The endpoint carbon content in the converter is critical for the quality of steel products,and accurately predicting this parameter is an effective way to reduce alloy consumption and improve smelting efficiency.Howev...The endpoint carbon content in the converter is critical for the quality of steel products,and accurately predicting this parameter is an effective way to reduce alloy consumption and improve smelting efficiency.However,most scholars currently focus on modifying methods to enhance model accuracy,while overlooking the extent to which input parameters influence accuracy.To address this issue,in this study,a prediction model for the endpoint carbon content in the converter was developed using factor analysis(FA)and support vector machine(SVM)optimized by improved particle swarm optimization(IPSO).Analysis of the factors influencing the endpoint carbon content during the converter smelting process led to the identification of 21 input parameters.Subsequently,FA was used to reduce the dimensionality of the data and applied to the prediction model.The results demonstrate that the performance of the FA-IPSO-SVM model surpasses several existing methods,such as twin support vector regression and support vector machine.The model achieves hit rates of 89.59%,96.21%,and 98.74%within error ranges of±0.01%,±0.015%,and±0.02%,respectively.Finally,based on the prediction results obtained by sequentially removing input parameters,the parameters were classified into high influence(5%-7%),medium influence(2%-5%),and low influence(0-2%)categories according to their varying degrees of impact on prediction accuracy.This classi-fication provides a reference for selecting input parameters in future prediction models for endpoint carbon content.展开更多
The rivers in Nepal are classified in terms of geographical regions but a more scientific classification such as on the ba-sis of morphology is clearly lacking. This study was done in 9 rivers namely Jhikhukhola of th...The rivers in Nepal are classified in terms of geographical regions but a more scientific classification such as on the ba-sis of morphology is clearly lacking. This study was done in 9 rivers namely Jhikhukhola of the Koshi system, Aandhikhola, Arungkhola, East Rapti, Karrakhola, Seti and main channel Narayani of the Gandaki system, and two independent systems within Nepal, Bagmati and Tinau. Among the morphologies, river bed or the substratum was taken as the main variable for the analysis which was categorized into 7 types as rocks, boulders, cobbles, pebbles, gravels, sand and silt. There were 23 sampling sites each with 2 stretches of around 100m in those rivers. The data were taken as a percentage, and to avoid biases it was observed visually by the same person for a complete year in every season. With 23 sites each with 2 stretches and 4 replicates corresponding to 4 seasons, there are altogether 184 observations, each termed as a case, that constitute this work. Canonical Discrimination Analysis (CDA) which is most suitable when the data pool is huge was applied to see if the rivers studied distinguish themselves in terms of its morphology. The result was remarkably successful and was close to the established regional classification of the rivers. This kind of river classification has great application in the utilization, conservation and restoration of the most important natural re-source of the country.展开更多
Average pulse profiles of pulsar signals are analyzed using the bispectrum technique. The result shows that there are nonlinear phase couplings between the two frequency axes of the bispectrum charts, which indicate n...Average pulse profiles of pulsar signals are analyzed using the bispectrum technique. The result shows that there are nonlinear phase couplings between the two frequency axes of the bispectrum charts, which indicate nonlinear factors in the generation and propagation of pulsar signals. Bispectra can be used as feature vectors of pulsar signals because of their being translation invariant. A one-dimension selected line spectrum algorithm for extracting pulsar signal characteristic is proposed. Compared with selected bispectra, the proposed selected line spectra have the maximum interclass separability measurements from the point of view of the whole one-dimension feature vector. Recognition experiments on several pulsar signals received at several frequency bands are carried out. The result shows that the selected line spectrum algorithm is suitable for extracting pulsar signal characteristics and has a good classification performance.展开更多
基金financially supported by the National Natural Science Foundation of China(No.52174297).
文摘The endpoint carbon content in the converter is critical for the quality of steel products,and accurately predicting this parameter is an effective way to reduce alloy consumption and improve smelting efficiency.However,most scholars currently focus on modifying methods to enhance model accuracy,while overlooking the extent to which input parameters influence accuracy.To address this issue,in this study,a prediction model for the endpoint carbon content in the converter was developed using factor analysis(FA)and support vector machine(SVM)optimized by improved particle swarm optimization(IPSO).Analysis of the factors influencing the endpoint carbon content during the converter smelting process led to the identification of 21 input parameters.Subsequently,FA was used to reduce the dimensionality of the data and applied to the prediction model.The results demonstrate that the performance of the FA-IPSO-SVM model surpasses several existing methods,such as twin support vector regression and support vector machine.The model achieves hit rates of 89.59%,96.21%,and 98.74%within error ranges of±0.01%,±0.015%,and±0.02%,respectively.Finally,based on the prediction results obtained by sequentially removing input parameters,the parameters were classified into high influence(5%-7%),medium influence(2%-5%),and low influence(0-2%)categories according to their varying degrees of impact on prediction accuracy.This classi-fication provides a reference for selecting input parameters in future prediction models for endpoint carbon content.
文摘The rivers in Nepal are classified in terms of geographical regions but a more scientific classification such as on the ba-sis of morphology is clearly lacking. This study was done in 9 rivers namely Jhikhukhola of the Koshi system, Aandhikhola, Arungkhola, East Rapti, Karrakhola, Seti and main channel Narayani of the Gandaki system, and two independent systems within Nepal, Bagmati and Tinau. Among the morphologies, river bed or the substratum was taken as the main variable for the analysis which was categorized into 7 types as rocks, boulders, cobbles, pebbles, gravels, sand and silt. There were 23 sampling sites each with 2 stretches of around 100m in those rivers. The data were taken as a percentage, and to avoid biases it was observed visually by the same person for a complete year in every season. With 23 sites each with 2 stretches and 4 replicates corresponding to 4 seasons, there are altogether 184 observations, each termed as a case, that constitute this work. Canonical Discrimination Analysis (CDA) which is most suitable when the data pool is huge was applied to see if the rivers studied distinguish themselves in terms of its morphology. The result was remarkably successful and was close to the established regional classification of the rivers. This kind of river classification has great application in the utilization, conservation and restoration of the most important natural re-source of the country.
基金the National Natural Science Foundation of China.
文摘Average pulse profiles of pulsar signals are analyzed using the bispectrum technique. The result shows that there are nonlinear phase couplings between the two frequency axes of the bispectrum charts, which indicate nonlinear factors in the generation and propagation of pulsar signals. Bispectra can be used as feature vectors of pulsar signals because of their being translation invariant. A one-dimension selected line spectrum algorithm for extracting pulsar signal characteristic is proposed. Compared with selected bispectra, the proposed selected line spectra have the maximum interclass separability measurements from the point of view of the whole one-dimension feature vector. Recognition experiments on several pulsar signals received at several frequency bands are carried out. The result shows that the selected line spectrum algorithm is suitable for extracting pulsar signal characteristics and has a good classification performance.
