Life cycle assessment (LCA) is an important content of green design;the major phase of LCA is impact assessment. After classifying the impact factors, with grey-system theory, the evaluating grey-groups and their whit...Life cycle assessment (LCA) is an important content of green design;the major phase of LCA is impact assessment. After classifying the impact factors, with grey-system theory, the evaluating grey-groups and their whitening weighing functions are defined;the grey-cluster analysis of each classified impact is performed;based on analyzing results, the calculating method of classified impact index is given. By range of action, the impact classes are grouped to three groups - global impact, regional impact, and local impact;the calculating methods of grouped and overall impact index are presented. Finally, an application example of comparative choice of a category of products - three materials, steel, aluminum and engineering plastics is given.展开更多
We propose a novel method that combines gray system theory and robust M-estimation method to suppress the interference in controlled-source electromagnetic data. We estimate the standard deviation of the data using a ...We propose a novel method that combines gray system theory and robust M-estimation method to suppress the interference in controlled-source electromagnetic data. We estimate the standard deviation of the data using a gray model because of the weak dependence of the gray system on data distribution and size. We combine the proposed and threshold method to identify and eliminate outliers. Robust M-estimation is applied to suppress the effect of the outliers and improve the accuracy. We treat the M-estimators of the preserved data as the true data. We use our method to reject the outliers in simulated signals containing noise to verify the feasibility of our proposed method. The processed values are observed to be approximate to the expected values with high accuracy. The maximum relative error is 3.6676%, whereas the minimum is 0.0251%. In processing field data, we observe that the proposed method eliminates outliers, minimizes the root-mean-square error, and improves the reliability of controlled-source electromagnetic data in follow-up processing and interpretation.展开更多
文摘Life cycle assessment (LCA) is an important content of green design;the major phase of LCA is impact assessment. After classifying the impact factors, with grey-system theory, the evaluating grey-groups and their whitening weighing functions are defined;the grey-cluster analysis of each classified impact is performed;based on analyzing results, the calculating method of classified impact index is given. By range of action, the impact classes are grouped to three groups - global impact, regional impact, and local impact;the calculating methods of grouped and overall impact index are presented. Finally, an application example of comparative choice of a category of products - three materials, steel, aluminum and engineering plastics is given.
基金supported by the National Natural Science Foundation of China(No.41227803)the State High-Tech Development Plan of China(No.2014AA06A602)the Fundamental Research Funds for the Central Universities of Central South University(No.2017557)
文摘We propose a novel method that combines gray system theory and robust M-estimation method to suppress the interference in controlled-source electromagnetic data. We estimate the standard deviation of the data using a gray model because of the weak dependence of the gray system on data distribution and size. We combine the proposed and threshold method to identify and eliminate outliers. Robust M-estimation is applied to suppress the effect of the outliers and improve the accuracy. We treat the M-estimators of the preserved data as the true data. We use our method to reject the outliers in simulated signals containing noise to verify the feasibility of our proposed method. The processed values are observed to be approximate to the expected values with high accuracy. The maximum relative error is 3.6676%, whereas the minimum is 0.0251%. In processing field data, we observe that the proposed method eliminates outliers, minimizes the root-mean-square error, and improves the reliability of controlled-source electromagnetic data in follow-up processing and interpretation.
