To solve the premature convergence problem of the basic Yin-Yang-Pair Optimization (YYPO) algorithm, the chaos search was introduced to the algorithm to explore more areas based on the ergodicity of chaos, so as to improve the global exploration capability. Besides, based on the intricate operator of I Ching, opposition-based learning was adopted to search for the opposite solutions to the current ones in order to improve the local exploitation ability. The design of parallel programming was also added to the algorithm to make full use of computing resources such as multi-core processors. Benchmark functions were used for numerical experiments to test the performance of the improved YYPO algorithm combined with chaos search and intricate operator, namely CSIOYYPO. Experimental results show that, compared with YYPO algorithms including basic YYPO algorithm and adaptive YYPO algorithm as well as other intelligent optimization algorithms, CSIOYYPO algorithm has higher calculation accuracy and higher convergence speed.

There are two problems to be solved in the traditional System Theoretic Process Analysis (STPA) method. One is the lack of automation means of realization, the other is the ambiguity problem caused by natural language result analysis. To solve these problems, a software safety requirement analysis and verification method based on STPA was proposed. Firstly, the software safety requirements were extracted and converted into formal expressions by the algorithm. Secondly, the state diagram model was built to describe the logic of software safety control behaviors and converted the logic into the readable formal language. Finally, the formal verification was carried out by model checking technology. The effectiveness of the method was verified by the case of a weapon launch control system. The results show that the proposed method can generate the safety requirements automatically and perform formal verification to them, avoid the dependence on manual intervention and solve the natural language description problems in traditional methods.

Focusing on the issue that the Road Side Unit (RSU) has inefficient response to the request of the vehicles in Vehicular Ad Hoc Network (VANET), a data scheduling algorithm based on Software Defined Network (SDN) architecture, namely SDDS, was proposed. Firstly, a graph of conflicting policies was generated based on status information of vehicles, and a maximum weighted independent set of the graph was solved to maximize the number of satisfied requests in current cycle. Secondly, the redundancy of data in vehicles was analyzed to figure out the optimum parameter, and a selection mechanism for collaborative vehicles was designed based on geographical position. Finally, the characteristics of handover vehicles and some factors that would affect the multi-RSU cooperation were analyzed, and a multi-RSU cooperation mechanism was put forward based on collision avoidance. In addition, a new evaluation indicator, service efficiency, was proposed to estimate the overall quality of service. Simulation results showed that compared with Most Requests First (MRF) and Cooperative Data Dissemination (CDD) algorithms, the service efficiency of SDDS algorithm was increased up to 15% and 20% respectively. The simulation results prove that SDDS algorithm can observably improve the sevice eficiency and quality of scheduling system.

The payload of Distribute Denial of Service (DDoS) attack changes drastically, the manual intervention of setting warning threshold relies on experience and the signature of abnormal traffic updates not timely, an improved DDoS attack detection algorithm based on Binary Stream Point Bag of Words (BSP-BoW) model was proposed. The Stream Point (SP) was extracted automatically from current network traffic data, the adaptive anomaly detection was carried out for different topology networks, and the labor cost was reduced by decreasing frequently updated feature set. Firstly, the mean clustering was carried out for the existing attack traffic and normal traffic to look for SP in the network traffic. Then, the original traffic was mapped to the corresponding SP for formalized expression by histogram. Finally, the DDoS was detected and classified by Euclidean distance. The experimental results on public database DARPA LLDOS1.0 show that, compared with Locally Weighted Learning (LWL), Support Vector Machine (SVM), Random Tree (RT), Logistic regression analysis (Logistic), Naive Bayes (NB), the proposed algorithm has higher recognition rate of abnormal network traffic. The proposed algorithm based on BoW model has the good recognition effect and generalization ability in abnormal network traffic recognition of denial of service attack, which is suitable for the deployment in the Small Medium Enterprise (SME) network traffic equipment.

The existing boundary prior based saliency algorithm model has the problem of improper selection of reasonable saliency prior region, which leads to the inaccurate foreground region and influence the final result. Aiming at this problem, a salient target detection algorithm based on contrast optimized manifold ranking was proposed. The image boundary information was utilized to find the background prior. An algorithm for measuring the priori quality was designed by using three indexes, namely, saliency expection, local contrast and global contrast. A priori quality design with weighted addition replaced simple multiplication fusion to make the saliency prior more accurate. When the salient regions were extracted from the a priori, the strategy of selecting the threshold was changed, the foreground region was selected more rationally, and the saliency map was obtained by using the manifold ranking, so that the saliency detection result was more accurate. The experimental results show that the proposed algorithm outperforms the similar algorithms, reduces the noise, which is more suitable for human visual perception, and ahead of the depth learning method in processing time.

The spatial pyramid matching method provides the spatial information by splitting an image into different cells. However, spatial pyramid matching can not match different parts of the objects well. A hierarchical image representation method based on Color Level (CL) was proposed. The class-specific discriminative colors of different levels were obtained from the viewpoint of feature fusion in CL algorithm, and then an image was iteratively split into different levels based on these discriminative colors. Finally, image representation was constructed by concatenating the histograms of different levels. To reduce the dimensionality of image representation, the Divisive Information-Theoretic feature Clustering (DITC) method was used to cluster the dictionary, and the generated compact dictionary was used for final image representation. Classification results on Soccer, Flower 17 and Flower 102 datasets, demonstrate that the proposed method can obtain satisfactory results in these datasets.

