950 resultados para finite mixture models
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Työssä on tutkittu laboratoriokokeen ja elementtimenetelmän avulla eri geometrioiden vaikutusta sekundääriseen momentin syntymiseen K-liitoksella. Kokeen liitos on tehty S700-lujuusluokan Ruukin Optim 700 plus MH nelikulmaisista rakenneputkista (RHS). Elementtimalleissa on käytetty geometrista ja materiaalista epälineaarisuutta ennustamaan liitoksen muodonmuutoskykyä ja laskennallista kestävyyttä. Liitoksen elementtimalleissa muutettavia geometrioita ovat: vapaaväli, uumasauvan ja paarteen välinen kulma, paarteen seinämän paksuus, liitoksen eksentrisyys ja uumasauvan ja paarteen leveyden suhde. Laboratoriokokeen liitoksen vetouumasauvassa vaikuttava sekundäärisen momentin aiheuttama jännitys on noin 25 % vetouumasauvan myötörajasta. Suurin sekundäärinen momentti syntyy, kun vapaaväliä pienennetään ja uumasauvaa kavennetaan paarteeseen nähden. Eurocode 3:n mitoitusohjeita voidaan elementtimallien perusteella soveltaa tietyille geometrioille turvallisesti.
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In this research, the effectiveness of Naive Bayes and Gaussian Mixture Models classifiers on segmenting exudates in retinal images is studied and the results are evaluated with metrics commonly used in medical imaging. Also, a color variation analysis of retinal images is carried out to find how effectively can retinal images be segmented using only the color information of the pixels.
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Affiliation: Département de Biochimie, Faculté de médecine, Université de Montréal
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Les modèles à sur-représentation de zéros discrets et continus ont une large gamme d'applications et leurs propriétés sont bien connues. Bien qu'il existe des travaux portant sur les modèles discrets à sous-représentation de zéro et modifiés à zéro, la formulation usuelle des modèles continus à sur-représentation -- un mélange entre une densité continue et une masse de Dirac -- empêche de les généraliser afin de couvrir le cas de la sous-représentation de zéros. Une formulation alternative des modèles continus à sur-représentation de zéros, pouvant aisément être généralisée au cas de la sous-représentation, est présentée ici. L'estimation est d'abord abordée sous le paradigme classique, et plusieurs méthodes d'obtention des estimateurs du maximum de vraisemblance sont proposées. Le problème de l'estimation ponctuelle est également considéré du point de vue bayésien. Des tests d'hypothèses classiques et bayésiens visant à déterminer si des données sont à sur- ou sous-représentation de zéros sont présentées. Les méthodes d'estimation et de tests sont aussi évaluées au moyen d'études de simulation et appliquées à des données de précipitation agrégées. Les diverses méthodes s'accordent sur la sous-représentation de zéros des données, démontrant la pertinence du modèle proposé. Nous considérons ensuite la classification d'échantillons de données à sous-représentation de zéros. De telles données étant fortement non normales, il est possible de croire que les méthodes courantes de détermination du nombre de grappes s'avèrent peu performantes. Nous affirmons que la classification bayésienne, basée sur la distribution marginale des observations, tiendrait compte des particularités du modèle, ce qui se traduirait par une meilleure performance. Plusieurs méthodes de classification sont comparées au moyen d'une étude de simulation, et la méthode proposée est appliquée à des données de précipitation agrégées provenant de 28 stations de mesure en Colombie-Britannique.
