For the facts that WeChat red packet and fund transfer functions are used for illegal activities such as red packet gambling and illegal transactions, and the existing research work in this field is difficult to identify the specific numbers of sending and receiving red packets and fund transfers in WeChat, and there are problems of low recognition rate and high resource consumption, a method for dividing large and small burst blocks of traffic was proposed to extract the characteristics of traffic, so as to effectively identify the sending and receiving of red packets and the transfer behaviors. Firstly, by taking advantage of the suddenness of sending and receiving red packets and fund transfers, a large burst time threshold was set to define the burst blocks of such behaviors. Then, according to the feature that the behaviors of sending and receiving red packets and fund transfers consist of several consecutive user operations, a small burst threshold was set to further divide the traffic block into small bursts. Finally, synthesizing the features of small burst blocks in the big burst block, the final features were obtained. The experimental results show that the proposed method is generally better than the existing research on WeChat payment behavior recognition in terms of time efficiency, space occupancy rate, recognition accuracy and algorithm universality, with an average accuracy rate up to 97.58%. The test results of the real environment show that the proposed method can basically accurately identify the numbers of sending and receiving red packets and fund transfers for a user in a period of time.

Advanced applications such as fraud detection and trend learning lead to the development of frequent pattern mining over data streams. Data stream mining has to face more problems than static data mining like spatio-temporal constraint and combinatorial explosion of itemsets. In the paper, the existing frequent pattern mining algorithms over data streams were reviewed, and some classical algorithms and some newest algorithms were analyzed. According to the completeness of pattern set, frequent patterns of data stream could be divided into complete patterns and compressed patterns. Compressed patterns include closed frequent patterns, maximal frequent patterns, top- k frequent patterns and combinations of them. Between them, only closed frequent patterns are losslessly compressed. And constrained frequent pattern mining was used to narrow the result set obtained, satisfying the user's demand more. Algorithms based on sliding window model and time decay model were used to better handle recent transactions which occupy an important position in data stream mining. Moreover, two of the common algorithms, sequential pattern mining and high utility pattern mining algorithms were introduced. At last, further research direction of frequent pattern mining over data streams were discussed.

Aiming at the problems of insufficient use of semantic information and the poor summarization precision in the current generated text summarization method, a text summarization method was proposed based on dual encoder. Firstly, the dual encoder was used to provide richer semantic information for Sequence to Sequence (Seq2Seq) architecture. And the attention mechanism with dual channel semantics and the decoder with empirical distribution were optimized. Then, position embedding and word embedding were merged in word embedding technology, and Term Frequency-Inverse Document Frequency (TF-IDF), Part Of Speech (POS), key Score (Soc) were added to word embedding, as a result, the word embedding dimension was optimized. The proposed method aims to optimize the traditional sequence mapping of Seq2Seq and word feature representation, enhance the model's semantic understanding, and improve the quality of the summarization. The experimental results show that the proposed method has the performance improved in the Rouge evaluation system by 10 to 13 percentage points compared with traditional Recurrent Neural Network method with attention (RNN+atten) and Multi-layer Bidirectional Recurrent Neural Network method with attention (Bi-MulRNN+atten). It can be seen that the proposed method has more accurate semantic understanding of text summarization and the generation effect better, and has a better application prospect.

Aiming at the characteristics of large passenger flow, complex and variable indoor environment in airport terminals, an indoor positioning algorithm based on Spectral Regression Kernel Discriminant Analysis (SRKDA) was proposed. In the offline phase, the Received Signal Strength (RSS) data of known location was collected, and the non-linear features of the Original Location Fingerprint (OLF) were extracted by SRKDA algorithm to generate a new feature fingerprint database. In the online phase, SRKDA was firstly used to process the RSS data of the point to be positioned, and then Weighted K-Nearest Neighbor (W KNN) algorithm was used to estimate the position. In positioning simulation experiments, the Cumulative Distribution Function (CDF) and positioning accuracies of the proposed algorithm under 1.5 m positioning accuracy are 91.2% and 88.25% respectively in two different localization scenarios, which are 16.7 percentage points and 18.64 percentage points higher than those of the Kernel Principal Component Analysis (KPCA)+W KNN model, 3.5 percentage points and and 9.07 percentage points higher than those of the KDA+W KNN model. In the case of a large number of offline samples (more than 1100), the data processing time of the proposed algorithm is much shorter than that of KPCA and KDA. The experimental results show that, the proposed algorithm can effectively improve the indoor positioning accuracy, save data processing time and enhance the positioning efficiency.

To solve the contradiction between the estimation accuracy and the calculation amount of the RFID tag estimation method, and the instability of the estimation method performance caused by the randomness of the tag reading process in the field of aviation logistics networking information gathering. Based on the idea of complementary advantages, a method for estimating the number of RFID tags based on rough and fine estimation was proposed. By modeling and analyzing the tag reading process of framed ALOHA algorithm, the mathematical model between the average number of tags in the collision slot and the proportion of the collision slot was established. Rough number estimation based on the model was made, and then, according to the value of rough estimation, the reliability of rough estimation was evaluated. The Maximum A Posteriori (MAP) estimation algorithm based on the value of rough estimation as priori knowledge was used to improve the estimation accuracy. Compared to the original maximum posteriori probability estimation algorithm, the search range can be reduced up to 90%. The simulation results show that, the average error of the RFID tag number estimation based on rough and fine estimation is 3.8%, the stability of the estimation method is significantly improved, and the computational complexity is greatly reduced. The proposed algorithm can be effectively applied to the information collection process aviation logistics networking.

To improve the accuracy and automation of extracting water body by using remote sensing image, a method was proposed for water body extraction based on Stacked AutoEncoder (SAE). A deep network model was built by stacking sparse autoencoders and each layer was trained in turn with the greedy layerwise approach. Features were learnt without supervision from the pixel level to avoid the problem that methods such as traditional neural network needed artificial feature analysis and selection. Softmax classifier was trained with supervision by using the learnt features and corresponding labels. Back Propagation (BP) algorithm was used to fine-tune and optimize the whole model. The accuracy of SAE-based method reaches 94.73% by using the Tarim River's ETM+ data to do the experiment, which is 3.28% and 4.04% higher than that of Support Vector Machine (SVM) and BP neural network separately. The experimental results show that the proposed method can effectively improve the accuracy of water body extraction.

This paper presents an approach bases on ant colony optimization route algorithm of mobile Ad Hoc networks. It can effectively bear network load in Ad Hoc networks by using the self-adaptability of that ant colony algorithm. The simulations in NS-2 show that it performs very well on Ad Hoc environments, especially in throughput, average latency, and delivery ratio. The performance is better than that of Ad Hoc On-demand Distance Vector routing (AODV) and Dynamic Source Routing (DSR).