基于神经风格迁移网络的民族图案创新方法

针对传统民族图案符号手工提取效率低,图案组合创新设计困难的问题,以苗族 传统的蜡染和挑花图案创新设计为例提出传统民族图案风格创新模型。该模型将改进型的形状 文法与基于深度学习的神经风格迁移网络相结合,提取并编码民族图案构型框架,通过形状文 法生成大量民族图案构型框架图案,使用风格迁移网络快速提取民族图案中的底层特征,在框 架基础上迁移生成创新民族图案设计方案。实验结果表明,该模型可以在指定框架的基础上生 成全新的民族纹饰图案,相对于直接使用神经网络对空白图像进行风格迁移生成的图案更加有 序。生成图案最终应用于苗族织物图案设计中,验证了该方法和设计流程的可行性。     

基于OpenCV的蜡染图案快速定制系统

为实现蜡染图案的快速定制, 提高蜡染图案创新设计效率, 研究一种基于OpenCV计算机视觉库相关函数和Python语言的蜡染图案快速定制系统. 该方法通过对蜡染图像进行形态学预处理, 然后使用canny算子实现蜡染图案轮廓提取; 随后用户手动选取子图案进行编码提取构型; 通过Hu矩匹配自动获取纹样空间位置, 结合拓扑学中的邻接图方法建立蜡染图案构型规则; 提出构型规则重用方法, 在已提取的构型规则上进行纹样嵌入, 使用基于标识矩阵法的图案嵌入法避免轮廓重叠. 该方法能够使设计结果更加丰富, 有效提高了设计效率. 最后通过蜡染图案设计原型系统进行蜡染图案创新设计, 验证该方法的可行性.     

基于感性语意模糊因子评价的图案设计源码特征集筛选

目前感性研究多从意象到设计特征正面探究设计问题,缺乏从意象诱因设计源码 反向推理的感性研究,为此提出一种从用户角度出发并结合感性模糊因子评价与聚类分析的反 向意象诱因设计源码筛选方法,以提取引发用户感性意象的图案设计源码特征。首先筛选数据 样本以建立样本感性语义空间,通过契合评价法筛选出基础因子。其次运用模糊因子评价确定 因子权重,从而能获取图案集的评价及可靠度,并以因子为评价准则结合聚类分析筛选出图案 设计源码特征集。最后通过 T 检验验证该方法的有效性与可行性,并以苗族服饰图案为对象进 行了实例研究。研究结果表明,该方法基于感性认知理论基础能够有效地指导设计源码特征集 的建立。     

面向工艺品个性化定制的图案构型提取与重用

针对工艺品个性化定制过程中知识重用性较低的问题,提出一种图案构型提取与 重用的方法,并以蜡染图案创新设计为例开展研究。首先,将蜡染知识按照工艺品个性化定制 的应用需求进行分类整理,建立蜡染知识库和案例库,并构建两者之间的语义关系,通过本体 语义检索,推荐出符合用户需求的蜡染案例。采用形状上下文法对蜡染图案的纹样进行提取, 结合拓扑学方法建立图案的构型规则。在构型重用过程中提出基于字符编码的改进形状文法, 用于表述纹样填充过程中形状的变换,从而完成了面向工艺品个性化定制的蜡染图案重用。开 发蜡染工艺品个性化定制系统,通过实例验证了该方法的可行性。     

基于C-MKE 模型的交互界面设计

为了满足用户对交互界面设计的感性需求,提出了基于C-MKE 模型的交互界面 设计方法。首先应用感性工学(KE)理论构架中的类目层次法(CC)推论交互界面设计项目,并在 与现有交互界面对比分析的基础上建立设计变量空间。其次,通过感性意象认知实验构建感性 意象认知空间,运用数学模型方程法(MM)实现交互界面设计变量与感性意象的映射模型,进一 步通过T 检验法验证了该模型的有效性。最后依据C-MKE 模型推导出苗族银饰文化传播APP 首页界面设计的参考性结论,并进行了界面的创新设计。     

基于产品特征解构的多维感知信息模型构建

基于现有一维感性工学模型难以充分表达用户对产品的感知信息,提出基于产品特 征解构的多维感知信息模型构建方法。首先运用产品特征解构方法构建产品多维造型特征空间和 情感意象认知空间;其次结合相似度理论、多维尺度法和K-mean 聚类分析量化意象词汇,提取 代表性情感维度;之后运用主成分分析判定多维造型特征中的主要影响因子;最后运用多元线性 回归构建出多维变量表达的KE 模型。以微波炉为例,验证了该方法的可行性,结果表明该方法 能有效指导产品的精准化设计。     

面向产品设计的民族图案语义量化模型构建与应用EI北大核心CSCD

为了提高传统民族图案知识与现代设计的一致性,解决现有设计中传统知识关联度低、知识利用效率低的问题,提出一种面向产品设计的民族图案语义量化模型构建方法.该方法以苗族蜡染图案作为研究对象,将图案语义划分为6个维度,综合专家知识对图案语义解析并编码,构建语义量化模型并制定二阶段建模过程.第1阶段,利用解析降维后的图案语义编码数据对图案进行聚类重组,构建图案语义簇,表征各语义簇代表性语义元素;第2阶段,计算各维度语义相关性,挖掘各语义簇各维度语义元素关联度.将用户需求与量化模型进行匹配,根据匹配结果指导设计实践,形成设计方案,并对设计方案的语义相关度和视觉美观度进行评分,选取评分较高的设计方案,制作民族产品实例.实例结果表明,该方法作为析出知识指导设计实践,可以提升民族图案语义与用户需求以及现代民族产品设计的契合度.     

