Consumers’ emotion has become imperative in product design. In affective design field, Kansei Engineering (KE) has been recognized as a technology that enables discovery of consumer’s emotion and formulation of guid...Consumers’ emotion has become imperative in product design. In affective design field, Kansei Engineering (KE) has been recognized as a technology that enables discovery of consumer’s emotion and formulation of guide to design products that win consumers in the competitive market. Albeit powerful technology, there is no rule of thumb in its analysis and interpretation process. KE expertise is required to determine sets of related Kansei and the significant concept of emotion. Many research endeavours become handicapped with the limited number of available and accessible KE experts. This work is performed to simulate the role of experts with the use of Natphoric algorithm and thus provides solution to the complexity and flexibility in KE. The algorithm is designed to learn the process by implementing training datasets taken from previous KE research works. A framework for automated KE is then designed to realize the development of automated KE system.展开更多
A large number of 3D models are created on computers and available for networks. Some content-based retrieval technologies are indispensable to find out particular data from such anonymous datasets. Though several sha...A large number of 3D models are created on computers and available for networks. Some content-based retrieval technologies are indispensable to find out particular data from such anonymous datasets. Though several shape retrieval technologies have been developed, little attention has been given to the points on human＇s sense and impression （as known as Kansei） in the conventional techniques, In this paper, the authors propose a novel method of shape retrieval based on shape impression of human＇s Kansei. The key to the method is the Gaussian curvature distribution from 3D models as features for shape retrieval. Then it classifies the 3D models by extracted feature and measures similarity among models in storage.展开更多
文摘Consumers’ emotion has become imperative in product design. In affective design field, Kansei Engineering (KE) has been recognized as a technology that enables discovery of consumer’s emotion and formulation of guide to design products that win consumers in the competitive market. Albeit powerful technology, there is no rule of thumb in its analysis and interpretation process. KE expertise is required to determine sets of related Kansei and the significant concept of emotion. Many research endeavours become handicapped with the limited number of available and accessible KE experts. This work is performed to simulate the role of experts with the use of Natphoric algorithm and thus provides solution to the complexity and flexibility in KE. The algorithm is designed to learn the process by implementing training datasets taken from previous KE research works. A framework for automated KE is then designed to realize the development of automated KE system.
文摘A large number of 3D models are created on computers and available for networks. Some content-based retrieval technologies are indispensable to find out particular data from such anonymous datasets. Though several shape retrieval technologies have been developed, little attention has been given to the points on human＇s sense and impression （as known as Kansei） in the conventional techniques, In this paper, the authors propose a novel method of shape retrieval based on shape impression of human＇s Kansei. The key to the method is the Gaussian curvature distribution from 3D models as features for shape retrieval. Then it classifies the 3D models by extracted feature and measures similarity among models in storage.
