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

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 (R2 = 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.

Automated Classification of User Needs for Beginner User Experience Designers: A Kano Model and Text Analysis Approach Using Deep Learning

“…Many researchers have employed the Kano model or its variants to investigate the UX or demands of various products, including electronic devices, online websites, and mobile apps [22][23][24]. Researchers use machine learning or deep learning techniques on diverse online platforms, including Amazon, the Xiaomi community, and Ctrip [25][26][27]. In addition to the evident advantages of the Kano model and deep learning technologies in text categorization and user needs analysis, the outcomes of these methods are often presented in the forms of complex datasets or technical reports, which can be challenging for non-expert users to comprehend.…”

Research on the evaluation method of agricultural intelligent robot design solutions

“…Hu Zhanmei [ 11 ] used rough set theory and Kano model to classify users’ demand information for the user demand problem of jewelry packaging, and proposed a jewelry packaging design method for users’ expectation value. Yang Hao et al [ 12 ] used perceptual engineering and the Kano model to quantify customer perceptions of demand types to derive a prioritized ranking of UAV styling elements, and compared them with preference score for validation. Wu Xiaoli et al [ 13 ] integrated three methods.…”

Using Kansei Engineering for the Design Thinking Framework: Bamboo Pen Holder Product Design

“…On the one hand, KE [4], proposed by Nagamachi, is an effective way for designers to understand user demand. It can be applied to effective design methods in various product fields (see Table 1) such as cars [5,6], sofas [7], ceramics [8], basketball shoes [9], vehicle-mounted UAVs [10], and shared bicycles [11], etc. In previous studies, KE can transform the user's emotional needs into effective elements of product design through the Kano model [12], visual stimulus materials [13], deep learning [14], text mining [15], data mining [16], or product-integrated decision systems [17].…”