Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Performance Evaluation of Battery Remaining Time Estimation Methods According to Outlier Data Processing Policies in Mobile Devices

모바일 기기에서 이상치 데이터 처리 정책에 따른 배터리 잔여 시간 예측 기법의 평가

  • Tak, Sungwoo (School of Computer Science and Engineering, Pusan National University)
  • Received : 2022.05.12
  • Accepted : 2022.06.08
  • Published : 2022.07.31
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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.

모바일 기기 배터리의 잔여 시간 예측은 배터리 잔량별 사용 시간 데이터의 분포 특성에 영향을 받는다. 특히 이상치 데이터가 존재하는 경우, 통계적 회귀 기법의 예측 성능을 왜곡시킬 수 있다. 이에 본 논문에서는 통계적 회귀 기법의 예측 성능 향상을 위해 이상치 데이터를 탐지 및 처리하는 프레임워크를 제안하였다. 제안한 프레임워크는 먼저 배터리 잔여 시간 예측에 영향을 주는 이상치 데이터를 탐지한다. 탐지된 이상치 데이터는 평활 과정을 통해 새로운 값으로 치환된 후, 이상치 데이터와 치환된 데이터 간의 차이를 개별 데이터에 분배한다. 마지막으로 개별 데이터를 재강화하여 예측 성능을 향상시키고자 한다. 제안한 프레임워크의 성능 분석을 수행한 결과, 배터리 잔여 시간의 예측 성능이 향상됨을 확인하였다.

Keywords

Acknowledgement

This work was supported by a 2-Year Research Grant of Pusan National University.

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