To address the electric vehicle freight routing optimization problem considering the constrains of battery life and battery-swapping stations, a calculation method of electric vehicle carbon emissions considering multiple factors such as speed, load and distance was proposed. Firstly, with the goal of minimizing power consumption and travel time cost, a mixed integer programming model was established. Then, an adaptive genetic algorithm was proposed based on the mountain-climb optimization and batter-swapping neighborhood searching, and the crossover and mutation probabilities adaptively adjusting with the change of the population fitness were designed. Finally, the mountain-climb searching was used to enhance the local search capability of the algorithm. And the battery-swapping neighborhood searching strategy for the electric vehicle was designed to further improve the optimal solution, so as to meet the constraints of battery life and battery-swapping stations and obtain the final optimal feasible solution. The experimental results show that, the adaptive genetic algorithm can find satisfactory solution more quickly and effectively compared to the traditional genetic algorithm; the route arrangement considering power consumption and travel time can reduce the carbon emissions and total freight distribution costs; compared with the fixed parameter setting of the crossover and mutation probabilities, the adaptive parameter adjustment method can more effectively avoid the local optimum and improve the global search ability of the algorithm.

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.

There is an identity authentication mechanism in alliance blockchain, but even by using the mechanism, Byzantine malicious nodes still exist in the network, and the member nodes in the alliance may be controlled and utilized by the third-party enemies. To solve these problems, a detection-Practical Byzantine Fault Tolerance (d-PBFT) consensus algorithm that can monitor the node states was proposed. Firstly, the primary node was elected and the its state was checked to ensure that the elected primary node never be malicious before. Secondly, the consensus request submitted by the client was executed through the three-stage consensus process "pre-prepare-prepare-commit". Finally, the primary node state was assessed according to the three-stage achievement state. If primary node was unstable or malicious, it would be marked, and the malicious node would be added to the quarantine to wait for processing. In this algorithm, based on tolerating a specific number of Byzantine nodes, every node state was monitored all the time and the malicious nods would be isolated. In this case, the bad impact of malicious nodes on the alliance would be reduced. Experimental results show that the network with d-PBFT algorithm has high throughput and low consensus delay, and the consensus generation amount of the algorithm is 26.1% more than that of Practical Byzantine Fault Tolerance (PBFT) algorithm when alliance network includes Byzantine nodes. The d-PBFT algorithm not only improves the robustness of alliance network, but also improves the network throughput.

Aiming at the problem of navigation in the condition without artificial markers in farmland or wild environment, a precise visual navigation method for agricultural robot based on virtual navigation line was proposed. In this method, the robot can be guided to walk in a straight line without laying navigation lines or road signs. Firstly, the target area to be tracked was determined according to the requirements, and the robot was controlled to adjust the direction until the target moved to the center of vision field. Secondly, the reference target was determined according to the positions of the robot and the target, and the virtual navigation line was determined according to the positions of two targets. Thirdly, the navigation line was updated dynamically, and the offset angle and the offset distance were obtained by combining the virtual calibration line and the virtual navigation line. Finally, the fuzzy control table was constructed with the offset parameters, and the adjustment of rotation angle and walking speed of the robot was realized by the table. Experimental results show that the proposed algorithm can accurately recognize the navigation route and use the fuzzy control strategy to make the robot walk in a straight line to the target, and has the navigation accuracy within 10 cm.

To solve the problems of incomplete reconstruction, blurred boundary and residual noise in Magnetic Resonance (MR) image reconstruction, a non-convex total variation regularization reconstruction model based on L ₂ regularization was proposed. First, Moreau envelope and minmax-concave penalty function were used to construct the non-convex regularization of L ₂ norm, then it was applied into the total variation regularization to construct the sparse reconstruction model based on the isotropic non-convex total variation regularization. The proposed non-convex regularization was able to effectively avoid the underestimation of larger non-zero elements in convex regularization, so as to reconstruct the edge contour of the target more effectively. At the same time, it was able to guarantee the global convexity of objective function under certain conditions. Therefore, Alternating Direction Method of Multipliers (ADMM) was able to be used to solve the model. Simulation experiments were carried out to reconstruct several MR images under different sampling templates and sampling rates. Experimental results show that compared with several typical image reconstruction methods, the proposed model has better performance and lower relative error, its Peak Signal-to-Noise Ratio (PSNR) is significantly improved, which is 4 dB higher than that of traditional reconstruction method based on the non-convex regularization of L ₁ norm; in addition, the visual effects of the reconstructed images are promoted significantly, effectively maintaining the edge details of the original images.

