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系統識別號 U0026-3008201016322000
論文名稱(中文) 使用泛用型普氏分析與支援向量機建立汽車前視圖之電腦輔助設計系統
論文名稱(英文) Applying Generalized Procrustes Analysis and Support Vector Machine to Develop an Automobile Front View Computer Aided Design System
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 98
學期 2
出版年 99
研究生(中文) 周辰威
研究生(英文) Chen-Wei Chou
電子信箱 darktubu@hotmail.com
學號 p3697113
學位類別 碩士
語文別 中文
論文頁數 80頁
口試委員 指導教授-謝孟達
口試委員-蕭世文
口試委員-孫永年
口試委員-楊智傑
中文關鍵字 泛用型普式分析  支援向量回歸  集群分析 
英文關鍵字 General Procrustes Analysis  Support Vector Regression  Cluster Analysis 
學科別分類
中文摘要 本研究主要目的是要使用泛用型普氏分析(Generalized Procrustes Analysis,GPA)與支援向量機建立汽車前視圖之電腦輔助設計系統。以50台汽車前視圖輪廓為樣本,並把汽車前視圖分成九個部份,利用RIHNO繪製線稿,採取整台車前視圖輪廓的點座標,接著運用GPA來調整點資料後,再以集群分析去做分群。以往一般的樣本輪廓點資料,通常是直接利用集群分析,但分群效果不一定理想,因此本研究嘗試經過GPA把資料調整過後,再進行分群。本研究後段嘗試以支援向量回歸(Support Vector Regression),把分群結果(自變數)和感性語彙的評分(依變數)輸入SVR來進行感性模型之訓練,目的是為了比較經過GPA調整的數據和未經過GPA調整的數據何者具有較良好的分群效果。結果顯示了經過GPA調整的分群數據,具有較低的均方根誤差(Root Mean Square Error),證明了經過GPA調整的資料,可以增加分群的精準度。經過訓練的系統,可以用來當作專家預測系統,預測新車款的感性語彙評分。
英文摘要 The purpose of this study is to verify if General Procrustes Analysis (GPA) will increase the precision of the results of the sample cluster. This study uses the front view outline of each car (total 50 cars) as a sample, and we classify the sample into nine parts. We use RHINO to draw the outline and find out the coordinate of the points. After adjusting the points using GPA, we use cluster analysis to group our samples, In the past, coordinate of the points is usually classfied through cluster anaalysis directly. However, the clustering results were not always satisfied. In this study, we try to adjust the data through GPA before applying the cluster analysis. The study also tries to adopt Support Vector Regression (SVR) method. We input the clustering results and emotional vocabulary to train the emotional model. The purpose is to verify if there are any differences between the GPA adjusted data and non-adjusted ones. The results suggest that the clustered data after using GPA indeed show lower RMSE. The trained system could be adopted as an expert system to predict the emotional vocabulary scores of new cars.
論文目次 目錄

摘要 I
ABSTRACT II
目錄 III
表目錄 VI
圖目錄 VII
第一章 緒論 9
1-1 研究動機 9
1-2 研究目的 9
1-3 研究範圍與限制 10
1-4 研究架構 10
第二章 文獻探討 12
2-1 泛用型普氏分析相關應用文獻 12
2-2 SVR相關應用文獻 13
第三章 研究理論架構 14
3-1 研究理論 14
3-1-1感性工學(Kansei Engineering) 14
3-1-2集群分析 17
3-1-3泛用型普氏分析(Generalized Procrustes Analysis) 18
3-1-4支援向量回歸(Support Vector Regression) 22
3-2 泛用型普氏分析 ( GENERALIZED PROCRUSTES ANALYSIS )的應用 24
3-3支援向量機的應用 26
3-3-1 支援向量迴歸 26
3-3-2 操作流程 26
3-4 實驗流程圖 27
第四章 研究方法與步驟 29
4-1 前段測驗 29
4-1-1 收集樣本 29
4-1-2 樣本處理 29
4-1-3 輪廓描繪以及特徵點座標輸出 30
4-1-4 利用GPA調整點資料 32
4-1-5 集群分析 34
4-2 汽車相關感性語彙收集 42
4-2-1 收集感性語彙 42
4-2-2 感性語彙的篩選 43
4-3汽車造形與感性語彙對意象感覺評分實驗 44
4-4 感性語彙對意象感覺評分結果及編碼 44
4-5 五十台汽車的分群 45
4-6 將分群結果列表 47
4-7 分群結果二元化 55
4-8進行SVR訓練 55
4-8-1 準備輸入資料 56
4-8-2 使用matlab7.0 spider 進行訓練 57
4-9 訓練結果 60
4-10 訓練結果分析 61
4-11 後續發展研究 61
4-11-1預測新汽車造形感性語彙值 61
4-11-2 選定預測樣本 61
4-11-3 處理樣本資料 62
4-11-4 利用SVR來預測感性語彙 64
4-11-5 驗證新樣本感性語彙評分 66
第五章 結論與探討 68
5-1 結果與討論 68
5-2 未來研究 70
參考文獻 72
附錄一 50 台汽車樣本線稿 75
附錄二 50台車原始圖片 76
附錄三 調整前分群資料 77
附錄四 調整前分群資料二元化 78
附錄五 調整後分群資料 79
附錄六 調整後分群資料二元化 80
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