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http://dx.doi.org/10.6109/jkiice.2022.26.7.1078

Performance Evaluation of Battery Remaining Time Estimation Methods According to Outlier Data Processing Policies in Mobile Devices  

Tak, Sungwoo (School of Computer Science and Engineering, Pusan National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.7, 2022 , pp. 1078-1090 More about this Journal
Abstract
The distribution patterns of battery usage time data per battery level are able to affect the performance of estimating battery remaining time in mobile devices. Outliers may mainly affect the estimation performance of statistical regression methods. In this paper, we propose a software framework that detects and processes outliers to improve the estimation performance of statistical regression methods. The proposed framework first detects outliers that degrade the estimation performance. The proposed framework replaces outliers with smoothed data. The difference between an outlier and its replaced data will be properly distributed into individual data. Finally, individual data are reinforced to improve the estimation performance. The numerical results obtained by experimenting the proposed framework confirmed that it yielded good performance of estimating battery remaining time.
Keywords
Mobile Device; Outlier Data; Battery Remaining Time; Regression; Estimation Performance;
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