臺灣博碩士論文加值系統

本論文主要是結合支向機(Support Vector Machines)技術於人體脈搏波辨識系統之應用。採用非侵入性檢測(non-invasive measurement)的方法來取得人體脈搏波形，並轉換成數值向量型式表示，應用支向機(Support Vector Machines)技術，期望找出能夠代表人體脈搏的特徵，進而將脈搏波形作精準的分類。然而，在數值化後的脈搏波形會有許多的特徵值是多餘的(redundant)，當去掉多餘的特徵後，便能夠對這一些脈搏波形作精準的分類。它表示所選取的脈搏波形的特徵，對於代表一個人的脈搏波形特性具有高度的貢獻性。在本研究中，經由反覆不斷的試驗，發現在使用不同的特徵情況之下，人體的脈搏波特徵都能夠精準的被分類，所以人體的脈搏波形應有顯著的不同，如何選取脈搏波形中具有高貢獻的特徵將是本論文的重點。若找出能夠代表脈搏波形的適當的特徵，則足以代表個人脈搏波形的特性，進而達到非侵入性檢測的目的。

In this paper, we developed a finger pulse recognition system based on Support Vector Machines. The finger pulse recognition system record the finger pulse that is using non-invasive measurement method and transfer the finger pulse to a numerical vector. Hope to find the feature that could represent the human finger pulse and classify the numerical finger pulse vector. However, the numerical finger pulse vector has a lot of redundant attributes. If we could reduce the attributes, we can accurately classify the numerical finger pulse vector. The selected finger pulse attributes which can be represented a woman’s finger pulse are more important. In this research work, after over and over experimentations, we found the woman’s finger pulses attributes can be accurately classified in different situations, since the woman’s finger pulses should be different. How to select the finger pulse attributes will be the point of this thesis. If we could find the suitable finger pulse attributes which can represent a woman features, we can achieve the non-invasive measurement purposes.

摘 要 I
Abstract II
誌 謝 III
目 錄 IV
圖表索引 IX
第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 2
1.3 論文架構與符號說明 2
第二章 線性支向機(LSVM) 4
2.1 資料分離的情形 4
2.2 測試階段 9
2.3資料不分離的情形 9
2.4 平滑支向機(Smooth Support Vector Machines, SSVM) 15
第三章 系統架構 19
3.1 系統架構流程簡述 19
3.2 特徵萃取 21
3.3 特徵選取 23
3.4 系統分析步驟簡述 24
第四章 資料集與評定標準 26
4.1 資料集 26
4.2 評定標準 28
4.3 Cross-Validation 31
第五章 實驗結果 33
第六章 結論與未來展望 44
參考文獻 47
作者簡介 49

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