博碩士論文 108226029 詳細資訊




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姓名 余昆霖(Kun-Lin Yu)  查詢紙本館藏   畢業系所 光電科學與工程學系
論文名稱 以藍芽訊號強度指標及機器學習建構室內定位之研究
(Research on Bluetooth Signal Strength Indicator and Machine Learning to Construct Indoor Positioning)
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摘要(中) 近年來發展室內定位已有許多方向的技術發展,有著各種的解決方法,而這些解決方法皆有各自的優點與侷限,但高精度的定位方式需要較高單價的設備去做佈署,本研究目的為設計一套新型定位演算法,在準確度上與設備成本取得最佳平衡。隨著許多新型個人移動設備如手機、穿戴裝置等,我們只要將設備的藍芽功能開啟,就能夠對其場域進行定位,感測位置的應用可更加廣泛。
本研究取用12個藍芽模組平均佈置於室內空間中,藉由人員在室內中影響到這些藍芽訊號做為機器學習的訓練依據,利用TensorFlow的學習框架,以卷積神經網路的方式來做訓練與預測,設計最佳的模型參數,大幅降低了訓練模型時間,在本研究的場域之中,藉由不同的藍芽訊號強弱判斷室內人員位置,定位精準度達到93.46%。
摘要(英) In recent years, there have been technological developments in many directions in the development of indoor positioning, and there are various solutions, and these solutions have their advantages and limitations, but high-precision positioning methods require higher unit price equipment for deployment. This study′s purpose is to design a new positioning algorithm to achieve the best balance between accuracy and equipment cost. With many new personal mobile devices such as mobile phones, wearable devices, etc., we can locate the field as long as the Bluetooth function of the device is turned on, and the application of sensing location can be more extensive.
This study uses 12 Bluetooth modules to be evenly arranged in the indoor space. These Bluetooth signals are used as the basis for machine learning training by personnel in the room. The TensorFlow learning framework is used to convolutional neural networks. The training and prediction methods are used to design the best model parameters, which greatly reduces the training model time. In the field of this research, the location of indoor personnel is judged by different Bluetooth signal strengths, and the positioning accuracy reaches 93.46%.
關鍵字(中) ★ 機器學習
★ 室內定位
★ 低功耗藍芽
關鍵字(英) ★ Machine learning
★ Indoor positioning
★ Bluetooth Low Energy
論文目次 目錄
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 研究貢獻 1
1-2 研究背景 1
1-3 研究動機 2
1-4 研究目的 3
1-5 論文架構 4
第二章 研究內容與方法 5
2-1 研究內容 5
2-2 研究理論 5
2-2-1 迴歸分析 5
2-2-2 機器學習概論 9
2-2-3 類神經網路 13
2-2-4 接收訊號強度指標 15
2-3 室內定位技術討論 16
2-3-1 紅外線定位(Infrared radiation, IR) 16
2-3-2 Wi-Fi定位 (Wireless Fidelity) 16
2-3-3 無線射頻辨識 (Radio Frequency Identification, RFID) 18
2-4 研究設備 18
2-4-1 藍芽低功耗模組 18
2-4-2 實驗場域 22
2-4-3 分析軟體(TensorFlow) 23
第三章 實驗流程 24
3-1 實驗架構 24
3-2 實驗分析步驟 25
3-3 實驗分析與結果 26
3-3-1 藍芽發射功率與傳輸距離關係 26
3-3-2 偵測人員室內位置實驗 28
3-3-3 模組數量與準確度關係 32
3-3-4 數據量與準確度關係 35
3-4 實驗結論 36
第四章 研究結論與未來展望 37
4-1 研究結論 37
4-2 未來展望 38
參考文獻 39
附錄(論文投稿) 42
參考文獻 參考文獻
[1] 蕭文龍,多變量分析最佳入門實用書,第8章:迴歸分析2009/06/06。
[2] 陳宜君,「藍芽感測器貼片之智能監控應用研究」,國立中央大學,碩士論文,民國109年。
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learning-6-tricks-to-prevent-overfitting-in-machine-learning-820b091dc42.
[4] Online resources: “Dropout Note, ”
https://github.com/PetarV-/TikZ/tree/master/Dropout.
[5] Online resources: “Friis transmission equation,” https://en.wikipedia.org/wiki/Friis_transmission_equation.
[6] R. Want, A. Hopper, V. Falcao, and J. Gibbons, “The Active Badge Location System,” ACM Transactions on Information Systems (TOIS), 1992, vol. 10, pp. 91-102.
[7] B. Hanssens, D. Plets, E. Tanghe, C. Oestgesy, D. P. Gaillotz, M. Li´enardz, L. Martens, W. Joseph, “An Indoor Localization Technique Based on Ultra-Wideband AoD/AoA/ToA Estimation,” IEEE International Symposium on Antennas and Propagation, Fajardo, Puerto Rico, Jul. 2016.
[8] K. Keunecke, G. Scholl, “IEEE 802.11 n-Based TDOA Performance Evaluation in an Indoor Multipath Environment,” 8th European Conference on Antennas and Propagation, Hague, Netherlands, Apr. 2014.
[9] S. He and S. H. G. Chan, “Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons,” IEEE Communications Surveys & Tutorials, vol. 18, no. 1, Aug. 2015, pp. 466-490.
[10] M. Hasani, J. Talvitie, L. Sydänheimo, E.-S. Lohan, L. Ukkonen, “Hybrid WLAN-RFID Indoor Localization Solution Utilizing Textile Tag,” IEEE Antennas and Wireless Propagation Letters, Vol. 14, Feb. 2015.
[11] K. Deepika, J. Usha, “Design & Development of Location Identification using RFID with WiFi Positioning Systems,” Ninth International Conference on Ubiquitous and Future Networks (ICUFN), Jul. 2017.
[12] Online resources: “CC13x0, CC26x0 SimpleLink™ Wireless MCU Technical Reference Manual (Rev. I), ”
https://www.ti.com/product/CC2650keyMatch=CC2650&tisearch=Search-EN-Everything.
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https://www.ti.com/product/CC2650MODA
[14] Abadi, M., Agarwal, A., Barham, P., Brevdo, E.,Chen, Z., Citro, C., Corrado, G.S., Davis, A.,Dean, J., Devin, M., et al. (2016). TensorFlow:Large-scale machine learning on heterogeneous systems.arXiv preprint, 1603.04467.
指導教授 張榮森(Rong-Seng Chang) 審核日期 2021-7-8
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