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系統識別號 U0026-2804201118315600
論文名稱(中文) 客製化產品設計之電腦輔助配色系統研究
論文名稱(英文) A Computer-aided Color Planning System for Customized Product Design
校院名稱 成功大學
系所名稱(中) 工業設計學系碩博士班
系所名稱(英) Department of Industrial Design
學年度 99
學期 2
出版年 100
研究生(中文) 陳明熙
研究生(英文) Ming-Shi Chen
電子信箱 mschen.hinet@msa.hinet.net
學號 P3893112
學位類別 博士
語文別 英文
論文頁數 122頁
口試委員 指導教授-林銘泉
指導教授-蕭世文
口試委員-陳俊賢
口試委員-馬敏元
召集委員-林振陽
口試委員-劉以琳
口試委員-黃世輝
口試委員-王中行
口試委員-孫仲山
中文關鍵字 電腦輔助配色  視認性  客製化產品設計  資料探勘 
英文關鍵字 Computer-aided color planning  Legibility  Customized product design  Data mining 
學科別分類
中文摘要 3C產品(電腦、通訊及消費性電子)是現今資訊時代重要的產業,其普及的程度儼然已成為生活的一部份。大部分的3C產品所面臨的設計議題,可以分為軟體設計及硬體設計兩個部份。軟體設計包含使用者介面設計及數位內容設計等;而硬體設計則包含產品造型設計、產品配色設計及人體工學設計等。產品設計必須滿足使用者需求,才能成為市場上的熱門商品。
數位內容配色設計以及產品配色設計,通常以設計師主觀的感覺與認知來進行,缺乏客觀量化的評估方法。基於此,本研究即針對數位內容文字媒體配色及產品配色設計議題,發展一個客觀、量化的評估系統,協助設計師及使用者進行產品配色設計。本研究所發展的電腦輔助配色系統,可協助使用者進行數位內容文字媒體前景色與背景色的配色設計,以獲得良好的視認性。並且以大量客製化的方式,讓使用者確認符合個人配色偏好的產品配色。而這些大量客製化的交易資料利用資料探勘的途徑,挖掘有用的資訊提供設計師作為進行設計工作時的參考依據。
本研究所建立的電腦輔助配色系統,應用模糊理論、倒傳遞類神經網路、田口式品質工程法及資料探勘等方法,並以手機為案例進行個案研究。本研究所發展的電腦輔助配色系統採取網頁介面來建置,大幅提升系統應用效率與適用性,提供有用的資訊讓設計師作為產品設計時的參考依據,或者提供重要資訊讓產品行銷企劃人員可以順利展開行銷企劃。
英文摘要 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.
論文目次 Abstract (Chinese) I
Abstract (English) II
Acknowledgements IV
Contents V
List of Tables IX
List of Figures X
Nomenclature XIII
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
References 114
Publications 120
VITA 122
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