English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The traffic data of the intelligent network with the Space-Integrated Ground Information Network have the characteristics of robust traffic burst, large traffic data, and high volatility compared with the ground traffic. We propose a Gated Temporal Convolution Network (GTCN) algorithm to solve the above problems, the algorithm segments the traffic time series into equal length according to the autocorrelation of the network traffic, and creates the feature sequence by extracting the segmented features using Temporal Convolution Network (TCN), to learn the burst and fluctuation of the intelligent network traffic of the Space-Integrated Ground Information Intelligent Network and to improve the processing efficiency of the traffic time series. Then, the feature sequences of each subsection data are stored, screened, and retained through the gated unit. Finally, the network traffic data are accurately predicted using the characteristics of the sequences stored by the TCN and the gating unit. The simulation experiments showed that, compared with the comparison algorithms, the accuracy of the proposed algorithm is improved by at least 58.72%, the training speed of the algorithm is improved by 84.05%, and the algorithm execution speed is improved by 71.92%, which meet the requirements of the accuracy and timeliness of the traffic prediction model of the Space-Integrated Ground Information Intelligent Network.

Traffic Prediction of Space-Integrated Ground Information Network Using the GTCN Algorithm