||A Computer-aided Color Planning System for Customized Product Design
||Department of Industrial Design
Computer-aided color planning
Customized product design
Products known as 3C products (computer, communications and consumer electronics) fall under the scope of many important industries today and are prevalent and broad in modern life. The majority of the design issues related to 3C products can be divided into two aspects: software design and hardware design. Software design includes design of user interfaces and digital content; hardware design includes product design, product color planning and ergonomics. Product design must meet users’ needs to be successful in the market.
Color planning in digital content and products is usually based on designers’ subjective feelings and perceptions but lacks objective assessment methods. This report discusses issues related to color planning in digital-content design and product design. An objective and quantitative evaluation system that can assist designers and users in conducting product color planning is proposed. The computer-aided color planning system (CACPS) developed in this research may assist users in planning text and background colors in digital content that obtain good legibility. In addition, using a mass customization approach allows users to confirm that the product color planning meets their personal preferences. Transaction data from mass customization can be used in a data-mining approach to explore useful design information and to serve as a reference for designers during design undertakings.
This study applies fuzzy theory, a back-propagation neural network, the Taguchi method and data-mining approaches in the development of the CACPS. The evaluation of a two-colored mobile phone is provided to illustrate the effectiveness of the proposed method. The CACPS is designed as a web-based interface that greatly enhances efficiency and applicability and provides a useful reference for designers during product design. The information from the CACPS is also useful for marketing staff.
Abstract (Chinese) I
Abstract (English) II
List of Tables IX
List of Figures X
Chapter 1 Introduction 1
1.1 Research background 1
1.1.1 Legibility and readability for digital content design 2
1.1.2 Design for mass customization 5
1.2 Problem statements 6
1.3 Organization of this dissertation 8
Chapter 2 Literature Review 9
2.1 Color model and color space conversion 9
2.2.1 The RGB color model 9
2.2.2 The HSV color model 10
2.2.3 The conversion between HSV and RGB color models 11
2.2 Fuzzy theory for color quantization 13
2.3 Artificial neural network 15
2.4 The Taguchi method 16
2.4.1 Characteristics of quality engineering 16
2.4.2 Characteristics of Taguchi quality engineering 16
2.4.3 Signal-to-noise ratio 17
2.4.4 Parameter design 18
2.4.5 Application of the orthogonal array in experiment design 19
2.5 Principles and techniques of data mining 20
2.5.1 Introduction to data mining 21
2.5.2 Correlation analysis measures the relationship between items 21
2.5.3 Decision-tree learning 23
2.5.3 Bayesian classifier 26
Chapter 3 Research Orientation 30
3.1 Research objectives 30
3.2 Research approach 31
Chapter 4 Legibility for Digital Content Color Planning 34
4.1 The development of legibility for digital-content color planning 34
4.1.1 Legibility issues for digital content 35
4.1.2 The study of legibility in color planning 36
4.1.3 The research framework for legibility in digital-content color planning 37
4.2 Research approach and steps in legible digital content color planning 37
4.3 Color gamut experiment 39
4.4 Legibility experiment 46
4.5 BPNN construction and training 48
4.6 Results and discussions of this study 52
4.7 Practical applications of legibility for digital content color planning 54
Chapter 5 Using Taguchi Method in Mass Customized Product Color Planning Service 56
5.1 The development of a color planning system for customized product design 56
5.1.1 Color planning in product design 57
5.1.2 Color planning for mass customization product design 58
5.1.3 The research framework of MCPCPSP 58
5.2 Research methods and procedures of MCPCPSP 59
5.2.1 Three fundamental characteristics of color 61
5.2.2 Types of color planning 63
5.3 Experiment of product color planning 66
5.3.1 The parameters and levels of color planning 66
5.3.2 TOA parameter design 68
5.4 Construction of the MCPCPSP 70
5.4.1 Selection of the primary color in MCPCPSP 72
5.4.2 The color planning experiment in MCPCPSP 73
5.4.3 Selection of the secondary color in MCPCPSP 75
5.5 Online consumer involvement in product color planning 76
Chapter 6 Using a Data Mining Approach in the Product Color Planning Information System 79
6.1 Mining retail e-commerce data 79
6.1.1 Consumer behavior on the Internet 80
6.1.2 Data mining for design information 80
6.2 The development of data mining for product color planning 81
6.2.1 Research steps of the developed data mining approach 82
6.3 The establishment of Web database system 84
6.3.1 Data acquisition for the Web database system 84
6.3.2 Data pre-processing and data conversion 90
6.3.3 Web database design for product color planning information system 93
6.4 Establishment of the data mining system 95
6.4.1 Apply the decision tree classification to confirming key parameters 95
6.4.2 Modular programming of Bayesian classifier 99
6.4.3 Operation of the Bayesian classifier in the data mining system 100
6.4.4 The integration of the decision tree classification and Bayesian classifier 104
6.4.5 Explanation and evaluation of analytical outcome 107
6.5 Data mining for design information acquisition 107
Chapter 7 Summary and Conclusions 109
7.1 Summary 109
7.2 Contributions 110
7.3 Recommendations for the future work 113
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