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DC Field | Value | Language |
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dc.contributor.author | Werayuth Charoenruengkit | - |
dc.contributor.author | Ramunya Jongfungfeuang | - |
dc.contributor.author | Sunisa Saejun | - |
dc.date.accessioned | 2020-04-16T08:29:00Z | - |
dc.date.available | 2020-04-16T08:29:00Z | - |
dc.date.issued | 2019 | - |
dc.identifier.uri | https://ir.swu.ac.th/jspui/handle/123456789/120 | - |
dc.description.abstract | Indoor positioning system is a challenging problem due to the variety of environment and unreliable of data that are used for a prediction of the position. For Wi-Fi based indoor positioning system, signal intensity used to predict the co-ordinate of the device are known to fluctuate greatly despite being measured at the same position. Therefore, significant errors are often found when solving this problem with regression algorithms. A quantization of co-ordinate data into position IDs can mitigate the fluctuated noises in the data and is able to reformulate the problem into a multi-class classification problem. The error in positioning can then be computed from the distance between the true co-ordinate and the predicted co-ordinate. The experiment shows that Random forest classification can predict the position with the error in positing at 5.65 meters on average when the quantization is applied with threshold setting to 1 meter | - |
dc.language.iso | en | th_TH |
dc.publisher | Srinakharinwirot University | th_TH |
dc.subject | Position Quantization | th_TH |
dc.title | Position Quantization Approach with Multi-class Classification for Wi-Fi Indoor Positioning System | th_TH |
dc.type | Article | th_TH |
Appears in Collections: | ComSci-Journal Articles |
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default.html | 340 B | HTML | View/Open | |
Sci_Weerayuth_C.pdf | 472.14 kB | View/Open |
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