Traditional relational data storage systems have been criticized by poor performance and lacking of fault tolerance, therefore it cannot satisfy the efficiency requirement of the massive unstructured data management. A non-relational storage management mechanism with high-performance and high-availability was proposed. First, a user-friendly application interface was designed, and data could be distributed to multiple storage nodes through efficient consistent hashing algorithm. Second, a configurable data replication mechanism was presented to enhance availability of the storage system. Finally, a query fault handling mechanism was proposed to improve the storage system's fault-tolerance and avoid service outages, which were caused by the node failure. The experimental results show that the concurrent access capacity of the proposed storage system increases by 30% and 50% respectively compared to traditional file system and relational database under different user workloads; meanwhile, the availability loss of the storage system under the fault state is less than 14% in a reasonable response time. Therefore, it is applicable for efficient storage management of massive unstructured data.

Focusing on the issue that the inflection points are hard to forecast in stock price volatility degrades the forecast accuracy, a kind of Lag Risk Degree Threshold Generalized Autoregressive Conditional Heteroscedastic in Mean (LRD-TGARCH-M) model was proposed. Firstly, hysteresis was defined based on the inconsistency phenomenon of stock price volatility and index volatility, and the Lag Degree (LD) calculation model was proposed through the energy volatility of the stock. Then the LD was used to measure the risk, and put into the average share price equation in order to overcome the Threshold Generalized Autoregressive Conditional Heteroscedastic in Mean (TGARCH-M) model's deficiency for predicting inflection points. Then the LD was put into the variance equation according to the drastic volatility near the inflection points, for the purpose of optimizing the change of variance and improving the forecast accuracy. Finally, the volatility forecasting formulas and accuracy analysis of the LRD-TGARCH-M algorithm were given out. The experimental results from Shanghai Stock, show that the forecast accuracy increases by 3.76% compared with the TGARCH-M model and by 3.44% compared with the Exponential Generalized Autoregressive Conditional Heteroscedastic in Mean (EGARCH-M) model, which proves the LRD-TGARCH-M model can degrade the errors in the price volatility forecast.

MapReduce computation model can not satisfy the efficiency requirement of graph data processing in the Hadoop cloud platform. In order to address the issue, a novel computation framework of graph data processing, called MyBSP (My Bulk Synchronous Parallel), was proposed. MyBSP is similar with Pregel developed from Google. Firstly, the running mechanism and shortcomings of MapReduce were analyzed. Secondly, the structure, workflow and principal interfaces of MyBSP framework were described. Finally, the principle of the PageRank algorithm for graph data processing was analyzed. Subsequently, the design and implementation of the PageRank algorithm for graph data processing were presented. The experimental results show that, the iteration processing performance of graph data processing algorithm based on the MyBSP framework is raised by 1.9-3 times compared with the algorithm based on MapReduce. Furthermore, the execution time of the MyBSP algorithm is reduced by 67% compared with MapReduce approach. Thus, MyBSP can efficiently meet the application prospect of graph data processing.

In order to investigate the cascading invulnerability attack strategy of complex network via community detection, the initial load of the node was defined by the betweenness of the node and its neighbors, this defining method comprehensively considered the information of the nodes, and the load on the broken nodes were redistributed to its neighbors according to the local preferential probability. When the network being intentionally attacked based on community detection, the couple strength, the invulnerability of Watts-Strogatz (WS) network, Barabási-Albert (BA) network, Erds-Rényi (ER) network and World-Local (WL) network, as well as network with overlapping and non-overlapping community under differet attack strategies were studied. The results show that the network's cascading invulnerability is negatively related with couple strength; as to different types of networks, under the premise that fast division algorithm correctly detects community structure, the networks invulnerability is lowest when the node with largest betweenness was attacked; after detecting overlapping community using the Clique Percolation Method (CPM), the network invulnerability is lowest when the overlapping node with largest betweenness was attacked. It comes to conclusion that the network will be largest destoryed when using the attack strategy of complex network via community detection.

This paper concerned the output tracking problem for a class of high-order stochastic nonlinear systems. Based on the backstepping control by adding a power integrator, an adaptive smooth state-feedback dynamic surface controller was proposed. The derivative of the designed adaption law was continuous by making use of the Sigmoid function. “Explosion of complexity” phenomenon in the adding a power integrator method design was eliminated by introducing a filter at each step of the recursive procedure and employing the dynamic surface control. The stability analysis was carried out by choosing an appropriate conol Lyapunov function. And its results show that the output can be regulated to the small neighborhood of the reference signal in probability. The results of a simulation example demonstrate the effectiveness of the proposed adaptive smooth state-feedback dynamic surface controller.

In order to deal with the problems of inefficient searching and low accuracy of Artificial Fish Swarm Algorithm （AFSA） for multimodal function optimization, an improved AFSA for multimodal function optimization was proposed. In the algorithm, the strategy of the survival of the fitter suppression was adopted, eliminating artificial fish which was situated in food with low concentration of similar artificial fish to select elite artificial fish swarm. Optimization for swarming behavior and following behavior contributed to artificial fish careful search in a new optimization trajectory to enhance its local search capacity. Modifying for preying behavior, artificial fish was avoided sinking flat position. In combination with Pattern Search Method (PSM), its local accuracy search capacity was enhanced. The simulation results indicate that the proposed algorithm has stronger global optimization and local optimization capabilities, and the search for each optimal solution accuracy has reached the ideal value, and it is able to be used for complex multimodal function optimization.