Recommending appropriate tags for texts is an effective way to better organize and use the text content. At present, most tag recommendation methods mainly recommend tags by mining the text content. However, most of the data information does not exist independently, for example, the co-occurrence of words between texts in a corpus can form a complex network structure. Previous studies have shown that the network structure information between texts and the text content information can summarize the semantics of the same text from two different perspectives, and the information extracted from two aspects can complement and explain each other. Based on this, a tag recommendation method was proposed to simultaneously model the network structure information of text and the content information of text. Firstly, Graph Convolutional neural Network(GCN) was used to extract the structure information of the network between texts, then Recurrent Neural Network(RNN) was used to extract the text content information, and finally the attention mechanism was used to recommend tags by combining the network structure information between texts and the text content information. Compared with baseline methods, such as tag recommendation method based on GCN and tag recommendation method with Topical attention-based Long Short-Term Memory(TLSTM) neural network, the proposed tag recommendation method with attention mechanism combining network structure information and text content information has better performance. For example, on the Mathematics Stack Exchange dataset, the precision, recall and F1 of the proposed method are improved by 2.3%, 3.8%, and 7.0% respectively compared with the optimal baseline method.

When the Model Driven Development (MDD) method is used in real-time field, it is difficult to describe the whole control system in a single layer completely and clearly. A real-time multi-layer modeling method based on hierarchy theory was presented in this study. The extensible input port and output port were adopted to equip present meta-model technique in real-time field, then the eXtensible Markup Language (XML) was used to describe the ports and the message transfer mechanism based on channel was applied to realize communication between models in mutiple layers. The modeling results for real-time control system show that compared with single layer modeling method, the hierarchical modeling method can effectively support the description of parallel interactions between multiple tasks when using model driven development method in real-time field, as a result it enhances the visibility and reusability of real-time complex system models.

An improved Principal Component Analysis (PCA) method was proposed with the synchronous multi-dimensional data stream anomaly analysis techniques. In this method, the problem of the original data stream variation tendency was mapped to the eigenvector space, and the steady-state eigenvector was solved, then the abnormal changes of the synchronous multi-dimensional data stream could be diagnosed by the relationship between the instantaneous eigenvector and the steady-state eigenvector. This method was applied to the abnormality diagnosis of the tunnel strain monitoring data stream, and the real-time monitoring and warning system for the tunnel strain was realized by using VC++. The experimental results show that the proposed method can reflect the changes of the aperiodic variables timely and realize the anomaly monitoring and early warning for multi-dimensional data stream effectively.

A new codebook model was proposed based on HSV color space to eliminate the effect of complex dynamic background in the moving object detection. The merits of this new model lie in three aspects: 1) HSV color space was introduced to effectively distinguish foreground and background for false targets removal; 2) a 4-tuple codeword was proposed for fast codebook training and small storage in comparison with the traditional 9-tuple codeword; 3) a new codebook learning and updating scheme was designed for easy and fast codebook training and detection. A global quantitative evaluation method named recall-precision curve was also proposed for the video sequence. Qualitative and quantitative experiments demonstrate that the proposed codebook model can effectively detect moving object under complex dynamic background.