The current supervised face forgery video detection methods need a large amount of labeled data. In order to solve the practical problems of fast iteration and many kinds of video forgery methods， the unsupervised idea in temporal anomaly detection was introduced into face forgery video detection， the face forgery video detection task was transformed into unsupervised video anomaly detection task， and an unsupervised face forgery video detection method based on reconstruction error was proposed. Firstly， the facial landmark sequence of continuous frames in the video to be detected was extracted. Secondly， the facial landmark sequence in the video to be detected was reconstructed based on multi-granularity information such as deviation features， local features and temporal features. Thirdly， the reconstruction error between the original sequence and the reconstructed sequence was calculated. Finally， the score was calculated according to the peak frequency of the reconstruction error to detect the forgery video automatically. Experimental results show that compared with detection methods such as LRNet （Landmark Recurrent Network） and Xception-c23， the proposed method has the AUC （Area Under Curve） of the detection performance increased by up to 27.6%， and the AUC of the transplantation performance increased by 30.4%.

Span-based joint extraction model shares the semantic representation of entity spans in entity and Relation Extraction （RE） tasks， which effectively reduces the cascade error caused by pipeline models. However， the existing models cannot adequately integrate contextual information into the representation of entities and relations. To solve this problem， a Joint Entity and Relation extraction model based on Contextual semantic Enhancement （JERCE） was proposed. Firstly， the semantic feature representations of sentence-level text and inter-entity text were obtained by contrastive learning method. Then， the representations were added into the representations of entity and relation to predict entities and relations jointly. Finally， the loss values of the two tasks were adjusted dynamically to optimize the overall performance of the joint model. In experiments on public datasets CoNLL04， ADE and ACE05， compared with Trigger-sense Memory Flow framework （TriMF）， the proposed JERCE model has the F1 scores of entity recognition improved by 1.04， 0.13 and 2.12 percentage points respectively， and the F1 scores of RE increased by 1.19， 1.14 and 0.44 percentage points respectively. Experimental results show that the JERCE model can fully obtain semantic information in context.

To solve the problems of the mutual interference of multi-channel ElectroEncephaloGraphy （EEG）， the different classification results caused by individual differences， and the low recognition rate of single domain features， a method of channel selection and feature fusion was proposed. Firstly， the acquired EEG was preprocessed， and the important channels were selected by using Gradient Boosting Decision Tree （GBDT）. Secondly， the Generalized Predictive Control （GPC） model was used to construct the prediction signals of important channels and distinguish the subtle differences among multi-dimensional correlation signals， then the SE-TCNTA （Squeeze and Excitation block-Temporal Convolutional Network-Temporal Attention） model was used to extract temporal features between different frames. Thirdly， the Pearson correlation coefficient was used to calculate the relationship between channels， the frequency domain features of EEG and the control values of prediction signals were extracted as inputs， the spatial graph structure was established， and the Graph Convolutional Network （GCN） was used to extract the features of frequency domain and spatial domain. Finally， the above two features were input to the fully connected layer for feature fusion in order to realize the classification of EEG. Experimental results on public dataset BCICIV_2a show that in the case of channel selection， compared with the first EEG-inception model for ERP detection and DSCNN （Shallow Double-branch Convolutional Neural Network） model that also uses double branch feature extraction， the proposed method has the classification accuracy increased by 1.47% and 1.69% respectively， and has the Kappa value increased by 1.25% and 2.53% respectively. The proposed method can improve the classification accuracy of EEG and reduce the influence of redundant data on feature extraction， so it is more suitable for Brain-Computer Interface （BCI） systems.

To overcome the defects of the standard cubic Hermite interpolating splines, a class of quartic Hermite interpolating splines with parameters was presented in this paper, which inherited the same properties of the standard cubic Hermite interpolating splines. Given the set interpolating conditions, the shape of the proposed splines could be adjusted by changing the values of the parameters. If the parameters were chosen properly, the quartic Hermite interpolating splines could achieve C2 continuity and approximate to the interpolated functions better than the standard cubic Hermite interpolating splines. The proposed new splines further enriched the theories of Hermite interpolating splines, and provided a new method for constructing interpolation curves and surfaces.

In order to realize function imaging guided radiotherapy combined with image-guided radiotherapy and function imaging, this paper studied the possibility of Computed Tomography (CT) perfusion imaging using cone-beam CT on image-guided radiotherapy. To solve the problem that Cone-Beam Computed Tomography (CBCT) cannot obtain precise Time-Density Curve (TDC) because of low speed imaging, this paper proposed a method of modeling a mathematically model based on projection data. At first, the CBCT projection data was simulated with computer simulation technology. Then the density values of voxel were mathematical modeled. Finally, concrete parameters were calculated using computer programming and computer optimal solution technology. The experiment proves that the TDC received from the proposed model and the Dynamic Contrast Enhanced CT (DCE-CT) have high similarity. Hence, it can be proved that realizing CBCT in image-guided radiotherapy perfusion imaging by the proposed model was feasible.