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Categorizing biological information based on function–morphology for bioinspired conceptual design

Published online by Cambridge University Press:  05 December 2016

Sooyeon Lee ,
Daniel A. McAdams  and
Elissa Morris
Sooyeon Lee
Affiliation:
Department of Mechanical Engineering, Texas A&M University, College Station, Texas, USA
Daniel A. McAdams*
Affiliation:
Department of Mechanical Engineering, Texas A&M University, College Station, Texas, USA
Elissa Morris
Affiliation:
Department of Mechanical Engineering, Texas A&M University, College Station, Texas, USA
*
Reprint requests to: Daniel A. McAdams, Texas A&M University, Mechanical Engineering Office Building, 3123 TAMU, College Station, TX 77843, USA. E-mail: dmcadams@tamu.edu
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Abstract

A function-based keyword search is a concept generation methodology studied in the bioinspired design area that conveys textual biological inspiration for engineering design. Current keyword search methods are inefficient primarily due to the knowledge gap between engineering and biology domains. To improve current keyword search methods, we propose an algorithm that extracts and organizes morphology-based solutions from biological text. WordNet is utilized to discover morphological solutions in biological text. The novel algorithm also adapts latent semantic analysis and the expectation–maximization algorithm to categorize morphological solutions and group biological text. We introduce a novel penalty function that reflects the distance between functions (problems) and morphologies (solutions). The penalty function allows the algorithm to extract morphological solutions directly related to a design problem. We compare the output of the algorithm to manually extracted solutions for validation. A case study is included to exemplify the utility of the developed algorithm. Upon implementation of the algorithm, engineering designers can discover innovative solutions in biological text in a straightforward, efficient manner.

Keywords

Bioinspired DesignConceptual DesignKeyword SearchMorphologyText Mining
Type
Regular Articles
Information
AI EDAM , Volume 31 , Special Issue 3: Uncertainty Quantification for Engineering Design , August 2017 , pp. 359 - 375
Copyright
Copyright © Cambridge University Press 2016 

