Introduction of variability in pantograph-catenary dynamic simulations

Currently, pantograph-catenary dynamic simulations codes are mainly based on deterministic approaches. However, the contact force between catenary and pantograph depends on many key parameters that are not always quantified precisely. To get a better chance of addressing extreme or combinations of critical conditions, methodologies to consider variability are thus necessary. Aerodynamic forces and geometrical irregularities of catenaries are thought to be significant sources of variability in measurement and this paper proposes methods to take them into account. Results are compared with measurements to see the importance of the considered parameters with respect to global variability observed in measurements

A Primal-Dual Proximal Algorithm for Sparse Template-Based Adaptive Filtering: Application to Seismic Multiple Removal

Unveiling meaningful geophysical information from seismic data requires to deal with both random and structured "noises". As their amplitude may be greater than signals of interest (primaries), additional prior information is especially important in performing efficient signal separation. We address here the problem of multiple reflections, caused by wave-field bouncing between layers. Since only approximate models of these phenomena are available, we propose a flexible framework for time-varying adaptive filtering of seismic signals, using sparse representations, based on inaccurate templates. We recast the joint estimation of adaptive filters and primaries in a new convex variational formulation. This approach allows us to incorporate plausible knowledge about noise statistics, data sparsity and slow filter variation in parsimony-promoting wavelet frames. The designed primal-dual algorithm solves a constrained minimization problem that alleviates standard regularization issues in finding hyperparameters. The approach demonstrates significantly good performance in low signal-to-noise ratio conditions, both for simulated and real field seismic data

A constrained-based optimization approach for seismic data recovery problems

Random and structured noise both affect seismic data, hiding the reflections of interest (primaries) that carry meaningful geophysical interpretation. When the structured noise is composed of multiple reflections, its adaptive cancellation is obtained through time-varying filtering, compensating inaccuracies in given approximate templates. The under-determined problem can then be formulated as a convex optimization one, providing estimates of both filters and primaries. Within this framework, the criterion to be minimized mainly consists of two parts: a data fidelity term and hard constraints modeling a priori information. This formulation may avoid, or at least facilitate, some parameter determination tasks, usually difficult to perform in inverse problems. Not only classical constraints, such as sparsity, are considered here, but also constraints expressed through hyperplanes, onto which the projection is easy to compute. The latter constraints lead to improved performance by further constraining the space of geophysically sound solutions.Comment: International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014); Special session "Seismic Signal Processing

Une autre vision de la périphérie

International audienceImages of the urban fringes of French cities often tend either to condemn their ugliness or to revel in a strange fascination for these geometric spaces. Here, Laurent Devisme comments upon Jean-Christophe Bardot’s photographs, and shows how they give a visible form to the imagined perceptions of these urban spaces.Les images des périphéries urbaines oscillent souvent entre la dénonciation de la laideur et la fascination pour ces espaces géométriques. Commentant les photographies de Jean-Christophe Bardot, Laurent Devisme montre comment elles rendent visibles l’imaginaire des territoires urbains

A forward-backward view of some primal-dual optimization methods in image recovery

A wide array of image recovery problems can be abstracted into the problem of minimizing a sum of composite convex functions in a Hilbert space. To solve such problems, primal-dual proximal approaches have been developed which provide efficient solutions to large-scale optimization problems. The objective of this paper is to show that a number of existing algorithms can be derived from a general form of the forward-backward algorithm applied in a suitable product space. Our approach also allows us to develop useful extensions of existing algorithms by introducing a variable metric. An illustration to image restoration is provided