Video super-resolution methods based on deep learning mainly focus on the inter-frame and intra-frame spatio-temporal relationships in the video, but previous methods have many shortcomings in the feature alignment and fusion of video frames, such as inaccurate motion information estimation and insufficient feature fusion. Aiming at these problems, a video super-resolution model based on Attention Fusion Network(AFN) was constructed with the use of the back-projection principle and the combination of multiple attention mechanisms and fusion strategies. Firstly, at the feature extraction stage, in order to deal with multiple motions between neighbor frames and reference frame, the back-projection architecture was used to obtain the error feedback of motion information. Then, a temporal, spatial and channel attention fusion module was used to perform the multi-dimensional feature mining and fusion. Finally, at the reconstruction stage, the obtained high-dimensional features were convoluted to reconstruct high-resolution video frames. By learning different weights of features within and between video frames, the correlations between video frames were fully explored, and an iterative network structure was adopted to process the extracted features gradually from coarse to fine. Experimental results on two public benchmark datasets show that AFN can effectively process videos with multiple motions and occlusions, and achieves significant improvements in quantitative indicators compared to some mainstream methods. For instance, for 4-times reconstruction task, the Peak Signal-to-Noise Ratio(PSNR) of the frame reconstructed by AFN is 13.2% higher than that of Frame Recurrent Video Super-Resolution network(FRVSR) on Vid4 dataset and 15.3% higher than that of Video Super-Resolution network using Dynamic Upsampling Filter(VSR-DUF) on SPMCS dataset.

To solve the high storage cost and low retrieval speed problems in malicious Structure Query Language (SQL) detection faced by Nearest Neighbor (NN) method, a Hash learning based Malicious SQL Detection (HMSD) method was proposed. In this algorithm, Hash learning was used to learn the binary coding representation for SQL statements. Firstly, the SQL statements were presented as real-valued features by washing and deleting the duplicated SQL statements. Secondly, the isotropic hashing was used to learn the binary coding representation for SQL statements. Lastly, the retrieval procedure was performed and the detection speed was improved by using binary coding representation. Experimental results show that on the malicious SQL detection dataset Wafamole, the dataset is randomly divided so that the training set contains 10 000 SQL statements and the test set contains 30 000 SQL statements, at the length of 128 bits, compared with nearest neighbor method, the proposed algorithm has the detection accuracy increased by 1.3%, the False Positive Rate (FPR) reduced by 0.19%,the False Negative Rate (FNR) decreased by 2.41%, the retrieval time reduced by 94%, the storage cost dropped by 97.5%; compared with support vector machine method, the proposed algorithm has the detection accuracy increased by 0.17%, which demonstrate that the proposed algorithm can solve the problems of nearest neighbor method in malicious SQL detection.

Random multiple access communication is an indispensable part of computer communication research. A three-slot P-Persistent Carrier Sense Multiple Access (P-CSMA) protocol with variable collision duration in Wireless Sensor Network (WSN) was proposed to solve the problem of traditional P-CSMA protocol in transmitting and controlling WSN and energy consumption of system. In this protocol, the collision duration was added to the traditional two-time-slot P-CSMA protocol in order to change the system model to three-time-slot model, that is, the duration of information packet being sent successfully, the duration of packet collision and the idle duration of the system.Through the modeling, the throughput, collision rate and idle rate of the system under this model were analyzed. It was found that by changing the collision duration, the loss of the system was reduced. Compared with the traditional P-CSMA protocol, this protocol makes the system performance improved, and makes the lifetime of the system nodes obtained based on the battery model obviously extended. Through the analysis, the system simulation flowchart of this protocol is obtained. Finally, by comparing and analyzing the theoretical values and simulation values of different indexes, the correctness of the theoretical derivation is proved.

