Monitorización de estructuras aeronáuticas mediante técnicas de inteligencia artificial

Una de las barreras para la aplicación de las técnicas de monitorización de la integridad estructural (SHM) basadas en ondas elásticas guiadas (GLW) en aeronaves es la influencia perniciosa de las condiciones ambientales y de operación (EOC). En esta tesis se ha estudiado dicha influencia y la compensación de la misma, particularizando en variaciones del estado de carga y temperatura. La compensación de dichos efectos se fundamenta en Redes Neuronales Artificiales (ANN) empleando datos experimentales procesados con la Transformada Chirplet. Los cambios en la geometría y en las propiedades del material respecto al estado inicial de la estructura (lo daños) provocan cambios en la forma de onda de las GLW (lo que denominamos característica sensible al daño o DSF). Mediante técnicas de tratamiento de señal se puede buscar una relación entre dichas variaciones y los daños, esto se conoce como SHM. Sin embargo, las variaciones en las EOC producen también cambios en los datos adquiridos relativos a las GLW (DSF) que provocan errores en los algoritmos de diagnóstico de daño (SHM). Esto sucede porque las firmas de daño y de las EOC en la DSF son del mismo orden. Por lo tanto, es necesario cuantificar y compensar el efecto de las EOC sobre la GLW. Si bien existen diversas metodologías para compensar los efectos de las EOC como por ejemplo “Optimal Baseline Selection” (OBS) o “Baseline Signal Stretching” (BSS), estas, se emplean exclusivamente en la compensación de los efectos térmicos. El método propuesto en esta tesis mezcla análisis de datos experimentales, como en el método OBS, y modelos basados en Redes Neuronales Artificiales (ANN) que reemplazan el modelado físico requerido por el método BSS. El análisis de datos experimentales consiste en aplicar la Transformada Chirplet (CT) para extraer la firma de las EOC sobre la DSF. Con esta información, obtenida bajo diversas EOC, se entrena una ANN. A continuación, la ANN actuará como un interpolador de referencias de la estructura sin daño, generando información de referencia para cualquier EOC. La comparación de las mediciones reales de la DSF con los valores simulados por la ANN, dará como resultado la firma daño en la DSF, lo que permite el diagnóstico de daño. Este esquema se ha aplicado y verificado, en diversas EOC, para una estructura unidimensional con un único camino de daño, y para una estructura representativa de un fuselaje de una aeronave, con curvatura y múltiples elementos rigidizadores, sometida a un estado de cargas complejo, con múltiples caminos de daños. Los efectos de las EOC se han estudiado en detalle en la estructura unidimensional y se han generalizado para el fuselaje, demostrando la independencia del método respecto a la configuración de la estructura y el tipo de sensores utilizados para la adquisición de datos GLW. Por otra parte, esta metodología se puede utilizar para la compensación simultánea de una variedad medible de EOC, que afecten a la adquisición de datos de la onda elástica guiada. El principal resultado entre otros, de esta tesis, es la metodología CT-ANN para la compensación de EOC en técnicas SHM basadas en ondas elásticas guiadas para el diagnóstico de daño. ABSTRACT One of the open problems to implement Structural Health Monitoring techniques based on elastic guided waves in real aircraft structures at operation is the influence of the environmental and operational conditions (EOC) on the damage diagnosis problem. This thesis deals with the compensation of these environmental and operational effects, specifically, the temperature and the external loading, by the use of the Chirplet Transform working with Artificial Neural Networks. It is well known that the guided elastic wave form is affected by the damage appearance (what is known as the damage sensitive feature or DSF). The DSF is modified by the temperature and by the load applied to the structure. The EOC promotes variations in the acquired data (DSF) and cause mistakes in damage diagnosis algorithms. This effect promotes changes on the waveform due to the EOC variations of the same order than the damage occurrence. It is difficult to separate both effects in order to avoid damage diagnosis mistakes. Therefore it is necessary to quantify and compensate the effect of EOC over the GLW forms. There are several approaches to compensate the EOC effects such as Optimal Baseline Selection (OBS) or Baseline Signal Stretching (BSS). Usually, they are used for temperature compensation. The new method proposed here mixes experimental data analysis, as in the OBS method, and Artificial Neural Network (ANN) models to replace the physical modelling which involves the BSS method. The experimental data analysis studied is based on apply the Chirplet Transform (CT) to extract the EOC signature on the DSF. The information obtained varying EOC is employed to train an ANN. Then, the ANN will act as a baselines interpolator of the undamaged structure. The ANN generates reference information at any EOC. By comparing real measurements of the DSF against the ANN simulated values, the damage signature appears clearly in the DSF, enabling an accurate damage diagnosis. This schema has been applied in a range of EOC for a one-dimensional structure containing single damage path and two dimensional real fuselage structure with stiffener elements and multiple damage paths. The EOC effects tested in the one-dimensional structure have been generalized to the fuselage showing its independence from structural arrangement and the type of sensors used for GLW data acquisition. Moreover, it can be used for the simultaneous compensation of a variety of measurable EOC, which affects the guided wave data acquisition. The main result, among others, of this thesis is the CT-ANN methodology for the compensation of EOC in GLW based SHM technique for damage diagnosis

