MRI and Bidimensional relaxometry sequences for macro and microstructure assessment in food models.

Physico-chemical and organoleptic characteristics of food depend largely on the microscopic level distribution of gases and water, and connectivity and mobility through the pores. Microstructural characterization of food can be accomplished by Magnetic Resonance Imaging (MRI) and Nuclear Magnetic Spectroscopy (NMR) combined with the application of methods of dissemination and multidimensional relaxometry. In this work, funded by the EC Project InsideFood, several artificial food models, based on foams and gels were studied using MRI and 2D relaxometry. Two different kinds of foams were used: a sugarless and a sugar foam. Then, a half of a syringe was filled with the sugarless foam and the other half with the sugar foam. Then, MRI and NMR experiments were performed and the sample evolution was observed along 3 days in order to quantify macrostructural changes through proton density images and microstructural ones using T1T2 maps, using an inversion CPMG sequence. On the proton density images it may be seen that after 16 hours it was possible to differentiate the macrostructural changes, as the apparition of free water due to a syneresis phenomenon. On the interface it can be seen a brighter area after 16 hours, due to the occurrence of free water. Moreover, thanks to the bidimensional relaxometry (T1-T2) it was possible to differentiate among microscopic changes. Differences between the pores size can be observed as well as the microstructure evolution after 30.5 hours, as a consequence differences are shown on free water redistribution through larger pores and capillarity phenomena between both foams.

Modelo de arquitectura para gestión cooperativa de sistemas y servicios distribuidos basado en agentes autónomos

