Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
An investigation into affective design using sorting technique and Kohonen self-organising map
Advances in Engineering Software
Design concept evaluation in product development using rough sets and grey relation analysis
Expert Systems with Applications: An International Journal
Dominance-based rough set approach to incomplete interval-valued information system
Data & Knowledge Engineering
Study on the perception of car appearance based on fuzzy inference
IDGD'11 Proceedings of the 4th international conference on Internationalization, design and global development
Dominance-based rough set model in intuitionistic fuzzy information systems
Knowledge-Based Systems
A multi-objective genetic algorithm approach to rule mining for affective product design
Expert Systems with Applications: An International Journal
FuzEmotion as a backward kansei engineering tool
International Journal of Automation and Computing
Employing rough sets and association rule mining in KANSEI knowledge extraction
Information Sciences: an International Journal
ANFIS modeling for predicting affective responses to tactile textures
Human Factors in Ergonomics & Manufacturing
Information Sciences: an International Journal
Disparate attributes algorithm for semantic assembly design rule management
Advanced Engineering Informatics
Affective and cognitive design for mass personalization: status and prospect
Journal of Intelligent Manufacturing
Hi-index | 12.05 |
Keen competitions in the global market have led product development to a more knowledge-intensive activity than ever, which requires not only tremendous expert knowledge but also effective analysis of design information. Kansei Engineering as a customer-oriented methodology for product development, often has to analyse imprecise design information inherent with nonlinearity and uncertainty. This paper proposes a systematic approach to Kansei Engineering based on the dominance-based rough set theory. Two novel concepts known as category score and partition quality have been developed and incorporated into the proposed approach. The new approach proposed is able to identify and analyse two types of inconsistencies caused by indiscernibility relations and dominance principles respectively. The result of an illustrative case study shows that the proposed approach can effectively extract Kansei knowledge from imprecise design information, and it can be easily integrated into an expert system for customer-oriented product development.