For the existing aspect category sentiment analysis research,most of the aspects are given for sentiment extraction,and this pipeline method is prone to error accumulation,and the use of graph convolutional neural net...For the existing aspect category sentiment analysis research,most of the aspects are given for sentiment extraction,and this pipeline method is prone to error accumulation,and the use of graph convolutional neural network for aspect category sentiment analysis does not fully utilize the dependency type information between words,so it cannot enhance feature extraction.This paper proposes an end-to-end aspect category sentiment analysis(ETESA)model based on type graph convolutional networks.The model uses the bidirectional encoder representation from transformers(BERT)pretraining model to obtain aspect categories and word vectors containing contextual dynamic semantic information,which can solve the problem of polysemy;when using graph convolutional network(GCN)for feature extraction,the fusion operation of word vectors and initialization tensor of dependency types can obtain the importance values of different dependency types and enhance the text feature representation;by transforming aspect category and sentiment pair extraction into multiple single-label classification problems,aspect category and sentiment can be extracted simultaneously in an end-to-end way and solve the problem of error accumulation.Experiments are tested on three public datasets,and the results show that the ETESA model can achieve higher Precision,Recall and F1 value,proving the effectiveness of the model.展开更多
Drug-target interaction(DTI)is a widely explored topic in the field of bioinformatics and plays a pivotal role in drug discovery.However,the traditional bio-experimental process of drug-target interaction identificati...Drug-target interaction(DTI)is a widely explored topic in the field of bioinformatics and plays a pivotal role in drug discovery.However,the traditional bio-experimental process of drug-target interaction identification requires a large investment of time and labor.To address this challenge,graph neural network(GNN)approaches in deep learning are becoming a prominent trend in the field of DTI research,which is characterized by multimodal processing of data,feature learning and interpretability in DTI.Nevertheless,some methods are still limited by homogeneous graphs and single features.To address the problems,we mechanistically analyze graph convolutional neural networks(GCNs)and graph attentional neural networks(GATs)to propose a new model for the prediction of drug-target interactions using graph neural networks named BiTGNN[Bidirectional Transformer(Bi-Transformer)-graph neural network].The method first establishes drug-target pairs through the pseudo-position specificity scoring matrix(PsePSSM)and drug fingerprint data,and constructs a heterogeneous network by utilizing the relationship between the drug and the target.Then,the computational extraction of drug and target attributes is performed using GCNs and GATs for the purpose of model information flow extension and graph information enhancement.We collect interaction data using the proposed Bi-Transformer architecture,in which we design a bidirectional cross-attention mechanism for calculating the effects of drugtarget interactions for realistic biological interaction simulations.Finally,a feed-forward neural network is used to obtain the feature matrices of the drug and the target,and DTI prediction is performed by fusing the two feature matrices.The Enzyme,Ion Channel(IC),G Protein-coupled Receptor(GPCR)and Nuclear Receptor(NR)datasets are used in the experiments,and compared with several existing mainstream models,our model outperforms in Area Under the ROC Curve(AUC),Specificity,Accuracy and the metric Area Under the Precision-Recall Curve(AUPR).展开更多
在一些修船企业建立的修船结算系统和电子价格库中,人工匹配结算编码步骤易出错且耗时长,直接影响结算效率。为解决该问题,提出一种基于多特征融合的修船结算编码智能匹配复合模型。采用来自变换器的双向编码器表示(Bidirectional Encod...在一些修船企业建立的修船结算系统和电子价格库中,人工匹配结算编码步骤易出错且耗时长,直接影响结算效率。为解决该问题,提出一种基于多特征融合的修船结算编码智能匹配复合模型。采用来自变换器的双向编码器表示(Bidirectional Encoder Representations from Transformers,BERT)模型将工程内容文本表示为词向量,采用卷积神经网络(Convolutional Neural Network,CNN)模型提取文本的局部特征,采用双向长短期记忆网络结合注意力机制(Bidirectional Long Short-Term Memory with Attention Mechanism,BiLSTM-Attention)模型提取上下文特征,得到对应的结算编码。试验结果表明,所提出的复合模型在整体准确率方面实现显著提升,充分证明该复合模型在处理复杂文本分类任务中的优势。展开更多
