33 resultados para linearity


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Ancient columns, made with a variety of materials such as marble, granite, stone or masonry are an important part of the
European cultural heritage. In particular columns of ancient temples in Greece and Sicily which support only the architrave are
characterized by small axial load values. This feature together with the slenderness typical of these structural members clearly
highlights as the evaluation of the rocking behaviour is a key aspect of their safety assessment and maintenance. It has to be noted
that the rocking response of rectangular cross-sectional columns modelled as monolithic rigid elements, has been widely investigated
since the first theoretical study carried out by Housner (1963). However, the assumption of monolithic member, although being
widely used and accepted for practical engineering applications, is not valid for more complex systems such as multi-block columns
made of stacked stone blocks, with or without mortar beds. In these cases, in fact, a correct analysis of the system should consider
rocking and sliding phenomena between the individual blocks of the structure. Due to the high non-linearity of the problem, the
evaluation of the dynamic behaviour of multi-block columns has been mostly studied in the literature using a numerical approach
such as the Discrete Element Method (DEM). This paper presents an introductory study about a proposed analytical-numerical
approach for analysing the rocking behaviour of multi-block columns subjected to a sine-pulse type ground motion. Based on the
approach proposed by Spanos (2001) for a system made of two rigid blocks, the Eulero-Lagrange method to obtain the motion
equations of the system is discussed and numerical applications are performed with case studies reported in the literature and with a
real acceleration record. The rocking response of single block and multi-block columns is compared and considerations are made
about the overturning conditions and on the effect of forcing function’s frequency.
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In recent years modern numerical methods have been employed in the design of Wave Energy Converters (WECs), however the high computational costs associated with their use makes it prohibitive to undertake simulations involving statistically relevant numbers of wave cycles. Experimental tests in wave tanks could also be performed more efficiently and economically if short time traces, consisting of only a few wave cycles, could be used to evaluate the hydrodynamic characteristics of a particular device or design modification. Ideally, accurate estimations of device performance could be made utilizing results obtained from investigations with a relatively small number of wave cycles. However the difficulty here is that many WECs, such as the Oscillating Wave Surge Converter (OWSC), exhibit significant non-linearity in their response. Thus it is challenging to make accurate predictions of annual energy yield for a given spectral sea state using short duration realisations of that sea. This is because the non-linear device response to particular phase couplings of sinusoidal components within those time traces might influence the estimate of mean power capture obtained. As a result it is generally accepted that the most appropriate estimate of mean power capture for a sea state be obtained over many hundreds (or thousands) of wave cycles. This ensures that the potential influence of phase locking is negligible in comparison to the predictions made. In this paper, potential methods of providing reasonable estimates of relative variations in device performance using short duration sea states are introduced. The aim of the work is to establish the shortness of sea state required to provide statistically significant estimations of the mean power capture of a particular type of Wave Energy Converter. The results show that carefully selected wave traces can be used to reliably assess variations in power output due to changes in the hydrodynamic design or wave climate. 

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We address the problem of 3D-assisted 2D face recognition in scenarios when the input image is subject to degradations or exhibits intra-personal variations not captured by the 3D model. The proposed solution involves a novel approach to learn a subspace spanned by perturbations caused by the missing modes of variation and image degradations, using 3D face data reconstructed from 2D images rather than 3D capture. This is accomplished by modelling the difference in the texture map of the 3D aligned input and reference images. A training set of these texture maps then defines a perturbation space which can be represented using PCA bases. Assuming that the image perturbation subspace is orthogonal to the 3D face model space, then these additive components can be recovered from an unseen input image, resulting in an improved fit of the 3D face model. The linearity of the model leads to efficient fitting. Experiments show that our method achieves very competitive face recognition performance on Multi-PIE and AR databases. We also present baseline face recognition results on a new data set exhibiting combined pose and illumination variations as well as occlusion.