Machine learning-based analysis of multiple simultaneous disturbances applied on a transmission-reflection analysis based distributed sensor using a nanoparticle-doped fiber

Abstract

Photonic technology combined with artificial intelligence plays a key role in the development of the latest smart system trends, integrating cutting-edge technology with machine learning models. This paper proposes a transmission-reflection analysis based system using dielectric nanoparticle-doped fiber combined with artificial intelligence to address one of the major problems in the distributed sensing approach: reducing the cost while maintaining high spatial resolution to close the gap between distributed sensors and the general public. Machine learning-based models are designed to classify the perturbed positions when the same force is used and force regression when different forces are applied on each position. The results show an accuracy of 99.43% in the position classification of multiple disturbances and an rms error of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m1"> <mml:mrow> <mml:mn>1.53</mml:mn> <mml:mtext> </mml:mtext> <mml:mi mathvariant="normal">N</mml:mi> </mml:mrow> </mml:math> in the force regression, which represents 5% of the force range. In addition, a smart environment using the current system is proposed, which presented 100% accuracy in identifying the positions of different persons in the environment. This smart environment enables remote home care of patients with high reliability, intelligent decision-making, and a predictive capability.