Specifications
Corresponding author: Ge, C. (shenyi17@gmail.com)
Source title: Applied Mechanics and Materials
Abbreviated source title: Appl. Mech. Mater.
Volume: 271
Issue: PART 1
Monograph title: Frontiers of Manufacturing and Design Science III
Issue date: 2013
Publication year: 2013
Pages: 417-421
Language: English
ISSN: 16609336
E-ISSN: 16627482
ISBN-13: 9783037855782
Document type: Conference article (CA)
Conference name: 3rd International Conference on Frontiers of Manufacturing and Design
Science, ICFMD 2012
Conference date: December 11, 2012 - December 13, 2012
Conference location: Hong kong
Conference code: 95055
Sponsor: Control Eng. Inf. Sci. Res. Assoc.; International Frontiers of science; and technology
Research Association; National Chin-Yi University of Technology; Integrated Research Center for
Green Living Techniques; Trans Tech Publication
Publisher: Trans Tech Publications, P.O. Box 1254, Clausthal-Zellerfeld, D-38670,
Germany
Abstract: In the process of product satisfaction solution by SEM (Structural Equation
Modeling), the model of product satisfaction has been revised aiming at solving the weights of
satisfaction index distribution in the multi-sample situation. According to the characteristic of
satisfaction data sampling, the partial least square is introduced, and the algorithmic method of
satisfaction weights based on SEM is presented. The proposed method has been validated by an
example of digital photo frame. © (2013) Trans Tech Publications, Switzerland.
Number of references: 9
Main heading: Design
Controlled terms: Manufacture
Uncontrolled terms: Algorithmic methods - Data sampling - Digital photos -
Index distribution - Index weight - Partial least square (PLS) - Product satisfaction -
Structural equation modeling - Structure equations - Weights
Classification code: 408 Structural Design - 537.1 Heat Treatment Processes
DOI: 10.4028/www.scientific.net/AMM.271-272.417
Database: Compendex
Compilation and indexing terms, © 2013 Elsevier Inc.
6.
Accession number: 20130415932638
Title: Algorithms of data mining and knowledge discovery of correlativity in
two-dimensional time series