An evolution inequality of Sobolev type involving a nonlinear convolution term is considered.By using the nonlinear capacity method and the contradiction argument,the non-existence of the nontrivial local weak solutio...An evolution inequality of Sobolev type involving a nonlinear convolution term is considered.By using the nonlinear capacity method and the contradiction argument,the non-existence of the nontrivial local weak solution is proved.展开更多
Federated learning(FL)is a promising technique to build a power generation model in photovoltaic(PV)scenarios.However,due to the heterogeneity of power generation data,there is a problem of slow convergence of the glo...Federated learning(FL)is a promising technique to build a power generation model in photovoltaic(PV)scenarios.However,due to the heterogeneity of power generation data,there is a problem of slow convergence of the global model or even deviation from the optimal solution during model training.Therefore,to improve the prediction accuracy and accelerate the model convergence speed,this paper proposes a model functional blocking and differentiated scheduling mechanism under personalized FL framework for intermittent PV power generation.Firstly,cluster analysis is conducted according to longitude,latitude,and altitude to form a model collaborative training region(MCTR).Then,based on the constructed MCTRs,a personalized FL model training method is proposed.This method is based on a combination of global shared convolutional neural network(CNN)model and local personalized long short term memory(LSTM)model,where CNN model block is responsible for feature extraction and LSTM model block is responsible for prediction.It adopts synchronous aggregation for global shared CNN and asynchronous aggregation for personalized LSTM.Furthermore,the FL server performs block scheduling of the CNN-LSTM models and aggregates them based on the regional membership which can provide differentiated and accurate prediction models with different power generation patterns.The simulation results show that the proposed algorithm has the highest accuracy of 85.1%and the best performance on mean absolute error(MAE),root mean square error(RMSE)and mean absolute percentage error(MAPE),with 0.1105,0.1224 and 0.4383 respectively.展开更多
Predictive beamforming design is an essential task in realizing high-mobility integrated sensing and communication(ISAC),which highly depends on the accuracy of the channel prediction(CP),i.e.,predicting the angular p...Predictive beamforming design is an essential task in realizing high-mobility integrated sensing and communication(ISAC),which highly depends on the accuracy of the channel prediction(CP),i.e.,predicting the angular parameters of users.However,the performance of CP highly depends on the estimated historical channel stated information(CSI)with estimation errors,resulting in the performance degradation for most traditional CP methods.To further improve the prediction accuracy,in this paper,we focus on the ISAC in vehicle networks and propose a convolutional long-short term memory(CLSTM)recurrent neural network(CLRNet)to predict the angle of vehicles for the design of predictive beamforming.In the developed CLRNet,both the convolutional neural network(CNN)module and the LSTM module are adopted to exploit the spatial features and the temporal dependency from the estimated historical angles of vehicles to facilitate the angle prediction.Finally,numerical results demonstrate that the developed CLRNet-based method is robust to the estimation error and can significantly outperform the state-of-the-art benchmarks,achieving an excellent sum-rate performance for ISAC systems.展开更多
基金Supported by Scientific Research Fund of Hunan Provincial Education Departmen(t23A0361)。
文摘An evolution inequality of Sobolev type involving a nonlinear convolution term is considered.By using the nonlinear capacity method and the contradiction argument,the non-existence of the nontrivial local weak solution is proved.
基金supported by the Science and Technology Project of State Grid Corporation of China(5108-202218280A-2-394-XG)。
文摘Federated learning(FL)is a promising technique to build a power generation model in photovoltaic(PV)scenarios.However,due to the heterogeneity of power generation data,there is a problem of slow convergence of the global model or even deviation from the optimal solution during model training.Therefore,to improve the prediction accuracy and accelerate the model convergence speed,this paper proposes a model functional blocking and differentiated scheduling mechanism under personalized FL framework for intermittent PV power generation.Firstly,cluster analysis is conducted according to longitude,latitude,and altitude to form a model collaborative training region(MCTR).Then,based on the constructed MCTRs,a personalized FL model training method is proposed.This method is based on a combination of global shared convolutional neural network(CNN)model and local personalized long short term memory(LSTM)model,where CNN model block is responsible for feature extraction and LSTM model block is responsible for prediction.It adopts synchronous aggregation for global shared CNN and asynchronous aggregation for personalized LSTM.Furthermore,the FL server performs block scheduling of the CNN-LSTM models and aggregates them based on the regional membership which can provide differentiated and accurate prediction models with different power generation patterns.The simulation results show that the proposed algorithm has the highest accuracy of 85.1%and the best performance on mean absolute error(MAE),root mean square error(RMSE)and mean absolute percentage error(MAPE),with 0.1105,0.1224 and 0.4383 respectively.
基金supported by the National Natural Science Foundation of China under Grant 61801082supported in part by the National Natural Science Foundation of China under Grant 62101232in part by the Guangdong Provincial Natural Science Foundation under Grant 2022A1515011257.
文摘Predictive beamforming design is an essential task in realizing high-mobility integrated sensing and communication(ISAC),which highly depends on the accuracy of the channel prediction(CP),i.e.,predicting the angular parameters of users.However,the performance of CP highly depends on the estimated historical channel stated information(CSI)with estimation errors,resulting in the performance degradation for most traditional CP methods.To further improve the prediction accuracy,in this paper,we focus on the ISAC in vehicle networks and propose a convolutional long-short term memory(CLSTM)recurrent neural network(CLRNet)to predict the angle of vehicles for the design of predictive beamforming.In the developed CLRNet,both the convolutional neural network(CNN)module and the LSTM module are adopted to exploit the spatial features and the temporal dependency from the estimated historical angles of vehicles to facilitate the angle prediction.Finally,numerical results demonstrate that the developed CLRNet-based method is robust to the estimation error and can significantly outperform the state-of-the-art benchmarks,achieving an excellent sum-rate performance for ISAC systems.
