As an integrated application of modern information technologies and artificial intelligence,Prognostic and Health Management(PHM)is important for machine health monitoring.Prediction of tool wear is one of the symboli...As an integrated application of modern information technologies and artificial intelligence,Prognostic and Health Management(PHM)is important for machine health monitoring.Prediction of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and industry.In this paper,a multi-scale Convolutional Gated Recurrent Unit network(MCGRU)is proposed to address raw sensory data for tool wear prediction.At the bottom of MCGRU,six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network,which augments the adaptability to features of different time scales.These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations.At the top of the MCGRU,a fully connected layer and a regression layer are built for cutting tool wear prediction.Two case studies are performed to verify the capability and effectiveness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.展开更多
由于传统文本评论情感分类方法通常忽略用户性格对于情感分类结果的影响,提出一种基于用户性格和语义-结构特征的文本评论情感分类方法(User Personality and Semantic-structural Features based Sentiment Classification Method for ...由于传统文本评论情感分类方法通常忽略用户性格对于情感分类结果的影响,提出一种基于用户性格和语义-结构特征的文本评论情感分类方法(User Personality and Semantic-structural Features based Sentiment Classification Method for Text Comments,BF_Bi GAC).依据大五人格模型能够有效表达用户性格的优势,通过计算不同维度性格得分,从评论文本中获取用户性格特征.利用双向门控循环单元(Bidirectional Gated Recurrent Unit,Bi GRU)和卷积神经网络(Convolutional Neural Network,CNN)可以有效提取文本上下文语义特征和局部结构特征的优势,提出一种基于Bi GRU、CNN和双层注意力机制的文本语义-结构特征获取方法.为区分不同类型特征的影响,引入混合注意力层实现对用户性格特征和文本语义-结构特征的有效融合,以此获得最终的文本向量表达.在IMDB、Yelp-2、Yelp-5及Ekman四个评论数据集上的对比实验结果表明,BF_Bi GAC在分类准确率(Accuracy)和加权macro F_(1)值(F_(w))上均获得较好表现,相对于拼接Bi GRU、CNN的情感分类方法(Sentiment Classification Method Concatenating Bi GRU and CNN,Bi G-RU_CNN)在Accuracy值上分别提升0.020、0.012、0.017及0.011,相对于拼接CNN、Bi GRU的情感分类方法(Sentiment Classification Method Concatenating CNN and Bi GRU,Conv Bi LSTM)F_(w)值上分别提升0.022、0.013、0.028及0.023;相对于预训练模型BERT和Ro BERTa,BF_Bi GAC在保证分类精度的情况下获得了较高的运行效率.展开更多
基金Supported in part by Natural Science Foundation of China(Grant Nos.51835009,51705398)Shaanxi Province 2020 Natural Science Basic Research Plan(Grant No.2020JQ-042)Aeronautical Science Foundation(Grant No.2019ZB070001).
文摘As an integrated application of modern information technologies and artificial intelligence,Prognostic and Health Management(PHM)is important for machine health monitoring.Prediction of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and industry.In this paper,a multi-scale Convolutional Gated Recurrent Unit network(MCGRU)is proposed to address raw sensory data for tool wear prediction.At the bottom of MCGRU,six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network,which augments the adaptability to features of different time scales.These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations.At the top of the MCGRU,a fully connected layer and a regression layer are built for cutting tool wear prediction.Two case studies are performed to verify the capability and effectiveness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.
基金This study was jointly funded by the National Key R&D Program of China[grant number 2022YFC3004103]the National Natural Foundation of China[grant number 42275003]+2 种基金the Beijing Science and Technology Program[grant number Z221100005222012]the Beijing Meteorological Service Science and Technology Program[grant number BMBKJ202302004]the China Meteorological Administration Youth Innovation Team[grant number CMA2023QN10].
文摘由于传统文本评论情感分类方法通常忽略用户性格对于情感分类结果的影响,提出一种基于用户性格和语义-结构特征的文本评论情感分类方法(User Personality and Semantic-structural Features based Sentiment Classification Method for Text Comments,BF_Bi GAC).依据大五人格模型能够有效表达用户性格的优势,通过计算不同维度性格得分,从评论文本中获取用户性格特征.利用双向门控循环单元(Bidirectional Gated Recurrent Unit,Bi GRU)和卷积神经网络(Convolutional Neural Network,CNN)可以有效提取文本上下文语义特征和局部结构特征的优势,提出一种基于Bi GRU、CNN和双层注意力机制的文本语义-结构特征获取方法.为区分不同类型特征的影响,引入混合注意力层实现对用户性格特征和文本语义-结构特征的有效融合,以此获得最终的文本向量表达.在IMDB、Yelp-2、Yelp-5及Ekman四个评论数据集上的对比实验结果表明,BF_Bi GAC在分类准确率(Accuracy)和加权macro F_(1)值(F_(w))上均获得较好表现,相对于拼接Bi GRU、CNN的情感分类方法(Sentiment Classification Method Concatenating Bi GRU and CNN,Bi G-RU_CNN)在Accuracy值上分别提升0.020、0.012、0.017及0.011,相对于拼接CNN、Bi GRU的情感分类方法(Sentiment Classification Method Concatenating CNN and Bi GRU,Conv Bi LSTM)F_(w)值上分别提升0.022、0.013、0.028及0.023;相对于预训练模型BERT和Ro BERTa,BF_Bi GAC在保证分类精度的情况下获得了较高的运行效率.
