Present-day handheld battery-enabled devices such as smartphones and tablets attract rich user experience but are often criticized for their short battery lives. Battery life is a subjective term and depends on a user's perceptions. A novel work to achieve power optimization for these devices, according to users’ perceptions, was the design of user-satisfaction-aware power management approach, perceptual computer power management approach (Per-C PMA). But we have found that the design of Per-C PMA requires collection of data intervals from a group of subjects. This limits the practical viability of Per-C PMA for highly personal handheld battery-enabled devices such as smartphones and tablets. So, here we propose a user-satisfaction-aware PMA called Per-C for Personalized Power Management Approach or “Per-C PPMA,” one that achieves significant reductions in power consumption compared to existing PMAs and noticeable improvements in the overall user satisfaction. Per-C PPMA uses the mathematical technique of person footprint of uncertainty (FOU) to process users’ linguistic opinions. Person FOU can either use an interval approach (IA) or Hao–Mendel approach (HMA) for data processing. The recommendations generated using IA and HMA are the same. However, IA takes a much higher computational time than HMA, even though both have the same asymptotic complexity of $\boldsymbol{O}({\boldsymbol{w}*\boldsymbol{n}})$. We strongly believe that Per-C PPMA is a novel technique and our work is the first such application of Person FOU on any hardware platform. An important outcome of this study is a ready-to-use mobile app “Per-C PPMA” (currently freely available on the website http://www.sau.int/∼cilab/).

中文翻译：

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基于不确定性的手持设备功率优化 CWW 模型的人员足迹

当今的手持电池设备，如智能手机和平板电脑，吸引了丰富的用户体验，但往往因其电池寿命短而受到批评。电池寿命是一个主观术语，取决于用户的看法。根据用户的看法，为这些设备实现电源优化的一项新颖工作是设计用户满意度感知电源管理方法，感知计算机电源管理方法（Per-C PMA）。但是我们发现 Per-C PMA 的设计需要从一组受试者中收集数据区间。这限制了 Per-C PMA 在高度个人化的手持电池供电设备（如智能手机和平板电脑）中的实际可行性。因此，在这里我们提出了一种用户满意度感知 PMA，称为 Per-C for Personalized Power Management Approach 或“Per-C PPMA，” 与现有 PMA 相比，功耗显着降低，整体用户满意度显着提高。Per-C PPMA 使用不确定性人足迹（FOU）的数学技术来处理用户的语言意见。人 FOU 可以使用区间方法 (IA) 或 Hao-Mendel 方法 (HMA) 进行数据处理。使用 IA 和 HMA 生成的建议是相同的。然而，IA 需要比 HMA 更长的计算时间，即使两者具有相同的渐近复杂度 $\boldsymbol{O}({\boldsymbol{w}*\boldsymbol{n}})$。我们坚信 Per-C PPMA 是一项新技术，我们的工作是 Person FOU 在任何硬件平台上的第一个此类应用。