What does this research mean for the field?

A rough-set-induced hybrid-kernel support vector regression model accurately maps discrete HEV front-face morphological features to users' Kansei evaluations, enabling the quantitative generation of culturally oriented design concepts. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

Develop an objective Kansei engineering methodology to optimize hybrid electric vehicle front-face styling under functional requirements and cultural-semantic constraints.

September 15, 2026MathematicsOpen Access

A Rough-Set-Driven Kansei Design Method for Hybrid Electric Vehicle Front Faces Under Cultural Semantic Constraints

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Authors

YTYichen Tian ZCZimo Chen

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Overview

Computational modeling demonstrates accurate prediction of emotional responses to hybrid electric vehicle styling, highlighting an objective pathway for culturally integrated automotive design.

Key Points

Develop an objective Kansei engineering methodology to optimize hybrid electric vehicle front-face styling under functional requirements and cultural-semantic constraints.
Applied an entropy-weighted neighborhood rough-set method to extract core Kansei emotional requirements.
Constructed a rough-set-induced hybrid-kernel support vector regression (RSIHK-SVR) model to map front-face morphological features to user evaluations.
Synthesized model-predicted morphological configurations with traditional motifs (bronze animal-mask, ice-crackle lattice, and fangsheng) to generate conceptual designs.
Identified power, premium quality, and approachability as the three primary Kansei requirements for hybrid electric vehicle front faces.
Achieved a mean test-set R² of 0.927 and RMSE of 0.157 with the RSIHK-SVR model, outperforming standard RBF and single-kernel models by improving R² by 0.8% to 13.3% and decreasing RMSE by 3.1% to 35.9%.
Confirmed through user evaluations that the three motif-integrated concept designs effectively conveyed their targeted emotional semantics and achieved high cultural-semantic compatibility.

Cite This Study

Tian et al. (2026) studied this question.

synapsesocial.com/papers/6aa913819013453be30a16dc https://doi.org/10.3390/math14183328

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September 15, 2026MathematicsOpen Access

A Rough-Set-Driven Kansei Design Method for Hybrid Electric Vehicle Front Faces Under Cultural Semantic Constraints

View Full Paper

Ask AI

Bookmark

Authors

YTYichen Tian ZCZimo Chen

Discussion

Member takes

Overview

Computational modeling demonstrates accurate prediction of emotional responses to hybrid electric vehicle styling, highlighting an objective pathway for culturally integrated automotive design.

Key Points

Develop an objective Kansei engineering methodology to optimize hybrid electric vehicle front-face styling under functional requirements and cultural-semantic constraints.
Applied an entropy-weighted neighborhood rough-set method to extract core Kansei emotional requirements.
Constructed a rough-set-induced hybrid-kernel support vector regression (RSIHK-SVR) model to map front-face morphological features to user evaluations.
Synthesized model-predicted morphological configurations with traditional motifs (bronze animal-mask, ice-crackle lattice, and fangsheng) to generate conceptual designs.
Identified power, premium quality, and approachability as the three primary Kansei requirements for hybrid electric vehicle front faces.
Achieved a mean test-set R² of 0.927 and RMSE of 0.157 with the RSIHK-SVR model, outperforming standard RBF and single-kernel models by improving R² by 0.8% to 13.3% and decreasing RMSE by 3.1% to 35.9%.
Confirmed through user evaluations that the three motif-integrated concept designs effectively conveyed their targeted emotional semantics and achieved high cultural-semantic compatibility.

Cite This Study

Tian et al. (2026) studied this question.

synapsesocial.com/papers/6aa913819013453be30a16dc https://doi.org/10.3390/math14183328

View Full Paper

Ask AI

Bookmark