Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-26T05:08:37.522Z Has data issue: false hasContentIssue false
  • English
  • Français

Elliptic Fourier analysis and perceptual matching for the evaluation of bioinspired sketching in conceptual design

Published online by Cambridge University Press:  14 August 2017

Zhongliang Yang ,
Yumiao Chen  and
Zheng Liu
Zhongliang Yang*
Affiliation:
College of Mechanical Engineering, Donghua University, Shanghai, China
Yumiao Chen
Affiliation:
Fashion and Art Design Institute, Donghua University, Shanghai, China
Zheng Liu
Affiliation:
College of Art Design, China Academy of Art, Hangzhou, China
*
Reprint requests to: Zhongliang Yang, College of Mechanical Engineering, Donghua University, Shanghai 201620, China. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Biologically inspired design can be used to aid in conceptual design. Sketching is an important ideation process in conceptual design for recording and evaluating flashing moments of inspiration. The present study aims to provide a framework for exploring the effects of biological examples on the sketching contours of products, as well as the perceptual matching degree between design ideas generated via sketching and the desired functions. Elliptic Fourier descriptors with principal component analysis and perceptual matching were used to evaluate and compare the effects of biological examples, no examples, and human-engineered examples from different product categories and within one product category on the sketches in an experiment that involved 28 participants. The application of elliptic Fourier descriptors with principal component analysis shows that there are significant differences in the third and seventh principal components. It is also found that exposure to biological examples can produce more sketches with high perceptual matching degree than the other three conditions, but there are no significant effects of the example exposure on the Pearson correlation coefficients of semantic differential evaluation value vectors between design problems and sketches. These results demonstrate that exposure to biological examples will correlate with Elliptic Fourier descriptors of sketches and will not significantly increase the perceptual matching degree between sketches and the desired function.

Keywords

Biologically Inspired DesignConceptual DesignElliptic FourierPerceptual MatchingSketching
Type
Regular Articles
Information
AI EDAM , Volume 32 , Issue 1 , February 2018 , pp. 92 - 107
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Asano, T., & Honda, S. (2010). Visual interface system by character handwriting gestures in the air. Proc. IEEE Int. Workshop on Robot and Human Interactive Communication, pp. 56–61, Viareggio, Italy, September 13–15. doi:10.1109/roman.2010.5598705Google Scholar
Atilola, O., Tomko, M., & Linsey, J.S. (2016). The effects of representation on idea generation and design fixation: a study comparing sketches and function trees. Design Studies 42, 110–136. doi:10.1016/j.destud.2015.10.005 Google Scholar
Autumn, K., Sitti, M., Liang, Y.A., Peattie, A.M., Hansen, W.R., Sponberg, S., Kenny, T.W., Fearing, R., Israelachvii, J.N., & Full, R.J. (2002). Evidence for van der Waals adhesion in gecko setae. Proceedings of the National Academy of Sciences 99(19), 12252–12256. doi:10.1073/pnas.192252799 CrossRefGoogle Scholar
Bruck, H.A., Gershon, A.L., Golden, I., Gupta, S.K., Gyger, L.S., Magrab, E.B., & Spranklin, B.W. (2006). New educational tools and curriculum enhancements for motivating engineering students to design and realize bio-inspired products. WIT Transactions on Ecology and the Environment 87, 325–334. doi:10.2495/DN060321 Google Scholar
Cardoso, C., & Badke-Schaub, P. (2011). The influence of different pictorial representations during idea generation. Journal of Creative Behavior 45(2), 130–146.CrossRefGoogle Scholar
Carlo, J.M., Barbeitos, M.S., & Lasker, H.R. (2011). Quantifying complex shapes: elliptical Fourier analysis of octocoral sclerites. Biological Bulletin 220(3), 224–237. doi:220/3/224 CrossRefGoogle ScholarPubMed
