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All issues Volume 179 (2020) E3S Web Conf., 179 (2020) 02123 References
Open Access
Issue
E3S Web Conf.
Volume 179, 2020
2020 International Conference on Environment and Water Resources Engineering (EWRE 2020)
Article Number 02123
Number of page(s) 10
Section Environmental and Industrial Design
DOI https://doi.org/10.1051/e3sconf/202017902123
Published online 23 July 2020
  1. Guo, H.P. (2018) The form design and kansei evaluation of wheeled tractors based on the front face characteristic. Northwest A&F University. [Google Scholar]
  2. Liu, Y.K. (2010) Tractor Formative Design and Research on Formative Design Method. Henan University of science and technology. [Google Scholar]
  3. Meng, H., Ruan, Z.H., Liu, Q. (2016) Development history of tractor hood. Tractor & Farm Transporter, 43: 10-11. [Google Scholar]
  4. Cheng, S.W., Sun, L.Y. (2014) A Survey on Visualization for Eye Tracking Data. Journal of Computer-Aided Design and Computer Graphics, 26: 698-707. [Google Scholar]
  5. Hyeonggyu, P., Sangil, L., Minho, L., Mun-Seon, C., Ho-Wan, K. (2016) Using eye movement data to infer human behavioral intentions. Computers in Human Behavior, 63: 796-804. [Google Scholar]
  6. Han, Y.C. (2000) The development of eye tracker and eye movement experiment. Psychological science, 04: 454-457. [Google Scholar]
  7. Guo, G., Lin, L., Xu, N. (2018) User-perceived styling experience of smart vehicles: A method to combine eye tracking with semantic differences. IET Intelligent Transport Systems, 13: 72-78. [Google Scholar]
  8. Qiu, K. (2018) Research on Optimal Design of Product Image Modeling Based on Eye Movement Signal. Lanzhou University of Technology. [Google Scholar]
  9. Li, J., Guo, S.J. (2019) Research on image modeling design and user emotional cognition of humanoid robot. Journal of machine design, 36: 134-138. [Google Scholar]
  10. Lu, G.Y., Hou, G.H. (2018) Eye Tracking of Product Design Decision Making Strategy. Packaging engineering, 39: 215-220. [Google Scholar]
  11. Su, J.N., Qiu, K., Zhang, S.T., Xiao L.W., Zhang, X.X. (2017) Evaluation method study of product modeling design elements based on eye movement data. Journal of machine design, 34: 124-128. [Google Scholar]
  12. Zhou, X. (2017) Research on Design Elements of Industrial Robot Based on Kansei Image. Anhui University of Technology. [Google Scholar]
  13. Zhu, Y., Chen, G. (2016) Head shape design of service robots on Kansei engineering and BP neural network. Packaging engineering, 37: 63-67. [Google Scholar]
  14. Li, Y.F., Zhu, L.P. (2019) Research on Product Image Form Design Based on Neural Network. Packaging engineering, 30: 88-90. [Google Scholar]
  15. Su, J.N., Fan, Y.F., Zhang, S.T., OuYang, L.Z. (2011) Product form design based on kansei engineering and neural network. Journal of Lanzhou University of technology, 37: 47-50. [Google Scholar]
  16. Chen, S.B., Yang, S.J., Zhu, L., Li, M.M., Zhang, W.S. (2019) Design and Quantification of Cars Modeling Imagery Based on BP Neural Network. Packaging engineering, 40: 128-133. [Google Scholar]
  17. Xv, J., Sun, S.Q., Zhang, K.J. (2017) Optimization Design of Product Image Model Based on Genetic Algorithm. Journal of Mechanical Engineering, 04: 53-64. [Google Scholar]
  18. Xiao, W.Q., Cheng, J.X., Zhou, X., Wang, X.J. (2017) Research on perceptual image space of industrial robot modeling design based on eye movement experiment. Journal of mechine design, 34: 124-128. [Google Scholar]
  19. Zhao, W.Q. (2019) Evaluation of product model design based on BP neural network. Computer Engineering and Design, 30: 5715-5721. [Google Scholar]
  20. Lu, G.Y., Hou, G.H. (2017) Predictive model of user experience of engineering vehicle modeling design based on eye tracking. Journal of mechanical design, 34: 107-111. [Google Scholar]

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