The K-Means algorithms typically utilize Euclidean distance to calculate the similarity between data points when dealing with large-scale heterogeneous data. However， this method has problems of low efficiency and high computational complexity. Inspired by the significant advantage of Hamming distance in handling data similarity calculation， a Quantum K-Means Hamming （QKMH） algorithm was proposed to calculate similarity. First， the data was prepared and made into quantum state， and the quantum Hamming distance was used to calculate similarity between the points to be clustered and the K cluster centers. Then， the Grover’s minimum search algorithm was improved to find the cluster center closest to the points to be clustered. Finally， these steps were repeated until the designated number of iterations was reached or the clustering centers no longer changed. Based on the quantum simulation computing framework QisKit， the proposed algorithm was validated on the MNIST handwritten digit dataset and compared with various traditional and improved methods. Experimental results show that the F1 score of the QKMH algorithm is improved by 10 percentage points compared with that of the Manhattan distance-based quantum K-Means algorithm and by 4.6 percentage points compared with that of the latest optimized Euclidean distance-based quantum K-Means algorithm， and the time complexity of the QKMH algorithm is lower than those of the above comparison algorithms.

Aiming at the fact that the existing video person re-identification methods cannot effectively extract the spatiotemporal information between consecutive frames of the video, a person re-identification network based on non-local attention and multi-feature fusion was proposed to extract global and local representation features and time series information. Firstly, the non-local attention module was embedded to extract global features. Then, the multi-feature fusion was realized by extracting the low-level and middle-level features as well as the local features, so as to obtain the salient features of the person. Finally, the similarity measurement and sorting were performed to the person features in order to calculate the accuracy of video person re-identification. The proposed model has significantly improved performance compared to the existing Multi-scale 3D Convolution (M3D) and Learned Clip Similarity Aggregation (LCSA) models with the mean Average Precision (mAP) reached 81.4% and 93.4% respectively and the Rank-1 reached 88.7% and 95.3% respectively on the large datasets MARS and DukeMTMC-VideoReID. At the same time, the proposed model has the Rank-1 reached 94.8% on the small dataset PRID2011.

To solve the problem of commodity recommendation based on network information, a recommendation algorithm based on community mining and label propagation on bipartite network was proposed. Firstly, a weighted bipartite graph was used to represent the user-item scoring matrix, and the label propagation technology was adopted to perform the community mining to the bipartite network. Then, the items which the users might be interested in were mined based on the community structure information of the bipartite network and by making full use of the similarity between the communities that the users in as well as the similarity between items and the similarity between the users. Finally, the item recommendation was performed to the users. The experimental results on real world networks show that, compared with the Collaborative Filtering recommendation algorithm based on item rating prediction using Bidirectional Association Rules (BAR-CF), the Collaborative Filtering recommendation algorithm based on Item Rating prediction (IR-CF), user Preferences prediction method based on network Link Prediction (PLP) and Modified User-based Collaborative Filtering (MU-CF), the proposed algorithm has the Mean Absolute Error (MAE) 0.1 to 0.3 lower, and the precision 0.2 higher. Therefore, the proposed algorithm can obtain recommendation results with higher quality compared to other similar methods.

Since some computation in reachability Query Preserving Graph Compression （QPGC） algorithm are redundant， a high-performance compression strategy was proposed. In the stage of solving the vertex sets of ancestors and descendants， an algorithm named TSB （Topological Sorting Based algorithm for solving ancestor and descendant sets） was proposed for common graph data. Firstly， the vertices of the graph data were topological sorted. Then， the vertex sets were solved in the order or backward order of the topological sequence， avoiding the redundant computation caused by the ambiguous solution order. And an algorithm based on graph aggregation operation was proposed for graph data with short longest path， namely AGGB （AGGregation Based algorithm for solving ancestor and descendant sets）， so the vertex sets were able to be solved in a certain number of aggregation operations. In the stage of solving reachability equivalence class， a Piecewise Statistical Pruning （PSP） algorithm was proposed. Firstly， piecewise statistics of ancestors and descendants sets were obtained and then the statistics were compared to achieve the coarse matching， and some unnecessary fine matches were pruned off. Experimental results show that compared with QPGC algorithm： in the stage of solving the vertex sets of ancestors and descendants， TSB and AGGB algorithm have the performance averagely increased by 94.22% and 90.00% respectively on different datasets； and in the stage of solving the reachability equivalence class， PSP algorithm has the performance increased by more than 70% on most datasets. With the increasing of the dataset， using TSB and AGGB cooperated with PSP has the performance improved by nearly 28 times. Theoretical analysis and simulation results show that the proposed strategy has less redundant computation and faster compression speed compared to QPGC.

