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
Numerous works prove that existing neighbor-averaging graph neural networks(GNNs)cannot efficiently catch structure features,and many works show that injecting structure,distance,position,or spatial features can signi...Numerous works prove that existing neighbor-averaging graph neural networks(GNNs)cannot efficiently catch structure features,and many works show that injecting structure,distance,position,or spatial features can significantly improve the performance of GNNs,however,injecting high-level structure and distance into GNNs is an intuitive but untouched idea.This work sheds light on this issue and proposes a scheme to enhance graph attention networks(GATs)by encoding distance and hop-wise structure statistics.Firstly,the hop-wise structure and distributional distance information are extracted based on several hop-wise ego-nets of every target node.Secondly,the derived structure information,distance information,and intrinsic features are encoded into the same vector space and then added together to get initial embedding vectors.Thirdly,the derived embedding vectors are fed into GATs,such as GAT and adaptive graph diffusion network(AGDN)to get the soft labels.Fourthly,the soft labels are fed into correct and smooth(C&S)to conduct label propagation and get final predictions.Experiments show that the distance and hop-wise structures encoding enhanced graph attention networks(DHSEGATs)achieve a competitive result.展开更多
This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary obj...This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary objective is to explore the unknown environments to locate and track targets effectively. To address this problem, we propose a novel Multi-Agent Reinforcement Learning (MARL) method based on Graph Neural Network (GNN). Firstly, a method is introduced for encoding continuous-space multi-UAV problem data into spatial graphs which establish essential relationships among agents, obstacles, and targets. Secondly, a Graph AttenTion network (GAT) model is presented, which focuses exclusively on adjacent nodes, learns attention weights adaptively and allows agents to better process information in dynamic environments. Reward functions are specifically designed to tackle exploration challenges in environments with sparse rewards. By introducing a framework that integrates centralized training and distributed execution, the advancement of models is facilitated. Simulation results show that the proposed method outperforms the existing MARL method in search rate and tracking performance with less collisions. The experiments show that the proposed method can be extended to applications with a larger number of agents, which provides a potential solution to the challenging problem of multi-UAV autonomous tracking in dynamic unknown environments.展开更多
台区电力工单记录反映了台区运行工况和用户需求,是制定台区用电安全管理制度和满足台区用户用电需求的重要依据。针对台区电力工单高复杂性和强专业性给台区工单分类带来的难题,提出一种融合标签平滑(LS)与预训练语言模型的台区电力工...台区电力工单记录反映了台区运行工况和用户需求,是制定台区用电安全管理制度和满足台区用户用电需求的重要依据。针对台区电力工单高复杂性和强专业性给台区工单分类带来的难题,提出一种融合标签平滑(LS)与预训练语言模型的台区电力工单分类模型(MiniRBT-LSTM-GAT)。首先,利用预训练模型计算电力工单文本中的字符级特征向量表示;其次,采用双向长短期记忆网络(BiLSTM)捕捉电力文本序列中的依赖关系;再次,通过图注意力网络(GAT)聚焦对文本分类贡献大的特征信息;最后,利用LS改进损失函数以提高模型的分类精度。所提模型与当前主流的文本分类算法在农网台区电力工单数据集(RSPWO)、浙江省95598电力工单数据集(ZJPWO)和THUCNews(TsingHua University Chinese News)数据集上的实验结果表明,与电力审计文本多粒度预训练语言模型(EPAT-BERT)相比,所提模型在RSPWO、ZJPWO上的查准率和F1值分别提升了2.76、2.02个百分点和1.77、1.40个百分点;与胶囊神经网络模型BRsyn-caps(capsule network based on BERT and dependency syntax)相比,所提模型在THUCNews数据集上的查准率和准确率分别提升了0.76和0.71个百分点。可见,所提模型有效提升了台区电力工单分类的性能,并在THUCNews数据集上表现良好,验证了模型的通用性。展开更多
随着基于位置的社交网络的快速发展,下一个PoI(point of interest)推荐已成为推荐领域的研究热点。然而现有研究模型忽略了PoI的时空特征以及上下文信息对下一个PoI推荐的效果。针对该问题,提出一种时空上下文感知的下一个PoI推荐方法...随着基于位置的社交网络的快速发展,下一个PoI(point of interest)推荐已成为推荐领域的研究热点。然而现有研究模型忽略了PoI的时空特征以及上下文信息对下一个PoI推荐的效果。针对该问题,提出一种时空上下文感知的下一个PoI推荐方法。首先,利用图注意力网络(GAT)学习包含社交关系的用户表征;并且通过流行度增强二部图神经网络(PEBGNN)学习含有PoI交互偏好的用户表征和PoI表征;同时,利用时空图卷积网络(ST-GCN)学习PoI时空转移偏好的PoI表征;最后,通过融合所学到的用户表征和PoI表征,计算出用户对于各个PoI的预测评分,以此为基础为用户推荐下一个PoI。为了验证该方法的有效性,在Gowalla、Foursquare以及Yelp这三个公开的数据集上进行了测试。实验结果显示,相比于多个基准模型,所提方法在准确率和召回率方面均展现出了显著的优势,分别平均提升28.53%和7.65%。展开更多
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.
