Influencia de la Ciencia Ficción en las TIC y las Telecomunicaciones

Desde el comienzo de la Historia, el ser humano ha pensado en su futuro, ya fuera por el hecho de preocuparse por su supervivencia o por razones más filosóficas, como el porvenir como especie. Pensar en el futuro es anticipación. El género de la ciencia ficción es el que más se basa en la anticipación. En muchos subgéneros de la ciencia ficción se tratan temas futuristas, viajes espaciales a otros planetas, inteligencia artificial, etc. Por otra parte, en la actualidad vivimos un período donde los cambios en las TIC (Tecnologías de la Información y Comunicación) son prácticamente diarios, aunque podría llegar a decirse que los cambios se producen hora a hora. A veces, muchos de estos avances nos parecen producto de la fantasía o la ciencia ficción, o salidos de alguna película futurista. Sin embargo, dichos avances son perfectamente posibles y explicables a través de la ciencia actual. A través de este proyecto se pretenden analizar estos avances, relacionándolos con distintas obras de ciencia ficción. Una gran cantidad de los avances tecnológicos tienen su origen en alguna obra de la ciencia ficción, ya sea literatura o cine. Otro objetivo es realizar un estudio de diferentes propuestas tecnológicas de diferentes obras de esta rama que no se hayan realizado todavía, y analizar su viabilidad, su utilidad y los posibles cambios sociológicos que produciría en el mundo en el que vivimos. El tercer objetivo es evaluar la aptitud y actitud de los ingenieros de telecomunicación en cuanto a la innovación y la proyección hacia el futuro de los estos posibles cambios tecnológicos. Abstract Since the beginning of History, humans have thought about their future, either concerned about survival or by philosophical reasons like the future as species. Thinking about the future is speculation. The science fiction genre is the one that is based on speculation. Sub-genres of science fiction covers futuristic themes like space travel to other planets, artificial intelligence, etc. Today we live in a period where changes in ICT (Information and Communication Technologies) are almost daily, although we should say that changes occur in a matter of hours. Sometimes, many of these advances seem a product of fantasy or science fiction, or coming out of a futuristic movie. However, these advances are perfectly possible and explainable by current science. Through this project the intention is to analyze these developments, relating them to various works of science fiction. A great number of this technological advancements have their origin in a work of science fiction, either literature or film. Another objective is to study different technological proposals of this genre that have not been done yet, and analyze their feasibility, usefulness and potential sociological changes that occur in the world we live in. The third objective is to evaluate the ability and attitude of telecommunication engineers in terms of innovation and future projection of these potential technological changes.

Artificial metaplasticity prediction model for cognitive rehabilitation outcome in acquired brain injury patients

Objective The main purpose of this research is the novel use of artificial metaplasticity on multilayer perceptron (AMMLP) as a data mining tool for prediction the outcome of patients with acquired brain injury (ABI) after cognitive rehabilitation. The final goal aims at increasing knowledge in the field of rehabilitation theory based on cognitive affectation. Methods and materials The data set used in this study contains records belonging to 123 ABI patients with moderate to severe cognitive affectation (according to Glasgow Coma Scale) that underwent rehabilitation at Institut Guttmann Neurorehabilitation Hospital (IG) using the tele-rehabilitation platform PREVIRNEC©. The variables included in the analysis comprise the neuropsychological initial evaluation of the patient (cognitive affectation profile), the results of the rehabilitation tasks performed by the patient in PREVIRNEC© and the outcome of the patient after a 3–5 months treatment. To achieve the treatment outcome prediction, we apply and compare three different data mining techniques: the AMMLP model, a backpropagation neural network (BPNN) and a C4.5 decision tree. Results The prediction performance of the models was measured by ten-fold cross validation and several architectures were tested. The results obtained by the AMMLP model are clearly superior, with an average predictive performance of 91.56%. BPNN and C4.5 models have a prediction average accuracy of 80.18% and 89.91% respectively. The best single AMMLP model provided a specificity of 92.38%, a sensitivity of 91.76% and a prediction accuracy of 92.07%. Conclusions The proposed prediction model presented in this study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients. The ability to predict treatment outcomes may provide new insights toward improving effectiveness and creating personalized therapeutic interventions based on clinical evidence.

