The performance of feature learning for deep convolutional neural networks(DCNNs)is increasing promptly with significant improvement in numerous applications.Recent studies on loss functions clearly describing that be...The performance of feature learning for deep convolutional neural networks(DCNNs)is increasing promptly with significant improvement in numerous applications.Recent studies on loss functions clearly describing that better normalization is helpful for improving the performance of face recognition(FR).Several methods based on different loss functions have been proposed for FR to obtain discriminative features.In this paper,we propose an additive parameter depending on multiplicative angular margin to improve the discriminative power of feature embedding that can be easily implemented.In additive parameter approach,an automatic adjustment of the seedling element as the result of angular marginal seed is offered in a particular way for the angular softmax to learn angularly discriminative features.We train the model on publically available dataset CASIA-WebFace,and our experiments on famous benchmarks YouTube Faces(YTF)and labeled face in the wild(LFW)achieve better performance than the various state-of-the-art approaches.展开更多
The effects of Sr addition and pressure increase on the microstructure and casting defects of a low-pressure die cast （LPDC） AISi7Mg0.3 alloy have been studied. Metallographic and image analysis techniques have been...The effects of Sr addition and pressure increase on the microstructure and casting defects of a low-pressure die cast （LPDC） AISi7Mg0.3 alloy have been studied. Metallographic and image analysis techniques have been used to quantitatively examine the microstructural changes and the amount of porosity occurring at different Sr levels and pressure parameters. The results indicate that an increase in the filling pressure induces lower heat dissipation of the liquid close to the die/core surfaces, with the formation of slightly greater dendrite arms and coarser eutectic Si particles. On the other hand, the increase in the Sr level leads to finer microstructural scale and eutectic Si. The analysed variables, within the experimental conditions, do not affect the morphology of eutectic Si particles. Higher applied pressure and Sr content generate castings with lower amount of porosiW. However, as the filling pressure increases the flow of metal inside the die cavity is more turbulent, leading to the formation of oxide films and cold shots. In the analysed range of experimental conditions, the design of experiment methodology and the analysis of variance have been used to develop statistical models that accurately predict the average size of secondary dendrite arm spacing and the amount of porosity in the low-pressure die cast AISiTMg0.3 alloy.展开更多
基金The work is supported by the NSF of China(No.11871447)Anhui Initiative in Quantum Information Technologies(AHY150200).
文摘The performance of feature learning for deep convolutional neural networks(DCNNs)is increasing promptly with significant improvement in numerous applications.Recent studies on loss functions clearly describing that better normalization is helpful for improving the performance of face recognition(FR).Several methods based on different loss functions have been proposed for FR to obtain discriminative features.In this paper,we propose an additive parameter depending on multiplicative angular margin to improve the discriminative power of feature embedding that can be easily implemented.In additive parameter approach,an automatic adjustment of the seedling element as the result of angular marginal seed is offered in a particular way for the angular softmax to learn angularly discriminative features.We train the model on publically available dataset CASIA-WebFace,and our experiments on famous benchmarks YouTube Faces(YTF)and labeled face in the wild(LFW)achieve better performance than the various state-of-the-art approaches.
文摘The effects of Sr addition and pressure increase on the microstructure and casting defects of a low-pressure die cast （LPDC） AISi7Mg0.3 alloy have been studied. Metallographic and image analysis techniques have been used to quantitatively examine the microstructural changes and the amount of porosity occurring at different Sr levels and pressure parameters. The results indicate that an increase in the filling pressure induces lower heat dissipation of the liquid close to the die/core surfaces, with the formation of slightly greater dendrite arms and coarser eutectic Si particles. On the other hand, the increase in the Sr level leads to finer microstructural scale and eutectic Si. The analysed variables, within the experimental conditions, do not affect the morphology of eutectic Si particles. Higher applied pressure and Sr content generate castings with lower amount of porosiW. However, as the filling pressure increases the flow of metal inside the die cavity is more turbulent, leading to the formation of oxide films and cold shots. In the analysed range of experimental conditions, the design of experiment methodology and the analysis of variance have been used to develop statistical models that accurately predict the average size of secondary dendrite arm spacing and the amount of porosity in the low-pressure die cast AISiTMg0.3 alloy.
