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Institution

General Electric

CompanyBoston, Massachusetts, United States
About: General Electric is a company organization based out in Boston, Massachusetts, United States. It is known for research contribution in the topics: Turbine & Signal. The organization has 76365 authors who have published 110557 publications receiving 1885108 citations. The organization is also known as: General Electric Company & GE.
Topics: Turbine, Signal, Rotor (electric), Coating, Combustor


Papers
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Journal ArticleDOI
TL;DR: Optimisation de lhomogeneite du champ radiofrequence necessaire pour produire des sequences d'impulsions a multichocs and du rapport signal sur bruit.

786 citations

Journal ArticleDOI
TL;DR: An edit-distance algorithm for shock graphs that finds the optimal deformation path in polynomial time is employed and gives intuitive correspondences for a variety of shapes and is robust in the presence of a wide range of visual transformations.
Abstract: This paper presents a novel framework for the recognition of objects based on their silhouettes. The main idea is to measure the distance between two shapes as the minimum extent of deformation necessary for one shape to match the other. Since the space of deformations is very high-dimensional, three steps are taken to make the search practical: 1) define an equivalence class for shapes based on shock-graph topology, 2) define an equivalence class for deformation paths based on shock-graph transitions, and 3) avoid complexity-increasing deformation paths by moving toward shock-graph degeneracy. Despite these steps, which tremendously reduce the search requirement, there still remain numerous deformation paths to consider. To that end, we employ an edit-distance algorithm for shock graphs that finds the optimal deformation path in polynomial time. The proposed approach gives intuitive correspondences for a variety of shapes and is robust in the presence of a wide range of visual transformations. The recognition rates on two distinct databases of 99 and 216 shapes each indicate highly successful within category matches (100 percent in top three matches), which render the framework potentially usable in a range of shape-based recognition applications.

773 citations

Journal ArticleDOI
Frank S. Ham1
TL;DR: In this paper, a simple theory for diffusion-limited general precipitation from a supersaturated solution upon an array of particles is described, and the time dependence of the unprecipitated fraction of the excess solute for small spherical, spheroidal, and cylindrical particles is calculated.

771 citations

Journal ArticleDOI
L. L. Garver1
TL;DR: The use of linear programming for network analysis to determine where capacity shortages exist and, most importantly, where to add new circuits to relieve the shortages is presented.
Abstract: One aspect of long-range planning of electric power systems involves the exploration of various designs for the bulk power transmission network. The use of linear programming for network analysis to determine where capacity shortages exist and, most importantly, where to add new circuits to relieve the shortages is presented. The new method of network estimation produces a feasible transmission network with near-minimum circuit miles using as input any existing network plus a load and generation schedule. An example is used to present the two steps of the method: 1) linear flow estimation and 2) new circuit selection. The method has become a fundamental part of computer programs for transmission network synthesis.

771 citations

Journal ArticleDOI
Nguyen Q. Minh1
TL;DR: In this paper, the authors discuss and summarize the SOFC's features and provide an overview of this technology's potential applications, including flexibility in cell and stack designs, manufacturing processes, and power plant sizes.

765 citations


Authors

Showing all 76370 results

NameH-indexPapersCitations
Cornelia M. van Duijn1831030146009
Krzysztof Matyjaszewski1691431128585
Gary H. Glover12948677009
Mark E. Thompson12852777399
Ron Kikinis12668463398
James E. Rothman12535860655
Bo Wang119290584863
Wei Lu111197361911
Harold J. Vinegar10837930430
Peng Wang108167254529
Hans-Joachim Freund10696246693
Carl R. Woese10527256448
William J. Koros10455038676
Thomas A. Lipo10368243110
Gene H. Golub10034257361
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20231
202216
2021415
20201,027
20191,418
20181,862