The composition control of molten steel is one of the main functions in the ladle furnace(LF)refining process.In this study,a feasible model was established to predict the alloying element yield using principal compon...The composition control of molten steel is one of the main functions in the ladle furnace(LF)refining process.In this study,a feasible model was established to predict the alloying element yield using principal component analysis(PCA)and deep neural network(DNN).The PCA was used to eliminate collinearity and reduce the dimension of the input variables,and then the data processed by PCA were used to establish the DNN model.The prediction hit ratios for the Si element yield in the error ranges of±1%,±3%,and±5%are 54.0%,93.8%,and98.8%,respectively,whereas those of the Mn element yield in the error ranges of±1%,±2%,and±3%are 77.0%,96.3%,and 99.5%,respectively,in the PCA-DNN model.The results demonstrate that the PCA-DNN model performs better than the known models,such as the reference heat method,multiple linear regression,modified backpropagation,and DNN model.Meanwhile,the accurate prediction of the alloying element yield can greatly contribute to realizing a“narrow window”control of composition in molten steel.The construction of the prediction model for the element yield can also provide a reference for the development of an alloying control model in LF intelligent refining in the modern iron and steel industry.展开更多
为提升铁路货运审计工作的效率,针对铁路货运一口价议价策略(简称:一口价策略)的文本数据,设计了基于数据增强的RoBERTa(Robustly optimized Bidirectional Encoder Representation from Transformers)BiLSTM(Bidrectional Long Short T...为提升铁路货运审计工作的效率,针对铁路货运一口价议价策略(简称:一口价策略)的文本数据,设计了基于数据增强的RoBERTa(Robustly optimized Bidirectional Encoder Representation from Transformers)BiLSTM(Bidrectional Long Short Term Memory)CRF(Conditional Random Field)模型,介绍了数据标注策略,详细阐述了模型的总体架构和样本数据增强方法。对所设计的模型进行了应用验证,验证结果表明,RoBERTaBiLSTMCRF模型对一口价策略中命名实体识别的各项性能评价指标较其他2种传统模型均有显著提高,能够更准确地识别一口价策略中的命名实体信息,辅助铁路货运审计人员的审计工作。展开更多
基金supported by the National Natural Science Foundation of China(No.51974023)State Key Laboratory of Advanced Metallurgy,University of Science and Technology Beijing(No.41621005)。
文摘The composition control of molten steel is one of the main functions in the ladle furnace(LF)refining process.In this study,a feasible model was established to predict the alloying element yield using principal component analysis(PCA)and deep neural network(DNN).The PCA was used to eliminate collinearity and reduce the dimension of the input variables,and then the data processed by PCA were used to establish the DNN model.The prediction hit ratios for the Si element yield in the error ranges of±1%,±3%,and±5%are 54.0%,93.8%,and98.8%,respectively,whereas those of the Mn element yield in the error ranges of±1%,±2%,and±3%are 77.0%,96.3%,and 99.5%,respectively,in the PCA-DNN model.The results demonstrate that the PCA-DNN model performs better than the known models,such as the reference heat method,multiple linear regression,modified backpropagation,and DNN model.Meanwhile,the accurate prediction of the alloying element yield can greatly contribute to realizing a“narrow window”control of composition in molten steel.The construction of the prediction model for the element yield can also provide a reference for the development of an alloying control model in LF intelligent refining in the modern iron and steel industry.
文摘为提升铁路货运审计工作的效率,针对铁路货运一口价议价策略(简称:一口价策略)的文本数据,设计了基于数据增强的RoBERTa(Robustly optimized Bidirectional Encoder Representation from Transformers)BiLSTM(Bidrectional Long Short Term Memory)CRF(Conditional Random Field)模型,介绍了数据标注策略,详细阐述了模型的总体架构和样本数据增强方法。对所设计的模型进行了应用验证,验证结果表明,RoBERTaBiLSTMCRF模型对一口价策略中命名实体识别的各项性能评价指标较其他2种传统模型均有显著提高,能够更准确地识别一口价策略中的命名实体信息,辅助铁路货运审计人员的审计工作。
