Mo-SML-03 - Characterizing Resonance Scaling in Cattle Eructation Sounds for Methane Emission Estimation

Event

EUROSENSORS 2026
2026-09-06 - 2026-09-09
Zurich

Band

Poster

Chapter

Signal Analysis, Machine Learning, Physics-Informed Machine Learning And Artificial Intelligence For Sensing

Author(s)

Y. Hu, J. Fastier-Wooller, S. Muramatsu, M. Yamamoto, T. Itoh - The University of Tokyo,Tokyo (Japan)

Pages

692 - 693

DOI

10.5162/eurosensors2026/Mo-SML-03

ISBN

978-3-910600-12-6

Price

free

Abstract

Real-time monitoring of the trajectory and size of the particle, droplet or bubble is relevant in many multiphase processes, but imaging techniques often fail in optically inaccessible media. In that case, electrical impedance tomography (EIT) provides a non-invasive alternative. This work presents a simulation-based study of an eight-electrode EIT sensor for monitoring a circular conductivity anomaly. We introduce a preliminary study deploying a parametric physics-informed neural network (PINN) approach that uses the center estimate from conventional Jacobian-based reconstruction to refine the trajectory and estimate the radius of an anomaly from simulated boundary-voltage measurements.