In comparison with single kernel learning， Multiple Kernel Learning （MKL） methods obtain better performance in the tasks of classification and regression. However， all the traditional MKL methods are used for tackling two-class or multi-class classification problems. To make MKL methods fit for dealing with the problems of One-Class Classification （OCC）， a Centered Kernel Alignment （CKA） based multiple kernel One-Class Support Vector Machine （OCSVM） was proposed. Firstly，CKA was utilized to calculate the weight of each kernel matrix， and the obtained weights were used as the linear combination coefficients to linearly combine different types of kernel functions to construct the combination kernel function and introduce them into the traditional OCSVM to replace the single kernel function. The proposed method can not only avoid the selection of kernel function， but also improve the generalization and anti-noise performances. In comparison with other five related methods including OCSVM，Localized Multiple Kernel OCSVM （LMKOCSVM） and Kernel-Target Alignment based Multiple Kernel OCSVM （KTA-MKOCSVM） on 20 UCI benchmark datasets， the geometric mean （g-mean） values of the proposed algorithm were higher than those of the comparison methods on 13 datasets. At the time， the traditional single kernel OCSVM obtained better results on 2 datasets，LMKOCSVM and KTA-MKOCSVM achieved better classification effects on 5 datasets. Therefore， the effectiveness of the proposed method was sufficiently verified by experimental comparisons.

The automatic flight control system has complex structure with many relevant components, resulting in long time fault diagnosis, which affects the efficiency of aircraft operation. Aiming at the problems, a remote real-time fault diagnosis scheme based on Aircraft Communication Addressing & Reporting System (ACARS) was proposed. Firstly, the fault characteristics of the automatic flight control system were analyzed, and the detection filter was designed and built. Then, the key information of the automatic flight control system transmitted in real time by ACARS data-link was used to realize the residual calculation of the relevant components, and the fault diagnosis and location were carried out according to the residual decision algorithm. Finally, because of the large difference between residual errors of different fault components and inconsistent decision-making threshold, an improved residual decision-making algorithm based on quadratic difference was proposed. This algorithm reduced the overall change trend of the detection object, reduced the influence of random noise and interference, and avoided a transient fault being considered as a system fault. The simulation results show that the proposed algorithm avoids the complexity of multiple decision-making thresholds. Its fault detection time is about 2 seconds with 0.1 second sampling time, which shortens the fault detection time greatly, and the effective fault detection rate is more than 90%.

Networks heavily rely on middlebox to provide critical service functions, with the development of Software Defined Network (SDN) and Network Function Virtualization (NFV) technology, how to use the new technology to deploy middleboxs and guide flow through a specific sequence of middleboxs to complete the service function chain is still a problem to be solved. A construction method based on the optimal node utility maximization, namely NUM (Node Utility Maximization), was proposed for the service function chain construction problem, which took into account the deployment and steering of the virtual middleboxs in the meantime. Firstly, a service function chain collaborative construction mechanism was designed based on SDN+NFV technology. Secondly, the node selection model and the utility maximization model were introduced in this mechanism, according to the solution of middleware box deployment and traffic guidance problem. Finally, the model was solved by applying Tabu search-combined simulated annealing algorithm. The simulation results show that the proposed method NUM is superior to the traditional algorithm in terms of construction time, success rate and network congestion rate, and the utility of the nodes is improved by about 20% by using the proposed service function chain construction method.

When the traditional genetic algorithm is used to solve the computation-intensive task, the execution time of the fitness function increases rapidly, and the convergence rate of the algorithm is very low when the population size or generation increases. A "coarse-grained combined with master-slave" HyBrid Parallel Genetic Algorithm (HBPGA) was designed and implemented on Sunway "TaihuLight" supercomputer which is ranked first in the latest TOP500 list. Two-level parallel architecture was used and two different programming models, MPI and Athread were combined. Compared with the traditional genetic algorithm implemented on single-core or multi-core cluster with single-level parallel architecture, the algorithm using two-level parallel architecture was implemented on the Sunway many-core processors, better performance and higher speedup ratio were achieved. In the experiment, when using 16×64 CPEs (Computing Processing Elements), the maximum speedup can reach 544, and the CPE speedup ratio is more than 31.

