现有的基于BERT(bidirectional encoder representations from transformers)的方面级情感分析模型仅使用BERT最后一层隐藏层的输出,忽略BERT中间隐藏层的语义信息,存在信息利用不充分的问题,提出一种融合BERT中间隐藏层的方面级情感分...现有的基于BERT(bidirectional encoder representations from transformers)的方面级情感分析模型仅使用BERT最后一层隐藏层的输出,忽略BERT中间隐藏层的语义信息,存在信息利用不充分的问题,提出一种融合BERT中间隐藏层的方面级情感分析模型。首先,将评论和方面信息拼接为句子对输入BERT模型,通过BERT的自注意力机制建立评论与方面信息的联系;其次,构建门控卷积网络(gated convolutional neural network,GCNN)对BERT所有隐藏层输出的词向量矩阵进行特征提取,并将提取的特征进行最大池化、拼接得到特征序列;然后,使用双向门控循环单元(bidirectional gated recurrent unit,BiGRU)网络对特征序列进行融合,编码BERT不同隐藏层的信息;最后,引入注意力机制,根据特征与方面信息的相关程度赋予权值。在公开的SemEval2014 Task4评论数据集上的实验结果表明:所提模型在准确率和F 1值两种评价指标上均优于BERT、CapsBERT(capsule BERT)、BERT-PT(BERT post train)、BERT-LSTM(BERT long and short-term memory)等对比模型,具有较好的情感分类效果。展开更多
Pattern recognition is critical to map data handling and their applications.This study presents a model that combines the Shape Context(SC)descriptor and Graph Convolutional Neural Network(GCNN)to classify the pattern...Pattern recognition is critical to map data handling and their applications.This study presents a model that combines the Shape Context(SC)descriptor and Graph Convolutional Neural Network(GCNN)to classify the patterns of interchanges,which are indispensable parts of urban road networks.In the SC-GCNN model,an interchange is modeled as a graph,wherein nodes and edges represent the interchange segments and their connections,respectively.Then,a novel SC descriptor is implemented to describe the contextual information of each interchange segment and serve as descriptive features of graph nodes.Finally,a GCNN is designed by combining graph convolution and pooling operations to process the constructed graphs and classify the interchange patterns.The SC-GCNN model was validated using interchange samples obtained from the road networks of 15 cities downloaded from OpenStreetMap.The classification accuracy was 87.06%,which was higher than that of the image-based AlexNet,GoogLeNet,and Random Forest models.展开更多
城市交通事故一般都发生在公共道路上,然而现有的交通事故风险预测算法都通过对预测区域进行规则网格化来确定预测空间单位,导致预测精度不高且实用价值较低。本文将道路路段作为预测单位,采用图卷积和长短期记忆网络,构建了一种基于路...城市交通事故一般都发生在公共道路上,然而现有的交通事故风险预测算法都通过对预测区域进行规则网格化来确定预测空间单位,导致预测精度不高且实用价值较低。本文将道路路段作为预测单位,采用图卷积和长短期记忆网络,构建了一种基于路网结构的城市交通事故短期风险预测方法(traffic accidents risk prediction based on road network,TARPBRN)。该方法能对指定路段短期内的交通事故风险进行预测,从而可以有针对性地进行治理,减少交通事故的发生。本文使用杭州市西湖区的交通事故数据对模型进行了训练,并与4种常用的计量经济学模型和3种已有的深度学习预测算法进行了对比。实验结果证明本文算法在准确度、正确率和漏报率等方面都优于已有算法。展开更多
Operational reliability assessment (ORA),which evaluates the risk level of power systems,is hindered by accumulated computational burdens and thus cannot meet the demands of real-time assessment.Recently,data-driven m...Operational reliability assessment (ORA),which evaluates the risk level of power systems,is hindered by accumulated computational burdens and thus cannot meet the demands of real-time assessment.Recently,data-driven methods with fast calculation speeds have emerged as a research focus for online ORA.However,the diverse contingencies of transformers,power lines,and other components introduce numerous topologies,posing significant challenges to the learning capabilities of neural networks.To this end,this paper proposes a multi-kernel collaborative graph convolution neural network (GCNN) for ORA considering varying topologies.Specifically,a physics law-informed graph convolution kernel derived from the Gaussian-Seidel iteration is introduced.It effectively aggregates node features across different topologies.By integrating additional advanced graph convolution kernels with a novel self-attention mechanism,the multi-kernel collaborative GCNN is constructed,which enables the extraction of diverse features and the construction of representative node feature vectors,thereby facilitating high-precision reliability assessments.Furthermore,to enhance the robustness of multi-kernel collaborative GCNN,the inherent pattern of the load-shedding model is analyzed and utilized to design a specialized supervised loss function,which allows the neural network to explore a broader feature space.Compared with the existing data-driven methods,the multi-kernel collaborative GCNN,combined with supervised exploration,can accommodate a wider range of contingencies and achieve superior assessment accuracy.展开更多
