At present, scholars mainly focus on four perspectives of text, vocabulary, grammar and semantics in the field of clone code detection. However, few breakthroughs have been made in the effect of clone code detection for a long time. In view of this problem, a new method called Clone Code detection based on Image Similarity (CCIS) was proposed. Firstly, the source code was preprocessed by removing comments, white space, etc., from which a "clean" function fragment was able to be obtained, and the identifiers, keywords, etc. in the function were highlighted. Then the processed source code was converted into images and these images were normalized. Finally, Jaccard distance and perceptual Hash algorithm were used for detection, obtaining the clone code information from these images. In order to verify the validity of this method, six open source softwares were used to constitute the evaluation dataset for testing. The experimental results show that CCIS method can detect 100% type-1 clone code, 88% type-2 clone code and 60% type-3 clone code, which proves the good effect of CCIS method on clone code detection.

In addition to traditional reactive fault-tolerant technologies, proactive fault tolerance can be used to improve storage system reliability significantly. There is few research on reliability of proactive cloud storage systems, supposing exponential distribution of drive failure. Two reliability state transfer models were developed for proactive redundant arrays of independent disks RAID-5 and RAID-6 systems respectively. Based on the models, Monte Carlo simulations were designed to estimate the expected number of data-loss events in proactive RAID-5 and RAID-6 systems within a given time period. Weibull distribution was used to model time-based (decreasing, constant occurrence, or increasing) disk failure rates, and express the impact of proactive fault tolerance, operational failures, failure restoration, latent block defects, and drive scrubbing on the system's reliability. The proposed method can help system designers to evaluate the impact of different fault tolerance mechanisms and system parameters on the reliability of cloud storage systems, and help to create highly reliable storage systems.

For the problem of estimation error and feedback delay in the process of acquiring channel information in Multiple-Input Multiple-Output (MIMO) systems, a robust interference alignment algorithm based on weight adjustment was proposed to improve the system performance at low Signal-to-Noise Ratio (SNR). Firstly, the system model was reconstructed by considering the influence of channel error on the basis of the ideal channel; secondly, the signal space at the receiving terminal was decomposed by matrix projection and divided into two parts, including desired signal subspace and interference signal subspace; thirdly, considering the interaction between the desired signal and the interference signal, the weighted sum of the power leaked into the corresponding subspaces was used as the objective function, and the iterative idea was used to calculate the precoding and interference suppression matrix. Finally, the calculated pre-coding and interference suppression matrix was used to derive the sum-rate expression of the channel error. Simulation results show that compared with the robust minimum interference leakage algorithm, when the SNR is 10 dB and the channel error variance value is 0.05, the spectrum efficiency of the system is increased by 25% and the energy efficiency is increased by 38%. Therefore, the proposed algorithm can effectively improve system performance at low SNR.

Traditional TSV (Through Silicon Via) table in vertically partially connected three-Dimensional Network-on-Chip (3D NoC) only stores TSV address information, which easily causes network congestion. In order to solve this problem, a record table architecture was proposed. The record table stored not only the nearest four TSV addresses to the router, but also the input-buffer occupancy and fault information of the corresponding router. Based on the record table, a novel adaptive unicast routing algorithm for the shortest transmission path was proposed. Firstly, the coordinates of current node and destination node were calculated to determine the transmission mode of packets. Secondly, by using the proposed algorithm, whether the transmission path was faulty and got information of buffer occupancy was obtained simultaneously. Finally, the optimal transmission port was determined and the packets were transmitted to the neighboring router. The experimental results under two network sizes show that the proposed algorithm has obvious advantages in average delay and throughput compared with Elevator-First algorithm. Additionally, the rates of losing packet under Random model and Shuffle traffic model are 25.5% and 29.5% respectively when the network fault rate is 50%.

Since the traditional Pulse Coupled Neural Network-based (PCNN) image fusion methods cannot extract the focus region clearly, a multi-focus image fusion technique using Phase Congruency (PC) and Spatial Frequency (SF) combined with PCNN model in Non-Subsampled Contourlet Transform (NSCT) domain was proposed. Firstly, the source images were decomposed into high frequency subband and low frequency subband by NSCT. Secondly, the values of SF and PC were calculated to motivate PCNN neurons to fire to find the focus regions, and then the high and low frequency subbands were fused respectively. Lastly, the fused image was reconstructed through inverse NSCT. Multi-focus image datasets Clock, Pepsi and Lab were utilized as the experimental image sets. In comparison, four classical fusion methods and three newly put forward fusion algorithms were compared with the proposed algorithm. Objective indicators including mutual information, edge intensity, entropy, standard deviation and average gradient were calculated, and the values of the proposed method were greater than or very close to the maximum value of the comparison algorithms; meanwhile, it was clearly found from the difference maps between the experimental result image and the source image that the difference graph of the proposed method contained significantly fewer traces of the clear region of the source image. The experimental results indicate that the proposed method can better extract the clear region of the fused image, and it can better retain details such as edges and textures of the source images, thus, a superior fusion effect is acquired.