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Ce mémoire présente l’évaluation du remodelage osseux autour des composantes acétabulaires non cimentées press-fit d’une arthroplastie de resurfaçage (RH) et d’une prothèse totale de hanche (PTH) après un minimum de 21 mois d’implantation. Nous avons mesuré par l’absorptiométrie à rayons X en double énergie (DEXA) la densité minérale osseuse (DMO) supra acétabulaire chez 60 patients (44 RH, 16 PTH). Aucune différence significative de la moyenne des DMO au niveau de la zone globale et de la zone centrale de l’acétabulum n’a été trouvée entre la hanche opérée et la hanche controlatérale, dans les deux groupes de traitement. Cependant, la DMO des zones corticospongieuses médiale et latérale est plus élevée du côté opéré par rapport au côté non opéré avec la cupule en chrome cobalt de la RH; (p=0,014 et 0,031 respectivement). Alors que pour la PTH avec une cupule en titane, la différence de la DMO au niveau de ces zones n’est pas significative; (p=0,130 et 0,733). Ces données semblent démontrer qu’avec des cupules plus rigides, il y a transfert des contraintes de charges vers la périphérie corticale. C’est la première étude à évaluer le remodelage osseux péri acétabulaire avec un RH. Cela montre que la DMO est relativement préservée et que le transfert des contraintes vers la périphérie peut être favorable au maintien de la stabilité de l’implant primaire et aux éventuelles révisions de la cupule press-fit du RH.
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Cette étude aborde le thème de l’utilisation des modèles de mélange de lois pour analyser des données de comportements et d’habiletés cognitives mesurées à plusieurs moments au cours du développement des enfants. L’estimation des mélanges de lois multinormales en utilisant l’algorithme EM est expliquée en détail. Cet algorithme simplifie beaucoup les calculs, car il permet d’estimer les paramètres de chaque groupe séparément, permettant ainsi de modéliser plus facilement la covariance des observations à travers le temps. Ce dernier point est souvent mis de côté dans les analyses de mélanges. Cette étude porte sur les conséquences d’une mauvaise spécification de la covariance sur l’estimation du nombre de groupes formant un mélange. La conséquence principale est la surestimation du nombre de groupes, c’est-à-dire qu’on estime des groupes qui n’existent pas. En particulier, l’hypothèse d’indépendance des observations à travers le temps lorsque ces dernières étaient corrélées résultait en l’estimation de plusieurs groupes qui n’existaient pas. Cette surestimation du nombre de groupes entraîne aussi une surparamétrisation, c’est-à-dire qu’on utilise plus de paramètres qu’il n’est nécessaire pour modéliser les données. Finalement, des modèles de mélanges ont été estimés sur des données de comportements et d’habiletés cognitives. Nous avons estimé les mélanges en supposant d’abord une structure de covariance puis l’indépendance. On se rend compte que dans la plupart des cas l’ajout d’une structure de covariance a pour conséquence d’estimer moins de groupes et les résultats sont plus simples et plus clairs à interpréter.
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The thesis has covered various aspects of modeling and analysis of finite mean time series with symmetric stable distributed innovations. Time series analysis based on Box and Jenkins methods are the most popular approaches where the models are linear and errors are Gaussian. We highlighted the limitations of classical time series analysis tools and explored some generalized tools and organized the approach parallel to the classical set up. In the present thesis we mainly studied the estimation and prediction of signal plus noise model. Here we assumed the signal and noise follow some models with symmetric stable innovations.We start the thesis with some motivating examples and application areas of alpha stable time series models. Classical time series analysis and corresponding theories based on finite variance models are extensively discussed in second chapter. We also surveyed the existing theories and methods correspond to infinite variance models in the same chapter. We present a linear filtering method for computing the filter weights assigned to the observation for estimating unobserved signal under general noisy environment in third chapter. Here we consider both the signal and the noise as stationary processes with infinite variance innovations. We derived semi infinite, double infinite and asymmetric signal extraction filters based on minimum dispersion criteria. Finite length filters based on Kalman-Levy filters are developed and identified the pattern of the filter weights. Simulation studies show that the proposed methods are competent enough in signal extraction for processes with infinite variance.Parameter estimation of autoregressive signals observed in a symmetric stable noise environment is discussed in fourth chapter. Here we used higher order Yule-Walker type estimation using auto-covariation function and exemplify the methods by simulation and application to Sea surface temperature data. We increased the number of Yule-Walker equations and proposed a ordinary least square estimate to the autoregressive parameters. Singularity problem of the auto-covariation matrix is addressed and derived a modified version of the Generalized Yule-Walker method using singular value decomposition.In fifth chapter of the thesis we introduced partial covariation function as a tool for stable time series analysis where covariance or partial covariance is ill defined. Asymptotic results of the partial auto-covariation is studied and its application in model identification of stable auto-regressive models are discussed. We generalize the Durbin-Levinson algorithm to include infinite variance models in terms of partial auto-covariation function and introduce a new information criteria for consistent order estimation of stable autoregressive model.In chapter six we explore the application of the techniques discussed in the previous chapter in signal processing. Frequency estimation of sinusoidal signal observed in symmetric stable noisy environment is discussed in this context. Here we introduced a parametric spectrum analysis and frequency estimate using power transfer function. Estimate of the power transfer function is obtained using the modified generalized Yule-Walker approach. Another important problem in statistical signal processing is to identify the number of sinusoidal components in an observed signal. We used a modified version of the proposed information criteria for this purpose.