Focused on the issue that developers cannot quickly figure out the models they need from various models, an automatic annotation method of visual deep neural network based on natural language processing technology was proposed. Firstly, the field categories of visual neural networks were divided, the keywords and corresponding weights were calculated according to the word frequency and other information. Secondly, a keyword extractor was established to extract keywords from paper abstracts. Finally, the similarities between extracted keywords and the known weights were calculated in order to obtain the application fields of a specific model. With experimental data derived from the papers published in three top international conferences of computer vision: IEEE International Conference on Computer Vision(ICCV), IEEE Conference on Computer Vision and Pattern Recognition(CVPR) and European Conference on Computer Vision(ECCV), the experiments were carried out. The experimental results indicate that the proposed method provides highly accurate classification results with a macro average value of 0.89. The validity of this proposed method is verified.

In order to prolong the lifespan of heterogeneous directional sensor network, a node scheduling algorithm based on Enhanced Coral Reef Optimization algorithm (ECRO) and with different monitoring requirements for different monitoring targets was proposed. ECRO was utilized to divide the sensor set into multiple sets satisfying the coverage requirements, so that the network lifespan was able to be prolonged by the scheduling among sets. The improvement of Coral Reef Optimization algorithm (CRO) was reflected in four aspects. Firstly, the migration operation in biogeography-based optimization algorithm was introduced into the brooding of coral reef to preserve the excellent solutions of the original population. Secondly, the differential mutation operator with chaotic parameter was adopted in brooding to enhance the optimization ability of the offspring. Thirdly, a random reverse learning strategy were performed on the worst individual of population in order to improve the diversity of population. Forthly, by combining CRO and simulated annealing algorithm, the local searching capability of algorithm was increased. Extensive simulation experiments on both numerical benchmark functions and node scheduling were conducted. The results of numerical test show that, compared with genetic algorithm, simulated annealing algorithm, differential evolution algorithm and the improved differential evolution algorithm, ECRO has better optimization ability. The results of sensor network node scheduling show that, compared with greedy algorithm, the Learning Automata Differential Evolution (LADE) algorithm, the original CRO, ECRO has the network lifespan improved by 53.8%, 19.0% and 26.6% respectively, which demonstrates the effectiveness of the proposed algorithm.

Aiming at the problems of low utilization rate, high error rate, no recognition ability and manual participation in video surveillance processing in coastal and inland waters of China, a new ship tracking and recognition method based on Darknet network model and YOLOv3 algorithm was proposed to realize ship tracking and real-time detection and recognition of ship types, solving the problem of ship tracking and recognition in important monitored waters. In the Darknet network of the proposed method, the idea of residual network was introduced, the cross-layer jump connection was used to increase the depth of the network, and the ship depth feature matrix was constructed to extract advanced ship features for combination learning and obtaining the ship feature map. On the above basis, YOLOv3 algorithm was introduced to realize target prediction based on image global information, and target region prediction and target class prediction were integrated into a single neural network model. Punishment mechanism was added to improve the ship feature difference between frames. By using logistic regression layer for binary classification prediction, target tracking and recognition was able to be realized quickly with high accuracy. The experimental results show that, the proposed algorithm achieves an average recognition accuracy of 89.5% with the speed of 30 frame/s; compared with traditional and deep learning algorithms, it not only has better real-time performance and accuracy, but also has better robustness to various environmental changes, and can recognize the types and important parts of various ships.

Aiming at the problem of advanced persistent threats in power information networks, a hybrid Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) intrusion detection model was proposed, by which current network states were classified according to various statistical characteristics of network traffic. Firstly, pre-processing works such as feature encoding and normalization were performed on the network traffic obtained from log files. Secondly, spatial correlation features between different hosts' intrusion traffic were extracted by using deformable convolution kernels in CNN. Finally, the processed data containing spatial correlation features were staggered in time, and the temporal correlation features of the intrusion traffic were mined by RNN. The experimental results showed that the Area Under Curve (AUC) of the model was increased by 7.5% to 14.0% compared to traditional machine learning models, and the false positive rate was reduced by 83.7% to 52.7%. It indicates that the proposed model can accurately identify the type of network traffic and significantly reduce the false positive rate.

