Transforming materials with evolving microstructures is one of the most important classes of smart materials that have many potential technological applications, and an unconventional phase field approach based on the...Transforming materials with evolving microstructures is one of the most important classes of smart materials that have many potential technological applications, and an unconventional phase field approach based on the characteristic functions of transforming variants has been developed to simulate the formation and evolution of their microstructures. This approach is advantageous in its explicit material symmetry and energy well structure, minimal number of ma- terial coefficients, and easiness in coupling multiple physical processes and order parameters, and has been applied successfully to study the microstructures and macroscopic prop- erties of shape memory alloys, ferroelectrics, ferromagnetic shape memory alloys, and multiferroic magnetoelectric crys- tals and films with increased complexity. In this topical re- view, the formulation of this unconventional phase field approach will be introduced in details, and its applications to various transforming materials will be discussed. Some ex- amples of specific microstructures will also be presented.展开更多
The high-frequency components in the traditional multi-scale transform method are approximately sparse, which can represent different information of the details. But in the low-frequency component, the coefficients ar...The high-frequency components in the traditional multi-scale transform method are approximately sparse, which can represent different information of the details. But in the low-frequency component, the coefficients around the zero value are very few, so we cannot sparsely represent low-frequency image information. The low-frequency component contains the main energy of the image and depicts the profile of the image. Direct fusion of the low-frequency component will not be conducive to obtain highly accurate fusion result. Therefore, this paper presents an infrared and visible image fusion method combining the multi-scale and top-hat transforms. On one hand, the new top-hat-transform can effectively extract the salient features of the low-frequency component. On the other hand, the multi-scale transform can extract highfrequency detailed information in multiple scales and from diverse directions. The combination of the two methods is conducive to the acquisition of more characteristics and more accurate fusion results. Among them, for the low-frequency component, a new type of top-hat transform is used to extract low-frequency features, and then different fusion rules are applied to fuse the low-frequency features and low-frequency background; for high-frequency components, the product of characteristics method is used to integrate the detailed information in high-frequency. Experimental results show that the proposed algorithm can obtain more detailed information and clearer infrared target fusion results than the traditional multiscale transform methods. Compared with the state-of-the-art fusion methods based on sparse representation, the proposed algorithm is simple and efficacious, and the time consumption is significantly reduced.展开更多
基金supported by the NSF (DMR-1006194 and CMMI1100339)NSFC (10972189 and 11102175)NSC(100-2628-E-002-034-MY3)
文摘Transforming materials with evolving microstructures is one of the most important classes of smart materials that have many potential technological applications, and an unconventional phase field approach based on the characteristic functions of transforming variants has been developed to simulate the formation and evolution of their microstructures. This approach is advantageous in its explicit material symmetry and energy well structure, minimal number of ma- terial coefficients, and easiness in coupling multiple physical processes and order parameters, and has been applied successfully to study the microstructures and macroscopic prop- erties of shape memory alloys, ferroelectrics, ferromagnetic shape memory alloys, and multiferroic magnetoelectric crys- tals and films with increased complexity. In this topical re- view, the formulation of this unconventional phase field approach will be introduced in details, and its applications to various transforming materials will be discussed. Some ex- amples of specific microstructures will also be presented.
基金Project supported by the National Natural Science Foundation of China(Grant No.61402368)Aerospace Support Fund,China(Grant No.2017-HT-XGD)Aerospace Science and Technology Innovation Foundation,China(Grant No.2017 ZD 53047)
文摘The high-frequency components in the traditional multi-scale transform method are approximately sparse, which can represent different information of the details. But in the low-frequency component, the coefficients around the zero value are very few, so we cannot sparsely represent low-frequency image information. The low-frequency component contains the main energy of the image and depicts the profile of the image. Direct fusion of the low-frequency component will not be conducive to obtain highly accurate fusion result. Therefore, this paper presents an infrared and visible image fusion method combining the multi-scale and top-hat transforms. On one hand, the new top-hat-transform can effectively extract the salient features of the low-frequency component. On the other hand, the multi-scale transform can extract highfrequency detailed information in multiple scales and from diverse directions. The combination of the two methods is conducive to the acquisition of more characteristics and more accurate fusion results. Among them, for the low-frequency component, a new type of top-hat transform is used to extract low-frequency features, and then different fusion rules are applied to fuse the low-frequency features and low-frequency background; for high-frequency components, the product of characteristics method is used to integrate the detailed information in high-frequency. Experimental results show that the proposed algorithm can obtain more detailed information and clearer infrared target fusion results than the traditional multiscale transform methods. Compared with the state-of-the-art fusion methods based on sparse representation, the proposed algorithm is simple and efficacious, and the time consumption is significantly reduced.
