Commissariat General Au Developpement Durable

Sainte-Foy-lès-Lyon, France

Commissariat General Au Developpement Durable

Sainte-Foy-lès-Lyon, France
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Kalantari M.,National School of Geographic Sciences | Hashemi A.,Isfahan University of Technology | Jung F.,Commissariat General Au Developpement Durable | Guedon J.-P.,CNRS Research Institute of Communication and Cybernetics of Nantes
Journal of Mathematical Imaging and Vision | Year: 2011

This paper presents a new method to solve the relative pose between two images, using three pairs of homologous points and the knowledge of the vertical direction. The vertical direction can be determined in two ways: The first requires direct physical measurements such as the ones provided by an IMU (inertial measurement unit). The other uses the automatic extraction of the vanishing point corresponding to the vertical direction in an image. This knowledge of the vertical direction solves two unknowns among the three parameters of the relative rotation, so that only three homologous couples of points are requested to position a couple of images. Rewriting the coplanarity equations thus leads to a much simpler solution. The remaining unknowns resolution is performed by "hiding a variable" approach. The elements necessary to build a specific algebraic solver are given in this paper, allowing for a real-time implementation. The results on real and synthetic data show the efficiency of this method. © 2010 Springer Science+Business Media, LLC.

Kalantari M.,National School of Geographic Sciences | Kalantari M.,CNRS Research Institute of Communication and Cybernetics of Nantes | Hashemi A.,Isfahan University of Technology | Jung F.,Commissariat general au developpement durable | Guedon J.P.,CNRS Research Institute of Communication and Cybernetics of Nantes
Traitement du Signal | Year: 2010

This paper proposes to use the knowledge of the vertical direction to estimate the relative orientation of images. The presented algorithm can use information about the vertical direction, either computed by image based techniques (information taken from the vertical vanishing point), or obtained by a direct physical measurement. This knowledge solves 2 unknowns among the 3 parameters of the relative rotation, so that only 3 homologous points are requested to position a couple of images. The coplanarity constraint equations are re-written to lead to a simpler version. The remaining unknowns resolution is performed by an algebraic method using Gröbner bases. The elements necessary to build a specific algebraic solver are given in this paper, allowing for a real-time implementation. The results on real and synthetic data show the efficiency of this method. © 2010 Lavoisier, Paris.

Yang S.,Societe Nationale des Chemins de fer Francais | Yang S.,University Paris Est Creteil | Cremona C.,Commissariat General au Developpement Durable | Cremona C.,University Paris Est Creteil | And 2 more authors.
European Journal of Environmental and Civil Engineering | Year: 2010

Reynolds numbers play an important role in fluid dynamics, because flow separations and reattachments are often Reynolds-number dependent, even if the bodies have sharp edges. To satisfy Reynolds scaling in experiments, the model scale can be very large. Computational fluid dynamics (CFD) provides an interesting alternative to wind tunnel tests, especially when Reynolds effects have to be assessed for a design stage. In this paper, the particle strength exchange (PSE) of vortex methods is introduced for assessing Reynolds number effects on bridge deck cross-sections. For this purpose, this study analyses how the aerodynamic forces of two different bridges sections are influenced by these effects. The computation results with PSE are compared with wind tunnel tests. The relation between Strouhal numbers and Reynolds numbers are also analyzed and compared with experimental results. All the results show that the particle strength exchange method can be efficiently used to calculate Reynolds effects on 2-dimensional bridge deck sections. © 2011 Lavoisier, Paris.

Cury A.,Laboratoire Central des Ponts et Chaussees | Cremona C.,Commissariat General Au Developpement Durable
Structural Control and Health Monitoring | Year: 2012

Learning algorithms have extensively been applied to classification and pattern recognition problems in the past years. Some papers have addressed special attention to applications regarding damage assessment, especially how these algorithms could be used to classify different structural conditions. Nevertheless, few works present techniques in which vibration signatures can be directly used to provide insights about possible modification processes. This paper proposes a novel approach in which the concept of Symbolic Data Analysis (SDA) is introduced to manipulate not only vibration data (signals) but also modal properties (natural frequencies and mode shapes). These quantities (transformed into symbolic data) are combined to three well-known classification techniques: Bayesian Decision Trees, Neural Networks and Support Vector Machines. The objective is to explore the efficiency of this combined methodology. For this purpose, several numerical simulations are first performed for evaluating the probabilities of true detection (or true classification) in the presence of different damage conditions. Several noise levels are also applied to the data to attest the sensibility of each technique. Second, a set of experimental tests performed on a railway bridge in France is used to emphasize advantages and drawbacks of the proposed approach. Results show that the analysis combining the cited learning algorithms with the symbolic data concepts is efficient enough to classify and discriminate structural modifications with a high probability of true detection, either considering vibration data or modal parameters. Copyright © 2010 John Wiley & Sons, Ltd.

Cury A.,Laboratoire Central des Ponts et Chaussees | Cremona C.,Commissariat General au Developpement Durable | Diday E.,University of Paris Dauphine
Engineering Structures | Year: 2010

Structural health monitoring is a problem which can be addressed at many levels. One of the more promising approaches used in damage assessment problems is based on pattern recognition. The idea is to extract features from the data that characterize only the normal condition and to use them as a template or reference. During structural monitoring, data are measured and the appropriate features are extracted as well as compared (in some sense) to the reference. Any significant deviations from the reference are considered as signal novelty or damage. In this paper, the corpus of symbolic data analysis (SDA) is applied on the one hand for classifying different structural behaviors and on the other hand for comparing any structural behavior to the previous classification when new data become available. For this purpose, raw information (acceleration measurements) and also processed information (modal data) are used for feature extraction. Some SDA techniques are applied for data classification: hierarchy-divisive methods, dynamic clustering and hierarchy-agglomerative schemes. Results regarding experimental tests performed on a railway bridge in France are presented in order to show the efficiency of the described methodology. The results show that the SDA methods are efficient to classify and to discriminate structural modifications either considering the vibration data or the modal parameters. In general, both hierarchy-divisive and dynamic cloud methods produce better results compared to those obtained by using the hierarchy-agglomerative method. More robust results are given by modal data than by measurement data. © 2009 Elsevier Ltd. All rights reserved.