進階搜尋


   電子論文尚未授權公開,紙本請查館藏目錄
(※如查詢不到或館藏狀況顯示「閉架不公開」,表示該本論文不在書庫,無法取用。)
系統識別號 U0026-2301201605564600
論文名稱(中文) 應用約略集於產品設計可行性分析
論文名稱(英文) Application of rough set in the product design feasibility analysis
校院名稱 成功大學
系所名稱(中) 工業設計學系
系所名稱(英) Department of Industrial Design
學年度 104
學期 1
出版年 104
研究生(中文) 黃志龍
研究生(英文) Chih-Lung Huang
學號 P38941128
學位類別 博士
語文別 英文
論文頁數 110頁
口試委員 指導教授-謝孟達
口試委員-林振陽
口試委員-蕭世文
口試委員-劉說芳
口試委員-徐芳真
中文關鍵字 感性工學系統  約略集  產品外觀特徵  情感回饋  關鍵因子擷取  預測模型 
英文關鍵字 Kansei engineering system  rough sets  product exterior features  affective responses  crucial factor acquisition  prediction model 
學科別分類
中文摘要 近二十年來,感性工學系統已經成功應用各種的數學假設模型,解決了消費者導向式產品開發問題。然而,近期全球消費市場急劇成長與暗潮洶湧的競爭,導致開發面在擷取關鍵設計因子的需求倍增。因此,如何幫助設計師快速獲取消費者情感偏好的關鍵內容,並提供設計師更完善的產品特徵之屬性配對,已變成全球設計研究所矚目的焦點之一。約略集為一種應用法則基準擷取關鍵因子的工具與探求屬性間相互關係的方法,約略集可針對人類的感知行為不精確與非線性,提供柔性偏好法則作為評價的基準。然而,在感性工學系統研究當中,在人類感知相關的產品造形設計中,使用約略集合的方式來處理此類問題的研究為數甚少,且約略集融合感性工學系統來探求設計關鍵因子的研究亦是相當罕見。因此,本研究透過論述感性工學與約略集兩者相關的重要架構與概念,並且系統性的剖析感性工學已經成功的數學假設模型與設計相關案例,逐步比對約略集用於感性工學系統的評價方式,藉以探討以約略集方式用於感性工學的模型基礎在產品設計的可行性。本研究最後以實例帶入模型演練方式,評估此方法的模型精準度等各項指標,並以交叉驗證的方式評估所建構的約略集模型在產品設計的可行性。案例一以服裝的外觀為研究,探討產品外觀特徵與情感之間的關係法則,應用約略集方法特性找出關鍵的服裝特徵所對應的情感回饋。案例二則以牙刷的外型與顏色的複合特徵為研究,探討複合特徵中應用約略集所產生的關鍵法則,是否可為產品設計所用,並找出牙刷外型、色彩與人類感知三個維度之間的相互關係,並應用混淆矩陣、交叉驗證、接收者操作特徵分析與曲線下面積等評價與驗證方法來檢視本次實驗的模型好壞與可行性。最後,兩案例的模型皆有良好預測準確能力,在產品設計中可提供讓設計師可讀性高且易懂的設計參照法則。
英文摘要 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.
論文目次 ABSTRACT I
ACKNOWLEDGEMENT IV
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

REFERENCES 95
APPENDIX 105
LIST OF PUBLICATIONS 110
參考文獻 Akcay, Okan, Paul Sable and M. Halim Dalgin (2012), "The importance of color in product choice among young Hispanic, Caucasian, and African-American groups in the USA," International Journal of Business and Social Science, 3: 1-6.
Bae, Changseok, Wei-Chang Yeh, Yuk Ying Chung and Sin-Long Liu (2010), "Feature selection with Intelligent Dynamic Swarm and Rough Set," Expert Systems with Applications, 37: 7026-7032.
Bazan, J. G. (1998), A comparison of dynamic and non-dynamic rough set methods for extracting laws from decision tables, Rough Sets in Knowledge Discovery 2: Applications, Case Studies and Software Systems.
Bazan, J. G., A. Skowron and P. Synak (1994), Dynamic reducts as a tool for extracting laws from decision tables Methodologies for Intelligent Systems, Proceeding of the Eighth International Symposium (ISMIS'94).
