Sight distance studies on roads: influence of digital elevation models and roadside elements

Sight distance plays an important role in road traffic safety. Two types of Digital Elevation Models (DEMs) are utilized for the estimation of available sight distance in roads: Digital Terrain Models (DTMs) and Digital Surface Models (DSMs). DTMs, which represent the bare ground surface, are commonly used to determine available sight distance at the design stage. Additionally, the use of DSMs provides further information about elements by the roadsides such as trees, buildings, walls or even traffic signals which may reduce available sight distance. This document analyses the influence of three classes of DEMs in available sight distance estimation. For this purpose, diverse roads within the Region of Madrid (Spain) have been studied using software based on geographic information systems. The study evidences the influence of using each DEM in the outcome as well as the pros and cons of using each model.

Aprendizaje colaborativo en enseñanza a distancia: entorno genérico para configurar, realizar y analizar actividades en grupo

En esta tesis se ha profundizado en el estudio y desarrollo de modelos de soporte para el aprendizaje colaborativo a distancia, que ha permitido proponer una arquitectura fundamentada en los principios del paradigma CSCL (Computer Supported Collaborative Learning). La arquitectura propuesta aborda un tipo de problema concreto que requiere el uso de técnicas derivadas del Trabajo Colaborativo, la Inteligencia Artificial, Interfaces de Usuario así como ideas tomadas de la Pedagogía y la Psicología. Se ha diseñado una solución completa, abierta y genérica. La arquitectura aprovecha las nuevas tecnologías para lograr un sistema efectivo de apoyo a la educación a distancia. Está organizada en cuatro niveles: el de Configuración, el de Experiencia, el de Organización y el de Análisis. A partir de ella se ha implementado un sistema llamado DEGREE. En DEGREE, cada uno de los niveles de la arquitectura da lugar a un subsistema independiente pero relacionado con los otros. La aplicación saca partido del uso de espacios de trabajo estructurados. El subsistema Configurador de Experiencias permite definir los elementos de un espacio de trabajo y una experiencia y adaptarlos a cada tipo de usuario. El subsistema Manejador de Experiencias recoge las contribuciones de los usuarios para construir una solución conjunta de un problema. Las intervenciones de los alumnos se estructuran basándose en un grafo conversacional genérico. Además, se registran todas las acciones de los usuarios para representar explícitamente el proceso completo que lleva a la solución. Estos datos también se almacenan en una memoria común que constituye el subsistema llamado Memoria Organizativa de Experiencias. El subsistema Analizador estudia las intervenciones de los usuarios. Este análisis permite inferir conclusiones sobre la forma en que trabajan los grupos y sus actitudes frente a la colaboración, teniendo en cuenta además el conocimiento subjetivo del observador. El proceso de desarrollo en paralelo de la arquitectura y el sistema ha seguido un ciclo de refinamiento en cinco fases con sucesivas etapas de prototipado y evaluación formativa. Cada fase de este proceso se ha realizado con usuarios reales y se han considerado las opiniones de los usuarios para mejorar las funcionalidades de la arquitectura así como la interfaz del sistema. Esta aproximación ha permitido, además, comprobar la utilidad práctica y la validez de las propuestas que sustentan este trabajo.---ABSTRACT---In this thesis, we have studied in depth the development of support models for distance collaborative learning and subsequently devised an architecture based on the Computer Supported Collaborative Learning paradigm principles. The proposed architecture addresses a specific problem: coordinating groups of students to perform collaborative distance learning activities. Our approach uses Cooperative Work, Artificial Intelligence and Human-Computer Interaction techniques as well as some ideas from the fields of Pedagogy and Psychology. We have designed a complete, open and generic solution. Our architecture exploits the new information technologies to achieve an effective system for education purposes. It is organised into four levels: Configuration, Experience, Organisation and Reflection. This model has been implemented into a system called DEGREE. In DEGREE, each level of the architecture gives rise to an independent subsystem related to the other ones. The application benefits from the use of shared structured workspaces. The configuration subsystem allows customising the elements that define an experience and a workspace. The experience subsystem gathers the users' contributions to build joint solutions to a given problem. The students' interventions build up a structure based on a generic conversation graph. Moreover, all user actions are registered in order to represent explicitly the complete process for reaching the group solution. Those data are also stored into a common memory, which constitutes the organisation subsystem. The user interventions are studied by the reflection subsystem. This analysis allows us inferring conclusions about the way in which the group works and its attitudes towards collaboration. The inference process takes into account the observer's subjective knowledge. The process of developing both the architecture and the system in parallel has run through a five-pass cycle involving successive stages of prototyping and formative evaluation. At each stage of that process, we have considered the users' feedback for improving the architecture's functionalities as well as the system interface. This approach has allowed us to prove the usability and validity of our proposal.

