Weighted Data Fusion Use for Ruthenium Dioxide Thin Film pH Array Electrodes

Abstract

Data fusion is a useful statistical method that can be applied to multiple sensors or array sensors in many fields. The pH value is a very important parameter in clinical diagnosis, industrial wastewater treatment using a hydrogen ion selective electrode or pH meter. In this study, the examined data fusion methods consist of the arithmetic average and weighted data fusion coefficients used to process the measured pH data. A ruthenium dioxide (RuO 2 ) pH electrode was prepared. RuO 2 thin films were deposited onto silicon substrate using a sputtering system. Eight RuO 2 hydrogen ion selective electrodes were used to form pH array sensors. From the measurement process, we can simultaneously obtain eight data sets. The experimental results, using the data fusion weighted coefficients obtained a good measurement result even when an individual sensor failed within the array sensors.Index Terms-Array sensor, data fusion, ion selective electrode, ruthenium dioxide, weighted coefficient.

Reliability of Measured Data for pH Sensor Arrays with Fault Diagnosis and Data Fusion Based on LabVIEW

“…We denote that a set of pH data from the i th pH sensor is x = x 1 , x 2 , …,x n . The average of the measured data is typically defined as x̄ The average of the pH data of the i th pH sensor is used to calculate it using the following equation [10]: ${\overline{x}}_i=\frac{1}{n}\sum _{i=1}^n{x}_i$…”

“…We evaluated the weighted coefficients w ADF,i ( w ADF,1 , w ADF,2 , … w ADF,8 ) for each pH sensor and the sum of weighted factors for each pH sensor is equal to unity. The final fusion result with pH sensor array is obtained from the following equation [10]: $X_{\mathit{\text{ADF}}}=\sum _{i=1}^8{w}_{\text{italicADF},i}{\overline{x}}_i$…”

“…We evaluated the weighted coefficients w i (w 1 , w 2 , … w n ) for each sensor and the sum of weighted factors for each sensor is equal to unity. The estimated data fusion value μ y can be described as follows [10]: ${\mu }_y=\frac{1}{n}\sum _{i=1}^n{y}_{ij},(j=1,2,\dots ,n)$…”

“…The expressed equation for w SADF,i is obtained as follows [10]: $w_{S\mathit{\text{ADF}},i}=\frac{1}{{\sigma }_i^2\left(\text{∑}_{k=1}^n{\sigma }_k^{-2}\right)},(i=1,2,\dots ,n)$…”

Distributed Compressed Sensing-Based Data Fusion in Sensor Networks

“…How to determine the optimum weights is the key in fusion algorithm, which is depended on mean square deviation of recover sensing data [7]. That is, calculate the optimum weights w i with the minimal mean square deviation and constrained condition of weights.…”