Casakin, H. (2004). Visual analogy as a cognitive strategy in the design process. Expert versus novice performance. Journal of Design Research 4(2). doi:10.1504/JDR.2004.009846 Google Scholar
Chai, C., Cen, F., Ruan, W., Yang, C., & Li, H. (2015). Behavioral analysis of analogical reasoning in design: differences among designers with different expertise levels. Design Studies 36, 3–30. doi:10.1016/j.destud.2014.07.001 CrossRefGoogle Scholar
Chakrabarti, A., Sarkar, P., Leelavathamma, B., & Nataraju, B.S. (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. doi:10.1017/S0890060405050109 Google Scholar
Chen, Y., Yang, Z., Wang, J., & Gong, H. (2016). Physiological and subjective responses to breathing resistance of N95 filtering facepiece respirators in still-sitting and walking. International Journal of Industrial Ergonomics 53, 93–101. doi:10.1016/j.ergon.2015.11.002 CrossRefGoogle Scholar
Chen, Y., Zhang, Z., Xie, Y., & Zhao, M. (2015). A new model of conceptual design based on scientific ontology and intentionality theory. Part I: The conceptual foundation. Design Studies 37, 12–36. doi:10.1016/j.destud.2014.12.002 Google Scholar
Chen, Y., Zhao, M., Xie, Y., & Zhang, Z. (2015). A new model of conceptual design based on scientific ontology and intentionality theory. Part II: The process model. Design Studies 38, 139–160. doi:10.1016/j.destud.2015.01.003 CrossRefGoogle Scholar
Cheong, H., Hallihan, G.M., & Shu, L.H. (2014). Design problem solving with biological analogies: a verbal protocol study. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 28(1), 27–47.Google Scholar
Christensen, B.T., & Schunn, C.D. (2007). The relationship of analogical distance to analogical function and preinventive structure: the case of engineering design. Memory & Cognition 35(1), 29–38. doi:10.3758/BF03195939 CrossRefGoogle ScholarPubMed
Crampton, J.S. (1995). Elliptic Fourier shape analysis of fossil bivalves: some practical considerations. Lethaia 28(2), 179–186.CrossRefGoogle Scholar
Das, P., & Bhattacharyya, D. (2009). Person identification through IRIS recognition. Journal of Security and Its Applications 3(1), 129–148.Google Scholar
Direkolu, C., & Nixon, M.S. (2011). Shape classification via image-based multiscale description. Pattern Recognition 44(9), 2134–2146. doi:10.1016/j.patcog.2011.02.016 CrossRefGoogle Scholar
Dunbar, K. (1997). How scientists think: on-line creativity and conceptual change in science. Creative Thought: An Investigation of Conceptual Structures and Processes (Ward, T.B., Smith, S.M., & Viad, J., Eds.), pp. 461–493. Washington, DC: American Psychological Association.CrossRefGoogle Scholar
Fan, X., Haihong, F., & Yuan, M. (2012). PCA based on mutual information for acoustic environment classification. Proc. 2012 Int. Conf. Audio, Language and Image Processing, pp. 270–275, Shanghai, China, July 16–18.Google Scholar
Ferguson, E.S. (1994). Engineering and the Mind's Eye. Cambridge, MA: MIT Press.Google Scholar
Floyd, S., Keegan, T., Palmisano, J., & Sitti, M. (2006). A novel water running robot inspired by basilisk lizards. Proc. IEEE Int. Conf. Intelligent Robots and Systems, Vol. 1, pp. 5430–5436. doi:10.1109/iros.2006.282111 Google Scholar
Freeman, H. (1974). Computer processing of line-drawing images. ACM Computing Surveys 6(1), 57–97. doi:10.1145/356625.356627 Google Scholar
Fu, K., Chan, J., Cagan, J., & Kotovsky, K. (2013). The meaning of “near” and “far”: the impact of structuring design databases and the effect of distance of analogy on design output. Journal of Mechanical Design 135, 1–12. doi:10.1115/1.4023158 CrossRefGoogle Scholar
Furuta, N., Ninomiya, S., Takahashi, N., Ohmori, H., & Yasuo, U. (1995). Quantitative evaluation of soybean (glycine max L. Merr.) leaflet shape by principal component scores based on elliptic Fourier descriptor. Breeding Science 45(3), 315–320.Google Scholar
Gentner, D., Rattermann, M.J., & Forbus, K.D. (1993). The roles of similarity in transfer: separating retrievability from inferential soundness. Cognitive Psychology 25(4), 524–575.Google Scholar
