With the arrival of cyber physical world and an extensive support of advanced information technology (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 by utilizing these valuable sources 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 the deep belief nets (DBN) is proposed to automatically uncover design tasks and quantify their interactions from design document archives. First, a DBN topic model with real-valued units is developed to learn a set of intrinsic topic features from a simple word-frequency-based input representation. The trained DBN model is then utilized to discover design tasks by unfolding hidden units by sets of strongly connected words, followed by estimating the interactions among tasks on the basis of their co-occurrence frequency in a hidden topic space. Finally, the proposed approach is demonstrated through a real-life case study using a design email archive spanning for more than 2 yr.
Automatic Discovery of Design Task Structure Using Deep Belief Nets
Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received May 14, 2015; final manuscript received March 2, 2017; published online May 16, 2017. Editor: Bahram Ravani.
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Lan, L., Liu, Y., and Feng Lu, W. (May 16, 2017). "Automatic Discovery of Design Task Structure Using Deep Belief Nets." ASME. J. Comput. Inf. Sci. Eng. December 2017; 17(4): 041001. https://doi.org/10.1115/1.4036198
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