Relationship between eye fixation patterns and Kansei evaluation of 3D chair forms

Understanding how to induce Kansei (emotion or affect) in consumers through form is critical in product design and development. Conventional Kansei evaluations, which involve subjectively evaluating the overall form of a product, do not clarify the effects of the individual parts of a product on people’s Kansei evaluation. A microscale analysis of eye movement of people looking at product form may redeem this flaw in subjective evaluation. However, simultaneously recording eye movement when people making Kansei evaluation is challenging, previous studies have typically investigated either the relationship between form and eye movement or the relationship between form and Kansei separately. The eye movement of people while performing Kansei evaluations on product forms still has not been clarified. To address this issue, the present study used an eye tracking system to analyze the changes in the fixation points of people performing various Kansei evaluations. Twenty participants were recruited for 8 Kansei evaluations on the form of 16 chairs by using the semantic differential (SD) rating, while their eye movements on these evaluations were tracked simultaneously. Through factor analysis on the data of Kansei evaluations, two principal factors, valence (pleasure) and arousal, were extracted from the 8 Kansei scales to constitute a Kansei plane which is compatible to Russell’s circumplex model (plane) of affect By adopting the factor scores of the 16 chairs as coordinates, the 16 chairs were mapped into the Kansei plane. Further analysis on the eye fixation on the chairs located in this plane concluded the following results: (a) Pleasure had a more significant effect on the participants’ visual attention compared to arousal; the participants required more fixation points when evaluating the chair form that induced displeasure. (b) The participants typically fixated on two parts of the chairs during their Kansei evaluations, namely the seat and the backrest, indicating that seats and backrests are the two primary features people consider when evaluating chairs. The results clarify the effect of various Kansei on eye movements; thereby enable predicting people’s Kansei evaluations of product forms through analyzing their eye movement.     

由感性工学浅析建立设计统计学

随着计算机技术的不断发展,简化了传统统计学的计算过程,使统计技术得到更广泛的应用,并且促使其应用学科得到前所未有的发展。本研究旨在探讨在感性工学的基础上将其中原统计学的研究分析方法进行系统化的整理;并加入统计学的基础知识,参照医学、生物、工程等统计学,尝试建立完整的设计统计学的知识体系,从而为工业设计理论和方法的发展提供一个新的思路。     

基于感性工学的产品客户化配置设计

基于感性工学理论及产品平台设计思想,提出了一种产品客户化配置设计方法.通过感性评价的问卷调查方式及回归分析方法建立感性意象与设计参数间的量化关系,同时辨识出平台参数与个性参数;在保持平台参数不变的基础上,改变个性参数进行产品造型以作为第二次问卷调查的样本.通过喜好评价的问卷调查形式,采用聚类分析方法对顾客进行基于喜好相似度的客户群聚类,并采用多项式回归模型建立各自的喜好度与个性参数间的量化关系模型,从而得到各个客户群的符合其最佳满意度的个性参数配置.最后,以手机机身设计为例进行说明.     

浅谈主页设计

主页在一个网站建设中占重要的地位,设计主页应以需求为主体,在收集和提炼客户信息的基础上,整合主页设计的一些技巧,以此提高制作主页的综合能力。     

基于GA-NN的复杂工艺生产过程多目标优化研究

在复杂非线性多目标优化问题求解中,非线性模型结构很难事先给定,需要检验的参数也非常繁多,应用传统的建模方法和优化模型已难以解决更为复杂的现实问题。人工神经网络技术为解决复杂非线性系统建模问题提供了一条新的途径。将神经网络响应面作为目标函数或者约束条件,加上其他常规约束条件进行系统模型的建立,再应用遗传算法进行优化,从而实现设计分析与设计优化的分离。以某化工企业的生产过程优化问题为例,利用BP神经网络建立了工艺参数与性能目标之间的模型,然后利用遗传算法搜索最优工艺参数,获取了用于指导生产的样本点数据。研究结果表明,该方法能够获得高精度的多目标优化模型,从而使优化效率大为提高。     

