||Application of rough set in the product design feasibility analysis
||Department of Industrial Design
Kansei engineering system
product exterior features
crucial factor acquisition
Recent twenty years, Kansei engineering system (KES) has been successfully employed a variety of mathematical assumption models to overcome the customer-oriented product development problems. However, last few years globalized consumer market has become more competitive than ever in capture the critical design factor, a set of methods how developers can quickly capture consumer affective responses and to provide designers more completed preference information at product features, have become a focus. Rough set theory (RST) as a rule-based critical factor acquisition method, which can be targeted the imprecise and non-linear behavior of the human perception of the reference rules as the basis for evaluation. To the author surprise, in KES researches, especially product form features related to human cognition, RST is still quite rare and has not been specific development combined with KES. Therefore, this study describes important concepts related to KES and RST, and systematically reviewed from the literature which has been successfully applied KES to design related cases, and step by step compared with RST for assessment of the development in KES, in order to reference as a follow-up of KES merging RST. Two case studies brought into model exercises and verified rough set model construction. The first case study in apparel patterns is to explore the relationship between the appearance of the product characteristics and affective responses between rules, the application of the rough set method to identify the critical characteristics of the clothing features corresponding affective responses. The second case with a toothbrush form and color composited characteristics on the critical features in the application of rough sets for rules generated, whether used as product design, and identify toothbrush form, color and human perception of three the relationship between the dimensions and apply confusion matrix, cross-validation, receiver operating characteristic and area under the curve, etc. evaluation and verified capabilities and methods to examine the feasibility of the experimental model. Finally, the study brought into the model training examples and validated the constructed rough set model. Accurate predictive models of the two cases were presented an acceptable predictive ability and provided high readability and comprehensive reference rules.
LIST OF CONTENTS V
LIST OF TABLES IX
LIST OF FIGURES XI
CHAPTER 1. INTRODUCTION 1
1.1 RESEARCH BACKGROUND 1
1.2 MOTIVATIONS 3
1.3 RESEARCH OBJECTIVES 5
1.4 ORGANIZATION OF THE THESIS 6
CHAPTER 2. LITERATURE REVIEW 10
2.1 THE FRAMEWORK OF KANSEI ENGINEERING 10
2.2 PREDICTION MODEL FOR RELATING AFFECTIVE RESPONSES 12
2.3 FEATURE SELECTION TECHNOLOGY ON DESIGN PATTERNS 15
2.3.1 Variables screening for KES 15
2.3.2 Rough sets for feature selection 17
CHAPTER 3. ROUGH SET ANALYSIS PROBLEM MODELLING 20
3.1 ROUGH SET THEORY 20
3.1.1 Information table 20
3.1.2 Set approximation 21
3.1.3 Classification 25
3.1.4 Reduct and core 26
3.1.5 Generation rules 28
3.2 PROBLEM AND RESOLUTION UNDER ROUGH SET ANALYSIS PROCEDURES 30
3.2.1 Pre-processing phase 30
3.2.2 Selection of representative features form reduct modalities 32
3.2.3 Genetic algorithm for RS selection of critical attributes problem 33
3.2.4 Induction of critical rules from decision tables 34
3.3 THE PERFORMANCE EVALUATION OF PREDICTION MODEL 35
3.3.1 Confusion matrix (CM) 35
3.3.2 Cross validation (CV) 37
3.3.3 Receiver operating characteristic and area of under curves 40
CHAPTER 4. CRITICAL DESIGN FACTORS SELECTION USING ROUGH SET APPROACH 42
4.1 INTRODUCTION 42
4.2 IMPLEMENTATION PROCEDURES 43
4.2.1 Collecting representative clothing samples 43
4.2.2 Decomposing and encoding styling elements 43
4.2.3 Describing customer preferences with adjectives 44
4.2.4 Questionnaire investigation for adjective evaluation 46
4.2.5 Constructing rough set classification model 47
4.3 EXPERIMENTAL RESULTS 48
4.3.1 Analysis of reduct modalities 48
4.3.2 The induction of critical rules for class labels 51
4.3.3 Classification performance from external validation 55
4.4 SUMMARY 57
CHAPTER 5. EVALUATION OF THE EFFECTS OF MULTIPLE DESIGN FACTORS USING ROUGH SET ANALYSIS 59
5.1 INTRODUCTION 59
5.2 IMPLEMENTATION PROCEDURES 60
5.2.1 Determination of representative products 60
5.2.2 Extraction of image words for decision attributes 60
5.2.3 Morphological analysis of exterior attributes for condition attributes 61
5.2.4 Definition of representative color samples using PCCS 63
5.2.5 Construction of experimental samples 63
5.2.6 Conducting semantic meaning assessment 67
5.3 EXPERIMENTAL RESULTS 69
5.3.1 High adjective scores and preference rankings 69
5.3.2 Effects of form and color form on the evaluation of toothbrush images 71
5.3.3 Performance of mountain means analysis 75
5.3.4 Rough set data pre-processing 78
5.3.5 Selection and analysis of design factor ranking 79
5.3.6 Analysis of selected feature subset 80
5.3.7 Performance of induced rules 82
5.3.8 Relationship between design factors and affective responses 84
5.3.9 Performance of prediction model and validation 88
5.4 SUMMARY 90
CHAPTER 6. CONCLUSIONS AND SUGGESTIONS 91
6.1 OVERVIEW OF CONCLUSIONS 91
6.2 SUGGESTIONS FOR FUTURE RESEARCHES 92
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