Predicting travel trajectory of vehicles can not only provide personalized services to users,but also have a certain effect on traffic guidance and traffic control.In this paper,we build a Bayonet-Corpus based on the ...Predicting travel trajectory of vehicles can not only provide personalized services to users,but also have a certain effect on traffic guidance and traffic control.In this paper,we build a Bayonet-Corpus based on the context of traffic intersections,and use it to model a traffic network.Besides,Bidirectional Gated Recurrent Unit(Bi-GRU)is used to predict the sequence of traffic intersections in one single trajectory.Firstly,considering that real traffic networks are usually complex and disorder and cannot reflect the higher dimensional relationship among traffic intersections,this paper proposes a new traffic network modeling algorithm based on the context of traffic intersections:inspired by the probabilistic language model,a Bayonet-Corpus is constructed from traffic intersections in real trajectory sequence,so the high-dimensional similarity between corpus nodes can be used to measure the semantic relation of real traffic intersections.This algorithm maps vehicle trajectory nodes into a high-dimensional space vector,blocking complex structure of real traffic network and reconstructing the traffic network space.Then,the bayonets sequence in real traffic network is mapped into a matrix.Considering the trajectories sequence is bidirectional,and Bi-GRU can handle information from forward and backward simultaneously,we use Bi-GRU to bidirectionally model the trajectory matrix for the purpose of prediction.展开更多
The rising prevalence of diabetes in modern society underscores the urgent need for precise and efficient diagnostic tools to support early intervention and treatment.However,the inherent limitations of existing datas...The rising prevalence of diabetes in modern society underscores the urgent need for precise and efficient diagnostic tools to support early intervention and treatment.However,the inherent limitations of existing datasets,including significant class imbalances and inadequate sample diversity,pose challenges to the accurate prediction and classification of diabetes.Addressing these issues,this study proposes an innovative diabetes prediction framework that integrates a hybrid Convolutional Neural Network-Bidirectional Gated Recurrent Unit(CNN-BiGRU)model for classification with Adaptive Synthetic Sampling(ADASYN)for data augmentation.ADASYN was employed to generate synthetic yet representative data samples,effectively mitigating class imbalance and enhancing the diversity and representativeness of the dataset.This augmentation process is critical for ensuring the robustness and generalizability of the predictive model,particularly in scenarios where minority class samples are underrepresented.The CNN-BiGRU architecture was designed to leverage the complementary strengths of CNN in extracting spatial features and BiGRU in capturing sequential dependencies,making it well-suited for the complex patterns inherent in medical data.The proposed framework demonstrated exceptional performance,achieving a training accuracy of 98.74%and a test accuracy of 97.78%on the augmented dataset.These results validate the efficacy of the integrated approach in addressing the challenges of class imbalance and dataset heterogeneity,while significantly enhancing the diagnostic precision for diabetes prediction.This study provides a scalable and reliable methodology with promising implications for advancing diagnostic accuracy in medical applications,particularly in resource-constrained and data-limited environments.展开更多
Mechanical vibration defect is the key factor leading to sudden failure of gas-insulated switchgear(GIS)equipment.It is important to realise effective prediction of the me-chanical vibration state development trend of...Mechanical vibration defect is the key factor leading to sudden failure of gas-insulated switchgear(GIS)equipment.It is important to realise effective prediction of the me-chanical vibration state development trend of GIS equipment in order to improve its active safety protection level.This paper carried out research on the accurate prediction method and experimental validation of the mechanical vibration state and its defect severity development trend for the GIS equipment.Firstly,the deep and shallow vibration feature parameters for different mechanical defect signals were jointly extracted by time-domain features and deep belief network methods.Secondly,a new prediction model,incorporating the attention mechanism and the bidirectional gated recurrent unit(BiGRU),was constructed with the deep and shallow vibration feature parameters as inputs.Finally,the prediction trend effectiveness was verified based on the real-type GIS mechanical simulation platform and the field operation GIS equipment.Results show that the deep and shallow vibration feature extraction method proposed in this paper can characterise the mechanical defect information more comprehensively.The new prediction method of the vibration state trend based on the attention-BiGRU model shows ideal accuracy,and the predicted vibration state development trend is highly consistent with the actual,with an average absolute error of 0.063.The root mean square error(ERMSE)value of the prediction method is<5%,which reduces the relative error value at least 37% compared with the traditional prediction models.This paper provides a valuable reference for the proactive defence of GIS mechanical failure.展开更多