Translating bus information into sign language for deaf people

This paper describes the application of language translation technologies for generating bus information in Spanish Sign Language (LSE: Lengua de Signos Española). In this work, two main systems have been developed: the first for translating text messages from information panels and the second for translating spoken Spanish into natural conversations at the information point of the bus company. Both systems are made up of a natural language translator (for converting a word sentence into a sequence of LSE signs), and a 3D avatar animation module (for playing back the signs). For the natural language translator, two technological approaches have been analyzed and integrated: an example-based strategy and a statistical translator. When translating spoken utterances, it is also necessary to incorporate a speech recognizer for decoding the spoken utterance into a word sequence, prior to the language translation module. This paper includes a detailed description of the field evaluation carried out in this domain. This evaluation has been carried out at the customer information office in Madrid involving both real bus company employees and deaf people. The evaluation includes objective measurements from the system and information from questionnaires. In the field evaluation, the whole translation presents an SER (Sign Error Rate) of less than 10% and a BLEU greater than 90%.

Autonomous Acquisition of Natural Situated Communication

An important part of human intelligence, both historically and operationally, is our ability to communicate. We learn how to communicate, and maintain our communicative skills, in a society of communicators – a highly effective way to reach and maintain proficiency in this complex skill. Principles that might allow artificial agents to learn language this way are in completely known at present – the multi-dimensional nature of socio-communicative skills are beyond every machine learning framework so far proposed. Our work begins to address the challenge of proposing a way for observation-based machine learning of natural language and communication. Our framework can learn complex communicative skills with minimal up-front knowledge. The system learns by incrementally producing predictive models of causal relationships in observed data, guided by goal-inference and reasoning using forward-inverse models. We present results from two experiments where our S1 agent learns human communication by observing two humans interacting in a realtime TV-style interview, using multimodal communicative gesture and situated language to talk about recycling of various materials and objects. S1 can learn multimodal complex language and multimodal communicative acts, a vocabulary of 100 words forming natural sentences with relatively complex sentence structure, including manual deictic reference and anaphora. S1 is seeded only with high-level information about goals of the interviewer and interviewee, and a small ontology; no grammar or other information is provided to S1 a priori. The agent learns the pragmatics, semantics, and syntax of complex utterances spoken and gestures from scratch, by observing the humans compare and contrast the cost and pollution related to recycling aluminum cans, glass bottles, newspaper, plastic, and wood. After 20 hours of observation S1 can perform an unscripted TV interview with a human, in the same style, without making mistakes.

Distributed Estimation of Distribution Algorithms for continuous optimization: how does the exchanged information influence their behavior?

One of the most promising areas in which probabilistic graphical models have shown an incipient activity is the field of heuristic optimization and, in particular, in Estimation of Distribution Algorithms. Due to their inherent parallelism, different research lines have been studied trying to improve Estimation of Distribution Algorithms from the point of view of execution time and/or accuracy. Among these proposals, we focus on the so-called distributed or island-based models. This approach defines several islands (algorithms instances) running independently and exchanging information with a given frequency. The information sent by the islands can be either a set of individuals or a probabilistic model. This paper presents a comparative study for a distributed univariate Estimation of Distribution Algorithm and a multivariate version, paying special attention to the comparison of two alternative methods for exchanging information, over a wide set of parameters and problems ? the standard benchmark developed for the IEEE Workshop on Evolutionary Algorithms and other Metaheuristics for Continuous Optimization Problems of the ISDA 2009 Conference. Several analyses from different points of view have been conducted to analyze both the influence of the parameters and the relationships between them including a characterization of the configurations according to their behavior on the proposed benchmark.