Bloch, Peter H. (1995), "Seeking the ideal form: Product design and consumer response," Journal of Marketing, 59: 16-29.
Bose, Indranil (2006), "Deciding the financial health of dot-coms using rough sets," Information & Management, 43: 835-846.
Chang, Hua-Cheng, Hsin-Hsi Lai and Yu-Ming Chang (2006), "Expression modes used by consumers in conveying desire for product form: A case study of a car " International Journal of Industrial Ergonomics, 36: 3-10.
Chang, Pei-Ti, Kun-Feng Tsai and Xing- Wei Zhao (2014), Using rough set theory to investigate the tourist preference for hot spring hotels, 12th international academic conference prague.
Chen, Hui-Ling, Bo Yang, Jie Liu and Da-You Liu (2011), "A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis," Expert Systems with Applications, 38: 9014-9022.
Chen, Kuohsiang (1997), "Form language and style description," Design studies, 18: 249-274.
Choo, Sunhyung and Youngin Kim (2003), "Effect of color on fashion fabric image," Color Research and Application, 28(3): 221-226.
Chouchoulas, Alexios and Qiang Shen (2001), "Rough Set-Aided Keyword Reduction for Text Categorization," Applied Artificial Intelligence, 15: 843-873.
Coates, Del (2003). Watches Tell More than Time. New York, Mc.Graw Hill.
Craven, Mark W. and Jude W. Shavlik (1997), "Using neural networks for data mining," Future Generation Computer Systems, 13: 211-229.
Demirbilek, Oya and Bahar Sener (2003), "Product design, semantics and emotional response," Ergonomics, 46: 1346-1360.
Geisser, Seymour (1993). Predictive Inference. New York, CHAPMAN & HALL.
Goh, Carey and Rob Law (2003), "Incorporating the rough sets theory into travel demand analysis," Tourism Management, 24: 511-517.
Grzymala-Busse, Jerzy W. (1992), "LERS-A System for Learning from Examples Based on Rough Sets," Intelligent Decision Support, 11: 3-18.
Han, Sung H. and Jongseo Kim (2003), "A comparison of screening methods: Selecting important design variables for modeling product usability," International Journal of Industrial Ergonomics, 32: 189-198.
Han, Sung H. and Huichul Yang (2004), "Screening important design variables for building a usability model: genetic algorithm-based partial least-squares approach," International Journal of Industrial Ergonomics, 33: 159-171.
Han, Sung H., Myung Hwan Yun, Kwang Jae Kim and Jiyoung Kwahk (2000), "Evaluation of product usability: development and validation of usability dimensions and design elements based on empirical models," International Journal of Industrial Ergonomics 26: 477-488.
Hotta, Hajime and Masafumi Hagiwara (2006), A fuzzy rule based personal Kansei modeling system, IEEE international conference on fuzzy systems, Vancouver, BC, Canada.
Hsiao, Kun-An and Lin-Lin Chen (2006), "Fundamental dimensions of affective responses to product shapes," International Journal of Industrial Ergonomics, 36: 553-564.
Hsiao, Shih Wen and H.C Huang (2002), "A neural network based approach for product form design," Design Studies, 23: 67-84.
Hsiao, Shih Wen and Hung Cheng Tsai (2005), "Applying a hybrid approach based on fuzzy neural network and genetic algorithm to product form design," International Journal of Industrial Ergonomics, 35: 411-428.
Hsiao, Shih-Wen and Ching-Hai Chen (1997), "A semantic and shape grammar based approach for product design," Design studies, 18: 275-296.
Hsu, Shang H, Ming C Chuang and Chen C Chang (2000), "A semantic differential study of designers' and users' product form perception," International Journal of Industrial Ergonomics, 25: 375-391.
Huang, Chun-Che, Wen-Yau Liang, Tzu-Liang Tseng and Ruo-Yin Wong (2015), "A rough set-based corporate memory for the case of ecotourism," Tourism Management, 47: 22-33.
Inuiguchi, Masahiro and Takuya Miyajima (2007), "Rough set based rule induction from two decision tables," European Journal of Operational Research 181: 1540-1553.
Ireneusz, Moraczewski, Barbara Sudnik-Wójcikowska and Wojciech Borkowski (1996), "Rough sets in floristic description of inner-city of Warsaw," FLORA: 253-260.