Aiming at the problems of long data acquisition delay and unstable data download in cloud storage system, a scheduling scheme based on storage node load information and erasure code technique was proposed. Firstly, erasure code was utilized to improve the delay performance of data retrieving in cloud storage, and parallel threads were used to download multiple data copies simultaneously. Secondly, a lot of load information about storage nodes was analyzed to figure out which performance indicators would affect delay performance, and a new scheduling algorithm was proposed based on load information. Finally, the open-source project OpenStack was used to build a real cloud computing platform to test algorithm performance based on real user request tracing and erasure coding. A large number of experiments show that the proposed scheme not only can achieve 15% lower average delay but also reduce 40% volatility of delay compared with other scheduling policies. It proves that the scheduling policy can effectively improve delay performance and stability of data retrieving in real cloud computing platform, achieving a better user experience.

Much lately, a leveled fully homomorphic encryption scheme was proposed based on the Learning With Rounding (LWR) problem. The LWR problem is a variant of the Learning With Errors (LWE) problem, but it dispenses with the costly Gaussian noise sampling. Thus, compared with the existing LWE-based fully homomorphic encryption schemes, the proposed LWR-based fully homomorphic encryption scheme has much higher efficiency. But then, the user's evaluation key was needed to be obtained in the homomorphic evaluator of the proposed LWR-based fully homomorphic encryption scheme. Accordingly, a new leveled fully homomorphic encryption scheme was constructed based on the LWR problem, and the user's evaluation key was not needed to be obtained in the homomorphic evaluator of the new fully homomorphic encryption scheme. Since the new proposed fully homomorphic encryption scheme can be used to construct the schemes such as identity-based fully homomorphic encryption schemes, and attribute-based fully homomorphic encryption schemes, the new proposed scheme has wider application than the lately proposed LWR-based fully homomorphic encryption scheme.

To research the thermal behavior of Caenorhabditis elegans (C.elegans), a new method was proposed to model and simulate the thermal behavior of C.elegans based on the artificial neural network. Firstly, the motion model of the nematode was established. Then, a nonlinear function was designed to approximate the movement logic of the thermotaxis of the nematode. Thirdly, the speed and the orientation change capabilities were implemented, and these capabilities had been realized by the artificial neural network. Finally, the experimental simulation was carried out in the Matlab environment, and the thermal behavior of the nematode was simulated. The experimental results show that Back Propagation (BP) neural network can simulate the thermal behavior of C.elegans better than Radical Basis Function (RBF) neural network. The experimental results also demonstrate that the proposed method can successfully model the thermal behavior of C.elegans, and reveal the essence of the thermotaxis of C.elegans to some extent, which theoretically supports the research on the thermotaxis of the crawling robot.

An automatic segmentation method based on image patch matching strategy was proposed to realize the automatic segmentation of glomerular basement membrane automatically. First of all, according to the characteristics of the glomerular basement membrane, the search range was extended from a reference image to multiple reference images, and an improved searching method was adopted to improve matching efficiency. Then,the optimal patches were searched out and the label image patches corresponding to the optimal patches were extracted, which were weighted by matching similarity. Finally, the weighted label patches were rearranged as the initial segmentation of glomerular basement membrane, from which the final segmentation could be obtained after morphological processing. On the glomerular Transmission Electron Microscopy (TEM) dataset, the Jaccard coefficient is between 83% and 95%. The experimental results show that the proposed method can achieve higher accuracy.

