To solve the problem that the current graph summarization methods have high compression ratios and the graph compression algorithms cannot be directly used in downstream tasks， a fusion algorithm of graph summarization and graph compression was proposed， which called Graph Summarization algorithm based on Node Similarity grouping and graph Compression （GSNSC）. Firstly， the nodes were initialized as super nodes， and the super nodes were grouped according to the similarity. Secondly， the super nodes of each group were merged until the specified number of times or nodes were reached. Thirdly， super edges and corrected edges were added between the super nodes for reconstructing the original graph. Finally， for the graph compression part， the cost of compressing and summarizing the adjacent edges of each super node were judged， and the less expensive one in these two was selected to execute. Experiments of graph compression ratio and graph query were conducted on six datasets such as Web-NotreDame， Web-Google and Web-Berkstan. Experimental results on six datasets show that， the proposed algorithm has the compression ratio reduced by at least 23 percentage points compared with SLUGGER （Scalable Lossless sUmmarization of Graphs with HiERarchy） algorithm， and the compression ratio decreased by at least 13 percentage points compared with SWeG （Summarization of Web-scale Graphs） algorithm. Experimental results on Web-NotreDame dataset show that the degree error of the proposed algorithm is reduced by 41.6% compared with that of SWeG algorithm. The above verifies that the proposed algorithm has better graph compression ratio and graph query accuracy.

Concerning the problem that existing availability models of Infrastructure as a Service (IaaS) are difficult to calculate the probability of the existence of multiple available Physical Machines (PMs), a new availability analysis method based on Markov process was proposed for IaaS clouds. Firstly, the computing resources were divided into three types:hot PM, warm PM and cold PM. Then, the impact of availability was modeled by combining the corresponding stages of the resource allocation process, separately generating three kinds of allocation sub-models. These sub-models cooperated with each other through the transformation relationships of different types of computing resources, so as to construct the overall model of the system. After that, the availability model was solved by equations constructed based on Markov process. Finally, the proposed analysis model was verified with a practical example, and the key factors such as PM transition rate were analyzed. Experimental results show that, increasing the number of PMs, especially cold PMs helps to improve the availability of IaaS. The proposed method can be used to estimate the probability of the existence of one or multiple available PMs.

With the development of the application of computer vision in the field of marine fisheries, fish image retrieval has played a huge role in fishery resource survey and fish behavior analysis. It is found that the background information of fish images can greatly interfere with fish image retrieval, and the fish image retrieval results only using color, texture, shape and other characteristics of fish images are not accurate due to the lack of spatial position information. To solve the above problems, a novel fish image retrieval algorithm based on HSVG (Hue, Saturation, Value, Gray) four-channel and spatial pyramid was proposed. Firstly, a visual saliency map was extracted to separate the foreground and the background, thereby reducing the interference of the image background on the retrieval. Then, in order to contain certain spatial position information, the fish image was converted into an HSVG four-channel map, and on this basis, the theory of spatial pyramid was used to segment the image and extract the SURF (Speed Up Robust Feature). Finally, the search results were obtained. In order to verify the effectiveness of the proposed algorithm, the recall and precision of the algorithm were compared with classic HSVG algorithm and saliency block algorithm on QUT_fish_data dataset and DLOU_fish_data dataset. Compared with traditional HSVG algorithm, the precision on two datasets is increased at most by 12% and 5%, and the recall is increased at most by 7% and 22%, respectively. Compared with saliency block algorithm, the precision on two datasets is increased at most by 15% and 5%, and the recall is increased at most by 36% and 22%, respectively. So, the proposed algorithm is effective and can improve the retrieval results significantly.

To solve the low efficiency problem of data retrieval in indoor three-dimensional (3D) maps, an indoor 3D map data retrieval method based on octree was proposed. Firstly, the data was stored according to the octree segmentation method. Secondly, the data was encoded to facilitate addressing. Thirdly, the search data was filtered by adding a room interval constraint to the data. Finally, the indoor map data was retrieved. Compared with the search method without constraints, the search cost of the proposed method was reduced by 25 percentage points on average, and the search time was more stable. Therefore, the proposed method can significantly improve the application efficiency of indoor 3D map data.

