The iron and steel industry in China has experienced vast changes over the past thirty years.To have a precise knowledge of the circumstances behind its evolution,it is essential to perform an iron flow analysis.Accor...The iron and steel industry in China has experienced vast changes over the past thirty years.To have a precise knowledge of the circumstances behind its evolution,it is essential to perform an iron flow analysis.Accordingly,iron flow analysis for the years 1990-2015 was conducted.Firstly,the iron natural resource efficiency,Chinese steel scrap index,and Chinese iron ore support ratio which can reflect the running status of China's iron and steel industry for these six years(1990,1995,2000,2005,2010,and 2015)were analyzed;thereafter,value chain and statistical entropy analyses were conducted based on the iron flow analysis,and some interesting results were obtained.Discussions and conclusions based on the results along with the recommendations for the China's iron and industry were proposed.展开更多
Strong energy sharing is shown by numerically investigating coupled multi-component Bose–Einstein condensates（BECs） with a harmonic trap to simulate the Fermi–Pasta–Ulam model（FPU）. For two-component BECs, the ...Strong energy sharing is shown by numerically investigating coupled multi-component Bose–Einstein condensates（BECs） with a harmonic trap to simulate the Fermi–Pasta–Ulam model（FPU）. For two-component BECs, the energy exchanging between each part, from regular, quantum beating to complete energy sharing, is explored by simulating their Husimi distributions, the time evolution of energies and the statistical entropy. Meanwhile, in the three-component case, a more complex energy sharing behavior is reported and a strong energy sharing is found.展开更多
The intensity of the micro-expression is weak,although the directional low frequency components in the image are preserved by many algorithms,the extracted micro-expression ft^ature information is not sufficient to ac...The intensity of the micro-expression is weak,although the directional low frequency components in the image are preserved by many algorithms,the extracted micro-expression ft^ature information is not sufficient to accurately represent its sequences.In order to improve the accuracy of micro-expression recognition,first,each frame image is extracted from,its sequences,and the image frame is pre-processed by using gray normalization,size normalization,and two-dimensional principal component analysis(2DPCA);then,the optical flow method is used to extract the motion characteristics of the reduced-dimensional image,the information entropy value of the optical flow characteristic image is calculated by the information entropy principle,and the information entropy value is analyzed to obtain the eigenvalue.Therefore,more micro-expression feature information is extracted,including more important information,which can further improve the accuracy of micro-expression classification and recognition;finally,the feature images are classified by using the support vector machine(SVM).The experimental results show that the micro-expression feature image obtained by the information entropy statistics can effectively improve the accuracy of micro-expression recognition.展开更多
基金supported by the National Key Research and Development Program of China(2019YFC1905204)the Soft Science Program Funded by Fujian Provincial Department of Science and Technology(2019R0067)+1 种基金the Project of Sichuan Mineral Resources Research Center(SCKCZY2020-YB01)the Fundamental Research Funds for the Central Universities of China(N182502045).
文摘The iron and steel industry in China has experienced vast changes over the past thirty years.To have a precise knowledge of the circumstances behind its evolution,it is essential to perform an iron flow analysis.Accordingly,iron flow analysis for the years 1990-2015 was conducted.Firstly,the iron natural resource efficiency,Chinese steel scrap index,and Chinese iron ore support ratio which can reflect the running status of China's iron and steel industry for these six years(1990,1995,2000,2005,2010,and 2015)were analyzed;thereafter,value chain and statistical entropy analyses were conducted based on the iron flow analysis,and some interesting results were obtained.Discussions and conclusions based on the results along with the recommendations for the China's iron and industry were proposed.
基金supported by the National Natural Science Foundation of China(Grant No.11374197)the Research Fund for the Doctoral Program of TYUST,China(Grant No.20122041)the Program for Changjiang Scholars and Innovative Research Team in University,China(Grant No.IRT13076)
文摘Strong energy sharing is shown by numerically investigating coupled multi-component Bose–Einstein condensates（BECs） with a harmonic trap to simulate the Fermi–Pasta–Ulam model（FPU）. For two-component BECs, the energy exchanging between each part, from regular, quantum beating to complete energy sharing, is explored by simulating their Husimi distributions, the time evolution of energies and the statistical entropy. Meanwhile, in the three-component case, a more complex energy sharing behavior is reported and a strong energy sharing is found.
基金the National Natural Science Foundation of China(Nos.61772417,61634004,and 61602377)the Key R&D Progrm Projects in Shaanxi Province(No.2017GY-060)the Shaanxi Natural Science Basic Research Project(No.018JM4018)。
文摘The intensity of the micro-expression is weak,although the directional low frequency components in the image are preserved by many algorithms,the extracted micro-expression ft^ature information is not sufficient to accurately represent its sequences.In order to improve the accuracy of micro-expression recognition,first,each frame image is extracted from,its sequences,and the image frame is pre-processed by using gray normalization,size normalization,and two-dimensional principal component analysis(2DPCA);then,the optical flow method is used to extract the motion characteristics of the reduced-dimensional image,the information entropy value of the optical flow characteristic image is calculated by the information entropy principle,and the information entropy value is analyzed to obtain the eigenvalue.Therefore,more micro-expression feature information is extracted,including more important information,which can further improve the accuracy of micro-expression classification and recognition;finally,the feature images are classified by using the support vector machine(SVM).The experimental results show that the micro-expression feature image obtained by the information entropy statistics can effectively improve the accuracy of micro-expression recognition.
