Fluid concentration estimation

Using acoustic waves to estimate fluid concentration is a promising technology due to its practicality and non-intrusive aspect, especially for medical applications. The existing approaches are exclusively based on the correlation between the reflection coefficient and the concentration. However, these techniques are limited by the high sensitivity of the reflection coefficient to environmental conditions changes, even slight ones. This introduces inaccuracies that cannot be tolerated in medical applications. This paper proposed a deep learning model, Fluid Concentration Estimation Convolutional Neural Network (FCE-CNN), to estimate fluid concentration. Instead of using only the reflection coefficient, we train our model to detect concentration-related patterns based on the whole received acoustic signal. FCE-CNN shows promising results that overcome the state-of-the-art limitations. Specifically, our model that is able to estimate fluid concentration with $98.5\%$ accuracy using ultra high-frequency acoustic waves.

利用声波估计流体浓度是一项兼具实用性和非侵入性的有前景技术，尤其在医疗应用领域。现有的方法仅基于反射系数与浓度之间的相关性。然而，这些技术受限于反射系数对环境条件变化的极高敏感性，即便是微小的变化也会导致误差，这在医疗应用中是无法容忍的。本文提出了一种深度学习模型——流体浓度估计卷积神经网络（FCE-CNN），用于估计流体浓度。不同于仅使用反射系数，我们的模型经过训练，能够根据接收到的完整声学信号检测与浓度相关的模式。FCE-CNN展现出有潜力的成果，克服了现有技术的局限性。具体而言，我们的模型能够使用超高频声波以98.5%的准确率估计流体浓度。