Methodological guidelines for reusing general ontologies

Currently, there is a great deal of well-founded explicit knowledge formalizing general notions, such as time concepts and the part_of relation. Yet, it is often the case that instead of reusing ontologies that implement such notions (the so-called general ontologies), engineers create procedural programs that implicitly implement this knowledge. They do not save time and code by reusing explicit knowledge, and devote effort to solve problems that other people have already adequately solved. Consequently, we have developed a methodology that helps engineers to: (a) identify the type of general ontology to be reused; (b) find out which axioms and definitions should be reused; (c) make a decision, using formal concept analysis, on what general ontology is going to be reused; and (d) adapt and integrate the selected general ontology in the domain ontology to be developed. To illustrate our approach we have employed use-cases. For each use case, we provide a set of heuristics with examples. Each of these heuristics has been tested in either OWL or Prolog. Our methodology has been applied to develop a pharmaceutical product ontology. Additionally, we have carried out a controlled experiment with graduated students doing a MCs in Artificial Intelligence. This experiment has yielded some interesting findings concerning what kind of features the future extensions of the methodology should have.

Bounded non-deterministic planning for multimedia adaptation

This paper proposes a novel combination of artificial intelligence planning and other techniques for improving decision-making in the context of multi-step multimedia content adaptation. In particular, it describes a method that allows decision-making (selecting the adaptation to perform) in situations where third-party pluggable multimedia conversion modules are involved and the multimedia adaptation planner does not know their exact adaptation capabilities. In this approach, the multimedia adaptation planner module is only responsible for a part of the required decisions; the pluggable modules make additional decisions based on different criteria. We demonstrate that partial decision-making is not only attainable, but also introduces advantages with respect to a system in which these conversion modules are not capable of providing additional decisions. This means that transferring decisions from the multi-step multimedia adaptation planner to the pluggable conversion modules increases the flexibility of the adaptation. Moreover, by allowing conversion modules to be only partially described, the range of problems that these modules can address increases, while significantly decreasing both the description length of the adaptation capabilities and the planning decision time. Finally, we specify the conditions under which knowing the partial adaptation capabilities of a set of conversion modules will be enough to compute a proper adaptation plan.

A Propose-and-revise System for Real-time Traffic Management

The aim of this paper is to describe an intelligent system for the problem of real time road traffic control. The purpose of the system is to help traffic engineers in the selection of the state of traffic control devices on real time, using data recorded by traffic detectors on motorways. The system follows an advanced knowledge-based approach that implements an abstract generic problem solving method, called propose-and-revise, which was proposed in Artificial Intelligence, within the knowledge engineering field, as a standard cognitive structure oriented to solve configuration design problems. The paper presents the knowledge model of such a system together with the strategy of inference and describes how it was applied for the case of the M-40 urban ring for the city of Madrid.

Speed-independent gait identification for mobile devices

Due to the intensive use of mobile phones for diferent purposes, these devices usually contain condential information which must not be accessed by another person apart from the owner of the device. Furthermore, the new generation phones commonly incorporate an accelerometer which may be used to capture the acceleration signals produced as a result of owner s gait. Nowadays, gait identication in basis of acceleration signals is being considered as a new biometric technique which allows blocking the device when another person is carrying it. Although distance based approaches as Euclidean distance or dynamic time warping have been applied to solve this identication problem, they show di±culties when dealing with gaits at diferent speeds. For this reason, in this paper, a method to extract an average template from instances of the gait at diferent velocities is presented. This method has been tested with the gait signals of 34 subjects while walking at diferent motion speeds (slow, normal and fast) and it has shown to improve the performance of Euclidean distance and classical dynamic time warping.

Aplicación de técnicas de inteligencia artificial para contribuir en la detección de microcalcificaciones en mamografía digitalizada

