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http://dx.doi.org/10.6109/jkiice.2020.24.4.536

A Data Preprocessing Framework for Improving Estimation Accuracy of Battery Remaining Time in Mobile Smart Devices  

Tak, Sungwoo (School of Electrical and Computer Engineering, Pusan National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.4, 2020 , pp. 536-545 More about this Journal
Abstract
When general statistical regression methods are applied to predict the battery remaining time of a mobile smart device, they yielded the poor accuracy of estimating battery remaining time as the deviations of battery usage time per battery level became larger. In order to improve the estimation accuracy of general statistical regression methods, a preprocessing task is required to refine the measured raw data with large deviations of battery usage time per battery level. In this paper, we propose a data preprocessing framework that preprocesses raw measured battery consumption data and converts them into refined battery consumption data. The numerical results obtained by experimenting the proposed data preprocessing framework confirmed that it yielded good performance in terms of accuracy of estimating battery remaining time under general statistical regression methods for given refined battery consumption data.
Keywords
Smart Devices; Data Preprocessing; Statistical Estimation; Battery Remaining Time; Estimation Accuracy;
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