Link prediction focuses on the design of prediction algorithms that can describe a given network mechanism more accurately to achieve the prediction result with higher accuracy. Based on an analysis of the existing research achievements, it is found that the similarity characteristics of a network has a great impact on the link prediction method used. In networks with low tag similarity between nodes, increasing the tag similarity is able to improve the prediction accuracy; in networks with high tag similarity between nodes, more attention should be paid to the contribution of structural information to link prediction to improve the prediction accuracy. Then, a tag-weighted similarity algorithm was proposed by weighting the tags, which was able to improve the accuracy of link prediction in networks with low similarity. Meanwhile, in networks with relatively high similarity, the structural information of the network was introduced into the node similarity calculation, and the accuracy of link prediction was improved through the preferential attachment mechanism. Experimental results on four real networks show that the proposed algorithm achieves the highest accuracy compared to the comparison algorithms Cosine Similarity between Tag Systems (CSTS), Preferential Attachment (PA), etc. According to the network similarity characteristics, using the proposed corresponding algorithm for link prediction can obtain more accurate prediction results.

The construction of binary Locally Repairable Code (LRC) achieving C-M (Cadambe-Mazumdar) bound has been fully studied while there are few researches on general fields. In order to solve the problem, the construction of LRC on general fields was studied. Firstly, a method for determining the number of elements in sunflower was proposed by projective geometry theory. Then, the parameters such as code length, dimension and locality of LRC were clearly described by depicting LRC through the disjoint repair group. Finally, based on the parity-check matrix with disjoint local repair group, two families of LRC on general fields with the minimum distance of 6 were constructed by sunflower, many of which were optimal or almost optimal. Compared with the existing LRC constructed by methods such as subfield subcode, generalized concatenated code and algebraic curve, the constructed two families of codes improve the information rate under the same code minimum distance and locality. These results can be applied to the construction of other LRC on general fields.

In order to solve the problem that parameter random assignment reduces the robustness of the algorithm and the performance is significantly affected by noise of Extreme Learning Machine (ELM), combining Denoising AutoEncoder (DAE) with ELM algorithm, a DAE based ELM (DAE-ELM) algorithm was proposed. Firstly, a denoising autoencoder was used to generate the input data, input weight and hidden layer parameters of ELM. Then, the hidden layer output was obtained through ELM to complete the training of classifier. On the one hand, the advantages of DAE were inherited by the algorithm, which means the features extracted automatically were more representative and robust and were impervious to noise. On the other hand, the randomness of parameter assignment of ELM was overcome and the robustness of the algorithm was improved. The experimental results show that, compared to ELM, Principal Component Analysis ELM (PCA-ELM), SAA-2, the classification error rate of DAE-ELM at least decreases 5.6% on MNIST, 3.0% on Fashion MINIST, 2.0% on Rectangles and 12.7% on Convex.

The rapid development of modern market economy is accompanied by higher risks. Forecasting regional investment in advance can find investment risks in advance so as to provide reference for investment decisions of countries and enterprises. Aiming at the lag of statistical data and the complexity of internal relations in macroeconomic forecasting, a prediction method of Long Short-Term Memory based on Weibo Sentiment Analysis (SA-LSTM) was proposed. Firstly, considering the strong timeliness of Weibo texts, a method of Weibo text crawling and sentiment analysis was determined to obtain Weibo text sentiment propensity scores. Then total investment in the region was forecasted by combing with structured economic indicators government statistics and Long Short-Term Memory (LSTM) networks. The experimental results in four actual datasets show that SA-LSTM can reduce the relative error of prediction by 4.95, 0.92, 1.21 and 0.66 percentage points after merging Weibo sentiment analysis. Compared with the best method in the four methods of AutoRegressive Integrated Moving Average model (ARIMA), Linear Regression (LR), Back Propagation Neural Network (BPNN), and LSTM, SA-LSTM can significantly reduce the relative error of prediction by 0.06, 0.92, 0.94 and 0.66 percentage points. In addition, the variance of the prediction relative error is the smallest, indicating that the proposed method has good robustness and good adaptability to data jitter.

