Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requir...Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requires more syntactic elements.Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence,recognizing the progress and comparing impacts.An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus.The conversational sentences are classified into four categories:information,question,directive,and commission.These classification label sequences are for analyzing the conversation progress and predicting the pecking order of the conversation.Ensemble of Bidirectional Encoder for Representation of Transformer(BERT),Robustly Optimized BERT pretraining Approach(RoBERTa),Generative Pre-Trained Transformer(GPT),DistilBERT and Generalized Autoregressive Pretraining for Language Understanding(XLNet)models are trained on conversation corpus with hyperparameters.Hyperparameter tuning approach is carried out for better performance on sentence classification.This Ensemble of Pre-trained Language Models with a Hyperparameter Tuning(EPLM-HT)system is trained on an annotated conversation dataset.The proposed approach outperformed compared to the base BERT,GPT,DistilBERT and XLNet transformer models.The proposed ensemble model with the fine-tuned parameters achieved an F1_score of 0.88.展开更多
In this paper,the bidirectional SK-Ramani equation is investigated by means of the extended homoclinic test approach and Riemann theta function method,respectively.Based on the Hirota bilinear method,exact solutions i...In this paper,the bidirectional SK-Ramani equation is investigated by means of the extended homoclinic test approach and Riemann theta function method,respectively.Based on the Hirota bilinear method,exact solutions including one-soliton wave solution are obtained by using the extended homoclinic approach and one-periodic wave solution is constructed by using the Riemann theta function method.A limiting procedure is presented to analyze in detail the relations between the one periodic wave solution and one-soliton solution.展开更多
An effective and reliable prediction of the remaining useful life(RUL)of a tool is important to a metal forming process because it can significantly reduce unexpected maintenance,avoid machine shutdowns and increase s...An effective and reliable prediction of the remaining useful life(RUL)of a tool is important to a metal forming process because it can significantly reduce unexpected maintenance,avoid machine shutdowns and increase system stability.This study proposes a new data-driven approach to the RUL prediction for metal forming processes under multiple contact sliding conditions.The data-driven approach took advantage of bidirectional long short-term memory(BLSTM)and convolutional neural networks(CNN).A pre-trained lightweight CNN-based network,WearNet,was re-trained to classify the wear states of workpiece surfaces with a high accuracy,then the classification results were passed into a BLSTM-based regression model as inputs for RUL estimation.The experimental results demonstrated that this approach was able to predict the RUL values with a small error(below 5%)and a low root mean square error(RMSE)(around 1.5),which was more superior and robust than the other state-of-the-art methods.展开更多
文摘Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requires more syntactic elements.Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence,recognizing the progress and comparing impacts.An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus.The conversational sentences are classified into four categories:information,question,directive,and commission.These classification label sequences are for analyzing the conversation progress and predicting the pecking order of the conversation.Ensemble of Bidirectional Encoder for Representation of Transformer(BERT),Robustly Optimized BERT pretraining Approach(RoBERTa),Generative Pre-Trained Transformer(GPT),DistilBERT and Generalized Autoregressive Pretraining for Language Understanding(XLNet)models are trained on conversation corpus with hyperparameters.Hyperparameter tuning approach is carried out for better performance on sentence classification.This Ensemble of Pre-trained Language Models with a Hyperparameter Tuning(EPLM-HT)system is trained on an annotated conversation dataset.The proposed approach outperformed compared to the base BERT,GPT,DistilBERT and XLNet transformer models.The proposed ensemble model with the fine-tuned parameters achieved an F1_score of 0.88.
文摘In this paper,the bidirectional SK-Ramani equation is investigated by means of the extended homoclinic test approach and Riemann theta function method,respectively.Based on the Hirota bilinear method,exact solutions including one-soliton wave solution are obtained by using the extended homoclinic approach and one-periodic wave solution is constructed by using the Riemann theta function method.A limiting procedure is presented to analyze in detail the relations between the one periodic wave solution and one-soliton solution.
基金supported by the Baosteel Australia Research and Development Centre(BAJC)Portfolio(Grant No.BA17001)the ARC Hub for Computational Particle Technology(Grant No.IH140100035)+1 种基金the Chinese Guangdong Specific Discipline Project(Grant No.2020ZDZX2006)the Shenzhen Key Laboratory Project of Cross-scale Manufacturing Mechanics(Grant No.ZDSYS20200810171201007).
文摘An effective and reliable prediction of the remaining useful life(RUL)of a tool is important to a metal forming process because it can significantly reduce unexpected maintenance,avoid machine shutdowns and increase system stability.This study proposes a new data-driven approach to the RUL prediction for metal forming processes under multiple contact sliding conditions.The data-driven approach took advantage of bidirectional long short-term memory(BLSTM)and convolutional neural networks(CNN).A pre-trained lightweight CNN-based network,WearNet,was re-trained to classify the wear states of workpiece surfaces with a high accuracy,then the classification results were passed into a BLSTM-based regression model as inputs for RUL estimation.The experimental results demonstrated that this approach was able to predict the RUL values with a small error(below 5%)and a low root mean square error(RMSE)(around 1.5),which was more superior and robust than the other state-of-the-art methods.
