Blast furnace data processing is prone to problems such as outliers.To overcome these problems and identify an improved method for processing blast furnace data,we conducted an in-depth study of blast furnace data.Bas...Blast furnace data processing is prone to problems such as outliers.To overcome these problems and identify an improved method for processing blast furnace data,we conducted an in-depth study of blast furnace data.Based on data samples from selected iron and steel companies,data types were classified according to different characteristics;then,appropriate methods were selected to process them in order to solve the deficiencies and outliers of the original blast furnace data.Linear interpolation was used to fill in the divided continuation data,the Knearest neighbor(KNN)algorithm was used to fill in correlation data with the internal law,and periodic statistical data were filled by the average.The error rate in the filling was low,and the fitting degree was over 85%.For the screening of outliers,corresponding indicator parameters were added according to the continuity,relevance,and periodicity of different data.Also,a variety of algorithms were used for processing.Through the analysis of screening results,a large amount of efficient information in the data was retained,and ineffective outliers were eliminated.Standardized processing of blast furnace big data as the basis of applied research on blast furnace big data can serve as an important means to improve data quality and retain data value.展开更多
We investigate the K* production in the KN → Kπp the isobar model. To describe this reaction, we first take into account the contributions from the π, ρ and ω exchanges, as in previous studies. We find that altho...We investigate the K* production in the KN → Kπp the isobar model. To describe this reaction, we first take into account the contributions from the π, ρ and ω exchanges, as in previous studies. We find that although the experimental data can be generally described, there are some obvious discrepancies between the model and the experiments. To improve the model, we consider the contributions of the axial-vector meson and hyperon exchange. It is shown that a large contribution of the axial-vector meson exchange can significantly improve the results. This may indicate that the coupling of the axial-vector meson,e.g. a1（1260）, is large in the KK* density matrix elements of K*0 in the KLp → K*0 p tion for a future comparison.展开更多
基金This work is financially supported by the National Nature Science Foundation of China(No.52004096)the Hebei Province High-End Iron and Steel Metallurgical Joint Research Fund Project,China(No.E2019209314)+1 种基金the Scientific Research Program Project of Hebei Education Department,China(No.QN2019200)the Tangshan Science and Technology Planning Project,China(No.19150241E).
文摘Blast furnace data processing is prone to problems such as outliers.To overcome these problems and identify an improved method for processing blast furnace data,we conducted an in-depth study of blast furnace data.Based on data samples from selected iron and steel companies,data types were classified according to different characteristics;then,appropriate methods were selected to process them in order to solve the deficiencies and outliers of the original blast furnace data.Linear interpolation was used to fill in the divided continuation data,the Knearest neighbor(KNN)algorithm was used to fill in correlation data with the internal law,and periodic statistical data were filled by the average.The error rate in the filling was low,and the fitting degree was over 85%.For the screening of outliers,corresponding indicator parameters were added according to the continuity,relevance,and periodicity of different data.Also,a variety of algorithms were used for processing.Through the analysis of screening results,a large amount of efficient information in the data was retained,and ineffective outliers were eliminated.Standardized processing of blast furnace big data as the basis of applied research on blast furnace big data can serve as an important means to improve data quality and retain data value.
基金Supports from the National Natural Science Foundation of China(U1832160,11375137)the Natural Science Foundation of Shaanxi Province(2019JM-025)the Fundamental Research Funds for the Central Universities
文摘We investigate the K* production in the KN → Kπp the isobar model. To describe this reaction, we first take into account the contributions from the π, ρ and ω exchanges, as in previous studies. We find that although the experimental data can be generally described, there are some obvious discrepancies between the model and the experiments. To improve the model, we consider the contributions of the axial-vector meson and hyperon exchange. It is shown that a large contribution of the axial-vector meson exchange can significantly improve the results. This may indicate that the coupling of the axial-vector meson,e.g. a1（1260）, is large in the KK* density matrix elements of K*0 in the KLp → K*0 p tion for a future comparison.