Calidad de Experiencia en servicios multimedia sobre IP

En este trabajo abordamos un esquema de medida de calidad de experiencia para servicios multimedia sobre IP. Esta arquitectura, denominada QuEM (Qualitative Experience Measure), es más adecuada que los esquemas tipo MOS para la monitorización de un gran volumen de tráfico multimedia en tiempo real: facilita la agregación de resultados y la interpretación de las medidas por los operadores. La arquitectura se basa en la detección y caracterización de eventos que degraden la calidad de experiencia durante un tiempo determinado y que puedan ser descritos de forma cualitativa. Cada tipo de evento es monitorizado por un detector específico denominado QuID (Qualitative Impairment Detector). En el artículo desarrollamos la arquitectura QuEM y proponemos un conjunto de QuIDs adecuado para la monitorización de servicios como IPTV o videoconferencia

The potential risk induced by climate change in the context of mega-nourishments

Peer ReviewedPostprint (published version

Long-term and large-scale modeling of mega-nourishments

The Sand Engine, ZM (Zandmotor), is a hook-shaped mega-nourishment (21.5 millions m³) located on the Dutch coast with an alongshore length of 2.4 km and an offshore extension of 1 km. The mega-nourishment project was initiated as a coastal protection measure on decadal time scales to maintain the coastline under predicted sea level rise. It follows the philosophy of working in harmony with the forces of nature by taking advantage of the longshore transport as the main distributor of sand along the adjacent coast (Stive et al., 2013). In the present contribution we use the Q2Dmorfo model (van den Berg, et al., 2012) to predict the long-term dynamics of the ZM.Peer ReviewedPostprint (published version

Coordinating heterogeneous IoT devices by means of the centralized vision of the SDN controller

The IoT (Internet of Things) has become a reality during recent years. The desire of having everything connected to the Internet results in clearly identified benefits that will impact on socio economic development. However, the exponential growth in the number of IoT devices and their heterogeneity open new challenges that must be carefully studied. Coordination among devices to adapt them to their users' context usually requires high volumes of data to be exchanged with the cloud. In order to reduce unnecessary communications and network overhead, this paper proposes a novel network architecture based on the Software-Defined Networking paradigm that allows IoT devices coordinate and adapt them within the scope of a particular context.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Métodos de catalogación y tipología de cibermedios

Las tipologías sobre cibermedios se plantean como objetivo mostrar taxonomías o clasificaciones sobre una nueva realidad comunicacional (los cibermedios) que ha surgido al amparo del nacimiento de las TICs. Entre las pertinencias de elaborar tipologías nos encontramos, principalmente, el hecho de que sirven para estructurar/organizar/comprender una realidad que, por novedosa, se encuentra dispersa y/o poco definida. El discurso actual sobre los cibermedios requiere el estudio sobre tipologías, en el momento en que, efectivamente, mientras unos se encuentran más o menos consolidados, otros se hallan en una fase incluso de definición o conceptualización. Esbozar tipologías de cibermedios es también oportuno en el momento en que abarcan tres niveles de conocimiento: primero, el estado inicial de la cuestión (esto es: cuáles son esas estructuras de comunicación a las que nos referimos como cibermedios); segundo, qué características poseen (rasgos definitorios/identificativos); y, tercero, las dinámicas que se dan entre ellos; esto es: qué tipo de relación, influencia e interacción predomina entre unos y otros. Y ello, entre otros motivos, porque en el escenario que analizamos, caracterizado por la pluralidad de formas y niveles de la comunicación, la atención tradicionalmente prestada a la comunicación de masas ha de extenderse necesariamente a otros planteamientos con cada vez más presencia en los nuevos medios

