Detección de daño en materiales compuestos mediante fibra óptica

En este artículo se explora la aplicación del PCA (Principal Component Analysis), y mediciones estadísticas T 2 y Q para detectar daños en estructuras fabricadas en materiales compuestos mediante la utilización de FBGs (Fiber Bragg Grating). Un modelo PCA es construido usando datos de la estructura sin daños como un estado de referencia. Los defectos en la estructura son simulados causando pequeñas delaminaciones entre el panel y el rigidizador. Los datos de diferentes escenarios experimentales para la estructura sin daño y con daño son proyectados en el modelo PCA. Las proyecciones y los índices T 2 y Q son analizadas. Resultados de cada caso son presentados y discutidos demostrando la viabilidad y el potencial de usar esta formulación en SHM (Structural Health Monitoring

Sensibilidad de las variaciones en el campo de deformaciones en función de la aparición de daños en palas de aerogeneradores fabricadas en materiales compuestos

Se instrumentó un prototipo de pala de aerogenerador de 150 kW de 13 metros de longitud con 24 FBGs embebidas directamente en el material durante la fabricación. Posteriormente se realizaron mediciones de deformaciones en el prototipo de pala sin ningún daño, con el fin de determinar el baseline de la pala. Luego, se indujeron algunos daños artificiales de diferentes naturalezas y severidades con el fin de estudiar la susceptibilidad de la aparición de cambios en el campo de deformaciones y la rigidez global de la pala, en función de la aparición de dichos daños. Se realizó un estudio de esfuerzos diferenciales con el fin de determinar la variación de la rigidez en la estructura y determinar si los sensores embebidos eran capaces de detectar dicha variación. Los resultados se presentan en este artículo

A robust procedure for damage detection from strain measurements based on principal component analysis

FBGs are excellent strain sensors, because of its low size and multiplexing capability. Tens to hundred of sensors may be embedded into a structure, as it has already been demonstrated. Nevertheless, they only afford strain measurements at local points, so unless the damage affects the strain readings in a distinguishable manner, damage will go undetected. This paper show the experimental results obtained on the wing of a UAV, instrumented with 32 FBGs, before and after small damages were introduced. The PCA algorithm was able to distinguish the damage cases, even for small cracks. Principal Component Analysis (PCA) is a technique of multivariable analysis to reduce a complex data set to a lower dimension and reveal some hidden patterns that underlie

Damage Detection in Metallic Beams from Dynamic Strain Measurements under Different Load Cases by Using Automatic Clustering and Pattern Recognition Techniques

International audienceIn general, the change in the local strain field or global stiffness caused by damage in a structure is very small and the strain field tends to homogenize very quickly in the field close to the defect. Moreover, other environmental effects can fade the slight changes in the strain field. Only by comparing the response of the structure at several points some information about damage may be unveiled. By means of pattern recognition techniques based on the strain field, this task can be achieved. This is the basis of the strain measurements data-driven models. The main limitation of the strain field pattern recognition techniques lies in the susceptibility of the strain field to change depending on the load conditions. In the case of dynamic loads, this may reflect even a greater limitation. Robust automated techniques are required to manage these limitations. In first instance, automatic clustering techniques are needed so that data can be classified according to the load conditions and secondly, a dimensional reduction technique is needed in order to obtain patterns that often underlie from data. Within the context of this paper, a combination of Local Density-based Simultaneous Two-Level (DS2L-SOM) Clustering based on Self-Organizing Maps (SOM) and Principal Components Analysis (PCA) is proposed in order to firstly, classify load conditions and secondly, perform strain field pattern recognition. The clustering technique is the basis for an Optimal Baseline Selection. An experimental validation of the technique is discussed in this paper, comparing damages of different sizes and positions in an aluminum beam, under a set of combined loads under dynamic conditions. Strains were measured at several points by using Fiber Bragg Gratings

Structural health monitoring by means of strain field pattern recognition on the basis of PCA and automatic clustering techniques based on SOM

Peer ReviewedPostprint (published version

Evaluación científica de la eficacia del nuevo método cognitivo-constructivista de iniciación al aprendizaje de nuevas lenguas. Implantación y difusión de resultados

El objetivo general del proyecto es evaluar, corregir, implantar y difundir un modelo de espacio autoformativo virtual dedicado al aprendizaje de nuevas lenguas. En concreto este modelo se ha implementado en dos espacios formativos para dos lenguas diferentes: latín y alemán. Dichos espacios están localizados en: - Curso de iniciación al aprendizaje de latín: https://cv4.ucm.es/moodle/course/view.php?id=115039 - Curso de iniciación al aprendizaje de alemán: https://cv4.ucm.es/moodle/course/view.php?id=115038 Ambos cursos tienen como soporte sendos diccionarios didácticos digitales desde los que también son accesibles los cursos: - Diccionario Didáctico Digital de latín: http://repositorios.fdi.ucm.es/DiccionarioDidacticoLatin - Diccionario Didáctico Digital de alemán: http://repositorios.fdi.ucm.es/DiccionarioDidacticoAlema

Differential cross section measurements for the production of a W boson in association with jets in proton–proton collisions at √s = 7 TeV

Measurements are reported of differential cross sections for the production of a W boson, which decays into a muon and a neutrino, in association with jets, as a function of several variables, including the transverse momenta (pT) and pseudorapidities of the four leading jets, the scalar sum of jet transverse momenta (HT), and the difference in azimuthal angle between the directions of each jet and the muon. The data sample of pp collisions at a centre-of-mass energy of 7 TeV was collected with the CMS detector at the LHC and corresponds to an integrated luminosity of 5.0 fb[superscript −1]. The measured cross sections are compared to predictions from Monte Carlo generators, MadGraph + pythia and sherpa, and to next-to-leading-order calculations from BlackHat + sherpa. The differential cross sections are found to be in agreement with the predictions, apart from the pT distributions of the leading jets at high pT values, the distributions of the HT at high-HT and low jet multiplicity, and the distribution of the difference in azimuthal angle between the leading jet and the muon at low values.United States. Dept. of EnergyNational Science Foundation (U.S.)Alfred P. Sloan Foundatio

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis