Mathematical Model for Secondary Transport of Cations at the Root of Plants

Ban, Byunghyun

doi:10.20944/preprints201909.0219.v1

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…Nowadays, the replacement of experience based know-how is main trend of Farm-tech area. [2,3] On fertilization area, the hottest application is hydroponics aqua culture). Open hydroponic system does not recycle the nutrient solution while closed hydroponic system does.…”

Section: Introductionmentioning

confidence: 99%

Pan-artifact Removing with Deep Learning, on ISEs

Ban¹,

Ryu²,

Lee³

2020

Preprint

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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).

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Section: Introductionmentioning

confidence: 99%

Pan-artifact Removing with Deep Learning, on ISEs

Ban¹,

Ryu²,

Lee³

2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Deep learning method to remove chemical, kinetic and electric artifacts on ISEs

Ban¹

2020

2020 International Conference on Information and Communication Technology Convergence (ICTC)

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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 R 2 as 0.997. A randomly chosen value of AI-predicted value has MAPE less than 5% (p-value 0.016).

show abstract

Mathematical Model for Secondary Transport of Cations at the Root of Plants

Cited by 2 publications

References 13 publications

Pan-artifact Removing with Deep Learning, on ISEs

Pan-artifact Removing with Deep Learning, on ISEs

Deep learning method to remove chemical, kinetic and electric artifacts on ISEs

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