Interpretation of the pressuremeter test using numerical models based on deformation tensor equations

The pressuremeter test in boreholes has proven itself as a useful tool in geotechnical explorations, especially comparing its results with those obtained from a mathematical model ruled by a soil representative constitutive equation. The numerical model shown in this paper is aimed to be the reference framework for the interpretation of this test. The model analyses variables such as: the type of response, the initial state, the drainage regime and the constitutive equations. It is a model of finite elements able to work with a mesh without deformation or one adapted to it.

Cooperative Multi-robot Patrolling: A study of distributed approaches based on mathematical models of game theory to protect infrastructures

El principio de Teoría de Juegos permite desarrollar modelos estocásticos de patrullaje multi-robot para proteger infraestructuras criticas. La protección de infraestructuras criticas representa un gran reto para los países al rededor del mundo, principalmente después de los ataques terroristas llevados a cabo la década pasada. En este documento el termino infraestructura hace referencia a aeropuertos, plantas nucleares u otros instalaciones. El problema de patrullaje se define como la actividad de patrullar un entorno determinado para monitorear cualquier actividad o sensar algunas variables ambientales. En esta actividad, un grupo de robots debe visitar un conjunto de puntos de interés definidos en un entorno en intervalos de tiempo irregulares con propósitos de seguridad. Los modelos de partullaje multi-robot son utilizados para resolver este problema. Hasta el momento existen trabajos que resuelven este problema utilizando diversos principios matemáticos. Los modelos de patrullaje multi-robot desarrollados en esos trabajos representan un gran avance en este campo de investigación. Sin embargo, los modelos con los mejores resultados no son viables para aplicaciones de seguridad debido a su naturaleza centralizada y determinista. Esta tesis presenta cinco modelos de patrullaje multi-robot distribuidos e impredecibles basados en modelos matemáticos de aprendizaje de Teoría de Juegos. El objetivo del desarrollo de estos modelos está en resolver los inconvenientes presentes en trabajos preliminares. Con esta finalidad, el problema de patrullaje multi-robot se formuló utilizando conceptos de Teoría de Grafos, en la cual se definieron varios juegos en cada vértice de un grafo. Los modelos de patrullaje multi-robot desarrollados en este trabajo de investigación se han validado y comparado con los mejores modelos disponibles en la literatura. Para llevar a cabo tanto la validación como la comparación se ha utilizado un simulador de patrullaje y un grupo de robots reales. Los resultados experimentales muestran que los modelos de patrullaje desarrollados en este trabajo de investigación trabajan mejor que modelos de trabajos previos en el 80% de 150 casos de estudio. Además de esto, estos modelos cuentan con varias características importantes tales como distribución, robustez, escalabilidad y dinamismo. Los avances logrados con este trabajo de investigación dan evidencia del potencial de Teoría de Juegos para desarrollar modelos de patrullaje útiles para proteger infraestructuras. ABSTRACT Game theory principle allows to developing stochastic multi-robot patrolling models to protect critical infrastructures. Critical infrastructures protection is a great concern for countries around the world, mainly due to terrorist attacks in the last decade. In this document, the term infrastructures includes airports, nuclear power plants, and many other facilities. The patrolling problem is defined as the activity of traversing a given environment to monitoring any activity or sensing some environmental variables If this activity were performed by a fleet of robots, they would have to visit some places of interest of an environment at irregular intervals of time for security purposes. This problem is solved using multi-robot patrolling models. To date, literature works have been solved this problem applying various mathematical principles.The multi-robot patrolling models developed in those works represent great advances in this field. However, the models that obtain the best results are unfeasible for security applications due to their centralized and predictable nature. This thesis presents five distributed and unpredictable multi-robot patrolling models based on mathematical learning models derived from Game Theory. These multi-robot patrolling models aim at overcoming the disadvantages of previous work. To this end, the multi-robot patrolling problem was formulated using concepts of Graph Theory to represent the environment. Several normal-form games were defined at each vertex of a graph in this formulation. The multi-robot patrolling models developed in this research work have been validated and compared with best ranked multi-robot patrolling models in the literature. Both validation and comparison were preformed by using both a patrolling simulator and real robots. Experimental results show that the multirobot patrolling models developed in this research work improve previous ones in as many as 80% of 150 cases of study. Moreover, these multi-robot patrolling models rely on several features to highlight in security applications such as distribution, robustness, scalability, and dynamism. The achievements obtained in this research work validate the potential of Game Theory to develop patrolling models to protect infrastructures.

