The Secondary Air System(SAS)plays an important role in the safe operation and performance of aeroengines.The traditional 1D-3D coupling method loses information when used for secondary air systems,which affects the c...The Secondary Air System(SAS)plays an important role in the safe operation and performance of aeroengines.The traditional 1D-3D coupling method loses information when used for secondary air systems,which affects the calculation accuracy.In this paper,a Cross-dimensional Data Transmission method(CDT)from 3D to 1D is proposed by introducing flow field uniformity into the data transmission.First,a uniformity index was established to quantify the flow field parameter distribution characteristics,and a uniformity index prediction model based on the locally weighted regression method(Lowess)was established to quickly obtain the flow field information.Then,an information selection criterion in 3D to 1D data transmission was established based on the Spearman rank correlation coefficient between the uniformity index and the accuracy of coupling calculation,and the calculation method was automatically determined according to the established criterion.Finally,a modified function was obtained by fitting the ratio of the 3D mass-average parameters to the analytical solution,which are then used to modify the selected parameters at the 1D-3D interface.Taking a typical disk cavity air system as an example,the results show that the calculation accuracy of the CDT method is greatly improved by a relative 53.88%compared with the traditional 1D-3D coupling method.Furthermore,the CDT method achieves a speedup of 2 to 3 orders of magnitude compared to the 3D calculation.展开更多
Traffic characterization(e.g.,chat,video)and application identifi-cation(e.g.,FTP,Facebook)are two of the more crucial jobs in encrypted network traffic classification.These two activities are typically carried out se...Traffic characterization(e.g.,chat,video)and application identifi-cation(e.g.,FTP,Facebook)are two of the more crucial jobs in encrypted network traffic classification.These two activities are typically carried out separately by existing systems using separate models,significantly adding to the difficulty of network administration.Convolutional Neural Network(CNN)and Transformer are deep learning-based approaches for network traf-fic classification.CNN is good at extracting local features while ignoring long-distance information from the network traffic sequence,and Transformer can capture long-distance feature dependencies while ignoring local details.Based on these characteristics,a multi-task learning model that combines Transformer and 1D-CNN for encrypted traffic classification is proposed(MTC).In order to make up for the Transformer’s lack of local detail feature extraction capability and the 1D-CNN’s shortcoming of ignoring long-distance correlation information when processing traffic sequences,the model uses a parallel structure to fuse the features generated by the Transformer block and the 1D-CNN block with each other using a feature fusion block.This structure improved the representation of traffic features by both blocks and allows the model to perform well with both long and short length sequences.The model simultaneously handles multiple tasks,which lowers the cost of training.Experiments reveal that on the ISCX VPN-nonVPN dataset,the model achieves an average F1 score of 98.25%and an average recall of 98.30%for the task of identifying applications,and an average F1 score of 97.94%,and an average recall of 97.54%for the task of traffic characterization.When advanced models on the same dataset are chosen for comparison,the model produces the best results.To prove the generalization,we applied MTC to CICIDS2017 dataset,and our model also achieved good results.展开更多
基金supported by the National Science and Technology Major Project,China(No.2017-III-0010-0036).
文摘The Secondary Air System(SAS)plays an important role in the safe operation and performance of aeroengines.The traditional 1D-3D coupling method loses information when used for secondary air systems,which affects the calculation accuracy.In this paper,a Cross-dimensional Data Transmission method(CDT)from 3D to 1D is proposed by introducing flow field uniformity into the data transmission.First,a uniformity index was established to quantify the flow field parameter distribution characteristics,and a uniformity index prediction model based on the locally weighted regression method(Lowess)was established to quickly obtain the flow field information.Then,an information selection criterion in 3D to 1D data transmission was established based on the Spearman rank correlation coefficient between the uniformity index and the accuracy of coupling calculation,and the calculation method was automatically determined according to the established criterion.Finally,a modified function was obtained by fitting the ratio of the 3D mass-average parameters to the analytical solution,which are then used to modify the selected parameters at the 1D-3D interface.Taking a typical disk cavity air system as an example,the results show that the calculation accuracy of the CDT method is greatly improved by a relative 53.88%compared with the traditional 1D-3D coupling method.Furthermore,the CDT method achieves a speedup of 2 to 3 orders of magnitude compared to the 3D calculation.
基金supported by the People’s Public Security University of China central basic scientific research business program(No.2021JKF206).
文摘Traffic characterization(e.g.,chat,video)and application identifi-cation(e.g.,FTP,Facebook)are two of the more crucial jobs in encrypted network traffic classification.These two activities are typically carried out separately by existing systems using separate models,significantly adding to the difficulty of network administration.Convolutional Neural Network(CNN)and Transformer are deep learning-based approaches for network traf-fic classification.CNN is good at extracting local features while ignoring long-distance information from the network traffic sequence,and Transformer can capture long-distance feature dependencies while ignoring local details.Based on these characteristics,a multi-task learning model that combines Transformer and 1D-CNN for encrypted traffic classification is proposed(MTC).In order to make up for the Transformer’s lack of local detail feature extraction capability and the 1D-CNN’s shortcoming of ignoring long-distance correlation information when processing traffic sequences,the model uses a parallel structure to fuse the features generated by the Transformer block and the 1D-CNN block with each other using a feature fusion block.This structure improved the representation of traffic features by both blocks and allows the model to perform well with both long and short length sequences.The model simultaneously handles multiple tasks,which lowers the cost of training.Experiments reveal that on the ISCX VPN-nonVPN dataset,the model achieves an average F1 score of 98.25%and an average recall of 98.30%for the task of identifying applications,and an average F1 score of 97.94%,and an average recall of 97.54%for the task of traffic characterization.When advanced models on the same dataset are chosen for comparison,the model produces the best results.To prove the generalization,we applied MTC to CICIDS2017 dataset,and our model also achieved good results.
