Mines ParisTech

About: Mines ParisTech is a education organization based out in Paris, France. It is known for research contribution in the topics: Finite element method & Microstructure. The organization has 6564 authors who have published 11676 publications receiving 359898 citations. The organization is also known as: École nationale supérieure des mines de Paris & École des mines de Paris.

Stochastic optimization of unit commitment: a new decomposition framework

Abstract: This paper presents a new stochastic decomposition method well-suited to deal with large-scale unit commitment problems. In this approach, random disturbances are modeled as scenario trees. Optimization consists in minimizing the average generation cost over this "tree-shaped future". An augmented Lagrangian technique is applied to this problem. At each iteration, nonseparable terms introduced by the augmentation are linearized so as to obtain a decomposition algorithm. This algorithm may be considered as a generalization of price decomposition methods, which are now classical in this field, to the stochastic framework. At each iteration, for each unit, a stochastic dynamic subproblem has to be solved. Prices attached to nodes of the scenario trees are updated by the coordination level. This method has been applied to a daily generation scheduling problem. The use of an augmented Lagrangian technique, provides satisfactory convergence properties to the decomposition algorithm. Moreover, numerical simulations show that compared to a classical deterministic optimization with reserve constraints, this new approach achieves substantial savings.

The metamorphism in the Central Himalaya

Abstract: All along the Himalayan chain an axis of crystalline rocks has been preserved, made of the Higher Himalaya crystalline and the crystalline nappes of the Lesser Himalaya. The salient points of the metamorphism, as deduced from data collected in central Himalaya (central Nepal and Kumaun), are: 1 The Higher Himalaya crystalline, also called the Tibetan Slab, displays a polymetamorphic history with a first stage of Barrovian type overprinted by a lower pressure and/or higher temperature type metamorphism. The metamorphism is due to quick and quasi-adiabatic uplift of the Tibetan Slab by transport along an MCT ramp, accompanied by thermal refraction effects in the contact zone between the gneisses and their sedimentary cover. The resulting metamorphic pattern is an apparent (diachronic) inverse zonation, with the sillimanite zone above the kyanite zone. 2 Conversely, the famous inverted zonation of the Lesser Himalaya is basically a primary pattern, acquired during a one-stage prograde metamorphism. Its origin must be related to the thrusting along the MCT, with heat supplied from the overlying hot Tibetan Slab, as shown by synmetamorphic microstructures and the close geometrical relationships between the metamorphic isograds and the thrust. 3 Thermal equilibrium is reached between units above and below the MCT. Far behind the thrust tip there is good agreement between the maximum temperature attained in the hanging wall and the temperature of the Tibetan Slab during the second metamorphic stage; but closer to the MCT front, the thermal accordance between both sides of the thrust is due to a retrogressive metamorphic episode in the basal part of the Tibetan Slab.

Flatness, motion planning and trailer systems

Abstract: A solution of the motion planning without obstacles for the standard a-trailer system is proposed This solution relies basically on the fact that the system is flat with the Cartesian coordinates of the last trailer as a linearizing output The Frenet formulae are used to simplify the calculations and permit to deal with angle constraints The general 1-trailer system, where the trailer is not directly hitched to the car at the center of the rear axle, is also flat The geometric construction used for the standard 1-trailer system can be extended to this more realistic system MATLAB simulations illustrate this method >

Classification of microarray data using gene networks

Abstract: Microarrays have become extremely useful for analysing genetic phenomena, but establishing a relation between microarray analysis results (typically a list of genes) and their biological significance is often difficult. Currently, the standard approach is to map a posteriori the results onto gene networks in order to elucidate the functions perturbed at the level of pathways. However, integrating a priori knowledge of the gene networks could help in the statistical analysis of gene expression data and in their biological interpretation. We propose a method to integrate a priori the knowledge of a gene network in the analysis of gene expression data. The approach is based on the spectral decomposition of gene expression profiles with respect to the eigenfunctions of the graph, resulting in an attenuation of the high-frequency components of the expression profiles with respect to the topology of the graph. We show how to derive unsupervised and supervised classification algorithms of expression profiles, resulting in classifiers with biological relevance. We illustrate the method with the analysis of a set of expression profiles from irradiated and non-irradiated yeast strains. Including a priori knowledge of a gene network for the analysis of gene expression data leads to good classification performance and improved interpretability of the results.