In order to reduce the relatively high probability of missed and false detection in traditional hard threshold setting algorithm, improve the anti-interference performance of Transform Domain Communication System (TDCS), an adaptive N-sigma amplitude spectrum shaping algorithm was proposed. The amplitude information of environment power spectrum was got according to spectrum sensing, the mean and standard deviation of the environment power spectrum were calculated. According to the correlation theory of normal distribution, the threshold was adaptively set. Therefore, when the electromagnetic environment changed, the mean and standard deviation would be readjusted and the threshold would be updated. The simulation results show that, compared to the traditional hard threshold setting algorithm, the threshold setting of the adaptive N-sigma amplitude spectrum shaping algorithm is more flexible and accurate, which can reduce the missed detection probability and false detection probability of interference and improve the overall anti-interference performance of the system.

In order to solve the subband decomposition problem of the wideband signal with a large spectrum scope, a new method for nun-uniform subband decomposition based on Power Spectral Density (PSD) was proposed to dynamically adjust the number and bandwidth of subband, reasonably control the autocorrelation matrix eigenvalue spread of subband signals and improve the performance and efficiency of subband signal processing. For a given sequence, the number of subbands and the spectrum range of subband were determined through power spectrum estimation. Then the subband signal was shifted to zero frequency using subband modulation to achieve signal decomposition. The eigenvalue spread of subband signal and signal reconstruction performance were analyzed using Matlab. The experimental results show that, compared with the existing methods with equal number of subbands, the proposed one using the distribution information of PSD can effectively control the eigenvalue spread of subband while maintaining well signal reconstruction performance.

The existing methods of constructing Voronoi diagram have low efficiency and high complexity, to remedy the disadvantages, a new method of constructing and updating Voronoi diagram based on the hybrid methods was given to query the nearest neighbor of the given spatial data effectively, and a new method of searching the nearest neighbor based on Voronoi diagram and the minimum inscribed circle was presented. To deal with the frequent, changes of the query point position, the method based on Voronoi diagram and the minimum bounding rectangle was proposed. To improve the efficiency of the dual nearest neighbor pair and closest pair query, a new method was given based on Voronoi polygons and their minimum inscribed circles. The experimental results show that the proposed methods reduce the additional computation caused by the uneven distribution of data and have a large advantage for the big dataset and the frequent query.

The existing methods for backbone networks only analyze coarse-grained network traffic characteristic parameters. Thus, they cannot guarantee both the premise of abnormal flow identification and the real-time detection for DoS (Denial of Service) & DDoS (Distributed Denial of Service, DDoS) attacks. Concerning this problem, a DoSⅅoS attack detection and abnormal flow identification method for backbone networks was proposed. First, it analyzed coarse-grained network traffic characteristic parameters to determine the time points that abnormal behaviors occur; then, fine-grained traffic behavior characteristic parameters were analyzed in these time points to find the destination IP addresses that correspond to abnormal behaviors; finally, comprehensive analysis was conducted for extracted traffic that correspond to abnormal behaviors to determine DoS and DDoS attacks. The simulation results show that, the proposed method can effectively detect DoS attacks and DDoS attacks in backbone networks. Meanwhile, it could accurately identify the abnormal traffic, while real-time detection is ensured.

In order to reduce the repeated traversal times of path in the FP-tree, the conditional pattern bases of all frequent 1-itemsets in the FP-tree need to be saved in the existing algorithms. Concerning this problem, in the new algorithm, the data structure of FP-tree was improved that only the conditional pattern bases were saved which were constituted by the items in the path from every leaf node' parents to the root in the FP-tree, and 〖BP(〗the improved FP-tree could reduc 〖BP)〗the storage space of the conditional pattern bases was reduced. After studying search space and the method of data representation in the algorithm for mining maximal frequent itemsets, the pruning and compression strategies were developed through theoretical analysis and verification, which could decrease the search space and the scale of FP-tree. Finally, the new algorithm was compared with NHTFPG algorithm and FpMAX algorithm respectively in terms of accuracy and efficiency. The experimental results show that the new FP-tree algorithm saves the required conditions for model-based storage space more than 50% than NHTFPG algorithm, and the efficiency ratio improves by 2 to 3 times than FpMAX algorithm.

To realize the building and controlling of workflow patterns involving multiple instances, a solution was proposed from the perspective of network partition. The implementing method was discussed based on RTWD net proposed by HU Fei-hu, et al. in Patent China 201010114083.9. First, the sub-workflows involving multiple instances should be divided into a subnet. Then the related parameters of multiple instances were defined, and multiple instances were controlled based on it. The paper discussed the controlling of sequential, synchronous and asynchronous parallel workflow patterns involving multiple instances based on the method. Because the divided subnet keeps consistent with the definition of workflow model, multiple instances can be scheduled by original workflow engine, which simplifies the realization of multiple instance patterns.

A peer-to-peer (P2P) E-mail architecture was presented. It eliminated dependence on a single center server, and provided confidential E-mail service for all users. Its basic mechanisms of sending and reading E-mail were implemented in our design. Its prototype was implemented and discussed.

UML is an object-oriented modeling language, and has been widely applied to software developing driven by Use-case and centered on architecture structure. The idea of UML object-oriented analysis and design was described which was applied to the development of IFIS intelligent fingerprint identification system, including requirement analysis, system design and system application.