Before building interval concept lattice from the formal context, the interval parameters[ α, β] should be determined, which influence the concept extension, the lattice structure and the quantity and precision of extracted association rules. In order to obtain α and β with the biggest compression degree of interval concept lattice, firstly the definition of the similarity of binary relation pairs and covering-neighborhood-space from formal context were proposed, the similarity matrix of binary relation pairs was obtained, and the neighborhood of binary relation pairs was calculated by the covering which was obtained by similar class of γ. Secondly, update algorithm of concept sets based on change of parameters was raised, where concept sets were got on the basis of the non-reconstruction. Combining with covering-neighborhood of binary relation pairs on changing interval parameters, further the model of parameter optimization of interval concept lattice could be built based on compression theory. According to the size of the compression degree and its changing trend, the optimal values of interval parameters were found. Finally, the validity of the model was demonstrated by an example.

Content-based image retrieval has being faced the problem of "semantic gap", feature selection has a direct influence on semantic learning results; while traditional distance metric often calculates the similarity from a single perspective, which cannot well express the similarity between images. To resolve the above problem, a bilinear image similarity matching algorithm based on deep feature analysis was proposed. First, the image dataset was fine-tuning trained on the Convolutional Neural Network (CNN) model, then the image features were extracted by using the trained CNN. After getting the output features of the full connection layer, the image similarity was calculated by the bilinear similarity matching algorithm, and the most similar image instance was returned after sorting the similarity. Experimental results on Caltech101 and Caltech 256 datasets show that compared with the contrast algorithms, the proposed algorithm can get higher mean average precision, Top K precision and recall, which demonstrates the effectiveness of the proposed algorithm.

Aiming at the shortage of current mobile Wireless Sensor Network (WSN) localization, a localization algorithm based on improved flooding broadcast mechanism and particle filtering was proposed. For a given unknown node, firstly, by the improved flooding broadcast mechanism, the effective average hop distance of an unknown node from its closest anchor node was used to calculate the distances to its all neighbor nodes. Then a differential error correction scheme was devised to reduce the measurement error accumulated over multiple hops for the average hop distance. Secondly, the particle filter and the virtual anchor node were used to narrow the prediction area, and more effective particle prediction area was obtained so as to further decrease the estimation error of the position of unknown node. The simulation results show that compared with DV-Hop, Monte Carlo Baggio (MCB) and Range-based Monte Carlo Localization (MCL) algorithms, the proposed positioning algorithm can effectively inhibit the broadcast redundancy and reduce the message overhead related to the node localization, and can achieve higher-accuracy positioning performance with lower communication cost.

To solve the problem that traditional image classification methods deal with the whole image in a non-hierarchical way, an image classification method based on visual saliency detection was proposed. Firstly, the visual attention model was employed to generate the salient region. Secondly, the texture feature and time signature feature of the image were extracted by Gabor filter and pulse coupled neural network, respectively. Finally, the support vector machine was adopted to accomplish image classification according to the features of the salient region. The experimental results show that the image classification precision rates of the proposed method in SIMPLIcity and Caltech are 94.26% and 95.43%, respectively. Obviously, saliency detection and efficient image feature extraction are significant to image classification.

To solve the problem of preserving detailed features of model about the traditional skeleton driven deformation, a method of subdivision-based skeleton-driven mesh deformation was proposed. Firstly,after that skeleton and control mesh were generated on deformed region, the relationship of between skeleton and control mesh, subdivision surface of control mesh and deformed region were established. Secondly, when the skeleton was modified according to the desired deformation result, the change information of the corresponding subdivision surface was transformed into the alteration of the mesh gradient field for Poisson. Some examples show that the deformation method for different mesh models could get better editing effects and preserve detailed features after the deformation effectively. Compared with the traditional skeleton-driven deformation method, it is proved to be easy to operate, and can be employed to preserve detailed features effectively. The method is suitable for editing the models with complex and rich geometric details.

