During multi-slice interactive visualization for out-of-core seismic volume on common computing platform, traditional cache scheduling approaches, which take no account of the spacial relationship between blocks and slices, lead to the low cache hit rate while interacting. And it's also difficult to achieve high rendering quality by use of common multi-resolution rendering methods. In view of these problems, a cache scheduling strategy named Maximum Distance First Out (MDFO) was designed. Firstly, according to the spatial position of the interactive slice, the scheduling priority of the block in the cache was improved, which ensures that the candidate block has a higher hit rate when the slice interacts continuously. Then, a two-stage slice interaction method was proposed. By using the fixed resolution body block to ensure the real-time interaction, the final display quality was improved by the step-by-step refinement, and the information entropy of the block data was further combined to enhance the resolution of the user's region of interest. The experimental results show that the proposed method can effectively improve the overall hit rate of the body block and reach the proportion of more than 60%. Meanwhile, the two-stage strategy can achieve higher quality images for application-oriented requirements and resolve the contradiction between interaction efficiency and rendering quality for out-of-core seismic data visualization.

Focusing on the issue that the current related research about social network do not consider subsets for neighborhood's privacy preserving, and the specific properties of neighborhood subsets also lead individual privacy disclosure, a new (θ, k)-anonymous model was proposed. According to the k-isomorphism ideology, the model removed labels of neighborhood subsets which needed to be protected in social network, made use of neighborhood component coding technique and the method of node refining to process nodes in candidate set and their neighborhood information, then completed the operation of specific subsets isomorphism with considering the sensitive attribute distribution. Ultimately, the model satisfies that each node in neighborhood subset meets neighborhood isomorphism with at least k-1 nodes, as well the model requires the difference between the attribute distribution of each node in the neighborhood subset and the throughout subsets is not bigger than θ. The experimental results show that, (θ, k)-anonymous model can reduce the anonymization cost and maximize the utility of the data.

Given the feature extraction of the furnace flame image produced in boilers and industrial production, a hierarchical adaptive method to extract salient points was proposed. First the Block Difference of Inverse Probabilities (BDIP) model was used to change the original image into BDIP image. On the basis of this, the BDIP image was made into Haar wavelet transform, the salient value of two-dimensional image was calculated by the improved weighted method, and then a non-equilibrium quadtree was built through the proposed adaptive method. The root of quadtree represented the salient value of the image, and the salient points number of subtree was determined according to the ratio of the salient value of every subtree to the salient value of parent node. The proposed extracting algorithm was salient points compared with the extracting algorithms based on BDIP and based on Haar wavelet transform. The experimental results show that edge accuracy and comprehensive feature retrieval accuracy at least increase by 10% and 3.5% respectively. The proposed method overcomes the shortcoming of traditional way that it extracts too many salient points and some extracted points are not salient, at the same time the method avoids local gather of salient points.

The existing Time Division Multiple Access (TDMA) scheduling methods for industrial emergency data under the conditions of asynchronous and multi-channel medium have the problems of high delay, saturated Control Channel (CC), and large energy consumption. To solve these problems, an Emergency data scheduling algorithm Oriented Asynchronous Multi-channel industrial wireless sensor networks, called EOAM, was proposed. First, the receiver-based strategy was adopted to solve the problem of saturated control channel during asynchronous multi-channel scheduling. Then a well-designed Special Channel (SC) together with the priority indication method was proposed to provide fast channel switch and real-time transmission of emergency data; additionally, the non-urgent data was allowed to occupy channel by a backoff-based mechanism indicated by the priority indication method, which could ensure the utilization of special channel. EOAM was suitable for both unicast and broadcast communications. The simulation results show that, compared with the Distributed Control Algorithm (DCA), the transmission delay of EOAM can reach 8 ms, the reliability is above 95%, and the energy consumption is reduced by 12.8%, which can meet the transmission requirements of industrial emergency data.