基于图卷积神经网络(GCNN)的指静脉识别方法不仅可以解决传统指静脉识别方法识别率较低的问题,还可以解决其计算量大的问题。针对目前指静脉图模型结构不稳定和匹配效率因模型增大而下降的问题,采用SLIC(Simple Linear Iterative Cluste...基于图卷积神经网络(GCNN)的指静脉识别方法不仅可以解决传统指静脉识别方法识别率较低的问题,还可以解决其计算量大的问题。针对目前指静脉图模型结构不稳定和匹配效率因模型增大而下降的问题,采用SLIC(Simple Linear Iterative Clustering)超像素分割算法来构建加权图并改变GCNN提取加权图的图级特征。为了有效抓取图数据中的高阶特征并避免过平滑,建立一种双分支多交互的深度图卷积网络(GCN),旨在提升节点对高阶特征的掌握能力。首先根据节点特征对图结构进行调整;然后结合原始和重构后的图结构,构建了双分支网络架构以充分挖掘高阶特征;最后设计一种通道信息互动机制,以促进不同分支间的信息交流,从而提高特征的多样性。实验结果显示,在多个标准数据集上进行指静脉识别任务时,该网络能减少单张图片识别时间,提高识别效率,并有效减轻过平滑现象,相较于单分支的GCN,在识别精度上平均取得了超过1.5百分点的性能提升。展开更多
文摘现有的基于BERT(bidirectional encoder representations from transformers)的方面级情感分析模型仅使用BERT最后一层隐藏层的输出,忽略BERT中间隐藏层的语义信息,存在信息利用不充分的问题,提出一种融合BERT中间隐藏层的方面级情感分析模型。首先,将评论和方面信息拼接为句子对输入BERT模型,通过BERT的自注意力机制建立评论与方面信息的联系;其次,构建门控卷积网络(gated convolutional neural network,GCNN)对BERT所有隐藏层输出的词向量矩阵进行特征提取,并将提取的特征进行最大池化、拼接得到特征序列;然后,使用双向门控循环单元(bidirectional gated recurrent unit,BiGRU)网络对特征序列进行融合,编码BERT不同隐藏层的信息;最后,引入注意力机制,根据特征与方面信息的相关程度赋予权值。在公开的SemEval2014 Task4评论数据集上的实验结果表明:所提模型在准确率和F 1值两种评价指标上均优于BERT、CapsBERT(capsule BERT)、BERT-PT(BERT post train)、BERT-LSTM(BERT long and short-term memory)等对比模型,具有较好的情感分类效果。
基金supported by the National Natural Science Foundation of China[grant numbers 42071450 and 42001415].
文摘Pattern recognition is critical to map data handling and their applications.This study presents a model that combines the Shape Context(SC)descriptor and Graph Convolutional Neural Network(GCNN)to classify the patterns of interchanges,which are indispensable parts of urban road networks.In the SC-GCNN model,an interchange is modeled as a graph,wherein nodes and edges represent the interchange segments and their connections,respectively.Then,a novel SC descriptor is implemented to describe the contextual information of each interchange segment and serve as descriptive features of graph nodes.Finally,a GCNN is designed by combining graph convolution and pooling operations to process the constructed graphs and classify the interchange patterns.The SC-GCNN model was validated using interchange samples obtained from the road networks of 15 cities downloaded from OpenStreetMap.The classification accuracy was 87.06%,which was higher than that of the image-based AlexNet,GoogLeNet,and Random Forest models.
文摘城市交通事故一般都发生在公共道路上,然而现有的交通事故风险预测算法都通过对预测区域进行规则网格化来确定预测空间单位,导致预测精度不高且实用价值较低。本文将道路路段作为预测单位,采用图卷积和长短期记忆网络,构建了一种基于路网结构的城市交通事故短期风险预测方法(traffic accidents risk prediction based on road network,TARPBRN)。该方法能对指定路段短期内的交通事故风险进行预测,从而可以有针对性地进行治理,减少交通事故的发生。本文使用杭州市西湖区的交通事故数据对模型进行了训练,并与4种常用的计量经济学模型和3种已有的深度学习预测算法进行了对比。实验结果证明本文算法在准确度、正确率和漏报率等方面都优于已有算法。
基金supported by the National Natural Science Foundation of China(No.52377076).
文摘Operational reliability assessment (ORA),which evaluates the risk level of power systems,is hindered by accumulated computational burdens and thus cannot meet the demands of real-time assessment.Recently,data-driven methods with fast calculation speeds have emerged as a research focus for online ORA.However,the diverse contingencies of transformers,power lines,and other components introduce numerous topologies,posing significant challenges to the learning capabilities of neural networks.To this end,this paper proposes a multi-kernel collaborative graph convolution neural network (GCNN) for ORA considering varying topologies.Specifically,a physics law-informed graph convolution kernel derived from the Gaussian-Seidel iteration is introduced.It effectively aggregates node features across different topologies.By integrating additional advanced graph convolution kernels with a novel self-attention mechanism,the multi-kernel collaborative GCNN is constructed,which enables the extraction of diverse features and the construction of representative node feature vectors,thereby facilitating high-precision reliability assessments.Furthermore,to enhance the robustness of multi-kernel collaborative GCNN,the inherent pattern of the load-shedding model is analyzed and utilized to design a specialized supervised loss function,which allows the neural network to explore a broader feature space.Compared with the existing data-driven methods,the multi-kernel collaborative GCNN,combined with supervised exploration,can accommodate a wider range of contingencies and achieve superior assessment accuracy.
文摘基于图卷积神经网络(GCNN)的指静脉识别方法不仅可以解决传统指静脉识别方法识别率较低的问题,还可以解决其计算量大的问题。针对目前指静脉图模型结构不稳定和匹配效率因模型增大而下降的问题,采用SLIC(Simple Linear Iterative Clustering)超像素分割算法来构建加权图并改变GCNN提取加权图的图级特征。为了有效抓取图数据中的高阶特征并避免过平滑,建立一种双分支多交互的深度图卷积网络(GCN),旨在提升节点对高阶特征的掌握能力。首先根据节点特征对图结构进行调整;然后结合原始和重构后的图结构,构建了双分支网络架构以充分挖掘高阶特征;最后设计一种通道信息互动机制,以促进不同分支间的信息交流,从而提高特征的多样性。实验结果显示,在多个标准数据集上进行指静脉识别任务时,该网络能减少单张图片识别时间,提高识别效率,并有效减轻过平滑现象,相较于单分支的GCN,在识别精度上平均取得了超过1.5百分点的性能提升。
