JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
Two Machine Learning Models for Mobile Phone Battery Discharge Rate Prediction Based on Usage Patterns |
| 영문제목(English Title) |
Two Machine Learning Models for Mobile Phone Battery Discharge Rate Prediction Based on Usage Patterns |
| 저자(Author) |
Chantana Chantrapornchai
Paingruthai Nusawat
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| 원문수록처(Citation) |
VOL 12 NO. 03 PP. 0436 ~ 0454 (2016. 09) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
This research presents the battery discharge rate models for the energy consumption of mobile phone batteries based on machine learning by taking into account three usage patterns of the phone: the standby state, video playing, and web browsing. We present the experimental design methodology for collecting data, preprocessing, model construction, and parameter selections. The data is collected based on the HTC One X hardware platform. We considered various setting factors, such as Bluetooth, brightness, 3G, GPS, Wi-Fi, and Sync. The battery levels for each possible state vector were measured, and then we constructed the battery prediction model using different regression functions based on the collected data. The accuracy of the constructed models using the multi-layer perceptron (MLP) and the support vector machine (SVM) were compared using varying kernel functions. Various parameters for MLP and SVM were considered. The measurement of prediction efficiency was done by the mean absolute error (MAE) and the root mean squared error (RMSE). The experiments showed that the MLP with linear regression performs well overall, while the SVM with the polynomial kernel function based on the linear regression gives a low MAE and RMSE. As a result, we were able to demonstrate how to apply the derived model to predict the remaining battery charge.
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| 키워드(Keyword) |
Battery Discharge Rate
Mobile Battery Usage
Multi-Layer Perceptron
Prediction Model
Support Vector Machine
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| 파일첨부 |
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