FE modeling of the cooling and tempering steps of bimetallic rolling mill rolls

Numerical simulations enable the analysis of the stress and strain histories of bimetallic rolling mill rolls. The history of rolling mill rolls is simulated by thermo-mechanical metallurgical finite element code while considering two steps: post-casting cooling and subsequent tempering heat treatment. The model requires a notably large set of material parameters. For different phases and temperatures, Young modulus, yield limit and tangent plastic modulus are determined through compression tests. Rupture stresses and strains are obtained by tensile tests. Thermo-physical parameters are measured by such experimental methods as dilatometry, DSC (Differential Scanning Calorimetry) and Laser Flash methods. Such parameters as the transformation plasticity coefficients for the ferrite, pearlite and martensite phases are identified through an inverse method. From the simulation results, the profile of the stresses evolution at different critical times is presented. An analysis of the potential damage is proposed by comparing the predicted axial stress with rupture stresses. The perspective of the Ghosh and McClintock damage criteria is also investigated.     

Static and Dynamic Program Compilation by Interpreter Specialization

Interpretation and run-time compilation techniques are increasingly important because they can support heterogeneous architectures, evolving programming languages, and dynamically-loaded code. Interpretation is simple to implement, but yields poor performance. Run-time compilation yields better performance, but is costly to implement. One way to preserve simplicity but obtain good performance is to apply program specialization to an interpreter in order to generate an efficient implementation of the program automatically. Such specialization can be carried out at both compile time and run time.Recent advances in program-specialization technology have significantly improved the performance of specialized interpreters. This paper presents and assesses experiments applying program specialization to both bytecode and structured-language interpreters. The results show that for some general-purpose bytecode languages, specialization of an interpreter can yield speedups of up to a factor of four, while specializing certain structured-language interpreters can yield performance comparable to that of an implementation in a general-purpose language, compiled using an optimizing compiler.     

Segmentation of Vectorial Image Features Using Shape Gradients and Information Measures

In this paper, we propose to focus on the segmentation of vectorial features (e.g. vector fields or color intensity) using region-based active contours. We search for a domain that minimizes a criterion based on homogeneity measures of the vectorial features. We choose to evaluate, within each region to be segmented, the average quantity of information carried out by the vectorial features, namely the joint entropy of vector components. We do not make any assumption on the underlying distribution of joint probability density functions of vector components, and so we evaluate the entropy using non parametric probability density functions. A local shape minimizer is then obtained through the evolution of a deformable domain in the direction of the shape gradient. The first contribution of this paper lies in the methodological approach used to differentiate such a criterion. This approach is mainly based on shape optimization tools. The second one is the extension of this method to vectorial data. We apply this segmentation method on color images for the segmentation of color homogeneous regions. We then focus on the segmentation of synthetic vector fields and show interesting results where motion vector fields may be separated using both their length and their direction. Then, optical flow is estimated in real video sequences and segmented using the proposed technique. This leads to promising results for the segmentation of moving video objects. Ariane Herbulot received the M. Engineering degree in computer science from the Ecole Superieure en Sciences Informatiques (ESSI), Sophia Antipolis,France in 2001, and the M.S. degree in computer vision from the University of Nice-Sophia Antipolis (UNSA) in 2003. She is currently a Ph.D. student in image processing with the I3S laboratory, CNRS-UNSA. Her research interests focus on nonparametric methods for image and video segmentation. Stéphanie Jehan-Besson received the engineering degree from Ecole Centrale Nantes and a Ph.D. in computer vision from the University of Nice Sophia Antipolis. She is currently associate professor at ENSICAEN, engineering school of Caen. Her research interests include variational methods for image segmentation, geometric PDEs (Partial Differential Equations), video object detection for MPEG-4/7, medical image segmentation, motion estimation and tracking. Stefan Duffner was born in Schorndorf, Germany in 1978. He received the Bachelor's degree in Computer Science from the University of Applied Sciences Konstanz, Germany in 2002 and the Master's degree in Applied Computer Science from the University of Freiburg, Germany in 2004. He's currently pursuing a Ph.D. degree in Computer Science at the Research Laboratory of France Telecom in Rennes, France. His research interests include machine learning, neural networks and their application to object detection and recognition in images. Michel Barlaud received his These d'Etat from the University of Paris XII and Agregation de Physique. He is currently a Professor of Image Processing at the University of Nice-Sophia Antipolis, and the leader of the Image Processing group of I3S. His research topics are: Image and Video coding using Wavelet Transform, Inverse problem using Half Quadratic Regularization and, Region Based Image and Video Segmentation using Shape Gradient and Active Contours. He is a regular reviewer for several journals, a member of the technical committees of several scientific conferences. He leads several national research and development projects with French industries, and participates in several international academic collaborations: European Network of Excellence SCHEMA and SIMILAR (Louvain La Neuve (Belgium), ITI Greece, Imperial College ...) and NSF-CNRS Funding (Universities of Stanford and Boston). He is the author of a large number of publications in the area of image and video processing, and the Editor of the book “Wavelets and Image Communication” Elsevier, 1994. Gilles Aubert received the These d'Etat es-Sciences Mathematiques from the Univesity of Paris 6, France, in 1986. He is currently professor of mathematics at the University of Nice-Sophia Antipolis and member of the J.A. Dieudonne Laboratory at Nice, France. His research interests are calculus of variations, nonlinear partial differential equations. Fields of applications include image processing and, in particular, restoration, segmentation, decomposition models and optical flow.     