Delay Tolerant Network (DTN) has characteristics of dynamic topology changes and limited node storage space. A DTN Epidemic Routing with Congestion Control strategy (ERC²) method was proposed. The method was based on a Dynamic Storage State Model (DSSM). According to sensing network conditions, the threshold of node's semi-congested state was dynamically adjusted to reduce the possibility of network congestion by nodes. The ACK index and message management queue were added to make node storage state change randomly with network load, dynamically update and actively delete redundant packages. Single or mixed mode was selected for message forwarding according to different congestion states combining with advantages of Epidemic and Prophet routing, so as to achieve the purpose of preventing, avoiding and canceling congestion, realizing adaptive buffer management of nodes and dynamically controlling congestion of network. Simulations were conducted on the ONE(Opportunistic Networking Environment) platform using Working Day Movement (WDM) model. In the simulation, ERC² was 66.18% higher than Prophet in message delivery rate. The average latency of ERC² was decreased by 48.36%, and the forwarding number was increased by 22.83%. The simulation results show that ERC² has better network performance than Epidemic and Prophet routing algorithms in scenarios with different levels of congestion.

The precision of user identification is low since the subjective weighting algorithms ignore the special meanings and effects of attributes in applications. To solve this problem, an Information Entropy based Multiple Social Networks User Identification Algorithm (IE-MSNUIA) was proposed. Firstly, the data types and physical meanings of different attributes were analyzed, then different similarity calculation methods were correspondingly adopted. Secondly, the weights of attributes were determined according to their information entropies, thus the potential information of each attribute could be fully exploited. Finally, all chosen attributes were integrated to determine whether the account pair was the matched one. Theoretical analysis and experimental results show that, compared with machine learning based algorithms and subjective weighting algorithms, the performance of the proposed algorithm is improved, on different datasets, the average precision of it is up to 97.2%, the average recall of it is up to 94.1%, and the average comprehensive evaluation metric of it is up to 95.6%. The proposed algorithm can accurately identify user accounts across multiple social networks.

Extreme Learning Machine (ELM) has become a new method in soft-sensing due to its good generalization and fast training speed. However, ELM often needs more hidden layer nodes and its generalization is reduced in the parameter modeling for aluminum electrolysis production process. To solve the problem, a soft-sensing model based on Improved Extreme Learning Machine (IELM) was proposed. Firstly, rough set theory was applied to reduce the unnecessary, unrelated or reductant input variables, reducing the complexity of ELM input. After analyzing the relationship between the input variables and output variables by partial correlation coefficient, the input data was divided into two parts, namely the positive part and the negative part. Then, the corresponding ELM model was built according to the two parts. Finally, the soft-sensing model of molecular ratio based on the improved ELM was built. The simulation experimental results show that the soft-sensing model based on the IELM has better generalization and stability.

Focusing on the data lost or unavailable reference in cloud storage, a secure cloud storage method based on Three-Dimensional model (TD-model)was proposed. Firstly, base nodes of TD-model method were formed by encoding the data, which would be stored uniformly into two opposite sides in the TD-model. Secondly, normal nodes were formed in each side by mathematical computing, and the nodes of each side ensure connection. Finally, high data availability was achieved by the correlation of all the six sides. The experimental results show that compared with the traditional replica storage methods, the secure cloud storage method based on TD-model enhances data recovery efficiency and ensures data integrity. In addition, the proposed method can overcome the drawback of traditional methods that only the single node failure can be recovered.

With the increase of High Performance Computation (HPC) system scale, it's very important to increase the efficiency of the checkpoint. A model to compute the quasi-optimal period for hierarchical checkpoint protocol was proposed. First, the execution of an application in HPC system was assessed, and checkpoint period optimization problem was abstracted as the nonlinear checkpoint cost model. Second, the hierarchical checkpoint cost formula was derived by simulating the possible fault location; two deceleration parameters and an acceleration parameter were introduced to reflect the impact of message logging on the hierarchical checkpoint. The simulation results show that, compared with the quasi-optimal period checkpoint cost, the average error value of the proposed model is below 5%, which is 20% less than that of the traditional model based on Markov chain. The proposed model can signally increase the efficiency of the hierarchical checkpoint protocol; meanwhile enhance the availability of the HPC system.

