This paper proposes an AI-based approach for islanding detection in active distribution networks.A review of existing AI-based studies reveals several gaps,including model complexity and stability concerns,limited acc...This paper proposes an AI-based approach for islanding detection in active distribution networks.A review of existing AI-based studies reveals several gaps,including model complexity and stability concerns,limited accuracy in noisy conditions,and limited applicability to systems with different types of resources.To address these challenges,this paper proposes a novel approach that adapts the WaveNet generator into a classifier,enhanced with a denoising UNet model,to improve performance in varying signal-to-noise ratio(SNR)conditions.In designing this model,we deviate from state-of-the-art approaches that primarily rely on long short-term memory(LSTM)architectures by employing 1D convolutional layers.This enables the model to focus on spatial analysis of the input signal,making it particularly well-suited for processing long input sequences.Additionally,residual connections are incorporated to mitigate overfitting and significantly enhance the model’s generalizability.To verify the effectiveness of the proposed scheme,over 14000 islanding/non-islanding cases are tested,considering different load active/reactive power values,load switching transients,capacitor bank switching,fault conditions in the main grid,different load quality factors,SNR levels,changes in network topology,and both types of conventional and inverter-based sources.展开更多
Background:Early detection of harmful brain activity in critically ill patients using electroencephalography(EEG)is vital for timely and effective clinical intervention.Automating EEG analysis with deep learning techn...Background:Early detection of harmful brain activity in critically ill patients using electroencephalography(EEG)is vital for timely and effective clinical intervention.Automating EEG analysis with deep learning techniques holds significant promise for enhancing diagnostic efficiency and accuracy.Methods:We implemented EfficientNetB2,which leverages convolutional neural networks with a novel Temporal Squeeze-and-Excitation module to capture temporal EEG features,and WaveNet,a sequential model designed to effectively model temporal dependencies in EEG data using dilated causal convolutions and temporal self-attention.Both models were trained and evaluated using a publicly available EEG dataset,with performance assessed via 4-fold cross-validation and a step-wise learning rate reduction strategy.Results:Our results demonstrate a significant reduction in training loss from 0.6459 to 0.3055 and validation loss from 0.9602 to 0.5719 over six epochs.Consistent improvements were observed across cross-validation folds,highlighting the robustness of the models.Additionally,ensemble learning of the two architectures further enhanced classification performance.Conclusion:This comparative analysis sheds light on the strengths and limitations of EfficientNetB2 and WaveNet for automated harmful brain activity detection in EEG signals.The findings contribute to the advancement of reliable and efficient deep learning models,paving the way for their clinical application in managing critically ill patients.展开更多
The prosperity of deep learning has revolutionized many machine learning tasks(such as image recognition,natural language processing,etc.).With the widespread use of autonomous sensor networks,the Internet of Things,a...The prosperity of deep learning has revolutionized many machine learning tasks(such as image recognition,natural language processing,etc.).With the widespread use of autonomous sensor networks,the Internet of Things,and crowd sourcing to monitor real-world processes,the volume,diversity,and veracity of spatial-temporal data are expanding rapidly.However,traditional methods have their limitation in coping with spatial-temporal dependencies,which either incorporate too much data from weakly connected locations or ignore the relationships between those interrelated but geographically separated regions.In this paper,a novel deep learning model(termed RF-GWN)is proposed by combining Random Forest(RF)and Graph WaveNet(GWN).In RF-GWN,a new adaptive weight matrix is formulated by combining Variable Importance Measure(VIM)of RF with the long time series feature extraction ability of GWN in order to capture potential spatial dependencies and extract long-term dependencies from the input data.Furthermore,two experiments are conducted on two real-world datasets with the purpose of predicting traffic flow and groundwater level.Baseline models are implemented by Diffusion Convolutional Recurrent Neural Network(DCRNN),Spatial-Temporal GCN(ST-GCN),and GWN to verify the effectiveness of the RF-GWN.The Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and Mean Absolute Percentage Error(MAPE)are selected as performance criteria.The results show that the proposed model can better capture the spatial-temporal relationships,the prediction performance on the METR-LA dataset is slightly improved,and the index of the prediction task on the PEMS-BAY dataset is significantly improved.These improvements are extended to the groundwater dataset,which can effectively improve the prediction accuracy.Thus,the applicability and effectiveness of the proposed model RF-GWN in both traffic flow and groundwater level prediction are demonstrated.展开更多
