Kansei Engineering and Soft Computing: Theory and Practice
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Kansei Engineering and Soft Computing: Theory and Practice

Ying Dai, Basabi Chakraborty, Minghui Shi
Release Date: August, 2010|Copyright: © 2011 |Pages: 436
DOI: 10.4018/978-1-61692-797-4
ISBN13: 9781616927974|ISBN10: 1616927976|EISBN13: 9781616927998
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Description & Coverage
Description:

With the increasing concern regarding human factors in system development, the concepts of humanized technology and human-related systems have become the focus of more and more research.

Kansei Engineering and Soft Computing: Theory and Practice offers readers a comprehensive review of kansei engineering, soft computing techniques, and the fusion of these two fields from a variety of viewpoints. It explores traditional technologies, as well as solutions to real-world problems through the concept of kansei and the effective utilization of soft computing techniques. This publication is an essential read for professionals, researchers, and students in the field of kansei information processing and soft computing providing both theoretical and practical viewpoints of research in humanized technology.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

  • DNA Computing
  • Fuzzy Logic
  • High-dimensional Data Clustering
  • Humanoids
  • Kansei
  • Particle Swarm Optimizer
  • Physiological Measurement
  • Semantic Detection
  • Soft Computing
  • Text Mining
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Editor/Author Biographies
Ying Dai received her B.S. and M.S. degrees from Xian Jiaotong University, China in 1985 and 1988, respectively. After some years working in the same university, she attended Department of Information Engineering, Shinshu University, Japan in 1992. She had a Dr. Eng degree from Shinshu University in 1996. She was granted JSPS Research Fellowships for Young Scientists from 1995 to 1997. She joined the Iwate Pref. University in 1998. She has been an associate professor in the Faculty of Software and Information Science, Iwate Pref. University since 2002. Her main research interests are in the area of Pattern Recognition, Image Understanding, Kansei Information Processing, and Soft Computing Techniques. She is the member of IEEE, IEICE (the Institute of Electronics, Information and Communication Engineers), and JSKE (Japan Society of Kansei Engineering).
Basabi Chakraborty received B.Tech, M.Tech and Ph. D degrees in RadioPhysics and Electronics from Calcutta University, India. She worked in National Center for Knowledge based Computing Systems and Technology affiliated to Indian Statistical Institute, Calcutta, India until 1990. From 1991 to 1993 she worked as a part time researcher in Advanced Intelligent Communication Systems Laboratory in Sendai, Japan. She received another Ph. D in Information Science from Tohoku University, Sendai in 1996. From 1996 to 1998, she worked as a post doctoral research fellow in Research Institute of Electrical Communication, Tohoku University, Japan (under Telecommunication Advancement Organization (TAO) fellowship for a period of 10 months). In 1998 she joined as a faculty in Software and Information Science department of Iwate Prefectural University, Iwate, Japan and currently an Associate Professor in the same department. Her main research interests are in the area of Pattern Recognition, Image Processing, Soft Computing Techniques, Biometrics, Trust and Security in Computer Communication Network. She is a senior member of IEEE, member of ACM, Japanese Neural Network Society (JNNS) and Information Processing Society of Japan (IPSJ), executive committee member of IUPRAI (Indian Unit of Pattern Recognition and Artificial Intelligence), IEEE JC WIE (Women In Engineering) and ISAJ (Indian Scientists Association in Japan).
Minghui Shi received his M.S. degree in 2002 and Dr. degree in 2008 respectively from Jiangnan University and Xiamen University, China. He is currently an assistant professor at Xiamen University. In addition to working as a teacher, he is also a research member of Artificial Intelligence Institute of Xiamen University. His research interests involve Soft Computing Techniques, Machine Learning, Pattern Recognition, Information Processing in TCM (Traditional Chinese Medicine), and Feature Selection.
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