In extreme weather or traffic, passengers cannot get a taxi to the destination quickly, thus a taxi unified recommendation algorithm based on region partition was proposed to provide common taxi service and carpooling service. First of all, the region was regarded as the logo of journey, making the journey matching possible. Secondly, in the carpooling service, the similar routes of two passengers were matched in real-time to help passenger carpool sharing. Finally, the taxi with the minimum percentage of bypass time was selected to recommend to the user. The Global Positioning System (GPS) data of 14747 taxis was used to evaluate the proposed algorithm. Compared with CallCab system, the total mileage of the proposed algorithm was dropped by about 10%, while the carpooling time was only raised by 6% on average, as well as the total passenger mileage was reduced by 30%. Experimental results show that the proposed algorithm not only can significantly reduce the emission of automotive exhaust, but also has better performance in terms of time consumption.

The result of widely used k-means algorithm is a two-way decision result, namely each object either belongs to one cluster or not. The two-way decision method is difficult to apply to some situations with uncertainty. Therefore, a three-way decision clustering method was proposed to show the three relationships between an object and a cluster. That is, the object definitely belongs to the cluster, the object may belong to the cluster or the object does not belong to the cluster. Obviously, the two-way decision is a special case of the three-way decision. A new separation index and clustering validity index were defined from the perspective of two aspects, which were the compactness of cluster and the separation among clusters considering the nearest neighbors. Then, an automatic three-way decision clustering algorithm was put forward. The method provides a new way to solve the problem of automatically determining the number of clusters in the framework of k-means algorithm for the uncertain information. The preliminary comparison experimental results on the artificial and real UCI data sets show that the proposed method is effective.

Domain-specific term recognition methods based on statistical distribution characteristics neglect term semantics and domain feature, and the recognition result are unsatisfying. To resolve this problem, a domain-specific term recognition method based on word embedding and Conditional Random Field (CRF) was proposed. The strong semantic expression ability of word embedding and strong field expression ability of similarity between words and term were fully utilized. Based on statistical features, the similarity between word embedding of words and word embedding of term was increased to create the feature vector. term recognition was realized by CRF and a series of features. Finally, experiment was carried out on field text and SogouCA corpus, and the precision, recall and F measure of the recognition results reached 0.9855, 0.9439 and 0.9643, respectively. The results show that the proposed method is more effective than current methods.

Aiming at the solidity of code and the difficulty of upgrades of existing Virtual Data Center (VDC) management platform, a VDC management platform based on Software Defined Network (SDN) was proposed. The proposed platform was composed of VDC Management subsystem (VDCM), VDC Computing Resources Control subsystem (VDCCRC) and VDC Network Resources Control subsystem (VDCNRC). A loosely coupled architecture was built by RESTful API interaction between subsystems. VDCNRC managed data center network by SDN controller, VDCCRC managed computing resources of data center by open source cloud computing platform, and a VDC management algorithm framework was built in VDC management subsystem to develop rapidly VDC management algorithm suitable for production environment. By utilizing Mininet, Openstack and Floodlight to set up test environment, the results show the proposed platform can support running, migrating or deleting virtual machines by Openstack, implement bandwidth resource isolation between VDCs by Openflow controller, and support the operations of creating, deleting, or updating VDC.

For the potential link congestion problem during traffic migration caused by IP network updates, a Congestion Avoidance and Fast Traffic Migration based on Multi-Topology Routing (CAFTM-MTR) algorithm was proposed. Firstly, the link capacity constraints and the timing characteristic of source node traffic migration were considered, and a congestion avoidance migration sequence that each moves one source node was gotten. Secondly, to shorten the migration finishing time, the algorithm was improved based on the sequence independence of traffics to make each batch move multiple sequence independent traffics. By using typical topologies and Waxman topologies to validate the proposed algorithm, the proposed algorithm improved the success rate of avoiding congestion from 20%-60% to 100% in the comparison experiments with Non-Congestion Avoidance and Fast Traffic Migration based on MTR (NonCAFTM-MTR) method, and obtained less than 8-round migration sequence. In addition, the proposed algorithm had an adaptability of dynamic traffic and was able to accommodate the traffic growth ranging from 5% to 284%. The simulation results show that CAFTM-MTR algorithm can effectively improve the success rate of congestion avoidance, and meanwhile make the traffic migration fast.

