Convolution neural network on WIFI indoor localization
| Autor: | Yao-Ren Chang, 張耀仁 |
|---|---|
| Rok vydání: | 2018 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 106 The mobile payment has been growing very quickly in these year, our life has become more and more convenient. Once we can locate user’s position precisely, we can broadcast the advertisement to the user to increase sales performance. For example: when you walk into the restaurant, the system sent you the coupon of this restaurant immediately, when you walk into the apparel store, the system list all of the clothes you might like, when you are leaving parking lot, the system auto-debiting your parking fee. In the past, WIFI localization system is based on RFID localization, triangle localization. Nowadays, with the growing of machine learning such as DBSCAN, Deep learning, KNN, we can localize user’s location more precisely. In this paper, we use Alipay real-time payment dataset to do our experiment. We rebuild the geographic information from WIFI signal and train the model with convolution neural networks. Besides, we reduce the training/testing time on overhead by feature engineering. Then we evaluate the result with three most representative machine learning models: Lighgbm (multiple classifier), Lightgbm (binary Classifier), Keras (Deep Neural network). Finally, we evaluate the pros and cons for each machine learning model, and discuss the result. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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