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Institution

École normale supérieure de Cachan

EducationCachan, Île-de-France, France
About: École normale supérieure de Cachan is a education organization based out in Cachan, Île-de-France, France. It is known for research contribution in the topics: Decidability & Finite element method. The organization has 2717 authors who have published 5585 publications receiving 175925 citations.


Papers
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Book ChapterDOI
01 Jan 1996
TL;DR: A tridimensional model of variations is designed to treat the problem of transfer of dimensions in the description of the behaviour of a set of parts with variation.
Abstract: The analysis of the difficulties encountered with traditional dimension chains in the description of the behaviour of a set of parts, with variation, has led us to develop a tridimensional model of variations. It is therefore designed to treat the problem of transfer of dimensions.

63 citations

Journal ArticleDOI
TL;DR: A series of "push-pull" salts substituted with an electron-donating bis(N,N-dimethyl)aniline unit and different electron-withdrawing methyl or chelating pyridinium units designed and synthesized from Michler's ketone revealed that a lower HOMO-LUMO gap is obtained in all cases, showing the ability of the donor to increase the " push- pull" effect.

63 citations

Journal ArticleDOI
TL;DR: In this article, the optimal design of composite laminates under buckling load uncertainty is presented, where the laminate is subjected to biaxial compressive loads and the buckled load is maximized under worst case in-plane loading which is computed using an antioptimization approach.

63 citations

Journal ArticleDOI
TL;DR: In this paper, an original eddy current imager (ECI) was designed for the fast and accurate non-destructive evaluation of defects buried next to rivets in aeronautical lap-joints.
Abstract: The authors present an original eddy current imager (ECI) designed for the fast and accurate non-destructive evaluation of defects buried next to rivets in aeronautical lap-joints. The ECI is associated to a signal processing method based on a principal component analysis (PCA) followed by a maximum likelihood (ML) approach. The PCA was implemented using EC images obtained with selected excitation frequencies. These images are considered as resulting from a linear mixing of different sources including the presence of rivets and defects, and the PCA is used to separate these sources thanks to an eigen decomposition of the EC data covariance matrix. As a result, the defect signatures are enhanced and used to implement an automatic defect characterization. This characterization is carried out by the means of an ML approach which allows the length and depth of the defects to be estimated. The method was implemented for the evaluation of a laboratory made riveted lap joint mock-up featuring buried defects. It was experimentally optimized and successfully implemented for the characterization of calibrated defects ranging from 2 to 10 mm in length and 2 to 8 mm in depth.

63 citations

Journal ArticleDOI
TL;DR: Encapsulation of a naphthalimide moiety in the core of silica nanoparticles afforded nanospheres with a strong green excimeric emission and added contributions provide intense white fluorescence upon 330 nm UV excitation.
Abstract: Into the white: Encapsulation of a naphthalimide moiety in the core of silica nanoparticles afforded nanospheres with a strong green excimeric emission. Together with the blue emission of the monomeric naphthalimide and the yellow fluorescence of the tetrazine acceptor on the outer shell, the added contributions provide intense white fluorescence upon 330 nm UV excitation.

63 citations


Authors

Showing all 2722 results

NameH-indexPapersCitations
Shi Xue Dou122202874031
Olivier Hermine111102643779
John R. Reynolds10560750027
Shaul Mukamel95103040478
Tomás Torres8862528223
Ifor D. W. Samuel7460523151
Serge Abiteboul7327824576
Stéphane Roux6862719123
Zeger Debyser6740416531
Louis Nadjo6426412596
Praveen K. Thallapally6419012110
Andrew Travers6319313537
Shoji Takeuchi6369214704
Bineta Keita6327412053
Yves Mély6236813478
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20233
202222
202121
202029
201958
201879