An Elderly-Oriented Form Design of Low-Speed New Energy Vehicles Based on Rough Set Theory and Support Vector Regression
Abstract
:1. Introduction
2. Relative Methods
2.1. Kansei Engineering
2.2. Rough Set Theory
2.3. Support Vector Regression
3. Proposed Research Framework
3.1. Data Set Sample Determination
3.2. Factor Analysis Clustering of Elderly-Oriented Kansei Factors
3.3. RST: Identification of the Key Design Features of Low-Speed NEVs
3.4. SVR Build of the Mapping Model between Kansei Semantics and Key Features of Low-Speed NEVs
4. Analysis and Discussion of Results
4.1. Analysis of Results
4.2. Discussion
4.3. Impact of the Design of Elderly-Oriented NEVs on Market Value and Sustainability
5. Conclusions
- Using the research framework of KE, a combination method of KE, RST, and SVR for product form design is proposed.
- RST was used to identify the key design features of low-speed NEVs that have an important influence on elderly-oriented satisfaction.
- SVR was used to build a mapping model between elderly-oriented Kansei factors and product form design features in a nonlinear manner.
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Brief | Bright | Harmonious | Stable | Affinity | Clear | Distinctiveness | Modern | Environmentalism |
No. | Brief | Bright | Harmonious | Stable | Affinity | Clear | Distinctiveness | Modern | Environmentalism |
---|---|---|---|---|---|---|---|---|---|
1 | 4.50 | 2.95 | 3.80 | 3.70 | 3.60 | 3.80 | 3.50 | 3.85 | 3.75 |
2 | 3.90 | 3.90 | 4.15 | 4.05 | 3.80 | 3.10 | 3.80 | 3.50 | 3.65 |
3 | 4.10 | 4.55 | 4.20 | 4.10 | 3.90 | 3.90 | 3.85 | 4.15 | 3.80 |
4 | 4.25 | 3.40 | 3.85 | 3.70 | 3.75 | 3.70 | 3.25 | 3.80 | 3.95 |
5 | 4.05 | 3.45 | 4.15 | 4.00 | 3.60 | 3.45 | 3.85 | 3.75 | 3.95 |
6 | 4.05 | 3.75 | 3.95 | 3.70 | 3.95 | 4.05 | 3.75 | 3.90 | 3.55 |
7 | 3.75 | 3.95 | 3.90 | 3.95 | 3.65 | 3.30 | 3.65 | 3.55 | 3.60 |
8 | 4.20 | 4.65 | 4.40 | 4.30 | 4.15 | 3.85 | 4.15 | 3.85 | 4.00 |
9 | 4.15 | 3.55 | 3.80 | 3.65 | 3.70 | 3.60 | 3.75 | 3.80 | 3.70 |
10 | 3.95 | 3.90 | 3.95 | 3.90 | 3.80 | 3.90 | 3.70 | 3.75 | 4.00 |
11 | 4.10 | 4.15 | 4.00 | 3.85 | 3.90 | 3.80 | 3.80 | 3.55 | 3.65 |
12 | 3.95 | 3.90 | 4.10 | 3.55 | 3.70 | 3.50 | 3.60 | 3.55 | 3.60 |
13 | 4.30 | 3.00 | 3.85 | 4.20 | 3.60 | 3.70 | 3.30 | 3.70 | 3.90 |
14 | 3.95 | 3.20 | 4.00 | 3.80 | 3.35 | 3.80 | 3.20 | 3.20 | 3.40 |
15 | 4.05 | 3.90 | 3.90 | 4.10 | 3.75 | 3.90 | 3.90 | 3.85 | 3.85 |
16 | 3.90 | 3.95 | 3.85 | 4.10 | 3.85 | 3.65 | 3.85 | 3.70 | 3.60 |
17 | 4.00 | 3.95 | 4.05 | 3.75 | 3.55 | 3.85 | 3.65 | 3.45 | 3.40 |
18 | 4.00 | 4.20 | 3.95 | 4.00 | 4.00 | 3.55 | 3.90 | 3.90 | 3.80 |
19 | 4.00 | 4.35 | 3.50 | 3.90 | 3.60 | 3.50 | 3.75 | 3.40 | 3.55 |
20 | 4.10 | 4.15 | 4.10 | 4.00 | 3.85 | 4.05 | 3.80 | 3.85 | 3.70 |
21 | 3.65 | 2.80 | 3.05 | 3.35 | 2.95 | 3.30 | 3.20 | 2.95 | 3.20 |
