Dempster-Shafer and Bayesian networks for CAD-based feature extraction: A comparative investigation and analysis

Qiang Ji; Michael M. Marefat; Paul J.A. Lever

Dempster-Shafer and Bayesian networks for CAD-based feature extraction: A comparative investigation and analysis

Qiang Ji, Michael M. Marefat, Paul J.A. Lever

Electrical and Computer Engineering

Research output: Contribution to conference › Paper › peer-review

Abstract

The paper evaluates the performance the Dempster-Shafer theory (DS) and the Bayesian Belief Network (BBN) with regard to their ability to extract manufacturing features from the solid model description of objects.

Original language	English (US)
Number of pages	1
State	Published - Dec 1 1994
Event	Proceedings of the 12th National Conference on Artificial Intelligence. Part 1 (of 2) - Seattle, WA, USA Duration: Jul 31 1994 → Aug 4 1994

Other

Other	Proceedings of the 12th National Conference on Artificial Intelligence. Part 1 (of 2)
City	Seattle, WA, USA
Period	7/31/94 → 8/4/94

ASJC Scopus subject areas

Software
Artificial Intelligence

Cite this

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title = "Dempster-Shafer and Bayesian networks for CAD-based feature extraction: A comparative investigation and analysis",

abstract = "The paper evaluates the performance the Dempster-Shafer theory (DS) and the Bayesian Belief Network (BBN) with regard to their ability to extract manufacturing features from the solid model description of objects.",

author = "Qiang Ji and Marefat, {Michael M.} and Lever, {Paul J.A.}",

year = "1994",

month = dec,

day = "1",

language = "English (US)",

note = "Proceedings of the 12th National Conference on Artificial Intelligence. Part 1 (of 2) ; Conference date: 31-07-1994 Through 04-08-1994",

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T1 - Dempster-Shafer and Bayesian networks for CAD-based feature extraction

T2 - Proceedings of the 12th National Conference on Artificial Intelligence. Part 1 (of 2)

AU - Ji, Qiang

AU - Marefat, Michael M.

AU - Lever, Paul J.A.

PY - 1994/12/1

Y1 - 1994/12/1

N2 - The paper evaluates the performance the Dempster-Shafer theory (DS) and the Bayesian Belief Network (BBN) with regard to their ability to extract manufacturing features from the solid model description of objects.

AB - The paper evaluates the performance the Dempster-Shafer theory (DS) and the Bayesian Belief Network (BBN) with regard to their ability to extract manufacturing features from the solid model description of objects.

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M3 - Paper

AN - SCOPUS:0028596872

Y2 - 31 July 1994 through 4 August 1994

ER -

Dempster-Shafer and Bayesian networks for CAD-based feature extraction: A comparative investigation and analysis

Abstract

Other

ASJC Scopus subject areas

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