Ji, Yuan

Eddy Current Nondestructive Evaluation (NDE)

Automated Eddy Current Pipeline CUI Inspection System

  • Externally applied testing solution without insulation removal
  • Imperfections on aluminum or stainless jacketing raise no false positive.
  • High sensitivity with up to 5 inch of lift-off
  • Machine learning based signal processing.

Alternative Current Potential Drop System

  • Accurate characterization of material electrical conductivity and linear magnetic permeability.
  • Coating thickness measurement and crack sizing.
  • Ultra high precision resistance measurement (rms error <20 nΩ).

Machine Learning on Edge Devices

  • STM32 solutions for artificial neural networks.
  • NVIDIA® Jetson™ and Raspberry Pi ecosystem.
  • Xilinx® Vitis™ AI for FPGA accelerated model inference.
Automated Eddy Current Pipeline CUI Inspection System
Automated Eddy Current Pipeline CUI Inspection System

Yuan JiYuan Ji

Research Scientist II



  • Eddy current system design and testing.
  • Electromagnetic pipeline inspection.
  • Radio frequency, analog, digital, power and motor control circuit design.
  • Embedded system and machine learning FPGA acceleration.


  • Eddy current NDE, ACPD NDE, Embedded System, FPGA, Machine Learning

Selected Publications & Patents

  • John R Bowler, Theodoros P Theodoulidis, Hui Xie, and Yuan Ji, “Evaluation of eddy-current probe signals due to cracks in fastener holes.” IEEE Transactions on Magnetics, vol. 48, no. 3, pp. 1159-1170, 2011
  • US Patent 10816508 “Planar Array Pipeline Inspection Tool” July 18, 2019
  • US Patent 10031108 “Multi-Frequency Eddy Current Pipeline Inspection Apparatus and Method” July 24, 2018