Accurate and reliable power system data are fundamental for critical operations such as gridmonitoring,fault diagnosis,and load forecasting,underpinned by increasing intelligentization and digitalization.However,data ...Accurate and reliable power system data are fundamental for critical operations such as gridmonitoring,fault diagnosis,and load forecasting,underpinned by increasing intelligentization and digitalization.However,data loss and anomalies frequently compromise data integrity in practical settings,significantly impacting system operational efficiency and security.Most existing data recovery methods require complete datasets for training,leading to substantial data and computational demands and limited generalization.To address these limitations,this study proposes a missing data imputation model based on an improved Generative Adversarial Network(BAC-GAN).Within the BAC-GAN framework,the generator utilizes Bidirectional Long Short-Term Memory(BiLSTM)networks and Multi-Head Attention mechanisms to capture temporal dependencies and complex relationships within power system data.The discriminator employs a Convolutional Neural Network(CNN)architecture to integrate local features with global structures,effectivelymitigating the generation of implausible imputations.Experimental results on two public datasets demonstrate that the BAC-GAN model achieves superior data recovery accuracy compared to five state-of-the-art and classical benchmarkmethods,with an average improvement of 17.7%in reconstruction accuracy.The proposedmethod significantly enhances the accuracy of grid fault diagnosis and provides reliable data support for the stable operation of smart grids,showing great potential for practical applications in power systems.展开更多
针对异步电机信号的故障敏感性差异大和故障特征提取困难等问题,提出了一种基于自混合注意力机制和时-空特征挖掘模型的异步电机故障诊断方法。该方法首先将自注意力机制擅长处理长距离依赖和全局信息特征的优势与squeeze-and-excitatio...针对异步电机信号的故障敏感性差异大和故障特征提取困难等问题,提出了一种基于自混合注意力机制和时-空特征挖掘模型的异步电机故障诊断方法。该方法首先将自注意力机制擅长处理长距离依赖和全局信息特征的优势与squeeze-and-excitation(SE)网络增强通道特征相关性的优点相结合,设计了一种新型的自混合注意力机制(self-hybrid attention mechanisms,SHAM),可以有效地降低异步电机电信号的故障敏感性差异;其次,将一维卷积神经网络(1D-convolutional neural network,1D-CNN)和SHAM相结合形成卷积自混合注意力模块(C-SHAM)来提取不同视野域的空间特征和捕获信号特征长范围依赖,同时抑制不同通道下同源信号内无关分量的影响;随后,将双向长短时记忆网络(bidirectional long and short term memory networks,Bi-LSTM)和SHAM相结合提出时序自混合注意力模块(BL-SHAM),进一步实现信号时序特征的再提取和不同通道特征的自适应融合;最后,通过分类器实现电机的故障识别。实验结果表明,在异步电机实验平台上,所提出的方法能够对异步电机的故障电信号进行有效地分类,平均准确率大于98%。展开更多
基金Supported by the National Key Research and Development Program of China(No.2018YFB1702601).
文摘For the existing aspect category sentiment analysis research,most of the aspects are given for sentiment extraction,and this pipeline method is prone to error accumulation,and the use of graph convolutional neural network for aspect category sentiment analysis does not fully utilize the dependency type information between words,so it cannot enhance feature extraction.This paper proposes an end-to-end aspect category sentiment analysis(ETESA)model based on type graph convolutional networks.The model uses the bidirectional encoder representation from transformers(BERT)pretraining model to obtain aspect categories and word vectors containing contextual dynamic semantic information,which can solve the problem of polysemy;when using graph convolutional network(GCN)for feature extraction,the fusion operation of word vectors and initialization tensor of dependency types can obtain the importance values of different dependency types and enhance the text feature representation;by transforming aspect category and sentiment pair extraction into multiple single-label classification problems,aspect category and sentiment can be extracted simultaneously in an end-to-end way and solve the problem of error accumulation.Experiments are tested on three public datasets,and the results show that the ETESA model can achieve higher Precision,Recall and F1 value,proving the effectiveness of the model.
文摘针对不同磁密幅值、频率、谐波组合等复杂激励工况下磁致伸缩建模面临的精准性问题,该文利用空间注意力机制(spatial attention mechanism,SAM)对传统的卷积神经网络(convolutional neural network,CNN)进行改进,将SAM嵌套入CNN网络中,建立SAMCNN改进型网络。再结合双向长短期记忆(bidirectional long short-term memory,BiLSTM)网络,提出电工钢片SAMCNN-BiLSTM磁致伸缩模型。首先,利用灰狼优化算法(grey wolf optimization,GWO)寻优神经网络结构的参数,实现复杂工况下磁致伸缩效应的准确表征;然后,建立中低频范围单频与叠加谐波激励等复杂工况下的磁致伸缩应变数据库,开展数据预处理与特征分析;最后,对SAMCNN-BiLSTM模型开展对比验证。对比叠加3次谐波激励下的磁致伸缩应变频谱主要分量,SAMCNN-BiLSTM模型计算值最大相对误差为3.70%,其比Jiles-Atherton-Sablik(J-A-S)、二次畴转等模型能更精确地表征电工钢片的磁致伸缩效应。
基金supported by the National Key R&D Program of China under the Project No.2021YFB2802300National Natural Science Foundation of China under the Grant Nos.12271362 and 12061059.