La creciente complejidad, heterogeneidad y dinamismo inherente a las redes de telecomunicaciones, los sistemas distribuidos y los servicios avanzados de información y comunicación emergentes, así como el incremento de su criticidad e importancia estratégica, requieren la adopción de tecnologías cada vez más sofisticadas para su gestión, su coordinación y su integración por parte de los operadores de red, los proveedores de servicio y las empresas, como usuarios finales de los mismos, con el fin de garantizar niveles adecuados de funcionalidad, rendimiento y fiabilidad. Las estrategias de gestión adoptadas tradicionalmente adolecen de seguir modelos excesivamente estáticos y centralizados, con un elevado componente de supervisión y difícilmente escalables. La acuciante necesidad por flexibilizar esta gestión y hacerla a la vez más escalable y robusta, ha provocado en los últimos años un considerable interés por desarrollar nuevos paradigmas basados en modelos jerárquicos y distribuidos, como evolución natural de los primeros modelos jerárquicos débilmente distribuidos que sucedieron al paradigma centralizado. Se crean así nuevos modelos como son los basados en Gestión por Delegación, en el paradigma de código móvil, en las tecnologías de objetos distribuidos y en los servicios web. Estas alternativas se han mostrado enormemente robustas, flexibles y escalables frente a las estrategias tradicionales de gestión, pero continúan sin resolver aún muchos problemas. Las líneas actuales de investigación parten del hecho de que muchos problemas de robustez, escalabilidad y flexibilidad continúan sin ser resueltos por el paradigma jerárquico-distribuido, y abogan por la migración hacia un paradigma cooperativo fuertemente distribuido. Estas líneas tienen su germen en la Inteligencia Artificial Distribuida (DAI) y, más concretamente, en el paradigma de agentes autónomos y en los Sistemas Multi-agente (MAS). Todas ellas se perfilan en torno a un conjunto de objetivos que pueden resumirse en alcanzar un mayor grado de autonomía en la funcionalidad de la gestión y una mayor capacidad de autoconfiguración que resuelva los problemas de escalabilidad y la necesidad de supervisión presentes en los sistemas actuales, evolucionar hacia técnicas de control fuertemente distribuido y cooperativo guiado por la meta y dotar de una mayor riqueza semántica a los modelos de información. Cada vez más investigadores están empezando a utilizar agentes para la gestión de redes y sistemas distribuidos. Sin embargo, los límites establecidos en sus trabajos entre agentes móviles (que siguen el paradigma de código móvil) y agentes autónomos (que realmente siguen el paradigma cooperativo) resultan difusos. Muchos de estos trabajos se centran en la utilización de agentes móviles, lo cual, al igual que ocurría con las técnicas de código móvil comentadas anteriormente, les permite dotar de un mayor componente dinámico al concepto tradicional de Gestión por Delegación. Con ello se consigue flexibilizar la gestión, distribuir la lógica de gestión cerca de los datos y distribuir el control. Sin embargo se permanece en el paradigma jerárquico distribuido. Si bien continúa sin definirse aún una arquitectura de gestión fiel al paradigma cooperativo fuertemente distribuido, estas líneas de investigación han puesto de manifiesto serios problemas de adecuación en los modelos de información, comunicación y organizativo de las arquitecturas de gestión existentes. En este contexto, la tesis presenta un modelo de arquitectura para gestión holónica de sistemas y servicios distribuidos mediante sociedades de agentes autónomos, cuyos objetivos fundamentales son el incremento del grado de automatización asociado a las tareas de gestión, el aumento de la escalabilidad de las soluciones de gestión, soporte para delegación tanto por dominios como por macro-tareas, y un alto grado de interoperabilidad en entornos abiertos. A partir de estos objetivos se ha desarrollado un modelo de información formal de tipo semántico, basado en lógica descriptiva que permite un mayor grado de automatización en la gestión en base a la utilización de agentes autónomos racionales, capaces de razonar, inferir e integrar de forma dinámica conocimiento y servicios conceptualizados mediante el modelo CIM y formalizados a nivel semántico mediante lógica descriptiva. El modelo de información incluye además un “mapping” a nivel de meta-modelo de CIM al lenguaje de especificación de ontologías OWL, que supone un significativo avance en el área de la representación y el intercambio basado en XML de modelos y meta-información. A nivel de interacción, el modelo aporta un lenguaje de especificación formal de conversaciones entre agentes basado en la teoría de actos ilocucionales y aporta una semántica operacional para dicho lenguaje que facilita la labor de verificación de propiedades formales asociadas al protocolo de interacción. Se ha desarrollado también un modelo de organización holónico y orientado a roles cuyas principales características están alineadas con las demandadas por los servicios distribuidos emergentes e incluyen la ausencia de control central, capacidades de reestructuración dinámica, capacidades de cooperación, y facilidades de adaptación a diferentes culturas organizativas. El modelo incluye un submodelo normativo adecuado al carácter autónomo de los holones de gestión y basado en las lógicas modales deontológica y de acción.---ABSTRACT---The growing complexity, heterogeneity and dynamism inherent in telecommunications networks, distributed systems and the emerging advanced information and communication services, as well as their increased criticality and strategic importance, calls for the adoption of increasingly more sophisticated technologies for their management, coordination and integration by network operators, service providers and end-user companies to assure adequate levels of functionality, performance and reliability. The management strategies adopted traditionally follow models that are too static and centralised, have a high supervision component and are difficult to scale. The pressing need to flexibilise management and, at the same time, make it more scalable and robust recently led to a lot of interest in developing new paradigms based on hierarchical and distributed models, as a natural evolution from the first weakly distributed hierarchical models that succeeded the centralised paradigm. Thus new models based on management by delegation, the mobile code paradigm, distributed objects and web services came into being. These alternatives have turned out to be enormously robust, flexible and scalable as compared with the traditional management strategies. However, many problems still remain to be solved. Current research lines assume that the distributed hierarchical paradigm has as yet failed to solve many of the problems related to robustness, scalability and flexibility and advocate migration towards a strongly distributed cooperative paradigm. These lines of research were spawned by Distributed Artificial Intelligence (DAI) and, specifically, the autonomous agent paradigm and Multi-Agent Systems (MAS). They all revolve around a series of objectives, which can be summarised as achieving greater management functionality autonomy and a greater self-configuration capability, which solves the problems of scalability and the need for supervision that plague current systems, evolving towards strongly distributed and goal-driven cooperative control techniques and semantically enhancing information models. More and more researchers are starting to use agents for network and distributed systems management. However, the boundaries established in their work between mobile agents (that follow the mobile code paradigm) and autonomous agents (that really follow the cooperative paradigm) are fuzzy. Many of these approximations focus on the use of mobile agents, which, as was the case with the above-mentioned mobile code techniques, means that they can inject more dynamism into the traditional concept of management by delegation. Accordingly, they are able to flexibilise management, distribute management logic about data and distribute control. However, they remain within the distributed hierarchical paradigm. While a management architecture faithful to the strongly distributed cooperative paradigm has yet to be defined, these lines of research have revealed that the information, communication and organisation models of existing management architectures are far from adequate. In this context, this dissertation presents an architectural model for the holonic management of distributed systems and services through autonomous agent societies. The main objectives of this model are to raise the level of management task automation, increase the scalability of management solutions, provide support for delegation by both domains and macro-tasks and achieve a high level of interoperability in open environments. Bearing in mind these objectives, a descriptive logic-based formal semantic information model has been developed, which increases management automation by using rational autonomous agents capable of reasoning, inferring and dynamically integrating knowledge and services conceptualised by means of the CIM model and formalised at the semantic level by means of descriptive logic. The information model also includes a mapping, at the CIM metamodel level, to the OWL ontology specification language, which amounts to a significant advance in the field of XML-based model and metainformation representation and exchange. At the interaction level, the model introduces a formal specification language (ACSL) of conversations between agents based on speech act theory and contributes an operational semantics for this language that eases the task of verifying formal properties associated with the interaction protocol. A role-oriented holonic organisational model has also been developed, whose main features meet the requirements demanded by emerging distributed services, including no centralised control, dynamic restructuring capabilities, cooperative skills and facilities for adaptation to different organisational cultures. The model includes a normative submodel adapted to management holon autonomy and based on the deontic and action modal logics.

