04/05/2024
By Danielle Fretwell

The Francis College of Engineering, Department of Electrical and Computer Engineering, invites you to attend a Doctoral Dissertation Proposal defense by Lidan Cao on: "Reconstruction Algorithm Based on Embedded Optical Sensing System."

Candidate Name: Lidan Cao
Degree: Doctoral
Defense Date: Tuesday, April 16, 2024
Time: 10:30 a.m. to noon
Location: Perry Hall 215

Committee:

  • Advisor: Xingwei Wang, Professor, Electrical and Computer Engineering Department, UMass Lowell
  • Xuejun Lu, Professor, Electrical and Computer Engineering Department, UMass Lowell
  • Hualiang Zhang, Professor, Electrical and Computer Engineering Department, UMass Lowell

Brief Abstract:
The distribution of temperature and strain fields holds significant importance across various industrial applications, emphasizing the need for accurate monitoring and mapping of these fields. Optical fiber sensors (OFS), which have emerged over the past half-century, are essential components in numerous applications due to their inherent advantages, such as small size, immunity to electromagnetic interference, and high sensitivity. Among OFS, distributed optical sensing systems, particularly those based on Optical Frequency Domain Reflectometry (OFDR), are pivotal in harnessing these advantages. OFDR, leveraging Rayleigh backscattering, enables distributed sensing with millimeter-level spatial resolution and exceptional sensitivity, making it ideal for precise measurements in diverse industrial settings. Its distributed sensing capabilities and high spatial resolution enable intricate pattern designs for enhanced data collection at specific locations. Effective packaging is crucial for integrating optical fiber sensors into various applications, and recent research has focused on embedding them within different materials to enhance their utility and effectiveness.

Concurrently, as sensing technologies continue to advance, there is a parallel development of temperature reconstruction algorithms. These algorithms are designed to enhance performance and accuracy in temperature reconstruction. Several algorithms, such as the least square method (LSM), algebraic reconstruction technique (ART), and radial basis function approximation (RBF), have been proposed, with RBF emerging as the most utilized in recent decades.

The objective of this proposal is to devise a reconstruction algorithm leveraging an embedded distributed sensing system. To achieve this, we initially conducted preliminary calibration tests on various embedded sensing systems employing OFDR distributed optical fiber sensors. Materials such as elastomer, 3D printed material, high-temperature thin film, and soft silicon were evaluated for their suitability. Subsequently, we developed and assessed a novel reconstruction algorithm based on an optimized RBF algorithm, utilizing data collected from distributed sensors.