Aiming at the problem that weakly supervised action localization method cannot locate action directly and the localization accuracy is not high， a weakly supervised action localization method based on attention mechanism was proposed， and an action localization model based on the pre-frame and post-frame information of action frame and the distinguishing function was designed and realized. The attention value generation model of Conditional Variational AutoEncoder （CVAE） was used to generate frame-level attention values as pseudo-frame-level labels； which CAVE was improved to obtain the frame-level attention value by adding the pre-frame and post-frame information of the action frame； to train and optimize pseudo-frame-level labels repeatedly， the optimization model for attention value based on distinguishing function was used. The experimental results conducted on THUMOS14 and ActivityNet1.2 datasets show that the action localization model based on the pre- and post-frame information of the action frame and the distinguishing function has better action localization effect and accuracy， which missing detection rate reduced by 11.7% compared with the model without the pre-frame and post-frame information of action frame； compared with AutoLoc， Weakly-supervised Temporal Activity Localization and Classification framework （W-TALC）， 3C-Net and other weakly supervised action localization models， when Intersection over Union （IoU） value is set to 0.5， the mean Average Precision （mAP） value on THUMOS14 dataset is improved by more than 10.7%， and the mAP value on ActivityNet1.2 dataset is improved by more than 8.8%.

Current research on Application Program Interface （API） learning and code reuse focuses on mining frequent API usage patterns， extracting component information， and recommending personalized API services based on user requirements and target functions. However， as beginners in software development who lack professional knowledge， experience and skills to implement specific use cases， they often need real code use cases as a reference except reading official documents. Most of the existing research about code recommendation is in single fragment mode. The lack of cross function case in case selection is not conducive for beginners to learn to build a complete use scenario or a functional module. At the same time， the semantic description extracted from a single function annotation is not enough for learners to understand the complete function implementation method of the project. To solve the above problems， an API use case recommendation service based on open source community analysis was proposed. Taking the software development back?end framework Spring Boot as an example， a cross function case recommendation assistant learning service was constructed. Then， the feasibility and effectiveness of the proposed API use case recommendation service was verified through questionnaires and expert verification.

Traditional learning-based super-resolution algorithms generally adopt training images for learning dictionary pairs, they are time-consuming, and the results strongly depend on the training images. To address these problems, a new super-resolution approach from a single color image was proposed based on sparse coding model. According to image self-similarity and redundancy features, this algorithm utilized low-resolution image itself for training dictionary pairs, combined with image pyramid structure. Meanwhile, in view of color images, the sparse representation based color image storage technology was used, which concatenated the values of three channels to a single vector and directly represented them sparsely. The experimental results illustrate that the proposed method not only can generate high-resolution images with better visual effects and higher Peak Signal-to-Noise Ratio (PSNR) but also has less computation time.

A new method to design a 3D surface movement simulation system for A-SMGCS based on Linux operation system and FilightGear flight simulator was proposed. The simulation system was driven by real-time track data of ADS-B. An airport model was built up according to a real airport prototype by modeling of aircrafts, airport layout, terminal, tower and terrain. Because ADS-B track data had no flight attitude data, an algorithm to extract flight attitude from a space vector, which was connected by two pre and post track points, was proposed. Six degrees of freedom (6-DOF) aircraft movement simulation was realized by importing position and attitude data into FlightGear through its multiplayer interface to drive aircraft models. The simulation results show that, the system can simulate surface movement accurately and realistically. The simulation system is low-cost because it is based on free, open source software.

Using moving least squares, an image deformation method was proposed based on control curves. Firstly, control curves were created by setting key points according to shape information or topology relationship, and were moved to new positions. Then affine, similar and rigid transformations were computed to realize various image deformations using Moving Least Squares. Experiments show that image shape and outline information can be realized by this method and the deformation result is satisfactory and realistic.