Jindo, Tomio, Kiyomi Hirasago and Mitsuo Nagamachi (1995), "Development of a design support system for office chairs using 3-D graphics " International Journal of Industrial Ergonomics, 15: 49-62.
Jones, John Chris (1992). Design Methods. New York, Van Nostrand Reinhold.
Kawakita, J. (1986). KJ Method. Tokyo, Chuokoron-Sha.
Kaya, Yılmaz and Murat Uyar (2013), "A hybrid decision support system based on rough set and extreme learning machine for diagnosis of hepatitis disease," Applied Soft Computing, 13: 3429-3438.
Kim, Hee Su and Sung Bae Cho (2000), "Application of interactive genetic algorithm to fashion design," Engineering Applications of Artificial Intelligence 13: 635-344.
Komorowski, Jan and Aleksander Øhrn (1999), "Modelling prognostic power of cardiac tests using rough sets," Artificial Intelligence in Medicine, 15: 167-191.
Kryszkiewicz, Marzena and Henryk Rybinski (1993), Finding Reducts in Composed Information Systems, Fuzzy Sets and Knowledge Discovery.
Kubat, Miroslav, Robert C. Holte and Stan Matwin (1998), "Machine Learning for the Detection of Oil Spills in Satellite Radar Images," Machine Learning, 30: 195-215.
Kwahk, Jiyoung and Sung H Han (2002), "A methodology for evaluating the usability of audiovisual consumer electronic products," Applied Ergonomics, 33: 419-431.
Mak, Brenda and Toshinori Munakata (2002), "Rule extraction from expert heuristics: A comparative study of rough sets with neural networks and ID3," European Journal of Operational Research, 136: 212-229.
McKelvey, Kathryn (1996). Fashion Source Book, Wiley-Blackwell,Oxford.
Mienko, Robert, Daniel Vanderpooten, Khaled Toumi and Jerzy Stefanowski (1996). Discovery-oriented induction of decision rules. Paris, Universite de Paris Dauphine.
Nagamachi, Mitsuo (1995), "Kansei Engineering: A new ergonomic consumer-oriented technology for product development," International Journal of Industrial Ergonomics 15: 3-11.
Nayatani, Yoshinobu and Hitoshi Komatsubara (2005), "Relationships among chromatic tone, perceived lightness, and degree of vividness," Color Research and Application, 30(3): 221-234.
Øhrn, Aleksander (1999). Discernibility and Rough Sets in Medicine: Tools and Applications. Department of Computer and Information Science. Norway, Norwegian University of Science and Technology. Ph.D.
Park, Jungchul and Sung H Han (2004), "A fuzzy rule-based approach to modeling affective user satisfaction towards office chair design," International Journal of Industrial Ergonomics 34: 31-47.
Pawar, Kulwant S and Helen Driva (1999), "Performance measurement for product design and development in a manufacturing environment," International Journal of Production Economics, 60-61: 61-68.
Pawlak, Zdzislaw (1991). Rough Sets: Theoretical Aspects of Reasoning about Data. Dordrecht, The Netherlands, Kluwer Academic Publisher.
Pawlak, Zdzislaw (2002), "Rough sets and intelligent data analysis," Information Sciences, 147: 1-12.
Pawlak, Zdzisław and Andrzej Skowron (2007), "Rudiments of rough sets," Information Sciences 177: 3-27.
Provost, Foster and Tom Fawcett (1997), Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions, Proceedings of KDD-1997.
Provost, Foster, Tom Fawcett and Ron Kohavi (1998), The Case Against Accuracy Estimation for Comparing Induction Algorithms, Proceedings of the Fifteenth International Conference on Machine Learning.
Provost, Foster and Ron Kohavi (1998), "On Applied Research in Machine Learning," Machine Learning, 30: 127-132.
Schütte, Simon and Jörgen Eklund (2005), "Design of rocker switches for work-vehicles—an application of Kansei Engineering," Applied Ergonomics, 36(5): 557-567.
Shi, Fuqian, Shouqian Sun and Jiang Xu (2012), "Employing rough sets and association rule mining in KANSEI knowledge extraction," Information Sciences, 196: 118-128.
Shieh, Meng Dar and Fang Chen Hsu (2013), "Using FCM Clustering for Consumer Segmentation in Kansei Engineering System," International Journal of Digital Content Technology and its Applications, 7: 563-571.