A nivel mundial, el cáncer de mama es el tipo de cáncer más frecuente además de una de las principales causas de muerte entre la población femenina. Actualmente, el método más eficaz para detectar lesiones mamarias en una etapa temprana es la mamografía. Ésta contribuye decisivamente al diagnóstico precoz de esta enfermedad que, si se detecta a tiempo, tiene una probabilidad de curación muy alta. Uno de los principales y más frecuentes hallazgos en una mamografía, son las microcalcificaciones, las cuales son consideradas como un indicador importante de cáncer de mama. En el momento de analizar las mamografías, factores como la capacidad de visualización, la fatiga o la experiencia profesional del especialista radiólogo hacen que el riesgo de omitir ciertas lesiones presentes se vea incrementado. Para disminuir dicho riesgo es importante contar con diferentes alternativas como por ejemplo, una segunda opinión por otro especialista o un doble análisis por el mismo. En la primera opción se eleva el coste y en ambas se prolonga el tiempo del diagnóstico. Esto supone una gran motivación para el desarrollo de sistemas de apoyo o asistencia en la toma de decisiones. En este trabajo de tesis se propone, se desarrolla y se justifica un sistema capaz de detectar microcalcificaciones en regiones de interés extraídas de mamografías digitalizadas, para contribuir a la detección temprana del cáncer demama. Dicho sistema estará basado en técnicas de procesamiento de imagen digital, de reconocimiento de patrones y de inteligencia artificial. Para su desarrollo, se tienen en cuenta las siguientes consideraciones: 1. Con el objetivo de entrenar y probar el sistema propuesto, se creará una base de datos de imágenes, las cuales pertenecen a regiones de interés extraídas de mamografías digitalizadas. 2. Se propone la aplicación de la transformada Top-Hat, una técnica de procesamiento digital de imagen basada en operaciones de morfología matemática. La finalidad de aplicar esta técnica es la de mejorar el contraste entre las microcalcificaciones y el tejido presente en la imagen. 3. Se propone un algoritmo novel llamado sub-segmentación, el cual está basado en técnicas de reconocimiento de patrones aplicando un algoritmo de agrupamiento no supervisado, el PFCM (Possibilistic Fuzzy c-Means). El objetivo es encontrar las regiones correspondientes a las microcalcificaciones y diferenciarlas del tejido sano. Además, con la finalidad de mostrar las ventajas y desventajas del algoritmo propuesto, éste es comparado con dos algoritmos del mismo tipo: el k-means y el FCM (Fuzzy c-Means). Por otro lado, es importante destacar que en este trabajo por primera vez la sub-segmentación es utilizada para detectar regiones pertenecientes a microcalcificaciones en imágenes de mamografía. 4. Finalmente, se propone el uso de un clasificador basado en una red neuronal artificial, específicamente un MLP (Multi-layer Perceptron). El propósito del clasificador es discriminar de manera binaria los patrones creados a partir de la intensidad de niveles de gris de la imagen original. Dicha clasificación distingue entre microcalcificación y tejido sano. ABSTRACT Breast cancer is one of the leading causes of women mortality in the world and its early detection continues being a key piece to improve the prognosis and survival. Currently, the most reliable and practical method for early detection of breast cancer is mammography.The presence of microcalcifications has been considered as a very important indicator ofmalignant types of breast cancer and its detection and classification are important to prevent and treat the disease. However, the detection and classification of microcalcifications continue being a hard work due to that, in mammograms there is a poor contrast between microcalcifications and the tissue around them. Factors such as visualization, tiredness or insufficient experience of the specialist increase the risk of omit some present lesions. To reduce this risk, is important to have alternatives such as a second opinion or a double analysis for the same specialist. In the first option, the cost increases and diagnosis time also increases for both of them. This is the reason why there is a great motivation for development of help systems or assistance in the decision making process. This work presents, develops and justifies a system for the detection of microcalcifications in regions of interest extracted fromdigitizedmammographies to contribute to the early detection of breast cancer. This systemis based on image processing techniques, pattern recognition and artificial intelligence. For system development the following features are considered: With the aim of training and testing the system, an images database is created, belonging to a region of interest extracted from digitized mammograms. The application of the top-hat transformis proposed. This image processing technique is based on mathematical morphology operations. The aim of this technique is to improve the contrast betweenmicrocalcifications and tissue present in the image. A novel algorithm called sub-segmentation is proposed. The sub-segmentation is based on pattern recognition techniques applying a non-supervised clustering algorithm known as Possibilistic Fuzzy c-Means (PFCM). The aim is to find regions corresponding to the microcalcifications and distinguish them from the healthy tissue. Furthermore,with the aim of showing themain advantages and disadvantages this is compared with two algorithms of same type: the k-means and the fuzzy c-means (FCM). On the other hand, it is important to highlight in this work for the first time the sub-segmentation is used for microcalcifications detection. Finally, a classifier based on an artificial neural network such as Multi-layer Perceptron is used. The purpose of this classifier is to discriminate froma binary perspective the patterns built from gray level intensity of the original image. This classification distinguishes between microcalcifications and healthy tissue.