While encryption technology safeguards the security of network communications,malicious traffic also uses encryption protocols to obscure its malicious behavior.To address the issues of traditional machine learning me...While encryption technology safeguards the security of network communications,malicious traffic also uses encryption protocols to obscure its malicious behavior.To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic,we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features,called BERT-based Spatio-Temporal Features Network(BSTFNet).At the packet-level granularity,the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers(BERT)model.At the byte-level granularity,we initially employ the Bidirectional Gated Recurrent Unit(BiGRU)model to extract temporal features from bytes,followed by the utilization of the Text Convolutional Neural Network(TextCNN)model with multi-sized convolution kernels to extract local multi-receptive field spatial features.The fusion of features from both granularities serves as the ultimate multidimensional representation of malicious traffic.Our approach achieves accuracy and F1-score of 99.39%and 99.40%,respectively,on the publicly available USTC-TFC2016 dataset,and effectively reduces sample confusion within the Neris and Virut categories.The experimental results demonstrate that our method has outstanding representation and classification capabilities for encrypted malicious traffic.展开更多
Considering the nonlinear structure and spatial-temporal correlation of traffic network,and the influence of potential correlation between nodes of traffic network on the spatial features,this paper proposes a traffic...Considering the nonlinear structure and spatial-temporal correlation of traffic network,and the influence of potential correlation between nodes of traffic network on the spatial features,this paper proposes a traffic speed prediction model based on the combination of graph attention network with self-adaptive adjacency matrix(SAdpGAT)and bidirectional gated recurrent unit(BiGRU).First-ly,the model introduces graph attention network(GAT)to extract the spatial features of real road network and potential road network respectively in spatial dimension.Secondly,the spatial features are input into BiGRU to extract the time series features.Finally,the prediction results of the real road network and the potential road network are connected to generate the final prediction results of the model.The experimental results show that the prediction accuracy of the proposed model is im-proved obviously on METR-LA and PEMS-BAY datasets,which proves the advantages of the pro-posed spatial-temporal model in traffic speed prediction.展开更多
Memristor-based neuromorphic computing shows great potential for high-speed and high-throughput signal processing applications,such as electroencephalogram(EEG)signal processing.Nonetheless,the size of one-transistor ...Memristor-based neuromorphic computing shows great potential for high-speed and high-throughput signal processing applications,such as electroencephalogram(EEG)signal processing.Nonetheless,the size of one-transistor one-resistor(1T1R)memristor arrays is limited by the non-ideality of the devices,which prevents the hardware implementation of large and complex networks.In this work,we propose the depthwise separable convolution and bidirectional gate recurrent unit(DSC-BiGRU)network,a lightweight and highly robust hybrid neural network based on 1T1R arrays that enables efficient processing of EEG signals in the temporal,frequency and spatial domains by hybridizing DSC and BiGRU blocks.The network size is reduced and the network robustness is improved while ensuring the network classification accuracy.In the simulation,the measured non-idealities of the 1T1R array are brought into the network through statistical analysis.Compared with traditional convolutional networks,the network parameters are reduced by 95%and the network classification accuracy is improved by 21%at a 95%array yield rate and 5%tolerable error.This work demonstrates that lightweight and highly robust networks based on memristor arrays hold great promise for applications that rely on low consumption and high efficiency.展开更多
To predict renewable energy sources such as solar power in microgrids more accurately,a hybrid power prediction method is presented in this paper.First,the self-attention mechanism is introduced based on a bidirection...To predict renewable energy sources such as solar power in microgrids more accurately,a hybrid power prediction method is presented in this paper.First,the self-attention mechanism is introduced based on a bidirectional gated recurrent neural network(BiGRU)to explore the time-series characteristics of solar power output and consider the influence of different time nodes on the prediction results.Subsequently,an improved quantum particle swarm optimization(QPSO)algorithm is proposed to optimize the hyperparameters of the combined prediction model.The final proposed LQPSO-BiGRU-self-attention hybrid model can predict solar power more effectively.In addition,considering the coordinated utilization of various energy sources such as electricity,hydrogen,and renewable energy,a multi-objective optimization model that considers both economic and environmental costs was constructed.A two-stage adaptive multi-objective quantum particle swarm optimization algorithm aided by a Lévy flight,named MO-LQPSO,was proposed for the comprehensive optimal scheduling of a multi-energy microgrid system.This algorithm effectively balances the global and local search capabilities and enhances the solution of complex nonlinear problems.The effectiveness and superiority of the proposed scheme are verified through comparative simulations.展开更多