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Frames are the most widely used structural system for multistorey buildings. A building frame is a three dimensional discrete structure consisting of a number of high rise bays in two directions at right angles to each other in the vertical plane. Multistorey frames are a three dimensional lattice structure which are statically indeterminate. Frames sustain gravity loads and resist lateral forces acting on it. India lies at the north westem end of the Indo-Australian tectonic plate and is identified as an active tectonic area. Under horizontal shaking of the ground, horizontal inertial forces are generated at the floor levels of a multistorey frame. These lateral inertia forces are transferred by the floor slab to the beams, subsequently to the columns and finally to the soil through the foundation system. There are many parameters that affect the response of a structure to ground excitations such as, shape, size and geometry of the structure, type of foundation, soil characteristics etc. The Soil Structure Interaction (SS1) effects refer to the influence of the supporting soil medium on the behavior of the structure when it is subjected to different types of loads. Interaction between the structure and its supporting foundation and soil, which is a complete system, has been modeled with finite elements. Numerical investigations have been carried out on a four bay, twelve storeyed regular multistorey frame considering depth of fixity at ground level, at characteristic depth of pile and at full depth. Soil structure interaction effects have been studied by considering two models for soil viz., discrete and continuum. Linear static analysis has been conducted to study the interaction effects under static load. Free vibration analysis and further shock spectrum analysis has been conducted to study the interaction effects under time dependent loads. The study has been extended to four types of soil viz., laterite, sand, alluvium and layered.The structural responses evaluated in the finite element analysis are bending moment, shear force and axial force for columns, and bending moment and shear force for beams. These responses increase with increase in the founding depth; however these responses show minimal increase beyond the characteristic length of pile. When the soil structure interaction effects are incorporated in the analysis, the aforesaid responses of the frame increases upto the characteristic depth and decreases when the frame has been analysed for the full depth. It has been observed that shock spectrum analysis gives wide variation of responses in the frame compared to linear elastic analysis. Both increase and decrease in responses have been observed in the interior storeys. The good congruence shown by the two finite element models viz., discrete and continuum in linear static analysis has been absent in shock spectrum analysis.