基于民族图案基元可拓语义的产品设计方法

针对在面向民族图案的产品设计过程中,设计者容易忽略民族图案所隐含的复杂语义,导致所设计的产品脱离图案原本蕴含的文化内涵的问题,提出基于民族图案基元可拓语义的产品设计方法.利用可拓理论对民族图案基元的语义进行提取、量化分析、图形解析,以优化产品设计过程中民族图案的运用.以侗族织锦为例,首先,由当地文化持有者和设计人员共同对侗族织锦图案基元及其语义进行提取和选择;然后,对图案基元的语义进行分析,得出最大可拓设计区间的特征词汇,再对特征词汇进行图解,并分析出最大可拓设计区间的图解;最后,对最大可拓设计区间的图解进行分析,并以此指导设计实践.邀请设计师和当地文化持有者共同对设计方案的文化表现性、创新性及美观度进行优度评估,遴选优度较高的设计方案,设计制作出具体的产品,提升在产品设计过程中对民族图案运用的准确度与优度.     

基于BP神经网络的壁挂式充电桩产品形态研究

通过对壁挂式充电桩感性意象与形态设计要素的分析,建立两者间对应的BP神经网络设计模型,从而得到各感性意象对应的产品形态。利用感性工学中的李克特量法对收集到的壁挂式充电桩形态样本和感性词汇进行设计评价并得到感性评价结果,将形态设计要素作为BP神经网络模型的输入层参数,感性评价结果数值作为输出层参数,利用Matlab软件进行壁挂式充电桩BP神经网络模型的反复训练和测试,最终得到壁挂式充电桩形态设计要素BP神经网络模型。最后通过MSE度量该BP神经网络模型准确性,研究表明建立的壁挂式充电桩感性意象和形态设计要素间的BP神经网络模型具有可行性。     

基于非遗和可拓语义的服务产品设计

摘 要：针对运用非物质文化遗产(简称“非遗”)进行产品创新设计过程中,对非遗源文化隐 性知识表征与转换方法有效性不足的问题,提出一种基于非遗与可拓语义的产品创意设计方 法。通过分析源文化,提取其中代表性的文化元素和文化特征,建立其可拓语义表征模型,从 而得到文化特征与非遗的量化关系;计算文化特征的可拓设计空间,并运用图解思维将其转化 为图解语义,通过可拓变换得到特定非遗元素的核心图解语义集,并构建其可拓图形元素库, 为设计实践提供基础素材。以电动车充电服务设施的造型设计为例,验证该方法的可行性与合 理性,结果显示运用可拓语义分析可以有效地提高非遗元素的提取和利用,为非遗创意产品造 型设计提供设计方法支持。     

Application of Evolutionary Neural Networks on Optimization Design of Mobile Phone Based on User's Emotional Needs

Taking users’ emotional needs into consideration, this research aims to propose a new method to present product design features exactly and completely. On the basis of genetic algorithm integrated with back‐propagation (BP) neural networks, taking the mobile phone as research object, an optimization design algorithm was finally designed. First, the continuous and discrete design variables that describe mobile phones were screened with methods of dimensions, coordinate label, and morphological analysis. Forty three‐dimensional (3D) mobile phone models were designed by using 3D design software PROE. Accordingly, 12 representative mobile phones were selected through multidimensional scaling analysis and cluster analysis. Fourteen pairwise Kansei image words were obtained by collecting, screening, surveys, and statistical analysis method. Second, a BP neural networks model between design variables and user preference along with Kansei image words was established and verified with questionnaire survey data. Finally, the optimization design model for mobile phones was established considering design requirements and users’ emotional needs. A genetic algorithm integrated with BP neural networks was used to optimize mobile phone design. The results show that the optimization scheme is superior to others, and this paper will provide design suggestion for mobile phone designers.     

A Kansei mining system for affective design

Affective design has received much attention from both academia and industries. It aims at incorporating customers' affective needs into design elements that deliver customers' affective satisfaction. The main challenge for affective design originates from difficulties in mapping customers' subjective impressions, namely Kansei, to perceptual design elements. This paper intends to develop an explicit decision support to improve the Kansei mapping process by reusing knowledge from past sales records and product specifications. As one of the important applications of data mining, association rule mining lends itself to the discovery of useful patterns associated with the mapping of affective needs. A Kansei mining system is developed to utilize valuable affect information latent in customers' impressions of existing affective designs. The goodness of association rules is evaluated according to their achievements of customers' expectations. Conjoint analysis is applied to measure the expected and achieved utilities of a Kansei mapping relationship. Based on goodness evaluation, mapping rules are further refined to empower the system with useful inference patterns. The system architecture and implementation issues are discussed in detail. An application of Kansei mining to mobile phone affective design is presented.     

