论文标题
在ISE上删除化学，动力学和电伪像的深度学习方法
Deep learning method to remove chemical, kinetic and electric artifacts on ISEs
论文作者
论文摘要
我们建议一种基于深度学习的传感器信号处理方法，以从离子选择性电极的测量值中去除化学，动力学和电伪像。 ISE用于通过测量沿玻璃膜的Nern势来研究从水溶液中的特定离子的浓度。但是，在多个离子的混合物中应用ISE存在一些问题。第一个问题是化学伪影，称为离子干扰效应。电荷颗粒相互相互作用，并通过不同ISE的玻璃膜流动。第二个问题是由液体运动引起的动力学。水分子与玻璃膜碰撞，导致电压异常的峰值。最后一个工件是ISES的干扰。当多个ISE浸入同一溶液中时，一个电极的信号发射干扰电压测量其他电极。因此，建议将ISE应用于单离子溶液上，而无需同时使用任何其他传感器。深度学习方法可以同时删除这两个工件。提出的方法使用5层人工神经网络来回归正确的信号以通过一声计算去除复杂的伪影。它的MAPE小于1.8％，R2回归为0.997。 AI处理数据的随机选择值的MAPE小于5％（p值0.016）。
We suggest a deep learning based sensor signal processing method to remove chemical, kinetic and electrical artifacts from ion selective electrodes' measured values. An ISE is used to investigate the concentration of a specific ion from aqueous solution, by measuring the Nernst potential along the glass membrane. However, application of ISE on a mixture of multiple ion has some problem. First problem is a chemical artifact which is called ion interference effect. Electrically charged particles interact with each other and flows through the glass membrane of different ISEs. Second problem is the kinetic artifact caused by the movement of the liquid. Water molecules collide with the glass membrane causing abnormal peak values of voltage. The last artifact is the interference of ISEs. When multiple ISEs are dipped into same solution, one electrode's signal emission interference voltage measurement of other electrodes. Therefore, an ISE is recommended to be applied on single-ion solution, without any other sensors applied at the same time. Deep learning approach can remove both 3 artifacts at the same time. The proposed method used 5 layers of artificial neural networks to regress correct signal to remove complex artifacts with one-shot calculation. Its MAPE was less than 1.8% and R2 of regression was 0.997. A randomly chosen value of AI-processed data has MAPE less than 5% (p-value 0.016).