A non-linear model for forecasting the monthly demand for electricity in Colombia

ABSTRACT This article provides a comparison of the performance of an ARIMA model, a multilayer perceptron, and an autoregressive neural network for forecasting the monthly demand for electricity in Colombia for the following month. The available data were divided into two different sets, i.e. one set for estimating the model parameters, and the other for evaluating the forecast ability outside the range of the sample calibration data. The results show that the autoregressive neural network is able to forecast the demand more accurately than the other two models when the total available data are considered.

Self-organized kilometer-scale shoreline sand wave generation: sensitivity to model and physical parameters

The instability mechanisms for self-organized kilometer-scale shoreline sand waves have been extensively explored by modeling. However, while the assumed bathymetric perturbation associated with the sand wave controls the feedback between morphology and waves, its effect on the instability onset has not been explored. In addition, no systematic investigation of the effect of the physical parameters has been done yet. Using a linear stability model, we investigate the effect of wave conditions, cross-shore profile, closure depth, and two perturbation shapes (P1: cross-shore bathymetric profile shift, and P2: bed level perturbation linearly decreasing offshore). For a P1 perturbation, no instability occurs below an absolute critical angle ¿c0˜ 40-50°. For a P2 perturbation, there is no absolute critical angle: sand waves can develop also for low-angle waves. In fact, the bathymetric perturbation shape plays a key role in low-angle wave instability: such instability only develops if the curvature of the depth contours offshore the breaking zone is larger than the shoreline one. This can occur for the P2 perturbation but not for P1. The analysis of bathymetric data suggests that both curvature configurations could exist in nature. For both perturbation types, large wave angle, small wave period, and large closure depth strongly favor instability. The cross-shore profile has almost no effect with a P1 perturbation, whereas large surf zone slope and gently sloping shoreface strongly enhance instability under low-angle waves for a P2 perturbation. Finally, predictive statistical models are set up to identify sites prone to exhibit either a critical angle close to ¿c0 or low-angle wave instability.Postprint (author's final draft

Adiciones a la orquidoflora de la provincia de Valladolid y zonas limítrofes

Additions to the orchid flora obtained during field work and bibliographic search are presented in this work. With 5 new taxa for the province [Epipactis atrorubens (Hoffm.) Besser., Himantoglossum hircinum (L.) Spreng, Neotinea maculata (Desf.) Stearn., Orchis langei K. Richt. and Orchis papilionacea L.], nowadays, the orchid flora from Valladolid has a total of 27 taxa included in 11 genera, 2 of these genera are also new for the province (Himantoglossum Spreng. and Neotinea Rchb. fil.). In addition, we provide new presence data for 9 taxa

Analytical marginalisation over photometric redshift uncertainties in cosmic shear analyses

As the statistical power of imaging surveys grows, it is crucial to account for all systematic uncertainties. This is normally done by constructing a model of these uncertainties and then marginalizing over the additional model parameters. The resulting high dimensionality of the total parameter spaces makes inferring the cosmological parameters significantly more costly using traditional Monte-Carlo sampling methods. A particularly relevant example is the redshift distribution, $p(z)$, of the source samples, which may require tens of parameters to describe fully. However, relatively tight priors can be usually placed on these parameters through calibration of the associated systematics. In this paper we show, quantitatively, that a linearisation of the theoretical prediction with respect to these calibratable systematic parameters allows us to analytically marginalise over these extra parameters, leading to a factor $\sim30$ reduction in the time needed for parameter inference, while accurately recovering the same posterior distributions for the cosmological parameters that would be obtained through a full numerical marginalisation over 160 $p(z)$ parameters. We demonstrate that this is feasible not only with current data and current achievable calibration priors but also for future Stage-IV datasets.Comment: 11 pages, 8 figures, prepared for submission to MNRAS, comments welcom