Polynomial approximation using particle swarm optimization of lineal enhanced neural networks with no hidden layers.

This paper presents some ideas about a new neural network architecture that can be compared to a Taylor analysis when dealing with patterns. Such architecture is based on lineal activation functions with an axo-axonic architecture. A biological axo-axonic connection between two neurons is defined as the weight in a connection in given by the output of another third neuron. This idea can be implemented in the so called Enhanced Neural Networks in which two Multilayer Perceptrons are used; the first one will output the weights that the second MLP uses to computed the desired output. This kind of neural network has universal approximation properties even with lineal activation functions. There exists a clear difference between cooperative and competitive strategies. The former ones are based on the swarm colonies, in which all individuals share its knowledge about the goal in order to pass such information to other individuals to get optimum solution. The latter ones are based on genetic models, that is, individuals can die and new individuals are created combining information of alive one; or are based on molecular/celular behaviour passing information from one structure to another. A swarm-based model is applied to obtain the Neural Network, training the net with a Particle Swarm algorithm.

Guidelines for Linked Data generation and publication: an example in building energy consumption

Linked Data is the key paradigm of the Semantic Web, a new generation of the World Wide Web that promises to bring meaning (semantics) to data. A large number of both public and private organizations have published their data following the Linked Data principles, or have done so with data from other organizations. To this extent, since the generation and publication of Linked Data are intensive engineering processes that require high attention in order to achieve high quality, and since experience has shown that existing general guidelines are not always sufficient to be applied to every domain, this paper presents a set of guidelines for generating and publishing Linked Data in the context of energy consumption in buildings (one aspect of Building Information Models). These guidelines offer a comprehensive description of the tasks to perform, including a list of steps, tools that help in achieving the task, various alternatives for performing the task, and best practices and recommendations. Furthermore, this paper presents a complete example on the generation and publication of Linked Data about energy consumption in buildings, following the presented guidelines, in which the energy consumption data of council sites (e.g., buildings and lights) belonging to the Leeds City Council jurisdiction have been generated and published as Linked Data.

Aplicación de técnicas de inteligencia artificial a la predicción de contaminantes atmosféricos