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A sandwich construction is a special form of the laminated composite consisting of light weight core, sandwiched between two stiff thin face sheets. Due to high stiffness to weight ratio, sandwich construction is widely adopted in aerospace industries. As a process dependent bonded structure, the most severe defects associated with sandwich construction are debond (skin core bond failure) and dent (locally deformed skin associated with core crushing). Reasons for debond may be attributed to initial manufacturing flaws or in service loads and dent can be caused by tool drops or impacts by foreign objects. This paper presents an evaluation on the performance of honeycomb sandwich cantilever beam with the presence of debond or dent, using layered finite element models. Dent is idealized by accounting core crushing in the core thickness along with the eccentricity of the skin. Debond is idealized using multilaminate modeling at debond location with contact element between the laminates. Vibration and buckling behavior of metallic honeycomb sandwich beam with and without damage are carried out. Buckling load factor, natural frequency, mode shape and modal strain energy are evaluated using finite element package ANSYS 13.0. Study shows that debond affect the performance of the structure more severely than dent. Reduction in the fundamental frequencies due to the presence of dent or debond is not significant for the case considered. But the debond reduces the buckling load factor significantly. Dent of size 8-20% of core thickness shows 13% reduction in buckling load capacity of the sandwich column. But debond of the same size reduced the buckling load capacity by about 90%. This underscores the importance of detecting these damages in the initiation level itself to avoid catastrophic failures. Influence of the damages on fundamental frequencies, mode shape and modal strain energy are examined. Effectiveness of these parameters as a damage detection tool for sandwich structure is also assessed
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Hat Stiffened Plates are used in composite ships and are gaining popularity in metallic ship construction due to its high strength-to-weight ratio. Light weight structures will result in greater payload, higher speeds, reduced fuel consumption and environmental emissions. Numerical Investigations have been carried out using the commercial Finite Element software ANSYS 12 to substantiate the high strength-to-weight ratio of Hat Stiffened Plates over other open section stiffeners which are commonly used in ship building. Analysis of stiffened plate has always been a matter of concern for the structural engineers since it has been rather difficult to quantify the actual load sharing between stiffeners and plating. Finite Element Method has been accepted as an efficient tool for the analysis of stiffened plated structure. Best results using the Finite Element Method for the analysis of thin plated structures are obtained when both the stiffeners and the plate are modeled using thin plate elements having six degrees of freedom per node. However, one serious problem encountered with this design and analysis process is that the generation of the finite element models for a complex configuration is time consuming and laborious. In order to overcome these difficulties two different methods viz., Orthotropic Plate Model and Superelement for Hat Stiffened Plate have been suggested in the present work. In the Orthotropic Plate Model geometric orthotropy is converted to material orthotropy i.e., the stiffeners are smeared and they vanish from the field of analysis and the structure can be analysed using any commercial Finite Element software which has orthotropic elements in its element library. The Orthotropic Plate Model developed has predicted deflection, stress and linear buckling load with sufficiently good accuracy in the case of all four edges simply supported boundary condition. Whereas, in the case of two edges fixed and other two edges simply supported boundary condition even though the stress has been predicted with good accuracy there has been large variation in the deflection predicted. This variation in the deflection predicted is because, for the Orthotropic Plate Model the rigidity is uniform throughout the plate whereas in the actual Hat Stiffened Plate the rigidity along the line of attachment of the stiffeners to the plate is large as compared to the unsupported portion of the plate. The Superelement technique is a method of treating a portion of the structure as if it were a single element even though it is made up of many individual elements. The Superelement has predicted the deflection and in-plane stress of Hat Stiffened Plate with sufficiently good accuracy for different boundary conditions. Formulation of Superelement for composite Hat Stiffened Plate has also been presented in the thesis. The capability of Orthotropic Plate Model and Superelement to handle typical boundary conditions and characteristic loads in a ship structure has been demonstrated through numerical investigations.
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We present distribution independent bounds on the generalization misclassification performance of a family of kernel classifiers with margin. Support Vector Machine classifiers (SVM) stem out of this class of machines. The bounds are derived through computations of the $V_gamma$ dimension of a family of loss functions where the SVM one belongs to. Bounds that use functions of margin distributions (i.e. functions of the slack variables of SVM) are derived.
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Real-world learning tasks often involve high-dimensional data sets with complex patterns of missing features. In this paper we review the problem of learning from incomplete data from two statistical perspectives---the likelihood-based and the Bayesian. The goal is two-fold: to place current neural network approaches to missing data within a statistical framework, and to describe a set of algorithms, derived from the likelihood-based framework, that handle clustering, classification, and function approximation from incomplete data in a principled and efficient manner. These algorithms are based on mixture modeling and make two distinct appeals to the Expectation-Maximization (EM) principle (Dempster, Laird, and Rubin 1977)---both for the estimation of mixture components and for coping with the missing data.
