Early gestational diabetes mellitus risk predictor using neural network with NearMiss (基于神经网络和NearMiss的早期妊娠糖尿病风险预测模型)

期刊：Journal of Cloud Computing

文章号: Article 128

DOI: 10.1186/s13677-024-00693-x

作者：Min Zhao and Junwen Lu

英文摘要:

    Offloading some tasks from the local device to the remote cloud is one of the important methods to overcome  the drawbacks of the medical mobile device, such as the limitation in the execution time and energy supply. The  challenges of offloading task is how to meet multiple requirement while keeping energy-saving. We classify tasks  in the medical mobile device into two kinds: the first is the task that hopes to be executed as soon as possible, those  tasks always have a deadline; the second is the task that can be executed anytime and always has no deadlines. Past  work always neglects the energy consumption when the medical mobile device is charged. To the best of our knowledge, this paper is the first paper that focuses on the energy efficiency of charging from a power grid to a medical  device during work. By considering the energy consumption in different locations, the energy efficiency during working and energy transmission, the available energy of and the battery, we propose a scheduling method based  on DQN. Simulations show that our proposed method can reduce the number of un-completed tasks, while having  a minimum value in the average execution time and energy consumption.

中文摘要:

    将部分任务从本地设备卸载到远程云端是克服医疗移动设备（例如执行时间和能源供应的限制）缺陷的重要方法之一。任务卸载的挑战在于如何在满足多种需求的同时保持节能。我们将医疗移动设备中的任务分为两类：第一类是希望尽快执行的任务，这类任务始终有截止时间；第二类是可以随时执行且没有截止时间的任务。以往的研究通常忽略医疗移动设备充电时的能耗。据我们所知，本文首次关注医疗设备在工作期间从电网充电的能效。通过考虑不同位置的能耗、工作和能量传输期间的能效、设备及电池的可用能量，我们提出了一种基于DQN的调度方法。仿真结果表明，我们提出的方法可以减少未完成任务的数量，同时使平均执行时间和能耗达到最小值。

图一 用于卸载任务的 DQN