22 | 3.60 | 3.05 | 3.50 | 3.60 | 3.20 | 3.15 | 3.35 | 2.95 | 3.25 |
23 | 4.10 | 2.95 | 3.05 | 3.20 | 3.60 | 3.65 | 3.15 | 3.35 | 3.40 |
24 | 4.30 | 3.95 | 3.90 | 3.50 | 3.90 | 3.75 | 3.60 | 3.90 | 3.70 |
25 | 3.75 | 2.90 | 3.45 | 3.50 | 3.55 | 3.40 | 3.50 | 3.35 | 3.55 |
26 | 3.90 | 3.05 | 3.55 | 3.30 | 3.35 | 3.55 | 3.40 | 3.45 | 3.90 |
27 | 3.75 | 3.75 | 4.05 | 4.05 | 4.00 | 3.60 | 3.95 | 4.00 | 3.65 |
28 | 3.75 | 3.75 | 3.50 | 3.60 | 3.60 | 3.30 | 3.45 | 3.55 | 3.65 |
29 | 3.65 | 3.40 | 3.70 | 3.50 | 3.40 | 3.00 | 3.65 | 3.20 | 3.50 |
30 | 4.05 | 3.60 | 4.10 | 3.90 | 4.00 | 3.70 | 3.85 | 4.00 | 4.05 |
Kaiser–Meyer–Olkin Measure of Sampling Adequacy | 0.861 | |
---|---|---|
Bartlett’s Test of Sphericity | Approx. chi-square | 189.135 |
df | 36 | |
Sig. | 0.000 |
IE | Sum of Squared Loadings | Sum of Squared Rotated Loadings | |||||||
---|---|---|---|---|---|---|---|---|---|
Sum | Var./% | Cum./% | Sum | Var./% | Cum./% | Sum | Var./% | Cum./% | |
1 | 5.36 | 59.551 | 59.551 | 5.36 | 59.551 | 59.551 | 3.701 | 41.121 | 41.121 |
2 | 1.503 | 16.7 | 76.251 | 1.503 | 16.7 | 76.251 | 2.115 | 23.503 | 64.623 |
3 | 0.665 | 7.386 | 83.637 | 0.665 | 7.386 | 83.637 | 1.711 | 19.014 | 83.637 |
4 | 0.493 | 5.476 | 89.114 | ||||||
5 | 0.274 | 3.047 | 92.16 | ||||||
6 | 0.246 | 2.738 | 94.899 | ||||||
7 | 0.209 | 2.327 | 97.226 | ||||||
8 | 0.157 | 1.739 | 98.965 | ||||||
9 | 0.093 | 1.035 | 100 |
Kansei Word | Component | ||
---|---|---|---|
1 | 2 | 3 | |
Brief | 0.816 | ||
Bright | 0.911 | ||
Harmonious | 0.745 | ||
Stable | 0.75 | ||
Affinity | 0.725 | ||
Clear | 0.913 | ||
Distinctiveness | 0.908 | ||
Modern | 0.521 | 0.534 | 0.555 |
Environmentalism | 0.881 |
Kansei Word | Component | ||
---|---|---|---|
1 | 2 | 3 | |
Brief | −0.204 | 0.436 | 0.131 |
Bright | 0.39 | 0.093 | −0.435 |
Harmonious | 0.179 | −0.057 | 0.103 |
Stable | 0.2 | −0.163 | 0.145 |
Affinity | 0.162 | 0.103 | −0.003 |
Clear | 0.012 | 0.662 | −0.434 |
Distinctiveness | 0.318 | −0.159 | −0.045 |
Modern | 0.003 | 0.119 | 0.241 |
Environmentalism | −0.179 | −0.224 | 0.823 |
No. | Hub | Side Window | Intake Grille | Headlight | Car Side View | Rearview Mirror | Front Door | Fog Light |
---|---|---|---|---|---|---|---|---|
1 | 4 | 3 | 2 | 1 | 1 | 4 | 9 | 10 |
2 | 9 | 7 | 2 | 2 | 1 | 2 | 9 | 4 |
3 | 2 | 7 | 10 | 2 | 1 | 2 | 5 | 4 |
… | … | … | … | … | … | … | … | … |
98 | 6 | 3 | 7 | 9 | 2 | 10 | 1 | 7 |
99 | 4 | 10 | 10 | 4 | 10 | 3 | 5 | 8 |
100 | 2 | 8 | 3 | 10 | 10 | 4 | 5 | 7 |
Design Feature | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
---|---|---|---|---|---|---|---|---|---|---|
Hub | 1 | 2 | 3 | 2 | 2 | 3 | 3 | 3 | 2 | 1 |
Side window | 1 | 1 | 2 | 2 | 1 | 2 | 3 | 3 | 3 | 3 |
Intake grille | 3 | 3 | 2 | 2 | 2 | 1 | 3 | 2 | 2 | 3 |
Headlight | 2 | 3 | 2 | 1 | 1 | 1 | 3 | 2 | 3 | 2 |
Car side view | 3 | 2 | 1 | 3 | 3 | 2 | 1 | 2 | 2 | 3 |
