文章针对传统方法在复杂地层盾构掘进过程中关于土仓压力超前预测精度不高的问题,提出了一种融合时间卷积神经网络(TCN)、多头稀疏自注意力(SMHA)、长短时记忆网络(LSTM)和时间感知注意力(TPA)的混合神经网络模型,用于精准预测复杂地层...文章针对传统方法在复杂地层盾构掘进过程中关于土仓压力超前预测精度不高的问题,提出了一种融合时间卷积神经网络(TCN)、多头稀疏自注意力(SMHA)、长短时记忆网络(LSTM)和时间感知注意力(TPA)的混合神经网络模型,用于精准预测复杂地层盾构施工中的土仓压力。TCN模块通过多层卷积捕捉时间序列的局部特征,而多头稀疏自注意力机制进一步增强了模型对长距离依赖关系的建模能力,LSTM层则专注于捕获时间序列的长期依赖信息,TPA模块自适应地调整注意力权重,从而提高预测精度。该模型在盾构施工中的应用表明,其在复杂地质条件下对盾构土仓压力的超前预测具有较高的准确性和适应性,有助于盾构施工的实时监控与决策。To address the issue of low prediction accuracy in traditional methods for forecasting chamber pressure during shield tunneling in complex geological conditions, this article proposes a hybrid neural network model based on Temporal Convolutional Network, Sparse Multi-Head Attention, Long Short-Term Memory, and Temporal Pattern Attention. The model is designed for precise prediction of chamber pressure in shield construction. The TCN module captures local features of time series through multi-layer convolution, while the SMHA mechanism further strengthens the model’s ability to model long-range dependencies. The LSTM layer focuses on extracting long-term dependencies in time series, and the TPA module adaptively adjusts attention weights to improve prediction accuracy. The application of this model in shield tunneling demonstrates its high accuracy and adaptability in forecasting chamber pressure under complex geological conditions, contributing to real-time monitoring and decision-making in tunneling operations.展开更多
针对具有时频特性的雷达信号,传统的雷达信号识别方法已经无法满足对信号类型精准识别的需求,因此需要通过采集并分析雷达信号脉内的时频特征实现对目标雷达的具体信息进行有效评估。设计了一种卷积-双向长短时记忆(Convolution-Bidirec...针对具有时频特性的雷达信号,传统的雷达信号识别方法已经无法满足对信号类型精准识别的需求,因此需要通过采集并分析雷达信号脉内的时频特征实现对目标雷达的具体信息进行有效评估。设计了一种卷积-双向长短时记忆(Convolution-Bidirectional Long Short-Term Memory,CNN-BiLSTM)混合神经网络模型,主要通过BiLSTM的时序记忆特性深度挖掘雷达信号的时域特征,结合权值共享特性和CNN层捕获雷达信号的时频特征,再利用二者信号特征联合完成对雷达信号调制方式的识别。通过对比实验验证,所提方法对若干种雷达信号的识别具有较高的准确度,平均值达到95.349%;优于只使用单一特征的网络和传统算法,具有良好的抗噪声能力。展开更多
文摘文章针对传统方法在复杂地层盾构掘进过程中关于土仓压力超前预测精度不高的问题,提出了一种融合时间卷积神经网络(TCN)、多头稀疏自注意力(SMHA)、长短时记忆网络(LSTM)和时间感知注意力(TPA)的混合神经网络模型,用于精准预测复杂地层盾构施工中的土仓压力。TCN模块通过多层卷积捕捉时间序列的局部特征,而多头稀疏自注意力机制进一步增强了模型对长距离依赖关系的建模能力,LSTM层则专注于捕获时间序列的长期依赖信息,TPA模块自适应地调整注意力权重,从而提高预测精度。该模型在盾构施工中的应用表明,其在复杂地质条件下对盾构土仓压力的超前预测具有较高的准确性和适应性,有助于盾构施工的实时监控与决策。To address the issue of low prediction accuracy in traditional methods for forecasting chamber pressure during shield tunneling in complex geological conditions, this article proposes a hybrid neural network model based on Temporal Convolutional Network, Sparse Multi-Head Attention, Long Short-Term Memory, and Temporal Pattern Attention. The model is designed for precise prediction of chamber pressure in shield construction. The TCN module captures local features of time series through multi-layer convolution, while the SMHA mechanism further strengthens the model’s ability to model long-range dependencies. The LSTM layer focuses on extracting long-term dependencies in time series, and the TPA module adaptively adjusts attention weights to improve prediction accuracy. The application of this model in shield tunneling demonstrates its high accuracy and adaptability in forecasting chamber pressure under complex geological conditions, contributing to real-time monitoring and decision-making in tunneling operations.
文摘针对具有时频特性的雷达信号,传统的雷达信号识别方法已经无法满足对信号类型精准识别的需求,因此需要通过采集并分析雷达信号脉内的时频特征实现对目标雷达的具体信息进行有效评估。设计了一种卷积-双向长短时记忆(Convolution-Bidirectional Long Short-Term Memory,CNN-BiLSTM)混合神经网络模型,主要通过BiLSTM的时序记忆特性深度挖掘雷达信号的时域特征,结合权值共享特性和CNN层捕获雷达信号的时频特征,再利用二者信号特征联合完成对雷达信号调制方式的识别。通过对比实验验证,所提方法对若干种雷达信号的识别具有较高的准确度,平均值达到95.349%;优于只使用单一特征的网络和传统算法,具有良好的抗噪声能力。
