Scholarlink

<p>This study addresses the neglect of user emotional needs in digital cultural heritage displays by proposing a quantitative methodology to optimize the Panoramic Virtual Museum Interactive Interface (PVMUI). Leveraging Kansei Engineering, we first extracted three core affective dimensions (Technological, Innovative, Fancy) through factor analysis of 45 Kansei terms. Morphological decomposition and entropy-weighted TOPSIS objectively prioritized six critical design features from 14 candidates. A nonlinear mapping model integrating Particle Swarm Optimization Support Vector Regression (PSO-SVR) was developed to correlate these features with user emotions, validated empirically via 100 participants. Results identified an optimal configuration: top-bottom layout (A1-1), 30% screen occupancy (A2-3), transparent navigation icons (A5-5), planar maps (A7-3), arrow-shaped movement icons (A9-7), and no scene interaction (A10-6). The framework advances virtual museum design by establishing emotion-driven optimization principles, bridging theoretical gaps in affective computing for PVMUI while offering actionable guidelines for enhancing user immersion and heritage dissemination.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Design of panoramic virtual museum interactive interface based on entropy weight TOPSIS and PSO-SVR

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月21日

查看全文
  • Zikai Wang,
  • Fang Li

摘要

This study addresses the neglect of user emotional needs in digital cultural heritage displays by proposing a quantitative methodology to optimize the Panoramic Virtual Museum Interactive Interface (PVMUI). Leveraging Kansei Engineering, we first extracted three core affective dimensions (Technological, Innovative, Fancy) through factor analysis of 45 Kansei terms. Morphological decomposition and entropy-weighted TOPSIS objectively prioritized six critical design features from 14 candidates. A nonlinear mapping model integrating Particle Swarm Optimization Support Vector Regression (PSO-SVR) was developed to correlate these features with user emotions, validated empirically via 100 participants. Results identified an optimal configuration: top-bottom layout (A1-1), 30% screen occupancy (A2-3), transparent navigation icons (A5-5), planar maps (A7-3), arrow-shaped movement icons (A9-7), and no scene interaction (A10-6). The framework advances virtual museum design by establishing emotion-driven optimization principles, bridging theoretical gaps in affective computing for PVMUI while offering actionable guidelines for enhancing user immersion and heritage dissemination.