Existing robotic grasping operations are usually performed under well-illuminated conditions with clear object details and high regional contrast. At the same time， for low-light conditions caused by night and occlusion， where the objects’ visual features are weak， the detection accuracies of existing robotic grasp detection models decrease dramatically. In order to improve the representation ability of sparse and weak grasp features in low-light scenarios， a grasp detection model incorporating visual feature enhancement mechanism was proposed to use the visual enhancement sub-task to impose feature enhancement constraints on grasp detection. In grasp detection module， the U-Net like encoder-decoder structure was adopted to achieve efficient feature fusion. In low-light enhancement module， the texture and color information was respectively extracted from local and global level， thereby balancing the object details and visual effect in feature enhancement. In addition， two low-light grasp datasets called low-light Cornell dataset and low-light Jacquard dataset were constructed as new benchmark dataset of low-light grasp and used to conduct the comparative experiments. Experimental results show that the accuracies of the proposed low-light grasp detection model are 95.5% and 87.4% on the benchmark datasets respectively， which are 11.1， 1.2 percentage points higher on low-light Cornell dataset and 5.5， 5.0 percentage points higher on low-light Jacquard dataset than those of the existing grasp detection models， including Generative Grasping Convolutional Neural Network （GG-CNN）， and Generative Residual Convolutional Neural Network （GR-ConvNet）， indicating that the proposed model has good grasp detection performance.

Aiming at the problem that the existing Handwritten Mathematical Expression Recognition （HMER） methods reduce image resolution and lose feature information after multiple pooling operations in Convolutional Neural Network （CNN）， which leads to parsing errors， an encoder-decoder model for HMER based on attention mechanism was proposed. Firstly， Densely connected convolutional Network （DenseNet） was used as the encoder， so that the dense connections were used to enhance feature extraction， promote gradient propagation， and alleviate vanishing gradient. Secondly， Gated Recurrent Unit （GRU） was used as the decoder， and attention mechanism was introduced， so that， the attention was allocated to different regions of image to realize symbol recognition and structural analysis accurately. Finally， the handwritten mathematical expression images were encoded， and the encoding results were decoded into LaTeX sequences. Experimental results on Competition on Recognition of Online Handwritten Mathematical Expressions （CROHME） dataset show that the proposed model has the recognition rate improved to 40.39%. And within the allowable error range of three levels， the model has the recognition rate improved to 52.74%， 58.82% and 62.98%， respectively. Compared with the Bidirectional Long Short-Term Memory （BLSTM） network model， the proposed model increases the recognition rate by 3.17 percentage points. And within the allowable error range of three levels， the proposed model has the recognition rate increased by 8.52 percentage points， 11.56 percentage points， and 12.78 percentage points， respectively. It can be seen that the proposed model can accurately parse the handwritten mathematical expression images， generate LaTeX sequences， and improve the recognition rate.

Non-negative Matrix Tri-Factorization （NMTF） is an important part of the latent factor model. Because this algorithm decomposes the original data matrix into three mutually constrained latent factor matrices， it has been widely used in research fields such as recommender systems and transfer learning. However， there is no research work on the interpretability of non-negative matrix tri-factorization. From this view， by regarding the user comment text information as prior knowledge， Partially Explainable Non-negative Matrix Tri-Factorization （PE-NMTF） algorithm was designed based on prior knowledge. Firstly， sentiment analysis technology was used by to extract the emotional polarity preferences of user comment text information. Then， the objective function and updating formula in non-negative matrix tri-factorization algorithm were changed， embedding prior knowledge into the algorithm. Finally， a large number of experiments were carried out on the Yelp and Amazon datasets for the cold start task of the recommender system and the AwA and CUB datasets for the image zero-shot task to compare the proposed algorithm with the non-negative matrix factorization and the non-negative matrix three-factor decomposition algorithms. The experimental results show that the proposed algorithm performs well on RMSE （Root Mean Square Error）， NDCG （Normalized Discounted Cumulative Gain）， NMI （Normalized Mutual Information）， and ACC （ACCuracy）， and the feasibility and effectiveness of the non-negative matrix tri-factorization were verified by using prior knowledge.

To meet the requirements for encrypting and decrypting speed of high-capacity file in high speed network, an Advanced Encryption Standard (AES) security model based on multi-core technology named MACBC was presented. By using the characteristics such as multi-level buffer and sharing memory of multi-core computer, MACBC split the high-capacity file into some data blocks that were encrypted and decrypted in multi-threads, under the condition of security guarantee and basically invariable memory space. The experimental results indicate that acceleration effect of this model is obvious. And the larger the capacity of file is, the higher the acceleration ratio is.