Characteristics of the Internet traffic data flow are studied based on the chaos theory. A phase space that is isometric with the network dynamic system is reconstructed by using the single variable time series of a n...Characteristics of the Internet traffic data flow are studied based on the chaos theory. A phase space that is isometric with the network dynamic system is reconstructed by using the single variable time series of a network flow. Some parameters, such as the correlative dimension and the Lyapunov exponent are calculated, and the chaos characteristic is proved to exist in Internet traffic data flows. A neural network model is construct- ed based on radial basis function （RBF） to forecast actual Internet traffic data flow. Simulation results show that, compared with other forecasts of the forward-feedback neural network, the forecast of the RBF neural network based on the chaos theory has faster learning capacity and higher forecasting accuracy.展开更多
传统网络异常流量检测方法存在忽略网络拓扑结构、获取标注数据成本高等问题,导致模型的准确率和泛化性较低。为此,文中提出了一种基于图神经网络和自监督学习的检测方法。利用网络流量数据的特点构建自监督图对比学习任务,通过边特征...传统网络异常流量检测方法存在忽略网络拓扑结构、获取标注数据成本高等问题,导致模型的准确率和泛化性较低。为此,文中提出了一种基于图神经网络和自监督学习的检测方法。利用网络流量数据的特点构建自监督图对比学习任务,通过边特征变换和边遮掩进行流量图增强生成对比样本。改进基于GraphSAGE(Graph SAmple and aggreGatE)的图编码器以充分利用相关关系来丰富节点的特征表示。使用适合对比学习的InfoNCE损失函数训练图编码器的参数,实现自主学习特征表示,摆脱对网络流量标签数据的依赖,并提高网络异常流量检测的准确率。实验结果表明,所提模型在没有标签数据的情况下在检测异常网络流量性能方面表现良好,在两个公开数据集上的F1值分别达到了92.64%和90.97%。展开更多
In this paper, the network equation for the slow neutron capture process （s-process） of heavy element nucleosynthesis is investigated. Dividing the s-process network reaction chains into two standard forms and using...In this paper, the network equation for the slow neutron capture process （s-process） of heavy element nucleosynthesis is investigated. Dividing the s-process network reaction chains into two standard forms and using the technique of matrix decomposition, a group of analytical solutions for the network equation are obtained. With the analytical solutions, a calculation for heavy element abundance of the solar system is carried out and the results are in good agreement with the astrophysical measurements.展开更多
As sandstone layers in thin interbedded section are difficult to identify,conventional model-driven seismic inversion and data-driven seismic prediction methods have low precision in predicting them.To solve this prob...As sandstone layers in thin interbedded section are difficult to identify,conventional model-driven seismic inversion and data-driven seismic prediction methods have low precision in predicting them.To solve this problem,a model-data-driven seismic AVO(amplitude variation with offset)inversion method based on a space-variant objective function has been worked out.In this method,zero delay cross-correlation function and F norm are used to establish objective function.Based on inverse distance weighting theory,change of the objective function is controlled according to the location of the target CDP(common depth point),to change the constraint weights of training samples,initial low-frequency models,and seismic data on the inversion.Hence,the proposed method can get high resolution and high-accuracy velocity and density from inversion of small sample data,and is suitable for identifying thin interbedded sand bodies.Tests with thin interbedded geological models show that the proposed method has high inversion accuracy and resolution for small sample data,and can identify sandstone and mudstone layers of about one-30th of the dominant wavelength thick.Tests on the field data of Lishui sag show that the inversion results of the proposed method have small relative error with well-log data,and can identify thin interbedded sandstone layers of about one-15th of the dominant wavelength thick with small sample data.展开更多
文摘Characteristics of the Internet traffic data flow are studied based on the chaos theory. A phase space that is isometric with the network dynamic system is reconstructed by using the single variable time series of a network flow. Some parameters, such as the correlative dimension and the Lyapunov exponent are calculated, and the chaos characteristic is proved to exist in Internet traffic data flows. A neural network model is construct- ed based on radial basis function （RBF） to forecast actual Internet traffic data flow. Simulation results show that, compared with other forecasts of the forward-feedback neural network, the forecast of the RBF neural network based on the chaos theory has faster learning capacity and higher forecasting accuracy.
文摘传统网络异常流量检测方法存在忽略网络拓扑结构、获取标注数据成本高等问题,导致模型的准确率和泛化性较低。为此,文中提出了一种基于图神经网络和自监督学习的检测方法。利用网络流量数据的特点构建自监督图对比学习任务,通过边特征变换和边遮掩进行流量图增强生成对比样本。改进基于GraphSAGE(Graph SAmple and aggreGatE)的图编码器以充分利用相关关系来丰富节点的特征表示。使用适合对比学习的InfoNCE损失函数训练图编码器的参数,实现自主学习特征表示,摆脱对网络流量标签数据的依赖,并提高网络异常流量检测的准确率。实验结果表明,所提模型在没有标签数据的情况下在检测异常网络流量性能方面表现良好,在两个公开数据集上的F1值分别达到了92.64%和90.97%。
基金supported by the National Natural Science Foundation of China (Grant No 10447141)the Youth Foundation of Beijing University of Chemical Technology,China (Grant No QN0622)
文摘In this paper, the network equation for the slow neutron capture process （s-process） of heavy element nucleosynthesis is investigated. Dividing the s-process network reaction chains into two standard forms and using the technique of matrix decomposition, a group of analytical solutions for the network equation are obtained. With the analytical solutions, a calculation for heavy element abundance of the solar system is carried out and the results are in good agreement with the astrophysical measurements.
文摘As sandstone layers in thin interbedded section are difficult to identify,conventional model-driven seismic inversion and data-driven seismic prediction methods have low precision in predicting them.To solve this problem,a model-data-driven seismic AVO(amplitude variation with offset)inversion method based on a space-variant objective function has been worked out.In this method,zero delay cross-correlation function and F norm are used to establish objective function.Based on inverse distance weighting theory,change of the objective function is controlled according to the location of the target CDP(common depth point),to change the constraint weights of training samples,initial low-frequency models,and seismic data on the inversion.Hence,the proposed method can get high resolution and high-accuracy velocity and density from inversion of small sample data,and is suitable for identifying thin interbedded sand bodies.Tests with thin interbedded geological models show that the proposed method has high inversion accuracy and resolution for small sample data,and can identify sandstone and mudstone layers of about one-30th of the dominant wavelength thick.Tests on the field data of Lishui sag show that the inversion results of the proposed method have small relative error with well-log data,and can identify thin interbedded sandstone layers of about one-15th of the dominant wavelength thick with small sample data.