Traditional e-commerce recommendation systems often struggle with dynamic user preferences and a vast array of products,leading to suboptimal user experiences.To address this,our study presents a Personalized Adaptive...Traditional e-commerce recommendation systems often struggle with dynamic user preferences and a vast array of products,leading to suboptimal user experiences.To address this,our study presents a Personalized Adaptive Multi-Product Recommendation System(PAMR)leveraging transfer learning and Bi-GRU(Bidirectional Gated Recurrent Units).Using a large dataset of user reviews from Amazon and Flipkart,we employ transfer learning with pre-trained models(AlexNet,GoogleNet,ResNet-50)to extract high-level attributes from product data,ensuring effective feature representation even with limited data.Bi-GRU captures both spatial and sequential dependencies in user-item interactions.The innovation of this study lies in the innovative feature fusion technique that combines the strengths of multiple transfer learning models,and the integration of an attention mechanism within the Bi-GRU framework to prioritize relevant features.Our approach addresses the classic recommendation systems that often face challenges such as cold start along with data sparsity difficulties,by utilizing robust user and item representations.The model demonstrated an accuracy of up to 96.9%,with precision and an F1-score of 96.2%and 96.97%,respectively,on the Amazon dataset,significantly outperforming the baselines and marking a considerable advancement over traditional configurations.This study highlights the effectiveness of combining transfer learning with Bi-GRU for scalable and adaptive recommendation systems,providing a versatile solution for real-world applications.展开更多
Solar photovoltaic energy generation due to its high potential is being adopted as one of the main power sources by many countries to mitigate their climate and electrical power issues.Hence accurate forecasting becom...Solar photovoltaic energy generation due to its high potential is being adopted as one of the main power sources by many countries to mitigate their climate and electrical power issues.Hence accurate forecasting becomes important to make grid operations smoother,and for this purpose,modern-day artificial intelligence technologies can make a significant contribution.This study is an endeavor to target accurate forecasting for different weather conditions by using a simple recurrent neural network,long-short-term memory and gated recurrent unit-based hybrid model,and bidirectional gated recurrent unit.The experimental dataset has been acquired from Quaid-e-Azam Solar Park,Bahawalpur,Pakistan.This study observed that the bidirectional gated recurrent unit outperforms the hybrid model,whereas the simple recurrent neural network lags most in accuracy.The results confirm that the bidirectional gated recurrent unit technique can perform accurately in all critical weather types.Whereas the values of root-mean-square error,mean absolute error,and R-squared values also ensure the precision of the model for all weather conditions,and the best of these parameters for bidirectional gated recurrent unit observed are 0.0012,0.212,and 0.99,respectively,for the overcast dataset.展开更多
Electrochemical impedance spectroscopy plays a crucial role in monitoring the state of health of lithium-ion batteries.However,effective feature extraction often relies on limited information and prior knowledge.To ad...Electrochemical impedance spectroscopy plays a crucial role in monitoring the state of health of lithium-ion batteries.However,effective feature extraction often relies on limited information and prior knowledge.To add-ress this issue,this paper presents an innovative approach that utilizes the gramian angular field method to transform raw electrochemical impedance spectroscopy data into image data that is easily recognizable by convolutional neural networks.Subsequently,the convolutional block attention module is integrated with bidirectional gated recurrent unit for state of health prediction.First,convolu-tional block attention module is applied to the electro-chemical impedance spectroscopy image data to enhance key features while suppressing redundant information,thereby effectively extracting representative battery state features.Subsequently,the extracted features are fed into a bidirectional gated recurrent unit network for time series modeling to capture the dynamic changes in battery state of health.Experimental results show a significant im-provement in the accuracy of state of health predictions,highlighting the effectiveness of convolutional block atten-tion module in feature extraction and the advantages of bidirectional gated recurrent unit in time series forecasting.This research provides an attention mechanism-based feature extraction solution for lithium-ion battery health management,demonstrating the extensive application potential of deep learning in battery state monitoring.展开更多