产品外形设计中客户感性认知模型及应用

为了更好地利用客户的感性认知支持产品外形设计活动,提出一种产品外形设计中客户感性认知与产品外形特征关联模型.在分析产品外形设计中所涉及的客户感性认知特点的基础上,建立产品外形特征要素与客户感性认知要素的关联模式;以认知行为为标准,利用特征匹配实现了客户感性认知的识别与相似性分析;提出了基于模糊认知图的客户感性认知与产品外形特征关联模型,利用蚁群聚类算法确定模糊认知图的结构及邻接矩阵,从而获取客户感性认知以指导产品的外形设计.最后通过实例验证了该模型的有效性和实用性.     

Form design of product image using grey relational analysis and neural network models

This paper presents a new approach to determining the best design combination of product form elements for matching a given product image represented by a word pair. A grey relational analysis (GRA) model is used to examine the relationship between product form elements and product image, thus identifying the most influential elements of product form for a given product image. A grey prediction (GP) model and a neural network (NN) model are used individually and in conjunction with the GRA model, in order to predict and suggest the best form design combination. An experimental study on the form design of mobile phones is conducted to evaluate the performance of these models. Based on expert surveys, the concept of Kansei Engineering is used to extract and evaluate the experimental samples, and a morphological analysis is used to extract form elements from these sample mobile phones. The evaluation result shows that all the NN-based models outperform the GP-based models, suggesting that the NN model should be used to help product designers determine the best combination of form elements for achieving a desirable product image. The GRA model can be incorporated into the NN model to help designers focus on the most influential elements in form design of mobile phones.     

基于构型风格的新疆民族织物图案自动生成

以新疆民族织物图案为对象,通过分析图案特点,发现具有某些元素有规 律地重复排列组合的特征,分解这些元素找出基本构型,归纳为六边形结构堆砌、砖形结构 堆砌、田字结构堆砌、三角结构堆砌、菱形结构堆砌等。用户在创作时首先选择某一种基本 构型,然后设计或嵌入适当的纹样基元到初始构型中,最后通过折叠或旋转延展生成一幅精 美的对称图案,文中原型系统可以广泛应用于新疆民族织物图案设计、广告以及服装设计等 领域。     

A product configuration analysis method for emotional design using a personal construct theory

Identifying emotion-related product attributes (perceived by consumers) is no easy task in the realm of emotional design. Conventionally, this process relies heavily on the researchers who conduct the Kansei experiments selecting product attributes such as color, form, and texture for Kansei studies. However, in so doing, other product attributes that also play a vital role in product-emotion associations might be neglected by the researchers. More importantly, the identification of product attributes should be based on consumer's point of view (and feelings). Accordingly, a personal construct theory based product configuration analysis method is proposed in this work. The method develops the customer's mind map for each Kansei tag in order to capture replications of candidate products. A means-value chain is used to generate targets which are later compared with candidate products by consumers. The comparison results could suggest product attributes that are relevant to the desired Kansei. The proposed approach is presented and illustrated using a case study of Graffiti designs on notebooks. Results obtained are discussed. It appears that the proposed method is promising in identifying product attributes with desired Kansei impacts.     

Soccer shoe recommendation system based on multitechnology integration for digital transformation

Designing a soccer shoe that fits specific customer’s requirements can improve satisfaction. However, there is no sufficient information to bridge the semantic needs and design characteristics of soccer shoes. Hence, this study is aimed at integrating multiple technologies with semantic customer requirements, shoe-form categories, and appearance designs, and developing a practical Kansei soccer shoe recommendation system. The psychological responses of a customer and perceptions of soccer shoe products are evaluated using Kansei engineering method. Based on a factor analysis, customers’ requirements are classified as aesthetic, functional, and comfortable. A total of 203 soccer shoe images were used to evaluate and categorise the external shoe form into nine design elements using the Kawakida Jirou method, and the weight assigned to each shoe-form category under Kansei semantic adjectives was determined using grey system theory. The quantification theory Type 1 method was used to determine the priority of the design elements. The results indicate that 10 pairs of semantic adjectives used by customers are related to soccer shoe forms. Furthermore, the design priority of each design element of the form category under the three types is reported. A soccer shoe recommendation system that can generate suggested soccer shoe samples for customers is developed by integrating the above-mentioned technologies. A validation experiment was conducted to verify the feasibility of the proposed system. The overall satisfaction of the recommended samples generated via the system is 87.08%, as reported by 80 participants. The findings prove that a new soccer shoe business model can be launched using the proposed system, linking the semantic customer requirements to the soccer shoe-form categories.