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Institution

Mines ParisTech

EducationParis, France
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.


Papers
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Journal ArticleDOI
TL;DR: All alternatives to conventional biomolecules employed in the detection of various toxin molecules are discussed, including modified enzymes, nanozymes, nanobodies, aptamers, peptides, protein scaffolds and DNazymes.

142 citations

Journal ArticleDOI
TL;DR: In this paper, the authors examined whether natural daily surface temperature changes could have played a role in the occurrence of a rockfall on the Rochers de Valabres slope in France's Southern Alps region.

142 citations

Journal ArticleDOI
TL;DR: It is proved that the result that, for any constant angular velocity smaller than a critical one, this system can be stabilized by means of a feedback torque control law if there is damping also holds if there are no damping.
Abstract: This paper deals with the stabilization of a rotating body-beam system with torque control. The system we consider is the one studied by Baillieul and Levi (1987). Xu and Baillieul proved (1993) that, for any constant angular velocity smaller than a critical one, this system can be stabilized by means of a feedback torque control law if there is damping. We prove that this result also holds if there is no damping.

142 citations

Journal ArticleDOI
TL;DR: A comparative study based simulations demonstrate the effectiveness and the advantage of the proposed cooperative localization method using split covariance intersection filter.
Abstract: Vehicle localization (ground vehicles) is an important task for intelligent vehicle systems and vehicle cooperation may bring benefits for this task. A new cooperative multi-vehicle localization method using split covariance intersection filter is proposed in this paper. In the proposed method, each vehicle maintains an estimate of a decomposed group state and this estimate is shared with neighboring vehicles; the estimate of the decomposed group state is updated with both the sensor data of the ego-vehicle and the estimates sent from other vehicles; the covariance intersection filter which yields consistent estimates even facing unknown degree of inter-estimate correlation has been used for data fusion. A comparative study based simulations demonstrate the effectiveness and the advantage of the proposed cooperative localization method.

141 citations


Authors

Showing all 6591 results

NameH-indexPapersCitations
Francis Bach11048454944
Olivier Delattre10349039258
Richard M. Murray9771169016
Bruno Latour9636494864
George G. Malliaras9438228533
George S. Wilson8871633034
Zhong-Ping Jiang8159724279
F. Liu8042823869
Kazu Suenaga7532926287
Carlo Adamo7544436092
Edith Heard7519623899
Enrico Zio73112723809
John J. Jonas7037921544
Bernard Asselain6940923648
Eric Guibal6929416397
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202315
202264
2021274
2020260
2019250
2018249