Concerning the problem that the Data Envelopment Analysis (DEA) has some shortcomings in showing the differences of weights for the evaluation indexes and sorting and adjusting the efficient decision making units, an improved DEA was proposed. Firstly, the weights of indexes were confirmed by the Analytic Hierarchy Process (AHP) and the preference cone model was built. And then all the decision making units could be sorted in term of the cross efficiency and the parts of the decision making units were adjusted according to the attendance ratio and the ideal decision making units. Finally, the line planning for the Beijing-Shanghai high speed railway was evaluated. The results show that there are four efficient lines among the six lines and two inefficient lines and one of the efficient lines need to be adjusted. The experimental results show that the proposed method can provide theoretical basis for the the adjustment of the train line planning.

Worm, Address Resolution Protocol (ARP) broadcast and other abnormal behaviorS which attack the cloud computing platform from the virtual machines cannot be detected by traditional network security components. In order to solve the problem, abnormal behavior detection technology architecture for cloud computing platform was designed, abnormal behavior detection for worms which brought signature and non-signature behaviors based on mutation theory and "Detection-Isolation-Cure-Restore" intelligent processing for cloud security was proposed. Abnormal detection, management of event and defense, and ARP broadcast detection for cloud computing platform were merged in the system. The experimental results show that the abnormal behavior inside the cloud computing platform can be detected and defensed with the system, the collection and analysis of the abnormal behavior inside cloud computing platform can be provided by this system in real-time, the traffic information can be refreshed automatically every 5 seconds, the system throughput can reach to 640 Gb and the bandwith occupied by abnormal flow can be reduced to less than 5% of the total bandwith in protected link.

This paper proposed a method for analyzing the survivability of interdependent networks with incomplete information. Firstly, the definition of the structure information and the attack information were proposed. A novel model of interdependent network with incomplete attack information was proposed by considering the process of acquiring attack information as the unequal probability sampling by using information breadth parameter and information accuracy parameter in the condition of structure information was known. Secondly, with the help of generating function and the percolation theory, the interdependent network survivability analysis models with random incomplete information and preferential incomplete information were derived. Finally, the scale-free network was taken as an example for further simulations. The research result shows that both information breadth and information accuracy parameters have tremendous impacts on the percolation threshold of interdependent network, and information accuracy parameter has more impact than information breadth parameter. A small number of high accuracy nodes information has the same survivability performance as a large number of low accuracy nodes information. Knowing a small number of the most important nodes can reduce the interdependent network survivability to a large extent. The interdependent network has far lower survivability performance than the single network even in the condition of incomplete attack information.

By introducing the current research progress of Virtual Data Center (VDC) embedding, and in accordance with the reliability requirement of VDC, a new heuristic algorithm to address reliability-aware VDC embedding problem was proposed. It restricted the number of Virtual Machines (VMs) which can be embedded onto the same physical server to guarantee the VDC reliability, and then regarded reduction of the bandwidth consumption and energy consumption as main objective to embed the VDC. Firstly, it reduced bandwidth consumption of data center by consolidating the virtual machines, which had high communication services, into the same group and placed them onto the same physical server. Secondly, the consolidated groups were mapped onto the powered physical servers to decrease the number of powered servers, thus reducing the power consumption of servers. The results of experiment conducted on fat tree topology show that, compared with 2EM algorithm, the proposed algorithm can satisfy VDC reliability requirement, and effectively reduce a maximum of 30% bandwidth consumption of data center without increasing extra energy consumption.

