16 research outputs found

    Metamodel-based static and dynamic optimization of composite structures with ply drop-offs

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    Nowadays, there is no analytical equation able to analyze the issues related to the performance of structures with ply drop-offs. In order to address this issue, a metamodel using Design of Experiments and the SunFlower Algorithm for static and dynamic optimization of composite structures with ply drop-offs was developed in this study. Through numerical simulations and experimental tests, a characterization of the static and dynamic behavior of tapered hybrid and non-hybrid tubes was proposed. Then, a metamodel was developed considering the results obtained through numerical simulations, where the best ply drop-off location that provides the best static and dynamic conditions was identified, and, posteriorly, it was applied in the manufacture of the tubes. The numerical results revealed that the hybrid tube reinforced with carbon and glass of fibers supported a high loading in buckling conditions when compared with non-hybrid tubes. Before the manufacture of the tubular structures, an experimental comparative study using honeycomb sandwich structures with different face sheets and cores was proposed to analyze the fabric characteristics. The results showed that the hybrid fabric reinforced with glass and aramid of fibers was demonstrated to be not viable for tubular structure manufacture. Then, in the manufacture of the tubular structures, the carbon, glass, and carbon/aramid hybrid fabrics were applied. The experimental results obtained with the optimized structures revealed that the hybridization provided an increase in the level of damping. The modal analyses performed on the intact and damaged structures demonstrated a smooth reduction in the first natural frequency and in the damping factor for the damaged structures. Aiming a comparative analysis between tapered and non-tapered structures, tubular structures without ply drop-offs were manufactured and experimental tests were performed. The hybrid tapered structure manufactured with carbon, aramid, and glass of fibers proved to be a promising option in compression conditions, supporting a loading of 9.489 kN, while the non-tapered structure supported a loading of 13.283 kN. In addition, this hybrid structure revealed a lower manufacturing cost when compared with the other hybrid structures, and it was considered lighter with a mass of 53 grams. The non-tapered hybrid structure had a mass of 77 grams, 30% higher than the tapered structure’s mass. Therefore, metamodel-based static and dynamic optimization was demonstrated to be feasible and advantageous for determining the optimum ply drop-off location.Atualmente não existe uma equação analítica que seja capaz de analisar questões relacionadas ao desempenho de estruturas com ply drop-offs. Com intuito de suprir essa questão, um metamodelo usando Projeto de Experimentos e o algoritmo SunFlower para otimização do comportamento estático e dinâmico de estruturas com ply drop-offs foi proposto nesse estudo. Através de simulações numéricas e testes experimentais, uma caracterização sobre o comportamento estático e dinâmico de tubos escalonados híbridos e não híbridos foi proposta. Então, um metamodelo foi desenvolvido considerando os resultados obtidos com as simulações numéricas, onde a melhor localização para os ply drop-offs foi identificada e, posteriormente, esta localização ótima foi usada na manufatura dos tubos. Os resultados numéricos revelaram que o tubo híbrido reforçado com fibras de carbono e vidro suportou um maior carregamento em condições de flambagem quando comparado aos tubos não híbridos. Antes da manufatura dos tubos, um estudo comparativo experimental envolvendo estruturas sanduíches honeycomb considerando diferentes faces e núcleos foi desenvolvido com intuito de analisar as características de cada tecido aplicado na face das estruturas. Os resultados mostraram que o tecido híbrido reforçado com fibras de vidro e aramida não era viável na manufatura dos tubos. Então, para a manufatura das estruturas tubulares foram considerados os tecidos reforçados com fibras de carbono, vidro e carbono/aramida. Os resultados experimentais obtidos com as estruturas ótimas mostraram que a hibridização proporcionou um aumento no nível de amortecimento. As análises modais executadas com as estruturas intactas e danificadas demonstraram uma suave redução na primeira frequência natural e no fator de amortecimento para as estruturas danificadas. Então, estruturas tubulares sem drop-offs foram manufaturadas e testes experimentais foram realizados. A estrutura híbrida manufaturada com fibras de carbono, aramida e vidro provou ser uma opção promissora em condições de compressão, suportando um carregamento de 9,489 kN, enquanto a estrutura não escalonada suportou um carregamento de 13,283 kN. Além disso, essa estrutura foi considerada mais leve com massa de 53 gramas e revelou um custo de manufatura reduzido, quando comparado às outras estruturas híbridas. A estrutura híbrida não escalonada apresentou massa de 77 gramas, o que corresponde a uma massa 30% maior quando comparado à estrutura escalonada. Finalmente, o metamodelo baseado na otimização provou ser viável e vantajoso

