Based on the principle of piecewise linearization, the incremental forms of microstructure evolution models were integrated into the thermo-mechanical coupled finite element（FE） model to simulate nonlinear microstru...Based on the principle of piecewise linearization, the incremental forms of microstructure evolution models were integrated into the thermo-mechanical coupled finite element（FE） model to simulate nonlinear microstructure evolution during multi-pass hot deformation. This is an unsteady-state deformation where dynamic recrystallization（DRX）, meta-dynamic recrystallization（MDRX）, static recrystallization（SRX） and grain growth（GG） take place during hot deformation or deformation interval. The distributions of deformation and microstructure for cylindrical AZ31 sample during single-pass and double-pass hot compressions were quantitatively calculated and compared with the metallographic observation. It is shown that both the deformation and microstructure are non-uniformly distributed due to the presence of friction between the die and the flat end of sample. The average grain size and its standard deviation under the double-pass hot compression are slightly smaller than those under single-pass compression.The simulated average grain sizes agree well with the experiments, which validates that the developed FE model on the basis of incremental forms of microstructure evolution models is reasonable.展开更多
Incremental image compression techniques using priori information are of significance to deal with the explosively increasing remote-sensing image data. However, the potential benefi ts of priori information are still...Incremental image compression techniques using priori information are of significance to deal with the explosively increasing remote-sensing image data. However, the potential benefi ts of priori information are still to be evaluated quantitatively for effi cient compression scheme designing. In this paper, we present a k-nearest neighbor(k-NN) based bypass image entropy estimation scheme, together with the corresponding mutual information estimation method. Firstly, we apply the k-NN entropy estimation theory to split image blocks, describing block-wise intra-frame spatial correlation while avoiding the curse of dimensionality. Secondly, we propose the corresponding mutual information estimator based on feature-based image calibration and straight-forward correlation enhancement. The estimator is designed to evaluate the compression performance gain of using priori information. Numerical results on natural and remote-sensing images show that the proposed scheme obtains an estimation accuracy gain by 10% compared with conventional image entropy estimators. Furthermore, experimental results demonstrate both the effectiveness of the proposed mutual information evaluation scheme, and the quantitative incremental compressibility by using the priori remote-sensing frames.展开更多
基金the funding support from the National Natural Science Foundation of China (Grant No. 51573156, 51675335)
文摘Based on the principle of piecewise linearization, the incremental forms of microstructure evolution models were integrated into the thermo-mechanical coupled finite element（FE） model to simulate nonlinear microstructure evolution during multi-pass hot deformation. This is an unsteady-state deformation where dynamic recrystallization（DRX）, meta-dynamic recrystallization（MDRX）, static recrystallization（SRX） and grain growth（GG） take place during hot deformation or deformation interval. The distributions of deformation and microstructure for cylindrical AZ31 sample during single-pass and double-pass hot compressions were quantitatively calculated and compared with the metallographic observation. It is shown that both the deformation and microstructure are non-uniformly distributed due to the presence of friction between the die and the flat end of sample. The average grain size and its standard deviation under the double-pass hot compression are slightly smaller than those under single-pass compression.The simulated average grain sizes agree well with the experiments, which validates that the developed FE model on the basis of incremental forms of microstructure evolution models is reasonable.
基金supported by National Basic Research Project of China(2013CB329006)National Natural Science Foundation of China(No.61622110,No.61471220,No.91538107)
文摘Incremental image compression techniques using priori information are of significance to deal with the explosively increasing remote-sensing image data. However, the potential benefi ts of priori information are still to be evaluated quantitatively for effi cient compression scheme designing. In this paper, we present a k-nearest neighbor(k-NN) based bypass image entropy estimation scheme, together with the corresponding mutual information estimation method. Firstly, we apply the k-NN entropy estimation theory to split image blocks, describing block-wise intra-frame spatial correlation while avoiding the curse of dimensionality. Secondly, we propose the corresponding mutual information estimator based on feature-based image calibration and straight-forward correlation enhancement. The estimator is designed to evaluate the compression performance gain of using priori information. Numerical results on natural and remote-sensing images show that the proposed scheme obtains an estimation accuracy gain by 10% compared with conventional image entropy estimators. Furthermore, experimental results demonstrate both the effectiveness of the proposed mutual information evaluation scheme, and the quantitative incremental compressibility by using the priori remote-sensing frames.
