Wireless Power Transfer Sensing Approach for Milk Adulteration Detection Using Supervised Learning

Natalia Vallejo Montoya, Daniel Rodriguez, Changzhi Li
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Abstract

With the increasing demand for wireless sensors due to the growing Internet of Things (IoT) industry, it becomes desirable to use existing technologies to realize new sensing functions. As wireless power transfer (WPT) becomes a standard feature in smartphones, this paper studies the non-invasive classification of liquid solutions with different concentrations, based on the WPT technology already deployed in mobile devices. Average accuracies of up to 97.6% were achieved utilizing supervised machine learning for the classification of milk adulterated with different water volumes. For these experiments, milk concentrations of 100%, 80%, 60%, and 40% were used for classification. Additionally, singular value decomposition and boxplot analysis were used to reduce the radio frequency bandwidth needed for classification to 9.45 MHz, leading to a drastic reduction in hardware complexity and computational cost.
基于监督学习的牛奶掺假检测无线传输传感方法
随着物联网(IoT)行业的发展,对无线传感器的需求不断增加,利用现有技术实现新的传感功能成为人们所希望的。随着无线电力传输(WPT)成为智能手机的标准功能,本文基于已部署在移动设备中的WPT技术,研究了不同浓度液体溶液的无创分类。利用监督机器学习对掺入不同含水量的牛奶进行分类,平均准确率高达97.6%。在这些实验中,分别使用100%、80%、60%和40%的牛奶浓度进行分类。此外,使用奇异值分解和箱线图分析将分类所需的射频带宽降低到9.45 MHz,从而大大降低了硬件复杂性和计算成本。
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