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姓名 林琮皓(Tsung-Hao Lin)  查詢紙本館藏   畢業系所 資訊工程學系在職專班
論文名稱 基於機器學習的Beacon室內定位應用於智慧零售系統
(ML-based Beacon Indoor positioning for Smart Retail System)
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檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2027-7-13以後開放)
摘要(中) 零售業導入智慧零售的需求日益增長,但導入無人商店的智慧零售缺乏了互動性,也因此阻礙了智慧零售的發展。為了讓顧客不僅是體驗智慧零售而來,提升購物體驗則成了最大的課題,這也因此影響了零售業導入智慧零售的意願。本論文中提出一套基於機器學習的Beacon室內定位智慧零售互動平台,利用藍芽Beacon訊號進行特徵擷取,再透過遞迴式機率神經網路進行位置的辨識,搭配樹莓派作為電子紙互動平台閘道器,並將辨識結果傳至樹莓派控制電子紙並與顧客進行互動。透過MQTT作為樹莓派與PC之間的通訊方式,並使用PSO結合RPNN進行Beacon訊號的辨識,其平均辨識準確度可達到97.56%,與LSTM相比其效果提升6%。本論文規劃一套完整的智慧零售系統,使用藍芽Beacon結合機器學習提升定位準確度,並利用電子紙平台與顧客進行互動,以藍芽及樹莓派的應用降低智慧零售導入的成本,實現零售業的數位轉型。
摘要(英) There is a growing demand for smart retail in the retail industry, but the lack of interactivity in unmanned stores has hindered the development of smart retail. In order to let customers not only experience smart retailing, improving the shopping experience is the biggest issue, which affects the willingness of the retail industry to implement smart retailing. In this paper, we propose a machine-learning-based Beacon indoor location-based smart retailing interactive platform, which uses Bluetooth Beacon signals for feature acquisition, and then uses a recursive probabilistic neural network for location identification, and Raspberry Pi as the gateway to the e-paper interactive platform then transmits the identification results to Raspberry Pi to control the e-paper and interact with customers. By using MQTT as the communication method between Raspberry Pi and PC, then using PSO combined with RPNN for Beacon signal recognition, the average recognition accuracy can reach 97.56%, which is 6% better than LSTM. In this paper, we plan a complete smart retail system, using Bluetooth Beacon combined with machine learning to improve the positioning accuracy, and using the e-paper platform to interact with customers, using Bluetooth and Raspberry Pi to reduce the cost of smart retail implementation and realize the digital transformation of the retail industry.
關鍵字(中) ★ 智慧零售
★ 遞迴式機率神經網路
★ 藍芽Beacon
★ 室內定位
關鍵字(英) ★ Smart Retail
★ Recursive Probabilistic Neural Network
★ Bluetooth Beacon
★ Indoor Positioning
論文目次 摘要 i
Abstract ii
致謝 iii
目錄 iv
圖目錄 vii
表目錄 x
第一章、緒論 1
1.1 研究背景 1
1.2 研究目的 3
1.3 論文架構 3
第二章、技術回顧 4
2.1. 室內定位技術 4
2.1.1. 到達時間定位法(TOA) 4
2.1.2. 到達時間差定位法(TDOA) 5
2.1.3. 到達角度定位法(AOA) 6
2.1.4. 接收訊號強度指示(RSSI) 6
2.2. RSSI訊號過濾 8
2.2.1. 簡單移動平均線 8
2.2.2. 均值及中值濾波器 8
2.2.3. 卡爾曼濾波器(Kalman Filter,KF) 8
2.3. 機器學習的室內定位技術 10
2.3.1. 機率神經網路 10
2.3.2. 遞迴神經網路 12
2.3.3. 遞迴式機率神經網路 15
第三章、系統架計 17
3.1. 系統設計方法論 17
3.2. 智慧零售互動系統架構 19
3.2.1. 藍芽通訊子系統 21
3.2.2. 位置特徵擷取子系統 22
3.2.3. RPNN位置辨識子系統 24
3.2.4. 電子紙互動子系統 25
3.3. 系統程式合成 27
第四章、系統整合與實驗 30
4.1. 實驗平台 30
4.1.1. Raspberry pi 4B閘道器 30
4.1.2. Beacon 信標 31
4.1.3. 電子紙模組 33
4.1.4. 實驗環境及位置分割 34
4.1.5. MQTT通訊協定 35
4.2. 資料前處理 36
4.3. 位置特徵資料庫建立 39
4.4. 位置辨識模型實驗 41
4.4.1. 實驗模型評估指標 41
4.4.2. 模型設計與應用 43
4.4.3. 實驗結果 45
4.5. 智慧零售互動實驗 48
第五章、結論與未來展望 50
5.1. 結論 50
5.2. 未來展望 51
參考文獻 52
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指導教授 陳慶瀚(Ching-Han Chen) 審核日期 2022-7-25
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