Abstract Air pollution is a big threat and a phenomenon that has a specific impact on human health, in addition, changes that occur in the chemical composition of the atmosphere can change the weather and cause acid rain or ozone destruction. Those are phenomena of global importance. The World Health Organization (WHO) considerates air pollution as one of the most important global priorities. Salamanca, Gto., Mexico has been ranked as one of the most polluted cities in this country. The industry of the area led to a major economic development and rapid population growth in the second half of the twentieth century. The impact in the air quality is important and significant efforts have been made to measure the concentrations of pollutants. The main pollution sources are locally based plants in the chemical and power generation sectors. The registered concerning pollutants are Sulphur Dioxide (SO2) and particles on the order of ∼10 micrometers or less (PM10). The prediction in the concentration of those pollutants can be a powerful tool in order to take preventive measures such as the reduction of emissions and alerting the affected population. In this PhD thesis we propose a model to predict concentrations of pollutants SO2 and PM10 for each monitoring booth in the Atmospheric Monitoring Network Salamanca (REDMAS - for its spanish acronym). The proposed models consider the use of meteorological variables as factors influencing the concentration of pollutants. The information used along this work is the current real data from REDMAS. In the proposed model, Artificial Neural Networks (ANN) combined with clustering algorithms are used. The type of ANN used is the Multilayer Perceptron with a hidden layer, using separate structures for the prediction of each pollutant. The meteorological variables used for prediction were: Wind Direction (WD), wind speed (WS), Temperature (T) and relative humidity (RH). Clustering algorithms, K-means and Fuzzy C-means, are used to find relationships between air pollutants and weather variables under consideration, which are added as input of the RNA. Those relationships provide information to the ANN in order to obtain the prediction of the pollutants. The results of the model proposed in this work are compared with the results of a multivariate linear regression and multilayer perceptron neural network. The evaluation of the prediction is calculated with the mean absolute error, the root mean square error, the correlation coefficient and the index of agreement. The results show the importance of meteorological variables in the prediction of the concentration of the pollutants SO2 and PM10 in the city of Salamanca, Gto., Mexico. The results show that the proposed model perform better than multivariate linear regression and multilayer perceptron neural network. The models implemented for each monitoring booth have the ability to make predictions of air quality that can be used in a system of real-time forecasting and human health impact analysis. Among the main results of the development of this thesis we can cite: A model based on artificial neural network combined with clustering algorithms for prediction with a hour ahead of the concentration of each pollutant (SO2 and PM10) is proposed. A different model was designed for each pollutant and for each of the three monitoring booths of the REDMAS. A model to predict the average of pollutant concentration in the next 24 hours of pollutants SO2 and PM10 is proposed, based on artificial neural network combined with clustering algorithms. Model was designed for each booth of the REDMAS and each pollutant separately. Resumen La contaminación atmosférica es una amenaza aguda, constituye un fenómeno que tiene particular incidencia sobre la salud del hombre. Los cambios que se producen en la composición química de la atmósfera pueden cambiar el clima, producir lluvia ácida o destruir el ozono, fenómenos todos ellos de una gran importancia global. La Organización Mundial de la Salud (OMS) considera la contaminación atmosférica como una de las más importantes prioridades mundiales. Salamanca, Gto., México; ha sido catalogada como una de las ciudades más contaminadas en este país. La industria de la zona propició un importante desarrollo económico y un crecimiento acelerado de la población en la segunda mitad del siglo XX. Las afectaciones en el aire son graves y se han hecho importantes esfuerzos por medir las concentraciones de los contaminantes. Las principales fuentes de contaminación son fuentes fijas como industrias químicas y de generación eléctrica. Los contaminantes que se han registrado como preocupantes son el Bióxido de Azufre (SO2) y las Partículas Menores a 10 micrómetros (PM10). La predicción de las concentraciones de estos contaminantes puede ser una potente herramienta que permita tomar medidas preventivas como reducción de emisiones a la atmósfera y alertar a la población afectada. En la presente tesis doctoral se propone un modelo de predicción de concentraci ón de los contaminantes más críticos SO2 y PM10 para cada caseta de monitorización de la Red de Monitorización Atmosférica de Salamanca (REDMAS). Los modelos propuestos plantean el uso de las variables meteorol ógicas como factores que influyen en la concentración de los contaminantes. La información utilizada durante el desarrollo de este trabajo corresponde a datos reales obtenidos de la REDMAS. En el Modelo Propuesto (MP) se aplican Redes Neuronales Artificiales (RNA) combinadas con algoritmos de agrupamiento. La RNA utilizada es el Perceptrón Multicapa con una capa oculta, utilizando estructuras independientes para la predicción de cada contaminante. Las variables meteorológicas disponibles para realizar la predicción fueron: Dirección de Viento (DV), Velocidad de Viento (VV), Temperatura (T) y Humedad Relativa (HR). Los algoritmos de agrupamiento K-means y Fuzzy C-means son utilizados para encontrar relaciones existentes entre los contaminantes atmosféricos en estudio y las variables meteorológicas. Dichas relaciones aportan información a las RNA para obtener la predicción de los contaminantes, la cual es agregada como entrada de las RNA. Los resultados del modelo propuesto en este trabajo son comparados con los resultados de una Regresión Lineal Multivariable (RLM) y un Perceptrón Multicapa (MLP). La evaluación de la predicción se realiza con el Error Medio Absoluto, la Raíz del Error Cuadrático Medio, el coeficiente de correlación y el índice de acuerdo. Los resultados obtenidos muestran la importancia de las variables meteorológicas en la predicción de la concentración de los contaminantes SO2 y PM10 en la ciudad de Salamanca, Gto., México. Los resultados muestran que el MP predice mejor la concentración de los contaminantes SO2 y PM10 que los modelos RLM y MLP. Los modelos implementados para cada caseta de monitorizaci ón tienen la capacidad para realizar predicciones de calidad del aire, estos modelos pueden ser implementados en un sistema que permita realizar la predicción en tiempo real y analizar el impacto en la salud de la población. Entre los principales resultados obtenidos del desarrollo de esta tesis podemos citar: Se propone un modelo basado en una red neuronal artificial combinado con algoritmos de agrupamiento para la predicción con una hora de anticipaci ón de la concentración de cada contaminante (SO2 y PM10). Se diseñó un modelo diferente para cada contaminante y para cada una de las tres casetas de monitorización de la REDMAS. Se propone un modelo de predicción del promedio de la concentración de las próximas 24 horas de los contaminantes SO2 y PM10, basado en una red neuronal artificial combinado con algoritmos de agrupamiento. Se diseñó un modelo para cada caseta de monitorización de la REDMAS y para cada contaminante por separado.

Hydrologic models for emergency decision support using Bayesian networks

In the presence of a river flood, operators in charge of control must take decisions based on imperfect and incomplete sources of information (e.g., data provided by a limited number sensors) and partial knowledge about the structure and behavior of the river basin. This is a case of reasoning about a complex dynamic system with uncertainty and real-time constraints where bayesian networks can be used to provide an effective support. In this paper we describe a solution with spatio-temporal bayesian networks to be used in a context of emergencies produced by river floods. In the paper we describe first a set of types of causal relations for hydrologic processes with spatial and temporal references to represent the dynamics of the river basin. Then we describe how this was included in a computer system called SAIDA to provide assistance to operators in charge of control in a river basin. Finally the paper shows experimental results about the performance of the model.

