Chinese Train Control System level 3 （CTCS-3） train control on-board equipment plays a crucial role in ensuring train safety and improving operational efficiency. On-board interface equipment enables interaction between the on-board Automatic Train Protection （ATP） system， and ground equipment， drivers and trains. However， faults in on-board interface equipment account for a relatively high proportion of on-board equipment faults. In order to identify fault causes and ensure safety， a fault diagnosis method for on-board interface equipment based on temporal knowledge graph completion was proposed. In the method， travel logs and fault statistical data were integrated by introducing the temporal series， which extracted fault phenomena， performed entity alignment， and constructed a temporal knowledge graph. On the basis of the above， a fault diagnosis network based on knowledge graph completion was constructed； Temporal-Translating Embedding （T-TransE） vectorization， and Bidirectional Long Short-Term Memory （Bi-LSTM） network as well as Self-Attention （SA） mechanism were integrated for temporal feature extraction. Finally， the T-TransE vectorization model was pretrained using on-board interface equipment fault data from a railway administration in recent years， and the temporal introduction method with the best effect was selected. In order to validate superiority of the proposed method and effectiveness of the data integration method， the diagnostic network without data integration or temporal relationship introduction， as well as other common fault diagnostic networks， were tested using the on-board fault data. Experimental results show that with the same corpus， the temporal knowledge graph completion-based fault diagnosis model achieves the highest accuracy of 96.69% compared to other fault diagnosis frameworks.

The automatic segmentation of brain lesions provides a reliable basis for the timely diagnosis and treatment of stroke patients and the formulation of diagnosis and treatment plans， but obtaining large-scale labeled data is expensive and time-consuming. Semi-Supervised Learning （SSL） methods alleviate this problem by utilizing a large number of unlabeled images and a limited number of labeled images. Aiming at the two problems of pseudo-label noise in SSL and the lack of ability of existing Three-Dimensional （3D） networks to focus on smaller objects， a semi-supervised method was proposed， namely， a rectified cross pseudo supervised method with attention mechanism for stroke lesion segmentation RPE-CPS （Rectified Cross Pseudo Supervision with Project & Excite modules）. First， the data was input into two 3D U-Net segmentation networks with the same structure but different initializations， and the obtained pseudo-segmentation graphs were used for cross-supervised training of the segmentation networks， making full use of the pseudo-label data to expand the training set， and encouraging a high similarity between the predictions of different initialized networks for the same input image. Second， a correction strategy about cross-pseudo-supervised approach based on uncertainty estimation was designed to reduce the impact of the noise in pseudo-labels. Finally， in the segmentation network of 3D U-Net， in order to improve the segmentation performance of small object classes， Project & Excite （PE） modules were added behind each encoder module， decoder module and bottleneck module. In order to verify the effectiveness of the proposed method， evaluation experiments were carried out on the Acute Ischemic Stroke （AIS） dataset of the cooperative hospital and the Ischemic Stroke Lesion Segmentation Challenge （ISLES2022） dataset. The experimental results showed that when only using 20% of the labeled data in the training set， the Dice Similarity Coefficient （DSC）， 95% Hausdorff Distance （HD95）， and Average Surface Distance （ASD） on the public dataset ISLES2022 reached 73.87%， 6.08 mm and 1.31 mm； on the AIS dataset， DSC， HD95， and ASD reached 67.74%， 15.38 mm and 1.05 mm， respectively. Compared with the state-of-the-art semi-supervised method Uncertainty Rectified Pyramid Consistency（URPC）， DSC improved by 2.19 and 3.43 percentage points， respectively. The proposed method can effectively utilize unlabeled data to improve segmentation accuracy， outperforms other semi-supervised methods， and is robust.

Isometric feature mapping (Isomap) algorithm is topologically unstable if the input data are distorted. Therefore, an improved Isomap algorithm was proposed. In the improved algorithm, Image Euclidean Distance (IMED) was embedded into Isomap algorithm. Firstly, the authors transformed images into image Euclidean Distance (ED) space through a linear transformation by introducing metric coefficients and metric matrix; then, Euclidean distance matrix of images in the transformed space was calculated to find the neighborhood graph and geodesic distance matrix; finally, low-dimensional embedding was constructed by MultiDimensional Scaling (MDS) algorithm. Experiments with the improved algorithm and nearest-neighbor classifier were conducted on ORL and Yale face database. The results show that the proposed algorithm outperforms Isomap with average recognition rate by 5.57% and 3.95% respectively, and the proposed algorithm has stronger robustness for face recognition with small changes.

Concerning the shortage of the tamper localization accuracy and tamper recovery performance in the existing image tampers localization and recovery algorithms, the authors proposed a precise three-watermarking algorithm. It generated three types of watermarks such as detection watermark, localization watermark and recovery watermark by binary coding based on Least Significant Bit (LSB). The watermarks were imbedded into the low bits of image. Tamper detection and recovery were implemented by detection watermark and recovery watermark based on blocks, and precise localization was implemented by localization watermark based on single pixel. The simulation results show that the proposed algorithm has precise tamper localization to any size of brightness images and RGB images, and has good tamper recovery performance.