Rearview mirror | 1 | 3 | 2 | 2 | 3 | 2 | 1 | 2 | 3 | 2 |
Front door | 3 | 1 | 2 | 2 | 2 | 3 | 1 | 2 | 3 | 1 |
Fog light | 2 | 2 | 3 | 3 | 1 | 1 | 3 | 3 | 1 | 1 |
No. | Pts | No. | Pts | No. | Pts | No. | Pts | No. | Pts |
---|---|---|---|---|---|---|---|---|---|
1 | 3.45 | 21 | 3.20 | 41 | 3.20 | 61 | 3.65 | 81 | 3.55 |
2 | 3.35 | 22 | 3.30 | 42 | 3.25 | 62 | 3.25 | 82 | 3.50 |
3 | 3.25 | 23 | 3.25 | 43 | 3.40 | 63 | 3.05 | 83 | 3.15 |
… | … | … | … | … | … | … | … | … | … |
18 | 3.95 | 38 | 3.75 | 58 | 3.35 | 78 | 3.45 | 98 | 3.20 |
19 | 3.30 | 39 | 3.15 | 59 | 3.20 | 79 | 3.55 | 99 | 3.05 |
20 | 3.50 | 40 | 3.25 | 60 | 3.60 | 80 | 3.35 | 100 | 3.25 |
1 | 2 | 2 | 3 | 2 | 3 | 2 | 3 | 1 | 2 |
2 | 2 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 2 |
3 | 2 | 3 | 3 | 3 | 3 | 3 | 2 | 3 | 1 |
… | … | … | … | … | … | … | … | … | … |
98 | 3 | 2 | 3 | 3 | 2 | 2 | 3 | 3 | 1 |
99 | 2 | 3 | 3 | 1 | 3 | 2 | 2 | 3 | 1 |
100 | 2 | 3 | 2 | 2 | 3 | 2 | 2 | 3 | 1 |
Hub | 0.15789 | Side window | 0.05263 | Intake grille | 0.07895 | Headlight | 0.15789 |
Car side view | 0.10526 | Rearview mirror | 0.18421 | Front door | 0.21053 | Fog light | 0.05263 |
No. | Hub | Headlight | Car Side View | Rearview Mirror | Front Door | Welcome | Clarity | Ecomodern |
---|---|---|---|---|---|---|---|---|
1 | 4 | 1 | 1 | 4 | 9 | 3.40 | 3.10 | 3.20 |
2 | 9 | 2 | 1 | 2 | 9 | 3.25 | 3.35 | 3.15 |
3 | 2 | 2 | 1 | 2 | 5 | 3.45 | 3.35 | 3.35 |
… | … | … | … | … | … | … | … | … |
98 | 6 | 9 | 2 | 10 | 1 | 3.55 | 3.50 | 3.40 |
99 | 4 | 4 | 10 | 3 | 5 | 3.55 | 3.80 | 3.25 |
100 | 2 | 10 | 10 | 4 | 5 | 3.25 | 3.45 | 3.65 |
Kansei Word | Hyperparameters | Results on the Training Set | Results on the Test Set | |||||
---|---|---|---|---|---|---|---|---|
C | Gamma | MSE | MBE | MSE | MBE | |||
Welcome | 0.70711 | 11.3137 | 0.997943 | 1.3970 × 10−4 | −1.4698 × 10−4 | 0.99623 | 7.0492 × 10−2 | −5.3592 × 10−2 |
Clarity | 0.70711 | 32 | 0.998835 | 9.8112 × 10−5 | −2.8806 × 10−4 | 0.99725 | 2.76051 × 10−2 | −4.5223 × 10−2 |
Ecomodern | 0.5 | 22.6274 | 0.997911 | 1.59023 × 10−4 | −1.3444 × 10−4 | 0.99603 | 1.73261 × 10−2 | 3.368 × 10−3 |
No. | Welcome | Clarity | Ecomodern | Average |
---|---|---|---|---|
1 | 3.314446 | 3.326493 | 3.321654 | 3.320864 |
2 | 3.314595 | 3.326116 | 3.322058 | 3.320923 |
3 | 3.314595 | 3.326116 | 3.322058 | 3.320923 |
… | … | … | … | … |
58,191 | 3.540985 | 3.487989 | 3.540870 | 3.523282 |
… | … | … | … | … |
9998 | 3.308972 | 3.326236 | 3.321663 | 3.318957 |
9999 | 3.308972 | 3.326236 | 3.321663 | 3.318957 |
100,000 | 3.307404 | 3.326236 | 3.321654 | 3.318431 |
Publication | Methods | User | Construct Mapping Models | Research Field |
---|---|---|---|---|
This paper | SVR | elderly user | Kansei evaluation and product form features | Product design (low-speed NEV) |