Impact of automatic meetering infrastructure on multiutility business models

In the last decade energy utility sector has undergone major changes in terms of liberalization, increased competition, efforts in improving energy efficiency, and in new technological solution such as smart meter and grid operations. There are new information technology solutions (e.g. Advanced Metering Infrastructure /AMI ) on the horizon that will not only introduce new technical and organizational concepts, but have a very strong potential to radically change modus operandi of utility companies. Coordinated, multi-utility programs can help accelerate the development and market success of new high-efficiency technologies. These programs provide opportunities for researchers to develop new high-efficiency equipment for manufacturers to sell this new equipment with utility help, for utilities to increase the amount of energy they save from incentive programs, and for consumers to benefit from lower utility bills and a cleaner environment (as energy is reduced, pollutants produced at power plants decline).

Seven challenges for RESTful transaction models

The REpresentational State Transfer (REST) architectural style describes the design principles that made the World Wide Web scalable and the same principles can be applied in enterprise context to do loosely coupled and scalable application integration. In recent years, RESTful services are gaining traction in the industry and are commonly used as a simpler alternative to SOAP Web Services. However, one of the main drawbacks of RESTful services is the lack of standard mechanisms to support advanced quality-ofservice requirements that are common to enterprises. Transaction processing is one of the essential features of enterprise information systems and several transaction models have been proposed in the past years to fulfill the gap of transaction processing in RESTful services. The goal of this paper is to analyze the state-of-the-art RESTful transaction models and identify the current challenges.

Inteligencia artificial para la predicción y control del acabado superficial en procesos de fresado a alta velocidad

El objetivo principal de esta tesis doctoral es profundizar en el análisis y diseño de un sistema inteligente para la predicción y control del acabado superficial en un proceso de fresado a alta velocidad, basado fundamentalmente en clasificadores Bayesianos, con el prop´osito de desarrollar una metodolog´ıa que facilite el diseño de este tipo de sistemas. El sistema, cuyo propósito es posibilitar la predicción y control de la rugosidad superficial, se compone de un modelo aprendido a partir de datos experimentales con redes Bayesianas, que ayudar´a a comprender los procesos dinámicos involucrados en el mecanizado y las interacciones entre las variables relevantes. Dado que las redes neuronales artificiales son modelos ampliamente utilizados en procesos de corte de materiales, también se incluye un modelo para fresado usándolas, donde se introdujo la geometría y la dureza del material como variables novedosas hasta ahora no estudiadas en este contexto. Por lo tanto, una importante contribución en esta tesis son estos dos modelos para la predicción de la rugosidad superficial, que se comparan con respecto a diferentes aspectos: la influencia de las nuevas variables, los indicadores de evaluación del desempeño, interpretabilidad. Uno de los principales problemas en la modelización con clasificadores Bayesianos es la comprensión de las enormes tablas de probabilidad a posteriori producidas. Introducimos un m´etodo de explicación que genera un conjunto de reglas obtenidas de árboles de decisión. Estos árboles son inducidos a partir de un conjunto de datos simulados generados de las probabilidades a posteriori de la variable clase, calculadas con la red Bayesiana aprendida a partir de un conjunto de datos de entrenamiento. Por último, contribuimos en el campo multiobjetivo en el caso de que algunos de los objetivos no se puedan cuantificar en números reales, sino como funciones en intervalo de valores. Esto ocurre a menudo en aplicaciones de aprendizaje automático, especialmente las basadas en clasificación supervisada. En concreto, se extienden las ideas de dominancia y frontera de Pareto a esta situación. Su aplicación a los estudios de predicción de la rugosidad superficial en el caso de maximizar al mismo tiempo la sensibilidad y la especificidad del clasificador inducido de la red Bayesiana, y no solo maximizar la tasa de clasificación correcta. Los intervalos de estos dos objetivos provienen de un m´etodo de estimación honesta de ambos objetivos, como e.g. validación cruzada en k rodajas o bootstrap.---ABSTRACT---The main objective of this PhD Thesis is to go more deeply into the analysis and design of an intelligent system for surface roughness prediction and control in the end-milling machining process, based fundamentally on Bayesian network classifiers, with the aim of developing a methodology that makes easier the design of this type of systems. The system, whose purpose is to make possible the surface roughness prediction and control, consists of a model learnt from experimental data with the aid of Bayesian networks, that will help to understand the dynamic processes involved in the machining and the interactions among the relevant variables. Since artificial neural networks are models widely used in material cutting proceses, we include also an end-milling model using them, where the geometry and hardness of the piecework are introduced as novel variables not studied so far within this context. Thus, an important contribution in this thesis is these two models for surface roughness prediction, that are then compared with respecto to different aspects: influence of the new variables, performance evaluation metrics, interpretability. One of the main problems with Bayesian classifier-based modelling is the understanding of the enormous posterior probabilitiy tables produced. We introduce an explanation method that generates a set of rules obtained from decision trees. Such trees are induced from a simulated data set generated from the posterior probabilities of the class variable, calculated with the Bayesian network learned from a training data set. Finally, we contribute in the multi-objective field in the case that some of the objectives cannot be quantified as real numbers but as interval-valued functions. This often occurs in machine learning applications, especially those based on supervised classification. Specifically, the dominance and Pareto front ideas are extended to this setting. Its application to the surface roughness prediction studies the case of maximizing simultaneously the sensitivity and specificity of the induced Bayesian network classifier, rather than only maximizing the correct classification rate. Intervals in these two objectives come from a honest estimation method of both objectives, like e.g. k-fold cross-validation or bootstrap.

