Concerning the phenomenon that the queues in the entrance lanes of the intersections are imbalanced or overflowed during the peak hours, an incremental split selection method based on video queue length management was proposed. Firstly, the queueing state at the end of the red time and the queueing length level at the end of the green time were judged. Then， the increment or decrement of the green time of each phase was calculated. Finally, the dynamic balance between the green time of each phase and the queue length of each entrance lane was realized with the purpose of balancing the queue lengths of the entrance lanes. The experimental results show that, the proposed optimization method can effectively balance the queue lengths of the entrance lanes, reducing the traffic delay and traffic congestion at the intersection. When the split does not match the queue length, the optimization method can quickly adjust the split to adapt to the change of the queue length.

Hypertension is an important hazard to health. Blood pressure prediction is of great importance to avoid grave consequences caused by sudden increase of blood pressure. Based on traditional Long Short-Term Memory (LSTM) network, a multi-factor cue LSTM model for both short-term prediction (predicting blood pressure for the next day) and long-term prediction (predicting blood pressure for the next several days) was proposed to provide early warning of undesirable change of blood pressure. Multi-factor cues used in blood pressure prediction model included time series data cues (e.g. heart rate) and contextual information cues (e.g. age, BMI (Body Mass Index), gender, temperature).The change characteristics of time series data and data features of other associated attributes were extracted in the blood pressure prediction. Environment factor was firstly considered in blood pressure prediction and multi-task learning method was used to help the model to capture the relation between data and improve the generalization ability of the model. The experimental results show that compared with traditional LSTM model and the LSTM with Contextual Layer (LSTM-CL) model, the proposed model decreases prediction error and prediction bias by 2.5%, 3.8% and 1.9%, 3.2% respectively for diastolic blood pressure, and reduces prediction error and prediction bias by 0.2%, 0.1% and 0.6%, 0.3% respectively for systolic blood pressure.

Industrial Control System (ICS) is widely used in critical infrastructure projects related to the national economy and people's livelihood such as power generation, transmission and distribution, petrochemical industry, water treatment and transmission. Large-scale attack on ICS is a huge threat to critical infrastructure. At present, the proposed ransomware worm for ICS is limited by the isolation characteristics of industrial control network, and it is difficult to spread on a large scale. Based on the observed actual development scene of ICS, in order to solve the problem of high isolation for ICS, a novel ransomware worm threat model with a new attack path was proposed. Firstly, the engineer station was taken as the primary infection target. Then, the engineer station was used as the springboard to attack the industrial control devices in the internal network. Finally, the worm infection and ransom were realized. Based on the proposed threat model, ICSGhost, which was a ransomware worm prototype, was implemented. In the closed experimental environment, ICSGhost can realize worm infection for ICS with a predetermined attack path. At the same time, for the ransomware worm threat, the defense plan was discussed. The experimental results show that such threat exists, and because its propagation path is based on the actual development scene of ICS, it is difficult to detect and guard against.

The traditional literatures mainly consider the cooperative relationship while neglecting the competitive relationship between manufacturing and logistics enterprises during interactive development. An improved model, namely Logistic-Volterra model, was proposed based on the traditional Logistic model, which considered the contribution coefficients and competition coefficients at the same time. Firstly, the Logistic-Volterra model was built and the stability solution was sovled, then the mathematical conditions for achieving stability and the interpretation of reality were discussed. Secondly, the affecting factors on the interactive development of manufacturing and logistics enterprises were discovered by using Matlab numerical simulation, and the differences between the improved model and traditional model were also discussed. Finally, the manufacturing enterprise A and logistics enterprise B were taken as an example to analyze the competitive behavior in the process of cooperation; furthermore, the impact of coopetition behavior on the interests was also analyzed. The theoretical analysis and simulation results show that the stability of the system is highly affected by contribution coefficient, competition coefficient and environmental capability, the result is more reasonable when considering the competition relationship in the model. It means that manufacturing and logistics enterprises should fully address the effects of competition on both sides.

