Research Papers

Automatic Discovery of Design Task Structure Using Deep Belief Nets

[+] Author and Article Information
Lijun Lan

Department of Mechanical Engineering,
National University of Singapore,
Singapore 117576, Singapore
e-mail: lijunlan@u.nus.edu

Ying Liu

Mechanical and Manufacturing Engineering,
School of Engineering,
Cardiff University,
Cardiff CF24 3AA, UK
e-mail: LiuY81@Cardiff.ac.uk

Wen Feng Lu

Department of Mechanical Engineering,
National University of Singapore,
Singapore 117576, Singapore
e-mail: mpelwf@nus.edu.sg

1Corresponding author.

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.

J. Comput. Inf. Sci. Eng 17(4), 041001 (May 16, 2017) (8 pages) Paper No: JCISE-15-1167; doi: 10.1115/1.4036198 History: Received May 14, 2015; Revised March 02, 2017

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.

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Fig. 1

The framework of discovering design task structure via DBN-based topic modeling

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Fig. 2

The architecture of deep belief network (DBN): (a) an example DBN with one input layer and three hidden layers, where each pair of succeeded layers is treated as a RBM model and (b) the restricted Boltzmann machine (RBM)

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Fig. 3

Training process of DBN topic model: (a) pretraining process, in which a stack of RBMs are learned layer by layer and (b) fine-tuning process, where an extra layer of label information is added to fine tune the entire network

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Fig. 4

Illustration of mapping design tasks from hidden topic features. The thick lines indicate words with strongest connections to the jth topic.

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Fig. 5

Document retrieval effectiveness of DBNs: (a) comparison of DBNs of one hidden layer but different hidden units and (b) comparison of DBNs of the same hidden units in the top layer but different numbers of hidden layers. The DBN structure is indicated in the format of XX-XX, e.g., 1630-50 means a DBN model with one visible layer of size 1630, and one hidden layer of size 50.

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Fig. 6

Document retrieval effectiveness of one-hidden-layer DBNs and LDAs with the same number of hidden topics

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Fig. 7

Temporal frequency of task-relevant topics in Table 1 with a window size of 15 days

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Fig. 8

Illustration of interaction strengths between selected design tasks




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