文摘Numerous works prove that existing neighbor-averaging graph neural networks(GNNs)cannot efficiently catch structure features,and many works show that injecting structure,distance,position,or spatial features can significantly improve the performance of GNNs,however,injecting high-level structure and distance into GNNs is an intuitive but untouched idea.This work sheds light on this issue and proposes a scheme to enhance graph attention networks(GATs)by encoding distance and hop-wise structure statistics.Firstly,the hop-wise structure and distributional distance information are extracted based on several hop-wise ego-nets of every target node.Secondly,the derived structure information,distance information,and intrinsic features are encoded into the same vector space and then added together to get initial embedding vectors.Thirdly,the derived embedding vectors are fed into GATs,such as GAT and adaptive graph diffusion network(AGDN)to get the soft labels.Fourthly,the soft labels are fed into correct and smooth(C&S)to conduct label propagation and get final predictions.Experiments show that the distance and hop-wise structures encoding enhanced graph attention networks(DHSEGATs)achieve a competitive result.
基金supported by the National Natural Science Foundation of China(Nos.12272104,U22B2013).
文摘This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary objective is to explore the unknown environments to locate and track targets effectively. To address this problem, we propose a novel Multi-Agent Reinforcement Learning (MARL) method based on Graph Neural Network (GNN). Firstly, a method is introduced for encoding continuous-space multi-UAV problem data into spatial graphs which establish essential relationships among agents, obstacles, and targets. Secondly, a Graph AttenTion network (GAT) model is presented, which focuses exclusively on adjacent nodes, learns attention weights adaptively and allows agents to better process information in dynamic environments. Reward functions are specifically designed to tackle exploration challenges in environments with sparse rewards. By introducing a framework that integrates centralized training and distributed execution, the advancement of models is facilitated. Simulation results show that the proposed method outperforms the existing MARL method in search rate and tracking performance with less collisions. The experiments show that the proposed method can be extended to applications with a larger number of agents, which provides a potential solution to the challenging problem of multi-UAV autonomous tracking in dynamic unknown environments.
文摘台区电力工单记录反映了台区运行工况和用户需求,是制定台区用电安全管理制度和满足台区用户用电需求的重要依据。针对台区电力工单高复杂性和强专业性给台区工单分类带来的难题,提出一种融合标签平滑(LS)与预训练语言模型的台区电力工单分类模型(MiniRBT-LSTM-GAT)。首先,利用预训练模型计算电力工单文本中的字符级特征向量表示;其次,采用双向长短期记忆网络(BiLSTM)捕捉电力文本序列中的依赖关系;再次,通过图注意力网络(GAT)聚焦对文本分类贡献大的特征信息;最后,利用LS改进损失函数以提高模型的分类精度。所提模型与当前主流的文本分类算法在农网台区电力工单数据集(RSPWO)、浙江省95598电力工单数据集(ZJPWO)和THUCNews(TsingHua University Chinese News)数据集上的实验结果表明,与电力审计文本多粒度预训练语言模型(EPAT-BERT)相比,所提模型在RSPWO、ZJPWO上的查准率和F1值分别提升了2.76、2.02个百分点和1.77、1.40个百分点;与胶囊神经网络模型BRsyn-caps(capsule network based on BERT and dependency syntax)相比,所提模型在THUCNews数据集上的查准率和准确率分别提升了0.76和0.71个百分点。可见,所提模型有效提升了台区电力工单分类的性能,并在THUCNews数据集上表现良好,验证了模型的通用性。
文摘随着基于位置的社交网络的快速发展,下一个PoI(point of interest)推荐已成为推荐领域的研究热点。然而现有研究模型忽略了PoI的时空特征以及上下文信息对下一个PoI推荐的效果。针对该问题,提出一种时空上下文感知的下一个PoI推荐方法。首先,利用图注意力网络(GAT)学习包含社交关系的用户表征;并且通过流行度增强二部图神经网络(PEBGNN)学习含有PoI交互偏好的用户表征和PoI表征;同时,利用时空图卷积网络(ST-GCN)学习PoI时空转移偏好的PoI表征;最后,通过融合所学到的用户表征和PoI表征,计算出用户对于各个PoI的预测评分,以此为基础为用户推荐下一个PoI。为了验证该方法的有效性,在Gowalla、Foursquare以及Yelp这三个公开的数据集上进行了测试。实验结果显示,相比于多个基准模型,所提方法在准确率和召回率方面均展现出了显著的优势,分别平均提升28.53%和7.65%。