To solve the problem of complex context and data sparsity, a new algorithm for the tensor decomposition based on context similarity of mobile user was proposed, namely UCS-TF (User-Context-Service Tensor Factorization recommendation). Multi-dimensional context similarity model was established with combining the user context similarity and confidence of similarity. Then, K-neighbor information of the target user was applied to the three-dimensional tensor decomposition, composed by user, context and mobile-service. Therefore, the predicted value of the target user was obtained, and the mobile recommendation was generated. Compared with cosine similarity method, Pearson correlation coefficient method and the improved Cosine1 model, the Mean Absolute Error (MAE) of the proposed UCS-TF algorithm was reduced by 11.1%, 10.1% and 3.2% respectively; and the P@N index of it was also significantly improved, which is better than that of the above methods. In addition, compared with Cosine1 algorithm, CARS2 algorithm and TF algorithm, UCS-TF algorithm had the smallest prediction error on 5%, 20%, 50% and 80% of data density. The experimental results indicate that the proposed UCS-TF algorithm has better performance, and the user context similarity combining with the tensor decomposition model can effectively alleviate the impact of score sparsity.

Focusing on the issue that the Road Side Unit (RSU) has inefficient response to the request of the vehicles in Vehicular Ad Hoc Network (VANET), a data scheduling algorithm based on Software Defined Network (SDN) architecture, namely SDDS, was proposed. Firstly, a graph of conflicting policies was generated based on status information of vehicles, and a maximum weighted independent set of the graph was solved to maximize the number of satisfied requests in current cycle. Secondly, the redundancy of data in vehicles was analyzed to figure out the optimum parameter, and a selection mechanism for collaborative vehicles was designed based on geographical position. Finally, the characteristics of handover vehicles and some factors that would affect the multi-RSU cooperation were analyzed, and a multi-RSU cooperation mechanism was put forward based on collision avoidance. In addition, a new evaluation indicator, service efficiency, was proposed to estimate the overall quality of service. Simulation results showed that compared with Most Requests First (MRF) and Cooperative Data Dissemination (CDD) algorithms, the service efficiency of SDDS algorithm was increased up to 15% and 20% respectively. The simulation results prove that SDDS algorithm can observably improve the sevice eficiency and quality of scheduling system.

In Wireless Sensor Network (WSN), to deal with the energy limitation of nodes and the energy consumption of broadcast routing, a new WSN broadcast routing algorithm based on the improved Discrete Fruit fly Optimization Algorithm (DFOA) was proposed. Firstly, the swap and swap sequence were introduced into the Fruit fly Optimization Algorithm (FOA) to obtain DFOA, which expands the applications field of FOA. Secondly, the step of fruit fly was controlled by the Lévy flight to increase the diversity of the samples, and the position updating strategy of population was also improved by the roulette selection to avoid the local optimum. Finally,the improved DFOA was used to optimize the broadcast routing of WSN to find the broadcast path with minimum energy consumption. The simulation results show that the improved DFOA reduces the energy consumption of broadcast and has better performance than comparison algorithms including the original DFOA, Simulated Annealing Genetic Algorithm (SAGA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) in different network. The improved DFOA can increase the diversity of the samples, enhance the ability of escaping from local optimum and improve the network performance.

When the packets are broadcasted with the fixed power in Vehicular Ad-Hoc NETwork (VANET), the wireless channel may not be allocated reasonable. In order to solve this problem, a power control mechanism adapted to the variation of vehicle density was proposed. It is adaptive to the variation of vehicle density. The direct neighbor list of each node was constructed and updated in a power control period, the power that used to transmit the vehicle status message was adjusted according to the location of the direct neighbor to cover all the direct neighbors, thus wireless channel could be allocated more reasonable and the performance of router could also be optimized. The validity of the proposed mechanism was proved by the simulation results. It is also found that the proposed mechanism is useful for adjusting the transmission power according to the vehicular density, reducing channel busy ratio and enhancing the performance of packet delivery ratio among direct neighbors, which can ensure the effective transmission of the security information.