    A Study on Hand Gesture Recognition Technique

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    Hand gesture recognition system can be used for interfacing between computer and human using hand gesture. This work presents a technique for a human computer interface through hand gesture recognition that is able to recognize 25 static gestures from the American Sign Language hand alphabet. The objective of this thesis is to develop an algorithm for recognition of hand gestures with reasonable accuracy. The segmentation of gray scale image of a hand gesture is performed using Otsu thresholding algorithm. Otsu algorithm treats any segmentation problem as classification problem. Total image level is divided into two classes one is hand and other is background. The optimal threshold value is determined by computing the ratio between class variance and total class variance. A morphological filtering method is used to effectively remove background and object noise in the segmented image. Morphological method consists of dilation, erosion, opening, and closing operation. Canny edge detection technique is used to find the boundary of hand gesture in image. A contour tracking algorithm is applied to track the contour in clockwise direction. Contour of a gesture is represented by a Localized Contour Sequence (L.C.S) whose samples are the perpendicular distances between the contour pixels and the chord connecting the end-points of a window centered on the contour pixels. These extracted features are applied as input to classifier. Linear classifier discriminates the images based on dissimilarity between two images. Multi Class Support Vector Machine (MCSVM) and Least Square Support Vector Machine (LSSVM) is also implemented for the classification purpose. Experimental result shows that 94.2% recognition accuracy is achieved by using linear classifier and 98.6% recognition accuracy is achieved using Multiclass Support Vector machine classifier. Least Square Support Vector Machine (LSSVM) classifier is also used for classification purpose and shows 99.2% recognition accuracy

    Wastewater minimization under uncertain operational conditions

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    EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    Numerical Techniques for Stochastic Optimization

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    This is a comprehensive and timely overview of the numerical techniques that have been developed to solve stochastic programming problems. After a brief introduction to the field, where accent is laid on modeling questions, the next few chapters lay out the challenges that must be met in this area. They also provide the background for the description of the computer implementations given in the third part of the book. Selected applications are described next. Some of these have directly motivated the development of the methods described in the earlier chapters. They include problems that come from facilities location, exploration investments, control of ecological systems, energy distribution and generation. Test problems are collected in the last chapter. This is the first book devoted to this subject. It comprehensively covers all major advances in the field (both Western and Soviet). It is only because of the recent developments in computer technology, that we have now reached a point where our computing power matches the inherent size requirements faced in this area. The book demonstrates that a large class of stochastic programming problems are now in the range of our numerical capacities

    Forward and inverse American option pricing via a complementarity approach

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    This dissertation considers three topics. The first part discusses the pricing of American options under a local volatility model and two jump diffusion models: Kou's jump diffusion model and the Dupire system. In Chapter 2, we establish partial differential complementarity systems for pricing American options under the aforementioned three models. We also introduce two different discretization schemes, a finite difference method and a finite element method, for the discretization of the complementarity systems into a collection of linear complementarity problems (LCPs). In Chapter 3, we discuss four popular existing numerical algorithms---a PSOR method, a two phase active-set method, a semi-smooth Newton method and a pivoting method---for solving LCPs that arise under Kou's jump diffusion model and the Dupire system. The numerical results presented in the thesis summarize the effectiveness of each approach for solving the corresponding LCPs. %In particular, we are interested in the numerical evaluation of four algorithms pricing these options: a PSOR method, a two-phase active-set method, a semi-smooth Newton method, and a parametric pivoting method. In the second part, we consider the calibration problems of computing an implied volatility parameter for American options under the Dupire system and the local volatility model. In Chapter 4, we formulate the calibration problem as an inverse problem of the forward pricing problem, which is modeled as a discretized partial differential linear complementarity system in Chapter 2. The resulting inverse problem then becomes an instance of a mathematical program with complementarity constraints (MPCC). Two methods for solving MPCCs, an implicit programming algorithm (IMPA) and a new hybrid algorithm, are studied in this dissertation. We test both algorithms and report their numerical performance for solving MPCCs derived under the Dupire system and the local volatility model with synthetic and market data. In the third part of this thesis, we investigate a new class of MPCCs, a doubly uni-parametric MPCC, for which the calibration of American options under the Black-Scholes-Merton (BSM) model is a special case. In particular, we consider one new algorithm for solving this problem when the problem matrices are positive definite, and a second algorithm for the more general case when the matrices are merely positive semi-definite. We study the convergence of both algorithms based on the local stability of the solutions as well as the numerical performance of both algorithms for solving doubly uni-parametric MPCCs with tridiagonal matrices, which are applicable for the calibration problems under the BSM model

