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
In this paper, we mainly deal with a class of higher-order coupled Kirch-hoff-type equations. At first, we take advantage of Hadamard’s graph to get the equivalent form of the original equations. Then, the inertial m...In this paper, we mainly deal with a class of higher-order coupled Kirch-hoff-type equations. At first, we take advantage of Hadamard’s graph to get the equivalent form of the original equations. Then, the inertial manifolds are proved by using spectral gap condition. The main result we gained is that the inertial manifolds are established under the proper assumptions of M(s) and gi(u,v), i=1, 2.展开更多
铜是我国对外依存较高的金属资源,而斑岩铜矿是铜矿中最重要的矿床类型之一。为系统分析斑岩铜矿预测领域的研究现状、热点和前沿趋势,本研究以CNKI(中国知网)和WoS(Web of Science)数据库收录的1980—2024年斑岩铜矿预测相关文献为样本...铜是我国对外依存较高的金属资源,而斑岩铜矿是铜矿中最重要的矿床类型之一。为系统分析斑岩铜矿预测领域的研究现状、热点和前沿趋势,本研究以CNKI(中国知网)和WoS(Web of Science)数据库收录的1980—2024年斑岩铜矿预测相关文献为样本,采用CiteSpace和VOSviewer软件进行知识图谱构建和数据信息挖掘。通过国家发文情况、国际国内作者和机构、关键词等多维度解析,结果显示:(1)全球范围内,伊朗和中国在该领域研究最为活跃,发文量约占总发文量的50%,而国内已形成以成都理工大学、中国地质大学(北京)为核心的研究机构群;(2)作者合作关系网络和共被引分析显示国内外核心作者群体有待形成,但跨区域合作网络已显现集聚效应,研究正向系统化发展;(3)关键词聚类分析中,CNKI和WoS分别识别出11个聚类和16个聚类,突现图谱和云图分析显示成矿条件与规律、地质特征和地球化学等共同构成研究主轴且具成熟性,机器学习和知识图谱是新型技术增长点。研究构建的领域知识图谱可为斑岩铜矿预测研究提供全景式认知框架,也可为深部找矿技术创新和勘查战略制定提供一定理论支撑。展开更多
中点钳位(neutral point clamped,NPC)型三电平逆变器并网工作环境恶劣,IGBT面临单管与双管同时故障的挑战,这使得故障特征之间的差异变得非常微弱,进而导致双管故障的识别精度难以有效提升。为此,提出了一种新的故障诊断方法,该方法结...中点钳位(neutral point clamped,NPC)型三电平逆变器并网工作环境恶劣,IGBT面临单管与双管同时故障的挑战,这使得故障特征之间的差异变得非常微弱,进而导致双管故障的识别精度难以有效提升。为此,提出了一种新的故障诊断方法,该方法结合了多通道的二维递归融合图和轻量化多尺度残差(lightweightmultiscale convolutional residuals,LMCR)网络。首先,通过仿真获取三相电流信号作为故障信号;再利用递归图(recurrence plot,RP)将三相电流信号分别转化为二维图并进行多通道融合,以捕捉时间序列中的周期性、突变点和趋势等特征;最后,将递归融合图作为输入,输入到LMCR模型中进行故障识别,LMCR模型整合多级Inception结构和残差网络,用于提取不同尺度的特征并融合这些特征,从而保证网络的梯度消失和爆炸。实验结果显示,该方法在IGBT故障识别中表现出色,无噪声环境下平均识别准确率达100%,噪声环境中也达到了92.53%,充分证明了该方法具有较强的特征提取能力和优异的抗噪性能。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In this paper, we mainly deal with a class of higher-order coupled Kirch-hoff-type equations. At first, we take advantage of Hadamard’s graph to get the equivalent form of the original equations. Then, the inertial manifolds are proved by using spectral gap condition. The main result we gained is that the inertial manifolds are established under the proper assumptions of M(s) and gi(u,v), i=1, 2.
文摘铜是我国对外依存较高的金属资源,而斑岩铜矿是铜矿中最重要的矿床类型之一。为系统分析斑岩铜矿预测领域的研究现状、热点和前沿趋势,本研究以CNKI(中国知网)和WoS(Web of Science)数据库收录的1980—2024年斑岩铜矿预测相关文献为样本,采用CiteSpace和VOSviewer软件进行知识图谱构建和数据信息挖掘。通过国家发文情况、国际国内作者和机构、关键词等多维度解析,结果显示:(1)全球范围内,伊朗和中国在该领域研究最为活跃,发文量约占总发文量的50%,而国内已形成以成都理工大学、中国地质大学(北京)为核心的研究机构群;(2)作者合作关系网络和共被引分析显示国内外核心作者群体有待形成,但跨区域合作网络已显现集聚效应,研究正向系统化发展;(3)关键词聚类分析中,CNKI和WoS分别识别出11个聚类和16个聚类,突现图谱和云图分析显示成矿条件与规律、地质特征和地球化学等共同构成研究主轴且具成熟性,机器学习和知识图谱是新型技术增长点。研究构建的领域知识图谱可为斑岩铜矿预测研究提供全景式认知框架,也可为深部找矿技术创新和勘查战略制定提供一定理论支撑。
