Accurate short-term traffic prediction is essential for improving the efficiency of data transmission in low Earth orbit(LEO)satellite networks.However,traffic values may be missing due to collector failures,transmiss...Accurate short-term traffic prediction is essential for improving the efficiency of data transmission in low Earth orbit(LEO)satellite networks.However,traffic values may be missing due to collector failures,transmission errors,and memory failures in complex space environments.Incomplete traffic time series prevent the efficient utilization of data,which can significantly reduce the traffic prediction accuracy.To overcome this problem,we propose a novel spatio-temporal correlation-based incomplete time-series traffic prediction(ITP-ST)model,which consists of two phases:reconstituting incomplete time series by missing data imputation and making traffic prediction based on the reconstructed time series.In the first phase,we propose a novel missing data imputation model based on the improved denoising autoencoder(IDAE-MDI).Specifically,we combine DAE with the Gramian angular summation field(GASF)to establish the temporal correlation between different time intervals and extract the structural patterns from the time series.Taking advantage of the unique spatio-temporal correlation of the LEO satellite network traffic,we focus on improving the missing data initialization method for DAE.In the second phase,we propose a traffic prediction model based on a multi-channel attention convolutional neural network(TP-CACNN)by combining the spatio-temporally correlated traffic of the LEO satellite network.Finally,to achieve the ideal structure of these models,we use the multi-verse optimizer(MVO)algorithm to select the optimal combination of model parameters.Experiments show that the ITP-ST model outperforms the baseline models in terms of traffic prediction accuracy at different data missing rates,which demonstrates the effectiveness of our proposed model.展开更多
Non-blind audio bandwidth extension is a standard technique within contemporary audio codecs to efficiently code audio signals at low bitrates. In existing methods, in most cases high frequencies signal is usually gen...Non-blind audio bandwidth extension is a standard technique within contemporary audio codecs to efficiently code audio signals at low bitrates. In existing methods, in most cases high frequencies signal is usually generated by a duplication of the corresponding low frequencies and some parameters of high frequencies. However, the perception quality of coding will significantly degrade if the correlation between high frequencies and low frequencies becomes weak. In this paper, we quantitatively analyse the correlation via computing mutual information value. The analysis results show the correlation also exists in low frequency signal of the context dependent frames besides the current frame. In order to improve the perception quality of coding, we propose a novel method of high frequency coarse spectrum generation to improve the conventional replication method. In the proposed method, the coarse high frequency spectrums are generated by a nonlinear mapping model using deep recurrent neural network. The experiments confirm that the proposed method shows better performance than the reference methods.展开更多
The rapidly increasing wind power penetration presents new challenges to the operation of power systems.Improving the accuracy of wind power forecasting is a possible solution under this circumstance.In the power fore...The rapidly increasing wind power penetration presents new challenges to the operation of power systems.Improving the accuracy of wind power forecasting is a possible solution under this circumstance.In the power forecasting of mul-tiple wind farms,determining the spatio-temporal correlation of multiple wind farms is critical for improving the forecasting accuracy.This paper proposes a spatio-temporal convolutional network(STCN)that utilizes a directed graph convolutional structure.A temporal convolutional network is also adopted to characterize the temporal features of wind power.Historical data from 15 wind farms in Australia are used in the case study.The forecasting results show that the proposed model has higher accuracy than the existing methods.Based on the structure of the STCN,asymmetric spatial correlation at different temporal scales can be observed,which shows the effectiveness of the proposed model.展开更多
In order to address the issues of predefined adjacency matrices inadequately representing information in road networks,insufficiently capturing spatial dependencies of traffic networks,and the potential problem of exc...In order to address the issues of predefined adjacency matrices inadequately representing information in road networks,insufficiently capturing spatial dependencies of traffic networks,and the potential problem of excessive smoothing or neglecting initial node information as the layers of graph convolutional neural networks increase,thus affecting traffic prediction performance,this paper proposes a prediction model based on Adaptive Multi-channel Graph Convolutional Neural Networks(AMGCN).The model utilizes an adaptive adjacency matrix to automatically learn implicit graph structures from data,introduces a mixed skip propagation graph convolutional neural network model,which retains the original node states and selectively acquires outputs of convolutional layers,thus avoiding the loss of node initial states and comprehensively capturing spatial correlations of traffic flow.Finally,the output is fed into Long Short-Term Memory networks to capture temporal correlations.Comparative experiments on two real datasets validate the effectiveness of the proposed model.展开更多