BaTboT: a biologically inspired flapping and morphing bat robot actuated by SMA-based artificial muscles

Bats are animals that posses high maneuvering capabilities. Their wings contain dozens of articulations that allow the animal to perform aggressive maneuvers by means of controlling the wing shape during flight (morphing-wings). There is no other flying creature in nature with this level of wing dexterity and there is biological evidence that the inertial forces produced by the wings have a key role in the attitude movements of the animal. This can inspire the design of highly articulated morphing-wing micro air vehicles (not necessarily bat-like) with a significant wing-to-body mass ratio. This thesis presents the development of a novel bat-like micro air vehicle (BaTboT) inspired by the morphing-wing mechanism of bats. BaTboT’s morphology is alike in proportion compared to its biological counterpart Cynopterus brachyotis, which provides the biological foundations for developing accurate mathematical models and methods that allow for mimicking bat flight. In nature bats can achieve an amazing level of maneuverability by combining flapping and morphing wingstrokes. Attempting to reproduce the biological wing actuation system that provides that kind of motion using an artificial counterpart requires the analysis of alternative actuation technologies more likely muscle fiber arrays instead of standard servomotor actuators. Thus, NiTinol Shape Memory Alloys (SMAs) acting as artificial biceps and triceps muscles are used for mimicking the morphing wing mechanism of the bat flight apparatus. This antagonistic configuration of SMA-muscles response to an electrical heating power signal to operate. This heating power is regulated by a proper controller that allows for accurate and fast SMA actuation. Morphing-wings will enable to change wings geometry with the unique purpose of enhancing aerodynamics performance. During the downstroke phase of the wingbeat motion both wings are fully extended aimed at increasing the area surface to properly generate lift forces. Contrary during the upstroke phase of the wingbeat motion both wings are retracted to minimize the area and thus reducing drag forces. Morphing-wings do not only improve on aerodynamics but also on the inertial forces that are key to maneuver. Thus, a modeling framework is introduced for analyzing how BaTboT should maneuver by means of changing wing morphology. This allows the definition of requirements for achieving forward and turning flight according to the kinematics of the wing modulation. Motivated by the biological fact about the influence of wing inertia on the production of body accelerations, an attitude controller is proposed. The attitude control law incorporates wing inertia information to produce desired roll (φ) and pitch (θ) acceleration commands. This novel flight control approach is aimed at incrementing net body forces (Fnet) that generate propulsion. Mimicking the way how bats take advantage of inertial and aerodynamical forces produced by the wings in order to both increase lift and maneuver is a promising way to design more efficient flapping/morphing wings MAVs. The novel wing modulation strategy and attitude control methodology proposed in this thesis provide a totally new way of controlling flying robots, that eliminates the need of appendices such as flaps and rudders, and would allow performing more efficient maneuvers, especially useful in confined spaces. As a whole, the BaTboT project consists of five major stages of development: - Study and analysis of biological bat flight data reported in specialized literature aimed at defining design and control criteria. - Formulation of mathematical models for: i) wing kinematics, ii) dynamics, iii) aerodynamics, and iv) SMA muscle-like actuation. It is aimed at modeling the effects of modulating wing inertia into the production of net body forces for maneuvering. - Bio-inspired design and fabrication of: i) skeletal structure of wings and body, ii) SMA muscle-like mechanisms, iii) the wing-membrane, and iv) electronics onboard. It is aimed at developing the bat-like platform (BaTboT) that allows for testing the methods proposed. - The flight controller: i) control of SMA-muscles (morphing-wing modulation) and ii) flight control (attitude regulation). It is aimed at formulating the proper control methods that allow for the proper modulation of BaTboT’s wings. - Experiments: it is aimed at quantifying the effects of properly wing modulation into aerodynamics and inertial production for maneuvering. It is also aimed at demonstrating and validating the hypothesis of improving flight efficiency thanks to the novel control methods presented in this thesis. This thesis introduces the challenges and methods to address these stages. Windtunnel experiments will be oriented to discuss and demonstrate how the wings can considerably affect the dynamics/aerodynamics of flight and how to take advantage of wing inertia modulation that the morphing-wings enable to properly change wings’ geometry during flapping. Resumen: Los murciélagos son mamíferos con una alta capacidad de maniobra. Sus alas están conformadas por docenas de articulaciones que permiten al animal maniobrar gracias al cambio geométrico de las alas durante el vuelo. Esta característica es conocida como (alas mórficas). En la naturaleza, no existe ningún especimen volador con semejante grado de dexteridad de vuelo, y se ha demostrado, que