针对传统机器学习方法对特征依赖大,以及传统卷积神经网络只通过提取重要的局部特征来完成识别分类,收敛速度慢的问题,提出了一维多尺度卷积神经网络和门控循环单元相结合的入侵检测方法。该方法使用一维多尺度卷积神经网络加强对特征...针对传统机器学习方法对特征依赖大,以及传统卷积神经网络只通过提取重要的局部特征来完成识别分类,收敛速度慢的问题,提出了一维多尺度卷积神经网络和门控循环单元相结合的入侵检测方法。该方法使用一维多尺度卷积神经网络加强对特征的捕捉能力,加快收敛速度,采用门控循环单元把握空间特征,减少通道数量扩张,降低数据维度。使用KDD CUP 99数据集和密西西比州大学的天然气管道的数据集进行仿真实验,结果表明与经典的机器学习分类器相比,该方法具有较高的入侵检测性能和较好的泛化能力。展开更多
In order to solve the problem of low accuracy of construction project duration prediction, this paper proposes a CNN attention BP combination model </span><span style="font-family:"white-space:...In order to solve the problem of low accuracy of construction project duration prediction, this paper proposes a CNN attention BP combination model </span><span style="font-family:"white-space:normal;">project risk prediction model based on attention mechanism, one-dimensional </span><span style="font-family:"white-space:normal;">convolutional neural network (1d-cnn) and BP neural network. Firstly, the literature analysis method is used to select the risk evaluation index value of construction project, and the attention mechanism is used to determine the weight of risk factors on construction period prediction;then, BP neural network is used to predict the project duration, and accuracy, cross entropy loss function and F1 score are selected to comprehensively evaluate the performance of 1d-cnn-attention-bp combined model. The experimental results show that the duration risk prediction accuracy of the risk prediction model proposed in this paper is more than 90%, which can meet the risk prediction of construction projects with high accuracy.展开更多
剩余寿命预测对航空发动机的预防性维修有重要指导作用,是保障飞机安全运行,提高维修保障效率的重要手段。一维卷积神经网络(1-dimensional convolutional neural network,1D-CNN)和双向长短时记忆神经网络(Bidirectional long short me...剩余寿命预测对航空发动机的预防性维修有重要指导作用,是保障飞机安全运行,提高维修保障效率的重要手段。一维卷积神经网络(1-dimensional convolutional neural network,1D-CNN)和双向长短时记忆神经网络(Bidirectional long short memory, Bi-LSTM)被应用于航空发动机剩余寿命预测模型。首先,根据工程经验在多状态参数的主成分分析的基础上对退化过程进行随机分布拟合,得到综合性能退化量;然后将多变量时间序列样本和对应的性能退化量代入1D-CNN模型进行回归分析,从而得到性能退化分析模型;再通过Bi-LSTM对性能退化量进行时间序列预测,得到性能退化的未来趋势;最后通过设定性能退化阈值,得到剩余寿命预测结果,从而得到从多状态参数-性能退化分析-性能退化预测-剩余寿命预测的实时动态感知模型。实例分析结果表明,提出的混合模型与其他单一深度学习和传统模型相比,有更低的回归分析误差和退化预测误差,能够得到更准确可靠的剩余寿命预测结果。展开更多
文摘针对传统机器学习方法对特征依赖大,以及传统卷积神经网络只通过提取重要的局部特征来完成识别分类,收敛速度慢的问题,提出了一维多尺度卷积神经网络和门控循环单元相结合的入侵检测方法。该方法使用一维多尺度卷积神经网络加强对特征的捕捉能力,加快收敛速度,采用门控循环单元把握空间特征,减少通道数量扩张,降低数据维度。使用KDD CUP 99数据集和密西西比州大学的天然气管道的数据集进行仿真实验,结果表明与经典的机器学习分类器相比,该方法具有较高的入侵检测性能和较好的泛化能力。
文摘In order to solve the problem of low accuracy of construction project duration prediction, this paper proposes a CNN attention BP combination model </span><span style="font-family:"white-space:normal;">project risk prediction model based on attention mechanism, one-dimensional </span><span style="font-family:"white-space:normal;">convolutional neural network (1d-cnn) and BP neural network. Firstly, the literature analysis method is used to select the risk evaluation index value of construction project, and the attention mechanism is used to determine the weight of risk factors on construction period prediction;then, BP neural network is used to predict the project duration, and accuracy, cross entropy loss function and F1 score are selected to comprehensively evaluate the performance of 1d-cnn-attention-bp combined model. The experimental results show that the duration risk prediction accuracy of the risk prediction model proposed in this paper is more than 90%, which can meet the risk prediction of construction projects with high accuracy.
文摘剩余寿命预测对航空发动机的预防性维修有重要指导作用,是保障飞机安全运行,提高维修保障效率的重要手段。一维卷积神经网络(1-dimensional convolutional neural network,1D-CNN)和双向长短时记忆神经网络(Bidirectional long short memory, Bi-LSTM)被应用于航空发动机剩余寿命预测模型。首先,根据工程经验在多状态参数的主成分分析的基础上对退化过程进行随机分布拟合,得到综合性能退化量;然后将多变量时间序列样本和对应的性能退化量代入1D-CNN模型进行回归分析,从而得到性能退化分析模型;再通过Bi-LSTM对性能退化量进行时间序列预测,得到性能退化的未来趋势;最后通过设定性能退化阈值,得到剩余寿命预测结果,从而得到从多状态参数-性能退化分析-性能退化预测-剩余寿命预测的实时动态感知模型。实例分析结果表明,提出的混合模型与其他单一深度学习和传统模型相比,有更低的回归分析误差和退化预测误差,能够得到更准确可靠的剩余寿命预测结果。
