoring. Modal parameters (eigenfrequencies, damping ratios and mode shapes) of the benchmark structure have been estimated applying the proposed identification method to a set of 100 simulated cases. The numerical results show that the proposed method estimates all the modal parameters reasonably well in the presence of 30% measurement noise even. Finally, advantages and disadvantages of the method have been discussed.

A unified framework for linear function approximation of value functions in stochastic control

This paper contributes with a unified formulation that merges previ- ous analysis on the prediction of the performance ( value function ) of certain sequence of actions ( policy ) when an agent operates a Markov decision process with large state-space. When the states are represented by features and the value function is linearly approxi- mated, our analysis reveals a new relationship between two common cost functions used to obtain the optimal approximation. In addition, this analysis allows us to propose an efficient adaptive algorithm that provides an unbiased linear estimate. The performance of the pro- posed algorithm is illustrated by simulation, showing competitive results when compared with the state-of-the-art solutions.

Using the EM algorithm to estimate the state space model for OMAX

This paper presents a time-domain stochastic system identification method based on Maximum Likelihood Estimation and the Expectation Maximization algorithm that is applied to the estimation of modal parameters from system input and output data. The effectiveness of this structural identification method is evaluated through numerical simulation. Modal parameters (eigenfrequencies, damping ratios and mode shapes) of the simulated structure are estimated applying the proposed identification method to a set of 100 simulated cases. The numerical results show that the proposed method estimates the modal parameters with precision in the presence of 20% measurement noise even. Finally, advantages and disadvantages of the method have been discussed.

Quality-optimization algorithm based on stochastic dynamic programming for MPEG DASH video streaming

In contrast to traditional push-based protocols, adaptive streaming techniques like Dynamic Adaptive Streaming over HTTP (DASH) fix attention on the client, who dynamically requests different-quality portions of the content to cope with a limited and variable bandwidth but aiming at maximizing the quality perceived by the user. Since DASH adaptation logic at the client is not covered by the standard, we propose a solution based on Stochastic Dynamic Programming (SDP) techniques to find the optimal request policies that guarantee the users' Quality of Experience (QoE). Our algorithm is evaluated in a simulated streaming session and is compared with other adaptation approaches. The results show that our proposal outperforms them in terms of QoE, requesting higher qualities on average.

Energy and power aware scheduling algorithm modeling for battery powered embedded systems