Purpose-Multi-domain convolutional neural network(MDCNN)model has been widely used in object recognition and tracking in the field of computer vision.However,if the objects to be tracked move rapid or the appearances ...Purpose-Multi-domain convolutional neural network(MDCNN)model has been widely used in object recognition and tracking in the field of computer vision.However,if the objects to be tracked move rapid or the appearances of moving objects vary dramatically,the conventional MDCNN model will suffer from the model drift problem.To solve such problem in tracking rapid objects under limiting environment for MDCNN model,this paper proposed an auto-attentional mechanism-based MDCNN(AA-MDCNN)model for the rapid moving and changing objects tracking under limiting environment.Design/methodology/approach-First,to distinguish the foreground object between background and other similar objects,the auto-attentional mechanism is used to selectively aggregate the weighted summation of all feature maps to make the similar features related to each other.Then,the bidirectional gated recurrent unit(Bi-GRU)architecture is used to integrate all the feature maps to selectively emphasize the importance of the correlated feature maps.Finally,the final feature map is obtained by fusion the above two feature maps for object tracking.In addition,a composite loss function is constructed to solve the similar but different attribute sequences tracking using conventional MDCNN model.Findings-In order to validate the effectiveness and feasibility of the proposed AA-MDCNN model,this paper used ImageNet-Vid dataset to train the object tracking model,and the OTB-50 dataset is used to validate the AA-MDCNN tracking model.Experimental results have shown that the augmentation of auto-attentional mechanism will improve the accuracy rate 2.75%and success rate 2.41%,respectively.In addition,the authors also selected six complex tracking scenarios in OTB-50 dataset;over eleven attributes have been validated that the proposed AA-MDCNN model outperformed than the comparative models over nine attributes.In addition,except for the scenario of multi-objects moving with each other,the proposed AA-MDCNN model solved the majority rapid moving objects tracking scenarios and outperformed than the comparative models on such complex scenarios.Originality/value-This paper introduced the auto-attentional mechanism into MDCNN model and adopted Bi-GRU architecture to extract key features.By using the proposed AA-MDCNN model,rapid object tracking under complex background,motion blur and occlusion objects has better effect,and such model is expected to be further applied to the rapid object tracking in the real world.展开更多
Modern power grids face the challenge of increasing renewable energy penetration that is stochastic in nature and calls for accurate demand predictions to provide the optimized power supply.Hence,increasing the self-c...Modern power grids face the challenge of increasing renewable energy penetration that is stochastic in nature and calls for accurate demand predictions to provide the optimized power supply.Hence,increasing the self-consumption of renewable energy through demand response in households,local communities,and micro-grids is essential and calls for high demand prediction performance at lower levels of demand aggregations to achieve optimal performance.Although many of the recent studies have investigated both macro and micro scale short-term load forecasting(STLF),a comprehensive investigation on the effects of electrical demand aggregation size on STLF is minimal,especially with large sample sizes,where it is essential for optimal sizing of residential micro-grids,demand response markets,and virtual power plants.Hence,this study comprehensively investigates STLF of five aggregation levels(3,10,30,100,and 479)based on a dataset of 479 residential dwellings in Osaka,Japan,with a sample size of(159,47,15,4,and 1)per level,respectively,and investigates the underlying challenges in lower aggregation forecasting.Five deep learning(DL)methods are utilized for STLF and fine-tuned with extensive methodological sensitivity analysis and a variation of early stopping,where a detailed comparative analysis is developed.The test results reveal that a MAPE of(2.47-3.31%)close to country levels can be achieved on the highest aggregation,and below 10%can be sustained at 30 aggregated dwellings.Furthermore,the deep neural network(DNN)achieved the highest performance,followed by the Bi-directional Gated recurrent unit with fully connected layers(Bi-GRU-FCL),which had close to 15%faster training time and 40%fewer learnable parameters.展开更多
基金This research is partially supported by the National Natural Science Foundation of China(Grant No.61772098)Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.KJZD K201900603,KJQN201900629)Chongqing Grad-uate Education Teaching Reform Project(No.yjg183081).
