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Reinforcing conceptual engineering design with a hybrid computer vision, machine learning and knowledge based system framework

Kaloskampis, Ioannis, Hicks, Yulia Alexandrovna and Marshall, D. 2011. Reinforcing conceptual engineering design with a hybrid computer vision, machine learning and knowledge based system framework. Presented at: IEEE International Conference on Systems, Man, and Cybernetics, Anchorage, Alaska, USA, 9-12 October 2011. IEEE SMC 2011 International Conference on Systems, Man and Cybernetics October 9-12, 2011 : Anchorage Alaska USA : Conference proceedings. Piscataway, N.J.: IEEE, pp. 3242-3249. 10.1109/ICSMC.2011.6084169

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Abstract

We propose a novel system that aids engineers in the conceptual stage of design. Our system's goal is to support the engineer without limiting his creative role; thus, our proposed method does not produce ready study solutions but rather actively monitors the design procedure, verifying design stages and pointing out potential mistakes. This is achieved with a hybrid computer vision, machine learning and knowledge based system framework. Design stage identification is performed with a novel algorithm which comprises a classification stage based on Random Forests and examination of the temporal relationships between the engineer's actions with the aid of statistical graphical models. Experimental results captured in a complex, real life scenario demonstrate our system's ability to efficiently support the engineer's decisions during the conceptual stage of design.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Engineering
Subjects: Q Science > QA Mathematics > QA76 Computer software
T Technology > TA Engineering (General). Civil engineering (General)
Publisher: IEEE
ISBN: 9781457706523
Last Modified: 10 Sep 2019 22:22
URI: http://orca.cf.ac.uk/id/eprint/39824

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