Concerning the large computation and low search efficiency in ciphertext retrieval, a secure ranked search scheme based on Simhash was proposed. In this scheme, a Secure Multi-keyword Ranked search Index (SMRI) was constructed based on the dimensionality reduction idea of Simhash, the documents were processed into fingerprints and vectors, the B+ tree was built with the segmented fingerprints and encrypted vectors and the "filter-refine" strategy was adopted to searching and sorting. Firstly, the candidate result set was obtained by matching the segmented fingerprints to perform the quick retrieval, then the top-k results were ranked by calculating the Hamming distance and the vector inner product between candidate result set and query trapdoor, and the Simhash algorithm with secret key and the Secure k-Nearest Neighbors (SkNN) algorithm ensured the security of the retrieval process. Simulation results show that compared with the method based on Vector Space Model (VSM), the SMRI-based ranked search scheme has lower computational complexity, saves time and space cost, and has higher search efficiency. It is suitable for fast and secure retrieval of massive ciphertext data.
On-line detection of fabric defects is a major problem faced by textile industry. Aiming at the problems such as high false positive rate, high false negative rate and low real-time in the existing detection of fabric defects, an on-line detection algorithm for fabric defects based on deep learning was proposed. Firstly, based on GoogLeNet network architecture, and referring to classical algorithm of other classification models, a fabric defect classification model suitable for actual production environment was constructed. Secondly, a fabric defect database was set up by using different kinds of fabric pictures marked by quality inspectors, and the database was used to train the fabric defect classification model. Finally, the images collected by high-definition camera on fabric inspection machine were segmented, and the segmented small images were sent to the trained classification model in batches to realize the classification of each small image. Thereby the defects were detected and their positions were determined. The model was validated on a fabric defect database. The experimental results show that the average test time of each small picture is 0.37 ms by this proposed model, which is 67% lower than that by GoogLeNet, 93% lower than that by ResNet-50, and the accuracy of the proposed model is 99.99% on test set, which shows that its accuracy and real-time performance meet actual industrial demands.
With the development of remote sensing technology, the data type and data volume of remote sensing data has increased dramatically in the past decades which is a challenge for traditional storage mode. A combination of quadtree and Hilbert spatial index was proposed in this paper to solve the the low storage efficiency in HBase data storage. Firstly, the research status of traditional terrain data storage and data storage based on HBase was reviewed. Secondly the design idea on the combination of quadtree and Hilbert spatial index based on managing global data was proposed. Thirdly the algorithm for calculating the row and column number based on the longitude and latitude of terrain data, and the algorithm for calculating the final Hilbert code was designed. Finally, the physical storage infrastructure for the index was designed. The experimental results illustrate that the data loading speed in Hadoop cluster improved 63.79%-78.45% compared to the single computer, the query time decreases by 16.13%-39.68% compared to the traditional row key index, the query speed is at least 14.71 MB/s which can meet the requirements of terrain data visualization.
Since it's hard to analyze the cryptographic procedure using method of property scan or debugging for the variety and different implementation of cryptographic algorithms, a method was proposed based on library function prototype analysis and their calling-graph building. Library functions prototype analysis is analyzing cryptographic algorithm knowledge and library frame knowledge to form a knowledge base. Calling-graph building is building a calling-graph that reflects the function calling order according to parameter value of the functions. Finally cryptographic algorithm knowledge and library frame knowledge on the calling-graph were extracted. The method discriminated common cryptographic algorithm almost 100%, and it could not only recover cryptographic data, key and cryptographic mode, but also help to analyze the relationship between more than two cryptographic algorithms dealing with the same data. The method could be used to analyze Trojan, worm and test whether the library is used correctly.
To solve the defect of traditional network traffic prediction forecasting, and obtain good forecasting results of network traffic, a network traffic forecasting model based on Gaussian Process Regression (GPR) was proposed. Firstly, the time delay and embedding dimension of network traffic were calculated to construct the learning samples of GPR, and then training samples were input to Gaussian process to learn in which Invasive Weed Optimization (IWO) algorithm was used to optimize the parameters of Gaussian process, and finally, the forecasting model of network traffic was established based on the optimal parameters, and the performance was tested by network traffic data. The results show that the proposed model can improve the forecasting precision of network traffic and it has great practical application value.