Gero, J.S. (1990). Design prototypes: a knowledge representation schema for design. AI Magazine 11(4), 26. doi:10.1609/aimag.v11i4.854 Google Scholar
Gero, J.S., & Kannengiesser, U. (2004). The situated function-behaviour-structure framework. Design Studies 25(4), 373–391. doi:10.1016/j.destud.2003.10.010 Google Scholar
Goel, A.K. (2013). A 30-year case study and 15 principles: implications of an artificial intelligence methodology for functional modeling. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 27(3), 203–215.Google Scholar
Goel, A.K., McAdams, D.A., & Stone, R.B. (2015). Biologically Inspired Design. New York: Springer.Google Scholar
Goldschmidt, G., & Smolkov, M. (2006). Variances in the impact of visual stimuli on design problem solving performance. Design Studies 27(5), 549–569. doi:10.1016/j.destud.2006.01.002 Google Scholar
Helms, M., Vattam, S.S., & Goel, A.K. (2009). Biologically inspired design: process and products. Design Studies 30(5), 606–622. doi:10.1016/j.destud.2009.04.003 CrossRefGoogle Scholar
Holyoak, K.J., & Thagard, P. (1995). The analogical mind. American Psychologist 52(1), 35–44.Google Scholar
Hsu, S.H., Chuang, M.C., & Chang, C.C. (2000). A semantic differential study of designers’ and users’ product form perception. International Journal of Industrial Ergonomics 25(4), 375–391. doi:10.1016/S0169-8141(99)00026-8 Google Scholar
Iwata, H., Nesumi, H., Ninomiya, S., Takano, Y., & Ukai, Y. (2002). The evaluation of Genotype* Environment interactions of citrus leaf morphology using image analysis and elliptic Fourier descriptors. Breeding Science 52(4), 243–251.Google Scholar
Iwata, H., Niikura, S., Matsuura, S., Takano, Y., & Ukai, Y. (2000). Diallel analysis of root shape of Japanese radish (Raphanus sativus L.) based on elliptic Fourier descriptors. Breeding Science 50(2), 73–80.CrossRefGoogle Scholar
Iwata, H., & Ukai, Y. (2006). SHAPE: a computer program package for quantitative evaluation of biological shapes based on elliptic Fourier descriptors. Journal of Heredity 93(5), 384–385. doi:10.1093/jhered/93.5.384 CrossRefGoogle Scholar
Janan, F., & Brady, M. (2015). Shape description and matching using integral invariants on eccentricity transformed images. International Journal of Computer Vision 113(2), 92–112. doi:10.1007/s11263-014-0773-x Google Scholar
Jindo, T., & Hirasago, K. (1997). Application studies to car interior of Kansei engineering. International Journal of Industrial Ergonomics 19(2), 105–114. doi:10.1016/S0169-8141(96)00007-8 Google Scholar
Kalogerakis, K., Lüthje, C., & Herstatt, C. (2010). Developing innovations based on analogies: experience from design and engineering consultants. Journal of Innovation Management 27(3), 418–436. doi:10.1111/j/1540-5885.2010.00725.x Google Scholar
Kan, J., & Gero, J. (2009). Using the FBS ontology to capture semantic design information in design protocol studies. In About Designing: Analysing Design Meetings (McDonnell, J., & Lloyd, P., Eds.). Boca Raton, FL: CRC Press. Retrieved from http://www.cs.gmu.edu/~jgero/publications/2009/09kangerodtrsbookchapter.pdf Google Scholar
Komoto, H., & Tomiyama, T. (2012). A framework for computer-aided conceptual design and its application to system architecting of mechatronics products. CAD Computer Aided Design 44(10), 931–946. doi:10.1016/j.cad.2012.02.004 CrossRefGoogle Scholar
Krueger, L.E. (1978). A theory of perceptual matching. Psychological Review 85(4), 278.Google Scholar
Kuhl, F.P., & Giardina, C.R. (1982). Elliptic Fourier features of a closed contour. Computer Graphics and Image Processing 18(3), 236–258.CrossRefGoogle Scholar
Lahr, D.F., Yi, H., & Hong, D.W. (2016). Biologically inspired design of a parallel actuated humanoid robot. Advanced Robotics 30(2), 109–118.Google Scholar
Latecki, L.J., & Lakämper, R. (1999). Convexity rule for shape decomposition based on discrete contour evolution. Computer Vision and Image Understanding 73(3), 441–454. doi:10.1006/cviu.1998.0738 Google Scholar
