Proactive fault tolerance mechanism, which predicts disk failures and prompts the system to perform migration and backup for the data in danger in advance, can be used to enhance the storage system reliability. In view of the problem that the reliability of the replication-based storage systems with proactive fault tolerance cannot be evaluated by the existing research accurately, several state transition models were proposed for replication-based storage systems; then the models were implemented based on Monte Carlo simulation, so as to simulate the running of the replication-based storage systems with proactive fault tolerance; at last, the expected number of data-loss events during a period in the systems was counted. The Weibull distribution function was used to model the time distribution of device failure and failure repair events, and the impact of proactive fault tolerance mechanism, node failures, node failure repairs, disk failures and disk failure repairs on the system reliability were evaluated quantitatively. Experimental results showed that when the accuracy of the prediction model reached 50%, the reliability of the systems were able to be improved by 1-3 times, and compared with 2-way replication systems, 3-way replication systems were more sensitive to system parameters. By using the proposed models, system administrators can easily assess system reliability under different fault tolerance schemes and system parameters, and then build storage systems with high reliability and high availability.

For the problems that third party counting institution does not meet the decentralization and de-trusting characteristics of blockchain and is lack of credibility,a blockchain electronic counting scheme based on the Practical Byzantine Fault Tolerance (PBFT) algorithm was proposed. Firstly,the centerless counting model was built in the distributed environment,and the counting node was determined by the credibility level of the node. Secondly,the consensus of pending ballots was formed based on PBFT. Thirdly,the minimum number of honest nodes in PBFT was set as the threshold for threshold signature,and the threshold signature was only formed by results satisfying the threshold. Finally, the results satisfying the trusted state were recorded in the blockchain account book. Test and analysis results show that only when the honest nodes exceed two-thirds,the PBFT is satisfied,and the obtained counting result is credible.

Aiming at the tracking problem of multiple extended targets under the circumstances with unknown measurement noise covariance, an extension of standard Extended Target Probability Hypothesis Density (ET-PHD) filter and the way to realize its analysis were proposed by using ET-PHD filter and Variational Bayesian (VB) approximation theory. Firstly, on the basis of the target state equations and measurement equations of the standard ET-PHD filter, the augmented state variables of target state and measurement noise covariance as well as the joint transition function of the above variables were defined. Then, the prediction and update equations of the extended ET-PHD filter were established based on the standard ET-PHD filter. And finally, under the condition of linear Gaussian assumptions, the joint posterior intensity function was expressed as the Gaussian and Inverse-Gamma (IG) mixture distribution, and the analysis of the extended ET-PHD filter was realized. Simulation results demonstrate that the proposed algorithm can obtain reliable tracking results, and can effectively track multiple extended targets in the circumstances with unknown measurement noise covariance.

The functions of a parallel computer system heavily rely on the performance of interconnection network of the system. In order to measure the fault tolerance abilities of the parallel computer systems with k-ary n-cubes as underlying topologies, the reliability of the subnetworks of k-ary ( n-1)-cubes in a k-ary n-cube under the node fault model was studied. For odd k ≥ 3, the mean time to failure to maintain the fault free condition of different number of k-ary ( n-1)-cubes in a k-ary n-cube was analyzed under the fixed partition pattern and the flexible partition pattern, respectively. And the calculation formulas for the reliability evaluation parameter of subnetwork were obtained. Under the node fault model, the results indicate that the parallel computer system which is built based on k-ary n-cubes with odd k has better fault tolerance ability under the flexible partition pattern when subnetworks in the system are assigned for the user task execution.

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 existing steganography, it is hard to extract secret information without original cover-image and easy to be detected by steganalysts when the hiding capacity is high. To solve this problem, a new scheme of steganography based on fractal graph was proposed. In this scheme, firstly, Black-and-White Fractal Graph (BWFG) was created by utilizing affine transformation and fractal iterated function system. Then the BWFG was transformed to Black-and-White Pixel Image (BWPI) based on the idea of coordinate transformation. At last, the BWPI was divided into several non-overlapping blocks and the positions of black and white pixels in each block were altered to hide the secret information, generating stego-image. The receiver could create the cover-image by utilizing the parameters of affine transformation and times of iteration, and extract secret information by comparing the difference of pixels in corresponding blocks. Theoretical analysis and simulation experiments show that, compared with the information hiding algorithm in frequency domain, the proposed scheme has good imperceptibility and high hiding capacity, and can resist steganalysis based on image features and transform domain coefficient change.

