Epilepsy is a neurological disorder characterised by recurrent seizures due to abnormal neuronal discharges.Seizure detection via EEG signals has progressed,but two main challenges are still encountered.First,EEG data...Epilepsy is a neurological disorder characterised by recurrent seizures due to abnormal neuronal discharges.Seizure detection via EEG signals has progressed,but two main challenges are still encountered.First,EEG data can be distorted by physiological factors and external variables,resulting in noisy brain networks.Static adjacency matrices are typically used in current mainstream methods,which neglect the need for dynamic updates and feature refinement.The second challenge stems from the strong reliance on long-range dependencies through self-attention in current methods,which can introduce redundant noise and increase computational complexity,especially in long-duration data.To address these challenges,the Attention-based Adaptive Graph ProbSparse Hybrid Network(AA-GPHN)is proposed.Brain network structures are dynamically optimised using variational inference and the information bottleneck principle,refining the adjacency matrix for improved epilepsy classification.A Linear Graph Convolutional Network(LGCN)is incorporated to focus on first-order neighbours,minimising the aggregation of distant information.Furthermore,a ProbSparse attention-based Informer(PAT)is introduced to adaptively filter long-range dependencies,enhancing efficiency.A joint optimisation loss function is applied to improve robustness in noisy environments.Experimental results on both patient-specific and cross-subject datasets demonstrate that AA-GPHN outperforms existing methods in seizure detection,showing superior effectiveness and generalisation.展开更多
Accurate photovoltaic(PV)power forecasting ensures the stability and reliability of power systems.To address the complex characteristics of nonlinearity,volatility,and periodicity,a novel two-stage PV forecasting meth...Accurate photovoltaic(PV)power forecasting ensures the stability and reliability of power systems.To address the complex characteristics of nonlinearity,volatility,and periodicity,a novel two-stage PV forecasting method based on an optimized transformer architecture is proposed.In the first stage,an inverted transformer backbone was utilized to consider the multivariate correlation of the PV power series and capture its non-linearity and volatility.ProbSparse attention was introduced to reduce high-memory occupation and solve computational overload issues.In the second stage,a weighted series decomposition module was proposed to extract the periodicity of the PV power series,and the final forecasting results were obtained through additive reconstruction.Experiments on two public datasets showed that the proposed forecasting method has high accuracy,robustness,and computational efficiency.Its RMSE improved by 31.23%compared with that of a traditional transformer,and its MSE improved by 12.57%compared with that of a baseline model.展开更多
基金funded in part by the National Natural Science Foundation of China(Nos.U20A20398,62076005,and 61906002)the Natural Science Foundation of Anhui Province(2008085MF191 and 2008085QF306)the University Synergy Innovation Programme of Anhui Province,China(GXXT-2021-002).
文摘Epilepsy is a neurological disorder characterised by recurrent seizures due to abnormal neuronal discharges.Seizure detection via EEG signals has progressed,but two main challenges are still encountered.First,EEG data can be distorted by physiological factors and external variables,resulting in noisy brain networks.Static adjacency matrices are typically used in current mainstream methods,which neglect the need for dynamic updates and feature refinement.The second challenge stems from the strong reliance on long-range dependencies through self-attention in current methods,which can introduce redundant noise and increase computational complexity,especially in long-duration data.To address these challenges,the Attention-based Adaptive Graph ProbSparse Hybrid Network(AA-GPHN)is proposed.Brain network structures are dynamically optimised using variational inference and the information bottleneck principle,refining the adjacency matrix for improved epilepsy classification.A Linear Graph Convolutional Network(LGCN)is incorporated to focus on first-order neighbours,minimising the aggregation of distant information.Furthermore,a ProbSparse attention-based Informer(PAT)is introduced to adaptively filter long-range dependencies,enhancing efficiency.A joint optimisation loss function is applied to improve robustness in noisy environments.Experimental results on both patient-specific and cross-subject datasets demonstrate that AA-GPHN outperforms existing methods in seizure detection,showing superior effectiveness and generalisation.
基金Top Leading Talents Project of Gansu Province(B32722246002).
文摘Accurate photovoltaic(PV)power forecasting ensures the stability and reliability of power systems.To address the complex characteristics of nonlinearity,volatility,and periodicity,a novel two-stage PV forecasting method based on an optimized transformer architecture is proposed.In the first stage,an inverted transformer backbone was utilized to consider the multivariate correlation of the PV power series and capture its non-linearity and volatility.ProbSparse attention was introduced to reduce high-memory occupation and solve computational overload issues.In the second stage,a weighted series decomposition module was proposed to extract the periodicity of the PV power series,and the final forecasting results were obtained through additive reconstruction.Experiments on two public datasets showed that the proposed forecasting method has high accuracy,robustness,and computational efficiency.Its RMSE improved by 31.23%compared with that of a traditional transformer,and its MSE improved by 12.57%compared with that of a baseline model.
