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Related Concept Videos

IR Spectrometers01:25

IR Spectrometers

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There are two main infrared (IR) spectrophotometers: dispersive IR spectrometers and Fourier transform infrared (FTIR) spectrometers. In a dispersive IR spectrometer, a beam of infrared radiation produced by a hot wire is divided into two parallel equal-intensity beams using mirrors. One beam passes through the sample, while another is a reference beam. The beams then move through the monochromator, which separates the radiations into a continuous spectrum of different frequencies. The...
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Attenuated total reflectance (ATR) infrared spectroscopy is a powerful analytical technique used to study the composition of materials. It is widely employed in chemistry, materials science, forensic science, and other fields where sample characterization is required. ATR has several advantages over traditional transmission IR spectroscopy, including the requirement of little to no sample preparation and the ability to analyze a wide range of samples.
The ATR process begins by directing a beam...
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Multispectral radiometric thermometry inversion algorithm using multisource information fusion.

Nannan Zhang, Yitong Liu, Jian Xing

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    This study introduces a novel multispectral thermometry method using AI to accurately measure temperature even with unknown emissivity. The enhanced algorithm significantly improves precision and speed for industrial applications.

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    Area of Science:

    • Physics
    • Engineering
    • Artificial Intelligence

    Background:

    • Accurate temperature measurement is critical in industrial processes.
    • Traditional multispectral thermometry faces challenges with unknown emissivity, leading to inaccuracies.
    • Existing methods often lack the speed required for real-time industrial monitoring.

    Purpose of the Study:

    • To develop a precise and efficient multispectral temperature measurement method.
    • To overcome the limitations of unknown emissivity in temperature determination.
    • To provide a robust solution for industrial multispectral thermometry applications.

    Main Methods:

    • Utilized convolutional neural networks (CNNs) for image recognition to transform voltage data into spectral images.
    • Integrated emissivity trend inversion and emissivity constraint algorithms to enhance accuracy.
    • Incorporated a temperature-constraint function for improved computational speed.

    Main Results:

    • Achieved a relative error below 0.08% and absolute error below 2 K for temperatures between 1000 K and 4000 K.
    • Demonstrated an average runtime of under 5 ms, indicating high efficiency.
    • Validated the method through simulation experiments and comparative tests.

    Conclusions:

    • The proposed method significantly enhances temperature measurement accuracy in multispectral thermometry.
    • The algorithm effectively addresses efficiency issues, making it suitable for industrial use.
    • This technique offers crucial technical support for advanced industrial temperature monitoring.