Sapientia, 1972, Vol. XXVII, nº 104 (Índice general)

Contenido: Índice general de autores y materias. Año I, 1946 – Año XXV, 1970

Sequential decision making in general state space models

Many problems in control and signal processing can be formulated as sequential decision problems for general state space models. However, except for some simple models one cannot obtain analytical solutions and has to resort to approximation. In this thesis, we have investigated problems where Sequential Monte Carlo (SMC) methods can be combined with a gradient based search to provide solutions to online optimisation problems. We summarise the main contributions of the thesis as follows. Chapter 4 focuses on solving the sensor scheduling problem when cast as a controlled Hidden Markov Model. We consider the case in which the state, observation and action spaces are continuous. This general case is important as it is the natural framework for many applications. In sensor scheduling, our aim is to minimise the variance of the estimation error of the hidden state with respect to the action sequence. We present a novel SMC method that uses a stochastic gradient algorithm to find optimal actions. This is in contrast to existing works in the literature that only solve approximations to the original problem. In Chapter 5 we presented how an SMC can be used to solve a risk sensitive control problem. We adopt the use of the Feynman-Kac representation of a controlled Markov chain flow and exploit the properties of the logarithmic Lyapunov exponent, which lead to a policy gradient solution for the parameterised problem. The resulting SMC algorithm follows a similar structure with the Recursive Maximum Likelihood(RML) algorithm for online parameter estimation. In Chapters 6, 7 and 8, dynamic Graphical models were combined with with state space models for the purpose of online decentralised inference. We have concentrated more on the distributed parameter estimation problem using two Maximum Likelihood techniques, namely Recursive Maximum Likelihood (RML) and Expectation Maximization (EM). The resulting algorithms can be interpreted as an extension of the Belief Propagation (BP) algorithm to compute likelihood gradients. In order to design an SMC algorithm, in Chapter 8 uses a nonparametric approximations for Belief Propagation. The algorithms were successfully applied to solve the sensor localisation problem for sensor networks of small and medium size.

Zoología general

Es una asignatura básica en la formación del ingeniero en Recursos Naturales Renovables y tiene como precedencia los conocimientos de las asignaturas de Biología; Introducción a los Recursos Naturales; Botánica Básica y Ecología. Además sirve de base a diferentes asignaturas tanto básicas como específicas, ya que en ella se imparten conocimientos técnicos al estudiante con el fin de que pueda desarrollarse de manera satisfactoria en otros cursos afines a su carrera. La zoología brinda conocimientos, para poder analizar, comprender e interpretar procesos que favorecen en parte el desarrollo de un hábitat adecuado, además se estudia de manera general a las principales especies animales que causan daño o beneficio directo e indirectamente a las distintas especies animales y vegetales. Lo mismo que aquellas que por su naturaleza de hábito alimenticio contribuyen a la diseminación de frutos y semillas, también a las que se catalogan como plagas en los sistemas agroforestales.

An overview of sequential Monte Carlo methods for parameter estimation in general state-space models

Nonlinear non-Gaussian state-space models arise in numerous applications in control and signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters, are numerical techniques based on Importance Sampling for solving the optimal state estimation problem. The task of calibrating the state-space model is an important problem frequently faced by practitioners and the observed data may be used to estimate the parameters of the model. The aim of this paper is to present a comprehensive overview of SMC methods that have been proposed for this task accompanied with a discussion of their advantages and limitations.

Waverider configurations derived from general conical flowfields

A method based on the computational fluid dynamics (CFD) is presented for a flexible waverider's design. The generating bodies of this method could be any cones. In addition, either the leading edge or the profile of the scramjet's inlet is used as the waverider's definition curve, parameterized by the quadric function, the sigmoid function or the B-spline function. Furthermore, several numerical examples are carried out to validate the method and the relevant codes. The CFD results of the configurations show that all the designs are successful. Moreover, primary suggestions are proposed for practical design by comparing the geometrical and aerodynamic performances of the cone-derived waveriders at Mach 6.

Garci Sánchez de Badajoz y la propulsión del Cancionero General

Integran este número de la revista ponencias presentadas en Studia Hispanica Medievalia VIII : Actas de las X Jornadas Internacionales de Literatura Española Medieval, 2011, y de Homenaje al Quinto Centenario del Cancionero General de Hernando del Castillo.

A general theory on media with randomly distributed inclusions .1. The average field behaviors

In this paper, a theory is developed to calculate the average strain field in the materials with randomly distributed inclusions. Many previous researches investigating the average field behaviors were based upon Mori and Tanaka's idea. Since they were restricted to studying those materials with uniform distributions of inclusions they did not need detailed statistical information of random microstructures, and could use the volume average to replace the ensemble average. To study more general materials with randomly distributed inclusions, the number density function is introduced in formulating the average field equation in this research. Both uniform and nonuniform distributions of inclusions are taken into account in detail.

A ground hydrologic model with inclusion of a layer of vegetation canopy that can interact with general-circulation model

In this paper, a ground hydrologic model(GHM) is presented in which the vapor, heat and momentum exchanges between ground surface covers (including vegetation canopy) and atmosphere is described more realistically. The model is used to simulate three sets of field data and results from the numerical simulation agree with the field data well. GHM has been tested using input data generated by general circulation model (GCM) runs for both the North American regions and the Chinese regions, The results from GHM are quite different from those of GHMs in GCMs. It shows that a more active concerted effort on the land surface process study to provide a physically realistic GHM for predicting the exchange between land and atmosphere is important and necessary.