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應用IoT發展零組件智慧監控與補償技術

為了解決Visual Studio Commun的問題，作者陳高創 這樣論述：

Abstract............................ i摘要 .................................iiiAcknowledgments.......... ivTable of Contents ............... vList of Tables ...................viiiList of Figures................... ixChapter 1: Introduction ................................. 11.1. Motivation..........

..... 11.2. Outline ................... 10Chapter 2: Development Tools..................... 122.1 Altium Designer.................................... 122.2 Keil C51 Compiler ..... 132.3 Visual Studio............. 142.4 Android Studio............ 15Chapter 3: IoT System Development...................

.............. 163.1. System Architecture ............................ 163.2. System Devices ................................. 183.2.1. Laser R-test .......................... 193.2.2. iNode........................ 223.2.3. Sensors........................... 263.2.3.1 Temperature sensor.............

...... 273.2.3.2 Vibration sensor..................... 313.2.3.3 Current sensor.................... 323.2.3.4 Water level....................... 333.3.1. Window platform .......................... 353.3.2. Android platform......................... 393.3.3. Website ................................. 4

2viChapter 4: Length Distortion of a Spindle under The Influence of Thermal RisingDuring Machining Process and Compensation Solution................... 444.1 System Architecture ......................... 444.1.1. System Temperature Measurement Tool ........ 454.1.2. System Configuration.............

....... 484.2. Sampling and Data Analyses .................. 494.3. Modeling and Experiment .................... 584.3.1. Modeling......... 584.3.2. Experiment........ 604.4. Experiment Results and Conclusions....................... 63Chapter 5: Laser R-Test for Angular Positioning Calibration and C

ompensationof the Five-Axis Machine Tools ............................. 655.1. System Architecture .......................... 655.2. System Uncertainty Analysis and Calibration............... 655.2.1. Analysis ........... 655.2.2. Calibration......... 685.2.2.1. Translational Axis Calibration ......

... 685.2.2.2. Eccentricity Calibration ........ 705.3. System Measurement Method ............ 725.4. System Configuration........................... 765.5. Experiment and Results............................. 785.5.1. Angular Positioning Verification without Milling Workpiece .......... 785.5.2. Ang

ular Positioning Verification with Milling Workpiece ........... 805.6. Conclusions................................. 84Chapter 6: Key Components – Chip Conveyor................ 866.1. System Architecture ........................ 866.2. IoT Device ................................. 876.3. System User

Interface ...................... 886.2.1. Windows .................................. 886.2.2. Android ................................. 926.2.3. Websites................................. 976.3. Conclusions............................ 100Chapter 7: Conclusion and Future works................ 101Refer

ences....................................... 103Development and Application of IoT on Machine Tool Component forMonitoring and Compensation Technology................. 10Chapter 4: Length Distortion of a Spindle under The Influence of Thermal RisingDuring Machining Process and Compensation Solution.

................... 444.1 System Architecture ...................... 444.1.1. System Temperature Measurement Tool .......... 454.1.2. System Configuration................. 484.2. Sampling and Data Analyses ........ 494.3. Modeling and Experiment .............. 584.3.1. Modeling......................

............. 584.3.2. Experiment.......................... 604.4. Experiment Results and Conclusions............... 63Chapter 5: Laser R-Test for Angular Positioning Calibration and Compensationof the Five-Axis Machine Tools ........ 655.1. System Architecture .............. 655.2. System Uncertain

ty Analysis and Calibration........... 655.2.1. Analysis .................... 655.2.2. Calibration................................ 685.2.2.1. Translational Axis Calibration ......... 685.2.2.2. Eccentricity Calibration ........... 705.3. System Measurement Method ........... 725.4. System Configurat

ion....................... 765.5. Experiment and Results................ 785.5.1. Angular Positioning Verification without Milling Workpiece .......... 785.5.2. Angular Positioning Verification with Milling Workpiece ........... 805.6. Conclusions........................................ 84Chapter 6:

Key Components – Chip Conveyor........ 866.1. System Architecture .......... 866.2. IoT Device ..................... 876.3. System User Interface ......... 886.2.1. Windows ..................... 886.2.2. Android ..................... 92vii6.2.3. Websites.......................... 976.3. Conclusions

......................... 100Chapter 7: Conclusion and Future works.... 101References....................... 103Development and Application of IoT on Machine Tool Component forMonitoring and Compensation Technology.......... 10

應用AI 物件辨識技術發展於常規性天花 板之室內定位系統

為了解決Visual Studio Commun的問題，作者詹翔友 這樣論述：

目錄摘要 iAbstract ii致謝 iii目錄 iv表目錄 vii圖目錄 viii第一章 緒論 11.1研究背景 11.2研究動機與目的 11.3論文架構 2第二章 相關技術與研究 32.1室內定位技術 32.1.1非視覺定位技術 32.1.2視覺定位技術 72.2 AI物件檢測(Object Detection) 132.2.1 You Only Look Once version 3(YOLOv3) 142.2.2 Faster-RCNN 162.2.3 SSD(Single Shot Multibox Detector) 16第三章 材料與方法

173.1系統架構 173.2硬體架構 183.2.1嵌入式系統 193.2.2 USB影像擷取裝置 203.2.3移動式平台結構架設 203.2.4電源供應裝置 213.3空間定位演算法 223.3.1 影像相對座標 233.3.2 初始絕對座標設定 243.3.3 動態影像特徵點之擷取 253.3.4 常規性天花板絕對座標之擷取 293.3.5 動態影像比例尺校正 313.3.6空間絕對座標計算 323.4 AI物件辨識系統 353.4.1 Tiny-YOLOv3類神經網路架構 353.4.2影像庫收集 383.4.3數據擴充 393.4.4物件標記

483.4.5評估方法 503.4.6訓練資料集評估結果 533.4.7測試資料集評估結果 553.5 GUI介面設計 57LabWindows/CVI 57第四章 實驗設計 594.1場域設計與評估 594.2定位參數評估方法 604.1.1場域設計 604.2.1靜態定位點誤差量測 614.2.2路徑偏移誤差量測 634.2.3定位系統反應時間實驗 664.2.4定位成功率實驗 66第五章 結果與討論 685.1靜態定位點誤差 695.2路徑偏移誤差量測結果 715.2.1場域路徑偏移 715.2.2斜線段路徑偏移誤差 725.2.3實驗場域任意行走結果

745.3定位系統反應時間 755.4定位成功率 755.5討論 76第六章 結論 78參考文獻 80附錄 85附錄A 類神經網路 85附錄B 開發工具 88Python: 88OpenCV: 88Visual Studio 2019 C/C++ IDE: 88Linux(Ubuntu): 89附錄C 訓練用影像資料庫 90不同角度旋轉圖像: 90不同平均亮度圖像: 91不同動態影像模糊圖像: 91