There are two types of inverse problems: Optimization designation and parameter identification. Before the parameter identification of mathematical and physical inverse problems, it is necessary to determine the numbe...There are two types of inverse problems: Optimization designation and parameter identification. Before the parameter identification of mathematical and physical inverse problems, it is necessary to determine the number and position of measurement points in analysis and evaluation of a large number of measured data. In this paper, a mathematical methodology is proposed to describe the influence of the number and position of measurement points on the reconstruction precision. Information entropy and Bayesian theory are used in the description. Finally, a numerical experiment shows that the methodology is effective.展开更多
The partial least squares(PLS)method has been successfully applied for fault diagnosis in indus-trial production.Compared with the traditional PLS methods,the modified PLS(MPLS)approach is available for slow-time-vary...The partial least squares(PLS)method has been successfully applied for fault diagnosis in indus-trial production.Compared with the traditional PLS methods,the modified PLS(MPLS)approach is available for slow-time-varying data processing and quality-relevant fault detecting.How-ever,it encounters heavy computational load in model updating,and the static control limits often lead to the low fault detection rate(FDR)or high false alarm rate(FAR).In this article,we first introduce the recursive MPLS(RMPLS)method for quality-relevant fault detection and computational complexity reducing,and then combine the local information increment(LII)method to obtain the time-varying control limits.First,the proposed LII-RMPLS method is capa-ble of quality-relevant faults detection.Second,the adaptive threshold leads to higher FDRs and lower FARs compared with traditional methods.Third,the adaptive parameter-matrices-based model updating approach ensures that the proposed method has better robustness and lower computational complexity when dealing with time-varying factors.展开更多
Based on the research of predictingβ-hairpin motifs in proteins, we apply Random Forest and Support Vector Machine algorithm to predictβ-hairpin motifs in ArchDB40 dataset. The motifs with the loop length of 2 to 8 ...Based on the research of predictingβ-hairpin motifs in proteins, we apply Random Forest and Support Vector Machine algorithm to predictβ-hairpin motifs in ArchDB40 dataset. The motifs with the loop length of 2 to 8 amino acid residues are extracted as research object and thefixed-length pattern of 12 amino acids are selected. When using the same characteristic parameters and the same test method, Random Forest algorithm is more effective than Support Vector Machine. In addition, because of Random Forest algorithm doesn’t produce overfitting phenomenon while the dimension of characteristic parameters is higher, we use Random Forest based on higher dimension characteristic parameters to predictβ-hairpin motifs. The better prediction results are obtained;the overall accuracy and Matthew’s correlation coefficient of 5-fold cross-validation achieve 83.3% and 0.59, respectively.展开更多
基金the National Natural Science Foundation of China (Grant Nos. 60531010 and 60471045)
文摘There are two types of inverse problems: Optimization designation and parameter identification. Before the parameter identification of mathematical and physical inverse problems, it is necessary to determine the number and position of measurement points in analysis and evaluation of a large number of measured data. In this paper, a mathematical methodology is proposed to describe the influence of the number and position of measurement points on the reconstruction precision. Information entropy and Bayesian theory are used in the description. Finally, a numerical experiment shows that the methodology is effective.
基金gratefully acknowledge that this work is supported in part by National Natural Science Foundation of China[grant numbers 61903375 and 61673387]in part by theNatural Science Foundation of Shaanxi Province[grant number 2020JM-3].
文摘The partial least squares(PLS)method has been successfully applied for fault diagnosis in indus-trial production.Compared with the traditional PLS methods,the modified PLS(MPLS)approach is available for slow-time-varying data processing and quality-relevant fault detecting.How-ever,it encounters heavy computational load in model updating,and the static control limits often lead to the low fault detection rate(FDR)or high false alarm rate(FAR).In this article,we first introduce the recursive MPLS(RMPLS)method for quality-relevant fault detection and computational complexity reducing,and then combine the local information increment(LII)method to obtain the time-varying control limits.First,the proposed LII-RMPLS method is capa-ble of quality-relevant faults detection.Second,the adaptive threshold leads to higher FDRs and lower FARs compared with traditional methods.Third,the adaptive parameter-matrices-based model updating approach ensures that the proposed method has better robustness and lower computational complexity when dealing with time-varying factors.
文摘Based on the research of predictingβ-hairpin motifs in proteins, we apply Random Forest and Support Vector Machine algorithm to predictβ-hairpin motifs in ArchDB40 dataset. The motifs with the loop length of 2 to 8 amino acid residues are extracted as research object and thefixed-length pattern of 12 amino acids are selected. When using the same characteristic parameters and the same test method, Random Forest algorithm is more effective than Support Vector Machine. In addition, because of Random Forest algorithm doesn’t produce overfitting phenomenon while the dimension of characteristic parameters is higher, we use Random Forest based on higher dimension characteristic parameters to predictβ-hairpin motifs. The better prediction results are obtained;the overall accuracy and Matthew’s correlation coefficient of 5-fold cross-validation achieve 83.3% and 0.59, respectively.
