For the scheduling optimization problem of cross-over twin Automated Stacking Crane (ASC), a multi-objective mathematical formulation model was proposed with considering the conflicts between two ASCs while reaching the same bay. The operation sequences of ASC was optimized, the optimal task sequence, task finish time and ASC's idle time after avoiding the conflict were gained and the practicality of optimized model was proved. Four experiment scenarios were performed to further analyze the efficiency difference between twin ASC and single ASC, and the effects of parameter changes. The results illustrate that the utilization rate of equipment of twin ASC is 107% lower, and the operation efficiency is 35% higher than single ASC. The ASV's total operation time will be decreased while the number of containers decreases and the travel speed of ASC increases. While the ratio of storages and retrieves of containers is 1, the optimized tasks' total operation time and ASC's idle time will be gained. It shows that the container terminals can improve their operation through adjusting the ratio of storages and retrieves during a short period or optimizing ASC travel speed.

For the problem of that the particle easily falls into local optimum when avoiding obstacles and cannot walk forward, the method of retreating the position of the local optimal particle to its historical best position and searching the feasible and optimal location in the 8-neighborhood of this position was proposed. The position which is nearest to the target and is not a barrier could be found by this method, then the particle was moved to it. Meanwhile, the global optimal position of current generation particle swarm could be found, and the location of each particle was set for this position to continue iteration. In the experiments for obstacle avoidance in grid map, the fact was found that when encountering obstacles, the particles fell into local optimum by the traditional methods, but particles could successfully avoid obstacles and arrive the end by using the improved algorithm. The improved algorithm was introduced into the 3D visual simulation system, in large map with many obstacles scenarios, the probability of falling into local optimum of the particle was reached to 50% which resulted in routing failure. After adding a circular slope to the obstacles for the further improvement of the algorithm, the probability of successful path-finding of the particle was increased to 83%. The experimental results show that the search ability of the improved algorithm is increased and it can effectively plan path in a complex scene.

Pre-positioning facilities for collecting tents held by communities is helpful to improve the efficacy of tents collection in post-earthquake relief. A problem for pre-positioning facilities that collect the tents held by commnities considering various earthquake scenarios was studied. A bi-stage stochastic programming model was formulated for the facility location problem. The model aimed at minimizing economic costs and time-related costs. Considering the effects of weights and other parameters on the solutions, five experiments were performed and analyzed. A case was used to verify the practicability of the developed model that located the facilities of tents collection from communities in Pudong New District, Shanghai. Finally, a scenario based on Ya'an earthquake was used to analyze the influence of parameters on the solution under such a certain scenario.

With the growth of cloud technology, distributed application platform develops towards elasticity resources and dynamic migration environment. The refinement of distributed application access control policies was associated with resources and environment, which also needs to improve performance to adapt to the dynamics. Although present access control space policies conflict analysis methods could be used in the conflict analysis of distributed application access control policies refinement. The granularity of its calculating unit is too fine to make batter performance. In this article, the authors designed a conflict analysis algorithm used in distributed application access control policies refinement, the conflict analysis algorithm was based on recursive calculation the intersection of sets and the calculation unit of the algorithm was permission assignment unit which improved computing granularity. The experimental results and analysis show that the proposed algorithm has better performance, and fits the needs of improving computing performance of cloud platform access control policies refinement.

Large sample size and characteristics of high dimension affects the K-Nearest Neighbor (KNN) classification algorithm in classification performance. Therefore, an improved KNN method named AMI&CD-KNN was put forward, which had feature dimension reduction and pruning mechanism based on average mutual information and class distinction of the feature parameters. Firstly, it used the concept of average mutual information in entropy to measure the accuracy of the feature parameters reflecting the class characteristic information. Secondly, it described the class distinction through the feature parameters odd ratio relative class and its distribution probability in the dataset, measured the size of the amount of class information provided by feature parameters. Finally, the relation was established between average mutual information and class distinction, and the sample pruning method was designed. Therefore, the classification was speeded up while ensuring the classification accuracy. The theoretical analysis and simulation experiment show that compared with the KNN and other algorithms with pruning mechanism, the improved algorithm has better classification generalization.

Focusing on the random packet loss, the high failure rate of failure detection for network storage system with random packet loss was studied. A Fault Detection (FD) for network storage with random packet dropout was presented. The residual generation and residual evaluation as well as False Alarm Rate （FAR） were used in the approach. First, residual generation was carried out in the periodic system framework. Then, residual evaluation was got by making use of the stochastic properties of the random packet loss. Finally, performance evaluation of the computation of FAR is fulfilled with the assistance of Chebyshev's inequality, and the algorithms of fault detection were given. The simulation results show that this approach can effectively detect the fault. Moreover, this approach is sensitive to fault.

Considering massive scale of cloud storage solutions, the traditional data distribution strategy confronts challenges to improve scalability and flexibility. This paper proposed an efficient data distribution strategy. Based on consistent hashing algorithm, the strategy introduced the virtualization technology, and employed virtual node to improve load balance. Moreover, the strategy used a new capacity-aware method to improve the performance of the cloud storage system. The evaluation experiments demonstrate that the proposed data distribution strategy improves system performance in both homogeneous and heterogeneous distributed storage architectures.

The existing TCP multicast congestion control mechanisms do not have speed smoothness, Round-Trip Time (RTT)-fairness, and high efficiency in highspeed networks. To solve these inherent disadvantages derived from TCP, an Adaptive High-Speed Multicast (AHSM) congestion control mechanism was proposed based on population ecology theory. The new mechanism estimated the bottleneck bandwidth and the rate of background traffic at each receiver. Then the estimated values were used in population ecology model to calculate requested service rate. The receiver with lowest requested service rate was selected as representative to control the sending rate via feedbacks. The simulation results show that the new mechanism has speed smoothness and RTT-fairness, it also has high transmission efficiency in high-speed networks. It can share bandwidth with unicast flow in both low and high-speed networks.

It is necessary to study those watermark algorithms appropriate to the special feature of fractal transform. By analyzing related parameters in the process of image fractal transform and taking some trials, a new watermark algorithm embedding and extracting the watermark information was proposed by modulating the parameter of contrast factor which mapped the domain blocks to their best matched blocks in fractal transformation. Higher watermark capacity could be embedded into image at low algorithm complexity without having perceptible influence on the quality of the image. The extraction algorithm avoided the loss of watermark information because of the missed synchronization between original image and watermarked image, which improved the accuracy in extraction. Experiment results show the effectiveness of the algorithm.