Mobile social network data has complex network structure, mutual label influence between nodes, variety of information including interactive information, location information, and other complex information. As a result, it brings many challenges to identify the characteristics of the user. In response to these challenges, a real mobile network was studied, the differences between the tagged users with different characteristics were extracted using statistical analysis, then the user's features of age and sex were recognized using relational Markov network prediction model. Analysis shows that the user of different age and sex has significant difference in call probability at different times, call entropy, distribution and discreteness of location information, gather degree in social networks, as well as binary and ternary interaction frequency. With these features, an approach for inferring the user's age and gender was put forward, which used the binary and ternary interaction relation group template, combined with the user's own temporal and spatial characteristics, and calculated the total joint probability distribution by relational Markov network. The experimental results show that the prediction accuracy of the proposed recognition model is at least 8% higher compared to the traditional classification methods, such as C4.5 decision tree, random forest, Logistic regression and Naive Bayes.

Current research on statistical publication of differential privacy data stream only considers one-dimensional data stream. However, many applications require privacy protection publishing two-dimensional data stream, which makes traditional models and methods unusable. To solve the issue, firstly, a differential privacy statistical publication algorithm for fixed-length two-dimensional data stream, call PTDSS, was proposed. The tuple frequency of the two-dimensional data stream under certain condition was calculated by a one-time linear scan to the data stream with low-cost space. Basing on the result of sensitivity analysis, a certain amount of noise was added into the statistical results so as to meet the differential privacy requirement. After that, a differential privacy continuous statistical publication algorithm for any length two-dimensional data stream using sliding window model, called PTDSS-SW, was presented. The theoretical analysis and experimental results show that the proposed algorithms can safely preserve the privacy in the statistical publication of two-dimensional data stream and ensure the relative error of the released data in the range of 10% to 95%.

Concerning the problem that Particle Swarm Optimization (PSO) falls into local minima easily and converges slowly at the last stage, a kind of hybrid PSO algorithm with cooperation of multiple particle roles (MPRPSO) was proposed. The concept of particle roles was introduced into the algorithm to divide the population into three roles: Exploring Particle (EP), Patrolling Particle (PP) and Local Exploiting Particle (LEP). In each iteration, EP was used to search the solution space by the standard PSO algorithm, and then PP which was based on chaos was used to strengthen the global search capability and replace some EPs to restore population vitality when the algorithm trapped in local optimum. Finally, LEP was used to strengthen the local search to accelerate convergence by unidimensional asynchronous neighborhood search. The 30 times independent runs in the experiment show that, the proposed algorithm in the conditions that particle roles ratio is 0.8∶〖KG-*3〗0.1∶〖KG-*3〗0.1 has the mean value of 2.352E-72,4.678E-29,7.780E-14 and 2.909E-14 respectively in Sphere, Rosenbrock, Ackley and Quadric, and can converge to the optimal solution of 0 in Rastrigrin and Griewank, which is better than the other contrastive algorithms. The experimental results show that proposed algorithm improves the optimal performance with certain robustness.

To solve the problem of optimization learning models in Belief Rule Base (BRB), a new parameter training approach based on the Particle Swarm Optimization (PSO) algorithm was proposed, which is one of the swarm intelligence algorithms. The optimization learning model was converted to nonlinear optimization problem with constraints. During the optimization process, all particles were limited in the search space and the particles with no speed were given velocity in order to maintain the diversity of the population of particles and achieve parameter training. In the practical pipeline leak detection problem, the Mean Absolute Error (MAE) of the trained system was 0.166478. The experimental results show the proposed method has good accuracy and it can be used for parameter training.