文摘Predicting travel trajectory of vehicles can not only provide personalized services to users,but also have a certain effect on traffic guidance and traffic control.In this paper,we build a Bayonet-Corpus based on the context of traffic intersections,and use it to model a traffic network.Besides,Bidirectional Gated Recurrent Unit(Bi-GRU)is used to predict the sequence of traffic intersections in one single trajectory.Firstly,considering that real traffic networks are usually complex and disorder and cannot reflect the higher dimensional relationship among traffic intersections,this paper proposes a new traffic network modeling algorithm based on the context of traffic intersections:inspired by the probabilistic language model,a Bayonet-Corpus is constructed from traffic intersections in real trajectory sequence,so the high-dimensional similarity between corpus nodes can be used to measure the semantic relation of real traffic intersections.This algorithm maps vehicle trajectory nodes into a high-dimensional space vector,blocking complex structure of real traffic network and reconstructing the traffic network space.Then,the bayonets sequence in real traffic network is mapped into a matrix.Considering the trajectories sequence is bidirectional,and Bi-GRU can handle information from forward and backward simultaneously,we use Bi-GRU to bidirectionally model the trajectory matrix for the purpose of prediction.
基金supported by the National Natural Science Foundation of China(No.42075129)Hebei ProvinceNatural Science Foundation(No.E2021202179)Key Research andDevelopment Project fromHebei Province(No.21351803D).
文摘The rising prevalence of diabetes in modern society underscores the urgent need for precise and efficient diagnostic tools to support early intervention and treatment.However,the inherent limitations of existing datasets,including significant class imbalances and inadequate sample diversity,pose challenges to the accurate prediction and classification of diabetes.Addressing these issues,this study proposes an innovative diabetes prediction framework that integrates a hybrid Convolutional Neural Network-Bidirectional Gated Recurrent Unit(CNN-BiGRU)model for classification with Adaptive Synthetic Sampling(ADASYN)for data augmentation.ADASYN was employed to generate synthetic yet representative data samples,effectively mitigating class imbalance and enhancing the diversity and representativeness of the dataset.This augmentation process is critical for ensuring the robustness and generalizability of the predictive model,particularly in scenarios where minority class samples are underrepresented.The CNN-BiGRU architecture was designed to leverage the complementary strengths of CNN in extracting spatial features and BiGRU in capturing sequential dependencies,making it well-suited for the complex patterns inherent in medical data.The proposed framework demonstrated exceptional performance,achieving a training accuracy of 98.74%and a test accuracy of 97.78%on the augmented dataset.These results validate the efficacy of the integrated approach in addressing the challenges of class imbalance and dataset heterogeneity,while significantly enhancing the diagnostic precision for diabetes prediction.This study provides a scalable and reliable methodology with promising implications for advancing diagnostic accuracy in medical applications,particularly in resource-constrained and data-limited environments.
基金National Key R&D Program of China,Grant/Award Numbers:2022YFB2403700,2022YFB2403705Natural Science Foundation of Chongqing,Grant/Award Number:CSTB2022NSCQ-MSX1247。
文摘Mechanical vibration defect is the key factor leading to sudden failure of gas-insulated switchgear(GIS)equipment.It is important to realise effective prediction of the me-chanical vibration state development trend of GIS equipment in order to improve its active safety protection level.This paper carried out research on the accurate prediction method and experimental validation of the mechanical vibration state and its defect severity development trend for the GIS equipment.Firstly,the deep and shallow vibration feature parameters for different mechanical defect signals were jointly extracted by time-domain features and deep belief network methods.Secondly,a new prediction model,incorporating the attention mechanism and the bidirectional gated recurrent unit(BiGRU),was constructed with the deep and shallow vibration feature parameters as inputs.Finally,the prediction trend effectiveness was verified based on the real-type GIS mechanical simulation platform and the field operation GIS equipment.Results show that the deep and shallow vibration feature extraction method proposed in this paper can characterise the mechanical defect information more comprehensively.The new prediction method of the vibration state trend based on the attention-BiGRU model shows ideal accuracy,and the predicted vibration state development trend is highly consistent with the actual,with an average absolute error of 0.063.The root mean square error(ERMSE)value of the prediction method is<5%,which reduces the relative error value at least 37% compared with the traditional prediction models.This paper provides a valuable reference for the proactive defence of GIS mechanical failure.
