Aiming at the problem that the deraining methods based on tensor product wavelet cannot capture high-frequency rain streaks in all directions， a Dual U-Former Network （DUFN） based on non-separable lifting wavelet was proposed. Firstly， the isotropic non-separable lifting wavelet was used to capture high-frequency rain streaks in all directions. In this way， compared with tensor product wavelets such as Haar wavelet， which can only capture high-frequency rain streaks in three directions， DUFN was able to obtain more comprehensive rain streak information. Secondly， two U-Nets composed of Transformer Blocks （TBs） were connected in series at various scales， so that the semantic features of the shallow decoder were transferred to the deep stage， and the rain streaks were removed more thoroughly. At the same time， the scale-guide encoder was used to guide the coding stage by using the information of various scales in the shallow layer， and Gated Fusion Module （GFM） based on CBAM （Convolutional Block Attention Module） was used to make the fusion process put more focus on the rain area. Experimental results on Rain200H， Rain200L， Rain1200 and Rain12 synthetic datasets show that the Structure SIMilarity （SSIM） of DUFN is improved by 0.009 7 on average compared to that of the advanced method SPDNet （Structure-Preserving Deraining Network）. And on Rain200H， Rain200L and Rain12 synthetic datasets， the Peak Signal-to-Noise Ratio （PSNR） of DUFN is improved by 0.657 dB averagely. On real-world dataset SPA-Data， PSNR and SSIM of DUFN are improved by 0.976 dB and 0.003 1 respectively compared with those of the advanced method ECNetLL （Embedding Consistency Network+Layered Long short-term memory）. The above verifies that DUFN can improve the rain removal performance by enhancing the ability to capture high-frequency information.

With regard to the Location-Routing Problem （LRP） of domestic waste transfer stations and incineration stations， by considering the economic objective and the obnoxious effect of waste facilities， a piecewise function of obnoxious effect related to wind direction and distance was designed， a Two-Echelon Multi-Objective LRP （2E-MOLRP） model was formulated， and a non-dominated algorithm combining Whale Optimization Algorithm （WOA） and Simulated Annealing （SA） algorithm was proposed， namely WOA-SA. Firstly， the random method and Clarke and Wright （CW） saving algorithm were used to optimize the initial population. Secondly， a nonlinear dynamic inertia weight coefficient was adopted to adjust the convergence speed of the WOA-SA. Thirdly， the global optimization ability was enhanced by designing the parallel structure of WOA-SA. Finally， the Pareto solution set was obtained by using the non-dominated sorting method. The analysis was carried out on 35 benchmark cases such as Prins and Barreto as well as a simulated case of Tianjin. The results show that the WOA-SA can find the Best Known Solution （BKS） of 20 benchmark cases， and has the mean values of the difference between the solution results and the BKSs of 0.37% and 0.08% on Prins and Barreto cases， which proves the good convergence and stability of the WOA-SA. The proposed model and algorithm were applied to the instance， and provided three schemes with different obnoxious effect values and economic costs for decision makers with different decision preferences. Therefore， the cost of waste recycling and the obnoxious effect of facilities on environment were reduced.

Functional implementation of modern safety critical systems is increasingly dependent on software. As a result, software security is very important to system security, and the complexity of software makes it difficult to capture the dangers of component interactions by traditional security analysis methods. In order to ensure the security of safety critical systems, a software security verification method based on System Theoretic Process Analysis (STPA) was proposed. On the basis of the security control structure, by constructing the process model with software process model variables, the system context information of dangerous behavior occurrence was specified and analyzed, and the software security requirements were generated. Then, through the landing gear control system software design, the software security verification was carried out by the model checking technology. The results show that the proposed method can effectively identify the potential dangerous control paths in the software at the system level and reduce the dependence on manual analysis.

The critical issues in design of routing protocol for Wireless Sensor Network (WSN) are energy awareness and maximizing the lifetime. Focus on those challenges, a new routing algorithm named CTOR was proposed based on time synchronization sleeping schedule with low duty cycle. In CTOR, a node selected several proper forwarders in order to gain assured delivery ratio, and then broadcasted the packets to forwarders; in order to reduce replication packets and yield much gain efficiently, forwarders that received packets forwarded packets according to a probability. Then the sink node broadcasted control messages to make all nodes time synchronized, the other nodes turned into sleeping mode according to the permanent duty cycle. This mechanism can reduce the power consumption and hence the network can work longer. The experimental results show that CTOR can alleviate the routing hole problem, prolong the lifetime of the network and increase the delivery ration of packets.

In Web service matching, the concept similarity computation based on semantic distance plays an important role. Because the influence of semantic asymmetry and semantic density has not been considered in current concept similarity computation based on semantic distance, the computation result is not accurate. To enhance the accuracy of the concept similarity computation, the semantic distance-based concept similarity computation was improved by adding the asymmetry factor and density factor on the basis of the current research. Finally, the feasibility of the improved semantic distance-based concept similarity computation was verified via an example. According to the contrast analysis, the improved semantic distance-based concept similarity computation can reflect semantic relationship between concepts more truly and the computation result is more in line with the objective reality.