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研究生:李金展
研究生(外文):Li, Chin-Chan
論文名稱:整合ICA和SVM以識別在不同製程干擾分配下之同時發生型樣
論文名稱(外文):Recognition Of The Concurrent Control Chart Patterns On Different Process Noises Using Integrated ICA and SVM Schemes
指導教授:邵曰仁邵曰仁引用關係呂奇傑呂奇傑引用關係
指導教授(外文):Shao, Yueh-JenLu, Chi-Jie
口試委員:林豐澤侯家鼎
口試委員(外文):Lin, Feng-TseHou, Chia-Ding
口試日期:2012-07-19
學位類別:碩士
校院名稱:輔仁大學
系所名稱:統計資訊學系應用統計碩士班
學門:數學及統計學門
學類:統計學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:68
中文關鍵詞:同時發生管制圖型樣獨立成份分析支援向量機型樣辨識製程干擾
外文關鍵詞:Concurrent Control chart patternIndependent component analysisSupport vector machinePattern recognitionProcess noises
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在製程業中,管制圖型樣的識別已成為不可缺少的監控技術之一。透過識別不尋常的管制圖型樣,可探討出存在於失控製程中的可歸屬原因,並補強管制圖不易偵測到的不尋常型樣,藉此,不但可以改善製程亦可降低產品的不良率。目前大多數研究皆假設:監控的製程觀察值為單一的基本型樣並且發生於製程干擾分配為常態(Normal)的製程中;但在實務上,不同管制圖型樣可能同時發生在一個製程品質特性上(稱為同時發生管制圖型樣),且製程干擾分配可能不服從常態分配,而是其他在製程上出現的伽瑪(Gamma)及均勻(Uniform)分配。為了識別在不同製程干擾分配下的同時發生管制圖型樣,本研究藉由整合獨立成份分析(Independent component analysis, ICA)與支援向量機(Support vector machine, SVM),建構一個有效的管制圖型樣識別模式。本研究將先應用ICA將待識別的型樣資料拆解成數個獨立成分(Independent component, IC),並且從多個IC中找出能夠代表同時發生管制圖型樣的IC,接著將此IC作為SVM識別模型之輸入變數來進行型樣識別。實驗結果顯示,本研究所提出的ICA-SVM模式,可以有效的在不同製程干擾分配之下識別同時發生管制圖型樣。
For industrial processes, the recognition of the control chart patterns (CCPs) have become one of the indispensable monitoring technologies. Most studies assumed that the monitoring process observed value is the single types of unusual patterns. However, in practice, the observed process may be concurrent patterns where two patterns may exist together and happened to different process noise distribution, such as Normal, Gamma, and Uniform. In order to recognize the concurrent CCPs, this study integrates the ICA and SVM to construct an effective model for recognizing concurrent CCPs. In the proposed model, the ICA is applied to the concurrent patterns for generating independent components (ICs). Then the ICs used to represent the concurrent patterns are identified. The ICs are served as the input variables of the SVM model to recognize the concurrent CCPs. Simulations results showed that the proposed ICA-SVM is able to effectively recognize concurrent CCPs with different process noises.
目 錄

第壹章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 3
第三節 研究架構 3
第四節 研究流程 4
第貳章 文獻探討 5
第一節 管制圖型樣識別 5
第二節 獨立成份分析 10
第三節 支援向量機 13
第四節 本研究與其他文獻之差異 15
第參章 研究方法 16
第一節 獨立成份分析 16
(一) 獨立成份分析之基本假設與限制 16
(二) 獨立成份分析之概念 20
第二節 支援向量機 22
(一) 一對多(one-against-rest)分類形式: 27
(二) 一對一(one-against-one)分類形式: 27
(三) 分層(hierarchical)或樹狀(tree)分類形式: 28


第肆章 實證分析 29
第一節 ICA-SVM識別模型 29
數據模擬及建模流程 ...... 29
第二節 實例說明 37
壹. 找出目標獨立成份說明 37
貳. 單純SVM及ICA-SVM識別結果比較 43
一.在Normal(0,1)製程干擾分配情形下 …………………………….43
二.在不同參數Gamma製程干擾分配情形下 ……………………44
三.在Uniform製程干擾分配情形下 …………………………………48
四.不同製程干擾下識別同時發生管制圖型樣重複十次實驗
結果 ……………………………………………………..49
參. 整體正確率比較 52
肆. 其他文獻在同時發生型樣之識別結果 53
第伍章 結論與建議 54
第一節 研究發現 55
第二節 未來研究方向與建議 55
參考文獻 57

