Prediction and scheduling of multi-energy microgrid based on BiGRU self-attention mechanism and LQPSO

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.

BSTFNet:An Encrypted Malicious Traffic Classification Method Integrating Global Semantic and Spatiotemporal Features

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.

Adaptive spatial-temporal graph attention network for traffic speed prediction

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.

Bayonet-corpus:a trajectory prediction method based on bayonet context and bidirectional GRU

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.

Lightweight and highly robust memristor-based hybrid neural networks for electroencephalogram signal processing

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.

基于BERT-BiGRU模型的文本分类研究

文本分类是自然语言处理的典型应用,目前文本分类最常用的是深度学习的分类方法。针对中文文本数据具有多种特性,例如隐喻表达、语义多义性、语法特异性等,在文本分类中进行研究。提出基于编码器-解码器的双向编码表示法-双向门控制循环单元(bidirectional encoder representations from transformers-bidirectional gate recurrent unit,BERT-BiGRU)模型结构,使用BERT模型代替传统的Word2vec模型表示词向量,根据上下文信息计算字的表示,在融合上下文信息的同时还能根据字的多义性进行调整,增强了字的语义表示。在BERT模型后面增加了BiGRU,将训练后的词向量作为Bi GRU的输入进行训练,该模型可以同时从两个方向对文本信息进行特征提取,使模型具有更好的文本表示信息能力,达到更精确的文本分类效果。使用提出的BERT-BiGRU模型进行文本分类,最终准确率达到0.93,召回率达到0.94,综合评价数值F1达到0.93。通过与其他模型的试验结果对比,发现BERT-BiGRU模型在中文文本分类任务中有良好的性能。

Auto-attentional mechanism in multi-domain convolutional neural networks for improving object tracking

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.

Electrical demand aggregation effects on the performance of deep learning-based short-term load forecasting of a residential building

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.