End-to-end encrypted traffic classification with one-dimensional convolution neural networks

2017 ◽  
Author(s):  
Wei Wang ◽  
Ming Zhu ◽  
Jinlin Wang ◽  
Xuewen Zeng ◽  
Zhongzhen Yang
Keyword(s):  
Neural Networks ◽  
Traffic Classification ◽  
One Dimensional ◽  
Convolution Neural Networks ◽  
End To End ◽  
Encrypted Traffic

Ensemble One-Dimensional Convolution Neural Networks for Skeleton-Based Action Recognition

2018 ◽  
Vol 25 (7) ◽  
pp. 1044-1048 ◽  
Author(s):  
Yangyang Xu ◽  
Jun Cheng ◽  
Lei Wang ◽  
Haiying Xia ◽  
Feng Liu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Action Recognition ◽  
One Dimensional ◽  
Convolution Neural Networks

scholarly journals Transformer Fault Identification with an IF-1DCNN Based on Informative Integration of Heterogeneous Sources

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Huipeng Du ◽  
Gang Wang ◽  
Jiazhao Li
Keyword(s):  
Neural Networks ◽  
Power Transformer ◽  
Engineering Application ◽  
Feature Space ◽  
Fault Classification ◽  
Support Vector ◽  
One Dimensional ◽  
Convolution Neural Networks ◽  
Feature Information ◽  
Transformer Fault

Only using single feature information as input feature cannot fully reflect the transformer fault classification and improve the accuracy of transformer fault diagnosis. To address the above problem, the convolution neural networks’ model is applied for transformer fault assessment designed to implement an end-to-end “different space feature extraction + transformer state diagnosis classification” to enable information from possibly heterogeneous sources to be integrated. This method integrates various feature information of the power transformer operation state to form the isomeric feature, and the model can be used to automatically extract different feature spaces’ information from isomeric feature quantity using its unique one-dimensional convolution and pooling operations. The performance of the proposed approach is compared with that of other models, such as a support vector machine (SVM), backpropagation neural network (BPNN), deep belief network (DBNs), and others. The experimental results show that the proposed one-dimensional convolution neural networks based on an isomeric feature (IF-1DCNN) can accurately classify the fault state of transformer and reduce the adverse interaction between different feature space information in the mixed feature, which has a good engineering application prospect.

The machine training in problems of satellite images’s processing

Metrologiya ◽  
2020 ◽  
pp. 15-37
Author(s):  
L. P. Bass ◽  
Yu. A. Plastinin ◽  
I. Yu. Skryabysheva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Computer Vision ◽  
Satellite Images ◽  
Vision Systems ◽  
Earth Remote Sensing ◽  
Practical Applications ◽  
Convolution Neural Networks ◽  
Computer Vision Systems ◽  
Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

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