Social network node classification is widely used in solving practical problems. Most of the existing network node classification algorithms focus on unsigned social networks,while node classification algorithms on social networks with symbols on edges are rare. Based on the fact that the negative links contribute more on signed network analysis than the positive links. The classification of nodes on signed networks was studied. Firstly, positive and negative networks were projected to the corresponding latent spaces, and a mathematical model was proposed based on positive and negative links in the latent spaces. Then, an iterative algorithm was proposed to optimize the model, and the iterative optimization of latent space matrix and projection matrix was used to classify the nodes in the network. The experimental results on the dataset of the signed social network show that the F1 value of the classification results by the proposed algorithm is higher than 11 on Epinions dataset, and that is higher than 23.8 on Slashdo dataset,which indicate that the proposed algorithm has higher accuracy than random algorithm.

A Quantum-inspired Migrating Birds Co-Optimization (QMBCO) algorithm was proposed for minimizing the makespan in Lot-streaming Flow shop Scheduling Problem (LFSP). Firstly, the quantum coding based on Bloch coordinates was applied to expand the solution space. Secondly, an initial solution improvement scheme based on Framinan-Leisten (FL) algorithm was used to makeup the shortage of traditional initial solution and construct the random initial population with high quality. Finally, Migrating Birds Optimization (MBO) and Variable Neighborhood Search (VNS) algorithm were applied for iteration to achieve the information exchange between the worse individuals and superior individuals in proposed algorithm to improve the global search ability. A set of instances with different scales were generated randomly, and QMBCO was compared with Discrete Particle Swarm Optimization (DPSO), MBO and Quantum-inspired Cuckoo Co-Search (QCCS) algorithms on them. Experimental results show that compared with DPSO, MBO and QCCS, QMBCO has the Average Relative Percentage Deviation (ARPD) averagely reduced by 65%, 34% and 24% respectively under two types of running time, verifying the effectiveness and efficiency of the proposed QMBCO algorithm.

When traditional binary-coded evolutionary algorithms are applied to optimize functions, the mutual interference between different dimensions would prevent effective restructuring of some low-order modes. A new evolutionary algorithm, called Dynamic Chromosomal Translocation-based Evolutionary Algorithm (CTDEA), was proposed based on cytological findings. This algorithm simulated the structuralized process of organic chromosome inside cells by constructing gene matrixes, and realized modular translocations of homogeneous chromosomes on the basis of gene matrix, in order to maintain the diversity of populations. Moreover, the individual fitness-based population-dividing method was adopted to safeguard elite populations, ensure competitions among individuals and improve the optimization speed of the algorithm. Experimental results show that compared with existing Genetic Algorithm (GA) and distribution estimation algorithms, this evolutionary algorithm greatly improves the population diversity, keeping the diversity of populations around 0.25. In addition, this algorithm shows obvious advantages in accuracy, stability and speed of optimization.

Concerning the problem that the network access control of Virtual Machines (VM) in the cloud computing Infrastructure as a Service (IaaS) platforms, a method of communication access control for VM in IaaS platforms was proposed. The method based on Software Defined Networking (SDN) was realized to customize the communication access control rules from Layer 2 to Layer 4. The experimental results show that the method can manage communication access permissions of tenants' VM flexibly, and ensure the security of tenants' network.

Aiming at how to effectively monitor the network abnormal events, find the bottleneck of network performance and potential point of failure in large-scale data center network, based on the deep analysis of the characteristics of InfiniBand (IB) network and introducing the feature selection strategy and incremental learning strategy, an incremental learning method of fault diagnosis for large-scale IB network (IL_Bayes) which based on the Bayes classification and added incremental learning mechanism was proposed. It could effectively improve the accuracy of fault classification. Through testing and verifying the diagnostic accuracy and the rate of misdiagnosis of this method in the Tianhe-2's real network environment, the result shows that the IL_Bayes method has higher classification accuracy and lower misdiagnosis rate.

In order to solve the problem that network intrusion detection was weak in training speed, real-time process and high false positive rate when dealing with big data, a Dendritic Cell TWin Support Vector Machine (DCTWSVM) approach was proposed. The Dendritic Cell Algorithm (DCA) was firstly used for the basic intrusion detection, and then the TWin Support Vector Machine (TWSVM) was applied to optimize the first step detection outcome. The experiments were carried out for testing the performance of the approach. The experimental results show that DCTWSVM respectively improves the detection accuracy by 2.02%, 2.30%, and 5.44% compared with DCA, Support Vector Machine (SVM) and Back Propagation (BP) neural network, and reduces the false positive rate by 0.26%, 0.46%, and 0.90%. The training speed is approximately twice as the SVM, and the brief training time is another advantage. The results indicate that the DCTWSVM is suitable for the comprehensive intrusion detection environment and helpful to the real-time intrusion process.

