Sustainable Recognition Methods of Modeling Design Features of Light and Micro Vehicle-Mounted UAV: Based on Support Vector Regression and Kano Model

Hao Yang, Yunxiang Huo, Ruoyu Jia, Feng Sha, Naiqi Hu, Linglan Yu and Yueran Wang
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Hao Yang: College of Mechanical and Material Engineering, North China University of Technology, Beijing 100144, China
Yunxiang Huo: College of Mechanical and Material Engineering, North China University of Technology, Beijing 100144, China
Ruoyu Jia: College of Mechanical and Material Engineering, North China University of Technology, Beijing 100144, China
Feng Sha: College of Mechanical and Material Engineering, North China University of Technology, Beijing 100144, China
Naiqi Hu: College of Mechanical and Material Engineering, North China University of Technology, Beijing 100144, China
Linglan Yu: College of Mechanical and Material Engineering, North China University of Technology, Beijing 100144, China
Yueran Wang: School of International Art Education, Tianjin Academy of Fine Arts, Tianjin 300402, China

Sustainability, 2022, vol. 14, issue 13, 1-18

Abstract: In the era of Industry 4.0, intelligent algorithms provide an effective way to make design methods more sustainable through mining people’s demands continuously, especially in the field of evaluating and predicting the user preferences of phasic or interim design schemes. Vehicle-mounted unmanned aerial vehicles (UAVs) are of significance in mobility experience and traffic surveys. However, as a new type of product, UAVs lack general rules in modeling design and the process of development decision making presents some fuzzy characteristics, which make the evolution and iteration of modeling design more complex. Based on the theories of Kansei Engineering, this study utilized support vector regression (SVR) to establish a correlation model between design factors and preference degree. Because the perceptual evaluation knowledge is fuzzy and uncertain, the paper applied cross-validation and grid search methods to find the optimal parameters. The parameters of the SVR model were adjusted to meet the need for stable learning and for endurance of the noise from subjective experience data to improve the prediction effect and generalization ability. In addition, by means of the Kano model, the customers’ cognition of demand types was quantified to obtain the prioritization of UAV modeling design elements, as well as to compare with the preference scores to validate the feasibility of this research. It was found that the SVR model proposed in the study could effectively predict user preference (R 2 = 0.763, RMSE = 0.057). For the UAVs with a higher preference score, the modeling characteristics were consistent with the attractive, one-dimensional or must-be quality elements in the results of the Kano model, which verified the reliability of the study. The conclusion is expected to provide a sustainable design method for vehicle-mounted UAVs commonly used in citizen travel and outdoor activities.

Keywords: sustainable innovation; UAV; prediction model; SVR; Kano model; design elements; preference degree (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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