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面向感性需求的婚纱个性化设计 被引量:1

Customized design of wedding dresses based on Kansei needs
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摘要 为探究婚纱款式造型要素与消费者感性意象之间的关系,通过专家访谈、主观评价等实验方法获取感性评价数据,结合主成分分析、回归分析等方法构建了二者之间的关系模型。通过感性评价实验从7组感性词汇中提取出婚纱款式的三大感性因子,分别为时尚因子、活泼因子、复杂因子。在此基础上,构建5类婚纱款式要素(廓型、袖型、裙长、裙摆、肩领胸)与三大感性因子之间的回归模型。通过4款全新样本及8名受试者主观实验验证,该模型被证实具有良好的可信度。从感性工学的角度量化了服装款式设计要素与消费者感性意象间的联系,有助于建立服装设计师知识与消费者感性需求之间的联系,辅助设计师提高工作效率,更好地满足消费者对服装个性化的需求。 To explore the relationship between the elements of wedding dress style and consumers'emotional impressions,a relationship model was constructed using expert interviews,subjective evaluations,and other experimental methods to obtain perceptual evaluation data,combined with principal component analysis,regression analysis,and other methods.Through perceptual evaluation experiments,three major emotional factors of wedding dress styles were extracted from seven sets of emotional vocabulary,namely the“Fashion”,“Youth”,and“Exquisiteness”.On this basis,a regression model was constructed between the five elements of wedding dress styles(silhouette,sleeve type,dress length,hem,and collar)and the three major emotional factors.Through validation with four new samples and eight subjects,the model was found to have good reliability.This study quantifies the relationship between clothing design elements and consumers’emotional impressions from the perspective of emotional engineering,which helps to establish a bridge between fashion designers’knowledge and consumers’emotional needs,effectively assist designers’work,and better meet consumers’demands for personalized clothing.
作者 李青 刘欣雨 糜雨溪 薛哲彬 LI Qing;LIU Xinyu;MI Yuxi;XUE Zhebin(College of Textile and Clothing Engineering,Soochow University,Suzhou 215021,Jiangsu,China;School of Textiles and Clothing,Jiangnan University,Wuxi 214122,Jiangsu,China;Wuxi Big Bridge Academy,Wuxi 214111,Jiangsu,China)
出处 《纺织高校基础科学学报》 CAS 2023年第4期47-53,共7页 Basic Sciences Journal of Textile Universities
基金 教育部人文社科青年基金(22YJC760111) 国家丝绸重点实验室开放基金(KJS2057)。
关键词 感性工学 婚纱 量化模型 语义分析 特征提取 Kansei engineering wedding dress quantitative modeling semantic analysis feature extraction
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