Design of Chinese traditional Jiaoyi (Folding chair) based on Kansei Engineering and CNN-GRU-attention
BackgroundsThis study innovatively enhances personalized emotional responses and user experience quality in traditional Chinese folding armchair (Jiaoyi chair) design through an interdisciplinary methodology.GoalTo systematically extract user emotional characteristics, we developed a hybrid research...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2025.1591410/full |
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| author | Xinyan Yang Nan Zhang Jiufang Lv Jiufang Lv Jiufang Lv |
| author_facet | Xinyan Yang Nan Zhang Jiufang Lv Jiufang Lv Jiufang Lv |
| author_sort | Xinyan Yang |
| collection | DOAJ |
| description | BackgroundsThis study innovatively enhances personalized emotional responses and user experience quality in traditional Chinese folding armchair (Jiaoyi chair) design through an interdisciplinary methodology.GoalTo systematically extract user emotional characteristics, we developed a hybrid research framework integrating web-behavior data mining.Methods1) the KJ method combined with semantic crawlers extracts emotional descriptors from multi-source social data; 2) expert evaluation and fuzzy comprehensive assessment reduce feature dimensionality; 3) random forest and K-prototype clustering identify three core emotional preference factors: “Flexible Refinement,” “Uncompromising Quality,” and “ergonomic stability.”DiscussionA CNN-GRU-Attention hybrid deep learning model was constructed, incorporating dynamic convolutional kernels and gated residual connections to address feature degradation in long-term semantic sequences. Experimental validation demonstrated the superior performance of our model in three chair design preference prediction tasks (RMSE = 0.038953, 0.066123, 0.0069777), outperforming benchmarks (CNN, SVM, LSTM). Based on the top-ranked preference encoding, we designed a new Jiaoyi chair prototype, achieving significantly reduced prediction errors in final user testing (RMSE = 0.0034127, 0.0026915, 0.0035955).ConclusionThis research establishes a quantifiable intelligent design paradigm for modernizing cultural heritage through computational design. |
| format | Article |
| id | doaj-art-d1813edcd0da47dfa6e704fa2bb7d4f5 |
| institution | DOAJ |
| issn | 1662-453X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj-art-d1813edcd0da47dfa6e704fa2bb7d4f52025-08-20T03:08:14ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2025-05-011910.3389/fnins.2025.15914101591410Design of Chinese traditional Jiaoyi (Folding chair) based on Kansei Engineering and CNN-GRU-attentionXinyan Yang0Nan Zhang1Jiufang Lv2Jiufang Lv3Jiufang Lv4College of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing, ChinaSchool of Design Art and Media, Nanjing University of Science and Technology, Nanjing, ChinaCollege of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing, ChinaCo-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing, Jiangsu, ChinaNJFU Academy of Chinese Ecological Progress and Forestry Development Studies, Nanjing, Jiangsu, ChinaBackgroundsThis study innovatively enhances personalized emotional responses and user experience quality in traditional Chinese folding armchair (Jiaoyi chair) design through an interdisciplinary methodology.GoalTo systematically extract user emotional characteristics, we developed a hybrid research framework integrating web-behavior data mining.Methods1) the KJ method combined with semantic crawlers extracts emotional descriptors from multi-source social data; 2) expert evaluation and fuzzy comprehensive assessment reduce feature dimensionality; 3) random forest and K-prototype clustering identify three core emotional preference factors: “Flexible Refinement,” “Uncompromising Quality,” and “ergonomic stability.”DiscussionA CNN-GRU-Attention hybrid deep learning model was constructed, incorporating dynamic convolutional kernels and gated residual connections to address feature degradation in long-term semantic sequences. Experimental validation demonstrated the superior performance of our model in three chair design preference prediction tasks (RMSE = 0.038953, 0.066123, 0.0069777), outperforming benchmarks (CNN, SVM, LSTM). Based on the top-ranked preference encoding, we designed a new Jiaoyi chair prototype, achieving significantly reduced prediction errors in final user testing (RMSE = 0.0034127, 0.0026915, 0.0035955).ConclusionThis research establishes a quantifiable intelligent design paradigm for modernizing cultural heritage through computational design.https://www.frontiersin.org/articles/10.3389/fnins.2025.1591410/fullKansei Engineeringaffective cognitiondeep learningJiaoyi chair designuser preference prediction |
| spellingShingle | Xinyan Yang Nan Zhang Jiufang Lv Jiufang Lv Jiufang Lv Design of Chinese traditional Jiaoyi (Folding chair) based on Kansei Engineering and CNN-GRU-attention Frontiers in Neuroscience Kansei Engineering affective cognition deep learning Jiaoyi chair design user preference prediction |
| title | Design of Chinese traditional Jiaoyi (Folding chair) based on Kansei Engineering and CNN-GRU-attention |
| title_full | Design of Chinese traditional Jiaoyi (Folding chair) based on Kansei Engineering and CNN-GRU-attention |
| title_fullStr | Design of Chinese traditional Jiaoyi (Folding chair) based on Kansei Engineering and CNN-GRU-attention |
| title_full_unstemmed | Design of Chinese traditional Jiaoyi (Folding chair) based on Kansei Engineering and CNN-GRU-attention |
| title_short | Design of Chinese traditional Jiaoyi (Folding chair) based on Kansei Engineering and CNN-GRU-attention |
| title_sort | design of chinese traditional jiaoyi folding chair based on kansei engineering and cnn gru attention |
| topic | Kansei Engineering affective cognition deep learning Jiaoyi chair design user preference prediction |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2025.1591410/full |
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