基金This research was funded by National Natural Science Foundation of China under Grant No.61806171Sichuan University of Science&Engineering Talent Project under Grant No.2021RC15+2 种基金Open Fund Project of Key Laboratory for Non-Destructive Testing and Engineering Computer of Sichuan Province Universities on Bridge Inspection and Engineering under Grant No.2022QYJ06Sichuan University of Science&Engineering Graduate Student Innovation Fund under Grant No.Y2023115The Scientific Research and Innovation Team Program of Sichuan University of Science and Technology under Grant No.SUSE652A006.
文摘While encryption technology safeguards the security of network communications,malicious traffic also uses encryption protocols to obscure its malicious behavior.To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic,we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features,called BERT-based Spatio-Temporal Features Network(BSTFNet).At the packet-level granularity,the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers(BERT)model.At the byte-level granularity,we initially employ the Bidirectional Gated Recurrent Unit(BiGRU)model to extract temporal features from bytes,followed by the utilization of the Text Convolutional Neural Network(TextCNN)model with multi-sized convolution kernels to extract local multi-receptive field spatial features.The fusion of features from both granularities serves as the ultimate multidimensional representation of malicious traffic.Our approach achieves accuracy and F1-score of 99.39%and 99.40%,respectively,on the publicly available USTC-TFC2016 dataset,and effectively reduces sample confusion within the Neris and Virut categories.The experimental results demonstrate that our method has outstanding representation and classification capabilities for encrypted malicious traffic.
基金the National Natural Science Foundation of China(No.61461027,61762059)the Provincial Science and Technology Program supported the Key Project of Natural Science Foundation of Gansu Province(No.22JR5RA226)。
文摘Considering the nonlinear structure and spatial-temporal correlation of traffic network,and the influence of potential correlation between nodes of traffic network on the spatial features,this paper proposes a traffic speed prediction model based on the combination of graph attention network with self-adaptive adjacency matrix(SAdpGAT)and bidirectional gated recurrent unit(BiGRU).First-ly,the model introduces graph attention network(GAT)to extract the spatial features of real road network and potential road network respectively in spatial dimension.Secondly,the spatial features are input into BiGRU to extract the time series features.Finally,the prediction results of the real road network and the potential road network are connected to generate the final prediction results of the model.The experimental results show that the prediction accuracy of the proposed model is im-proved obviously on METR-LA and PEMS-BAY datasets,which proves the advantages of the pro-posed spatial-temporal model in traffic speed prediction.
基金Project supported by the National Key Research and Development Program of China(Grant No.2019YFB2205102)the National Natural Science Foundation of China(Grant Nos.61974164,62074166,61804181,62004219,62004220,and 62104256).
文摘Memristor-based neuromorphic computing shows great potential for high-speed and high-throughput signal processing applications,such as electroencephalogram(EEG)signal processing.Nonetheless,the size of one-transistor one-resistor(1T1R)memristor arrays is limited by the non-ideality of the devices,which prevents the hardware implementation of large and complex networks.In this work,we propose the depthwise separable convolution and bidirectional gate recurrent unit(DSC-BiGRU)network,a lightweight and highly robust hybrid neural network based on 1T1R arrays that enables efficient processing of EEG signals in the temporal,frequency and spatial domains by hybridizing DSC and BiGRU blocks.The network size is reduced and the network robustness is improved while ensuring the network classification accuracy.In the simulation,the measured non-idealities of the 1T1R array are brought into the network through statistical analysis.Compared with traditional convolutional networks,the network parameters are reduced by 95%and the network classification accuracy is improved by 21%at a 95%array yield rate and 5%tolerable error.This work demonstrates that lightweight and highly robust networks based on memristor arrays hold great promise for applications that rely on low consumption and high efficiency.