Energy management has always been recognized as a challenge in mobile systems, especially in modern OS-based mobile systems where multi-functioning are widely supported. Nowadays, it is common for a mobile system user to run multiple applications simultaneously while having a target battery lifetime in mind for a specific application. Traditional OS-level power management (PM) policies make their best effort to save energy under performance constraint, but fail to guarantee a target lifetime, leaving the painful trading off between the total performance of applications and the target lifetime to the user itself. This thesis provides a new way to deal with the problem. It is advocated that a strong energy-aware PM scheme should first guarantee a user-specified battery lifetime to a target application by restricting the average power of those less important applications, and in addition to that, maximize the total performance of applications without harming the lifetime guarantee. As a support, energy, instead of CPU or transmission bandwidth, should be globally managed as the first-class resource by the OS. As the first-stage work of a complete PM scheme, this thesis presents the energy-based fair queuing scheduling, a novel class of energy-aware scheduling algorithms which, in combination with a mechanism of battery discharge rate restricting, systematically manage energy as the first-class resource with the objective of guaranteeing a user-specified battery lifetime for a target application in OS-based mobile systems. Energy-based fair queuing is a cross-application of the traditional fair queuing in the energy management domain. It assigns a power share to each task, and manages energy by proportionally serving energy to tasks according to their assigned power shares. The proportional energy use establishes proportional share of the system power among tasks, which guarantees a minimum power for each task and thus, avoids energy starvation on any task. Energy-based fair queuing treats all tasks equally as one type and supports periodical time-sensitive tasks by allocating each of them a share of system power that is adequate to meet the highest energy demand in all periods. However, an overly conservative power share is usually required to guarantee the meeting of all time constraints. To provide more effective and flexible support for various types of time-sensitive tasks in general purpose operating systems, an extra real-time friendly mechanism is introduced to combine priority-based scheduling into the energy-based fair queuing. Since a method is available to control the maximum time one time-sensitive task can run with priority, the power control and time-constraint meeting can be flexibly traded off. A SystemC-based test-bench is designed to assess the algorithms. Simulation results show the success of the energy-based fair queuing in achieving proportional energy use, time-constraint meeting, and a proper trading off between them. La gestión de energía en los sistema móviles está considerada hoy en día como un reto fundamental, notándose, especialmente, en aquellos terminales que utilizando un sistema operativo implementan múltiples funciones. Es común en los sistemas móviles actuales ejecutar simultaneamente diferentes aplicaciones y tener, para una de ellas, un objetivo de tiempo de uso de la batería. Tradicionalmente, las políticas de gestión de consumo de potencia de los sistemas operativos hacen lo que está en sus manos para ahorrar energía y satisfacer sus requisitos de prestaciones, pero no son capaces de proporcionar un objetivo de tiempo de utilización del sistema, dejando al usuario la difícil tarea de buscar un compromiso entre prestaciones y tiempo de utilización del sistema. Esta tesis, como contribución, proporciona una nueva manera de afrontar el problema. En ella se establece que un esquema de gestión de consumo de energía debería, en primer lugar, garantizar, para una aplicación dada, un tiempo mínimo de utilización de la batería que estuviera especificado por el usuario, restringiendo la potencia media consumida por las aplicaciones que se puedan considerar menos importantes y, en segundo lugar, maximizar las prestaciones globales sin comprometer la garantía de utilización de la batería. Como soporte de lo anterior, la energía, en lugar del tiempo de CPU o el ancho de banda, debería gestionarse globalmente por el sistema operativo como recurso de primera clase. Como primera fase en el desarrollo completo de un esquema de gestión de consumo, esta tesis presenta un algoritmo de planificación de encolado equitativo (fair queueing) basado en el consumo de energía, es decir, una nueva clase de algoritmos de planificación que, en combinación con mecanismos que restrinjan la tasa de descarga de una batería, gestionen de forma sistemática la energía como recurso de primera clase, con el objetivo de garantizar, para una aplicación dada, un tiempo de uso de la batería, definido por el usuario, en sistemas móviles empotrados. El encolado equitativo de energía es una extensión al dominio de la energía del encolado equitativo tradicional. Esta clase de algoritmos asigna una reserva de potencia a cada tarea y gestiona la energía sirviéndola de manera proporcional a su reserva. Este uso proporcional de la energía garantiza que cada tarea reciba una porción de potencia y evita que haya tareas que se vean privadas de recibir energía por otras con un comportamiento más ambicioso. Esta clase de algoritmos trata a todas las tareas por igual y puede planificar tareas periódicas en tiempo real asignando a cada una de ellas una reserva de potencia que es adecuada para proporcionar la mayor de las cantidades de energía demandadas por período. Sin embargo, es posible demostrar que sólo se consigue cumplir con los requisitos impuestos por todos los plazos temporales con reservas de potencia extremadamente conservadoras. En esta tesis, para proporcionar un soporte más flexible y eficiente para diferentes tipos de tareas de tiempo real junto con el resto de tareas, se combina un mecanismo de planificación basado en prioridades con el encolado equitativo basado en energía. En esta clase de algoritmos, gracias al método introducido, que controla el tiempo que se ejecuta con prioridad una tarea de tiempo real, se puede establecer un compromiso entre el cumplimiento de los requisitos de tiempo real y el consumo de potencia. Para evaluar los algoritmos, se ha diseñado en SystemC un banco de pruebas. Los resultados muestran que el algoritmo de encolado equitativo basado en el consumo de energía consigue el balance entre el uso proporcional a la energía reservada y el cumplimiento de los requisitos de tiempo real.

Sustainability of irrigated farming systems in a Tunisian region: A recursive stochastic programming analysis

The aim of this study was to evaluate the sustainability of farm irrigation systems in the Cébalat district in northern Tunisia. It addressed the challenging topic of sustainable agriculture through a bio-economic approach linking a biophysical model to an economic optimisation model. A crop growth simulation model (CropSyst) was used to build a database to determine the relationships between agricultural practices, crop yields and environmental effects (salt accumulation in soil and leaching of nitrates) in a context of high climatic variability. The database was then fed into a recursive stochastic model set for a 10-year plan that allowed analysing the effects of cropping patterns on farm income, salt accumulation and nitrate leaching. We assumed that the long-term sustainability of soil productivity might be in conflict with farm profitability in the short-term. Assuming a discount rate of 10% (for the base scenario), the model closely reproduced the current system and allowed to predict the degradation of soil quality due to long-term salt accumulation. The results showed that there was more accumulation of salt in the soil for the base scenario than for the alternative scenario (discount rate of 0%). This result was induced by applying a higher quantity of water per hectare for the alternative as compared to a base scenario. The results also showed that nitrogen leaching is very low for the two discount rates and all climate scenarios. In conclusion, the results show that the difference in farm income between the alternative and base scenarios increases over time to attain 45% after 10 years.