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Prediction of shield machine attitude parameters based on decomposition and multi-head attention mechanism
Automation in Construction ( IF 9.6 ) Pub Date : 2025-01-17 , DOI: 10.1016/j.autcon.2025.105973
Qiushi Wang, Wenqi Ding, Kourosh Khoshelham, Yafei Qiao
Automation in Construction ( IF 9.6 ) Pub Date : 2025-01-17 , DOI: 10.1016/j.autcon.2025.105973
Qiushi Wang, Wenqi Ding, Kourosh Khoshelham, Yafei Qiao
To mitigate the impact of shield attitude prediction errors on operational decision-making, a framework centered on decomposition and deep learning is proposed to predict multiple shield attitudes. The shield time series data is decomposed into trends and fluctuations by integrating detrended fluctuation analysis and variational mode decomposition. A deep learning model augmented by the multi-head attention mechanism is proposed to independently predict trends and fluctuations. The multi-head attention mechanism enhances the prediction accuracy in simultaneously predicting six attitude parameters. Most prediction errors are allocated to fluctuations through decomposition. The precise prediction of trends provides significant insights into shield attitudes and reduces the risk of misleading outcomes. Compared with existing methods, the proposed method achieves greater precision while requiring fewer inference resources to predict all six attitude parameters. The contribution of multi-head attention and the reason behind prediction error allocation are analyzed via experiments and parameter sensitivity analysis.
中文翻译:
基于分解和多头注意力机制的盾机姿态参数预测
为了减轻盾牌姿态预测误差对作战决策的影响,提出了一种以分解和深度学习为中心的框架来预测多重盾牌姿态。通过整合去趋势波动分析和变分模态分解,将盾牌时间序列数据分解为趋势和波动。提出了一种由多头注意力机制增强的深度学习模型,用于独立预测趋势和波动。多头注意力机制提高了同时预测 6 个姿态参数的预测精度。大多数预测误差是通过分解分配给波动的。对趋势的精确预测为盾牌态度提供了重要的见解,并降低了误导结果的风险。与现有方法相比,所提出的方法实现了更高的精度,同时需要更少的推理资源来预测所有六个姿态参数。通过实验和参数敏感性分析分析多头注意力的贡献和预测误差分配背后的原因。
更新日期:2025-01-17
中文翻译:
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基于分解和多头注意力机制的盾机姿态参数预测
为了减轻盾牌姿态预测误差对作战决策的影响,提出了一种以分解和深度学习为中心的框架来预测多重盾牌姿态。通过整合去趋势波动分析和变分模态分解,将盾牌时间序列数据分解为趋势和波动。提出了一种由多头注意力机制增强的深度学习模型,用于独立预测趋势和波动。多头注意力机制提高了同时预测 6 个姿态参数的预测精度。大多数预测误差是通过分解分配给波动的。对趋势的精确预测为盾牌态度提供了重要的见解,并降低了误导结果的风险。与现有方法相比,所提出的方法实现了更高的精度,同时需要更少的推理资源来预测所有六个姿态参数。通过实验和参数敏感性分析分析多头注意力的贡献和预测误差分配背后的原因。