For large-scale data center network, how to monitor the network effectively, discover the bottleneck of network performance and potential point of failure, provide support for optimization of network performance becomes the new research subject. However, there are many factors which affect the network performance, and there are differences in the influence of performance factors. How to give an accurate performance evaluation has been a difficult problem. To solve these problems, a network performance evaluation index system was proposed in this paper. On this basis, a method for evaluating the network performance of data centers based on objective weight (PE-OWD) was put forward. By using the method of objective weight determination, the dynamic calculation of the weights of the indexes was adopted. And using the data normalization method based on historical parameters, a perfect network performance evaluation model was established. For the Tianhe2 real network environment, the performance indexes of the network equipment were evaluated, and the validity of the method for evaluating the network performance was verified.

To analyze the abnormal behavior of program induced by software defects, a characterization method of program behavior was proposed firstly, and then the baseline behavior and fault behavior of program got defined and formally described. A quantitative approach to represent the fault behavior of program was proposed afterwards. Furthermore, a Program Fault Behavior Analysis (PFBA) was delivered and implemented, which selected system-call as state granularity of program behavior. Based on specific embedded benchmarks, the experiment was followed through with fault injection method to obtain early-described indices of fault behavior. The experimental results show that there exists a difference among program behaviors under each individual fault type. Based on an in-depth analysis, it is demonstrated that the diversity of fault behaviors is induced by algorithm implementations and structural characteristics of embedded program themselves. Therefore, the analysis of fault behavior presented here can reveal the characteristics of embedded program response behavior under specific software defects, as well as providing important feedback to the process of program development.

Several problems exist in trawling algorithm, such as too many Web communities, high repetition rate between community-cores and isolated community formed by strict definition of community. Thus, an algorithm detecting Blog community based on Formal Concept Analysis (FCA) was proposed. Firstly, concept lattice was formed according to the linkage relations between Blogs，then clusters were divided from the lattice based on equivalence relation, finally communities were clustered in each cluster based on the similarity of concepts. The experimental results show that, the community-cores, which network density is greater than 40%, occupied 83.420% of all in testing data set, the network diameter of combined community is 3, and the content of community gets enriched significantly. The proposed algorithm can be effectively used to detect communities in Blog, micro-Blog and other social networks, and it has significant application value and practical meaning.

In order to solve the problems of high hardware cost and information isolated island in the constructing and operating of atmospheric monitoring network system, this paper proposed an atmospheric monitoring network system based on Microsoft's Windows Azure cloud computing platform. This system provided access for automatic weather stations by General Packet Radio Service (GPRS) network technology, adopted Microsoft's Blob, Table storage and SQL Azure database to store massive data, developed and established Web portal on Windows Azure platform with ASP.NET and Flash technology, realized a unified access platform for users. The results demonstrate that this system can effectively solve the problem and create constructive value for establishing atmospheric monitoring network system in various ranges.

Through analyzing the characteristics of the edge sets of the optimal solutions from Traveling Salesmen Problem (TSP), a kind of new model was proposed to produce the referring optimization edge sets for Lin-Kernighan algorithm on the basis of authors' previous research (WANG DONG, WU XIANG-BIN. Strategy for improving the performance of chained Lin-Kernighan algorithm. Journal of Computer Applications, 2007,27(11): 2826-2829). The number in the edge sets produced by the new model is less than those produced by normal algorithms or previous research. Meanwhile, the new edge sets include more edges that belong to the global optimal solution than them. Applying the new model to Lin-Kernighan algorithm, the execution time of the algorithm is further reduced, without losing the algorithm accuracy for a single call. Furthermore, the solution performance of Lin-Kernighan algorithm is improved also. With previous research achievement, the performance of all hybrid algorithms using Lin-Kernighan algorithm as the local search algorithm could be improved too.

Micro-Doppler signature produced by micro-motion contains movement and structure information, which is useful for radar classification and recognition. In this paper, complex micro-motion scattering point with rotation and acceleration was modeled. Based on a quantitative analysis on micro-Doppler modulation, the acceleration, rotational frequency and rotational radius were estimated by peak value extraction method which extracted time-frequency analysis matrix maximum along the frequency and least square fitting straight line method. Finally, the simulation results verify the correctness of the theoretical analysis and the validity of the parameter estimation.

Anti-collision protocol in EPC Gen2 protocol is flexible, so reasonable algorithm can evidently improve the system performance. Based on the research of its Q value adjusting method and the process of this important protocol, a new Q value adjusting method and an improved slotted random Aloha method were proposed. The simulation results show that the improved algorithms can increase the system throughput and lower the delay of tag identification, showing a good performance.

An adaptive multiple colony ant algorithm was presented to solve frenquency assignment problem of mobile communicaiton. Unlike the traditional ant colony algorithm which used only one ant colony, our algorithm used multiple ant colonies. For each ant colony, a coefficient of convergence was defined by which the ants adaptively could choose the path, update their local pheromone and exchange information between colonies. By using the adaptive strategy to update the pheromone, the balance between the diversity and convergence of every ant colony was kept. The simulation results on the fixed frequency assignment problem and minimal span frequency assignment problem show that our algorithm has global convergence and higher speed of optimization.