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 an information explosion era, the large scale and structure complexity of datasets become problems in approximation calculation. Dynamic computing is an efficient approach to solve these problems. With the development of existing updating method applied to the dynamic approximation in multi-granular rough sets, a vector matrix based method for computing and updating approximations in Variable Precision Multi-Granulation Rough Sets (VPMGRS) was proposed. Firstly, a static algorithm for computing approximations based on vector matrix for VPMGRS was presented. Secondly, the searching area for updating approximations in VPMGRS was reconsidered, and the area was shrunk according to the properties of VPMGRS, effectively improving the time efficiency of the approximation updating algorithm. Thirdly, according to the new searching area, a vector matrix based algorithm for updating approximations in VPMGRS was proposed based on the static algorithm for computing approximations. Finally, the effectiveness of the designed algorithm was verified by experiments.

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.

In the study of personalized facial attractiveness, due to lack of features and insufficient consideration of the influence factors of public aesthetics, the prediction of personal preferences cannot reach high prediction accuracy. In order to improve the prediction accuracy, a new personalized facial attractiveness prediction framework based on feature-level and decision-level information fusion was proposed. Firstly, the objective characteristics of different facial beauty features were fused together, and the representative facial attractive features were selected by a feature selection algorithm, the local and global features of face were fused by different information fusion strategies. Then, the traditional facial features were fused with the features extracted automatically through deep networks. At the same time, a variety of fusion strategies were proposed for comparison. The score information representing the public aesthetic preferences and the personalized score information representing the individual preferences were fused at the decision level. Finally, the personalized facial attractiveness prediction score was obtained. The experimental results show that, compared with the existing algorithms for personalized facial attractiveness evaluation, the proposed multi-level fusion method has a significant improvement in prediction accuracy, and can achieve the Pearson correlation coefficient more than 0.9. The proposed method can be used in the fields of personalized recommendation and face beautification.

Data distributed parallel applications like MapReduce are widely used. Focusing on the issues such as low execution efficiency and high cost of such applications, a case analysis of Hadoop was given. Firstly, based on the analyses of the execution processes of such applications, it was found that the data volume, the numbers of the nodes and tasks were the main factors that affected their execution efficiency. Secondly, the impacts of the factors mentioned above on the execution efficiency of an application were explored. Finally, based on a set of experiments, two important novel rules were derived as follows. Given a specific volume of data, the execution efficiency of a data distributed parallel application could not be improved remarkably only by increasing the number of nodes, but the execution cost would raise on the contrary. However, when the number of tasks was nearly equal to that of the nodes, a higher efficiency and lower cost could be got for such an application. The conclusions are useful for users to optimize their data distributed parallel applications and to estimate the necessary computing resources to be rented in a cloud environment.

Concerning that the existing researches on public opinion propagation model are seldom combined with the practical opinion data and digging out the inherent law of public opinion propagation from the opinion big data is becoming an urgent problem, a parameter inversion algorithm of public opinion propagation model using neural network was proposed based on the practical opinion big data. A network opinion propagation model was constructed by improving the classical disease spreading Susceptible-Infective-Recovered (SIR) model. Based on this model, the parameter inversion algorithm was used to predict the network public opinion's trend of actual cases. The proposed algorithm could accurately predict the specific heat value of public opinion compared with Markov prediction model.The experimental results show that the proposed algorithm has certain superiority in prediction and can be used for data fitting, process simulation and trend prediction of network emergency spreading.

In terms of the problem that a safety-critical system will be paused, detected and resumed when security tools alert, and the delay between the occurrence and discovery of the false alarms (false positive or false negative) results in an effect on the availability of the guest Operating System (OS), a scheme based on virtualization was proposed. When a false alarm occurred, the operations of the suspicious application were quarantined correctly to avoid substantial system-wide damages. Then the operations of the suspicious application were logged and application inter-dependency information was generated according to its interactions with other applications. When the false alarm was determined, measures such as resuming the application's operations and killing the relevant applications according to the operation logs and inter-dependency information were taken so that the guest OS could reach the correct operating status quickly. The experimental results show that the scheme can reduce the overhead caused by rollback and recovery when a false alarm occurs. Compared to the situation without the proposed scheme, the overhead of handling the false alarm is reduced by 20%-50%. The proposed scheme can effectively reduce the effect of false alarm on the availability of clients, and can be applied in the cloud platform which provides services to safety-critical clients.

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.

