Eliciting design knowledge from affective responses using rough sets and Kansei engineering system

There are few studies on the tricolor design optimization, and the influence of color‐area ratio on users' emotion has been ignored. This article aims to achieve multiobjective optimization of tricolor product color design. Two modes of color‐area ratio are put forward. Using the proposed method of generating tricolor schemes, 368 tricolor schemes of the representative baby carriage are designed. Through questionnaire survey, color images are screened, and combined with correlation analysis and factor analysis, five perceptual features of color design are determined as “Order,” “Excitement,” “Temperature,” “Color harmony,” and “Users' emotional preference.” From two perspectives of color information processing, radial basis function neural networks are used to construct two emotional evaluation models. By integrating the radial basis function neural networks and a genetic algorithm, this study achieves multiobjective optimization of tricolor product color design for two optimization objectives of color harmony and users' emotional preference. Verification results show that the optimization schemes are significantly better than other schemes. Due to similar product characteristics, children's electric car is utilized to verify the generalization capability of the optimization method proposed in this article. It has been demonstrated that the optimization schemes achieve higher scores than randomly selected color schemes and the actual subjective scores match with the predicted scores computed by the color optimization method.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on color optimization of tricolor product considering color harmony and users' emotion

Guo

Nagamachi

et al. 2019

“…The methods used in previous studies to solve MEPCD problems always involve weighted coefficient approaches, which are used to transform multiple objectives into a single objective. At first, various intelligent tools are used to construct a model that will relate product color to image perception by the users in each emotional dimension, such as gray theory (GT), neural network (NN), fuzzy logic (FL), support vector regression (SVR), and rough sets (RS) . Then, the designer defines the importance of each emotional dimension according to the design requirements .…”

Section: Introductionmentioning

confidence: 99%

“…At first, various intelligent tools are used to construct a model that will relate product color to image perception by the users in each emotional dimension, such as gray theory (GT), [10][11][12] neural network (NN), 13,14 fuzzy logic (FL), 15 support vector regression (SVR), 16 and rough sets (RS). 17 Then, the designer defines the importance of each emotional dimension according to the design requirements. 18,19 However, during the process of selecting a strategy, it is difficult to quantify the important relations among all emotional dimensions by using traditional weighted methods (it is difficult for users to determine these coefficients for optimization in MEPCD).…”

Section: Introductionmentioning

confidence: 99%

Multiemotional product color design using gray theory and nondominated sorting genetic algorithm‐III

Ding

Dong

2019

When users select products, they consider the emotional experience resulting from the color of the product. However, the emotional demands of users for product color are multidimensional and diverse. It is very important yet difficult to accurately grasp multiemotional image requirements and effectively convert them into design elements. Therefore, multiemotional product color design (MEPCD) has become a very important and challenging research topic. In this article, a novel MEPCD system using gray theory (GT) and nondominated sorting genetic algorithm‐III (NSGA‐III) is proposed to effectively solve the MEPCD problem. First, the image perception spaces of users, which exist in different emotional dimensions, were collected using factor analysis and the semantic differential technique. Then, GT was used to establish a multidimensional emotional product color image evaluation model. Finally, NSGA‐III was used to optimize and design a multiemotional color scheme for a product. Furthermore, according to actual conditions, an MEPCD system was established based on the proposed method. The design case study shows that the method and design system proposed in this article have a certain range of applicability and can effectively improve the practicality of MEPCD.

“…Hsiao established a fuzzy recognition module and an evaluation module by applying the analogue natural color mode; with this capability, he constructed the computer aided design (CAD) system for the product's color design, which can help designers obtain optimized natural colors . Shieh established an evaluation method and model for a product's color image by combining the KE system and rough sets …”

Section: Introductionmentioning

confidence: 99%

Product color emotional design considering color layout

Ding

Dong

2018

Emotional experience and demand for product colors are important factors in users' decisions to buy and use a product. Therefore, accurately characterizing users' emotional responses of the product's color has become a significant consideration for product color design. However, a product color design problem exists in which it is challenging to accurately and efficiently position users' color image space because consumers have completely different image perceptions when encountering a large number of color schemes generated by different color spatial distributions. For this reason, this article proposes a product color emotional design method that considers the color layout. A table is built of an elemental composition for product color design, which contains color layout forms. This article also establishes a mapping model based on the semantic difference and back propagation neural network between the users' color image perceptions and the elements of the product's color design. The system recommends a color layout form that matches the users' emotional image goal through the k‐nearest neighbor algorithm, and then the form is initialized using a genetic algorithm. The system can realize the solution to the optimal product color scheme by optimizing and evaluating the population. Designers can make intuitive choices and decisions through the product color recommendation system. Through an example of color design for industrial vacuum cleaners, this article shows that the method has satisfactory feasibility and applicability for solving the problem of the optimization of product color design with color layout forms.