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Automatic discovery of design task structure using deep belief nets

Lan, Lijun, Liu, Ying ORCID:, Lu, Wen Feng and Alghamdi, Awn 2015. Automatic discovery of design task structure using deep belief nets. Presented at: 27th International Conference on Design Theory and Methodology, Boston, MA, 2 - 5 August 2015. Print Proceedings of the ASME 2015 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (DETC2015). , vol.7 United States of America: American Society of Mechanical Engineers, 10.1115/DETC2015-47369

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With the arrival of cyber physical world and an extensive support of advanced IT infrastructure, nowadays it is possible to obtain the footprints of design activities through emails, design journals, change logs, and different forms of social data. In order to manage a more effective design process, it is essential to learn from the past and understand, for example, what design tasks are actually carried out, their interactions and how they impact each other. In this paper, a computational approach based on deep belief nets (DBN) is proposed to automatically uncover design tasks and quantify their interactions. A DBN topic modeling with real-valued units is to learn a set of intrinsic topic features from a simple word-frequency based input representation. Evaluated using a design email archive spanning for more than two years, the proposed approach has achieved a much higher accuracy in identifying design tasks compared to a prevailing approach

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Engineering
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Additional Information: Paper No. DETC2015-47369, pp. V007T06A026
Publisher: American Society of Mechanical Engineers
ISBN: 9780791857175
Last Modified: 31 Oct 2022 10:59

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