902 resultados para Math Applications in Computer Science
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This paper addresses the numerical solution of random crack propagation problems using the coupling boundary element method (BEM) and reliability algorithms. Crack propagation phenomenon is efficiently modelled using BEM, due to its mesh reduction features. The BEM model is based on the dual BEM formulation, in which singular and hyper-singular integral equations are adopted to construct the system of algebraic equations. Two reliability algorithms are coupled with BEM model. The first is the well known response surface method, in which local, adaptive polynomial approximations of the mechanical response are constructed in search of the design point. Different experiment designs and adaptive schemes are considered. The alternative approach direct coupling, in which the limit state function remains implicit and its gradients are calculated directly from the numerical mechanical response, is also considered. The performance of both coupling methods is compared in application to some crack propagation problems. The investigation shows that direct coupling scheme converged for all problems studied, irrespective of the problem nonlinearity. The computational cost of direct coupling has shown to be a fraction of the cost of response surface solutions, regardless of experiment design or adaptive scheme considered. (C) 2012 Elsevier Ltd. All rights reserved.
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Understanding the underlying mechanisms that account for the impact of potassium (K) fertilization and its replacement by sodium (Na) on tree growth is key to improving the management of forest plantations that are expanding over weathered tropical soils with low amounts of exchangeable bases. A complete randomized block design was planted with Eucalyptus grandis (W. Hill ex Maiden) to quantify growth, carbon uptake and carbon partitioning using a carbon budget approach. A combination of approaches including the establishment of allometric relationships over the whole rotation and measurements of soil CO2 efflux and aboveground litterfall at the end of the rotation were used to estimate aboveground net production (ANPP), total belowground carbon flux and gross primary production (GPP). The stable carbon isotope (delta C-13) of stem wood alpha-cellulose produced every year was used as a proxy for stomatal limitation of photosynthesis. Potassium fertilization increased GPP and decreased the fraction of carbon allocated belowground. Aboveground net production was strongly enhanced, and because leaf lifespan increased, leaf biomass was enhanced without any change in leaf production, and wood production (P-W) was dramatically increased. Sodium application decreased the fraction of carbon allocated belowground in a similar way, and enhanced GPP, ANPP and P-W, but to a lesser extent compared with K fertilization. Neither K nor Na affected delta C-13 of stem wood alpha-cellulose, suggesting that water-use efficiency was the same among the treatments and that the inferred increase in leaf photosynthesis was not only related to a higher stomatal conductance. We concluded that the response to K fertilization and Na addition on P-W resulted from drastic changes in carbon allocation.
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Reflections on the university campus usually focus on its relevancy as a research and teaching area; however, the need for preservation, protection, maintenance and cleaning only become visible in the event of inadequacy or lack thereof. The aim of this study is to address the characteristics of the preservation and security measures performed at the Science and Technology Park of the University of São Paulo (Parque de Ciência e Tecnologia da Universidade de São Paulo), agency subjected to the Dean’s Office for Culture and University Extension (Pró-Reitoria de Cultura e Extensão Universitária). Because of its history and location, the Park requires special care. The Park’s land formerly housed the Astronomical Observatory of São Paulo and the Institute of Astronomy and Geophysics of the University of São Paulo (Instituto de Astronomia e Geofísica – IAG-USP), within the Fontes do Ipiranga State Park (Parque Estadual das Fontes do Ipiranga – PEFI), in the city of São Paulo. Preservation and reconversion activities relative to historical buildings are developed at the Park. The institution’s location and its specificities require security in its borders, as well as in relation to the users of the park.
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Tesis en inglés. Eliminadas las páginas en blanco del pdf
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The present work proposes a method based on CLV (Clustering around Latent Variables) for identifying groups of consumers in L-shape data. This kind of datastructure is very common in consumer studies where a panel of consumers is asked to assess the global liking of a certain number of products and then, preference scores are arranged in a two-way table Y. External information on both products (physicalchemical description or sensory attributes) and consumers (socio-demographic background, purchase behaviours or consumption habits) may be available in a row descriptor matrix X and in a column descriptor matrix Z respectively. The aim of this method is to automatically provide a consumer segmentation where all the three matrices play an active role in the classification, getting homogeneous groups from all points of view: preference, products and consumer characteristics. The proposed clustering method is illustrated on data from preference studies on food products: juices based on berry fruits and traditional cheeses from Trentino. The hedonic ratings given by the consumer panel on the products under study were explained with respect to the product chemical compounds, sensory evaluation and consumer socio-demographic information, purchase behaviour and consumption habits.