表 目 錄
表4-1-1、七種型樣的計算公式 29
表4-1-2、六種同時發生管制圖型樣的計算公式 30
表4-2-1、圖4-2-1的相關係數(Normal) 39
表4-2-2、圖4-2-2的相關係數(Gamma) 40
表4-2-3、圖4-2-3的相關係數(Uniform) 42
表4-2-4、單純SVM模型之識別正確率矩陣列表(Normal(0,1)) 43
表4-2-5、ICA-SVM模型之識別正確率矩陣列表(Normal(0,1)) 44
表4-2-6、單純SVM模型之識別正確率矩陣列表(Gamma(1,1)) 45
表4-2-7、單純SVM模型之識別正確率矩陣列表(Gamma(2,1)) 45
表4-2-8、單純SVM模型之識別正確率矩陣列表(Gamma(3,1)) 46
表4-2-9、ICA-SVM模式之識別正確率矩陣列表(Gamma(1,1)) 46
表4-2-10、ICA-SVM模式之識別正確率矩陣列表(Gamma(2,1)) 47
表4-2-11、ICA-SVM模式之識別正確率矩陣列表(Gamma(3,1)) 47
表4-2-12、單純SVM模式之識別正確率矩陣列表(Uniform(-4,4)) 48
表4-2-13、ICA-SVM模式之分類正確率矩陣列表(Uniform(-4,4)) 49
表4-2-14、在干擾為Normal(0,1)情形下十次識別正確率與標準差 50
表4-2-15、在干擾為Gamma(1,1)情形下十次識別正確率與標準差 50
表4-2-16、在干擾為Gamma(2,1)情形下十次識別正確率與標準差 51
表4-2-17、在干擾為Gamma(3,1)情形下十次識別正確率與標準差 51
表4-2-18、在干擾為Uniform(-4,4)情形下十次識別正確率與標準差 .52
表4-2-19、整體正確率對照表 53
表4-2-20、同時發生型樣整體正確率(Yang et al. (2010)) 53

圖 目錄
圖1-1-1、研究流程圖 4
圖2-1-1、七種常見的管制圖型樣 6
圖2-1-2、同時發生管制圖型樣(a) US+UT;(b) US+DT ;(c) US+Cyc;(d) US+Sys;(e) DS+UT;(f) DS+DT;(g) DS+Cyc;(h) DS+Sys;(i) UT+Cyc;(j) UT+Sys;(k) DT+Cyc (l) DT+Sys;(m) Sys+Cyc 8
圖3-1-1、簡單的雞尾酒問題模擬 17
圖3-1-2、獨立成份分析範例 18
圖3-1-3、獨立成份分析拆解 19
圖3-2-1、支援向量機概念示意圖 23
圖3-2-2、映射示意圖 26
圖3-2-3、SVM多元分類形式 27
圖4-1-1、訓練階段流程圖 31
圖4-1-2、移動識別窗口沿著一個序列的製程觀測值產生的解說圖形(Guh & Shiue, 2005) 31
圖4-1-3、六種同時發生管制圖型樣訓練資料例子 33
圖4-1-4、測試階段流程圖 35
圖4-2-1、同時發生管制圖型樣ICA分解範例一: (a1)製程干擾為Normal之下之同時發生型樣; (a2)虛擬變數Normal; (a3) 虛擬變數Uniform; (a4)虛擬變數Gamma 38
圖4-2-2、同時發生管制圖型樣ICA分解範例二: (a1)製程干擾為Gamma之下之同時發生型樣; (a2)虛擬變數Gamma; (a3)虛擬變數Normal; (a4)虛擬變數Uniform 40
圖4-2-3、同時發生管制圖型樣ICA分解範例三: (a1)製程干擾Uniform之下之同時發生型樣; (a2)虛擬變數Uniform; (a3)虛擬變數Normal; (a4)虛擬變數Gamma 42

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吳若巗(2007)。結合管制圖與MLE法以估計均勻製程改變點之研究。天主教輔仁大學應用統計研究所碩士論文。
李柏勳(2010)。混合式及具權重變化之管制圖型樣辨識。天主教輔仁大學應用統計研究所碩士論文。
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