Lingbo, Z., Jianwen, X., Zhuqing, Z., Hess, D.W., & Wong, C.P. (2005). Lotus effect surface for prevention of microelectromechanical system (MEMS) striction. Proc. 55th Electronic Components and Technology Conf., Vol. 2, pp. 1798–1801, Lake Buena Vista, FL, May 31–June 3. doi:10.1109/ectc.2005.1442039 Google Scholar
Linsey, J.S., Wood, K.L., & Markman, A.B. (2008). Modality and representation in analogy. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 22(2), 85–100. doi:10.1017/S0890060408000061 Google Scholar
Luo, S.-J., Fu, Y.-T., & Korvenmaa, P. (2012). A preliminary study of perceptual matching for the evaluation of beverage bottle design. International Journal of Industrial Ergonomics 42(2), 219–232. doi:10.1016/j.ergon.2012.01.007 Google Scholar
Luo, S.-J., Fu, Y.-T., & Zhou, Y.-X. (2012). Perceptual matching of shape design style between wheel hub and car type. International Journal of Industrial Ergonomics 42(1), 90–102. doi:10.1016/j.ergon.2011.10.001 Google Scholar
Mak, T.W., & Shu, L.H. (2004). Use of biological phenomena in design by analogy. Proc. ASME DETC/CIE, Paper No. DETC2004/DETC-57303, Salt Lake City, UT, September 28–October 2. Retrieved from http://www.mie.utoronto.ca/labs/bidlab/pubs/Mak_Shu_DTM_04.pdf CrossRefGoogle Scholar
Malaga, R.A. (2000). Effect of stimulus modes and associative distance in individual creativity support systems. Decision Support Systems 29(2), 125–141. doi:10.1016/S0167-9236(00)00067-1 Google Scholar
Michl, J. (2009). E. H. Gombrich's adoption of the formula form follows function: a case of mistaken identity? Human Affairs 19(3), 274–288. doi:10.2478/v10023-009-0041-9 CrossRefGoogle Scholar
Moss, J., Kotovsky, K., & Cagan, J. (2007). The influence of open goals on the acquisition of problem-relevant information. Journal of Experimental Psychology. Learning, Memory, and Cognition 33, 876–891. doi:10.1037/0278-7393.33.5.876 Google Scholar
Nagel, J.K.S., Nagel, R.L., Stone, R.B., & McAdams, D.A. (2010). Function-based, biologically inspired concept generation. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 24(4), 521–535.Google Scholar
Nakrani, S., & Tovey, C. (2003). On honey bees and dynamic allocation in an internet server colony. Adaptive Behavior 12(3–4), 223–240. doi:10.1177/105971230401200308 Google Scholar
Nelson, B.A., Wilson, J.O., Rosen, D., & Yen, J. (2009). Refined metrics for measuring ideation effectiveness. Design Studies 30(6), 737–743. doi:10.1016/j.destud.2009.07.002 Google Scholar
Neto, J.C., Meyer, G.E., Jones, D.D., & Samal, A.K. (2006). Plant species identification using elliptic Fourier leaf shape analysis. Computers and Electronics in Agriculture 50(2), 121–134. doi:10.1016/j.compag.2005.09.004 Google Scholar
Pahl, G., & Beitz, W. (2013). Engineering Design: A Systematic Approach. New York: Springer Science & Business Media.Google Scholar
Pourmohamadi, M., & Gero, J.S. (2011). LINKOgrapher: an analysis tool to study design protocols based on FBS coding. Proc. Int. Conf. Engineering Design, Technical University of Denmark, August 15–18.Google Scholar
Qian, L., & Gero, J.S. (1996). Function–behavior–structure paths and their role in analogy-based design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 10(4), 289. doi:10.1017/S0890060400001633 Google Scholar
Rodgers, P.A., Green, G., & Mcgown, A. (2000). Using concept sketches to track design progress. Design Studies 21(5), 451–464.Google Scholar
Rohlf, F.J., & Archie, J.W. (1984). A comparison of Fourier methods for the description of wing shape in mosquitoes (Diptera: Culicidae). Systematic Biology 33(3), 302–317.Google Scholar
Römer, A., Leinert, S., & Sachse, P. (2000). External support of problem analysis in design problem solving. Research in Engineering Design 12(3), 144–151.Google Scholar
Schwert, P.M. (2007). Using sentence and picture clues to solve verbal insight problems. Creativity Research Journal 19(2–3), 293–306. doi:10.1080/10400410701397446 CrossRefGoogle Scholar