文摘Drug-target interaction(DTI)is a widely explored topic in the field of bioinformatics and plays a pivotal role in drug discovery.However,the traditional bio-experimental process of drug-target interaction identification requires a large investment of time and labor.To address this challenge,graph neural network(GNN)approaches in deep learning are becoming a prominent trend in the field of DTI research,which is characterized by multimodal processing of data,feature learning and interpretability in DTI.Nevertheless,some methods are still limited by homogeneous graphs and single features.To address the problems,we mechanistically analyze graph convolutional neural networks(GCNs)and graph attentional neural networks(GATs)to propose a new model for the prediction of drug-target interactions using graph neural networks named BiTGNN[Bidirectional Transformer(Bi-Transformer)-graph neural network].The method first establishes drug-target pairs through the pseudo-position specificity scoring matrix(PsePSSM)and drug fingerprint data,and constructs a heterogeneous network by utilizing the relationship between the drug and the target.Then,the computational extraction of drug and target attributes is performed using GCNs and GATs for the purpose of model information flow extension and graph information enhancement.We collect interaction data using the proposed Bi-Transformer architecture,in which we design a bidirectional cross-attention mechanism for calculating the effects of drugtarget interactions for realistic biological interaction simulations.Finally,a feed-forward neural network is used to obtain the feature matrices of the drug and the target,and DTI prediction is performed by fusing the two feature matrices.The Enzyme,Ion Channel(IC),G Protein-coupled Receptor(GPCR)and Nuclear Receptor(NR)datasets are used in the experiments,and compared with several existing mainstream models,our model outperforms in Area Under the ROC Curve(AUC),Specificity,Accuracy and the metric Area Under the Precision-Recall Curve(AUPR).
文摘在一些修船企业建立的修船结算系统和电子价格库中,人工匹配结算编码步骤易出错且耗时长,直接影响结算效率。为解决该问题,提出一种基于多特征融合的修船结算编码智能匹配复合模型。采用来自变换器的双向编码器表示(Bidirectional Encoder Representations from Transformers,BERT)模型将工程内容文本表示为词向量,采用卷积神经网络(Convolutional Neural Network,CNN)模型提取文本的局部特征,采用双向长短期记忆网络结合注意力机制(Bidirectional Long Short-Term Memory with Attention Mechanism,BiLSTM-Attention)模型提取上下文特征,得到对应的结算编码。试验结果表明,所提出的复合模型在整体准确率方面实现显著提升,充分证明该复合模型在处理复杂文本分类任务中的优势。
基金supported by the National Natural Science Foundation of China(No.51977113)the Science and Technology Project of State Grid Zhejiang Electric Power Co.,Ltd.(No.5211JX240001).
文摘Accurate and reliable power system data are fundamental for critical operations such as gridmonitoring,fault diagnosis,and load forecasting,underpinned by increasing intelligentization and digitalization.However,data loss and anomalies frequently compromise data integrity in practical settings,significantly impacting system operational efficiency and security.Most existing data recovery methods require complete datasets for training,leading to substantial data and computational demands and limited generalization.To address these limitations,this study proposes a missing data imputation model based on an improved Generative Adversarial Network(BAC-GAN).Within the BAC-GAN framework,the generator utilizes Bidirectional Long Short-Term Memory(BiLSTM)networks and Multi-Head Attention mechanisms to capture temporal dependencies and complex relationships within power system data.The discriminator employs a Convolutional Neural Network(CNN)architecture to integrate local features with global structures,effectivelymitigating the generation of implausible imputations.Experimental results on two public datasets demonstrate that the BAC-GAN model achieves superior data recovery accuracy compared to five state-of-the-art and classical benchmarkmethods,with an average improvement of 17.7%in reconstruction accuracy.The proposedmethod significantly enhances the accuracy of grid fault diagnosis and provides reliable data support for the stable operation of smart grids,showing great potential for practical applications in power systems.
文摘针对异步电机信号的故障敏感性差异大和故障特征提取困难等问题,提出了一种基于自混合注意力机制和时-空特征挖掘模型的异步电机故障诊断方法。该方法首先将自注意力机制擅长处理长距离依赖和全局信息特征的优势与squeeze-and-excitation(SE)网络增强通道特征相关性的优点相结合,设计了一种新型的自混合注意力机制(self-hybrid attention mechanisms,SHAM),可以有效地降低异步电机电信号的故障敏感性差异;其次,将一维卷积神经网络(1D-convolutional neural network,1D-CNN)和SHAM相结合形成卷积自混合注意力模块(C-SHAM)来提取不同视野域的空间特征和捕获信号特征长范围依赖,同时抑制不同通道下同源信号内无关分量的影响;随后,将双向长短时记忆网络(bidirectional long and short term memory networks,Bi-LSTM)和SHAM相结合提出时序自混合注意力模块(BL-SHAM),进一步实现信号时序特征的再提取和不同通道特征的自适应融合;最后,通过分类器实现电机的故障识别。实验结果表明,在异步电机实验平台上,所提出的方法能够对异步电机的故障电信号进行有效地分类,平均准确率大于98%。
