Institution
Mines ParisTech
Education•Paris, 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 published on a yearly basis
Papers
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TL;DR: A sensitivity-based methodology is presented to choose the best possible gains parameterization in a state Riccati dependent equation (SDRE) feedback controller and results will be validated and compared with other nonlinear optimal feedback controllers, from a realistic industrial simulator environment for vehicle dynamics.
Abstract: This paper presents a feedback steering control strategy for a vehicle in an automatic driving context. Two main contributions in terms of control are highlighted. On the one hand, the automatic reference trajectories generation from geometric path constraints (obstacles). Thanks to the flatness property of the considered model, the longitudinal velocity will be controlled around a quasi-constant value while lateral and yaw dynamics targets will allow to avoid obstacles. On the other hand, a sensitivity-based methodology will be presented to choose the best possible gains parameterization in a state Riccati dependent equation (SDRE) feedback controller. Both direct and adjoint sensitivity methods are used, together with a dynamic inversion of the system, in order to optimize the performances of the controller. Obstacle avoiding simulation results will be validated and compared with other nonlinear optimal feedback controllers, from a realistic industrial simulator environment for vehicle dynamics
111 citations
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TL;DR: In this paper, the segregation of metallic additives M and metalloid impurities I to the free surface of iron was studied by Auger electron spectroscopy in dilute binary and ternary iron-based alloys.
111 citations
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TL;DR: The proposed life expectancy framework provides information on the life expectancy change in time series studies, and it clarifies the relation between the results of time series, intervention, and cohort studies.
Abstract: Information on life expectancy change is of great concern for policy makers, as evidenced by the discussions of the so-called "harvesting" issue (i.e. the question being, how large a loss each death corresponds to in the mortality results of time series studies). Whereas most epidemiological studies of air pollution mortality have been formulated in terms of mortality risk, this paper shows that a formulation in terms of life expectancy change is mathematically equivalent, but offers several advantages: it automatically takes into account the constraint that everybody dies exactly once, regardless of pollution; it provides a unified framework for time series, intervention studies and cohort studies; and in time series and intervention studies, it yields the life expectancy change directly as a time integral of the observed mortality rate. Results are presented for life expectancy change in time series studies. Determination of the corresponding total number of attributable deaths (as opposed to the number of observed deaths) is shown to be problematic. The time variation of mortality after a change in exposure is shown to depend on the processes by which the body can repair air pollution damage, in particular on their time constants. Hypothetical results are presented for repair models that are plausible in view of the available intervention studies of air pollution and of smoking cessation. If these repair models can also be assumed for acute effects, the results of cohort studies are compatible with those of time series. The proposed life expectancy framework provides information on the life expectancy change in time series studies, and it clarifies the relation between the results of time series, intervention, and cohort studies.
111 citations
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TL;DR: It is demonstrated that the neural border specifiers Pax3 and Zic1 are direct upstream regulators of neural crest specifiers Snail1/2, Foxd3, Twist1, and Tfap2b and that Pax3 could maintain its own expression through a positive autoregulatory feedback loop.
111 citations
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15 Mar 2005-Materials Science and Engineering A-structural Materials Properties Microstructure and Processing
TL;DR: In this paper, the effect of silicon on the stability of secondary carbides was studied using transmission electron microscopy and small-angle neutron scattering, and it was concluded that silicon has a detrimental effect as it shifts the secondary hardening peak towards lower tempering temperatures.
Abstract: In comparison with the conventional AISI H11 tool steel, which contains approximately 1 wt.% silicon, the modified steel AISI H11 (∼0.35 wt.% silicon) exhibits improved tensile and fatigue properties at 550 °C – the estimated tool surface temperature during the high-pressure injection of aluminium alloys. The effect of silicon on the stability of secondary carbides was studied using transmission electron microscopy and small-angle neutron scattering. Silicon has a considerable influence on the precipitation of secondary carbides. A higher volume fraction and density of small particles were observed in the low-silicon-grade steel, both after heat treatment and after fatigue testing. The final discussion focuses on the influence of silicon in the precipitation sequence. It is concluded that silicon has a detrimental effect as it shifts the secondary hardening peak towards lower tempering temperatures.
111 citations
Authors
Showing all 6591 results
Name | H-index | Papers | Citations |
---|---|---|---|
Francis Bach | 110 | 484 | 54944 |
Olivier Delattre | 103 | 490 | 39258 |
Richard M. Murray | 97 | 711 | 69016 |
Bruno Latour | 96 | 364 | 94864 |
George G. Malliaras | 94 | 382 | 28533 |
George S. Wilson | 88 | 716 | 33034 |
Zhong-Ping Jiang | 81 | 597 | 24279 |
F. Liu | 80 | 428 | 23869 |
Kazu Suenaga | 75 | 329 | 26287 |
Carlo Adamo | 75 | 444 | 36092 |
Edith Heard | 75 | 196 | 23899 |
Enrico Zio | 73 | 1127 | 23809 |
John J. Jonas | 70 | 379 | 21544 |
Bernard Asselain | 69 | 409 | 23648 |
Eric Guibal | 69 | 294 | 16397 |