Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Pan-artifact Removing with Deep Learning, on ISEs

Version 1 : Received: 23 May 2020 / Approved: 24 May 2020 / Online: 24 May 2020 (11:43:21 CEST)
Version 2 : Received: 5 June 2020 / Approved: 7 June 2020 / Online: 7 June 2020 (17:40:24 CEST)
Version 3 : Received: 15 October 2020 / Approved: 15 October 2020 / Online: 15 October 2020 (16:51:23 CEST)

A peer-reviewed article of this Preprint also exists.

B. Ban, "Deep learning method to remove chemical, kinetic and electric artifacts on ISEs," 2020 International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, 2020, pp. 1242-1246, doi: 10.1109/ICTC49870.2020.9289389. B. Ban, "Deep learning method to remove chemical, kinetic and electric artifacts on ISEs," 2020 International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, 2020, pp. 1242-1246, doi: 10.1109/ICTC49870.2020.9289389.

Journal reference: 2020 International Conference on Information and Communication Technology Convergence (ICTC) 2020
DOI: 10.1109/ICTC49870.2020.9289389

Abstract

This paper presents a signal-processing method to remove pan-artifact on ISEs with artificial neural networks. An Ion Selective Electrode 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 multi-ion solution has problem. First problem is a chemicophysical 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 that movement of liquid directly interfere the glass membrane, causing inaccurate voltage measurement. When multiple ISEs are dipped into same solution, a sensor’s signal emission interference voltage measurement of other sensors. Therefore, an ISE is recommended to applied on single-ion solution, without any other sensors applied at the same time. Deep learning approach can remove both artifacts at the same time. The proposed method is designed to remove complex artifacts with one-shot calculation, with MAPE less than 1.8%, and R2 as 0.997. A randomly chosen value of AI-predicted value has MAPE less than 5% (p-value 0.016).

Keywords

AI; Machine Learning; ISE; Analog Signal Processing; Horticulture; Aqua Culture

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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