Focusing on the low efficiency of the rescue robot in the coal mine disaster environment, a new improved boundary exploration algorithm based on multiple Unmanned Aerial Vehicles (multi-UAV) was proposed. Based on the utility value boundary exploration algorithm, the flight angle parameter of UAV was considered, and the distribution function was introduced as a judgment mechanism to construct the objective function. Finally, the ant colony algorithm was used to solve the objective function. Simulation experiments were carried out on a rasterized map with Matlab software. The simulation results show that the improved boundary exploration algorithm can reduce the phenomenon of repeated coverage and crowding, shorten the detection time, meanwhile the energy required by UAV is reduced by about 30%, thus improving the overall exploration efficiency of multi-UAV system.

To solve the problem of irrelevant and redundant features in harmfulness prediction of clone code, a combination model for harmfulness feature selection of code clone was proposed based on relevance and influence. Firstly, a preliminary sorting for the correlation of feature data was proceeded by the information gain ratio, then the features with high correlation was preserved and other irrelevant features were removed to reduce the search space of features. Next, the optimal feature subset was determined by using the wrapper sequential floating forward selection algorithm combined with six kinds of classifiers including Naive Bayes and so on. Finally, the different feature selection methods were analyzed, and feature data was analyzed, filtered and optimized by using the advantages of various methods in different selection critera. Experimental results show that the prediction accuracy is increased by15.2-34 percentage pointsafter feature selection; and compared with other feature selection methods, F1-measure of this method is increased by 1.1-10.1 percentage points, and AUC measure is increased by 0.7-22.1 percentage points. As a result, this method can greatly improve the accuracy of harmfulness prediction model.

To solve the problems that the method of building clone genealogy is complicated, as well as evolution patterns need urgently expanding, new clone evolution patterns were proposed, and clone genealogy was built automatically based on the mapping relationships of code clones between versions. First, topics of code clones were extracted using Latent Dirichlet Allocation (LDA) from clone detection results in each released software version. Second, mapping relationships of code clones between of versions were confirmed by similarities of the topics. Third, evolution patterns were appended to code clones according to the existing mapping relationships, and evolution features were analyzed. Finally, clone genealogy was built by integrating mapping relationships and evolution patterns together. Experiments of building clone genealogy was conducted on four open source systems. The experimental results show that the proposed approach is feasible, and the proposed evolution patterns really exist in the procedure of software evolution. Further more, it is found that about 90% of code clones in the software systems are stable during evolution, and approximately 67% of clone groups live through less than half of the release versions. The experimental conclusions and relevant analysis provide strongly support for the future research as well as maintenance and management of code clones.

Complex graphics usually consist of geometric primitives. Based on the recognition of simple geometric primitives, complex graphics recognition focuses on the spatial relationship between the graphic elements. Geometric structure is too complex to use the heuristic rules, and the existing geometric structure analysis methods are also too complicated to use the traditional method. The core technical issues of the structural analysis for the hand-drawn geometry recognition was analyzed, a geometric structure description model based on Scalable Vector Graphics (SVG) tag was designed to represent the graphics by the formal description of the graphical elements and their constraints. It used SVG tags to store the graphical elements and their constraints, and identified the geometry shape and its internal relationship by parsing the SVG tags. It was validated in a prototype system, namely GeoSketch, with promising effect. The experimental results show that the proposed method is simple and low-dimensional, it is easy for determing the shape of the GeoSketch and its internal relations.

Since clone detection results cannot fully reflect the features of clones, clone genealogies extraction from multiple versions can be used to uncover the patterns and characteristics exhibited by clones in the evolving system. A clone genealogy extraction method named FCG was proposed. FCG first mapped clones between each adjacent versions and then identified clone evolution patterns. All of the results were combined to get clone genealogies. Experiments on 6 open source systems found that the average lifetime of clones in current version is over 70 percent of the total number of studied versions, and most of them do not change, which indicates that majority of clones can be well maintained. While some unstable clones may be defect potential, and needs to be modified or refactoring. Results show that FCG can efficiently extract clone genealogies, which contributes to a better understanding of clones and provides insights on targeted management of clones.

