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
开展公路隧道结构状态精准预测是掌握隧道结构状态变化、识别潜在安全风险和保障安全运营的重要技术手段。针对隧道监控量测测点的空间分布与时序特性,提出了一种基于河马优化(Hippopotamus Optimization, HO)算法和卷积神经网络(Convol...开展公路隧道结构状态精准预测是掌握隧道结构状态变化、识别潜在安全风险和保障安全运营的重要技术手段。针对隧道监控量测测点的空间分布与时序特性,提出了一种基于河马优化(Hippopotamus Optimization, HO)算法和卷积神经网络(Convolutional Neural Network, CNN)的双向长短期记忆(Bidirectional Long Short Term Memory, BiLSTM)网络公路隧道结构状态预测方法。量化分析测点间关联性,结合温度特征构建模型输入矩阵;利用CNN挖掘各测点的空间关联性,采用BiLSTM提取时间序列特征,引入HO算法优化模型参数;将预测结果映射为隧道结构状态等级,展示隧道整体受力状态。结果表明,建立的HO-CNN-BiLSTM模型能够有效提取空间和温度特征,在预测精度和稳定性方面均优于对比模型,可实现隧道结构状态精确评估,为公路隧道的安全运营及分级管控措施制定提供技术支撑。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
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。
文摘开展公路隧道结构状态精准预测是掌握隧道结构状态变化、识别潜在安全风险和保障安全运营的重要技术手段。针对隧道监控量测测点的空间分布与时序特性,提出了一种基于河马优化(Hippopotamus Optimization, HO)算法和卷积神经网络(Convolutional Neural Network, CNN)的双向长短期记忆(Bidirectional Long Short Term Memory, BiLSTM)网络公路隧道结构状态预测方法。量化分析测点间关联性,结合温度特征构建模型输入矩阵;利用CNN挖掘各测点的空间关联性,采用BiLSTM提取时间序列特征,引入HO算法优化模型参数;将预测结果映射为隧道结构状态等级,展示隧道整体受力状态。结果表明,建立的HO-CNN-BiLSTM模型能够有效提取空间和温度特征,在预测精度和稳定性方面均优于对比模型,可实现隧道结构状态精确评估,为公路隧道的安全运营及分级管控措施制定提供技术支撑。
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。
文摘为提高高超声速滑翔飞行器(HGV)轨迹预测的精度,提出一种基于时域卷积网络(temporal convolutional network,TCN)和双向长短时记忆网络(bidirectional long short-term memory network,BiLSTM)结合的HGV轨迹预测方法.该方法利用TCN的因果膨胀卷积提取HGV轨迹多尺度动态特征,融合BiLSTM的双向循环机制挖掘轨迹长时依赖与上下文关联,通过全连接层将预测结果映射到样本空间.引入贝叶斯优化(Bayesian optimization,BO)与灰狼优化(grey wolf optimization,GWO)组合优化模式,实现了网络超参数的全局优化,据此建立了深度学习框架下的HGV轨迹预测模型.数值仿真结果表明,在训练完备条件下,建立的预测模型能够有效预测HGV未来时刻的位置状态,相较于4种对比模型,该预测模型的均方根误差平均降低62.10%,平均绝对误差平均降低61.66%.