Aiming at such interferences as false detection and missed detection which can't be overcome by the existing virtual loop detection algorithm in multilane traffic flow detection, a novel traffic flow detection algorithm based on adaptive virtual loop was put forward. According to the image binarization principle, quadratic estimation was adopted in the foreground detection part of the Visual Background extractor (ViBe) algorithm, and the background updating mechanism was changed. A new improved ViBe algorithm was presented to achieve the purposes of rapidly eliminating the ghost and completing the foreground object extraction. Then, the fixed detection area was set on the road, and the mobile virtual loop was established or canceled according to the moving target trajectory of fixed detection area. The traffic flow algorithm based on virtual loop was further used to achieve traffic flow statistics. Three different scenarios:no vehicle lane change with 4 lanes, vehicle lane change with 2 lanes, vehicle lane change with 3 lanes and sudden environmental change, were chosen for experiments and the traffic flow detection accuracy of the proposed algorithm was 8.9, 25 and 16.6 percentage points higher than that of the traditional virtual loop detection algorithm. The experimental results show that the proposed algorithm is more suitable for multilane traffic flow detection.

Since fractional order chaotic time series prediction has low precision and slow speed, a prediction model of new orthogonal basis neural network based on Quantum Particle Swarm Optimization (QPSO) algorithm was proposed. Firstly, on the basis of Laguerre orthogonal basis function, a new orthogonal basis function was put forward combined with the neural network topology to form a new orthogonal basis neural network. Secondly, QPSO algorithm was used for parameter optimization of the new orthogonal basis neural network, thus the parameter optimization problem was transformed into a function optimization problem on multidimensional space. Finally, the prediction model was established based on the optimized parameters. Fractional order Birkhoff-shaw and Jerk chaotic systems were taken as models respectively, then chaotic time series produced according to Adams-Bashforth-Moulton estimation-correction algorithm were used as the simulation objects. In the comparison experiments on single-step prediction with Back Propagation (BP) neural network, Radical Basis Function (RBF) neural network and general new orthogonal basis neural network, Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of the new orthogonal basis neural network based on QPSO algorithm were significantly reduced, and Coefficients of Decision (CD) of it was closer to 1; meanwhile, Mean Modeling Time (MMT) of it was greatly shortened. The theoretical analysis and simulation results show that the new orthogonal basis neural network based on QPSO algorithm can improve the precision and speed of fractional order chaotic time series prediction, so the prediction model can be easily expanded and applied.

Concerning low precision and slow speed of traditional intelligent optimization algorithm for parameter identification in chaotic system, a new method of parameter identification in chaotic system based on feedback teaching-learning-based optimization algorithm was proposed. This method was based on the teaching-learning-based optimization algorithm, where the feedback stage was introduced at the end of the teaching and learning stage. At the same time the parameter identification problem was converted into a function optimization problem in parameter space. Three-dimensional quadratic autonomous generalized Lorenz system, Jerk system and Sprott-J system were taken as models respectively, intercomparison experiments among particle swarm optimization algorithm, quantum particle swarm optimization algorithm, teaching-learning-based optimization algorithm and feedback teaching-learning-based optimization algorithm were conducted. The identification error of the feedback teaching-learning-based optimization algorithm was zero, meanwhile, the search times was decreased significantly. The simulation results show that the feedback teaching-learning-based optimization algorithm improves the precision and speed of the parameter identification in chaotic system markedly, so the feasibility and effectiveness of the algorithm are well demonstrated.