Under the condition of being confronted with highly concurrent requests, the existing Web services would bring about the increase of the response time, even the problem that server goes down. To solve this problem, a kind of distributed self-elasticity architecture for the Web system named ECAP (self-Elasticity Cloud Application Platform) was proposed based on cloud computing. The architecture built on the Infrastructure as a Service (IaaS) platform of OpenStack. It combined Platform as a Service (PaaS) platform of Cloudify to realize the ECAP. In addition, it realized the fuzzy analytic hierarchy scheduling method by building the fuzzy matrix in the scale values of virtual machine resource template. At last, the test applications were uploaded in the cloud platform, and the test analysis was given by using the tool of pressure test. The experimental result shows that ECAP performs better in the average response time and the load performance than that of the common application server.

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.

To acquire an accurate patient positioning in image-guided radiotherapy, an improved Demons deformable registration method was developed. The FDK algorithm was adopted to reconstruct Cone Beam CT (CBCT) and the reconstruction result was visualized by a volume rendering method with Visualization ToolKit (VTK). Based on the Insight segmentation and registration ToolKit (ITK), the Demons algorithm was completed incorporating the gradient information of fixed image and floating image by the concept of symmetric gradient, and a new formula of Demons force was demonstrated. Registrion experiments were carried out using medical images both from single modality and multi-modality. The results show that the improved Demons algorithm achieves a faster convergence speed and a higher precision compared with the original demons algorithm, which indicates that the Demons algorithm based on symmetric gradient is more suitable for the registration of CBCT reconstruction image and CT plan image in image-guided radiotherapy.

In system call trace of a process, there are two kinds of invariability, program behavior invariability and user behavior invariability, of which the former can be further subdivided into temporal order invariability and frequency invariability. The existing researches on system call based intrusion detection techniques focus on program behavior invariability only, ignoring user behavior invariability. Based on frequency invariability embedded in process traces, the existence and description of user behavior invariability were studied, on which the least entropy length was proposed to measure the invariability. The experiment on Sendmail datasets shows that, least entropy length excellently describes user behavior invariability and significantly improves the performance of system call anomaly detection with the help of program behavior invariability.

When microblog users search someone, they would like to follow by keywords. Most service providers order their results list simply depending on the scale of followers. Unfortunately, this approach gives frauds quite a few opportunities to cheat the search engine. This paper, by regarding microblog users as Web pages, and the relationship between followers as the one between Web pages that linked each other, applied the basic idea of PageRank to rank microblog users. After introducing a statetransition matrix and an autoiterative MapReduce workflow to parallel the computation steps, this paper described a user ranking algorithm for microblog search. As shown in the experiment by using Hadoop platform, the algorithm increases the difficulty to cheat search engines, makes more important users get better rankings, and improves the relevance and quality of search results.

Through studying software network behavior, a new system model for analyzing the software network behavior based on dynamic binary analysis and message semantics analysis was proposed. The system consisted of dynamic binary analysis module, message semantics analysis module and network behavior analyzer. With the dynamic binary analysis, the Application Programming Interface (API) functions and system functions called by software could be obtained; by using the dynamic taint analysis, the message semantics could be extracted. The experimental results show that, combining the dynamic binary analysis and message semantics extraction can be used for analyzing the software network behavior.

With a comprehensive analysis on R-wave detection matching degree, power spectral density ratio and kurtosis,three indexes for electrocardiogram (ECG) signal quality, this paper established a quality assessment model for a wearable ECG-signal based on fuzzy comprehensive judgment, obtained the quality indexes and quality degree for ECG-signal. Then through a comparison and discussion for this algorithm, the results show that the using of fuzzy comprehensive assessment method can reduce the erroneous assessment in the condition of disturbance to some extent.

Considering the non-linearity of TCP model, uncertainty of Round Trip Time (RTT) and fluctuation of network load, an Active Queue Management (AQM) scheme based on Variable Structure Controller (VSC) using single neuron adaptive learning was proposed. The nonlinear VSC was used to guarantee the swiftness and robustness of queue response at router. However, the jitter of VSC would cause the queue fluctuation and performance degradation. Therefore, a single neuron was introduced to adjust the parameters of the VSC in order to alleviate the effect of jitter and modeling uncertainty. The proposed scheme can reduce the jitter and enhance the robustness for AQM control system greatly. Finally, the simulation results show the effectiveness of the proposed algorithm through NS-2 simulator.

