Kaloskampis, Ioannis  ORCID: https://orcid.org/0000-0002-4450-4935, Hicks, Yulia Alexandrovna ORCID: https://orcid.org/0000-0002-7179-4587 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
|
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: | 21 Oct 2022 10:19 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/39824 |
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