Non-Intrusive Load Monitoring （NILM） technology provides technical support for demand side management， and non-intrusive load identification is the key link in the process of load monitoring. The long-term sampling process of load data cannot be carried out in real time and high frequency， and the time sequence of the obtained load data is lost. At the same time， the defect of insufficient representation of low-level signal features occurs in Convolution Neural Network （CNN）. In view of the above two problems， a CNN based non-intrusive load identification algorithm with upsampling pyramid structure was proposed. In the proposed algorithm， with direct orientation to the collected load current signals， the time sequence of the data was compensated by the relevant information in the time dimension of the upsampling network expanded data， and the high-level and low-level features of load signals were extracted by the bidirectional pyramid one-dimensional convolution， so that the load characteristics were fully utilized. As a result， the purpose of identifying unknown load signals can be achieved. Experimental results show that the recognition accuracy of non-intrusive load identification algorithm based on CNN with upsampling pyramid structure can reach 95.21%， indicating that the proposed algorithm has a good generalization ability， and can effectively realize load identification.

To solve the problem of image reconstruction of incomplete projection data from cone-beam CT, a fast cone-beam CT reconstruction algorithm was proposed. In this work, the cone-beam CT reconstruction problem was reduced to an unconstrained optimization problem of minimizing an objective function which included a squared error term combined with a sparseness-inducing regularization term. The Lipschitz continuity of the objective function was analyzed and the Lipschitz constant was estimated based on its definition. The gradient descent step-size was calculated by the Lipschitz constant and the reconstructed image was updated by gradient method. Finally simultaneous algebraic reconstruction technique was used to reconstruct image from limited-angle projections and to meet the constraint of the projection data. An adaptive step-size technique was accommodated as so to accelerate the convergence of proposed algorithm. Simulation with noiseless Shepp-Logan shows: In comparison with simultaneous algebraic reconstruction technique, adaptive steepest descent-projection onto convex sets algorithm and gradient-projection Barzilari-Borwein algorithm, the proposed algorithm has higher SNR (Signal-to-Noise Ratio) by 13.7728dB, 12.8205dB, and 7.3580dB respectively. The algorithm has better performance in convergence speed and reconstruction accuracy, and can greatly improve the quality of images reconstructed from few projection data.

For the deficiency of the existing network virus models with immunization, considering the infectious disease model in biology, a Susceptible-Infected-Recovered-Susceptible (SIRS) computer virus propagation model with stage immunization was formulated. The varying probability of being vaccinated when the threshold was reached and its impact on the spread of the virus in the network were considered. Furthermore, with the help of the theory of dynamic stability analysis, the existence and stability conditions of equilibriums were studied. The numerical simulation results illustrate that improving the rate of vaccination and setting a reasonable threshold can effectively constrain virus prevalence in the network.