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References

REFERENCES

Bingham, E., & Mannila, H. (2001). Random projection in dimensionality reduction: applications to image and text data. Proc. 7th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, pp. 245–250. San Francisco, CA: ACM.CrossRefGoogle Scholar
Chakrabarti, A., Sarkar, P., Leelavathamma, B., & Nataraju, B. (2005). A functional representation for aiding biomimetic and artificial inspiration of new ideas. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 19(2), 113–132.CrossRefGoogle Scholar
Cheong, H., Chiu, I., Shu, L., Stone, R., & McAdams, D. (2011). Biologically meaningful keywords for functional terms of the functional basis. Journal of Mechanical Design 133(2), 021007.Google Scholar
Dempster, A.P., Laird, N.M., & Rubin, D.B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (Methodological) 39(1), 1–38.Google Scholar
Gero, J.S., & Kazakov, V. (1999). Adapting evolutionary computing for exploration in creative designing. Proc. Computational Models of Creative Design IV, pp. 175–186. Sydney, Australia: University of Sydney, Key Centre of Design Computing and Cognition.Google Scholar
Globerson, A., & Tishby, N. (2003). Sufficient dimensionality reduction. Journal of Machine Learning Research 3, 1307–1331.Google Scholar
Hacco, E., & Shu, L. (2002). Biomimetic concept generation applied to design for remanufacture. Proc. ASME Design Engineering Technical Conf., Vol. 3, pp. 239–246. New York: ASME.Google Scholar
Hirtz, J., Stone, R.B., McAdams, D.A., Szykman, S., & Wood, K.L. (2002). A functional basis for engineering design: reconciling and evolving previous efforts. Research in Engineering Design 13(2), 65–82.CrossRefGoogle Scholar
Jolliffe, I. (2002). Principal Component Analysis. New York: Wiley Online Library.Google Scholar
Kaiser, M., Hashemi Farzaneh, H., & Lindemann, U. (2014). Bioscrabble—the role of different types of search terms when searching for biological inspiration in biological research articles. Proc. DESIGN 2014 13th Int. Design Conf., Dubrovnik, Croatia, May 19–22.Google Scholar
Ke, J., Wallace, J., & Shu, L. (2009). Supporting biomimetic design through categorization of natural-language keyword-search results. Proc. ASME 2009 Int. Design Engineering Technical Conf./Computers and Information in Engineering Conf., pp. 775–784. San Diego, CA: ASME.Google Scholar
Laham, T., & Foltz, P. (1998). Learning human-like knowledge by singular value decomposition: a progress report. Advances in Neural Information Processing Systems 10: Proc. 1997 Conf., p. 45. Cambridge, MA: MIT Press.Google Scholar
Landauer, T.K., Foltz, P.W., & Laham, D. (1998). An introduction to latent semantic analysis. Discourse Processes 25(2–3), 259–284.CrossRefGoogle Scholar
Little, A., Wood, K., & McAdams, D. (1997). Functional analysis: a fundamental empirical study for reverse engineering, benchmarking, and redesign. Proc. 1997 Design Engineering Technical Conf., Paper No. 97-DETC/DTM-3879, Sacramento, CA.Google Scholar
Manning, C.D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge: Cambridge University Press.Google Scholar
Manning, C.D., & Schütze, H. (1999). Foundations of Statistical Natural Language Processing. Cambridge, MA: MIT Press.Google Scholar
Marks, P. (2011). Woodpecker inspires shock absorbers. New Scientist 209(2798), 21.Google Scholar
Nagel, J.K., Stone, R.B., & McAdams, D.A. (2010). An engineering-to-biology thesaurus for engineering design. Proc. ASME 2010 Int. Design Engineering Technical Conf./Computers and Information in Engineering Conf., pp. 117–128. New York: ASME.Google Scholar
Nakov, P., Popova, A., & Mateev, P. (2001). Weight functions impact on LSA performance. Proc. EuroConference RANLP, pp. 187–193, Tzigov Chark, Bulgaria, September 5–7.Google Scholar
Purves, W.K., Orians, G.H., Sadava, D., & Heller, H.C. (2003). Life: The Science of Biology: Vol. 3. Plants and Animals. New York: Macmillan.Google Scholar
Qian, L., & Gero, J. (1992). A design support system using analogy. Proc. Artificial Intelligence in Design'92, pp. 795–813. New York: Springer.Google Scholar
Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management 24(5), 513–523.Google Scholar
Schmid, H. (2013). Probabilistic part-of-speech tagging using decision trees. In New Methods in Language Processing (Jones, D.B., & Somers, H., Eds.), pp. 154–164. New York: Routledge.Google Scholar
Shu, L., Ueda, K., Chiu, I., & Cheong, H. (2011). Biologically inspired design. CIRP Annals—Manufacturing Technology 60(2), 673–693.Google Scholar
Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 63(2), 411–423.Google Scholar
Tsenn, J., McAdams, D.A., & Linsey, J.S. (2015). A comparison of mechanical engineering and biology students’ ideation and bioinspired design abilities. Proc. Design Computing and Cognition '14, pp. 645–662. New York: Springer.Google Scholar
Vandevenne, D., Verhaegen, P.-A., Dewulf, S., & Duflou, J.R. (2016). SEABIRD: scalable search for systematic biologically inspired design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 30(1), 78–95.Google Scholar
Vandevenne, D., Verhaegen, P.-A., & Duflou, R.J. (2014). Mention and focus organism detection and their applications for scalable systematic bio-ideation tools. Journal of Mechanical Design 136(11), 111104.Google Scholar
Vattam, S., Wiltgen, B., Helms, M., Goel, A.K., & Yen, J. (2011). DANE: fostering creativity in and through biologically inspired design. Proc. Design Creativity 2010 (Taura, T., & Nagai, Y., Eds.), pp. 115–122. London: Springer.Google Scholar
Villalon, J., & Calvo, R.A. (2009). Single document semantic spaces. Proc. 8th Australasian Data Mining Conf., Vol. 101, pp. 175–181. Melbourne: Australian Computer Society.Google Scholar
Wild, F., Stahl, C., Stermsek, G., & Neumann, G. (2005). Parameters driving effectiveness of automated essay scoring with LSA. Proc. 12th Int. Conf. Artificial Intelligence in Education, July 18–22, 2005, Amsterdam, The Netherlands.Google Scholar
Yoon, S.-H., & Park, S. (2011). A mechanical analysis of woodpecker drumming and its application to shock-absorbing systems. Bioinspiration & Biomimetics 6(1), 016003.Google Scholar
Zhu, M., & Ghodsi, A. (2006). Automatic dimensionality selection from the scree plot via the use of profile likelihood. Computational Statistics & Data Analysis 51(2), 918–930.CrossRefGoogle Scholar