Concerning the network service failure caused by the virtual network function failure of the service function chain in the network function virtualization environment, a resource utility maximization backup method of virtual network function was put forward to improve the network reliability. First, the backup problem of virtual network functions was analyzed in detail and a reliability evaluation model was established; then a corresponding backup mechanism was put forward, and the advantages of the proposed mechanism over other mechanisms were proved. Secondly, a global backup method and a backup selection strategy were designed to select the corresponding virtual network functions until the reliability requirement was satisfied. Finally, simulation and experimental results showed that compared with GREP, JP+random selection and DSP+random selection, the proposed method achieved excellent performance in terms of reliability and resource utilization, especially improved the rate of request acceptance by 18.8%-25% and the resource utilization ratio by 15%-20%. The experimental results demonstrate that the proposed method can effectively utilize the resources to improve the network reliability.

To deal with weak edge, intensity inhomogeneity and low contrast that may appear in finger-vein images, a new segmentation algorithm based on even-symmetric Gabor filter and level set method was proposed. Firstly, the even-symmetric Gabor filter was used to filter the finger-vein image through 8 different orientations; secondly, finger-vein image based on the 8 filtered results was reconstructed to obtain the high quality image with significantly improved gray contrast between target and background; finally, the level set algorithm combining local features and global features was applied to segment finger-vein image. Compared with the level set algorithm proposed by Li, et al. (LI C, HUANG R, DING Z, et al. A variational level set approach to segmentation and bias correction of images with intensity inhomogeneity. MICCAI'08: Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II. Berlin: Springer, 2008: 1083-1091), and Legendre Level Set (L2S) algorithm, the percentage of Area Difference (AD) of the proposed algorithm decreased by 1.116% and 0.370% respectively, and the Relative Difference Degree (RDD) reduced by 1.661% and 1.379% respectively. The experimental results show that the proposed algorithm can achieve better results compared with traditional level set image segmentation algorithms that only consider local information or global information.

Aiming at the poor suppression ability for the high-frequency noise in Huber-MRF prior model and the excessive punishment for the high frequency information of image in Gauss-MRF prior model, an adaptive regularization HL-MRF model was proposed. The method combined low frequency function of Huber edge punishment with high frequency function of Lorentzian edge punishment to realize a linear constraint for low frequency and a less punishment for high frequency. The model gained its optimal solution of parameters by using adaptive constraint method to determine regularization parameter. Compared with super-resolution reconstruction methods based on Gauss-MRF prior model and Huber-MRF prior model, the method based on HL-MRF prior model obtains higer Peak Signal-to-Noise Ratio (PSNR) and better performace in details, therefore it has ceratin advantage to suppress the high frequency noise and avoid excessively smoothing image details.

When Augmented Reality (AR) browser running in the Point of Interest (POI) dense region, there are some problems like data loading slowly, icon sheltered from the others, low positioning accuracy, etc. To solve above problems, this article proposed a new calculation method of the Global Positioning System (GPS) coordinate mapping which introduced the distance factor, improved the calculating way of coordinates based on the angle projection, and made the icon distinguished effectively after the phone posture changed. Secondly, in order to improve the user experience, a POI labels focus display method which is in more accord with human visual habits was proposed. At the same time, aiming at the low positioning accuracy problem of GPS, the distributed mass scene visual recognition technology was adopted to implement high-precision positioning of scenario.

