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Data processing method for simultaneous estimation of temperature and emissivity in multispectral thermometry.

Zhuangtao Tian, Kaihua Zhang, Yanfen Xu

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    Summary
    This summary is machine-generated.

    This study introduces a new model for multispectral thermometry, overcoming challenges with unknown emissivity. The developed Multiple-Population Genetic (MPG) algorithm accurately estimates temperature and emissivity simultaneously.

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    Area of Science:

    • Physics
    • Engineering
    • Materials Science

    Background:

    • Accurate temperature measurement is crucial in many industrial applications.
    • Multispectral thermometry offers a non-contact method for temperature determination.
    • Unknown emissivity poses a significant challenge in multispectral thermometry data processing.

    Purpose of the Study:

    • To develop a novel data processing model for multispectral thermometry that addresses the challenge of unknown emissivity.
    • To introduce and validate new algorithms for optimizing the model.
    • To enable simultaneous estimation of temperature and emissivity without prior assumptions.

    Main Methods:

    • A new data processing model was established by incorporating emissivity constraints into the object function.
    • Two novel algorithms, Sequential Randomized Coordinate Shrinking (SRCS) and Multiple-Population Genetic (MPG), were developed for model optimization.
    • The MPG algorithm was employed to calculate the temperature and emissivity of two experimental samples.

    Main Results:

    • The Multiple-Population Genetic (MPG) algorithm demonstrated validity in practical applications.
    • Experimental results showed a relative temperature error within 0.4%.
    • The average calculation time for the MPG algorithm was 0.36 seconds.

    Conclusions:

    • The proposed method enables simultaneous estimation of temperature and emissivity without requiring emissivity assumptions.
    • The developed model and algorithms offer a robust solution for accurate multispectral thermometry.
    • The approach is suitable for real-time temperature measurement in industrial settings.