Characterization of capacitive micromachined ultrasonic transducers

This paper describes numerical and experimental characterization of capacitive micromachined ultrasonic transducers (CMUTs) for ultrasound transmission. Simulations based on a finite elements method to model the electromechanical behaviour of CMUTs and to determine the dimensions of elementary cells are presented. In particular, we analyse how the collapse voltage and the capacitance are affected by different parameters of a circular cell and by different bias voltages. The fill factor is defined as the ratio of the top electrode radius to the membrane radius and we study the influence of the fill factor in the performances of CMUTs. The fabrication process of a CMUT uses anodic bonding of a SOI wafer on a borosilicate glass substrate and we compare experimental results with numerical results in terms of eigenfrequencies, bandwidth, quality factor and capacitance for non-metallized and metallized membranes.     

Intrinsic dimension estimation by maximum likelihood in isotropic probabilistic PCA

A central issue in dimension reduction is choosing a sensible number of dimensions to be retained. This work demonstrates the surprising result of the asymptotic consistency of the maximum likelihood criterion for determining the intrinsic dimension of a dataset in an isotropic version of probabilistic principal component analysis (PPCA). Numerical experiments on simulated and real datasets show that the maximum likelihood criterion can actually be used in practice and outperforms existing intrinsic dimension selection criteria in various situations. This paper exhibits and outlines the limits of the maximum likelihood criterion. It leads to recommend the use of the AIC criterion in specific situations. A useful application of this work would be the automatic selection of intrinsic dimensions in mixtures of isotropic PPCA for classification.     

Fast GPU-based denoising filter using isoline levels

In this study, we describe a GPU-based filter for image denoising, whose principle rests on Matheron’s level sets theory first introduced in 1975 but rarely implemented because of its high computation cost. We use the fact that, within a natural image, significant contours of objects coincide with parts of the image level-lines. The presented algorithm assumes an a priori knowledge of the corrupting noise type and uses the polygonal level-line modeling constraint to estimate the gray-level of each pixel of the denoised image by local maximum likelihood optimization. Over the 512 × 512 pixel test images, the freely available implementation of the state-of-the-art BM3D algorithm achieves 9.56 dB and 36 % of mean improvement in 4.3 s, respectively, for peak signal to noise ratio and mean structural similarity index. Over the same images, our implementation features a high quality/runtime ratio, with a mean improvement of 7.14 dB and 30 % in 9 ms, which is 470 times as fast and potentially allows processing high-definition video images at 19 fps.     

Aspectual templates in UML

UML Templates allow to capture reusable models through parameterization. The construct is general enough to be used in many ways, ranging from the representation of generic components (such as Java generics or C++ templates) to aspectual usage, including pattern-, aspect- and view-oriented modeling. We concentrate on this last usage and so-called aspectual templates which require that parameters must form a model of systems in which to inject new functionalities. Starting from this strict constraint, we derive an in-depth semantic enhancement of the standard. It is formalized as a fully UML-compliant interpretation in OCL of the template construct and its binding mechanism. In particular, this aspectual interpretation must be ensured in case of partial binding (not all parameters are valued). Partial binding of UML is a powerful technique which allows to obtain richer templates from the composition of other ones. As a major result, the present semantic enhancement is consistent with this capacity so that partial binding of aspectual templates produces aspectual templates. Finally, at an operational level, an algorithm for aspectual template (partial) binding operation is formulated and consequent reusable technology made available in EMF (Eclipse Modeling Framework) is presented.     

On Parallel Thinning Algorithms: Minimal Non-simple Sets,P-simple Points and Critical Kernels

Critical kernels constitute a general framework in the category of abstract complexes for the study of parallel homotopic thinning in any dimension. In this article, we present new results linking critical kernels to minimal non-simple sets (MNS) and P-simple points, which are notions conceived to study parallel thinning in discrete grids. We show that these two previously introduced notions can be retrieved, better understood and enriched in the framework of critical kernels. In particular, we propose new characterizations which hold in dimensions 2, 3 and 4, and which lead to efficient algorithms for detecting P-simple points and minimal non-simple sets.