Polynomial regression using a perceptron with axo-axonic connections

Social behavior is mainly based on swarm colonies, in which each individual shares its knowledge about the environment with other individuals to get optimal solutions. Such co-operative model differs from competitive models in the way that individuals die and are born by combining information of alive ones. This paper presents the particle swarm optimization with differential evolution algorithm in order to train a neural network instead the classic back propagation algorithm. The performance of a neural network for particular problems is critically dependant on the choice of the processing elements, the net architecture and the learning algorithm. This work is focused in the development of methods for the evolutionary design of artificial neural networks. This paper focuses in optimizing the topology and structure of connectivity for these networks

Two-dimensional direct rainfall hydraulic models applied to direct calculation of hydrologic events in dams to prevent overtopping

One of the main concerns when conducting a dam test is the acute determination of the hydrograph for a specific flood event. The use of 2D direct rainfall hydraulic mathematical models on a finite elements mesh, combined with the efficiency of vector calculus that provides CUDA (Compute Unified Device Architecture) technology, enables nowadays the simulation of complex hydrological models without the need for terrain subbasin and transit splitting (as in HEC-HMS). Both the Spanish PNOA (National Plan of Aereal Orthophotography) Digital Terrain Model GRID with a 5 x 5 m accuracy and the CORINE GIS Land Cover (Coordination of INformation of the Environment) that allows assessment of the ground roughness, provide enough data to easily build these kind of models

Predicción de tráfico de contenedores a corto plazo mediante técnicas de minería de datos : redes neuronales artificiales y redes bayesianas