基金supported by the National Natural Science Foundation of China under Grant 51977004the Beijing Natural Science Foundation under Grant 4212042.
文摘To predict renewable energy sources such as solar power in microgrids more accurately,a hybrid power prediction method is presented in this paper.First,the self-attention mechanism is introduced based on a bidirectional gated recurrent neural network(BiGRU)to explore the time-series characteristics of solar power output and consider the influence of different time nodes on the prediction results.Subsequently,an improved quantum particle swarm optimization(QPSO)algorithm is proposed to optimize the hyperparameters of the combined prediction model.The final proposed LQPSO-BiGRU-self-attention hybrid model can predict solar power more effectively.In addition,considering the coordinated utilization of various energy sources such as electricity,hydrogen,and renewable energy,a multi-objective optimization model that considers both economic and environmental costs was constructed.A two-stage adaptive multi-objective quantum particle swarm optimization algorithm aided by a Lévy flight,named MO-LQPSO,was proposed for the comprehensive optimal scheduling of a multi-energy microgrid system.This algorithm effectively balances the global and local search capabilities and enhances the solution of complex nonlinear problems.The effectiveness and superiority of the proposed scheme are verified through comparative simulations.
文摘Traditional e-commerce recommendation systems often struggle with dynamic user preferences and a vast array of products,leading to suboptimal user experiences.To address this,our study presents a Personalized Adaptive Multi-Product Recommendation System(PAMR)leveraging transfer learning and Bi-GRU(Bidirectional Gated Recurrent Units).Using a large dataset of user reviews from Amazon and Flipkart,we employ transfer learning with pre-trained models(AlexNet,GoogleNet,ResNet-50)to extract high-level attributes from product data,ensuring effective feature representation even with limited data.Bi-GRU captures both spatial and sequential dependencies in user-item interactions.The innovation of this study lies in the innovative feature fusion technique that combines the strengths of multiple transfer learning models,and the integration of an attention mechanism within the Bi-GRU framework to prioritize relevant features.Our approach addresses the classic recommendation systems that often face challenges such as cold start along with data sparsity difficulties,by utilizing robust user and item representations.The model demonstrated an accuracy of up to 96.9%,with precision and an F1-score of 96.2%and 96.97%,respectively,on the Amazon dataset,significantly outperforming the baselines and marking a considerable advancement over traditional configurations.This study highlights the effectiveness of combining transfer learning with Bi-GRU for scalable and adaptive recommendation systems,providing a versatile solution for real-world applications.
文摘Solar photovoltaic energy generation due to its high potential is being adopted as one of the main power sources by many countries to mitigate their climate and electrical power issues.Hence accurate forecasting becomes important to make grid operations smoother,and for this purpose,modern-day artificial intelligence technologies can make a significant contribution.This study is an endeavor to target accurate forecasting for different weather conditions by using a simple recurrent neural network,long-short-term memory and gated recurrent unit-based hybrid model,and bidirectional gated recurrent unit.The experimental dataset has been acquired from Quaid-e-Azam Solar Park,Bahawalpur,Pakistan.This study observed that the bidirectional gated recurrent unit outperforms the hybrid model,whereas the simple recurrent neural network lags most in accuracy.The results confirm that the bidirectional gated recurrent unit technique can perform accurately in all critical weather types.Whereas the values of root-mean-square error,mean absolute error,and R-squared values also ensure the precision of the model for all weather conditions,and the best of these parameters for bidirectional gated recurrent unit observed are 0.0012,0.212,and 0.99,respectively,for the overcast dataset.
文摘Electrochemical impedance spectroscopy plays a crucial role in monitoring the state of health of lithium-ion batteries.However,effective feature extraction often relies on limited information and prior knowledge.To add-ress this issue,this paper presents an innovative approach that utilizes the gramian angular field method to transform raw electrochemical impedance spectroscopy data into image data that is easily recognizable by convolutional neural networks.Subsequently,the convolutional block attention module is integrated with bidirectional gated recurrent unit for state of health prediction.First,convolu-tional block attention module is applied to the electro-chemical impedance spectroscopy image data to enhance key features while suppressing redundant information,thereby effectively extracting representative battery state features.Subsequently,the extracted features are fed into a bidirectional gated recurrent unit network for time series modeling to capture the dynamic changes in battery state of health.Experimental results show a significant im-provement in the accuracy of state of health predictions,highlighting the effectiveness of convolutional block atten-tion module in feature extraction and the advantages of bidirectional gated recurrent unit in time series forecasting.This research provides an attention mechanism-based feature extraction solution for lithium-ion battery health management,demonstrating the extensive application potential of deep learning in battery state monitoring.