The hard disk of cloud computing platform is not reliable. This paper proposed to use Self-Monitoring Analysis and Reporting Technology (SMART) log to predict hard disk failure based on Classification using lOcal clusterinG with Over-Sampling (COG-OS) framework. First, faultless hard disks were divided into multiple disjoint sample subsets by using DBScan or K-means clustering algorithm. And then these subsets and another sample set of faulty hard disks were mixed, and Synthetic Minority Over-sampling TEchnique (SMOTE) was used to make the overall sample set tend to balance. At last, faulty hard disks was predicted by using LIBSVM classification algorithm. The experimental results show that the method is feasible. COG-OS improves SMOTE+Support Vector Machine (SVM) on faulty hard disks' recall and overall performance, when using K-means method to divide samples of faultless hard disks and using LIBSVM method with Radial Basis Function (RBF) kernel to predict faulty hard disks.

There is a potential security risk in the communication process by using QR (Quick Response) code. This paper, therefore, proposed an encryption algorithm. Ising model has a matrix form, in which it is same with QR code, and its process of state change supports parallel operation. An encryption matrix was generated according to the model’s change rule, and then the ciphertext of QR code was produced, using the matrix and combining coding theory of the code. It was proven by the experiments that the algorithm did well in terms of randomness and sensitivity of key. With its simplicity, security and high efficiency, it could be applied to the secure communication for QR code.

Chip layer assignment is a key step in analytical placement of 3D Integrated Circuits (ICs). Analytical placement could face the conversion from 3D continuous space in z-direction to several connected 2D chip layer spaces by layer assignment. However, layer assignment may destroy the previous optimal solution in 3D continuous space. To realize the transition from an optimal 3D placement to a legalized, layer-assigned placement smoothly, a layer assignment method was proposed by using the minimum cost flow, which protected solution space and inherited optimal wirelength at most. The layer assignment method was embedded in a multilevel non-linear placement of 3D ICs which minimized the weighted sum of total wirelength and Through Silicon Via (TSV) number subject to area density constraints. The proposed placement algorithm can achieve better wirelength results, TSV number and run time in comparison with the recent 3D placement methods.

This paper proposed an Improved Quantum Genetic Algorithm (IQGA) for the problem of slow convergence in test data generation. There are two main improvements. First, every bit of every individual was reversed to conduct the evolution; second, the binary individuals were mutated after measurement, instead of the traditional exchange of the probability amplitude of quantum bits. IQGA was applied into test data generation. The experiments on three basic programs prove that IQGA is better than QGA in terms of coverage rate and the number of iterations. IQGA can not only ensure the right direction of the evolution of populations, but also avoid premature phenomenon, and it can get the solution at a faster convergence speed.

Because of the cloud computings characteristics of being dynamic and open, continuous malicious attacks happen frequently. With the idea of trusted cloud, a defense system model based on trust for cloud computing was constructed to guarantee the cloud security. Through real-time monitoring, users behavior evidences could be obtained and standardized; a new method for users trust evaluation based on fuzzy Analytic Hierarchy Process (AHP) was presented, which gradually determined the weights of behavior evidences, achieved quantitative assessment of behavioral trust; so as to provide great security defense for users. Besides, multiple detection engines were used to conduct a comprehensive inspection of suspicious files and to make integrated decisions. The experimental results show the system model can effectively eliminate the malicious behaviors from undesirable users, reduce users damages caused by virus and achieve a two-way defense for both cloud and client.

The principle of DDoS(Distributed Denial of Service) useing the limitation of TCP/IP was introduced.Based on the monitor on the network packet traffic and the useage of the system resource,a method that leads the RSA algorithm into the SYN Cookie mechanism to encrypt and filtrate the IP packets was proposed,which can detect and reduce the damage of DDoS.The method have maken a good performance in the experimentation.

The method was introduced to encapsulate the detailed controls of the concrete DVB-CI device with device driver and provide the uplayer program an abstract device on the embedded Linux system. A DVB-CI device driver based on MontaVista's Hard Hat Linux system and IBM STB02500 Set-Top Box Integrated Controller was implemented as an example.