Derivación deductiva de programas funcionales con patrones

Una de las dificultades principales en el desarrollo de software es la ausencia de un marco conceptual adecuado para su estudio. Una propuesta la constituye el modelo transformativo, que entiende el desarrollo de software como un proceso iterativo de transformación de especificaciones: se parte de una especificación inicial que va transformándose sucesivamente hasta obtener una especificación final que se toma como programa. Este modelo básico puede llevarse a la práctica de varias maneras. En concreto, la aproximación deductiva toma una sentencia lógica como especificación inicial y su proceso transformador consiste en la demostración de la sentencia; como producto secundario de la demostración se deriva un programa que satisface la especificación inicial. La tesis desarrolla un método deductivo para la derivación de programas funcionales con patrones, escritos en un lenguaje similar a Hope. El método utiliza una lógica multigénero, cuya relación con el lenguaje de programación es estudiada. También se identifican los esquemas de demostración necesarios para la derivación de funciones con patrones, basados en la demostración independiente de varias subsentencias. Cada subsentencia proporciona una subespecificación de una ecuación del futuro programa a derivar. Nuestro método deductivo está inspirado en uno previo de Zohar Manna y Richard Waldinger, conocido como el cuadro deductivo, que deriva programas en un lenguaje similar a Lisp. El nuevo método es una modificación del cuadro de estos autores, que incorpora géneros y permite demostrar una especificación mediante varios cuadros. Cada cuadro demuestra una subespecificación y por tanto deriva una ecuación del programa. Se prevén mecanismos para que los programas derivados puedan contener definiciones locales con patrones y variables anónimas y sinónimas y para que las funciones auxiliares derivadas no usen variables de las funciones principales. La tesis se completa con varios ejemplos de aplicación, un mecanismo que independentiza el método del lenguaje de programación y un prototipo de entorno interactivo de derivación deductiva. Categorías y descriptores de materia CR D.l.l [Técnicas de programación]: Programación funcional; D.2.10 [Ingeniería de software]: Diseño - métodos; F.3.1 [Lógica y significado de los programas]: Especificación, verificación y razonamiento sobre programas - lógica de programas; F.3.3 [Lógica y significado de los programas]: Estudios de construcciones de programas - construcciones funcionales; esquemas de programa y de recursion; 1.2.2 [Inteligencia artificial]: Programación automática - síntesis de programas; 1.2.3 [Inteligencia artificial]: Deducción y demostración de teoremas]: extracción de respuesta/razón; inducción matemática. Términos generales Programación funcional, síntesis de programas, demostración de teoremas. Otras palabras claves y expresiones Funciones con patrones, cuadro deductivo, especificación parcial, inducción estructural, teorema de descomposición.---ABSTRACT---One of the main difficulties in software development is the lack of an adequate conceptual framework of study. The transformational model is one such proposal that conceives software development as an iterative process of specifications transformation: an initial specification is developed and successively transformed until a final specification is obtained and taken as a program. This basic model can be implemented in several ways. The deductive approach takes a logical sentence as the initial specification and its proof constitutes the transformational process; as a byproduct of the proof, a program which satisfies the initial specification is derived. In the thesis, a deductive method for the derivation of Hope-like functional programs with patterns is developed. The method uses a many-sorted logic, whose relation to the programming language is studied. Also the proof schemes necessary for the derivation of functional programs with patterns, based on the independent proof of several subsentences, are identified. Each subsentence provides a subspecification of one equation of the future program to be derived. Our deductive method is inspired on a previous one by Zohar Manna and Richard Waldinger, known as the deductive tableau, which derives Lisp-like programs. The new method incorporates sorts in the tableau and allows to prove a sentence with several tableaux. Each tableau proves a subspecification and therefore derives an equation of the program. Mechanisms are included to allow the derived programs to contain local definitions with patterns and anonymous and synonymous variables; also, the derived auxiliary functions cannot reference parameters of their main functions. The thesis is completed with several application examples, i mechanism to make the method independent from the programming language and an interactive environment prototype for deductive derivation. CR categories and subject descriptors D.l.l [Programming techniques]: Functional programming; D.2.10 [Software engineering]: Design - methodologies; F.3.1 [Logics and meanings of programa]: Specifying and verifying and reasoning about programs - logics of programs; F.3.3 [Logics and meanings of programs]: Studies of program constructs - functional constructs; program and recursion schemes; 1.2.2 [Artificial intelligence]: Automatic programming - program synthesis; 1.2.3 [Artificial intelligence]: Deduction and theorem proving - answer/reason extraction; mathematical induction. General tenas Functional programming, program synthesis, theorem proving. Additional key words and phrases Functions with patterns, deductive tableau, structural induction, partial specification, descomposition theorem.