Aiming at the fair and efficient bandwidths allocation for the geographically distributed control systems, a distributed and dynamic bandwidth allocation algorithm was proposed.Firstly, the bandwidth allocation problem was formulated as a convex optimization problem, namely, the sum of utilities of all the control systems was maximized. Further, the idea of the distributed bandwidth allocation algorithm was adopted to make the control systems vary their sampling periods based on fed-back congestion information from the network, and get the maximum sampling rate or maximum transmission rate which could be used. Then the interaction between control systems and links was modelled as a time-delay dynamical system, and Proportional-Integral (PI) controller was used as the link queue controller to realize the algorithm; The simulation results show that the proposed bandwidth allocation algorithm can not only make the transmission rates of all plants converge to the value where all plants share the bandwidth equally in 10 seconds. At the same time, for the PI controller, its queue stabilizes around the desired set point of 50 packets, and can accurately and steadily track the input signal to maximize the performance of all control systems.

The reliability of Embedded System Hardware (ESH) is very important, which is directly related to the quality and longevity of the embedded system. To analyze the reliability of ESH, it was studied on the perspective of hardware using Copula function. At first, abstract formalization of the ESH was defined from composition level. Then reliability modeling of each function module of the ESH was given by considering integration of hardware and software, as well as using Copulas function to establish the reliability model of ESH. Finally, the parameters of the proposed reliability model were estimated, and a specific calculation example by using this proposed model was put forward and compared with some other Copulas functions. The result shows that the proposed model using Copula function is effective.

Concerning the problem that traditional fault diagnosis of High-Speed Rail (HSR) vibration signal is slow and cannot meet the actual requirement of real-time processing, an accelerated fault diagnosis method for HSR vibration signal was proposed based on Compute Unified Device Architecture (CUDA). First, the data of HSR was processed by Empirical Mode Decomposition (EMD) based on CUDA, then the fuzzy entropy of each result component was calculated. Finally, K-Nearest Neighbor (KNN) classification algorithm was used to classify feature space which consisted of multiple fuzzy entropy features. The experimental results show that the proposed method is efficient on fault classification of HSR vibration signal, and the processing speed is significantly improved compared with the traditional method.

Concerning the problem that Ontology Web Language for Services (OWL-S) process model lacks capacity for dynamic interaction and timing characteristics, a formalization method based on temporal description logic for process model was proposed. It described the atomic processes and composite processes of the OWL-S process model, and then the dynamic semantic of OWL-S process model was obtained. Finally, the formal modeling of OWL-S process model was realized. The experimental results show that the proposed method is feasible, and it provides the foundation for the analysis and validation.

As one of the most popular routing protocols proposed by Internet Engineering Task Force (IETF), Ad Hoc on Demand Distance Vector Routing (AODV) has been implanted for many applications in Mobile Ad Hoc Network (MANET). In AODV, enormous broadcasting messages are generated during route discovery procedure, which consumes lots of bandwidth and degrades significantly the Quality of Service (QoS) of networks. To solve this problem, a cross-layer mechanism with a routing protocol Enhanced AODV (E-AODV) was proposed. In E-AODV, the Signal to Noise Ratio (SNR) of received signals was considered as the key criterion to select the next hop. Furthermore, Wireless Transmission Control Protocol (WTCP) was implanted as one important way in E-AODV to obtain a better QoS. The simulation results show that the proposed mechanism can reduce the Data Delivery Latency (DDL) up to 56% and improve the Data Delivery Ratio (DDR) up to 24%.

Considering the problem of limited energy in Delay/Disruption Tolerant Network (DTN), a node energy-aware probabilistic routing algorithm was proposed. Nodes in network were distinguished according to energy situation, and different message delivery mechanism and energy-efficient buffer management strategy were adopted in order to achieve the balance between delivery ratio and energy consumption. Simulations indicate that the algorithm improves delivery ratio and reduces overhead ratio on low energy consumption, and has better performance on network lifetime compared with other algorithms.

Concerning the medium access control shortcoming of wireless network, a sort of new algorithm was proposed in this paper. The algorithm was based on the dynamic p-persistent algorithm and its kernel idea was derived from virtual transmission or virtual thread. The aim that provided more information for the dynamic p-persistent algorithm to obtain the optimal transmission probability was to increase available efficiency of wireless bandwidth. The simulations show that the proposed algorithm increases throughput about 27%, and reduces collision rate about 28% on average.

