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使用深度强化学习的可穿戴设备的自适应电源管理SmartAPM框架
R Sunder1, Umesh Kumar Lilhore1, Anjani Kumar Rai2
1School of Computer Science and Engineering, Galgotias University, Greater Noida, UP, India.
Scientific reports
|February 26, 2025
概括
智能自适应电源管理 (SmartAPM) 使用深度强化学习来优化可穿戴设备的功率. 这种创新框架显著延长了电池寿命,并通过智能地适应使用模式来提高用户满意度.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 人与计算机的交互
背景情况:
- 可穿戴设备因电池寿命的限制而扎,影响性能和用户体验.
- 频繁充电是一个常见的问题,减少了用户对当前可穿戴技术的满意度.
研究的目的:
- 引入智能自适应电源管理 (SmartAPM) 框架,以优化可穿戴设备的功率.
- 通过智能,自适应的电源管理策略,增强电池寿命和用户满意度.
主要方法:
- 开发了一种利用深度强化学习 (DRL) 进行自适应电源管理的新框架.
- 集成多种数据集,包括用户活动,传感器数据和功率指标,用于全面培训.
- 采用多代理的DRL方法,包括设备/云端学习和转移学习以实现个性化.
主要成果:
- 与传统方法相比,SmartAPM显示电池寿命增加了36%.
- 随着SmartAPM框架的实施,用户满意度得到了25%的改善.
- 该系统在24小时内适应新的使用模式,使用不到5%的设备资源.
结论:
- 在可穿戴设备能源管理方面,SmartAPM提供了显著的进步.
- 该框架有可能彻底改变可穿戴设备中的电池效率和用户体验.
- 这种方法为新一代持久且用户友好的可穿戴设备铺平了道路.
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