To solve the problems of small number of anomaly samples and inefficient feedback of anomalies in the anomaly detection tasks of large-scale software system, a log-level anomaly detection algorithm based on Generative Adversarial Network (GAN) and attention mechanism. First, the unstructured logs were converted into structured events through the log templates, and each event included timestamps, signature and parameters. Second, through sliding window method, the sequence of the parsed events were divided into patterns, and the real training dataset was comprised combination of the divided event patterns and the corresponding following events. Third, the real event patterns were used as the training samples to train the attention mechanism-based GAN, and the Recurrent Neural Network (RNN) based generator was trained through the adversarial learning mechanism until it converged. Finally, through the input flow event pattern, the generator generated the possibility distribution of normal and abnormal events based on the previous pattern. When the threshold was set, whether the specific log of next moment is a normal event or an abnormal event was determined automatically. Experimental results show that the proposed anomaly detection algorithm, which uses a gated recurrent unit network as the attention weight and a Long Short-Term Memory (LSTM) network to fit event patterns, has a 21.7% increase in precision compared to the algorithm only using the gated recurrent unit network. In addition, compared to the log-level anomaly detection algorithm LogGAN, the proposed algorithm improves the precision of anomaly detection by 7.8% over the performance of LogGAN.

To solve the problem that Non Line Of Sight (NLOS) state identification requires classification of known channel types, a channel environment classification algorithm based on Convolutional Neural Network (CNN) was proposed. Firstly, an Ultra-WideBand (UWB) channel was sampled, and a sample set was constructed. Then, a CNN was trained by the sample set to extract features of different channel scenes. Finally, the classification of UWB channel environment was realized. The experimental results show that the overall accuracy of the model using the proposed algorithm is about 93.40% and the algorithm can effectively realize the classification of channel environments.

In recent years, more and more multimedia contents were used on social media to share users' experiences and emotions. Compared to single text or image, the complementation of text and image can further fully reveal the real emotion of users. Concerning the sentiment shortage of single text or image, a method based on Convolutional Neural Network (CNN) was proposed for multimedia sentiment analysis. In order to explore the influence of semantic representation in different level, image features were combined with different level (word-level, phrase-level and sentence-level) text features to construct CNN. The experimental results on two real-world datasets demonstrate that the proposed method gets more accurate prediction on multimedia sentiment analysis by capturing the internal relations between text and image.

In modeling and simulation of meteor burst communication system, concerning the problem of network delay caused by Hybrid Automatic Repeat Request (HARQ), an estimation model of transmission delay based on HARQ was proposed. Firstly, in consideration of the network structure and channel characters in meteor burst communication, a network delay model was constructed by analyzing the theory of HARQ. Then, based on queuing theories, the improvement mechanism of HARQ was introduced to establish an estimation model of transmission delay of Type-Ⅰ HARQ and one of Type-Ⅱ HARQ. Finally, the simulation was realized to compare and analyze the transmission delay performance of two kinds of HARQ. When packet transmission accuracy or packet transmission time changes independently, the transmission delay of Type-Ⅱ HARQ is less than that of Type-Ⅰ HARQ. The experimential results show that Type-Ⅱ HARQ has advantages of network delay performance in meteor burst communication compared to Type-Ⅰ HARQ.