las fuerzas inerciales producidas por el batir de las alas juega un papel fundamental en los movimientos que orientan al animal en vuelo. Estas características pueden inspirar el diseño de un micro vehículo aéreo compuesto por alas mórficas con redundantes grados de libertad, y cuya proporción entre la masa de sus alas y el cuerpo del robot sea significativa. Esta tesis doctoral presenta el desarrollo de un novedoso robot aéreo inspirado en el mecanismo de ala mórfica de los murciélagos. El robot, llamado BaTboT, ha sido diseñado con parámetros morfológicos muy similares a los descritos por su símil biológico Cynopterus brachyotis. El estudio biológico de este especimen ha permitido la definición de criterios de diseño y modelos matemáticos que representan el comportamiento del robot, con el objetivo de imitar lo mejor posible la biomecánica de vuelo de los murciélagos. La biomecánica de vuelo está definida por dos tipos de movimiento de las alas: aleteo y cambio de forma. Intentar imitar como los murciélagos cambian la forma de sus alas con un prototipo artificial, requiere el análisis de métodos alternativos de actuación que se asemejen a la biomecánica de los músculos que actúan las alas, y evitar el uso de sistemas convencionales de actuación como servomotores ó motores DC. En este sentido, las aleaciones con memoria de forma, ó por sus siglas en inglés (SMA), las cuales son fibras de NiTinol que se contraen y expanden ante estímulos térmicos, han sido usados en este proyecto como músculos artificiales que actúan como bíceps y tríceps de las alas, proporcionando la funcionalidad de ala mórfica previamente descrita. De esta manera, los músculos de SMA son mecánicamente posicionados en una configuración antagonista que permite la rotación de las articulaciones del robot. Los actuadores son accionados mediante una señal de potencia la cual es regulada por un sistema de control encargado que los músculos de SMA respondan con la precisión y velocidad deseada. Este sistema de control mórfico de las alas permitirá al robot cambiar la forma de las mismas con el único propósito de mejorar el desempeño aerodinámico. Durante la fase de bajada del aleteo, las alas deben estar extendidas para incrementar la producción de fuerzas de sustentación. Al contrario, durante el ciclo de subida del aleteo, las alas deben contraerse para minimizar el área y reducir las fuerzas de fricción aerodinámica. El control de alas mórficas no solo mejora el desempeño aerodinámico, también impacta la generación de fuerzas inerciales las cuales son esenciales para maniobrar durante el vuelo. Con el objetivo de analizar como el cambio de geometría de las alas influye en la definición de maniobras y su efecto en la producción de fuerzas netas, simulaciones y experimentos han sido llevados a cabo para medir cómo distintos patrones de modulación de las alas influyen en la producción de aceleraciones lineales y angulares. Gracias a estas mediciones, se propone un control de vuelo, ó control de actitud, el cual incorpora información inercial de las alas para la definición de referencias de aceleración angular. El objetivo de esta novedosa estrategia de control radica en el incremento de fuerzas netas para la adecuada generación de movimiento (Fnet). Imitar como los murciélagos ajustan sus alas con el propósito de incrementar las fuerzas de sustentación y mejorar la maniobra en vuelo es definitivamente un tópico de mucho interés para el diseño de robots aéros mas eficientes. La propuesta de control de vuelo definida en este trabajo de investigación podría dar paso a una nueva forma de control de vuelo de robots aéreos que no necesitan del uso de partes mecánicas tales como alerones, etc. Este control también permitiría el desarrollo de vehículos con mayor capacidad de maniobra. El desarrollo de esta investigación se centra en cinco etapas: - Estudiar y analizar el vuelo de los murciélagos con el propósito de definir criterios de diseño y control. - Formular modelos matemáticos que describan la: i) cinemática de las alas, ii) dinámica, iii) aerodinámica, y iv) actuación usando SMA. Estos modelos permiten estimar la influencia de modular las alas en la producción de fuerzas netas. - Diseño y fabricación de BaTboT: i) estructura de las alas y el cuerpo, ii) mecanismo de actuación mórfico basado en SMA, iii) membrana de las alas, y iv) electrónica abordo. - Contro de vuelo compuesto por: i) control de la SMA (modulación de las alas) y ii) regulación de maniobra (actitud). - Experimentos: están enfocados en poder cuantificar cuales son los efectos que ejercen distintos perfiles de modulación del ala en el comportamiento aerodinámico e inercial. El objetivo es demostrar y validar la hipótesis planteada al inicio de esta investigación: mejorar eficiencia de vuelo gracias al novedoso control de orientación (actitud) propuesto en este trabajo. A lo largo del desarrollo de cada una de las cinco etapas, se irán presentando los retos, problemáticas y soluciones a abordar. Los experimentos son realizados utilizando un túnel de viento con la instrumentación necesaria para llevar a cabo las mediciones de desempeño respectivas. En los resultados se discutirá y demostrará que la inercia producida por las alas juega un papel considerable en el comportamiento dinámico y aerodinámico del sistema y como poder tomar ventaja de dicha característica para regular patrones de modulación de las alas que conduzcan a mejorar la eficiencia del robot en futuros vuelos.