Hu et al. [64] | CNN | young user | Kansei evaluation and product form features | Product design (weight scales and goblets) |
Li et al. [65] | BPNN | young user | Kansei evaluation and product key form features | Product design (footwear) |
Kang et al. [66] | IGA | young user | Kansei evaluation and cultural symbols | Product design (creative industries) |
Lian et al. [67] | SVR | young user | Kansei evaluation and product form features | Product design (numerical control machine) |
Lin et al. [68] | QTTI, BPNN | young user | Kansei evaluation and sound elements | Product design (electric shaver) |
Yang et al. [69] | SVM | young user | Product performance and product form features | Product design (digital camera) |
Algorithm | Algorithm Type | Scope of Application | Data Requirements | Adjustable Parameters | Training Speed | Prediction Speed | Memory Consumption |
---|---|---|---|---|---|---|---|
BPNN | Neural Network | Complex pattern recognition, nonlinear relationships | Bulk labeled data, normalized inputs | Number of layers, number of nodes, learning rate | Slow | Fast | High |
GST | Ensemble Method | Regression and classification issues | Strong ability to handle missing values, numerical type characterization | Tree depth, learning rate, subsampling | Medium | Fast | Medium |
SVR | Support Vector Machine | Regression on issues | Linearly separable or approved separable data | Kernel function types, regularized arguments | Medium | Medium | Low |
IGA | Evolutionary Algorithm | Optimization Issues | No need for gradient information, multimodal problems | Population size, crossover rate, mutation rate | Medium | Low | Low |
PCA | Dimensionality Reduction | Feature extraction, data compression | Numerical data, linear relationships | Principal component, explained variance ratio | Fast | Fast | Low |
QT-I | Metaheuristic Algorithm | Regression on issues | No need for gradient information, multimodal problems | Taboo length, number of iterations, neighborhood search | Slow | Slow | Low |
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© 2024 by the author. Published by MDPI on behalf of the World Electric Vehicle Association. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Chen, Z. An Elderly-Oriented Form Design of Low-Speed New Energy Vehicles Based on Rough Set Theory and Support Vector Regression. World Electr. Veh. J. 2024, 15, 389. https://doi.org/10.3390/wevj15090389
Chen Z. An Elderly-Oriented Form Design of Low-Speed New Energy Vehicles Based on Rough Set Theory and Support Vector Regression. World Electric Vehicle Journal. 2024; 15(9):389. https://doi.org/10.3390/wevj15090389
Chicago/Turabian StyleChen, Zimo. 2024. "An Elderly-Oriented Form Design of Low-Speed New Energy Vehicles Based on Rough Set Theory and Support Vector Regression" World Electric Vehicle Journal 15, no. 9: 389. https://doi.org/10.3390/wevj15090389