arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2004.10021eess.SP
收藏

基于掩码R-CNN的智能无线电能传输目标检测

Mask R-CNN Based Object Detection for Intelligent Wireless Power Transfer

Aozhou Wu, Qingqing Zhang, Wen Fang, Hao Deng, Sai Jiang, Qingwen Liu

摘要:

共振波束充电(RBC)是一种具有安全性、移动性和可重复充电能力的多瓦数和数米数无线电能传输方法。然而,RBC系统操作依赖于信息可用性,包括功率接收器位置、类别标签和接收器编号。由于智能手机是最广泛使用的移动终端,我们提出了一个面具R-CNN的智能手机检测模型在RBC系统。实验表明,我们的模型将智能手机扫描时间减少到三分之一。因此,这种机器学习检测方法提供了一种智能方式来改善移动和物联网(IoT)设备无线电力传输的用户体验。

英文摘要:

Resonant Beam Charging (RBC) is a promising multi-Watt and multi-meter wireless power transfer method with safety, mobility and simultaneously-charging capability. However, RBC system operation relies on information availability including power receiver location, class label and the receiver number. Since smartphone is the most widely-used mobile device, we propose a Mask R-CNN based smartphone detection model in the RBC system. Experiments illustrate that our model reduces the smartphone scanning time to one third. Thus, this machine learningdetectionapproachprovidesanintelligentwaytoimprove the user experience in wireless power transfer for mobile and Internet of Things (IoT) devices.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