基於磁感測陣列的磁力異常探勘系統

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姓名

游騰淵(Teng-Yuan Yu) 查詢紙本館藏

畢業系所

資訊工程學系在職專班

論文名稱

基於磁感測陣列的磁力異常探勘系統
(Magnetic Abnormal Prospecting System Based On Magnetic Sensor Array)

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至系統瀏覽論文 (2029-7-20以後開放)

摘要(中)

現今的磁力感測技術廣泛應用於許多不同的領域，特別是在磁力異常檢測。本研究利用低成本、體積小的微機電系統（MEMS）磁力感測器，提出一種垂直堆疊的磁感測陣列架構，結合特殊的特徵提取，及機率神經網路（PNN），來強化磁力異常檢測的效能，並透過MIAT方法論來進行系統架構設計，採用軟體高階合成的方式整合至微控制器單元（MCU）。本實驗蒐集一組無磁性物質的資料進行PNN訓練，並使用兩組相同數量的有無磁性物質各一半的資料進行測試，實驗顯示，在預測第一組測試資料的最佳超參數及閥值，驗證在第二組測試資料依然能達到很好的精確性，證明設定超參數及閥值，在本系統中的有效性。在磁力異常探勘實驗中，從原始資料的蒐集，到PNN測試完成的磁力異常值，均採用三維分布圖與真實資料的方式來呈現，對單一感測器與磁感測陣列進行了詳細的比較。結果確認了磁感測陣列相較於單一感測器不僅提供了更高的分辨率，也具備更高的可靠性和精確度。

摘要(英)

Magnetic sensing technology is extensively applied across various fields, especially in detecting magnetic anomalies. This study leverages low-cost, compact microelectromechanical systems (MEMS) magnetic sensors to propose a vertically stacked magnetic sensor array architecture. This setup combines unique feature extraction and a Probabilistic Neural Network (PNN) to enhance the detection capabilities of magnetic anomalies. The system design is guided by the MIAT methodology, incorporating software high-level synthesis into the microcontroller unit (MCU). In our experiments, data without magnetic materials were collected for training the PNN, and two sets of data, each consisting of equal parts magnetic and non-magnetic materials, were used for testing. The experiments demonstrate that the optimal hyperparameters and threshold settings determined from the first test dataset maintained high accuracy in the second test dataset, validating the effectiveness of these settings in our system. The exploration of magnetic anomalies, from the collection of raw data to the completion of PNN tests on magnetic anomaly values, is visualized using three-dimensional distribution plots and real data, providing a detailed comparison between individual sensors and the magnetic sensor array. The results confirm that the magnetic sensor array not only offers higher resolution but also ensures greater reliability and precision compared to individual sensors.

關鍵字(中)

★ 磁力
★ 探勘
★ 感測陣列
★ 統計動量
★ 機率神經網路

關鍵字(英)

★ Magnetic
★ Prospecting
★ Sensor Array
★ Momentum
★ PNN

論文目次

摘要 I
Abstract II
誌謝 III
目錄 IV
圖目錄 VII
表目錄 IX
第一章緒論 1
1.1 研究背景 1
1.2 研究目的 2
1.3 論文架構 3
第二章技術回顧 4
2.1 磁傾角、磁偏角、磁場強度 4
2.2 機率神經網路 6
2.3 統計動量 7
2.4 快速傅立葉轉換 8
2.5 MIAT系統設計方法論 9
2.5.1 IDEF0階層式系統設計 10
2.5.2 Grafcet離散事件建模 13
2.5.3 軟體高階合成 17
第三章系統架構設計 21
3.1 系統架構設計方塊圖 21
3.1.1 主系統架構 21
3.1.2 感測器系統 21
3.1.3 資料蒐集系統 22
3.1.4 資料處理系統 22
3.1.5 PNN模型訓練及測試 23
3.2 結合MIAT方法論的系統架構設計IDEF0 23
3.2.1 主系統架構IDEF0 24
3.2.2 感測器系統IDEF0 24
3.2.3 初始化系統IDEF0 25
3.2.4 資料蒐集系統IDEF0 26
3.2.5 資料處理系統IDEF0 27
3.2.6 計算時間特徵IDEF0 28
3.2.7 PNN模型訓練及測試IDEF0 29
3.3 結合MIAT方法論的系統架構設計Grafcet 29
3.3.1 主系統架構Grafcet 30
3.3.2 感測器系統Grafcet 31
3.3.3 初始化系統Grafcet 32
3.3.4 資料蒐集系統Grafcet 33
3.3.5 資料處理系統Grafcet 35
3.3.6 計算時間特徵Grafcet 36
3.3.7 PNN模型訓練及測試Grafcet 37
3.4 軟體高階合成 38
第四章系統整合與實驗 39
4.1實驗平台 39
4.1.1 MCU開發板ESP32-S3-DevKitC-1U 40
4.1.2 磁力感測器MMC5983MA-B 41
4.1.3 I^2C擴充模組TCA9548A 42
4.2 資料蒐集 43
4.2.1 訓練資料蒐集過程 43
4.2.2 測試資料蒐集過程 45
4.3 資料計算 46
4.3.1 計算及驗證平台 46
4.3.2 原始資料 47
4.3.3 計算差分 48
4.3.4 計算時間特徵 51
4.4 訓練及測試 52
4.4.1 資料描述及前處理 52
4.4.2 資料前處理 52
4.4.3 模型評估指標 53
4.4.4 調整超參數及閥值 54
4.5 磁力異常探勘實驗 58
4.5.1實驗環境 58
4.5.2實驗規劃 60
4.5.3實驗過程及結果 60
第五章結論與未來展望 68
5.1 結論 68
5.2 未來展望 69
參考文獻 70

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指導教授

陳慶瀚(Ching-Han Chen)

審核日期

2024-7-22

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