To address the problem of lack of labeled data in low-resource languages， which prevents the use of existing mature deep learning methods for Named Entity Recognition （NER）， a cross-lingual NER model based on sentence-level Generative Adversarial Network （GAN）， namely SLGAN-XLM-R （Sentence Level GAN based on XLM-R）， was proposed. Firstly， the labeled data of the source language was used to train the NER model on the basis of the pre-trained model XLM-R （XLM-Robustly optimized BERT pretraining approach）. At the same time， the linguistic adversarial training was performed on the embedding layer of XLM-R model by combining the unlabeled data of the target language. Then， the soft labels of the unlabeled data of the target language were predicted by using the NER model， Finally the labeled data of the source language and the target language was mixed to fine-tune the model again to obtain the final NER model. Experiments were conducted on four languages， English， German， Spanish， and Dutch， in two datasets， CoNLL2002 and CoNLL2003. The results show that with English as the source language， the F1 scores of SLGAN-XLM-R model on the test sets of German， Spanish， and Dutch are 72.70%， 79.42%， and 80.03%， respectively， which are 5.38， 5.38， and 3.05 percentage points higher compared to those of the direct fine-tuning on XLM-R model.

Three-way concept analysis is a very important topic in the field of artificial intelligence. The biggest advantage of this theory is that it can study “attributes that are commonly possessed” and “attributes that are commonly not possessed” of the objects in the formal context at the same time. It is well known that the new formal context generated by attribute clustering has a strong connection with the original formal context， and there is a close internal connection between the original three-way concepts and the new three-way concepts obtained by attribute clustering. Therefore， the comparative study and analysis of three-way concepts under attribute clustering were carried out. Firstly， the concepts of pessimistic， optimistic and general attribute clusterings were proposed on the basis of attribute clustering， and the relationship among these three concepts was studied. Moreover， the difference between the original three-way concepts and the new ones was studied by comparing the clustering process with the formal process of three-way concepts. Furthermore， two minimum constraint indexes were put forward from the perspective of object-oriented and attribute-oriented respectively， and the influence of attribute clustering on three-way concept lattice was explored. The above results further enrich the theory of three-way concept analysis and provide feasible ideas for the field of visual data processing.

In the Remaining Useful Life （RUL） prediction methods of aero-engine， the data at different time steps are not weighted simultaneously， including the original data and the extracted features， which leads to the problem of low accuracy of RUL prediction.Therefore， an RUL prediction method based on optimized hybrid model was proposed. Firstly， three different paths were chosen to extract features. 1） The mean value and trend coefficient of the original data were input into the fully connected network. 2） The original data were input into Bidirectional Long Short-Term Memory （Bi-LSTM） network， and the attention mechanism was used to process the obtained features. 3） The attention mechanism was used to process the original data， and the weighted features were input into Convolutional Neural Network （CNN） and Bi-LSTM network. Then， the idea of fusing multi-path features for prediction was adopted， the above-mentioned extracted features were fused and input into the fully connected network to obtain the RUL prediction result. Finally， the Company-Modular Aero-Propulsion System Simulation （C-MAPSS） datasets were used to verify the effectiveness of the method. Experimental results show that the proposed method performs well on all the four datasets. Taking FD001 dataset as an example， the Root Mean Square Error （RMSE） of the proposed method is reduced by 9.01% compared to that of Bi-LSTM network.