Mutual Information and Perplexity based Clustering of Dialogue Information for Dynamic Adaptation of Language Models

We present two approaches to cluster dialogue-based information obtained by the speech understanding module and the dialogue manager of a spoken dialogue system. The purpose is to estimate a language model related to each cluster, and use them to dynamically modify the model of the speech recognizer at each dialogue turn. In the first approach we build the cluster tree using local decisions based on a Maximum Normalized Mutual Information criterion. In the second one we take global decisions, based on the optimization of the global perplexity of the combination of the cluster-related LMs. Our experiments show a relative reduction of the word error rate of 15.17%, which helps to improve the performance of the understanding and the dialogue manager modules.

Conductance interaction identification by means of Boltzmann distribution and mutual information analysis in conductance-based neuron models

Conductance interaction identification by means of Boltzmann distribution and mutual information analysis in conductance-based neuron models.

Mutual Information and Perplexity based Clustering of Dialogue Information for Dynamic Adaptation of Language Models

We present two approaches to cluster dialogue-based information obtained by the speech understanding module and the dialogue manager of a spoken dialogue system. The purpose is to estimate a language model related to each cluster, and use them to dynamically modify the model of the speech recognizer at each dialogue turn. In the first approach we build the cluster tree using local decisions based on a Maximum Normalized Mutual Information criterion. In the second one we take global decisions, based on the optimization of the global perplexity of the combination of the cluster-related LMs. Our experiments show a relative reduction of the word error rate of 15.17%, which helps to improve the performance of the understanding and the dialogue manager modules.

Information integration: generating functional models from structural ones

Information integration is a very important topic. Reusing the knowledge and having common representations have been (and it is) an active research topic in the process systems community. Conventional (structural) But only structural models have been dealt with so far. In this paper the issue of integration is related with two types of different knowledge, functional and structural. Functional representation and analysis have proved very useful, but still it is developed and presented in a completely isolated way from the classic structural description of the process. This paper presents an architecture to integrate both representations.

Information integration: from P&I diagrams to functional models

Information integration is a very important topic. Reusing the knowledge and having common and exchangeable representations have been an active research topic in process systems engineering. In this paper we deal with information integration in two different ways, the first one sharing knowledge between different heterogeneous applications and the second one integrating two different (but complementary) types of knowledge: functional and structural. A new architecture to integrate these representation and use for several purposes is presented in this paper.