Robust thermal face recognition using region classifiers

This paper presents a robust approach for recognition of thermal face images based on decision level fusion of 34 different region classifiers. The region classifiers concentrate on local variations. They use singular value decomposition (SVD) for feature extraction. Fusion of decisions of the region classifier is done by using majority voting technique. The algorithm is tolerant against false exclusion of thermal information produced by the presence of inconsistent distribution of temperature statistics which generally make the identification process difficult. The algorithm is extensively evaluated on UGC-JU thermal face database, and Terravic facial infrared database and the recognition performance are found to be 95.83% and 100%, respectively. A comparative study has also been made with the existing works in the literature.

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%.

System failure prediction through rare-events elastic-net logistic regression

Predicting failures in a distributed system based on previous events through logistic regression is a standard approach in literature. This technique is not reliable, though, in two situations: in the prediction of rare events, which do not appear in enough proportion for the algorithm to capture, and in environments where there are too many variables, as logistic regression tends to overfit on this situations; while manually selecting a subset of variables to create the model is error- prone. On this paper, we solve an industrial research case that presented this situation with a combination of elastic net logistic regression, a method that allows us to automatically select useful variables, a process of cross-validation on top of it and the application of a rare events prediction technique to reduce computation time. This process provides two layers of cross- validation that automatically obtain the optimal model complexity and the optimal mode l parameters values, while ensuring even rare events will be correctly predicted with a low amount of training instances. We tested this method against real industrial data, obtaining a total of 60 out of 80 possible models with a 90% average model accuracy.

Real-time incidents detection in the highways of the future

Due to ever increasing transportation of people and goods, automatic traffic surveillance is becoming a key issue for both providing safety to road users and improving traffic control in an efficient way. In this paper, we propose a new system that, exploiting the capabilities that both computer vision and machine learning offer, is able to detect and track different types of real incidents on a highway. Specifically, it is able to accurately detect not only stopped vehicles, but also drivers and passengers leaving the stopped vehicle, and other pedestrians present in the roadway. Additionally, a theoretical approach for detecting vehicles which may leave the road in an unexpected way is also presented. The system works in real-time and it has been optimized for working outdoor, being thus appropriate for its deployment in a real-world environment like a highway. First experimental results on a dataset created with videos provided by two Spanish highway operators demonstrate the effectiveness of the proposed system and its robustness against noise and low-quality videos.

Optimal Control Using Feedback Linearization for a Generalized T-S Model

In this paper, a fuzzy feedback linearization is used to control nonlinear systems described by Takagi-Suengo (T-S) fuzzy systems. In this work, an optimal controller is designed using the linear quadratic regulator (LQR). The well known weighting parameters approach is applied to optimize local and global approximation and modelling capability of T-S fuzzy model to improve the choice of the performance index and minimize it. The approach used here can be considered as a generalized version of T-S method. Simulation results indicate the potential, simplicity and generality of the estimation method and the robustness of the proposed optimal LQR algorithm.

Sistemas de ayuda al diagnóstico y a la terapia funcional en enfermedades neurodegenerativas