In the fire strategy of RoboCup rescue simulation, controlling the spread of fire plays an important role in rescue effect. Most teams use single-objective selection method based on various indicators of the single building. It is difficult for this method to control the fire effectively when more buildings are in fire or the fire spreads quickly. This paper proposed a new partitioning method based on density cluster. In this method, all the buildings were clustered and seperated into different regions according to the fire spread speed, the target region and buildings extinguishing the fire were selected by the weight considering both the fire spread speed and the building attribute, thus the fire could be controlled even completely extinguished. Finally, the validity of this method has been confirmed through simulation test and competition.

A risk assessment model based on Bayesian network was proposed. In this model, each risk event influencing the Trusted Platform Control Module (TPCM)'s trust was analyzed. According to the relation among risks, the Bayesian network evaluation model was built. According to the evaluation from expert, Bayesian network inferring method was used to evaluate the risk probability and its influence. The whole system's risk value and risk priority were determined. An example was given to verify the model's correctness and validation.

Based on Brightness preserving Bi-Histogram Equalization (BBHE), an improved algorithm of gray image enhancement was proposed. An appropriate threshold, which was selected based on the entropy of the output image and the difference between the mean brightness of input and output images, was selected to cut the image, then BBHE and gray lever homogenization were performed respectively. This method makes the brightness mean error as small as possible and the entropy of output image as large as possible. Meanwhile, it can prevent over-enhancement. The experimental results prove that the new method has better performance on image enhancement.

In order to align partly overlapped data clouds measured from different view points, a multi-scale feature points detection algorithm was proposed. A few feature points can be extracted from large number of original data quickly. This algorithm consists of three steps: discrete curvature computing, bilateral filtering and feature points detecting. The number of feature points can be controlled by scale parameter approximately. For each feature point, the authors proposed local shape spectral descriptor to identify its local shape characteristic. Firstly, an affinity matrix was constructed using distance and curvature information of points in neighborhood of a feature point, and then a few of eigenvalues of affinity matrix were used to form a shape descriptor, with which the correspondence between different data sets can be computed easily. Some examples prove that the method is robust and efficient for aligning large number of data with noise.

Simulation of radar scanning is one of the difficulties in the visual simulation of battlefield situation. To introduce a real-time simulation method of three-dimensional scanning radar based on particle system. According to the rule of radar scan, the traditional particle system model was changed, and the radar afterglow particles were rendered with point. It was implemented on Personal Computer with OpenGL. The result shows that the method is able to meet the needs of reality and real time.

A fingerprint image watermarking algorithm was proposed based on Contourlet transform and the quantization of parity. After Contourlet transform, original image was decomposed into a series of multi scale, local, and directional subimages. The original binary watermark was firstly scrambled by two-dimensional Arnold transform, and then embedded into the Contourlet transform low frequency sub-band. Then the coefficients of low frequency sub-band were modified by using the quantization of parity. The retrieving watermark algorithm is a blind detecting process, and it does not need original image. The experimental result shows that, this algorithm can resist attacks, such as crop, JPEG compression, and overlaid noise. The watermarking is invisible and robust, and it improves the reliability of fingerprint recognition.

Focusing on WAP technology and its application in mobile Internet, which is popular at present, there was a research about using mature MVC pattern on designing such applications. It gave a brief introduction of WAP, WAP network and MVC pattern, and also introduced MVC pattern to WAP system, which made WAP program optimized. A concrete application was given as an example lastly.

The regular topological structure was introduced. For solving the low dimensional data with regular structure, the measure of the regularity was constructed and then the dimensionality reduction was brought forward. Compared with the kernel eigenmaps, for example Locally Linear Embedding(LLE) and Laplacian Eigenmap, the method makes the results approximately regular. The last results prove the theory results and show that this technique can greatly discover the topological structure of data, compared to the LLE and Laplacian Eigenmap.