Shah, J., Smith, S.M., & Vargas-Hernandez, N. (2003). Metrics for measuring ideation effectiveness. Design Studies 24(2), 111–134. doi:10.1016/S0142-694X(02)00034-0 Google Scholar
Siangliulue, P., Chan, J., Gajos, K.Z., & Dow, S.P. (2015). Providing timely examples improves the quantity and quality of generated ideas. Proc. 2015 ACM SIGCHI Conf. Creativity and Cognition, pp. 83–92, Glasgow, June 22–25. doi:10.1145/2757226.2757230 Google Scholar
Sio, U.N., Kotovsky, K., & Cagan, J. (2015). Fixation or inspiration? A meta-analytic review of the role of examples on design processes. Design Studies 39, 70–99. doi:10.1016/j.destud.2015.04.004 Google Scholar
Smith, V. (2015). Species discrimination in Carcharhinus shark teeth using elliptic Fourier analysis . PhD Thesis. Tulane University School of Science and Engineering.Google Scholar
Spiliopoulou, E., Rugaber, S., Goel, A., Chen, L., Wiltgen, B., & Jagannathan, A.K. (2015). Intelligent search for biologically inspired design. Proc. 20th Int. Conf. Intelligent User Interfaces Companion, pp. 77–80. New York: ACM.Google Scholar
Sun, L., Xiang, W., Chai, C., Wang, C., & Huang, Q. (2014). Creative segment: A descriptive theory applied to computer-aided sketching. Design Studies 35(1), 54–79. doi:10.1016/j.destud.2013.10.003 Google Scholar
Sun, L., Xiang, W., Chai, C., Wang, C., & Liu, Z. (2013). Impact of text on idea generation: an electroencephalography study. International Journal of Technology and Design Education 23(4), 1047–1062. doi:10.1007/s10798-013-9237-9 Google Scholar
Tseng, I., Moss, J., Cagan, J., & Kotovsky, K. (2008). The role of timing and analogical similarity in the stimulation of idea generation in design. Design Studies 29(3), 203–221. doi:10.1016/j.destud.2008.01.003 Google Scholar
Umeda, Y., & Ishii, M. (1996). Supporting conceptual design based on the function-behavior-state modeler. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 10(4), 275–288. doi:10.1017/S0890060400001621 Google Scholar
Vandevenne, D., Verhaegen, P.A., Dewulf, S., & Duflou, J.R. (2014). A scalable approach for ideation in biologically inspired design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 29(1), 19–31.Google Scholar
van Dijk, C.G.C. (1995). New insights in computer-aided conceptual design. Design Studies 16(1), 62–80. doi:10.1016/0142-694X(95)90647-X Google Scholar
Vasconcelos, L.A., & Crilly, N. (2016). Inspiration and fixation: questions, methods, findings, and challenges. Design Studies 42, 1–32. doi:10.1016/j.destud.2015.11.001 CrossRefGoogle Scholar
Vincent, J.F.V., & Mann, D.L. (2002). Systematic technology transfer from biology to engineering. Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences 360(1791), 159–173. doi:10.1098/rsta.2001.0923 Google Scholar
Viscusi, D.J., Bergman, M.S., Novak, D.A., Faulkner, K.A., Palmiero, A., Powell, J., & Shaffer, R.E. (2011). Impact of three biological decontamination methods on filtering facepiece respirator fit, odor, comfort, and donning ease. Journal of Occupational and Environmental Hygiene 8(7), 426–436. doi:10.1080/15459624.2011.585927 Google Scholar
Wang, L., Shen, W., Xie, H., Neelamkavil, J., & Pardasani, A. (2002). Collaborative conceptual design—state of the art and future trends. Computer-Aided Design 34(13), 981–996.Google Scholar
Wilson, J.O. (2008). A Systematic Approach to Bio-inspired Conceptual Design . Doctoral Thesis, Georgia Institute of Technology).Google Scholar
Wilson, J.O., Rosen, D., Nelson, B.A., & Yen, J. (2010). The effects of biological examples in idea generation. Design Studies 31(2), 169–186. doi:10.1016/j.destud.2009.10.003 Google Scholar
Yan, H., Huynh, B.V.N., Murai, T., & Nakamori, Y. (2008). Kansei evaluation based on prioritized multi-attribute fuzzy target-oriented decision analysis. Information Sciences 178(21), 4080–4093. doi:10.1016/j.ins.2008.06.023 Google Scholar
Yoshioka, Y., Iwata, H., Ohsawa, R., & Ninomiya, S. (2004). Analysis of petal shape variation of Primula sieboldii by elliptic fourier descriptors and principal component analysis. Annals of Botany 94(5), 657–664. doi:10.1093/aob/mch190 CrossRefGoogle ScholarPubMed