Microstructures of as-cast and extruded ZK60-xRE （RE=Dy, Ho and Gd, x=0-5, mass fraction） alloys were investigated. Meanwhile, the impact toughness was tested and then the relationship was discussed. The results sho...Microstructures of as-cast and extruded ZK60-xRE （RE=Dy, Ho and Gd, x=0-5, mass fraction） alloys were investigated. Meanwhile, the impact toughness was tested and then the relationship was discussed. The results show that as-cast microstructure is refined gradually with increasing the RE content. Mg-Zn-RE new phase increases gradually, while MgZn2 phase decreases gradually to disappear. Second phase tends to distribute along grain boundary in continuous network. Extruded microstructure is refined obviously to reach the micron level. Broken second phase tends to distribute along the extrusion direction in zonal shape. Impact toughness value -nK increases from 9-17 J/cm2 for as-cast state to 26-54 J/cm2 for extruded state. With increasing the value of -nK, fracture macro-morphology changes from a rough plane via multi-plane with step to V-type plane; and from single radiation zone to two zones of fiber and shear lip, respectively. Fracture micro-morphology changes from the brittle fracture to the ductile fracture. Fine grain and few fine dispersed second phase can enhance the impact toughness of magnesium alloys effectively.展开更多
Background subtraction is a challenging problem in surveillance scenes. Although the low-rank and sparse decomposition(LRSD) methods offer an appropriate framework for background modeling, they fail to account for ima...Background subtraction is a challenging problem in surveillance scenes. Although the low-rank and sparse decomposition(LRSD) methods offer an appropriate framework for background modeling, they fail to account for image's local structure, which is favorable for this problem. Based on this, we propose a background subtraction method via low-rank and SILTP-based structured sparse decomposition, named LRSSD. In this method, a novel SILTP-inducing sparsity norm is introduced to enhance the structured presentation of the foreground region. As an assistance, saliency detection is employed to render a rough shape and location of foreground. The final refined foreground is decided jointly by sparse component and attention map. Experimental results on different datasets show its superiority over the competing methods, especially under noise and changing illumination scenarios.展开更多
基金Projects(2010A090200078,2011A080403008)supported by the Major Science and TechnologyProject of Guangdong Province,China
文摘Microstructures of as-cast and extruded ZK60-xRE （RE=Dy, Ho and Gd, x=0-5, mass fraction） alloys were investigated. Meanwhile, the impact toughness was tested and then the relationship was discussed. The results show that as-cast microstructure is refined gradually with increasing the RE content. Mg-Zn-RE new phase increases gradually, while MgZn2 phase decreases gradually to disappear. Second phase tends to distribute along grain boundary in continuous network. Extruded microstructure is refined obviously to reach the micron level. Broken second phase tends to distribute along the extrusion direction in zonal shape. Impact toughness value -nK increases from 9-17 J/cm2 for as-cast state to 26-54 J/cm2 for extruded state. With increasing the value of -nK, fracture macro-morphology changes from a rough plane via multi-plane with step to V-type plane; and from single radiation zone to two zones of fiber and shear lip, respectively. Fracture micro-morphology changes from the brittle fracture to the ductile fracture. Fine grain and few fine dispersed second phase can enhance the impact toughness of magnesium alloys effectively.
基金supported in part by the EU FP7 QUICK project under Grant Agreement No.PIRSES-GA-2013-612652*National Nature Science Foundation of China(No.61671336,61502348,61231015,61671332,U1736206)+3 种基金Hubei Province Technological Innovation Major Project(No.2016AAA015,No.2017AAA123)the Fundamental Research Funds for the Central Universities(413000048)National High Technology Research and Development Program of China(863 Program)No.2015AA016306Applied Basic Research Program of Wuhan City(2016010101010025)
文摘Background subtraction is a challenging problem in surveillance scenes. Although the low-rank and sparse decomposition(LRSD) methods offer an appropriate framework for background modeling, they fail to account for image's local structure, which is favorable for this problem. Based on this, we propose a background subtraction method via low-rank and SILTP-based structured sparse decomposition, named LRSSD. In this method, a novel SILTP-inducing sparsity norm is introduced to enhance the structured presentation of the foreground region. As an assistance, saliency detection is employed to render a rough shape and location of foreground. The final refined foreground is decided jointly by sparse component and attention map. Experimental results on different datasets show its superiority over the competing methods, especially under noise and changing illumination scenarios.