基于改进型遗传算法的前馈神经网络优化设计

阐明了遗传算法和神经网络结合的可行性，提出了一种改进的面向神经网络权值学习的遗传算法。通过对XOR问题的实验，显示出其快速学习网络权值的能力，且能摆脱局部极值的困扰和初始权值的限制，从各方面都表现出优于标准遗传算法和BP算法的性能。     

A support vector regression based prediction model of affective responses for product form design

In this paper, a state-of-the-art machine learning approach known as support vector regression (SVR) is introduced to develop a model that predicts consumers’ affective responses (CARs) for product form design. First, pairwise adjectives were used to describe the CARs toward product samples. Second, the product form features (PFFs) were examined systematically and then stored them either as continuous or discrete attributes. The adjective evaluation data of consumers were gathered from questionnaires. Finally, prediction models based on different adjectives were constructed using SVR, which trained a series of PFFs and the average CAR rating of all the respondents. The real-coded genetic algorithm (RCGA) was used to determine the optimal training parameters of SVR. The predictive performance of the SVR with RCGA (SVR–RCGA) is compared to that of SVR with 5-fold cross-validation (SVR–5FCV) and a back-propagation neural network (BPNN) with 5-fold cross-validation (BPNN–5FCV). The experimental results using the data sets on mobile phones and electronic scooters show that SVR performs better than BPNN. Moreover, the RCGA for optimizing training parameters for SVR is more convenient for practical usage in product form design than the timeconsuming CV.     

基于感性工学的文具产品形态设计研究

随着社会发展,消费者对于产品的精神和情感需求越来越高。因此,如何有效的获取消费者的心理情感需求,并进行有效转化至产品设计之中,成为设计中的新课题。感性工学正是在这种情形下产生,其旨在探求消费者情感与产品特性的对应关系,服务于消费者。本文将以文具设计为例,简述感性工学在文具设计中的应用。     

A New Design of Student Apartment Bed Based on Kansei Engineering and Ergonomics

Aiming at the defects of the products available on the market at present and basing on the Kansei Engineering and Ergonomics, a new kind of student apartment bed which can prove its humanity more clearly is designed, and its design direction is gotten through the questionnaire and the design starts from the real actual needs of students. After the original design was finished, the later investigation on the students was engaged with and made the engineering analysis. The conclusion is that the sum of ＂perfect＂ and ＂good＂ membership rates is up to 64%, hence the comprehensive assessment of the design is perfect. That is to say, this design is able to realize and meet the needs of users, and there is no doubt that it will have a good business prospects.     

基于产品感性定制模型的顾客协同设计系统

为使产品定制模型更加适合缺少相关领域专业知识的大众消费者,建立了基于感性工学的产品感性定制模型。引入配件感性性能指数、产品感性性能矩阵对产品感性性能进行量化。使用层次分析法实现了求解与顾客对产品感性性能需求对应的产品工程配置的方法。并应用产品感性定制模型,构建了基于Web和虚拟现实技术的顾客协同设计系统。     

基于产品感性定制模型的顾客协同设计系统

为使产品定制模型更加适合缺少相关领域专业知识的大众消费者,建立了基于感性工学的产品感性定制模型.引入配件感性性能指数、产品感性性能矩阵对产品感性性能进行量化.使用层次分析法实现了求解与顾客对产品感性性能需求对应的产品工程配置的方法.并应用产品感性定制模型,构建了基于Web和虚拟现实技术的顾客协同设计系统.     

Constructing a hybrid Kansei engineering system based on multiple affective responses: Application to product form design

This study proposes an expert system, which is called hybrid Kansei engineering system (HKES) based on multiple affective responses (MARs), to facilitate the development of product form design. HKES is consists of two sub-systems, namely forward Kansei engineering system (FKES) and backward Kansei engineering system (BKES). FKES is utilized to generate product alternatives and BKES is utilized to predict affective response of new product designs. Although the idea of HKES and similar hybrid systems have already been applied in various fields, such as product design, engineering design, and system optimization, most of existing methodologies are limited by searching optimal design solutions using single-objective optimization (SOO), instead of multi-objective optimization (MOO). Hence the applicability of HKES is limited while adapting to real-world problems, such as product form design discussed in this paper. To overcome this shortcoming, this study integrates the methodologies of support vector regression (SVR) and multi-objective genetic algorithm (MOGA) into the scheme of HEKS. BKES was constructed by training SVR prediction model of every single affective response (SAR). The form features of these product samples were treated as input data while the average utility scores obtained from all the consumers were used as output values. FKES generates optimal design alternatives using the MOGA-based searching method according to MARs specified by a product designer as the system supervisor. A case study of mobile phone design was given to demonstrate the analysis results. The proposed HKES based on MARs can be applied to a wide variety of product design problems, as well as other MOO problems involving with subjective human perceptions.     

