Overall Plan of Henan Power Dispatching Data Network Based on IP over SDH

2012 ◽  
Vol 198-199 ◽  
pp. 391-395
Author(s):  
Bo Xie ◽  
Shi Wen Wang
Keyword(s):  
Network Architecture ◽  
Access Network ◽  
Security Requirement ◽  
Data Network ◽  
Dual Plane ◽  
Backbone Network ◽  
Network Protection ◽  
Access Mode ◽  
Power Dispatching ◽  
Kernel Design

In order to meet the security requirement of the dispatching data network and the features of its main applications, the principle of IP over SDH and the overall plan of Henan Power dispatching data network (HNDnet) based on IP over SDH is presented, together with the network architecture of the dual-plane backbone network and access network. Kernel design, network protection on communication side and routers access mode of Henan data dispatching network based on the dual-plane power communication network are illustrated in detail and some examples are given. Then a safer dispatching data network is provided.

mCRAN: A radio access network architecture for 5G indoor communications

2015 ◽  
Author(s):  
Kishor Chandra ◽  
Zizheng Cao ◽  
T.M. Bruintjes ◽  
R. Venkatesha Prasad ◽  
G. Karagiannis ◽  
...  
Keyword(s):  
Network Architecture ◽  
Access Network ◽  
Radio Access Network ◽  
Radio Access ◽  
Indoor Communications

scholarly journals Wheel fault diagnosis model based on multichannel attention and supervised contrastive learning

2021 ◽  
Vol 13 (12) ◽  
pp. 168781402110670
Author(s):  
Yanxiang Chen ◽  
Zuxing Zhao ◽  
Euiyoul Kim ◽  
Haiyang Liu ◽  
Juan Xu ◽  
...  
Keyword(s):  
Network Architecture ◽  
Feature Learning ◽  
Feature Representation ◽  
Diagnostic Methods ◽  
Learning Ability ◽  
Noise Interference ◽  
Backbone Network ◽  
Mechanical Methods ◽  
Wheel Flat ◽  
Wheel Radius

As wheels are important components of train operation, diagnosing and predicting wheel faults are essential to ensure the reliability of rail transit. Currently, the existing studies always separately deal with two main types of wheel faults, namely wheel radius difference and wheel flat, even though they are both reflected by wheel radius changes. Moreover, traditional diagnostic methods, such as mechanical methods or a combination of data analysis methods, have limited abilities to efficiently extract data features. Deep learning models have become useful tools to automatically learn features from raw vibration signals. However, research on improving the feature-learning capabilities of models under noise interference to yield higher wheel diagnostic accuracies has not yet been conducted. In this paper, a unified training framework with the same model architecture and loss function is established for two homologous wheel faults. After selecting deep residual networks (ResNets) as the backbone network to build the model, we add the squeeze and excitation (SE) module based on a multichannel attention mechanism to the backbone network to learn the global relationships among feature channels. Then the influence of noise interference features is reduced while the extraction of useful information features is enhanced, leading to the improved feature-learning ability of ResNet. To further obtain effective feature representation using the model, we introduce supervised contrastive loss (SCL) on the basis of ResNet + SE to enlarge the feature distances of different fault classes through a comparison between positive and negative examples under label supervision to obtain a better class differentiation and higher diagnostic accuracy. We also complete a regression task to predict the fault degrees of wheel radius difference and wheel flat without changing the network architecture. The extensive experimental results show that the proposed model has a high accuracy in diagnosing and predicting two types of wheel faults.

Access Network Architecture

Femtocells ◽  
2009 ◽  
pp. 39-67
Author(s):  
Enjie Liu ◽  
Guillaume De La Roche
Keyword(s):  
Network Architecture ◽  
Access Network

Radio Access Network Architecture

2005 ◽  
pp. 75-98 ◽  
Author(s):  
Fabio Longoni ◽  
Atte Länsisalmi ◽  
Antti Toskala
Keyword(s):  
Network Architecture ◽  
Access Network ◽  
Radio Access Network ◽  
Radio Access
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