It is a hot research topic to solve the low performance of Virtual Network Function (VNF) in SDN/NFV (Software Defined Networking/Network Function Virtualization) architecture by designing hardware acceleration mechanism. After introducing the hardware acceleration resources to VNF, how to control and deploy these acceleration resources has been an urgent problem. To solve the problems above, a uniform hardware acceleration management architecture based on the accelerator cards on servers and OpenFlow switches was proposed. Based on this architecture, the model of acceleration resource deployment was built, and the evaluation indicators for the resource deployment mechanism was proposed by analyzing the impact of acceleration resources on service chain mapping. Finally, a two-stage acceleration resource deployment algorithm was designed. The experimental results show that, compared with Single-attribute Acceleration Resource Deployment algorithm (SARD) and Uniform Acceleration Resource Deployment algorithm (UARD), the proposed mechanism can optimize the deployment of the acceleration resources and improve the total traffic handled by acceleration resources and the utilization of acceleration resources by 41.4% and 14.5% respectively.

The performances and correctness of system are affected by the state transition real-time process of the cyber-physical system. In order to solve the problem, aiming at the state transition process of actor's output-event driven system, a new scheduling strategy of value evaluation for output-event of actor named Value Evaluation-Information Entropy and Quality of Data (VE-IE&QoD) was proposed. Firstly, the real-time performance of event was expressed through the super dense time model. The self-information of the output-event, the information entropy of the actor and the quality of data were defined as the function indexes of value evaluation. Then, the value evaluation mission was executed for the process of the actor in performing task and it was considered about suitably increasing the weighting coefficient for parametric equation. Finally, the discrete event models which contain the proposed VE-IE&QoD scheduling strategy, the traditional Earliest Deadline First (EDF) scheduling algorithm and Information Entropy ^* (IE ^*) scheduling strategy were built by Ptolemy Ⅱ platform. The operation situation of different algorithm models was analyzed, the change of value evaluation and execution time of different algorithm models were compared. The experimental results show that, the VE-IE&QoD scheduling strategy can reduce the system average execution time, improve the memory usage efficiency and task value evaluation. The proposed VE-IE&QoD scheduling strategy can improve the system performance and correctness to some extent.

In order to apply reasoning rules of the description logic to analyze and solve the simple contradiction problem, the extension set was introduced to be the set theory foundation of the description logic SHOQ, and a new description logic named D-SHOQES (Dynamic Description Logic SHOQ Based on Extension Set) was proposed. The cut sets of extension concepts and extension roles were defined as atomic concepts and atomic roles, and the action theory was injected to obtain the qualitative change domain and the quantitative change domain of the concepts and roles. The semantics of concepts, roles and actions in D-SHOQES were given, as well as the Tableau-algorithm reasoning rules. Finally, the method of solving contradiction problem was researched, which offered a strategy for the solution to contradiction problem.

An access control scheme for cloud storage proposed by Tang et al. (TANG Y，LEE P，LUI J，〖WTBX〗et al.〖WTBZ〗 Secure overlay cloud storage with access control and assured deletion. IEEE Transactions on Dependable and Secure Computing，2012，9(6)：903-916） was analyzed and proved to be vulnerable to collusion attacks, and a detailed attack method was given. To address this problem of the given scheme, this paper took the advantage of the property of collusion attack resistant of the attribute based encryption algorithm and improved a ciphertext-policy attribute-based encryption (CP-ABE) algorithm which used the access tree. Then the improved algorithm could be used seamlessly into the access control and assured deletion scheme for cloud storage without changing the cloud server. Finally, the security against the chosen plaintext attack was proved under the Decision Bilinear Diffie-Hellman (DBDH) assumption, and the ability of resisting the collusion attack of the scheme was proved by analyzing a practical cloud situation.

Signal decomposition is a process that obtains information from signals and it is a foundational and key technique for many fields such as pattern recognition, intelligent system and machinery fault diagnosis. It is very important to study non-stationary signal decomposition which always includes lots of information that can reflect the changing of the system and widely exists. After improving the sparsity of signal representation, the engineering background of feature extraction for non-stationary signal was studied in this paper, the characteristics, mechanisms, development history and current and future challenges of five types of methods were analyzed in depth, the models of these methods were compared, together with the state-of-the-art of feature extraction models in signal processing and analysis and some successful applications available were systematically reviewed. Finally, several main problems and a few deficiencies were pointed out, and future research directions were anticipated.

The model learned by ranking algorithm based on traditional pairwise method does not work well by ranking measure, such as Normalized Discounted Cumulative Gain (NDCG). To solve this problem, a novel ranking algorithm is proposed. The algorithm uses the same train data as the traditional way, what different is defining a new object function faced to NDCG. For the problem that the function is non-smooth, difficult to directly optimize, the algorithm presents to use the cutting plane algorithm which not only solve the problem above but also make the number of iteration not depending on the training size. Experimental results on the benchmark datasets prove the effectiveness of the proposed algorithm.