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We formulate density estimation as an inverse operator problem. We then use convergence results of empirical distribution functions to true distribution functions to develop an algorithm for multivariate density estimation. The algorithm is based upon a Support Vector Machine (SVM) approach to solving inverse operator problems. The algorithm is implemented and tested on simulated data from different distributions and different dimensionalities, gaussians and laplacians in $R^2$ and $R^{12}$. A comparison in performance is made with Gaussian Mixture Models (GMMs). Our algorithm does as well or better than the GMMs for the simulations tested and has the added advantage of being automated with respect to parameters.
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La aplicación de materiales compuestos de matriz polimérica reforzados mediante fibras largas (FRP, Fiber Reinforced Plastic), está en gradual crecimiento debido a las buenas propiedades específicas y a la flexibilidad en el diseño. Uno de los mayores consumidores es la industria aeroespacial, dado que la aplicación de estos materiales tiene claros beneficios económicos y medioambientales. Cuando los materiales compuestos se aplican en componentes estructurales, se inicia un programa de diseño donde se combinan ensayos reales y técnicas de análisis. El desarrollo de herramientas de análisis fiables que permiten comprender el comportamiento mecánico de la estructura, así como reemplazar muchos, pero no todos, los ensayos reales, es de claro interés. Susceptibilidad al daño debido a cargas de impacto fuera del plano es uno de los aspectos de más importancia que se tienen en cuenta durante el proceso de diseño de estructuras de material compuesto. La falta de conocimiento de los efectos del impacto en estas estructuras es un factor que limita el uso de estos materiales. Por lo tanto, el desarrollo de modelos de ensayo virtual mecánico para analizar la resistencia a impacto de una estructura es de gran interés, pero aún más, la predicción de la resistencia residual después del impacto. En este sentido, el presente trabajo abarca un amplio rango de análisis de eventos de impacto a baja velocidad en placas laminadas de material compuesto, monolíticas, planas, rectangulares, y con secuencias de apilamiento convencionales. Teniendo en cuenta que el principal objetivo del presente trabajo es la predicción de la resistencia residual a compresión, diferentes tareas se llevan a cabo para favorecer el adecuado análisis del problema. Los temas que se desarrollan son: la descripción analítica del impacto, el diseño y la realización de un plan de ensayos experimentales, la formulación e implementación de modelos constitutivos para la descripción del comportamiento del material, y el desarrollo de ensayos virtuales basados en modelos de elementos finitos en los que se usan los modelos constitutivos implementados.
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Preferred structures in the surface pressure variability are investigated in and compared between two 100-year simulations of the Hadley Centre climate model HadCM3. In the first (control) simulation, the model is forced with pre-industrial carbon dioxide concentration (1×CO2) and in the second simulation the model is forced with doubled CO2 concentration (2×CO2). Daily winter (December-January-February) surface pressures over the Northern Hemisphere are analysed. The identification of preferred patterns is addressed using multivariate mixture models. For the control simulation, two significant flow regimes are obtained at 5% and 2.5% significance levels within the state space spanned by the leading two principal components. They show a high pressure centre over the North Pacific/Aleutian Islands associated with a low pressure centre over the North Atlantic, and its reverse. For the 2×CO2 simulation, no such behaviour is obtained. At higher-dimensional state space, flow patterns are obtained from both simulations. They are found to be significant at the 1% level for the control simulation and at the 2.5% level for the 2×CO2 simulation. Hence under CO2 doubling, regime behaviour in the large-scale wave dynamics weakens. Doubling greenhouse gas concentration affects both the frequency of occurrence of regimes and also the pattern structures. The less frequent regime becomes amplified and the more frequent regime weakens. The largest change is observed over the Pacific where a significant deepening of the Aleutian low is obtained under CO2 doubling.