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.