文摘Accurate short-term traffic prediction is essential for improving the efficiency of data transmission in low Earth orbit(LEO)satellite networks.However,traffic values may be missing due to collector failures,transmission errors,and memory failures in complex space environments.Incomplete traffic time series prevent the efficient utilization of data,which can significantly reduce the traffic prediction accuracy.To overcome this problem,we propose a novel spatio-temporal correlation-based incomplete time-series traffic prediction(ITP-ST)model,which consists of two phases:reconstituting incomplete time series by missing data imputation and making traffic prediction based on the reconstructed time series.In the first phase,we propose a novel missing data imputation model based on the improved denoising autoencoder(IDAE-MDI).Specifically,we combine DAE with the Gramian angular summation field(GASF)to establish the temporal correlation between different time intervals and extract the structural patterns from the time series.Taking advantage of the unique spatio-temporal correlation of the LEO satellite network traffic,we focus on improving the missing data initialization method for DAE.In the second phase,we propose a traffic prediction model based on a multi-channel attention convolutional neural network(TP-CACNN)by combining the spatio-temporally correlated traffic of the LEO satellite network.Finally,to achieve the ideal structure of these models,we use the multi-verse optimizer(MVO)algorithm to select the optimal combination of model parameters.Experiments show that the ITP-ST model outperforms the baseline models in terms of traffic prediction accuracy at different data missing rates,which demonstrates the effectiveness of our proposed model.
基金supported by the National Natural Science Foundation of China under Grant No. 61762005, 61231015, 61671335, 61702472, 61701194, 61761044, 61471271National High Technology Research and Development Program of China (863 Program) under Grant No. 2015AA016306+2 种基金 Hubei Province Technological Innovation Major Project under Grant No. 2016AAA015the Science Project of Education Department of Jiangxi Province under No. GJJ150585The Opening Project of Collaborative Innovation Center for Economics Crime Investigation and Prevention Technology, Jiangxi Province, under Grant No. JXJZXTCX-025
文摘Non-blind audio bandwidth extension is a standard technique within contemporary audio codecs to efficiently code audio signals at low bitrates. In existing methods, in most cases high frequencies signal is usually generated by a duplication of the corresponding low frequencies and some parameters of high frequencies. However, the perception quality of coding will significantly degrade if the correlation between high frequencies and low frequencies becomes weak. In this paper, we quantitatively analyse the correlation via computing mutual information value. The analysis results show the correlation also exists in low frequency signal of the context dependent frames besides the current frame. In order to improve the perception quality of coding, we propose a novel method of high frequency coarse spectrum generation to improve the conventional replication method. In the proposed method, the coarse high frequency spectrums are generated by a nonlinear mapping model using deep recurrent neural network. The experiments confirm that the proposed method shows better performance than the reference methods.
基金National Key Research and Development Program(No.2020YFB0905900)National Natural Science Foundation of China(No.51777065).
文摘The rapidly increasing wind power penetration presents new challenges to the operation of power systems.Improving the accuracy of wind power forecasting is a possible solution under this circumstance.In the power forecasting of mul-tiple wind farms,determining the spatio-temporal correlation of multiple wind farms is critical for improving the forecasting accuracy.This paper proposes a spatio-temporal convolutional network(STCN)that utilizes a directed graph convolutional structure.A temporal convolutional network is also adopted to characterize the temporal features of wind power.Historical data from 15 wind farms in Australia are used in the case study.The forecasting results show that the proposed model has higher accuracy than the existing methods.Based on the structure of the STCN,asymmetric spatial correlation at different temporal scales can be observed,which shows the effectiveness of the proposed model.
文摘In order to address the issues of predefined adjacency matrices inadequately representing information in road networks,insufficiently capturing spatial dependencies of traffic networks,and the potential problem of excessive smoothing or neglecting initial node information as the layers of graph convolutional neural networks increase,thus affecting traffic prediction performance,this paper proposes a prediction model based on Adaptive Multi-channel Graph Convolutional Neural Networks(AMGCN).The model utilizes an adaptive adjacency matrix to automatically learn implicit graph structures from data,introduces a mixed skip propagation graph convolutional neural network model,which retains the original node states and selectively acquires outputs of convolutional layers,thus avoiding the loss of node initial states and comprehensively capturing spatial correlations of traffic flow.Finally,the output is fed into Long Short-Term Memory networks to capture temporal correlations.Comparative experiments on two real datasets validate the effectiveness of the proposed model.
