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A finite element based level-set method for multiphase flow applications

computing and Visualization in science 2014年第1期3卷 93-101页

作者： Anna-Karin Tornberg Björn Engquist Department of Numerical Analysis and Computing Science Royal Institute of Technology S-100 44 Stockholm Sweden (e-mail: annak@nada.kth.se/engquist@nada.kth.se) SE

A numerical method for simulating incompressible two-dimensional multiphase flow is presented. The method is based on a level-set formulation discretized by a finite-element technique. The treatment of the specific features of this problem, such as surface tension forces acting at the interfaces separating two immiscible fluids, as well as the density and viscosity jumps that in general occur across such interfaces, have been integrated into the finite-element framework. Using a method based on the weak formulation of the Navier-Stokes equations has its advantages. In this formulation, the singular surface tension forces are included through line integrals along the interfaces, which are easily approximated quantities. In addition, differentiation of the discontinuous viscosity is avoided. The discontinuous density and viscosity are included in the finite element integrals. A strategy for the evaluation of integrals with discontinuous integrands has been developed based on a rigorous analysis of the errors associated with the evaluation of such integrals. numerical tests have been performed. For the case of a rising buoyant bubble the results are in good agreement with results from a front-tracking method. The run presented here is a run including topology changes, where initially separated areas of one fluid merge in different stages due to buoyancy effects.

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Experience from 10 years of student projects oriented towards graphic interaction

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COMPUTERS & GRAPHICS 1996年第3期20卷 463-471页

作者： Kjelldahl, L Sundblad, Y Numerical Analysis and Computing Science—NADA Royal Institute of Technology—KTH S-100 44 Stockholm Sweden

The characteristics of a project course (160 h of work for the students) oriented towards graphical interaction is described. Project proposals are presented to the students by researchers from different applications. The students get a high degree of freedom during the project and partly as a result of this they are enthusiastic about the work and produce very impressive prototypes which they present at the end of the course. Copyright (C) 1996 Elsevier science Ltd

关键词： Interactive computer graphics

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A study on how depth perception is affected by different presentation methods of 3D objects on a 2D display

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Computers and Graphics 1995年第2期19卷 199-202页

作者： Kjelldahl, Lars Prime, Martin Numerical Analysis and Computing Science-NADA KTH S-100 44 Stockholm Sweden Rutherford Appleton Laboratory Chilton Didcot Oxfordshire OX11 0QX United Kingdom

The experimental results of viewing 3D objects on a 2D display are presented. The effect of object relationship;presentation quality (wireframe and shaded);and illumination on depth perception were studied. After the depth estimation phase, subjects were then asked to make a subjective judgement: whether they thought better estimates had been made with the shaded rather than wireframe form. Subsequent analysis, showed that the experimental figures did not support this guess. Lighting influenced accuracy, which illustrates the importance of careful choices of light sources. Relative object placement was also established as an important factor. © 1995.

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Invariance constraints for improving generalization in probabilistic neural networks

Invariance constraints for improving generalization in proba...

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International Conference on Neural Networks

作者： H.G.C. Traven Studies of Artificial Neural Systems (SANS) research group Department of Numerical Analysis and Computing Science (NADA) Royal Institute of Technology Stockholm Sweden

Probabilistic neural networks can approximate class conditional densities in optimal (Bayesian) pattern classifiers. In natural pattern recognition applications, the size of the training set is always limited, making the approximation task difficult. Invariance constraints can significantly simplify the task of density approximation. A technique is presented for learning invariant representations, based on a statistical approach to ground invariance. An iterative method is developed formally for computing the maximum likelihood estimate to the parameters of an invariant mixture model. The method can be interpreted as a competitive training strategy for a radial basis function (RBF) network. It can be used for self-organizing formation of both invariant templates and features.< >

关键词： Intelligent networks Neural networks Artificial neural networks Pattern recognition Computer networks Bayesian methods Storage area networks numerical analysis Parameter estimation Organizing

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A NEURAL NETWORK APPROACH TO STATISTICAL PATTERN-CLASSIFICATION BY SEMIPARAMETRIC ESTIMATION OF PROBABILITY DENSITY-FUNCTIONS

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1991年第3期2卷 366-377页

作者： TRAVEN, HGC Department of Numerical Analysis and Computing Science (NADA) Royal Institute of Technology Stockholm Sweden

The paper outlines a method for designing near optimal nonlinear classifiers based on a self-organizing technique for estimating probability density functions when only weak assumptions are made about the densities. The classical parametric and nonparametric methods for estimating density functions have a number of drawbacks;parametric methods give weak results on unknown distributions, while nonparametric methods require extensive amounts of design samples, storage capacity, and computing power. The present method avoids these disadvantages by parameterizing a set of component densities from which the actual densities are constructed. The parameters of the component densities are optimized by a self-organizing algorithm, reducing to a minimum the labeling of design samples. All the required computations are realized with the simple "sum of product" units commonly used in connectionist models. The density approximations produced by the method are illustrated in two dimensions for a multispectral image classification task. The practical use of the method is illustrated by a small speech recognition problem, that of recognizing 18 Swedish consonants. Related issues of invariant projections, cross-class pooling of data, and subspace partitioning are also discussed.

关键词： statistical pattern recognition Probability density function self-organizing algorithm Speech recognition Pattern recognition Neural Networks (Computer)

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Doctoral Theses in Computer Graphics

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Computer Graphics Forum 1989年第4期8卷 353-357页

作者： Kjelldahl, Lars Numerical Analysis and Computing Science (NADA) Royal Institute of Technology (KTH) Stockholm s ‐ 100 44 Sweden

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EXTRACTION OF DIFFUSE EDGES BY EDGE FOCUSING

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PATTERN RECOGNITION LETTERS 1988年第3期7卷 181-190页

作者： SJOBERG, F BERGHOLM, F Department of Numerical Analysis and Computing Science NADA Royal Institute of Technology S-100 44 Stockholm Sweden

The edge focusing method produces a series of edge images ranging from coarser to finer scale resolution. The displacements of these extracted edges in this series are discussed. A three-step method of labelling the extracted edges as coming from objects or as coming from shadows and other illumination phenomena using this series is tried. More precisely, we show that it seems possible to label edges into the categories ‘diffuse’ and ‘non-diffuse’ from a binary multi-scale representation, i.e. without using the image intensities directly.

关键词： Classification of edge elements diffuse edges edge focusing illumination edges

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