Aiming at the problems of less clone code detection tools and low efficiency for the current Type-3, an effective clone code detection method for Type-3 based on the levenshtein distance of token was proposed. Type-1, Type-2 and Type-3 clone codes could be detected by the proposed method in an efficient way. Firstly, the source codes of a subject system were tokenized into some token sequences with specified code size. Secondly, each definite-sized substring of the token sequences was mapped with corresponding index. Thirdly, the clone pairs were built by the levenshtein distance algorithm and the clone groups were built by the disjoint-set algorithm on the basis of the mapping information query. Finally, the feedback information of clone codes were given. A prototype tool named FClones was implemented. It was evaluated by the code mutation-based framework and compared with two state-of-the-art tools SimCad and NiCad. The experimental results show that the recall of FCloens is equal to or greater than 95% and its precision is not lower than 98% in detecting all of these three types of clone codes. FClones can do better in detecting Type-3 clones than others.

The activities of the programmers including copy, paste and modify result in a lot of code clone in the software systems. However, the inconsistent change of code clone is the main reason that causes program error and increases maintenance costs in the evolutionary process of the software version. To solve this problem, a new research method was proposed. The mapping relationship between the clone groups was built at first. Then the theme of lineal cloning cluster was extracted using Latent Dirichlet Allocation (LDA) model. Finally, the inconsistent change probability of code clone was predicted. A software which contains eight versions was tested and an obvious discrimination was got. The experimental results show that the method can effectively predict the probability of inconsistent change and be used for evaluating quality and credibility of software.

In view of the kinematic model of mobile robot, a tracking controller of global asymptotic stability was proposed. The design of tracking controller was divided into two parts: The first part designed the control law of angular velocity by using global fast terminal sliding mode in order to asymptotically stabilize the tracking error of the heading angle; the second part designed the control law of linear velocity by using the Lyapunov method in order to asymptotically stabilize the tracking error of the planar coordinate. By combining Lyapunov stability theorem and two control laws, the mobile robot can track the desired trajectory in a global asymptotic sense when the angular velocity and the linear velocity satisfy these control laws. The experimental results show that the mobile robot can track desired trajectory effectively. It is helpful for promoting the practical application.

For the Chameleon algorithm using distance function to measure the similarity of data points, resulting in that the two proximate points may only have a few common characteristics, minimum half has practical difficulties, the merger needs artificial specified threshold value, and can not be revoked once the merger is completed. Therefore, the authors improved Chameleon algorithm and proposed a new Chameleon algorithm using Weighted Shared nearest neighbors Graph (WSnnG). Firstly, it measured the similarity by using the number of shared nearest neighbors, further constructed the WSnnG. Secondly, it resolved minimum half through the introduction of the network module evaluation function, then according to the structural equivalence similarity degree as a basis for merger. Finally, a new cohesion measure was discussed to solve problems that can not be revoked after the merger. The experimental results on UCI data sets and four two-dimensional artificial data sets show that the improved Chameleon algorithm using WSnnG has greatly improved in clustering accuracy and running time.

In order to solve the semantic conflict of emergency cases understanding in Case-Based Reasoning (CBR), an emergency case ontology model based on CBR was defined on the basis of the extended ABC ontology model according to the analysis of emergency cases, and the key elements of the model were described in detail, such as concepts, relations, axioms and instances. Finally, the model was validated by the analysis on the case of the nuclear crises of the first nuclear power station in Fukushima.

To solve the classification problem of multi-dimensional time series and obtain understandable classification rules, the concept of time series entropy and the method of structuring time series entropy were introduced. And the decision tree model was expanded based on both attribute selection and attribute value. Two algorithms for structuring decision tree model of multi-dimensional time series classification were presented. Finally, process decision tree was tested on mobile customer churn data, and the feasibility of the proposed method was demonstrated.

The updating of computers has great impact on the dynamics of worms. To contain the propagation of worms, it is necessary to characterize this factor. A model was proposed in this paper, which took account of the influence of computer updating. Furthermore, the model's equilibria and their stability conditions were obtained mathematically and then verified by simulations. The analytical and simulated results show that the updating of computers can lead to the persistence of worms, which will die out otherwise. The simulation results also show that the updating rate has bi-effects on the spreading of worms. Under the guidance of basic reproduction number, the negative effect can be alleviated and worms can be terminated by introducing a anti-virus system of high initial installation rate.