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<p>Fast and accurate coal-gangue identification techniques are essential for intelligent integrated mining and improved coal quality. However, existing methods are susceptible to high dust, noise, and other disturbances, resulting in unstable recognition results that cannot meet the demands of industrial applications. To address these challenges, this paper proposes a coal-gangue recognition method based on the fusion of a multi-scale parallel MCNN-BITCN network with an attention mechanism and the Improved Sparrow Search Algorithm (ISSA). The method combines a bidirectional spatio-temporal convolutional network (BITCN) with a multi-branch convolutional neural network (MCNN) to deeply mine and expand the extracted features. The time-frequency features are then fused through a cross-attention mechanism and fed into a fully connected layer. An improved sparrow search algorithm (ISSA) is used to generate the input weights and biases of the hidden layer nodes. This paper constructs an experimental platform to simulate the impact of coal-gangue on the tail beam of hydraulic support and conducts several comparative experiments. Results show that the MCNN-BITCN model maintains 83.76% accuracy even in high noise environments with a signal-to-noise ratio (SNR) of -5dB. After ISSA optimization, the model’s accuracy improves to 87.69%. These findings highlight the effectiveness and robustness of the proposed ISSA-MCNN-BITCN model under complex noise conditions.</p>

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An attentional fusion-based method for coal-gangue recognition in noisy environment of generalised workface

  • 期刊论文

  • 发表日期：

    2025年7月11日

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  • Qingjun Song,
  • Shirong Sun,
  • Qinghui Song,
  • Xinrui Jiang,
  • Haiyan Jiang,
  • Lina Lu

摘要

Fast and accurate coal-gangue identification techniques are essential for intelligent integrated mining and improved coal quality. However, existing methods are susceptible to high dust, noise, and other disturbances, resulting in unstable recognition results that cannot meet the demands of industrial applications. To address these challenges, this paper proposes a coal-gangue recognition method based on the fusion of a multi-scale parallel MCNN-BITCN network with an attention mechanism and the Improved Sparrow Search Algorithm (ISSA). The method combines a bidirectional spatio-temporal convolutional network (BITCN) with a multi-branch convolutional neural network (MCNN) to deeply mine and expand the extracted features. The time-frequency features are then fused through a cross-attention mechanism and fed into a fully connected layer. An improved sparrow search algorithm (ISSA) is used to generate the input weights and biases of the hidden layer nodes. This paper constructs an experimental platform to simulate the impact of coal-gangue on the tail beam of hydraulic support and conducts several comparative experiments. Results show that the MCNN-BITCN model maintains 83.76% accuracy even in high noise environments with a signal-to-noise ratio (SNR) of -5dB. After ISSA optimization, the model’s accuracy improves to 87.69%. These findings highlight the effectiveness and robustness of the proposed ISSA-MCNN-BITCN model under complex noise conditions.