To address these limitations, the Optical
Carrier Microwave Interferometry (OCMI) technique has emerged as a promising
alternative. OCMI combines the advantages of optics and microwaves, offering
features like distributed sensing, high positioning accuracy, and insensitivity
to optical polarization. It has been successfully used for measuring physical
parameters like temperature, strain, pressure, and vibration. However,
conventional demodulation methods used with OCMI—such as dip frequency tracking
and phase demodulation—are typically limited to detecting one parameter at a
time and may struggle with low sensitivity or resolution, especially when
applied to complex systems like gas-liquid two-phase flow. These limitations
highlight the need for an improved approach that can handle the simultaneous
measurement of multiple flow parameters under dynamic industrial conditions.
In
response, this study proposes a new sensing method that integrates OCMI
technology with machine learning, specifically artificial neural networks
(ANN). By learning from data patterns, ANN models can simultaneously predict
gas and liquid flow rates, overcoming the limitations of traditional OCMI
demodulation. This approach not only enhances measurement accuracy and
adaptability to different flow patterns but also provides a foundation for
real-time monitoring in complex flow environments. The effectiveness of
different input schemes to the neural network is also investigated,
demonstrating the potential of combining advanced sensing techniques with
intelligent data processing to revolutionize multiphase flow measurement in
industrial applications.
International Conference on Computer Vision
The International Research Awards on Computer Vision recognize groundbreaking contributions in the field of computer vision, honoring researchers, scientists and innovators whose work has significantly advanced the domain. This prestigious award highlights excellence in fundamental theories, novel algorithms and real-world applications, fostering progress in artificial intelligence, image processing and deep learning.
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