基金supported by the Education and Scientific Research Project for Young and Middle-aged Teachers in Fujian Province(No.JAT200581).
文摘Purpose-Multi-domain convolutional neural network(MDCNN)model has been widely used in object recognition and tracking in the field of computer vision.However,if the objects to be tracked move rapid or the appearances of moving objects vary dramatically,the conventional MDCNN model will suffer from the model drift problem.To solve such problem in tracking rapid objects under limiting environment for MDCNN model,this paper proposed an auto-attentional mechanism-based MDCNN(AA-MDCNN)model for the rapid moving and changing objects tracking under limiting environment.Design/methodology/approach-First,to distinguish the foreground object between background and other similar objects,the auto-attentional mechanism is used to selectively aggregate the weighted summation of all feature maps to make the similar features related to each other.Then,the bidirectional gated recurrent unit(Bi-GRU)architecture is used to integrate all the feature maps to selectively emphasize the importance of the correlated feature maps.Finally,the final feature map is obtained by fusion the above two feature maps for object tracking.In addition,a composite loss function is constructed to solve the similar but different attribute sequences tracking using conventional MDCNN model.Findings-In order to validate the effectiveness and feasibility of the proposed AA-MDCNN model,this paper used ImageNet-Vid dataset to train the object tracking model,and the OTB-50 dataset is used to validate the AA-MDCNN tracking model.Experimental results have shown that the augmentation of auto-attentional mechanism will improve the accuracy rate 2.75%and success rate 2.41%,respectively.In addition,the authors also selected six complex tracking scenarios in OTB-50 dataset;over eleven attributes have been validated that the proposed AA-MDCNN model outperformed than the comparative models over nine attributes.In addition,except for the scenario of multi-objects moving with each other,the proposed AA-MDCNN model solved the majority rapid moving objects tracking scenarios and outperformed than the comparative models on such complex scenarios.Originality/value-This paper introduced the auto-attentional mechanism into MDCNN model and adopted Bi-GRU architecture to extract key features.By using the proposed AA-MDCNN model,rapid object tracking under complex background,motion blur and occlusion objects has better effect,and such model is expected to be further applied to the rapid object tracking in the real world.
文摘Modern power grids face the challenge of increasing renewable energy penetration that is stochastic in nature and calls for accurate demand predictions to provide the optimized power supply.Hence,increasing the self-consumption of renewable energy through demand response in households,local communities,and micro-grids is essential and calls for high demand prediction performance at lower levels of demand aggregations to achieve optimal performance.Although many of the recent studies have investigated both macro and micro scale short-term load forecasting(STLF),a comprehensive investigation on the effects of electrical demand aggregation size on STLF is minimal,especially with large sample sizes,where it is essential for optimal sizing of residential micro-grids,demand response markets,and virtual power plants.Hence,this study comprehensively investigates STLF of five aggregation levels(3,10,30,100,and 479)based on a dataset of 479 residential dwellings in Osaka,Japan,with a sample size of(159,47,15,4,and 1)per level,respectively,and investigates the underlying challenges in lower aggregation forecasting.Five deep learning(DL)methods are utilized for STLF and fine-tuned with extensive methodological sensitivity analysis and a variation of early stopping,where a detailed comparative analysis is developed.The test results reveal that a MAPE of(2.47-3.31%)close to country levels can be achieved on the highest aggregation,and below 10%can be sustained at 30 aggregated dwellings.Furthermore,the deep neural network(DNN)achieved the highest performance,followed by the Bi-directional Gated recurrent unit with fully connected layers(Bi-GRU-FCL),which had close to 15%faster training time and 40%fewer learnable parameters.