El correcto pronóstico en el ámbito de la logística de transportes es de vital importancia para una adecuada planificación de medios y recursos, así como de su optimización. Hasta la fecha los estudios sobre planificación portuaria se basan principalmente en modelos empíricos; que se han utilizado para planificar nuevas terminales y desarrollar planes directores cuando no se dispone de datos iniciales, analíticos; más relacionados con la teoría de colas y tiempos de espera con formulaciones matemáticas complejas y necesitando simplificaciones de las mismas para hacer manejable y práctico el modelo o de simulación; que requieren de una inversión significativa como para poder obtener resultados aceptables invirtiendo en programas y desarrollos complejos. La Minería de Datos (MD) es un área moderna interdisciplinaria que engloba a aquellas técnicas que operan de forma automática (requieren de la mínima intervención humana) y, además, son eficientes para trabajar con las grandes cantidades de información disponible en las bases de datos de numerosos problemas prácticos. La aplicación práctica de estas disciplinas se extiende a numerosos ámbitos comerciales y de investigación en problemas de predicción, clasificación o diagnosis. Entre las diferentes técnicas disponibles en minería de datos las redes neuronales artificiales (RNA) y las redes probabilísticas o redes bayesianas (RB) permiten modelizar de forma conjunta toda la información relevante para un problema dado. En el presente trabajo se han analizado dos aplicaciones de estos casos al ámbito portuario y en concreto a contenedores. En la Tesis Doctoral se desarrollan las RNA como herramienta para obtener previsiones de tráfico y de recursos a futuro de diferentes puertos, a partir de variables de explotación, obteniéndose valores continuos. Para el caso de las redes bayesianas (RB), se realiza un trabajo similar que para el caso de las RNA, obteniéndose valores discretos (un intervalo). El principal resultado que se obtiene es la posibilidad de utilizar tanto las RNA como las RB para la estimación a futuro de parámetros físicos, así como la relación entre los mismos en una terminal para una correcta asignación de los medios a utilizar y por tanto aumentar la eficiencia productiva de la terminal. Como paso final se realiza un estudio de complementariedad de ambos modelos a corto plazo, donde se puede comprobar la buena aceptación de los resultados obtenidos. Por tanto, se puede concluir que estos métodos de predicción pueden ser de gran ayuda a la planificación portuaria. The correct assets’ forecast in the field of transportation logistics is a matter of vital importance for a suitable planning and optimization of the necessary means and resources. Up to this date, ports planning studies were basically using empirical models to deal with new terminals planning or master plans development when no initial data are available; analytical models, more connected to the queuing theory and the waiting times, and very complicated mathematical formulations requiring significant simplifications to acquire a practical and easy to handle model; or simulation models, that require a significant investment in computer codes and complex developments to produce acceptable results. The Data Mining (DM) is a modern interdisciplinary field that include those techniques that operate automatically (almost no human intervention is required) and are highly efficient when dealing with practical problems characterized by huge data bases containing significant amount of information. These disciplines’ practical application extends to many commercial or research fields, dealing with forecast, classification or diagnosis problems. Among the different techniques of the Data Mining, the Artificial Neuronal Networks (ANN) and the probabilistic – or Bayesian – networks (BN) allow the joint modeling of all the relevant information for a given problem. This PhD work analyses their application to two practical cases in the ports field, concretely to container terminals. This PhD work details how the ANN have been developed as a tool to produce traffic and resources forecasts for several ports, based on exploitation variables to obtain continuous values. For the Bayesian networks case (BN), a similar development has been carried out, obtaining discreet values (an interval). The main finding is the possibility to use ANN and BN to estimate future needs of the port’s or terminal’s physical parameters, as well as the relationship between them within a specific terminal, that allow a correct assignment of the necessary means and, thus, to increase the terminal’s productive efficiency. The final step is a short term complementarily study of both models, carried out in order to verify the obtained results. It can thus be stated that these prediction methods can be a very useful tool in ports’ planning.

Artificial analysis of molecular marker loci linked to tree resistance response by an artificial neural network

One of the biggest challenges that software developers face is to make an accurate estimate of the project effort. Radial basis function neural networks have been used to software effort estimation in this work using NASA dataset. This paper evaluates and compares radial basis function versus a regression model. The results show that radial basis function neural network have obtained less Mean Square Error than the regression method.