Focusing on the issue that the communication path selected by random routing algorithm of the onion router (Tor) can not be controlled, thus leading to problems such as the abuse of anonymous techniques and the failure of tracing methods, a Tor anonymous circuit control method based on node failure was proposed. To effectively control the circuit, the fake TCP reset information was sent to mimic the node failure, so that the Tor client would not stop choosing nodes until it selected the controlled ones. The results of theoretic analysis of Tor network path selection algorithm and the real test in a private Tor network composed of 256 onion routers demonstrate the effectiveness of the proposed approach. Compared with traditional methods which deploy high bandwidth routers to attract users to select the controlled nodes, the proposed method can improve the probability of choosing controlled entry node from 4.8% to about 60%, when entry guard was generally enabled by Tor client by default. The results also show, as the length of a controlled path increases, the success rate of building path decreases. Therefore the proposed method is suitable for controlling short paths.

To achieve simple and convenient facial expression recognition, a method combining multi-scale Local Binary Pattern Histogram Fourier (LBP-HF) and Active Shape Model (ASM) was proposed. Firstly, the face regions were detected and segmented by ASM to reduce the influence of unrelated regions, and then LBP-HF were extracted to form recognition vectors. Finally, the nearest neighborhood classifier was applied to recognize expressions. The influences of various scale LBP-HF features on facial expression recognition were studied through extracting LBP-HF features from different scales. At last, multi-scale LBP-HF features were concatenated to discriminate expressions, and more effective expression features were obtained. By comparison with the experimental result of Gabor features, its feasibility and simplication are validated, and the highest mean recognition rate is 93.50%. The experimental results demonstrate that the method can be used for human-computer interaction.

In order to improve the security of secure communication, a new Generalized Hybrid Dislocated Function Projective Synchronization (GHDFPS) based on generalized hybrid dislocated projective synchronization and function projective synchronization was researched by Lyapunov stability theory and adaptive active control method. At the same time, the control methods of GHDFPS between two different-order chaotic systems with uncertain parameter and parameter identification were presented, and the application of the novel synchronization on secure communication was analyzed. By strict mathematical proof and numerical simulation, the GHDFPS between two different-order chaotic systems with uncertain parameter were achieved, the uncertain parameter was identified. Because of the variety of function scaling factor matrix, the security of secure communication has been increased by GHDFPS. Moreover, this synchronization form and method of control were applied to secure communication via chaotic masking modulation. Many information signals can be recovered and validated.

Due to the threats of Cross-Site Scripting (XSS) attack in Online Social Network (OSN), a approach combined classifiers and improved n-gram model was proposed to detect the malicious OSN webpages infected with XSS code. Firstly, similarity-based features and difference-based features were extracted to build classifiers and the improved n-gram model. After that, the classifiers and model were combined to detect malicious webpages in OSN. The experimental results show that compared with the traditional classifier detection methods, the proposed approach is more effective and the false positive rate is about 5%.

To effectively capture the dynamic information of the gait and accelerate the authentication and identification, a novel gait recognition algorithm was presented in this paper, which employed the row mass vector of the Frame Difference Energy Image (FDEI) as the gait features. The gait contour images were extracted through the object detection, binarization, morphological process and connectivity analysis of the original images. Using the width of the contour images sequence, the quasi-periodicity analysis and the row mass vector of the frame difference image were obtained, then the Continuous Hidden Markov Model (CHMM) was employed to train and recognize the parameters of model. The proposed algorithm was applied to Central Asia Student International Academic (CASIA) gait database. The experimental results show that it can easily extract the features of the gait with low dimension, achieving fast recognition speed and high recognition rate, so it can be used for real-time gait recognition.

Due to problems of over-compression by a non-adaptive mapping function, and changes of perceived contrasts for luminance shift during mapping, a hierarchical tone-mapping algorithm for detail-preserving was proposed. In this algorithm, the luminance-response curve adapting to each local luminance in High Dynamic Range (HDR) images, as a mapping function, was used to map luminances of the base layer. Then, compensation coefficients of the detail layer, for stretching or compressing details, were computed according to values of luminance shift based on Stevens' effect. The experimental results show that the proposed algorithm has good performance on preserving perceived details.

