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研究生: 曾品勳
Pin-Hsun Tseng
論文名稱: 結合拉曼光譜與機器學習方法加速鋰離子電池電解液之開發
Raman Spectroscopy Combined with Machine Learning Methods to Accelerate on Developing Electrolyte in Lithium-ion Battery
指導教授: 黃炳照
Bing-Joe Hwang
蘇威年
Wei-Nien Su
吳溪煌
She-Huang Wu
口試委員: 黃炳照
Bing-Joe Hwang
蘇威年
Wei-Nien Su
吳溪煌
She-Huang Wu
楊延齡
Yan-Ling Yang
學位類別: 碩士
Master
系所名稱: 工程學院 - 化學工程系
Department of Chemical Engineering
論文出版年: 2021
畢業學年度: 109
語文別: 中文
論文頁數: 118
中文關鍵詞: 鋰離子電池機器學習離子導電度拉曼光譜卷積神經網路累積式學習二維拉曼影像領域自適應
外文關鍵詞: Li-ion battery, machine learning, Raman spectroscopy, ionic conductivity, convolutional neural network, cumulative learning, 2D Raman image, domain adaptation
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  • 摘要 I ABSTRACT III 致謝 V 目錄 VII 圖目錄 IX 表目錄 X 第 1 章 簡介 1 1.1 前言 1 1.2 鋰離子電池的發展 2 1.3 鋰離子電池的反應機制與組成 3 1.4 拉曼光譜 7 1.5 機器學習 10 1.6 研究動機與目的 25 第 2 章 文獻回顧 27 2.1 機器學習應用於離子導電度預測 27 2.1.1 物質性質作為特徵資料 27 2.1.2 分子結構作為特徵資料 32 2.2 拉曼光譜作為機器學習資料特徵 34 第 3 章 研究方法 40 3.1 實驗藥品 40 3.2 實驗儀器 41 3.3 目標資料庫設計 41 3.4 離子導電度量測方法 42 3.5 拉曼光譜量測方法 46 3.6 機器學習方法 48 3.6.1 資料分析 49 3.6.2 模型評價函數 49 3.6.3 傳統機器學習方法 50 3.6.4 深度學習方法 51 3.6.5 小資料集改善方法 55 3.7 二維拉曼影像 59 第 4 章 結果與討論 61 4.1 資料分析 61 4.1.1 皮爾森相關係數分析 63 4.1.2 PCA與t-SNE的降維結果分析 64 4.2 經典機器學習方法模型表現 66 4.2.1 使用全譜資料訓練 66 4.2.2 使用PCA轉換後的資料訓練 67 4.3 神經網路架構與光譜前處理比較 71 4.4 資料增強影響 73 4.5 知識遷移方法效果 76 4.5.1 遷移式學習(TL)效果 76 4.5.2 累積式學習(CL)效果 77 4.6 二維拉曼影像討論 81 4.7 模型泛化性測試 87 第 5 章 研究結論 91 第 6 章 未來展望 93 第 7 章 參考文獻 95

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