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Uncertainty Evaluation of Two-Dimensional Horizontal Distributed Photometric Sensor Based on MCM for Illuminance

Jianguo Sun1, Yueyao Wang1, Yinbao Cheng1

  • 1College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China.

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|August 14, 2025
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Summary

This study presents a new framework for evaluating measurement uncertainty in distributed photometric sensors used for LED fill lights. It found the Monte Carlo method offers more accurate uncertainty assessment than the GUM method.

Keywords:
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Area of Science:

  • Optics and Photonics
  • Metrology
  • Optical Engineering

Background:

  • Precise illuminance measurement is crucial for LED fill light quality control and optical design.
  • Distributed photometric sensors offer advantages but face challenges in uncertainty assessment.
  • Existing methods may not fully capture the complexities of these advanced measurement systems.

Purpose of the Study:

  • To propose and validate an uncertainty evaluation framework for photometric parameter measurement using a two-dimensional horizontal distributed photometric sensor.
  • To compare the Guide to the Expression of Uncertainty in Measurement (GUM) and Monte Carlo method (MCM) for uncertainty synthesis in this context.
  • To enhance the reliability of optical metrology systems through improved uncertainty assessment.

Main Methods:

  • Development of an uncertainty analysis model for the two-dimensional horizontal distributed photometric sensor system.
  • Implementation and comparison of two uncertainty synthesis methods: GUM and MCM.
  • Design and execution of illuminance measurement experiments to validate the proposed framework.

Main Results:

  • The probability distribution of measurement data was found to follow a trapezoidal distribution.
  • The expanded uncertainty calculated using the GUM method was 21.1% higher than that obtained using the MCM.
  • The proposed framework effectively addresses uncertainty evaluation challenges for this type of sensor.

Conclusions:

  • The Monte Carlo method provides a more accurate uncertainty evaluation for distributed photometric sensors compared to the GUM method.
  • The findings are valuable for assessing uncertainty in high-precision optical instruments.
  • This research contributes significantly to enhancing the reliability of optical metrology systems.