Region-based moving object detection using spatially conditioned nonparametric models in a GPU

A novel GPU-based nonparametric moving object detection strategy for computer vision tools requiring real-time processing is proposed. An alternative and efficient Bayesian classifier to combine nonparametric background and foreground models allows increasing correct detections while avoiding false detections. Additionally, an efficient region of interest analysis significantly reduces the computational cost of the detections.

Cooperative Learning Model based on Multi-Agent Architecture for Embedded Intelligent Systems

Cooperative systems are suitable for many types of applications and nowadays these system are vastly used to improve a previously defined system or to coordinate multiple devices working together. This paper provides an alternative to improve the reliability of a previous intelligent identification system. The proposed approach implements a cooperative model based on multi-agent architecture. This new system is composed of several radar-based systems which identify a detected object and transmit its own partial result by implementing several agents and by using a wireless network to transfer data. The proposed topology is a centralized architecture where the coordinator device is in charge of providing the final identification result depending on the group behavior. In order to find the final outcome, three different mechanisms are introduced. The simplest one is based on majority voting whereas the others use two different weighting voting procedures, both providing the system with learning capabilities. Using an appropriate network configuration, the success rate can be improved from the initial 80% up to more than 90%.

A Semantic Approach to Problem-Based Learning with Conceptual Models

El aprendizaje basado en problemas se lleva aplicando con éxito durante las últimas tres décadas en un amplio rango de entornos de aprendizaje. Este enfoque educacional consiste en proponer problemas a los estudiantes de forma que puedan aprender sobre un dominio particular mediante el desarrollo de soluciones a dichos problemas. Si esto se aplica al modelado de conocimiento, y en particular al basado en Razonamiento Cualitativo, las soluciones a los problemas pasan a ser modelos que representan el compotamiento del sistema dinámico propuesto. Por lo tanto, la tarea del estudiante en este caso es acercar su modelo inicial (su primer intento de representar el sistema) a los modelos objetivo que proporcionan soluciones al problema, a la vez que adquieren conocimiento sobre el dominio durante el proceso. En esta tesis proponemos KaiSem, un método que usa tecnologías y recursos semánticos para guiar a los estudiantes durante el proceso de modelado, ayudándoles a adquirir tanto conocimiento como sea posible sin la directa supervisión de un profesor. Dado que tanto estudiantes como profesores crean sus modelos de forma independiente, estos tendrán diferentes terminologías y estructuras, dando lugar a un conjunto de modelos altamente heterogéneo. Para lidiar con tal heterogeneidad, proporcionamos una técnica de anclaje semántico para determinar, de forma automática, enlaces entre la terminología libre usada por los estudiantes y algunos vocabularios disponibles en la Web de Datos, facilitando con ello la interoperabilidad y posterior alineación de modelos. Por último, proporcionamos una técnica de feedback semántico para comparar los modelos ya alineados y generar feedback basado en las posibles discrepancias entre ellos. Este feedback es comunicado en forma de sugerencias individualizadas que el estudiante puede utilizar para acercar su modelo a los modelos objetivos en cuanto a su terminología y estructura se refiere. ABSTRACT Problem-based learning has been successfully applied over the last three decades to a diverse range of learning environments. This educational approach consists of posing problems to learners, so they can learn about a particular domain by developing solutions to them. When applied to conceptual modeling, and particularly to Qualitative Reasoning, the solutions to problems are models that represent the behavior of a dynamic system. Therefore, the learner's task is to move from their initial model, as their first attempt to represent the system, to the target models that provide solutions to that problem while acquiring domain knowledge in the process. In this thesis we propose KaiSem, a method for using semantic technologies and resources to scaffold the modeling process, helping the learners to acquire as much domain knowledge as possible without direct supervision from the teacher. Since learners and experts create their models independently, these will have different terminologies and structure, giving rise to a pool of models highly heterogeneous. To deal with such heterogeneity, we provide a semantic grounding technique to automatically determine links between the unrestricted terminology used by learners and some online vocabularies of the Web of Data, thus facilitating the interoperability and later alignment of the models. Lastly, we provide a semantic-based feedback technique to compare the aligned models and generate feedback based on the possible discrepancies. This feedback is communicated in the form of individualized suggestions, which can be used by the learner to bring their model closer in terminology and structure to the target models.

Dynamic topic-based adaptation of language models: a comparison between different approaches

This paper presents a dynamic LM adaptation based on the topic that has been identified on a speech segment. We use LSA and the given topic labels in the training dataset to obtain and use the topic models. We propose a dynamic language model adaptation to improve the recognition performance in "a two stages" AST system. The final stage makes use of the topic identification with two variants: the first on uses just the most probable topic and the other one depends on the relative distances of the topics that have been identified. We perform the adaptation of the LM as a linear interpolation between a background model and topic-based LM. The interpolation weight id dynamically adapted according to different parameters. The proposed method is evaluated on the Spanish partition of the EPPS speech database. We achieved a relative reduction in WER of 11.13% over the baseline system which uses a single blackground LM.

