一种基于智能手机嵌入Wi-Fi技术和机器学习算法的有毒水体检测方法
A Novel Poisoned Water Detection Method Using Smartphone Embedded Wi-Fi Technology and Machine Learning Algorithms
摘要：
水是人体必需的流体，其质量和清洁度的自动检查是一个正在进行的研究领域。一种这样的方法是将液体呈现给各种类型的信号，并使信号衰减的量指示液体类别。在本文中，我们利用Wi-Fi信号通过训练不同的机器学习算法来区分干净的水和有毒的水。通过等效的智能手机嵌入式Wi-Fi芯片组获取Wi-Fi接入点（WAP）信号，然后提取网络状态信息CSI度量并将其转换为特征向量，以用作机器学习分类算法的输入。CSI数据的测量的幅度和相位被选择作为四个分类器k-NN、SVM、LSTM和Entrance的输入特征。实验结果表明，该模型是足够的，以区分有毒水和清洁水的分类精度为89%时，LSTM的应用，而92%的分类精度时，应用AdaBoost-Enhancement分类器。
英文摘要：
Water is a necessary fluid to the human body and automatic checking of its quality and cleanness is an ongoing area of research. One such approach is to present the liquid to various types of signals and make the amount of signal attenuation an indication of the liquid category. In this article, we have utilized the Wi-Fi signal to distinguish clean water from poisoned water via training different machine learning algorithms. The Wi-Fi access points (WAPs) signal is acquired via equivalent smartphone-embedded Wi-Fi chipsets, and then Channel-State-Information CSI measures are extracted and converted into feature vectors to be used as input for machine learning classification algorithms. The measured amplitude and phase of the CSI data are selected as input features into four classifiers k-NN, SVM, LSTM, and Ensemble. The experimental results show that the model is adequate to differentiate poison water from clean water with a classification accuracy of 89% when LSTM is applied, while 92% classification accuracy is achieved when the AdaBoost-Ensemble classifier is applied.
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。