Bibliography information network is a typical heterogeneous information network and the similarity search based on it is a hot topic of graph mining. However, current methods mainly adopt meta path or meta structure to search similar objects, do not consider semantic features of node itself which leads to a deviation in the search results. To fill this gap, a vector-based semantic feature extraction method was proposed, and a vector-based node similarity calculation method called VSim was designed and implemented. In addition, a similarity search algorithm VPSim (Similarity computation Based on Vector and meta Path) based on semantic features was designed by combining the meta-paths. In order to improve the execution efficiency of the algorithm, a pruning strategy based on the characteristics of bibliographic network data was designed. Experiments on real-world data sets demonstrate that VSim is applicative for searching entities with similar semantic features and VPSim is effective, efficient and extensible.

Focusing on medical image secure retrieval in cloud server, an encrypted medical image retrieval algorithm based on Discrete Wavelet Transform (DWT) and perceptual hash was proposed. Firstly, the image was encrypted in frequency domain based on the characteristics of Henon mapping. Secondly, the encrypted medical image was decomposed by wavelet to obtain the sub-image close to the original image. According to the characteristics of Discrete Cosine Transform (DCT), the perceptual hash sequence of the image was obtained by comparing the relationship between the coefficients of DCT and the mean of the coefficients. Finally, the encrypted medical image retrieval was achieved by comparing the normalized correlation coefficients between the perceived hash sequences. Compared with the hash algorithm based on Non-negative Matrix Factorization (NMF), the proposed algorithm improves the retrieval accuracy by nearly 40% under Gaussian noise, which is not changed obviously under the JPEG compression attack, median filter attack, scaling attack and ripple distortion attack. Experimental results show that the proposed algorithm has strong robustness against geometric attack and conventional attack, as well as reduce the time complexity of image encryption.

Adjacent Vertex-Distinguishing Equitable V-Total Coloring (AVDEVTC) of a graph means on the basis of adjacent vertex-distinguishing V-total coloring, the differences between every two colors used in coloring are no more than one. The minimum number of colors used for completing AVDEVTC is called Adjacent Vertex-Distinguishing Equitable V-Total Chromatic Number (AVDEVTCN). A multi-objective optimization coloring algorithm was proposed to resolve the problem of AVDEVTC of the graph. A main objective function and four subobjective functions were designed according to the conditions of AVDEVTC. Every subobjective function was optimized to meet the requirements of the main objective function by the iterative exchange operation of the color set of every vertex on the coloring matrix, thus completed the coloring. Theoretical analysis and experimental comparison show that all of the simple connected graphs within eight vertices have the AVDEVTC, and their AVDEVTCN are between the maximum degree plus 1 and the maximum degree plus 2. The experimental result indicates that the proposed coloring algorithm can correctly calculate the AVDEVTCN of graphs within 1000 vertices in a short period of time.

Concerning the constrained resources and low detection rate of Android, a software behavior dynamic monitoring framework based on ROM was constructed by considering behavior characteristics of Android in installation mode, trigger mode and malicious load, and the effectivenesses of Support Vector Machine (SVM), decision tree, k-Nearest Neighbor (KNN) and Naive Bayesian (NB) classifier were evaluated using information gain, chi square test and Fisher Score. The results of evaluation on overall classification of the behavior log of 20916 malicious samples and 17086 normal samples show that SVM has the best performance in the detection of malicious software, its accuracy rate can reach 93%, and the False Positive Rate (FPR) is less than 2%. It can be applied to the online cloud analysis environment and detection platform, as well as meeting the needs of mass sample processing.

At present, the parameters of high sulfur gas purification process present timing autocorrelation characteristics, resulting in poor static multivariate statistical process monitoring for abnormal condition. An anomaly detection and diagnosis method called Dynamic Kernel Independent Component Analysis (DKICA) was proposed, which considered the timing autocorrelation of parameters. Firstly, Auto-Regression (AR) model was introduced. The model order was determined by the parameter identification to describe the timing of autocorrelation in the monitoring process. Secondly, original variables were projected to a kernel independent space, their T ² and SPE statistics were monitored to realize anomaly detection by judging whether they exceeded control limit of normal condition. Finally, the first order partial derivative of the T ² statistic to original variable was calculated, and the contribution plot was given to achieve abnormality diagnosis. The data collected from a high sulfur gas purification plant was analyzed, and the results showed the detection accuracy of DKICA was prior to that of Kernel Independent Component Analysis (KICA).

