Aiming at the problems of low accuracy and poor robustness of traditional point cloud registration algorithms and the inability of accurate radiotherapy for cancer patients before and after radiotherapy， an Attention Dynamic Graph Convolutional Neural Network Lucas-Kanade （ADGCNNLK） was proposed. Firstly， residual attention mechanism was added to Dynamic Graph Convolutional Neural Network （DGCNN） to effectively utilize spatial information of point cloud and reduce information loss. Then， the DGCNN added with residual attention mechanism was used to extract point cloud features， this process was not only able to capture the local geometric features of the point cloud while maintaining the invariance of the point cloud replacement， but also able to semantically aggregate the information， thereby improving the registration efficiency. Finally， the extracted feature points were mapped to a high-dimensional space， and the classic image iterative registration algorithm LK （Lucas-Kanade） was used for registration of the nodes. Experimental results show that compared with Iterative Closest Point （ICP）， Globally optimal ICP （Go-ICP） and PointNetLK， the proposed algorithm has the best registration effect with or without noise. Among them， in the case without noise， compared with PointNetLK， the proposed algorithm has the rotation mean squared error reduced by 74.61%， and the translation mean squared error reduced by 47.50%； in the case with noise， compared with PointNetLK， the proposed algorithm has the rotation mean squared error reduced by 73.13%， and the translational mean squared error reduced by 44.18%， indicating that the proposed algorithm is more robust than PointNetLK. And the proposed algorithm is applied to the registration of human point cloud models of cancer patients before and after radiotherapy， assisting doctors in treatment， and realizing precise radiotherapy.

Imbalanced data classification is an important research content in machine learning， but most of the existing imbalanced data classification algorithms foucus on binary classification， and there are relatively few studies on imbalanced multi?class classification. However， datasets in practical applications usually have multiple classes and imbalanced data distribution， and the diversity of classes further increases the difficulty of imbalanced data classification， so the multi?class classification problem has become a research topic to be solved urgently. The imbalanced multi?class classification algorithms proposed in recent years were reviewed. According to whether the decomposition strategy was adopted， imbalanced multi?class classification algorithms were divided into decomposition methods and ad?hoc methods. Furthermore， according to the different adopted decomposition strategies， the decomposition methods were divided into two frameworks： One Vs. One （OVO） and One Vs. All （OVA）. And according to different used technologies， the ad?hoc methods were divided into data?level methods， algorithm?level methods， cost?sensitive methods， ensemble methods and deep network?based methods. The advantages and disadvantages of these methods and their representative algorithms were systematically described， the evaluation indicators of imbalanced multi?class classification methods were summarized， the performance of the representative methods were deeply analyzed through experiments， and the future development directions of imbalanced multi?class classification were discussed.

The cooperative rollback recovery mechanism based on checkpointing is an effective mechanism for the survivability of distributed system. The existing cooperative rollback recovery mechanism based on checkpointing presumes that the communication channel is reliable. However, this assumption is not always true in actual application scenarios. For the actual application scenarios of distributed system, a reliable assurance model for the survivability of distributed system was proposed, based on the checkpointing-based rollback recovery mechanism. Through the creation of redundant communication channel and process migration mechanism, the proposed model assures the survivability of distributed system in actual application scenarios where the communication channel is not reliable.

Simultaneous Algebraic Reconstruction Technique (SART) is able to generate Computed Tomography (CT) images with higher quality compared to Filtered Back-Projection (FBP) method when the projection data is incomplete or noisy. However, it is very time-consuming; and parallel computation is one of those efficient approaches to manage the problem. In this study, a new parallel implementation of SART based on the platform of Compute Unified Device Architecture (CUDA) was proposed. The experimental results show that there are no differences between the images reconstructed by this new method and those by serial implementation, but the reconstruction time is greatly decreased, more applicable to clinical application.