Statistical models for short-term wind power ramp forecasting

La predicción de energía eólica ha desempeñado en la última década un papel fundamental en el aprovechamiento de este recurso renovable, ya que permite reducir el impacto que tiene la naturaleza fluctuante del viento en la actividad de diversos agentes implicados en su integración, tales como el operador del sistema o los agentes del mercado eléctrico. Los altos niveles de penetración eólica alcanzados recientemente por algunos países han puesto de manifiesto la necesidad de mejorar las predicciones durante eventos en los que se experimenta una variación importante de la potencia generada por un parque o un conjunto de ellos en un tiempo relativamente corto (del orden de unas pocas horas). Estos eventos, conocidos como rampas, no tienen una única causa, ya que pueden estar motivados por procesos meteorológicos que se dan en muy diferentes escalas espacio-temporales, desde el paso de grandes frentes en la macroescala a procesos convectivos locales como tormentas. Además, el propio proceso de conversión del viento en energía eléctrica juega un papel relevante en la ocurrencia de rampas debido, entre otros factores, a la relación no lineal que impone la curva de potencia del aerogenerador, la desalineación de la máquina con respecto al viento y la interacción aerodinámica entre aerogeneradores. En este trabajo se aborda la aplicación de modelos estadísticos a la predicción de rampas a muy corto plazo. Además, se investiga la relación de este tipo de eventos con procesos atmosféricos en la macroescala. Los modelos se emplean para generar predicciones de punto a partir del modelado estocástico de una serie temporal de potencia generada por un parque eólico. Los horizontes de predicción considerados van de una a seis horas. Como primer paso, se ha elaborado una metodología para caracterizar rampas en series temporales. La denominada función-rampa está basada en la transformada wavelet y proporciona un índice en cada paso temporal. Este índice caracteriza la intensidad de rampa en base a los gradientes de potencia experimentados en un rango determinado de escalas temporales. Se han implementado tres tipos de modelos predictivos de cara a evaluar el papel que juega la complejidad de un modelo en su desempeño: modelos lineales autorregresivos (AR), modelos de coeficientes variables (VCMs) y modelos basado en redes neuronales (ANNs). Los modelos se han entrenado en base a la minimización del error cuadrático medio y la configuración de cada uno de ellos se ha determinado mediante validación cruzada. De cara a analizar la contribución del estado macroescalar de la atmósfera en la predicción de rampas, se ha propuesto una metodología que permite extraer, a partir de las salidas de modelos meteorológicos, información relevante para explicar la ocurrencia de estos eventos. La metodología se basa en el análisis de componentes principales (PCA) para la síntesis de la datos de la atmósfera y en el uso de la información mutua (MI) para estimar la dependencia no lineal entre dos señales. Esta metodología se ha aplicado a datos de reanálisis generados con un modelo de circulación general (GCM) de cara a generar variables exógenas que posteriormente se han introducido en los modelos predictivos. Los casos de estudio considerados corresponden a dos parques eólicos ubicados en España. Los resultados muestran que el modelado de la serie de potencias permitió una mejora notable con respecto al modelo predictivo de referencia (la persistencia) y que al añadir información de la macroescala se obtuvieron mejoras adicionales del mismo orden. Estas mejoras resultaron mayores para el caso de rampas de bajada. Los resultados también indican distintos grados de conexión entre la macroescala y la ocurrencia de rampas en los dos parques considerados. Abstract One of the main drawbacks of wind energy is that it exhibits intermittent generation greatly depending on environmental conditions. Wind power forecasting has proven to be an effective tool for facilitating wind power integration from both the technical and the economical perspective. Indeed, system operators and energy traders benefit from the use of forecasting techniques, because the reduction of the inherent uncertainty of wind power allows them the adoption of optimal decisions. Wind power integration imposes new challenges as higher wind penetration levels are attained. Wind power ramp forecasting is an example of such a recent topic of interest. The term ramp makes reference to a large and rapid variation (1-4 hours) observed in the wind power output of a wind farm or portfolio. Ramp events can be motivated by a broad number of meteorological processes that occur at different time/spatial scales, from the passage of large-scale frontal systems to local processes such as thunderstorms and thermally-driven flows. Ramp events may also be conditioned by features related to the wind-to-power conversion process, such as yaw misalignment, the wind turbine shut-down and the aerodynamic interaction between wind turbines of a wind farm (wake effect). This work is devoted to wind power ramp forecasting, with special focus on the connection between the global scale and ramp events observed at the wind farm level. The framework of this study is the point-forecasting approach. Time series based models were implemented for very short-term prediction, this being characterised by prediction horizons up to six hours ahead. As a first step, a methodology to characterise ramps within a wind power time series was proposed. The so-called ramp function is based on the wavelet transform and it provides a continuous index related to the ramp intensity at each time step. The underlying idea is that ramps are characterised by high power output gradients evaluated under different time scales. A number of state-of-the-art time series based models were considered, namely linear autoregressive (AR) models, varying-coefficient models (VCMs) and artificial neural networks (ANNs). This allowed us to gain insights into how the complexity of the model contributes to the accuracy of the wind power time series modelling. The models were trained in base of a mean squared error criterion and the final set-up of each model was determined through cross-validation techniques. In order to investigate the contribution of the global scale into wind power ramp forecasting, a methodological proposal to identify features in atmospheric raw data that are relevant for explaining wind power ramp events was presented. The proposed methodology is based on two techniques: principal component analysis (PCA) for atmospheric data compression and mutual information (MI) for assessing non-linear dependence between variables. The methodology was applied to reanalysis data generated with a general circulation model (GCM). This allowed for the elaboration of explanatory variables meaningful for ramp forecasting that were utilized as exogenous variables by the forecasting models. The study covered two wind farms located in Spain. All the models outperformed the reference model (the persistence) during both ramp and non-ramp situations. Adding atmospheric information had a noticeable impact on the forecasting performance, specially during ramp-down events. Results also suggested different levels of connection between the ramp occurrence at the wind farm level and the global scale.

On the dynamic adaptation of language models based on dialogue information

We present an approach to adapt dynamically the language models (LMs) used by a speech recognizer that is part of a spoken dialogue system. We have developed a grammar generation strategy that automatically adapts the LMs using the semantic information that the user provides (represented as dialogue concepts), together with the information regarding the intentions of the speaker (inferred by the dialogue manager, and represented as dialogue goals). We carry out the adaptation as a linear interpolation between a background LM, and one or more of the LMs associated to the dialogue elements (concepts or goals) addressed by the user. The interpolation weights between those models are automatically estimated on each dialogue turn, using measures such as the posterior probabilities of concepts and goals, estimated as part of the inference procedure to determine the actions to be carried out. We propose two approaches to handle the LMs related to concepts and goals. Whereas in the first one we estimate a LM for each one of them, in the second one we apply several clustering strategies to group together those elements that share some common properties, and estimate a LM for each cluster. Our evaluation shows how the system can estimate a dynamic model adapted to each dialogue turn, which helps to improve the performance of the speech recognition (up to a 14.82% of relative improvement), which leads to an improvement in both the language understanding and the dialogue management tasks.

Dam seepage analysis based on artificial neural networks: the hysteresis phenomenon

Seepage flow measurement is an important behavior indicator when providing information about dam performance. The main objective of this study is to analyze seepage by means of an artificial neural network model. The model is trained and validated with data measured at a case study. The dam behavior towards different water level changes is reproduced by the model and a hysteresis phenomenon detected and studied. Artificial neural network models are shown to be a powerful tool for predicting and understanding seepage phenomenon.