云计算的快速发展使得服务器面临的负载压力逐渐增加,如何精准预测负载资源成为云中心资源分配与服务器安全运行的重要课题。现有的单一模型在捕捉全局特征方面存在不足,而组合模型在处理时序数据时的平稳性和解释性方面有所欠缺。因此...云计算的快速发展使得服务器面临的负载压力逐渐增加,如何精准预测负载资源成为云中心资源分配与服务器安全运行的重要课题。现有的单一模型在捕捉全局特征方面存在不足,而组合模型在处理时序数据时的平稳性和解释性方面有所欠缺。因此,提出一种基于NeuralProphet分解的卷积神经网络(CNN)-长短期记忆(LSTM)网络-注意力(Attention)机制的组合模型。NeuralProphet将负载数据分解为趋势、季节和自回归项分量,增强数据的平稳性和解释性,从而使模型能更高效地捕捉全局特征和长期依赖关系;并通过注意力机制动态权重分配,聚焦影响预测结果的关键特征,进一步提高对未来时刻的预测精度。在Alibaba Cluster Data V2018数据集上的实验结果表明,所提出的组合模型在预测精度和性能方面优于其他深度学习模型。与单一模型NeuralProphet及CNN-BiLSTM组合模型相比,该模型在R2评分上提高了17.9%,均方根误差(RMSE)降低了73.6%,平均绝对误差(MAE)降低了69.7%,对称平均绝对百分比误差(sMAPE)降低了65.3%,具备更高的预测准确性和鲁棒性,有助于提高云资源利用效率。展开更多
Taking Jiuhong Modern Agriculture Demonstration Park of Heilongjiang Province as the base for rice disease image acquisition,a total of 841 images of the four different diseases,including rice blast,stripe leaf blight...Taking Jiuhong Modern Agriculture Demonstration Park of Heilongjiang Province as the base for rice disease image acquisition,a total of 841 images of the four different diseases,including rice blast,stripe leaf blight,red blight and bacterial brown spot,were obtained.In this study,an interleaved attention neural network(IANN)was proposed to realize the recognition of rice disease images and an interleaved group convolutions(IGC)network was introduced to reduce the number of convolutional parameters,which realized the information interaction between channels.Based on the convolutional block attention module(CBAM),attention was paid to the features of results of the primary group convolution in the cross-group convolution to improve the classification performance of the deep learning model.The results showed that the classification accuracy of IANN was 96.14%,which was 4.72%higher than that of the classical convolutional neural network(CNN).This study showed a new idea for the efficient training of neural networks in the case of small samples and provided a reference for the image recognition and diagnosis of rice and other crop diseases.展开更多
针对能量收集无线传感器网络中,能量预测精度不佳、节点能量利用效率过低和网络难以持续运行等问题,提出了一种改进样本卷积交互神经网络(sample convolution and interaction network,SCINet)预测模型,并引入概率稀疏自注意力机制,在...针对能量收集无线传感器网络中,能量预测精度不佳、节点能量利用效率过低和网络难以持续运行等问题,提出了一种改进样本卷积交互神经网络(sample convolution and interaction network,SCINet)预测模型,并引入概率稀疏自注意力机制,在新特征序列的每个时间步上计算注意力权重,捕捉重要特征,提高模型预测精度.最后,根据节点剩余能量、预测未来可收集的太阳能能量,对分簇路由算法进行改进.仿真实验结果表明,该能量预测模型具备更高的预测精度和泛化能力.在能量预测模型的基础上,改进的分簇路由算法,能有效地延长无线传感器网络的生命周期.展开更多
文摘云计算的快速发展使得服务器面临的负载压力逐渐增加,如何精准预测负载资源成为云中心资源分配与服务器安全运行的重要课题。现有的单一模型在捕捉全局特征方面存在不足,而组合模型在处理时序数据时的平稳性和解释性方面有所欠缺。因此,提出一种基于NeuralProphet分解的卷积神经网络(CNN)-长短期记忆(LSTM)网络-注意力(Attention)机制的组合模型。NeuralProphet将负载数据分解为趋势、季节和自回归项分量,增强数据的平稳性和解释性,从而使模型能更高效地捕捉全局特征和长期依赖关系;并通过注意力机制动态权重分配,聚焦影响预测结果的关键特征,进一步提高对未来时刻的预测精度。在Alibaba Cluster Data V2018数据集上的实验结果表明,所提出的组合模型在预测精度和性能方面优于其他深度学习模型。与单一模型NeuralProphet及CNN-BiLSTM组合模型相比,该模型在R2评分上提高了17.9%,均方根误差(RMSE)降低了73.6%,平均绝对误差(MAE)降低了69.7%,对称平均绝对百分比误差(sMAPE)降低了65.3%,具备更高的预测准确性和鲁棒性,有助于提高云资源利用效率。
基金Supported by the Heilongjiang Provincial Key Research and Development Program Guidance Project(GZ20210103)。
文摘Taking Jiuhong Modern Agriculture Demonstration Park of Heilongjiang Province as the base for rice disease image acquisition,a total of 841 images of the four different diseases,including rice blast,stripe leaf blight,red blight and bacterial brown spot,were obtained.In this study,an interleaved attention neural network(IANN)was proposed to realize the recognition of rice disease images and an interleaved group convolutions(IGC)network was introduced to reduce the number of convolutional parameters,which realized the information interaction between channels.Based on the convolutional block attention module(CBAM),attention was paid to the features of results of the primary group convolution in the cross-group convolution to improve the classification performance of the deep learning model.The results showed that the classification accuracy of IANN was 96.14%,which was 4.72%higher than that of the classical convolutional neural network(CNN).This study showed a new idea for the efficient training of neural networks in the case of small samples and provided a reference for the image recognition and diagnosis of rice and other crop diseases.
文摘针对能量收集无线传感器网络中,能量预测精度不佳、节点能量利用效率过低和网络难以持续运行等问题,提出了一种改进样本卷积交互神经网络(sample convolution and interaction network,SCINet)预测模型,并引入概率稀疏自注意力机制,在新特征序列的每个时间步上计算注意力权重,捕捉重要特征,提高模型预测精度.最后,根据节点剩余能量、预测未来可收集的太阳能能量,对分簇路由算法进行改进.仿真实验结果表明,该能量预测模型具备更高的预测精度和泛化能力.在能量预测模型的基础上,改进的分簇路由算法,能有效地延长无线传感器网络的生命周期.
