research-article

Deriving Equations from Sensor Data Using Dimensional Function Synthesis

Authors:

Vasileios Tsoutsouras,

Phillip Stanley-MarbellAuthors Info & Claims

ACM Transactions on Embedded Computing Systems (TECS), Volume 18, Issue 5s

Article No.: 84, Pages 1 - 22

https://doi.org/10.1145/3358218

Published: 08 October 2019 Publication History

Abstract

We present a new method for deriving functions that model the relationship between multiple signals in a physical system. The method, which we call dimensional function synthesis, applies to data streams where the dimensions of the signals are known. The method comprises two phases: a compile-time synthesis phase and a subsequent calibration using sensor data.

We implement dimensional function synthesis and use the implementation to demonstrate efficiently summarizing multi-modal sensor data for two physical systems using 90 laboratory experiments and 10 000 synthetic idealized measurements. We evaluate the performance of the compile-time phase of dimensional function synthesis as well as the calibration phase overhead, inference latency, and accuracy of the models our method generates.

The results show that our technique can generate models in less than 300 ms on average across all the physical systems we evaluated. When calibrated with sensor data, our models outperform traditional regression and neural network models in inference accuracy in all the cases we evaluated. In addition, our models perform better in training latency (over 8660× improvement) and required arithmetic operations in inference (over 34× improvement). These significant gains are largely the result of exploiting information on the physics of signals that has hitherto been ignored.

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Deriving Equations from Sensor Data Using Dimensional Function Synthesis

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cover image ACM Transactions on Embedded Computing Systems

ACM Transactions on Embedded Computing Systems Volume 18, Issue 5s

Special Issue ESWEEK 2019, CASES 2019, CODES+ISSS 2019 and EMSOFT 2019

October 2019

1423 pages

ISSN:1539-9087

EISSN:1558-3465

DOI:10.1145/3365919

Editor:
Sandeep K. Shukla
Indian Institute of Technology, India

Issue’s Table of Contents

Copyright © 2019 ACM.

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 08 October 2019

Accepted: 01 July 2019

Revised: 01 June 2019

Received: 01 April 2019

Published in TECS Volume 18, Issue 5s

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https://dl.acm.org/doi/10.1145/3640537.3641576
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https://doi.org/10.23939/istcipa2023.57.013
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https://dl.acm.org/doi/10.1145/3633804
Lindemann LJiang LMatni NPappas G(2023)Risk of Stochastic Systems for Temporal Logic SpecificationsACM Transactions on Embedded Computing Systems10.1145/358049022:3(1-31)Online publication date: 19-Apr-2023
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