Recuperación cruzada de información pública clínico-genómica a partir de consultas sobre repositorios clínicos privados

Parte de la investigación biomédica actual se encuentra centrada en el análisis de datos heterogéneos. Estos datos pueden tener distinto origen, estructura, y semántica. Gran cantidad de datos de interés para los investigadores se encuentran en bases de datos públicas, que recogen información de distintas fuentes y la ponen a disposición de la comunidad de forma gratuita. Para homogeneizar estas fuentes de datos públicas con otras de origen privado, existen diversas herramientas y técnicas que permiten automatizar los procesos de homogeneización de datos heterogéneos. El Grupo de Informática Biomédica (GIB) [1] de la Universidad Politécnica de Madrid colabora en el proyecto europeo P-medicine [2], cuya finalidad reside en el desarrollo de una infraestructura que facilite la evolución de los procedimientos médicos actuales hacia la medicina personalizada. Una de las tareas enmarcadas en el proyecto P-medicine que tiene asignado el grupo consiste en elaborar herramientas que ayuden a usuarios en el proceso de integración de datos contenidos en fuentes de información heterogéneas. Algunas de estas fuentes de información son bases de datos públicas de ámbito biomédico contenidas en la plataforma NCBI [3] (National Center for Biotechnology Information). Una de las herramientas que el grupo desarrolla para integrar fuentes de datos es Ontology Annotator. En una de sus fases, la labor del usuario consiste en recuperar información de una base de datos pública y seleccionar de forma manual los resultados relevantes. Para automatizar el proceso de búsqueda y selección de resultados relevantes, por un lado existe un gran interés en conseguir generar consultas que guíen hacia resultados lo más precisos y exactos como sea posible, por otro lado, existe un gran interés en extraer información relevante de elevadas cantidades de documentos, lo cual requiere de sistemas que analicen y ponderen los datos que caracterizan a los mismos. En el campo informático de la inteligencia artificial, dentro de la rama de la recuperación de la información, existen diversos estudios acerca de la expansión de consultas a partir de retroalimentación relevante que podrían ser de gran utilidad para dar solución a la cuestión. Estos estudios se centran en técnicas para reformular o expandir la consulta inicial utilizando como realimentación los resultados que en una primera instancia fueron relevantes para el usuario, de forma que el nuevo conjunto de resultados tenga mayor proximidad con los que el usuario realmente desea. El objetivo de este trabajo de fin de grado consiste en el estudio, implementación y experimentación de métodos que automaticen el proceso de extracción de información trascendente de documentos, utilizándola para expandir o reformular consultas. De esta forma se pretende mejorar la precisión y el ranking de los resultados asociados. Dichos métodos serán integrados en la herramienta Ontology Annotator y enfocados a la fuente de datos de PubMed [4].---ABSTRACT---Part of the current biomedical research is focused on the analysis of heterogeneous data. These data may have different origin, structure and semantics. A big quantity of interesting data is contained in public databases which gather information from different sources and make it open and free to be used by the community. In order to homogenize thise sources of public data with others which origin is private, there are some tools and techniques that allow automating the processes of integration heterogeneous data. The biomedical informatics group of the Universidad Politécnica de Madrid cooperates with the European project P-medicine which main purpose is to create an infrastructure and models to facilitate the transition from current medical practice to personalized medicine. One of the tasks of the project that the group is in charge of consists on the development of tools that will help users in the process of integrating data from diverse sources. Some of the sources are biomedical public data bases from the NCBI platform (National Center for Biotechnology Information). One of the tools in which the group is currently working on for the integration of data sources is called the Ontology Annotator. In this tool there is a phase in which the user has to retrieve information from a public data base and select the relevant data contained in it manually. For automating the process of searching and selecting data on the one hand, there is an interest in automatically generating queries that guide towards the more precise results as possible. On the other hand, there is an interest on retrieve relevant information from large quantities of documents. The solution requires systems that analyze and weigh the data allowing the localization of the relevant items. In the computer science field of the artificial intelligence, in the branch of information retrieval there are diverse studies about the query expansion from relevance feedback that could be used to solve the problem. The main purpose of this studies is to obtain a set of results that is the closer as possible to the information that the user really wants to retrieve. In order to reach this purpose different techniques are used to reformulate or expand the initial query using a feedback the results that where relevant for the user, with this method, the new set of results will have more proximity with the ones that the user really desires. The goal of this final dissertation project consists on the study, implementation and experimentation of methods that automate the process of extraction of relevant information from documents using this information to expand queries. This way, the precision and the ranking of the results associated will be improved. These methods will be integrated in the Ontology Annotator tool and will focus on the PubMed data source.

