摘要
乐甫波器件负载液体时,液体的密度和粘度相互耦合,难以通过乐甫波器件直接实现其并行检测。基于上述背景,建立了基底与薄膜同时采用压电材料的双压电结构乐甫波器件液体传感理论模型,提出了基于人工神经网络的乐甫波液体密度粘度并行检测方法,以理论模型计算出的数据作为人工神经网络的训练数据,采用乐甫波的波速和衰减来并行预测液体密度和粘度,预测结果与理论仿真的对比表明了该方法的有效性。
When Love wave device is loaded with liquid, the liquid density and viscosity are coupling together, it＇s hard to use the Love wave device for the parallel measurement of liquid density and viscosity directly. Based on the background mentioned above, the theoretical model of liquid sensing by the Love wave device composed of both piezoelectric substrate and piezoelectric film is established, the parallel measurement method of liquid density and viscosity based on the artificial neural networks is presented. Utilizing the numerical values calculated by the theoretical model as the training data, the liquid density and viscosity are forecasted parallel by the Love wave propagation velocity and attenuation. The comparison between the forecast results and the theoretical simulation shows the effectiveness of the method.
出处
《计量学报》
CSCD
北大核心
2017年第6期721-724,共4页
Acta Metrologica Sinica
基金
国家自然科学基金(51475240)
航空科学基金(2014ZD52053)
关键词
计量学
乐甫波
密度
粘度
并行检测
人工神经网络
metrology
Love wave
density
viscosity
parallel measurement
artificial neural networks