Adopting embedded framework and network communication mode, a kind of multi-axis embedded motion controller hardware platform design for Panasonic A5N drive was proposed, which was based on the modular control core (ARM + FPGA) and could adapt to the new real-time network communication named RTEX. It was transplanted in μC/OS-Ⅱ. The processes of controller's functional design, hardware design and software design were explained in detail. Up to now, the motion controller's hardware platform had been completed and verified by communication experiments and position and velocity control experiments based on SCARA robot. The experimental results show that the controller has good communication function and stable performance, and can well accomplish the functions of servo control.

It is difficult for the object tracking algorithm based on single feature, to track the object in complex cases. Therefore, this paper proposed an algorithm fusing multiple features for object tracking based on adaptive background information. The algorithm was based on the use of color feature and gray level co-occurrence matrix texture feature to represent the object. Under the frame of particle filter, it analyzed the particle space distribution, particle value distribution and ability to distinguish the background information with different feature. Then it presented an efficient fusion coefficient calculation. According to the object's appearance of changes in the process of tracking, it updated the object template adaptively. The experimental results in different settings show that this algorithm greatly improves the resistance to background interference, under the premise of not reducing the real-time. In all sorts of situations, it has good stability and robustness.

Considering the limitation of research on ordinary Voronoi diagram and low efficiency of the capabilities to build weighted voronoi diagram, a method based on quadtree structure for generating weighted Voronoi diagram was proposed in this paper. The key idea of this method was to obtain searched correlated seeds region of the nonexpansion nodes by the quadtree structure, calculate the time consumption value to replace weighted distance, and determine the ownership seed according to the node shortest time consumption value. The computing model based on quadtree structure and several basic characteristics of the method were given. The test result shows that the seeds are rapidly dilated, the time complexity gets effectively lower than uniform grid structure. The algorithm is simple, and it has a strong maneuverability and practical value.

Since the traditional Geographic Information System (GIS)'s structured or semi-structured attribute query poses sort of limitation on input accuracy and scope of query sentences, a GIS descriptive query method for text-related non-structured text data was suggested based on the expanded version of a dictionary of English synonyms, TongYiCi CiLin. compiled by Harbin Institute of Technology. Basic process is to calculate correlation between descriptive query sentence and text connected to the geographic element, then getting general query results according to it. The comparison experiment shows that descriptive query method not only supports diversity of input query sentence, but also effectively gets geographic element related to input descriptive query.

This paper proposed an image denoising method using transform-domain sparse representation with the characteristic that fewer Discrete Cosine Transformation (DCT) nonzero coefficients exist in image smoothing-domain. This method overcame the shortcoming of traditional denoising method, i.e. losing information of edge and texture. Firstly, similar image block was grouped by computing l2 norm; secondly, according to transform-domain coefficient sparsity, denoising was performed by threshold. To improve it, Principal Component Analysis (PCA) was used on these groups, processing groups with PC components. Lastly, the image with processed groups was reconstructed using Kaiser windows method. Compared to traditional method, this method preserves image edge and texture information, so that the noise could be preferably removed and the effect of image visual could be improved.

Concerning the specific application environment of embedded microprocessor, the branch prediction technology was researched in this paper, and a new scheme of branch prediction was proposed. Compatible with cache design, jump direction and destination address of branch prediction happened on extended instruction bus. The unexecuted instruction and address pointer were saved for possible recovery after misprediction, which reduced misprediction penalty, simultaneously guaranteed the instruction flow to execute correctly. The study shows this scheme is of little hardware spending, high prediction efficiency and low misprediction penalty.

Query reformulation is a key issue of the data integration, and it automatically rewrites the users query request to the data source request directly. Recently the University of Michigan and the IBM AImaden Research Center presented a novel algorithm for constraintbased XML query reformulation, but it did not consider the problem of complex schema matching and limited the algorithm application. Based on the original query reformulation algorithm, an extended algorithm for XML query reformulation was presented, expanding the application scope of the original algorithm. The correctness and time complexity of the extended algorithm were also given.