Shieh, Meng Dar, Jhung Hsin Wang and Chih Chieh Yang (2011), "A clustering approach to affective dimension responses selection for product design," Journal of Convergence Information Technology, 6(2): 197-206.
Shieh, Meng Dar and Chih Chieh Yang (2008a), "Classification model for product form design using fuzzy support vector machines," Computers & Industrial Engineering, 55(1): 150-164.
Shieh, Meng Dar and Chih Chieh Yang (2008b), "Multiclass SVM-RFE for product form feature selection," Expert Systems with Applications, 35(1-2): 531-541.
Shimizu, Youji and Tomio Jindo (1995), "A fuzzy logic analysis method for evaluating human sensitivities," International Journal of Industrial Ergonomics, 15: 39-47.
Shyng, Jhieh-Yu, Fang-Kuo Wang, Gwo-Hshiung Tzeng and Kun-Shan Wu (2007), "Rough Set Theory in analyzing the attributes of combination values for the insurance market," Expert Systems with Applications, 32: 56-64.
Singh, Satyendra (2006), "Impact of color on marketing," Management Decision, 44(6): 783-789.
Skowron, Andrzej (1993), "Boolean reasoning for decision rules generation," Methodologies for Intelligent Systems, Lecture Notes in Computer Science 295-305.
Skowron, Andrzej and Cecylia Rauszer (1992), "The discernibility matrices and functions in information systems," Intelligent Decision Support, 11: 331-362.
Stefanowski, Jerzy and Daniel Vanderpooten (1994), "A General Two-Stage Approach to Inducing Rules from Examples," Rough Sets, Fuzzy Sets and Knowledge Discovery: 317-325.
Susmaga, Robert (1998), Parallel Computation of Reducts, Rough Sets and Current Trends in Computing.
Swets (1988), "Measuring the accuracy of diagnostic systems," Science, 240: 1285-1293.
Swiniarski, Roman W. and Andrzej Skowron (2003), "Rough set methods in feature selection and recognition," Pattern Recognition Letters, 24: 833-849.
Tan, Raymond R. (2005), "Rule-based life cycle impact assessment using modified rough set induction methodology," Environmental Modelling & Software, 20: 509-513.
Tanoue, Chitoshi, Kenji Ishizaka and Mitsuo Nagamachi (1997), "Kansei Engineering: A study on perception of vehicle interior image," International Journal of Industrial Ergonomics 19: 115-128.
Tay, Francis E.H. and Lixiang Shen (2002), "Economic and financial prediction using rough sets model," European Journal of Operational Research, 141: 641-659.
Tsai, Jeanne L., Brian Knutson and Helene H. Fung (2006), "Cultural variation in affect valuation," Journal of Personality and Social Psychology, 90: 288-307.
Tseng, Tzu-Liang and Chun-Che Huang (2007), "Rough set-based approach to feature selection in customer relationship management," Omega, 35: 365-383.
Tseng, Tzu-Liang (Bill), Yongjin Kwon and Yalcin M. Ertekin (2005), "Feature-based rule induction in machining operation using rough set theory for quality assurance," Robotics and Computer-Integrated Manufacturing, 21: 559-567.
Tsuchiya, Toshio, Tatsushi Maeda, Yukihiro Matsubara and Mitsuo Nagamachi (1996), "A fuzzy rule induction method using genetic algorithm," International Journal of Industrial Ergonomics 18: 135-145.
Tsumoto, Shusaku (2004), "Mining diagnostic rules from clinical databases using rough sets and medical diagnostic model," Information Sciences, 162: 65-80.
Vinterbo, Staal and Aleksander Øhrn (2000), "Minimal approximate hitting sets and rule templates," International Journal of Approximate Reasoning, 25: 123-143.
Wakulicz-Deja and Paszek (1997), "Diagnose progressive encephalopathy applying the rough set theory," International Journal of Medical Informatics, 46: 119-127.
Walczak, B. and D.L. Massart (1999), "Rough sets theory," Chemometrics and Intelligent Laboratory Systems, 47: 1-16.
Wallace, David R. (1991). A computer model of aesthetic industrial design. Department of Mechanical Engineering, Massachusetts Institute of Technology.
