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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Automatic rock classification of LIBS combined with 1DCNN based on an improved Bayesian optimization.

Guangdong Song, Shengen Zhu, Wenhao Zhang

    Applied Optics
    |January 6, 2023
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    This study combines laser-induced breakdown spectroscopy (LIBS) with one-dimensional convolutional neural networks (1DCNNs) for automated rock classification. An improved Bayesian optimization (BO) algorithm significantly enhances classification efficiency and accuracy.

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    Area of Science:

    • Geoscience and Spectroscopy
    • Artificial Intelligence in Materials Science

    Background:

    • Automated rock classification is crucial for efficient geological surveys and resource exploration.
    • Traditional methods often lack the speed and accuracy required for large-scale analysis.
    • Integrating spectroscopic data with advanced machine learning offers a promising alternative.

    Purpose of the Study:

    • To develop an automated rock classification system.
    • To improve the accuracy and efficiency of rock structure analysis.
    • To investigate the synergy between Laser-Induced Breakdown Spectroscopy (LIBS) and one-dimensional Convolutional Neural Networks (1DCNNs).

    Main Methods:

    • Utilized Laser-Induced Breakdown Spectroscopy (LIBS) for elemental composition analysis of rocks.
    • Employed one-dimensional Convolutional Neural Networks (1DCNNs) for pattern recognition in spectral data.
    • Developed and applied an improved Bayesian Optimization (BO) algorithm for hyperparameter tuning of the 1DCNN model.

    Main Results:

    • The proposed improved Bayesian Optimization (BO) algorithm significantly reduced modeling time by approximately 65%.
    • Achieved high classification accuracy: 99.33% on the validation set and 99.00% on the test set using the 1DCNN model.
    • Demonstrated the effectiveness of combining LIBS data with 1DCNNs for precise automated rock classification.

    Conclusions:

    • The integration of LIBS and 1DCNN, optimized by an improved BO algorithm, provides a highly accurate and efficient method for automated rock classification.
    • This approach offers substantial improvements in speed and performance compared to existing algorithms.
    • The methodology holds significant potential for advancing geological studies and material analysis.