Application of Agent Technology for Fault Diagnosis of Telecommunication Networks

La presente tesis doctoral contribuye al problema del diagnóstico autonómico de fallos en redes de telecomunicación. En las redes de telecomunicación actuales, las operadoras realizan tareas de diagnóstico de forma manual. Dichas operaciones deben ser llevadas a cabo por ingenieros altamente cualificados que cada vez tienen más dificultades a la hora de gestionar debidamente el crecimiento exponencial de la red tanto en tamaño, complejidad y heterogeneidad. Además, el advenimiento del Internet del Futuro hace que la demanda de sistemas que simplifiquen y automaticen la gestión de las redes de telecomunicación se haya incrementado en los últimos años. Para extraer el conocimiento necesario para desarrollar las soluciones propuestas y facilitar su adopción por los operadores de red, se propone una metodología de pruebas de aceptación para sistemas multi-agente enfocada en simplificar la comunicación entre los diferentes grupos de trabajo involucrados en todo proyecto de desarrollo software: clientes y desarrolladores. Para contribuir a la solución del problema del diagnóstico autonómico de fallos, se propone una arquitectura de agente capaz de diagnosticar fallos en redes de telecomunicación de manera autónoma. Dicha arquitectura extiende el modelo de agente Belief-Desire- Intention (BDI) con diferentes modelos de diagnóstico que gestionan las diferentes sub-tareas del proceso. La arquitectura propuesta combina diferentes técnicas de razonamiento para alcanzar su propósito gracias a un modelo estructural de la red, que usa razonamiento basado en ontologías, y un modelo causal de fallos, que usa razonamiento Bayesiano para gestionar debidamente la incertidumbre del proceso de diagnóstico. Para asegurar la adecuación de la arquitectura propuesta en situaciones de gran complejidad y heterogeneidad, se propone un marco de argumentación que permite diagnosticar a agentes que estén ejecutando en dominios federados. Para la aplicación de este marco en un sistema multi-agente, se propone un protocolo de coordinación en el que los agentes dialogan hasta alcanzar una conclusión para un caso de diagnóstico concreto. Como trabajos futuros, se consideran la extensión de la arquitectura para abordar otros problemas de gestión como el auto-descubrimiento o la auto-optimización, el uso de técnicas de reputación dentro del marco de argumentación para mejorar la extensibilidad del sistema de diagnóstico en entornos federados y la aplicación de las arquitecturas propuestas en las arquitecturas de red emergentes, como SDN, que ofrecen mayor capacidad de interacción con la red. ABSTRACT This PhD thesis contributes to the problem of autonomic fault diagnosis of telecommunication networks. Nowadays, in telecommunication networks, operators perform manual diagnosis tasks. Those operations must be carried out by high skilled network engineers which have increasing difficulties to properly manage the growing of those networks, both in size, complexity and heterogeneity. Moreover, the advent of the Future Internet makes the demand of solutions which simplifies and automates the telecommunication network management has been increased in recent years. To collect the domain knowledge required to developed the proposed solutions and to simplify its adoption by the operators, an agile testing methodology is defined for multiagent systems. This methodology is focused on the communication gap between the different work groups involved in any software development project, stakeholders and developers. To contribute to overcoming the problem of autonomic fault diagnosis, an agent architecture for fault diagnosis of telecommunication networks is defined. That architecture extends the Belief-Desire-Intention (BDI) agent model with different diagnostic models which handle the different subtasks of the process. The proposed architecture combines different reasoning techniques to achieve its objective using a structural model of the network, which uses ontology-based reasoning, and a causal model, which uses Bayesian reasoning to properly handle the uncertainty of the diagnosis process. To ensure the suitability of the proposed architecture in complex and heterogeneous environments, an argumentation framework is defined. This framework allows agents to perform fault diagnosis in federated domains. To apply this framework in a multi-agent system, a coordination protocol is defined. This protocol is used by agents to dialogue until a reliable conclusion for a specific diagnosis case is reached. Future work comprises the further extension of the agent architecture to approach other managements problems, such as self-discovery or self-optimisation; the application of reputation techniques in the argumentation framework to improve the extensibility of the diagnostic system in federated domains; and the application of the proposed agent architecture in emergent networking architectures, such as SDN, which offers new capabilities of control for the network.