感性微分法在产品设计中的探讨

感性工学是将感性与工学相结合的一种技术,其在产品设计中的应用,主要通过分析人的感性来建立产品构想模型,并据此来设计产品。感性微分法是感性工学中的一种定性研究方法,这种方法可以通过感性概念的逐步细分从而和设计元素建立起一一对应关系,本文试图应用感性微分法在产品设计中的作用进行探讨,并以行李箱的拉杆设计为例就感性微分法的应用进行探讨。     

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.     

基于可拓表征与神经网络的苗族图案设计

针对苗族图案的文化传承及设计应用问题,提出基于可拓表征和神经网络的民族图案创新设计方法,对苗族蜡染图案进行解构、映射和重构.首先对苗族蜡染图案进行可拓表征,运用发散树法构建设计生长阶段模型对苗族图案基元进行拓展分析.其次基于感性工学对苗族蜡染图案进行感性意象分析,提出一种面向图案构型、纹样语义和种类的图案解构方法,构建...     

基于神经网络的干扰波形优化研究

对干扰信号进行快速而有效的波形优化是实施多目标电子干扰的必要条件。由于干扰波形的约束条件复杂，变量的维数很多．现有的波形优化方法优化的效果不太理想。本文采用神经网络方法对干扰波形进行了分析和研究，提出了解决干扰波形优化问题的神经网络方法。     

基于改进遗传算法的神经网络优化方法

为了克服神经网络反向传播算法收敛速度慢,易陷入局部极小值,初始权值和阈值的选择缺乏依据,具有很大随机性等缺陷,采用基于自适应遗传算法的神经网络优化方法,方法结合了两者的优点,但是仍存在种群早期进化速度慢的缺点,于是提出了一种改进的自适应遗传算法,将其应用于神经网络的权值和阈值的优化设计中,并将此模型用于对某城市污水厂难测参数SVI的预测.仿真结果表明,算法不仅可克服BP算法的缺陷,而且与BP和GA-BP网络模型比较,大大提高了收敛速度与收敛精度,获得了良好的测量效果.     

基于遗传算法的RBF神经网络的优化设计方法

该文提出了一种新的RBF神经网络的设计方法，采用遗传算法对RBF神经网络的隐层节点中心值进行进化优选，用自适应梯度下降法选择隐层节点高斯函数的宽度，用递推的最小二乘法训练RBF神经网络的权值，仿真结果证明了该方法的有效性。     

一种基于区间优化的神经网络学习算法

神经网络的学习算法通常是采用梯度下降法,此方法容易陷入局部极小而得到次最优解。另外,对于有些应用来说,用于训练网络的样本的输入／输出数据无法精确给出,而只能以一定的范围的形式给出,这就给传统的神经网络带来了困难。该文提出了一种基于区间优化的神经网络学习算法,可以很好地解决上面所提到的传统神经网络学习算法的缺点。     

基于遗传小波神经网络的语音识别分类器设计

分类在语音识别中是很重要的,由于小波神经网络的学习效果对网络隐层节点数、初始权值(包括阈值)、伸缩和平移因子以及学习率和动量因子的依赖性较大,致使其全局搜索能力弱,易陷入局部极小,收敛速度减慢,甚至不收敛。而遗传算法具有的高度并行、随机、自适应搜索性能,使它在处理用传统搜索方法解决不了的复杂和非线性问题时具有明显的优势。因此,考虑把遗传算法和神经网络相结合,采用遗传算法选取初值进行训练,用小波神经网络完成给定精度的学习。仿真实验结果表明,该模型有效地提高了语音的识别率,并缩短了识别时间,实现了效率与时间的双赢,为算法的实用性莫定了基础。