Institution
Ford Motor Company
Company•Dearborn, Michigan, United States•
About: Ford Motor Company is a company organization based out in Dearborn, Michigan, United States. It is known for research contribution in the topics: Internal combustion engine & Clutch. The organization has 36123 authors who have published 51450 publications receiving 855200 citations. The organization is also known as: Ford Motor & Ford Motor Corporation.
Topics: Internal combustion engine, Clutch, Control theory, Torque, Exhaust gas
Papers published on a yearly basis
Papers
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TL;DR: Simulated results demonstrate the desired driving behaviors of an autonomous vehicle using both the reinforcement learning and inverse reinforcement learning techniques.
172 citations
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TL;DR: Analyse par topographie de the diffusion des atomes d'or a temperature ambiante de la diffusion est independante de the valeur ou du signe du courant de l'effet tunnel and des parametres de tension.
Abstract: Time-lapse topography has been done with a scanning tunneling microscope on a clean, annealed Au(111) surface showing the effects of surface diffusion of Au atoms at room temperature. Within several minutes, features such as marks made by a gentle touch of the tunnel tip are seen to change as a result of the diffusion of Au atoms over the surface. The diffusion does not depend on the magnitude or sign of the tunneling current and voltage parameters. These experiments demonstrate the unique ability of the scanning tunneling microscope to obtain directly information about surface diffusion on an atomic scale.
172 citations
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TL;DR: The method (Bayesian Rule Sets - BRS) is applied to characterize and predict user behavior with respect to in-vehicle context-aware personalized recommender systems and has a major advantage over classical associative classification methods and decision trees.
Abstract: We present a machine learning algorithm for building classifiers that are comprised of a small number of short rules. These are restricted disjunctive normal form models. An example of a classifier of this form is as follows: If X satisfies (condition A AND condition B) OR (condition C) OR ..., then Y = 1. Models of this form have the advantage of being interpretable to human experts since they produce a set of rules that concisely describe a specific class. We present two probabilistic models with prior parameters that the user can set to encourage the model to have a desired size and shape, to conform with a domain-specific definition of interpretability. We provide a scalable MAP inference approach and develop theoretical bounds to reduce computation by iteratively pruning the search space. We apply our method (Bayesian Rule Sets - BRS) to characterize and predict user behavior with respect to in-vehicle context-aware personalized recommender systems. Our method has a major advantage over classical associative classification methods and decision trees in that it does not greedily grow the model.
172 citations
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TL;DR: In this article, the first-order saddlepoint approximation for reliability analysis is proposed to improve the accuracy of reliability analysis, which reduces the chance of an increase in the nonlinearity of the limit-state function.
Abstract: In the approximation methods of reliability analysis, nonnormal random variables are transformed into equivalent standard normal random variables. This transformation tends to increase the nonlinearity of a limit-state function and, hence, results in less accurate reliability approximation. The first-order saddlepoint approximation for reliability analysis is proposed to improve the accuracy of reliability analysis. By the approximation of a limit-state function at the most likelihood point in the original random space and employment of the accurate saddlepoint approximation, the proposed method reduces the chance of an increase in the nonlinearity of the limit-state function. This approach generates more accurate reliability approximation than the first-order reliability method without an increase in the computational effort. The effectiveness of the proposed method is demonstrated with two examples and is compared with the first- and second-order reliability methods.
172 citations
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TL;DR: In this paper, a full-wave analysis for the problem of scattering frequency selective surfaces from (FSS) comprised of periodic arrays of cross dipoles and Jerusalem crosses is presented, where the formulation is carried out in the spectral domain where the convolution form of the integral equation for the induced current is reduced to an algebraic one.
Abstract: A full-wave analysis for the problem of scattering frequency selective surfaces from (FSS) comprised of periodic arrays of cross dipoles and Jerusalem crosses is presented. The formulation is carried out in the spectral domain where the convolution form of the integral equation for the induced current is reduced to an algebraic one. The equation is then solved using the Galerkin's procedure applied in the spectral domain. A set of entire-domain type "junction basis functions," which, is demonstrated in this paper to be essential to account correctly for the discontinuous nature of the induced current at the junction of the cross, is included in the expansion for the unknown induced current. This analysis is computationally efficient, and its accuracy is verified by the agreement between the computed theoretical data and the experimental results reported by other authors.
172 citations
Authors
Showing all 36140 results
Name | H-index | Papers | Citations |
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Anil K. Jain | 183 | 1016 | 192151 |
Markus Antonietti | 176 | 1068 | 127235 |
Christopher M. Dobson | 150 | 1008 | 105475 |
Jack Hirsh | 146 | 734 | 86332 |
Galen D. Stucky | 144 | 958 | 101796 |
Federico Capasso | 134 | 1189 | 76957 |
Peter Stone | 130 | 1229 | 79713 |
Gerald R. Crabtree | 128 | 371 | 60973 |
Douglas A. Lauffenburger | 122 | 705 | 55326 |
Abass Alavi | 113 | 1298 | 56672 |
Mark E. Davis | 113 | 568 | 55334 |
Keith Beven | 110 | 514 | 61705 |
Naomi Breslau | 107 | 254 | 42029 |
Fei Wang | 107 | 1824 | 53587 |
Jun Yang | 107 | 2090 | 55257 |