    Hybridization of machine learning for advanced manufacturing

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    Tesis por compendio de publicacioines[ES] En el contexto de la industria, hoy por hoy, los términos “Fabricación Avanzada”, “Industria 4.0” y “Fábrica Inteligente” están convirtiéndose en una realidad. Las empresas industriales buscan ser más competitivas, ya sea en costes, tiempo, consumo de materias primas, energía, etc. Se busca ser eficiente en todos los ámbitos y además ser sostenible. El futuro de muchas compañías depende de su grado de adaptación a los cambios y su capacidad de innovación. Los consumidores son cada vez más exigentes, buscando productos personalizados y específicos con alta calidad, a un bajo coste y no contaminantes. Por todo ello, las empresas industriales implantan innovaciones tecnológicas para conseguirlo. Entre estas innovaciones tecnológicas están la ya mencionada Fabricación Avanzada (Advanced Manufacturing) y el Machine Learning (ML). En estos campos se enmarca el presente trabajo de investigación, en el que se han concebido y aplicado soluciones inteligentes híbridas que combinan diversas técnicas de ML para resolver problemas en el campo de la industria manufacturera. Se han aplicado técnicas inteligentes tales como Redes Neuronales Artificiales (RNA), algoritmos genéticos multiobjetivo, métodos proyeccionistas para la reducción de la dimensionalidad, técnicas de agrupamiento o clustering, etc. También se han utilizado técnicas de Identificación de Sistemas con el propósito de obtener el modelo matemático que representa mejor el sistema real bajo estudio. Se han hibridado diversas técnicas con el propósito de construir soluciones más robustas y fiables. Combinando técnicas de ML específicas se crean sistemas más complejos y con una mayor capacidad de representación/solución. Estos sistemas utilizan datos y el conocimiento sobre estos para resolver problemas. Las soluciones propuestas buscan solucionar problemas complejos del mundo real y de un amplio espectro, manejando aspectos como la incertidumbre, la falta de precisión, la alta dimensionalidad, etc. La presente tesis cubre varios casos de estudio reales, en los que se han aplicado diversas técnicas de ML a distintas problemáticas del campo de la industria manufacturera. Los casos de estudio reales de la industria en los que se ha trabajado, con cuatro conjuntos de datos diferentes, se corresponden con: • Proceso de fresado dental de alta precisión, de la empresa Estudio Previo SL. • Análisis de datos para el mantenimiento predictivo de una empresa del sector de la automoción, como es la multinacional Grupo Antolin. Adicionalmente se ha colaborado con el grupo de investigación GICAP de la Universidad de Burgos y con el centro tecnológico ITCL en los casos de estudio que forman parte de esta tesis y otros relacionados. Las diferentes hibridaciones de técnicas de ML desarrolladas han sido aplicadas y validadas con conjuntos de datos reales y originales, en colaboración con empresas industriales o centros de fresado, permitiendo resolver problemas actuales y complejos. De esta manera, el trabajo realizado no ha tenido sólo un enfoque teórico, sino que se ha aplicado de modo práctico permitiendo que las empresas industriales puedan mejorar sus procesos, ahorrar en costes y tiempo, contaminar menos, etc. Los satisfactorios resultados obtenidos apuntan hacia la utilidad y aportación que las técnicas de ML pueden realizar en el campo de la Fabricación Avanzada

    Models and Analysis of Vocal Emissions for Biomedical Applications

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    The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy. This edition celebrates twenty years of uninterrupted and succesfully research in the field of voice analysis
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