To address the problems of insufficient interpretability and long sequence dependency in the deep knowledge tracing model based on Recurrent Neural Network （RNN）， a model named Temporal Convolutional Knowledge Tracing with Attention mechanism （ATCKT） was proposed. Firstly， the student historical interactions embedded representations were learned in the training process. Then， the exercise problem-based attention mechanism was used to learn a specific weight matrix to identify and strengthen the influences of student historical interactions on the knowledge state at each moment. Finally， the student knowledge states were extracted by Temporal Convolutional Network （TCN）， in which dilated convolution and deep neural network were used to expand the scope of sequence learning， and alleviate the problem of long sequence dependency. Experimental results show that compared with four models such as Deep Knowledge Tracing （DKT） and Convolutional Knowledge Tracing （CKT） on four datasets （ASSISTments2009、ASSISTments2015、Statics2011 and Synthetic-5）， ATCKT model has the Area Under the Curve （AUC） and Accuracy （ACC） significantly improved， especially on ASSISTments2015 dataset， with an increase of 6.83 to 20.14 percentage points and 7.52 to 11.22 percentage points respectively， at the same time， the training time of the proposed model is decreased by 26% compared with that of DKT model. In summary， this model can accurately capture the student knowledge states and efficiently predict student future performance.

To address the privacy leakage problem caused by the model parameters memorizing some features of the data during the training process of the Convolutional Neural Network （CNN） models， a Gradient Descent with Momentum algorithm based on Differential Privacy in CNN （DPGDM） was proposed. Firstly， the Gaussian noise meeting differential privacy was added to the gradient in the backpropagation process of model optimization， and the noise-added gradient value was used to participate in the model parameter update process， so as to achieve differential privacy protection for the overall model. Secondly， to reduce the impact of the introduction of differential privacy noise on convergence speed of the model， a learning rate decay strategy was designed and then the gradient descent with momentum algorithm was improved. Finally， to reduce the influence of noise on the accuracy of the model， the value of the noise scale was adjusted dynamically during model optimization， thereby changing the amount of noise that needs to be added to the gradient in each round of iteration. Experimental results show that compared with DP-SGD （Differentially Private Stochastic Gradient Descent） algorithm， the proposed algorithm can improve the accuracy of the model by about 5 and 4 percentage points at privacy budget of 0.3 and 0.5， respectively， proving that by using the proposed algorithm， the model usability is improved and privacy protection of the model is achieved.

Concerning the defect that the traditional filtering window cannot be adaptively extended and the standard mean filter algorithm could blur edges easily, a new adaptive weighted mean filtering algorithm based on city block distance was proposed. First, the noise points can be detected with switch filtering ideas. Then, for each noise point, the window was extended according to the city block distance, and the window size was adaptively adjusted based on the number of signal points within the window. Last, the weighted mean of the signal points in the window was taken as the gray value of the noise points to achieve the effective recovery of the noise points. The experimental results show that the algorithm can effectively filter out salt-and-pepper noise, especially for the larger-noise-density image, and denoising effect is more significant.

The functional model and architecture of collaborative design system for automatic weapon were analyzed. A unified module template instance and its information access relationship model of automatic weapon were presented to achieve practical collaborative design. The proposed system includes configuration management, model-lib management, integrated service model, hierarchical collaborative design process model and cooperative behavior prediction and analysis module which assist collaborative design of modularized products. Combining the API functions development technology of CAD software and the supports of Web services technology with common language interface, the system has been implemented in reliable distributed interactive supporting environment.