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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations

Reducing Energy Consumption of Pressure Sensor Calibration Using Polynomial HyperNetworks with Fourier Features

February 1, 2023

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Authors

Muhammad Sarmad

Norwegian University of Science and Technology


Mishal Fatima

Endress + Hauser


Jawad Tayyub

Endress + Hauser


DOI:

10.1609/aaai.v36i11.21474


Abstract:

Our research aims to reduce the cost of pressure sensor calibration through machine learning. Pressure sensor calibration is a standard process whereby freshly manufactured pressure sensors are subjected to various controlled temperature and pressure setpoints to compute a mapping between the sensor's output and true pressure. Traditionally this mapping is calculated by fitting a polynomial with calibration data. Obtaining this data is costly since a large spectrum of temperature and pressure setpoints are required to model the sensor's behavior. We present a machine learning approach to predict a pre-defined calibration polynomial's parameters while requiring only one-third of the calibration data. Our method learns a pattern from past calibration sessions to predict the calibration polynomial's parameters from partial calibration setpoints for any newly manufactured sensor. We design a novel polynomial hypernetwork coupled with Fourier features and a weighted loss to solve this problem. We perform extensive evaluations and show that the current industry-standard method fails under similar conditions. In contrast, our approach saves two-thirds of the calibration time and cost. Furthermore, we conduct comprehensive ablations to study the effect of Fourier mapping and weighted loss. Code and a novel calibration dataset validated by calibration engineers are also made public.

Topics: AAAI

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HOW TO CITE:

Muhammad Sarmad||Mishal Fatima||Jawad Tayyub Reducing Energy Consumption of Pressure Sensor Calibration Using Polynomial HyperNetworks with Fourier Features Proceedings of the AAAI Conference on Artificial Intelligence (2022) 12145-12153.

Muhammad Sarmad||Mishal Fatima||Jawad Tayyub Reducing Energy Consumption of Pressure Sensor Calibration Using Polynomial HyperNetworks with Fourier Features AAAI 2022, 12145-12153.

Muhammad Sarmad||Mishal Fatima||Jawad Tayyub (2022). Reducing Energy Consumption of Pressure Sensor Calibration Using Polynomial HyperNetworks with Fourier Features. Proceedings of the AAAI Conference on Artificial Intelligence, 12145-12153.

Muhammad Sarmad||Mishal Fatima||Jawad Tayyub. Reducing Energy Consumption of Pressure Sensor Calibration Using Polynomial HyperNetworks with Fourier Features. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.12145-12153.

Muhammad Sarmad||Mishal Fatima||Jawad Tayyub. 2022. Reducing Energy Consumption of Pressure Sensor Calibration Using Polynomial HyperNetworks with Fourier Features. "Proceedings of the AAAI Conference on Artificial Intelligence". 12145-12153.

Muhammad Sarmad||Mishal Fatima||Jawad Tayyub. (2022) "Reducing Energy Consumption of Pressure Sensor Calibration Using Polynomial HyperNetworks with Fourier Features", Proceedings of the AAAI Conference on Artificial Intelligence, p.12145-12153

Muhammad Sarmad||Mishal Fatima||Jawad Tayyub, "Reducing Energy Consumption of Pressure Sensor Calibration Using Polynomial HyperNetworks with Fourier Features", AAAI, p.12145-12153, 2022.

Muhammad Sarmad||Mishal Fatima||Jawad Tayyub. "Reducing Energy Consumption of Pressure Sensor Calibration Using Polynomial HyperNetworks with Fourier Features". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.12145-12153.

Muhammad Sarmad||Mishal Fatima||Jawad Tayyub. "Reducing Energy Consumption of Pressure Sensor Calibration Using Polynomial HyperNetworks with Fourier Features". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 12145-12153.

Muhammad Sarmad||Mishal Fatima||Jawad Tayyub. Reducing Energy Consumption of Pressure Sensor Calibration Using Polynomial HyperNetworks with Fourier Features. AAAI[Internet]. 2022[cited 2023]; 12145-12153.


ISSN: 2374-3468


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