Wang, Jue, Abdel-Rahman Hedar, Shouyang Wang and Jian Ma (2012), "Rough set andscatter search metaheuristic based feature selection for credit scoring," Expert Systems with Applications, 39: 6123-6128.
Wang, Xiangyang, Jie Yang, Richard Jensen and Xiaojun Liu (2006), "Rough set feature selection and rule induction for prediction of malignancy degree in brain glioma," computer methods and programs in biomedicine, 83: 147-156.
Wang, Xiangyang, Jie Yang, Xiaolong Teng, Weijun Xia and Richard Jensen (2007), "Feature selection based on rough sets and particle swarm optimization," Pattern Recognition Letters, 28: 459-471.
Wilk, Sz., Słowinski, W. Michałowsk and S. Greco (2005), "Supporting triage of children with abdominal pain in the emergency room," European Journal of Operational Research, 160: 696-709.
Witlox, Frank and Hans Tindemans (2004), "The application of rough sets analysis in activity-based modelling," Expert Systems with Applications, 27: 585-592.
Wroblewski, J. (1995), Finding minimal reducts using genetic algorithm, Proceedings of the International Workshop on Rough Sets Soft Computing at Second Annual Joint Conference on Information Sciences (JCIS'95)
Wróblewski, Jakub (1998), Covering with reducts - a fast algorithm for rule generation, Rough Sets and Current Trends in Computing.
Yamaguchi, D., G.D. Li, T. Akabane, K. Mizutani, M. Nagai and M. Kitaoka (2006), Grey-rough set approach for commodity evaluation, Proceedings of 8th Annual Conference on Kansei Engineering, Tokyo.
Yang, Bo Suk, Dong Soo Lim and Andy Chit Chiow Tan (2005), "VIBEX: an expert system for vibration fault diagnosis of rotating machinery using decision tree and decision table," Expert Systems with Applications 28: 735-742.
Yang, Chih Chieh (2011), "Constructing a hybrid Kansei engineering system based on multiple affective responses: Application to product form design," Computers & Industrial Engineering 60(4): 760-768.
Yang, Chih Chieh, Meng Dar Shieh, Kuang Hsiung Chen and Pei Ju Lin (2011), "Product form feature selection for mobile phone design using LS-SVR and ARD," Journal of Convergence Information Technology, 6(2): 138-150.
Yang, Chih Chieh, Chih Cheng Sun, Pei Ju Lin and Chi Chen Tsai (2011), "A general framework for Kansei engineering system," International Journal of Digital Content Technology and its Applications, 5: 173-180.
Yang, Hsu-Hao, Tzu-Chiang Liu and Yen-Ting Lin (2007), "Applying rough sets to prevent customer complaints for IC packaging foundry," Expert Systems with Applications, 32: 151-156.
Yang, Sun Mo, Mitsuo Nagamachi and Soon Yo Lee (1999), "Rule-based inference model for the Kansei Engineering System," International Journal of Industrial Ergonomics 24: 459-471.
Yeh, Ching-Chiang, Der-Jang Chi and Ming-Fu Hsu (2010), "A hybrid approach of DEA, rough set and support vector machines for business failure prediction," Expert Systems with Applications, 37: 1535-1541.
Yun, Myung Hwan, Sung H Han, Sang W Hong and Jongseo Kim (2003), "Incorporating user satisfaction into the look-and-feel of mobile phone design," 46: 1423-1440.
Zhai, Lian Yin, LI Pheng Khoo and Zhao Wei Zhong (2009), "A dominance-based rough set approach to Kansei Engineering in product development," Expert Systems with Applications, 36: 393-402.
Ziarko, Wojciech, Robert Golan and Donald Edwards (1993), An application of dialogic knowledge discovery tool to identify strong predictive rules in stock market data, In Proc. AAAI Workshop on Knowledge Discovery in Databases.
Zou, Zhonghai, Tzu-Liang Tseng, Hansuk Sohn, Guofang Song and Rafael Gutierrez (2011), "A rough set based approach to distributor selection in supply chain management," Expert Systems with Applications, 38: 106-115.
論文全文使用權限
  • 同意授權校內瀏覽/列印電子全文服務,於2021-01-29起公開。


  • 如您有疑問,請聯絡圖書館
    聯絡電話:(06)2757575#65773
    聯絡E-mail:etds@email.ncku.edu.tw