When a link weight changes in network with Internet Protocol (IP), routing loops may occur. Such loops increase the network latency and cause packet losses, which cannot meet the needs of high-level real-time service. A fast routing micro-loop avoidance algorithm using a weight sequence was proposed. The link weights were reallocated according to the weight sequence so that no loops would occur during convergence phase. In order to calculate the weight sequence, a safety weight interval was defined to describe the condition for avoiding loops, then the safety interval was used to search a set of safety weight ranges. During calculation, the prunning technology was used to reduce search range and improve efficiency. At last, the final weight sequence was obtained from these ranges. The simulation test results using typical network topology algorithm show that in average five times of link weight reallocation can successfully avoid loops in 87% of topologies. In addition, compared with other existing algorithms using iterative adjustment link weights to solve the routing micro-loop, the computational complexity of the proposed algorithm was greatly reduced by an order of magnitude and the computational efficiency was improved by 30%-80%. The proposed algorithm can greatly shorten the calculation time and more efficiently solve the problem of routing micro-loop, which will avoid network latency and packet loss to provide a high level of service quality.

Concerning the problem that single UML model can not test the software sufficiently, this paper proposed a new method of automatically generating software test clues by combining the characteristics of UML2.0 interaction overview diagram. First, this paper gave the formal definition of UML class diagrams, sequence diagrams and Interaction Overview Diagrams (IOD) . Second, the Node Control Flow Graph (NCFG) was constructed by extracting the process information of the interaction overview diagram while the Message Sequence Diagrams (MSD) were constructed by extracting the object interaction information. The testable model of IOD was constructed by embedding the MSD's message path into NCFG. At last, the paper adopted two-two coverage criterion to generate test clues. The experiment verifies that this method which automatically generates test clues avoids the combinatorial explosion while guaranteeing the test adequacy.

Equipment failure often brings some defects on the surface of batteries in the battery production process. The traditional artificial detection has weakness on the timeliness and durability. But there has not been any efficient automatic detection means for the ordinary battery by now. Concerning the distribution and morphological characteristics of the defects, a new automatic optical detection method based on computer vision was proposed. The proposed method used Canny operator and virtual granule collision method with the minimum value searching method to determine the area to be detected based on the battery anode surface morphology features. Considering the sharpness of the defect, Harris corner points were used to mark the defects as mark points. False mark points were filtered by the degree of aggregation of the points. The defect region would be extracted at last according to the location of mark points. The experimental results illustrate the detection success rate of the proposed method is over 90% and the method can work more efficiently than the popular wavelet analytical method. The study achievement provides a reference for product quality automatic detection on battery production.

In Public Key Infrastructure (PKI) systems, the Certificate Authority (CA) signature is not easy to forge, thus, intrusions to these certificate revocation systems which are based on Certificate Revocation List (CRL) usually aim at destroying system usability and data integration. Concerning this intrusion feature, an intrusion tolerance CRL service system was designed in this paper. Within the system, CRL was stored on multiple redundant servers. In order to copy and use data among these servers, a passive replication algorithm of randomly selecting main server and a simple vote algorithm of selecting the most recent updated CRL were proposed. Under the given experiment intrusion conditions, although system expenses were increased, the query accuracy of certificate revocation of a system that tolerated intrusions was about 20% higher than that of a system that did not. The experimental results show that adding more servers properly increases the query accuracy of certificate revocation and controls the system expenses.

Arnold cat transformation is widely applied and has the best scrambling effect. However, it has some disadvantages, such as small key quantities and poor generalizability. From the scrambling essential, the concept of even scrambling was presented. At the same time, an algorithm of image position scrambling on the basis of improving Arnold cat transformation was put forward. The experimental results show that the key quantities and scrambling effect of the proposed algorithm are improved obviously, and it can resist cut attack.

Based on the analysis of the factors which affect the routing protocol design, the key technique issues of ad hoc routing protocols were summarized, including route loop-freedom, adaptation to dynamic topology, control overhead, energy efficiency, and combination with orientation technique, etc. At last, the problems in routing protocol research aspects, such as protocol requirements, and protocol evaluation and comparison methods, were described.

UML Sequence Diagram emphasizes particularly the process of message interaction of objects. But it lacks formal description of dynamic semantics, therefore goes against comprehension of the diagram and test case generation based on the diagram. Based on UML specification (version 1.5), this paper defined formal syntax of the diagram firstly and brought forward concept of Action-Point afterwards; thus discuss and put forward the structured operational semantics of message action of single object and the whole sequence diagram, provided the precondition of model checking and the test cases generation which based on sequence diagram.