Electrical and Computer Engineering Department, University of Toronto, 10 King's College Circle, Toronto, ON M5S3G4, Canada.
Electrical and Computer Engineering Department, University of Alberta, 116 St., Edmonton, AB T6G 2R3, Canada.
Sensors (Basel). 2023 Jul 7;23(13):6236. doi: 10.3390/s23136236.
This research explores the application of an artificial intelligence (AI)-assisted approach to enhance the selectivity of microwave sensors used for liquid mixture sensing. We utilized a planar microwave sensor comprising two coupled rectangular complementary split-ring resonators operating at 2.45 GHz to establish a highly sensitive capacitive region. The sensor's quality factor was markedly improved from 70 to approximately 2700 through the incorporation of a regenerative amplifier to compensate for losses. A deep neural network (DNN) technique is employed to characterize mixtures of methanol, ethanol, and water, using the frequency, amplitude, and quality factor as inputs. However, the DNN approach is found to be effective solely for binary mixtures, with a maximum concentration error of 4.3%. To improve selectivity for ternary mixtures, we employed a more sophisticated machine learning algorithm, the convolutional neural network (CNN), using the entire transmission response as the 1-D input. This resulted in a significant improvement in selectivity, limiting the maximum percentage error to just 0.7% (≈6-fold accuracy enhancement).
这项研究探讨了人工智能 (AI) 辅助方法在增强用于液体混合物感应的微波传感器选择性方面的应用。我们利用包含两个耦合矩形互补开口环谐振器的平面微波传感器，在 2.45 GHz 下工作，以建立一个高度灵敏的电容区域。通过引入再生放大器来补偿损耗，传感器的品质因数从 70 显著提高到约 2700。使用频率、幅度和品质因数作为输入，深度神经网络 (DNN) 技术用于表征甲醇、乙醇和水的混合物。然而，发现 DNN 方法仅对二元混合物有效，最大浓度误差为 4.3%。为了提高对三元混合物的选择性，我们使用更复杂的机器学习算法——卷积神经网络 (CNN)，使用整个传输响应作为一维输入。这显著提高了选择性，将最大百分比误差限制在仅 0.7%（≈6 倍的准确性提高）。