Analytical models for the power subsystem and the attitude control subsystem of a microsatellite

El trabajo realizado en la presente tesis doctoral se debe considerar parte del proyecto UPMSat-2, que se enmarca dentro del ámbito de la tecnología aeroespacial. El UPMSat-2 es un microsatélite (de bajo coste y pequeño tamaño) diseñado, construido, probado e integrado por la Universidad Politécnica de Madrid (España), para fines de demostración tecnológica y educación. El objetivo de la presente tesis doctoral es presentar nuevos modelos analíticos para estudiar la interdependencia energética entre los subsistemas de potencia y de control de actitud de un satélite. En primer lugar, se estudia la simulación del subsistema de potencia de un microsatélite, prestando especial atención a la simulación de la fuente de potencia, esto es, los paneles solares. En la tesis se presentan métodos sencillos pero precisos para simular la producción de energía de los paneles en condiciones ambientales variables a través de su circuito equivalente. Los métodos propuestos para el cálculo de los parámetros del circuito equivalente son explícitos (o al menos, con las variables desacopladas), no iterativos y directos; no se necesitan iteraciones o valores iniciales para calcular los parámetros. La precisión de este método se prueba y se compara con métodos similares de la literatura disponible, demostrando una precisión similar para mayor simplicidad. En segundo lugar, se presenta la simulación del subsistema de control de actitud de un microsatélite, prestando especial atención a la nueva ley de control propuesta. La tesis presenta un nuevo tipo de control magnético es aplicable a la órbita baja terrestre (LEO). La ley de control propuesta es capaz de ajustar la velocidad de rotación del satélite alrededor de su eje principal de inercia máximo o mínimo. Además, en el caso de órbitas de alta inclinación, la ley de control favorece la alineación del eje de rotación con la dirección normal al plano orbital. El algoritmo de control propuesto es simple, sólo se requieren magnetopares como actuadores; sólo se requieren magnetómetros como sensores; no hace falta estimar la velocidad angular; no incluye un modelo de campo magnético de la Tierra; no tiene por qué ser externamente activado con información sobre las características orbitales y permite el rearme automático después de un apagado total del subsistema de control de actitud. La viabilidad teórica de la citada ley de control se demuestra a través de análisis de Monte Carlo. Por último, en términos de producción de energía, se demuestra que la actitud propuesto (en eje principal perpendicular al plano de la órbita, y el satélite que gira alrededor de ella con una velocidad controlada) es muy adecuado para la misión UPMSat-2, ya que permite una área superior de los paneles apuntando hacia el sol cuando se compara con otras actitudes estudiadas. En comparación con el control de actitud anterior propuesto para el UPMSat-2 resulta en un incremento de 25% en la potencia disponible. Además, la actitud propuesto mostró mejoras significativas, en comparación con otros, en términos de control térmico, como la tasa de rotación angular por satélite puede seleccionarse para conseguir una homogeneización de la temperatura más alta que apunta satélite y la antena. ABSTRACT The work carried out in the present doctoral dissertation should be considered part of the UPMSat-2 project, falling within the scope of the aerospace technology. The UPMSat-2 is a microsatellite (low cost and small size) designed, constructed integrated and tested for educational and technology demonstration purposes at the Universidad Politécnica de Madrid (Spain). The aim of the present doctoral dissertation is to present new analytical models to study the energy interdependence between the power and the attitude control subsystems of a satellite. First, the simulation of the power subsystem of a microsatellite is studied, paying particular attention to the simulation of the power supply, i.e. the solar panels. Simple but accurate methods for simulate the power production under variable ambient conditions using its equivalent circuit are presented. The proposed methods for calculate the equivalent circuit parameters are explicit (or at least, with decoupled variables), non-iterative and straight forward; no iterations or initial values for the parameters are needed. The accuracy of this method is tested and compared with similar methods from the available literature demonstrating similar precision but higher simplicity. Second, the simulation of the control subsystem of a microsatellite is presented, paying particular attention to the new control law proposed. A new type of magnetic control applied to Low Earth Orbit (LEO) satellites has been presented. The proposed control law is able to set the satellite rotation speed around its maximum or minimum inertia principal axis. Besides, the proposed control law favors the alignment of this axis with the normal direction to the orbital plane for high inclination orbits. The proposed control algorithm is simples, only magnetorquers are required as actuators; only magnetometers are required as sensors; no estimation of the angular velocity is needed; it does not include an in-orbit Earth magnetic field model; it does not need to be externally activated with information about the orbital characteristics and it allows automatic reset after a total shutdown of attitude control subsystem. The theoretical viability of the control law is demonstrated through Monte Carlo analysis. Finally, in terms of power production, it is demonstrated that the proposed attitude (on principal axis perpendicular to the orbit plane, and the satellite rotating around it with a controlled rate) is quite suitable for the UPMSat-2 mission, as it allows a higher area of the panels pointing towards the sun when compared to other studied attitudes. Compared with the previous attitude control proposed for the UPMSat-2 it results in a 25% increment in available power. Besides, the proposed attitude showed significant improvements, when compared to others, in terms of thermal control, as the satellite angular rotation rate can be selected to achieve a higher temperature homogenization of the satellite and antenna pointing.