In order to create novel artistic effects, a period-dynamic-image model was proposed, in which each element is a periodic function. Instead of using an array of color pixels to represent a digital image, a Fourier model was used to represent a periodic dynamic image as an array of functional pixels, and the output of each pixel was computed by a Fourier synthesis process. Then three applications with three rendering styles were put forward, including dynamic painting, dynamic distortion effects and dynamic speech balloons, to visually display the periodic dynamic images. A prototype system was constructed and a series of experiments were performed. The results demonstrate that the proposed method can effectively explore the novel artistic effects of periodic dynamic images, and it can be used as a new art media.

The research on the equitable total coloring is limited to some special graphs such as complete-graph and join-graph. For the normal equitable total coloring of the simple connected graph, there is not any feasible method in the published paper. In order to research the equitable total coloring of the normal graph, a new heuristic intelligent algorithm was proposed according to four constraint rules including vertex constraint rule, edge constraint rule, vertex-edge constraint rule and equitable constraint rule of the equitable total coloring. First, four sub-functions and one total function were ascertained. Second, by using the dyeing matrix and complementary matrix in each sub-function, the iterative exchange did not stop until each sub-function value was zero, that meant the subgoal-coloring was completed. If each sub-function value was 0, the total function value was 0, which meant coloring was successful. The experimental results show that the proposed algorithm can generate all of the simple connected graphs in which the number of vertices is no more than 8, and it can achieve the corresponding coloring, and then obtains the equitable total chromatic number. Also when any positive integer k is not less than 3 and not more than 9, random graph G has k-equitable total coloring. At the same time, the proposed algorithm chooses 72 graphs whose vertex number is between 20 and 400, and draws the diagram about the vertex number, edge number and color number according to the equitable total coloring results.

To improve the workshop environment of textile mill and enhance the automatic control level on the environment, an intelligent environment measuring and controlling system of textile workshop based on Internet of Things (IoT) was proposed. The overall design scheme of the system was given. In order to reduce traffic loads of sink nodes and improve the data transmission rate of network, the wireless network topology structure of single-hop multi-sink nodes was designed. The concrete implementation scheme of hardware design and software work process of sensing nodes, controlling nodes and other nodes were represented detailedly. The improved Newton interpolation algorithm was used as the fitting function to process the detection data, which improved the precision of detection and control of system. The application results show that the system is simple, stable and reliable, low in cost, easy to maintain and upgrade, and obtains good application effect.

In Mobile Ad Hoc Network (MANET), the movements of nodes are liable to cause link failures, while the local repair in the classic Ad Hoc On-demand Distance Vector (AODV) routing algorithm is performed only after the link breaks, which has some limitations and may result in the cached data packet loss when the repair process fails or goes on too slowly. In order to solve this problem, an optimized AODV routing algorithm named ARB-AODV was proposed, which can avoid route breaks. In ARB-AODV algorithm, the link which seemed to break was predicted and the stability degrees of the nodes' neighbors were calculated. Then the node with the highest stability was added to the weak link to eliminate the edge effect of nodes and avoid route breaks. Experiments were conducted on NS-2 platform using Random Waypoint Mobility Model (RWM) and Constant Bit Rate (CBR) data. When the nodes moved at a speed higher than 10m/s, the packet delivery ratio of ARB-AODV algorithm maintained at 80% or even higher, the average end-to-end delay declined up to 40% and the overhead of normalized routing declined up to 15% compared with AODV. The simulation results show that ARB-AODV outperforms AODV, and it can effectively improve network performance.

Concerning the problems of complicated structure and revolution of 2D traditional neighborhood cellular automata, low encrypting efficiency, little key space of 1D cellular automata, low diffusion speed and needing multiple rounds iteration to produce avalanche effect, a new encryption algorithm based on 2D X-type reversible cellular automata and Arnold transformation was proposed. Firstly, the plaintext was evolved by the proposed cellular automata, then it was transformed by Arnold transformation and cyclic shift transformation after every evolution, until the ciphertext was encrypted well enough. The experimental result shows that the key space is increased by 16.8% and has perfect robustness in resisting brute force attack. In addition the diffusion and confusion is so excellent that it can produce higher avalanche effect and resist chosen plaintext attack.

