In the field of open social text， the generated text content lacks personalized features. In order to solve the problem， a user-level fine-grained control generation model was proposed， namely PTG-GPT2-Chinese （Personalized Text Generation Generative Pre-trained Transformer 2-Chinese）. In the proposed model， on the basis of the GPT2 （Generative Pre-trained Transformer 2.0） structure， an Encoder-Decoder model framework was designed. First， the static personalized information of a user was modeled and encoded on the Encoder side， a bidirectional independent attention module was added on the Decoder side to receive the static personalized feature vector， and the attention module in the original GPT2 structure was used for capturing the dynamic personalized features in the user’s text. Then， the scores of different attention modules were weighted and fused dynamically， and were participated in the subsequent decoding， thereby automatically generating social text constrained by the user’s personalized feature attributes. However， the semantic sparsity of the user’s basic information may cause conflicts between the generated text and some personalized features. Aiming at this problem， the BERT （Bidirectional Encoder Representations from Transformers） model was used to perform the secondary enhanced generation of consistent understanding between the output data of the Decoder side and the user’s personalized features， and finally the personalized social text generation was realized. Experimental results show that compared with the GPT2 model， the proposed model has the fluency improved by 0.36% to 0.72%， and on the basis of no loss of language fluency， the secondary generation makes the two evaluation indicators： personalization and consistency increase by 10.27% and 13.24% respectively. It is proved that the proposed model can assist user’s creation effectively and generate social text that is fluent and personalized for the user.

Aiming at the problem of insufficient adaptive ability of network intrusion detection models， the large-scale fast search ability of Sparrow Search Algorithm （SSA） was introduced into Particle Swarm Optimization （PSO） algorithm， and a network intrusion detection algorithm based on Sparrow Search Algorithm and improved Particle Swarm Optimization Algorithm （SSAPSO） was proposed. In the algorithm， by optimizing the parameters that are difficult to set in Light Gradient Boosting Machine （LightGBM） algorithm， PSO algorithm converged quickly while ensuring the optimization accuracy， and an optimal network intrusion detection model was obtained. Simulation results show that on the four benchmark functions， SSAPSO converged faster than basic PSO algorithm. Compared with Categorical features+gradient Boosting （CatBoost） algorithm， SSAPSO optimized LightGBM （SSAPSO-LightGBM） has the accuracy， recall， precision and F1_score improved by 15.12%， 3.25%， 21.26% and 12.25% respectively on KDDCUP99 dataset. Compared with LightGBM algorithm， SSAPSO-LightGBM has the detection accuracy for Normal， Remote-to-Login （R2L） attack， User-to-Root （U2R） attack and Probeing （PROBE） attack on the above dataset improved by 0.61%， 3.14%， 4.24%， 1.04% and 5.03% respectively.

Wearable ElectroEncephaloGram （EEG） device is a wireless EEG system to daily real-time monitoring. It is developed rapidly and widely applied because of its portability， real-time performance， non-invasiveness， and low-cost advantages. This system is mainly composed of hardware parts such as signal acquisition module， signal processing module， micro-control module， communication module and power supply module， and software parts such as mobile terminal module and cloud storage module. The key technologies of wearable EEG devices were discussed. First， the improvement of EEG signal acquisition module was explained. In addition， the comparisons of wearable EEG device signal preprocessing module， signal noise reduction， artifact processing and feature extraction technology were performed. Then， the advantages and disadvantages of machine learning and deep learning classification algorithms were analyzed， and the application fields of wearable EEG device were summarized. Finally， future development trends of the key technologies of wearable EEG device were proposed.