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[EN]An accurate estimation of the number of people entering / leaving a controlled area is an interesting capability for automatic surveil- lance systems. Potential applications where this technology can be ap- plied include those related to security, safety, energy saving or fraud control. In this paper we present a novel con guration of a multi-sensor system combining both visual and range data specially suited for trou- blesome scenarios such as public transportation. The approach applies probabilistic estimation lters on raw sensor data to create intermediate level hypothesis that are later fused using a certainty-based integration stage. Promising results have been obtained in several tests performed on a realistic test bed scenario under variable lightning conditions.
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[EN]In this paper, we experimentally study the combination of face and facial feature detectors to improve face detection performance. The face detection problem, as suggeted by recent face detection challenges, is still not solved. Face detectors traditionally fail in large-scale problems and/or when the face is occluded or di erent head rotations are present. The combination of face and facial feature detectors is evaluated with a public database. The obtained results evidence an improvement in the positive detection rate while reducing the false detection rate. Additionally, we prove that the integration of facial feature detectors provides useful information for pose estimation and face alignment.
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[EN]Re-identi fication is commonly accomplished using appearance features based on salient points and color information. In this paper, we make an study on the use of di fferent features exclusively obtained from depth images captured with RGB-D cameras. The results achieved, using simple geometric features extracted in a top-view setup, seem to provide useful descriptors for the re-identi fication task.
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[EN]This paper focuses on four different initialization methods for determining the initial shape for the AAM algorithm and their particular performance in two different classification tasks with respect to either the facial expression DaFEx database and to the real world data obtained from a robot’s point of view.
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In my PhD thesis I propose a Bayesian nonparametric estimation method for structural econometric models where the functional parameter of interest describes the economic agent's behavior. The structural parameter is characterized as the solution of a functional equation, or by using more technical words, as the solution of an inverse problem that can be either ill-posed or well-posed. From a Bayesian point of view, the parameter of interest is a random function and the solution to the inference problem is the posterior distribution of this parameter. A regular version of the posterior distribution in functional spaces is characterized. However, the infinite dimension of the considered spaces causes a problem of non continuity of the solution and then a problem of inconsistency, from a frequentist point of view, of the posterior distribution (i.e. problem of ill-posedness). The contribution of this essay is to propose new methods to deal with this problem of ill-posedness. The first one consists in adopting a Tikhonov regularization scheme in the construction of the posterior distribution so that I end up with a new object that I call regularized posterior distribution and that I guess it is solution of the inverse problem. The second approach consists in specifying a prior distribution on the parameter of interest of the g-prior type. Then, I detect a class of models for which the prior distribution is able to correct for the ill-posedness also in infinite dimensional problems. I study asymptotic properties of these proposed solutions and I prove that, under some regularity condition satisfied by the true value of the parameter of interest, they are consistent in a "frequentist" sense. Once I have set the general theory, I apply my bayesian nonparametric methodology to different estimation problems. First, I apply this estimator to deconvolution and to hazard rate, density and regression estimation. Then, I consider the estimation of an Instrumental Regression that is useful in micro-econometrics when we have to deal with problems of endogeneity. Finally, I develop an application in finance: I get the bayesian estimator for the equilibrium asset pricing functional by using the Euler equation defined in the Lucas'(1978) tree-type models.
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[EN]Different researches suggest that inner facial features are not the only discriminative features for tasks such as person identification or gender classification. Indeed, they have shown an influence of features which are part of the local face context, such as hair, on these tasks. However, object-centered approaches which ignore local context dominate the research in computational vision based facial analysis. In this paper, we performed an analysis to study which areas and which resolutions are diagnostic for the gender classification problem. We first demonstrate the importance of contextual features in human observers for gender classification using a psychophysical ”bubbles” technique.
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[ES]This paper describes some simple but useful computer vision techniques for human-robot interaction. First, an omnidirectional camera setting is described that can detect people in the surroundings of the robot, giving their angular positions and a rough estimate of the distance. The device can be easily built with inexpensive components. Second, we comment on a color-based face detection technique that can alleviate skin-color false positives. Third, a simple head nod and shake detector is described, suitable for detecting affirmative/negative, approval/dissaproval, understanding/disbelief head gestures.