Owing to that different users focus on attributes of the same item is not exactly the same, individuals' weight distribution for goods attributes are not the same. A method of the generalized interval-valued trapezoidal fuzzy soft set was proposed to deal with this kind of recommendation problems. First, the concept of generalized interval-valued trapezoidal fuzzy soft set was established by combining the concepts of generalized interval-valued trapezoidal fuzzy set and soft set, some basic operations on a generalized interval-valued trapezoidal fuzzy soft set were defined, such as “and” operation, and “or” operation. Using these operations, as well as the center of gravity method of the generalized interval-valued trapezoidal fuzzy numbers, commodities could be ranked. A group preference model from the preferences of the group members could be constructed. Finally, this paper used the car recommendation as an example to introduce the group preference aggregation algorithm and this numerical example was given to illustrate the feasibility and effectiveness of the proposed method.

Through analyzing the advantages and disadvantages of traditional immersive display systems, a new immersive display system was designed and implemented. In this system, the light illuminated by one projector was reflected by a cylindrical reflector to a cambered rear projection screen. Seamless projection picture can be obtained and coherent displaying of wideangle virtual scene could be implemented. By properly designing the surface of cylindrical reflector, it implemented uniform enlargement of the image on the horizontal direction. The distortion of the projection picture caused by the curvature of the screen could be basically removed through prior distortion of projected image and participants could acquire more moving freedom by means of rear projection. This system overcame the problem of image mosaics in traditional multiprojector and multidisplay immersive display systems. It is simple for manufacturing and can achieve satisfying immersive display quality, which is verified by the experimental result.

For the poor precision of region-based image retrieval, Multi-Instance Learning (MIL) prototype selection algorithm and feedback mechanism with reference to textual information were proposed. In the process of instance prototype selection, textual information was used to extend the positive examples, and negative example distribution was used to select initial instances and the iterative optimization process of instance updating and classifier training were used to obtain the true instance prototypes. In the process of relevance feedback, active learning with the combined learning methods was adopted. The switch of active learning strategy was controlled by the information value in the feedback process. The experimental results show that this algorithm is feasible, and the performance is superior to other MIL algorithms.

The conventional steganalysis techniques does not work in zero-watermarking technology because it does not modify carrier text. However, active attack model on text zero-watermarking has not been well studied. In order to solve this problem, an active attack algorithm for text zero-watermarking was proposed. Making use of synonym substitution technology, sync-attack and birthday-attack were defined. Through combining the two active attack methods, a lexical active attack algorithm on text zero-watermarking was designed and implemented. The experimental result shows the proposed algorithm can effectively destroy zero-watermarking without modifying carrier text massively.

Abstract: A seam tracking algorithm based on multi-information sensor of vision and arc is proposed. The algorithm is applied to the automatic control process of MIG pulse welding quality. This paper captures different description information for the effective feature extraction and transmission with the vision sensor and the arc sensor, and can be used in multi-sensor information fusion algorithm for seam tracking. The vision sensor obtains image information by industrial CCD for the lateral deviation control of the weld torch, the arc sensor obtains current information by data acquisition card for the height deviation control of the weld torch, The two kinds of sensor information in a complementary way to integrate for the lateral and height corrective control of the welding process. Experimental results show that the proposed algorithm can improve the welding quality, and thus verify the algorithm efficiency and rationality.

Four distributed localization algorithms in wireless sensor networks(WSN) were discussed.Their performances were compared.The energy of WSN was consumed during computation and communication.So the computational cost and communication cost of these algorithms were analysed.Finally,recommendations for future study and possible improvements were put forward.

RBMoM is a mobile multicast protocol with good integrated performance,which integrates two basic methods called Remote Subscription and Bi-directional Tunneling in Mobile IP of IETF.The problems in RBMoM mobile multicast protocol were analysed,and a DMSP-sharing mechanism was proposed,on which an efficient mobile multicast scheme was provided based.This scheme lessens efficiently multicast traffic in network,and reduces multicast tree maintenance costs,but does not cause multicast data delivery to incur too large delivery delay.

It doesn’t adapt very well to the dynamic and distributed nature of Web services composition that researches based on traditional workflow technique. A Web services composition model MAS-WS based on multi-agent was proposed,the focus was on monitoring and exception handling in Web services composition execution. The key techniques including dynamic formation of agent-team, proactive collaboration, hierarchical management of Web services composition progress, interleaved planning with plan execution and so on were described. And then advantages of MAS-WS were discussed.