Most of the existing methods to compute text similarity based on Vector Space Model (VSM) use TF-IDF scores as the weights of feature terms in text, which ignores the word sense relationships among feature terms and lead to inaccurate text similarity. To improve the accuracy of text similarities calculated by methods based on VSM, a new term weight computing method by integrating word sense was proposed in this paper. Firstly, word sense similarities among feature terms were computed based on the Chinese WordNet. And then, the TF-IDF weights were revised according to the word sense similarities for the purpose of reflecting both the frequency and the word sense of feature terms in text. The experimental results on the HIT IR-lab Multi-Document Summarization Corpus show that to use the weights calculated by the proposed method can efficiently improve the differentiation among document clusters.

Concerning the problem that the network traffic data has been polluted by noise so that accurate modeling and predicting cannot be achieved, an integrated network traffic online predicting method based on lifting wavelet de-noising and online Least Squares Support Vector Machines (LSSVM) was proposed. First, the Lifting Wavelet De-noising (LWD) was used to pre-process network traffic data, then the phase space reconstruction theory was introduced to calculate the delay time and embedded dimension. On this basis, the training samples were formed and the online LSSVM prediction model was constructed to predict the network traffic. The experimental results show that this prediction model can eliminate the noise effectively and predict the network traffic.

As most of network failures are transient single link failures, a new approach of using Particle Swarm Optimization (PSO) algorithm to optimize link weights for enhancing network survivability was proposed. A cost function was introduced to put high cost on links with high utilizations for avoiding link overloaded. The objective function was a weighted sum of two proportions: one is the maximum cost under normal state, and the other is the maximum link cost under all single link failures. Then the algorithm model was built and PSO algorithm was used to find the optimal weights. The experimental results show that the weight calculated by the proposed method can keep lower link utilization under failure states, and prevent the network from congestion due to traffic diversion. Therefore, the network survivability can be improved.

In order to improve the accuracy, speed and efficiency of the Hierarchical Cellular Genetic Algorithm (HCGA) in solving complex problem, in this paper, a new polycentric HCGA on the basis of HCGA and with reference to the central city theory of western economics was proposed. The new algorithm chose a few individuals with high fitness in population as the central cities. Individuals around the central city moved towards the center，and the optimal solution was generated from these central cities. The algorithm greatly improves populations diversity and thus the searching efficiency. The numerical simulations show that the improved algorithm is more effective for realizing the global optimization and can avoid premature effectively.

A method that segmented fracture surfaces for automatic reassembly of broken 3D solids was presented. Firstly, the fragments were segmented into a set of surfaces bounded by sharp curves according to the angle of normal vectors of adjacent triangles. Then according to perturbation value and perturbation image of the normal vectors, after the second segmentation, surfaces were divided into the original surface and fractures. The experimental results show that the proposed method can distinguish fractured surfaces of complex fragment correctly and quickly.

To solve the problems of huge storage, difficult management, poor display of data and decision support, a Migration Information System (MIS) based on three-dimension GIS named World Wind was designed and developed. Its system architecture and functional modules were designed, MIS was developed based on World Wind Java SDK, Digital Elevation Mode (DEM) and image data were divided, stored, organized and scheduled, based on which integration and application of migration information were accomplished. A case study shows that the system has good extensibility and 3D effect.

ent service matching from the perspective of combining process and capability, so the matching precision is affected. To solve this problem, with the use of automata, Web services described by Web Ontology Language for Services (OWL-S) were expressed as formalized processes. Meanwhile, a service capability matching algorithm based on process-similarity was proposed. In this algorithm, whether request and service is process-similar was decided by similarity judgment of formalized processes, and the result obtained by process-similarity judgment was used to match capability; process-similarity judgment was performed through structure similarity computation and behavior similarity checking. The proposed algorithm is proved to be feasible and effective by comparative experiments.