El incremento de la esperanza de vida en los países desarrollados (más de 80 años en 2013), está suponiendo un crecimiento considerable en la incidencia y prevalencia de enfermedades discapacitantes, que si bien pueden aparecer a edades tempranas, son más frecuentes en la tercera edad, o en sus inmediaciones. Enfermedades neuro-degenerativas que suponen un gran hándicap funcional, pues algunas de ellas están asociadas a movimientos involuntarios de determinadas partes del cuerpo, sobre todo de las extremidades. Tareas cotidianas como la ingesta de alimento, vestirse, escribir, interactuar con el ordenador, etc… pueden llegar a ser grandes retos para las personas que las padecen. El diagnóstico precoz y certero resulta fundamental para la prescripción de la terapia o tratamiento óptimo. Teniendo en cuenta incluso que en muchos casos, por desgracia la mayoría, sólo se puede actuar para mitigar los síntomas, y no para sanarlos, al menos de momento. Aun así, acertar de manera temprana en el diagnóstico supone proporcionar al enfermo una mayor calidad de vida durante mucho más tiempo, por lo cual el esfuerzo merece, y mucho, la pena. Los enfermos de Párkinson y de temblor esencial suponen un porcentaje importante de la casuística clínica en los trastornos del movimiento que impiden llevar una vida normal, que producen una discapacidad física y una no menos importante exclusión social. Las vías de tratamiento son dispares de ahí que sea crítico acertar en el diagnóstico lo antes posible. Hasta la actualidad, los profesionales y expertos en medicina, utilizan unas escalas cualitativas para diferenciar la patología y su grado de afectación. Dichas escalas también se utilizan para efectuar un seguimiento clínico y registrar la historia del paciente. En esta tesis se propone una serie de métodos de análisis y de identificación/clasificación de los tipos de temblor asociados a la enfermedad de Párkinson y el temblor esencial. Empleando técnicas de inteligencia artificial basadas en clasificadores inteligentes: redes neuronales (MLP y LVQ) y máquinas de soporte vectorial (SVM), a partir del desarrollo e implantación de un sistema para la medida y análisis objetiva del temblor: DIMETER. Dicho sistema además de ser una herramienta eficaz para la ayuda al diagnóstico, presenta también las capacidades necesarias para proporcionar un seguimiento riguroso y fiable de la evolución de cada paciente. ABSTRACT The increase in life expectancy in developed countries in more than 80 years (data belongs to 2013), is assuming considerable growth in the incidence and prevalence of disabling diseases. Although they may appear at an early age, they are more common in the elderly ages or in its vicinity. Nuero-degenerative diseases that are a major functional handicap, as some of them are associated with involuntary movements of certain body parts, especially of the limbs. Everyday tasks such as food intake, dressing, writing, interact with the computer, etc ... can become large debris for people who suffer. Early and accurate diagnosis is crucial for prescribing optimal therapy or treatment. Even taking into account that in many cases, unfortunately the majority, can only act to mitigate the symptoms, not to cure them, at least for now. Nevertheless, early diagnosis may provide the patient a better quality of life for much longer time, so the effort is worth, and much, grief. Sufferers of Parkinson's and essential tremor represent a significant percentage of clinical casuistry in movement disorders that prevent a normal life, leading to physical disability and not least social exclusion. There are various treatment methods, which makes it necessary the immediate diagnosis. Up to date, professionals and medical experts, use a qualitative scale to differentiate the disease and degree of involvement. Therefore, those scales are used in clinical follow-up. In this thesis, several methods of analysis and identification / classification of types of tremor associated with Parkinson's disease and essential tremor are proposed. Using artificial intelligence techniques based on intelligent classification: neural networks (MLP and LVQ) and support vector machines (SVM), starting from the development and implementation of a system for measuring and objective analysis of the tremor: DIMETER. This system besides being an effective tool to aid diagnosis, it also has the necessary capabilities to provide a rigorous and reliable monitoring of the evolution of each patient.

Estimación de la resistencia de estructuras de hormigón armado reforzadas a cortante mediante FRP