Focusing on the issue that multi-view correlation analysis are not effective to exploit the correlation information and neglect latent discriminant information in images, a Dual-View Discriminant Correlation Analysis (DVDCA) approach based on dual view was proposed. Firstly, the supervised within-class correlation variation and between-class correlation variation were designed; secondly, within-class correlation variation was maximized and between-class correlation variation was minimized to extract the discriminant feature; finally, constrained dual-view discriminant correlation model was designed to exploit rich view information of both within-view and between-view. Compared with multi-view linear discriminant analysis, Canonical Correlation Analysis (CCA), Multi-view Discriminant Latent Space (MDLS), Uncorrelated Multi-view Discrimination Dictionary Learning (UMDDL) on the Multi-PIE dataset, the proposed algorithm can achieve recognition rate increase of 1.45-4.73 percentage points; on the MFD dataset, the proposed algorithm can achieve increase of 1.25-5.29 percentage points.

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).

Concerning the problem that a large number of services' massive behavior data leads to inefficiency in anomaly detection of services and dynamic composition of services leads to uncertainty in service under the distributed environment, a new distributed service anomaly detection model based on danger theory was proposed. Firstly, inspired by the biological processes of artificial immune recognizing abnormalities, this paper used differentiation to describe the variation of massive services' behavior data, and constructed characteristic triad to detect abnormal source. Then, service guided by the idea of cloud model, this paper resolved uncertainty among services by constructing status cloud of the services and computing the degree of membership between services, and calculated the danger zone. Finally, the simulation experiments of student for selecting courses were carried out. According to the simulation results, the model not only detects abnormal services dynamically, but also describes of the dependencies between services accurately, and improves the anomaly detection efficiency. The simulation results verify the validity and effectiveness of the model.

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.

Due to the lack of physiological and ecological characteristics while modeling plant morphology, the law of plant development cannot be expressed in the model. To solve this problem, a new plant morphology modeling method was proposed based on growth kinetics. Taking the plant-reed as an example, firstly, the growth kinetics of plant was studied, and the morphological model of plant was constructed based on the effective accumulated temperature and growth rate. Then the topological change of plant canopy structure was described using Open L-systems (Open-L) method. Finally, the algorithm of constructing virtual plant development model was presented by coupling with the geometric model and displaying model of plant topology and organs. The simulation results demonstrate that the proposed method is effective and feasible in visualizing the morphogenesis of plant and reflecting its growth mechanism, which also provides valuable evidences for dynamical control and prediction of plant development.

Since the segmentation performance of existing binary segmentation algorithm for video is excessively low, a binary probability segmentation algorithm in real-time based on Graphics Processing Unit (GPU) was proposed. The algorithm implemented a probabilistic segmentation based on the Quadratic Markov Measure Field (QMMF) model by regularizing the likelihood of each pixel of frame belonging to forground class or background class. In this algorithm, first two kinds of likelihood models, Static Background Likelihood Model (SBLM) and Unstable Background Likelihood Model (UBLM) were proposed. Secondly, the probability of each pixel belonging to background was computed by tonal transforming, cast shadow detecting and camouflage detecting algorithm. Finally, the probability of background which makes the energy function have a minimum value was computed by Gauss-Seidel model iteration and the binary value of each pixel was calculated. Moreover, illumination change, cast shadow and camouflage were included to improve the accuracy of segmentation algorithm. In order to fulfill the real-time requirement, a parallel version of our algorithm was implemented in a NVIDIA GPU. The accuracy and GPU execution time of the segmentation algorithm were analyzed. The experimental results show that the average missing rate and false detection rate of ViBe+ and GMM+ are 3 and 6 times those of QMMF, the average execution time of GPU of ViBe+ and GMM+ is about 1.3 times that of QMMF. Moreover, the average speedup of algorithm was computed and it is about 76.8.

At present, in the field of target recognition, the highest accuracy algorithm is the Deformable Part Model (DPM) for human detection. Aiming at the disadvantage of large amount of calculation, a parallel solution method based on Graphics Processing Unit (GPU) was proposed. In this paper, with the GPU programming model of OpenCL, the details of the whole DPM algorithm were implemented by the parallel methods,and optimization of the memory model and threads allocation was made. Through the comparison of the OpenCV library and the GPU implementation, under the premise of ensuring the detection effect, the execution efficiency of the algorithm was increased by nearly 8 times.

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.

For automatic detection of tire crown cord overlap defect, a detection method based on the crown X ray image was presented. Firstly, the gray cumulative projection curves that X-ray image was projected along different angles were obtained. Secondly, the local peak energy distribution of curves were calculated and the energy feature vector was constructed by the n largest peak energy values. Thirdly, the tire crown crack image was recognized by the maximum projection curve which could be distinguished through the energy feature vector by Support Vector Machine (SVM). Lastly, using the position inverse calculation, the tire crown crack was located. The experimental results demonstrate that the proposed approach was effective to detect the defects of tire crown which caused by tire cord overlap. The highest rate of correct detection can reach 97.7% in the 1000 crown images collected by the process of production.