In temporal networks with time attributes， conventional network motif discovery methods based on frequent subgraph statistics are easily affected by the differences in network size and structure. And an accurate benchmark for characteristic mining of empirical network can be provided by the null model network with same scale and some properties of the empirical network. Therefore， a temporal network motif discovery method based on null model was proposed to use relative values after comparing the features of the two network subgraphs to identify the subgraphs with significant structural meaning in temporal networks. At the same time， in order to determine when null model network reached stability， the method of successful scrambling times was adopted to improve the temporal network’s null model construction methods based on time scrambling or time randomization. In experiment stage， simulations were conducted on 46-node Global Positioning System （GPS） constellation containing satellites and ground stations， the number of successful scrambles times when the subgraph features of null model network reached stability was determined. Ten null model networks were constructed and compared with the satellite network. It was found that the number of occurrences of subgraph reflecting the continuity characteristics of node connection is only 1/34 of that of the subgraph with the highest frequency， but the former subgraph is the most important motif in the satellite network. Experimental results show that the temporal network’s motif discovery method with null model as reference can identify motifs that reflects network structural characteristics and dynamic change process more accurately.

A Filtered Back-Projection （FBP） ultrasonic tomography reconstruction algorithm based on sparse representation was proposed to solve the difficulty of traditional ultrasonic Lamb wave in detecting and vividly describing the delamination defects composite materials. Firstly， the Lamb wave time-of-flight signals in the composite plate with defect were used as the projection values， the one-dimensional Fourier transform of the projection was equivalent to the two-dimensional Fourier transform of the original image， and the FBP reconstructed image was obtained by convolution with the filter function and projection along different directions. Then， the sparse super-resolution model was constructed and jointly trained by constructing a dictionary of low-resolution image blocks and high-resolution image blocks in order to strengthen the sparse similarity between low- and high-resolution blocks and real image blocks， and a complete dictionary was constructed using low- and high-resolution blocks. Finally， the images obtained by FBP were substituted into the constructed dictionary to obtain the complete high-resolution images. Experimental results show that the proposed algorithm improves Peak Signal-to-Noise Ratio （PSNR）， Structural Similarity （SSIM）， and Edge Structural Similarity （ESSIM） values in the reconstructed image by 9.22%， 2.90%， 80.77%， and 4.75%， 1.52%， 16.5%， respectively compared with the linear interpolation and bicubic spline interpolation algorithms. The proposed algorithm can detect delamination defects in composite materials， improve the resolution of the obtained images with delamination defects and enhance the edge details of the images.

A multi-stage low-illuminance image enhancement network based on attention mechanism was proposed to solve the problem that the details of low-illuminance images are lost due to the overlapping of image contents and large brightness differences in some regions during the enhancement process of low-illuminance images. At the first stage， an improved multi-scale fusion module was used to perform preliminary image enhancement. At the second stage， the enhanced image information of the first stage was cascaded with the input of this stage， and the result was used as the input of the multi-scale fusion module in this stage. At the third stage， the enhanced image information of the second stage was cascaded with the input of the this stage， and the result was used as the input of the multi-scale fusion module in this stage. In this way， with the use of multi-stage fusion， not only the brightness of the image was improved adaptively， but also the details were retained adaptively. Experimental results on open datasets LOL and SICE show that compared to the algorithms and networks such as MSR （Multi-Scale Retinex） algorithm， gray Histogram Equalization （HE） algorithm and RetinexNet （Retina cortex Network）， the proposed network has the value of Peak Signal-to-Noise Ratio （PSNR） 11.0% to 28.9% higher， and the value of Structural SIMilarity （SSIM） increased by 6.8% to 46.5%. By using multi-stage method and attention mechanism to realize low-illuminance image enhancement， the proposed network effectively solves the problems of image content overlapping and large brightness difference， and the images obtained by this network are more detailed and subjective recognizable with clearer textures.

First, the maintenance spare parts supply process of a two-echelon support system under cannibalization was studied, and the system availabilities under three strategies including non-cannibalization strategy, cannibalization strategy and partial cannibalization strategy were analyzed. Based on this, the inventory optimization model was established. The system availability was used as the constraint while the minimize support costs were taken as optimization objective, and the marginal analysis method was selected to solve the model. The results by comparing to different strategies indicate that taking cannibalization strategy can reduce inventory and total costs under minimum availability, and can effectively improve equipment availability under certain inventory.