Wen-Xin Hu

The effect of the extrusion process on the microstructure, corrosion, and mechanical properties of Mg–Zn–Ca–Zr alloy has been investigated. Zn and Ca were both in a solid solution and only the Zr-rich phase was observed in the homogenized and extruded alloys. The Zr-rich phase was obviously refined after extrusion. The corrosion rate of the homogenized alloy decreased by about 25% after extrusion. This is because the refined Zr-rich phase was easier to cover with the deposited corrosion products, which reduced the cathodic reaction activity of the Zr-rich phase. The corrosion rate is similar for the alloys extruded at 320 °C and 350 °C since the size and distribution of the Zr-rich phase were not different in the two conditions. The alloy extruded at 320 °C has a smaller grain size and better comprehensive mechanical properties.

Recently, deep-learning based method has achieved great success in remote sensing image segmentation. Deep neural networks for semantic segmentation always require a large number of samples, which becomes a tough problem especially in remote sensing area. Unsupervised domain adaptation utilizes the labeled source dataset to train the unlabeled target dataset, which can reduce the labeling cost greatly. The key challenge of this task is how to alleviate the data distribution discrepancy of feature between the two datasets meanwhile keep the detail information in target dataset. This paper proposes a novel framework based on AdaptSegNet model that combined with remote sensing derived indices. We concatenate the NDWI index to the output layer of segmentation module, which can reflect waterbody at pixel-level with value from -1 to 1 and improve the segmentation accuracy greatly. we adopt 3 × 3 convolution to learn a weight that update the class probability after concatenation. Experiments are conducted from the GID dataset to the Landsat8 datasets. Our method achieves 90.0% mean intersection-over-union (IoU) and outperforms all of the baselines

Demographic attributes play an important role in adjusting the market strategy of film companies and customizing recommendation for their user. However, it's difficult to obtain full information of demographic attributes of online user and previous work only adapt single-source data (user historical record) for prediction, which is not enough for user representation. In this paper, we proposed a novel approach named CNN-ETNA to predict user demographic attributes from their movie-watching behavior. Our model CNN-ETNA is further improved by extracting semantic feature from movie to learn informative user representations. Experiments conducted on Movielens dataset demonstrate that our approach effectively improves the performance of demographic prediction compared to baseline algorithms.