El empleo de refuerzos de FRP en vigas de hormigón armado es cada vez más frecuente por sus numerosas ventajas frente a otros métodos más tradicionales. Durante los últimos años, la técnica FRP-NSM, consistente en introducir barras de FRP sobre el recubrimiento de una viga de hormigón, se ha posicionado como uno de los mejores métodos de refuerzo y rehabilitación de estructuras de hormigón armado, tanto por su facilidad de montaje y mantenimiento, como por su rendimiento para aumentar la capacidad resistente de dichas estructuras. Si bien el refuerzo a flexión ha sido ampliamente desarrollado y estudiado hasta la fecha, no sucede lo mismo con el refuerzo a cortante, debido principalmente a su gran complejidad. Sin embargo, se debería dedicar más estudio a este tipo de refuerzo si se pretenden conservar los criterios de diseño en estructuras de hormigón armado, los cuales están basados en evitar el fallo a cortante por sus consecuencias catastróficas Esta ausencia de información y de normativa es la que justifica esta tesis doctoral. En este pro-yecto se van a desarrollar dos metodologías alternativas, que permiten estimar la capacidad resistente de vigas de hormigón armado, reforzadas a cortante mediante la técnica FRP-NSM. El primer método aplicado consiste en la implementación de una red neuronal artificial capaz de predecir adecuadamente la resistencia a cortante de vigas reforzadas con este método a partir de experimentos anteriores. Asimismo, a partir de la red se han llevado a cabo algunos estudios a fin de comprender mejor la influencia real de algunos parámetros de la viga y del refuerzo sobre la resistencia a cortante con el propósito de lograr diseños más seguros de este tipo de refuerzo. Una configuración óptima de la red requiere discriminar adecuadamente de entre los numerosos parámetros (geométricos y de material) que pueden influir en el compor-tamiento resistente de la viga, para lo cual se han llevado a cabo diversos estudios y pruebas. Mediante el segundo método, se desarrolla una ecuación de proyecto que permite, de forma sencilla, estimar la capacidad de vigas reforzadas a cortante con FRP-NSM, la cual podría ser propuesta para las principales guías de diseño. Para alcanzar este objetivo, se plantea un pro-blema de optimización multiobjetivo a partir de resultados de ensayos experimentales llevados a cabo sobre vigas de hormigón armado con y sin refuerzo de FRP. El problema multiobjetivo se resuelve mediante algoritmos genéticos, en concreto el algoritmo NSGA-II, por ser más apropiado para problemas con varias funciones objetivo que los métodos de optimización clásicos. Mediante una comparativa de las predicciones realizadas con ambos métodos y de los resulta-dos de ensayos experimentales se podrán establecer las ventajas e inconvenientes derivadas de la aplicación de cada una de las dos metodologías. Asimismo, se llevará a cabo un análisis paramétrico con ambos enfoques a fin de intentar determinar la sensibilidad de aquellos pa-rámetros más sensibles a este tipo de refuerzo. Finalmente, se realizará un análisis estadístico de la fiabilidad de las ecuaciones de diseño deri-vadas de la optimización multiobjetivo. Con dicho análisis se puede estimar la capacidad resis-tente de una viga reforzada a cortante con FRP-NSM dentro de un margen de seguridad espe-cificado a priori. ABSTRACT The use of externally bonded (EB) fibre-reinforced polymer (FRP) composites has gained acceptance during the last two decades in the construction engineering community, particularly in the rehabilitation of reinforced concrete (RC) structures. Currently, to increase the shear resistance of RC beams, FRP sheets are externally bonded (EB-FRP) and applied on the external side surface of the beams to be strengthened with different configurations. Of more recent application, the near-surface mounted FRP bar (NSM-FRP) method is another technique successfully used to increase the shear resistance of RC beams. In the NSM method, FRP rods are embedded into grooves intentionally prepared in the concrete cover of the side faces of RC beams. While flexural strengthening has been widely developed and studied so far, the same doesn´t occur to shearing strength mainly due to its great complexity. Nevertheless, if design criteria are to be preserved more research should be done to this sort of strength, which are based on avoiding shear failure and its catastrophic consequences. However, in spite of this, accurately calculating the shear capacity of FRP shear strengthened RC beams remains a complex challenge that has not yet been fully resolved due to the numerous variables involved in the procedure. The objective of this Thesis is to develop methodologies to evaluate the capacity of FRP shear strengthened RC beams by dealing with the problem from a different point of view to the numerical modeling approach by using artificial intelligence techniques. With this purpose two different approaches have been developed: one concerned with the use of artificial neural networks and the other based on the implementation of an optimization approach developed jointly with the use of artificial neural networks (ANNs) and solved with genetic algorithms (GAs). With these approaches some of the difficulties concerned regarding the numerical modeling can be overcome. As an alternative tool to conventional numerical techniques, neural networks do not provide closed form solutions for modeling problems but do, however, offer a complex and accurate solution based on a representative set of historical examples of the relationship. Furthermore, they can adapt solutions over time to include new data. On the other hand, as a second proposal, an optimization approach has also been developed to implement simple yet accurate shear design equations for this kind of strengthening. This approach is developed in a multi-objective framework by considering experimental results of RC beams with and without NSM-FRP. Furthermore, the results obtained with the previous scheme based on ANNs are also used as a filter to choose the parameters to include in the design equations. Genetic algorithms are used to solve the optimization problem since they are especially suitable for solving multi-objective problems when compared to standard optimization methods. The key features of the two proposed procedures are outlined and their performance in predicting the capacity of NSM-FRP shear strengthened RC beams is evaluated by comparison with results from experimental tests and with predictions obtained using a simplified numerical model. A sensitivity study of the predictions of both models for the input parameters is also carried out.