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An Information Entropy-Based Modeling Method for the Measurement System.

Li Kong1, Hao Pan1, Xuewei Li2

  • 1School of Artificial Intelligence and Automation, Key Laboratory of Image Processing and Intelligent Control, Ministry of Education, Huazhong University of Science and Technology, Wuhan 430074, China.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel information entropy-based model for measurement systems. This approach enhances understanding of information acquisition and loss, complementing existing theories.

Keywords:
information acquisitioninformation entropymeasurement systemmodelinguncertainty

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Area of Science:

  • Metrology and Measurement Science
  • Information Theory
  • Systems Engineering

Background:

  • Measurement systems are crucial for real-world information acquisition but existing models are often too abstract.
  • This abstraction hinders a clear understanding of system operation and information processing.
  • Limitations in current models restrict their practical application and optimization.

Purpose of the Study:

  • To propose a unified, information entropy-based modeling method for measurement systems.
  • To address the abstract nature of existing models by focusing on information and uncertainty.
  • To provide a more intuitive and comprehensive framework for understanding measurement processes.

Main Methods:

  • Developed a modeling concept rooted in information theory and uncertainty principles.
  • Introduced an entropy balance equation, derived from the chain rule for entropy, for system modeling.
  • Applied the proposed entropy balance equation to construct an information entropy-based measurement system model.

Main Results:

  • Successfully modeled three typical measurement units/processes using the information entropy approach.
  • The proposed model offers an intuitive description of information processing and transformation within measurement systems.
  • Demonstrated that the method focuses on information loss and the role of measurement units.

Conclusions:

  • The information entropy-based model provides a complementary perspective to existing measurement system models.
  • It enhances the understanding of information flow and uncertainty within measurement systems.
  • This approach enriches measurement theory by offering a more concrete and applicable modeling framework.