文摘This paper proposes an AI-based approach for islanding detection in active distribution networks.A review of existing AI-based studies reveals several gaps,including model complexity and stability concerns,limited accuracy in noisy conditions,and limited applicability to systems with different types of resources.To address these challenges,this paper proposes a novel approach that adapts the WaveNet generator into a classifier,enhanced with a denoising UNet model,to improve performance in varying signal-to-noise ratio(SNR)conditions.In designing this model,we deviate from state-of-the-art approaches that primarily rely on long short-term memory(LSTM)architectures by employing 1D convolutional layers.This enables the model to focus on spatial analysis of the input signal,making it particularly well-suited for processing long input sequences.Additionally,residual connections are incorporated to mitigate overfitting and significantly enhance the model’s generalizability.To verify the effectiveness of the proposed scheme,over 14000 islanding/non-islanding cases are tested,considering different load active/reactive power values,load switching transients,capacitor bank switching,fault conditions in the main grid,different load quality factors,SNR levels,changes in network topology,and both types of conventional and inverter-based sources.
文摘Background:Early detection of harmful brain activity in critically ill patients using electroencephalography(EEG)is vital for timely and effective clinical intervention.Automating EEG analysis with deep learning techniques holds significant promise for enhancing diagnostic efficiency and accuracy.Methods:We implemented EfficientNetB2,which leverages convolutional neural networks with a novel Temporal Squeeze-and-Excitation module to capture temporal EEG features,and WaveNet,a sequential model designed to effectively model temporal dependencies in EEG data using dilated causal convolutions and temporal self-attention.Both models were trained and evaluated using a publicly available EEG dataset,with performance assessed via 4-fold cross-validation and a step-wise learning rate reduction strategy.Results:Our results demonstrate a significant reduction in training loss from 0.6459 to 0.3055 and validation loss from 0.9602 to 0.5719 over six epochs.Consistent improvements were observed across cross-validation folds,highlighting the robustness of the models.Additionally,ensemble learning of the two architectures further enhanced classification performance.Conclusion:This comparative analysis sheds light on the strengths and limitations of EfficientNetB2 and WaveNet for automated harmful brain activity detection in EEG signals.The findings contribute to the advancement of reliable and efficient deep learning models,paving the way for their clinical application in managing critically ill patients.
文摘The prosperity of deep learning has revolutionized many machine learning tasks(such as image recognition,natural language processing,etc.).With the widespread use of autonomous sensor networks,the Internet of Things,and crowd sourcing to monitor real-world processes,the volume,diversity,and veracity of spatial-temporal data are expanding rapidly.However,traditional methods have their limitation in coping with spatial-temporal dependencies,which either incorporate too much data from weakly connected locations or ignore the relationships between those interrelated but geographically separated regions.In this paper,a novel deep learning model(termed RF-GWN)is proposed by combining Random Forest(RF)and Graph WaveNet(GWN).In RF-GWN,a new adaptive weight matrix is formulated by combining Variable Importance Measure(VIM)of RF with the long time series feature extraction ability of GWN in order to capture potential spatial dependencies and extract long-term dependencies from the input data.Furthermore,two experiments are conducted on two real-world datasets with the purpose of predicting traffic flow and groundwater level.Baseline models are implemented by Diffusion Convolutional Recurrent Neural Network(DCRNN),Spatial-Temporal GCN(ST-GCN),and GWN to verify the effectiveness of the RF-GWN.The Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and Mean Absolute Percentage Error(MAPE)are selected as performance criteria.The results show that the proposed model can better capture the spatial-temporal relationships,the prediction performance on the METR-LA dataset is slightly improved,and the index of the prediction task on the PEMS-BAY dataset is significantly improved.These improvements are extended to the groundwater dataset,which can effectively improve the prediction accuracy.Thus,the applicability and effectiveness of the proposed model RF-GWN in both traffic flow and groundwater level prediction are demonstrated.
