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

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.

[1]  Jonathan T. Barron,et al.  Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains , 2020, NeurIPS.

[2]  Jacek Pieniazek,et al.  Temperature and Nonlinearity Compensation of Pressure Sensor With Common Sensors Response , 2020, IEEE Transactions on Instrumentation and Measurement.

[3]  Pratul P. Srinivasan,et al.  NeRF , 2020, ECCV.

[4]  Natalia Gimelshein,et al.  PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.

[5]  Yoshua Bengio,et al.  On the Spectral Bias of Neural Networks , 2018, ICML.

[6]  Leonidas J. Guibas,et al.  PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Matthias Nießner,et al.  Shape Completion Using 3D-Encoder-Predictor CNNs and Shape Synthesis , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Quoc V. Le,et al.  HyperNetworks , 2016, ICLR.

[9]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Minh N. Do,et al.  Semantic Image Inpainting with Perceptual and Contextual Losses , 2016, ArXiv.

[11]  Eugenio Culurciello,et al.  An Analysis of Deep Neural Network Models for Practical Applications , 2016, ArXiv.

[12]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Xiaoou Tang,et al.  Image Super-Resolution Using Deep Convolutional Networks , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Seunghoon Hong,et al.  Learning Deconvolution Network for Semantic Segmentation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[15]  G. Feiertag,et al.  A systematic MEMS sensor calibration framework , 2015 .

[16]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[17]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[19]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[20]  Jafar Ghaisari,et al.  Intelligent Selection of Calibration Points Using a Modified Progressive Polynomial Method , 2012, IEEE Transactions on Instrumentation and Measurement.

[21]  Mariano Carrillo-Romero,et al.  Quantitative evaluation of self compensation algorithms applied in intelligent sensors , 2010, 2010 IEEE Instrumentation & Measurement Technology Conference Proceedings.

[22]  J. Rivera,et al.  Improved progressive polynomial algorithm for self-calibration and optimal response in smart sensors , 2009 .

[23]  José Rivera,et al.  Self-Calibration and Optimal Response in Intelligent Sensors Design Based on Artificial Neural Networks , 2007, Sensors (Basel, Switzerland).

[24]  Jagdish Chandra Patra,et al.  Auto-compensation of nonlinear influence of environmental parameters on the sensor characteristics using neural networks. , 2005, ISA transactions.

[25]  R. Pallas-Areny,et al.  Optimal two-point static calibration of measurement systems with quadratic response , 2004, Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.04CH37510).

[26]  Vivekanand Gopalkrishnan,et al.  Neural Network-Based Self-Calibration/ Compensation of Sensors Operating in Harsh Environments , 2004 .

[27]  J.C. Patra,et al.  Neural network-based self-calibration/compensation of sensors operating in harsh environments [smart pressure sensor example] , 2004, Proceedings of IEEE Sensors, 2004..

[28]  Santanu Kumar Rath,et al.  An intelligent pressure sensor with self-calibration capability using artificial neural networks , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[29]  Patra,et al.  Auto-calibration and -compensation of a capacitive pressure sensor using multilayer perceptrons , 2000, ISA transactions.

[30]  Johan H. Huijsing,et al.  Integrated Smart Sensor Calibration , 1997 .

[31]  Johan H. Huijsing,et al.  A noniterative polynomial 2-D calibration method implemented in a microcontroller , 1997 .

[32]  Kensall D. Wise,et al.  Digital compensation of high-performance silicon pressure transducers , 1990 .

[33]  Lawrence D. Jackel,et al.  Handwritten Digit Recognition with a Back-Propagation Network , 1989, NIPS.