Autonomous classification models in ubiquitous environments

Stream-mining approach is defined as a set of cutting-edge techniques designed to process streams of data in real time, in order to extract knowledge. In the particular case of classification, stream-mining has to adapt its behaviour to the volatile underlying data distributions, what has been called concept drift. Moreover, it is important to note that concept drift may lead to situations where predictive models become invalid and have therefore to be updated to represent the actual concepts that data poses. In this context, there is a specific type of concept drift, known as recurrent concept drift, where the concepts represented by data have already appeared in the past. In those cases the learning process could be saved or at least minimized by applying a previously trained model. This could be extremely useful in ubiquitous environments that are characterized by the existence of resource constrained devices. To deal with the aforementioned scenario, meta-models can be used in the process of enhancing the drift detection mechanisms used by data stream algorithms, by representing and predicting when the change will occur. There are some real-world situations where a concept reappears, as in the case of intrusion detection systems (IDS), where the same incidents or an adaptation of them usually reappear over time. In these environments the early prediction of drift by means of a better knowledge of past models can help to anticipate to the change, thus improving efficiency of the model regarding the training instances needed. By means of using meta-models as a recurrent drift detection mechanism, the ability to share concepts representations among different data mining processes is open. That kind of exchanges could improve the accuracy of the resultant local model as such model may benefit from patterns similar to the local concept that were observed in other scenarios, but not yet locally. This would also improve the efficiency of training instances used during the classification process, as long as the exchange of models would aid in the application of already trained recurrent models, that have been previously seen by any of the collaborative devices. Which it is to say that the scope of recurrence detection and representation is broaden. In fact the detection, representation and exchange of concept drift patterns would be extremely useful for the law enforcement activities fighting against cyber crime. Being the information exchange one of the main pillars of cooperation, national units would benefit from the experience and knowledge gained by third parties. Moreover, in the specific scope of critical infrastructures protection it is crucial to count with information exchange mechanisms, both from a strategical and technical scope. The exchange of concept drift detection schemes in cyber security environments would aid in the process of preventing, detecting and effectively responding to threads in cyber space. Furthermore, as a complement of meta-models, a mechanism to assess the similarity between classification models is also needed when dealing with recurrent concepts. In this context, when reusing a previously trained model a rough comparison between concepts is usually made, applying boolean logic. The introduction of fuzzy logic comparisons between models could lead to a better efficient reuse of previously seen concepts, by applying not just equal models, but also similar ones. This work faces the aforementioned open issues by means of: the MMPRec system, that integrates a meta-model mechanism and a fuzzy similarity function; a collaborative environment to share meta-models between different devices; a recurrent drift generator that allows to test the usefulness of recurrent drift systems, as it is the case of MMPRec. Moreover, this thesis presents an experimental validation of the proposed contributions using synthetic and real datasets.