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A Novel Reconstruction Method for Temperature Distribution Measurement Based on Ultrasonic Tomography
Summary
A new two-step method using an improved equilibrium optimizer (IEO) and Gaussian process regression (GPR) enhances ultrasonic tomography (UT) for precise temperature measurement. This approach achieves high-resolution images with low errors in industrial applications.
Area of Science:
- Industrial instrumentation
- Non-invasive sensing technologies
- Computational imaging
Background:
- Precise temperature distribution measurement is critical across various industrial sectors.
- Ultrasonic tomography (UT) offers significant potential for non-invasive temperature monitoring.
- Existing UT methods face challenges in achieving both high resolution and accuracy in temperature reconstruction.
Purpose of the Study:
- To develop a novel two-step reconstruction method for improving the resolution and accuracy of temperature distribution images obtained via ultrasonic tomography.
- To address limitations in current UT temperature measurement techniques.
- To enhance the industrial applicability of ultrasonic tomography for thermal imaging.
Main Methods:
- A two-step reconstruction approach combining an improved equilibrium optimizer (IEO) with Gaussian process regression (GPR).
- The IEO, featuring a new nonlinear time strategy and population update rules, is used for initial low-resolution, high-precision reconstruction.
- GPR is subsequently applied to enhance image resolution while maintaining low reconstruction errors.
Main Results:
- The proposed IEO-GPR method effectively reconstructs high-resolution temperature distribution images with minimal errors.
- Numerical simulations and experiments demonstrate the method's robust performance.
- For a complex three-peak temperature distribution, the method achieved competitive accuracy with 3.10% RMSE and 2.37% ARE.
- In practical experiments, IEO-GPR yielded a 0.72% RMSE, outperforming conventional algorithms by at least 0.89%.
Conclusions:
- The IEO-GPR method provides an effective solution for high-resolution and accurate temperature distribution measurement using ultrasonic tomography.
- The study highlights the successful integration of optimization algorithms and machine learning for advanced imaging applications.
- The developed technique shows significant promise for improving industrial process monitoring and control.
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