Abstract:City memory resources are important assets which enable us to preserve histories and pass on cultures. In terms of photograph resources amongst them, they can help to present city’s historical and cultural changes directly. These resources scatter around and are preserved in various places, by different groups such as archives, libraries and individuals. This research combines decentralized photographic resources with Geographic Information System (GIS), focusing on the figure, event, time and location of the photos to map them with geographic information in webpage and to display them productively. In order to meet the demand of reality, a metadata description proposal is designed, referring to DC and VRA standards. Another essential procedure is to formulate a four-tier classification system to correspond with the metadata proposals. As for visualization, we use Photo Waterfall and Time Line to display our resources in front end. Last but not the least, led by the Web 2.0 trend, we exploit an artistic, friendly, user involvement, expandable, and general historical display platform to show the historical and culture precipitation of Beijing.

Abstract:In order to improve the analysis of reservoir properties and oil exploration and development process, this paper analyzes data and finds relationships between reservoir properties using Spark parallel computing framework and data mining algorithm, and classifies and predicts different reservoir segments. The main work in this paper includes: building the Spark distributed clustering and data processing and analysis platform, Spark being a popular big data parallel computing framework, which can achieve fast and accurate data mining tasks compared with some traditional analysis methods and tools; establishing a multidimensional outlier detection function according to the characteristics of reservoir data and adding a new discriminant attribute Pr; proposing a cross-recall training model and optimized cost function for logistic regression classification in dealing with the imbalanced data. KR-SMOTE is used to oversample for decession tree classification that both improve the classification precision.

Abstract:First, this article analyzes the navigation risk brought by the difference between the river boundary displayed on the electronic river chart and the real and practical river boundary. Then, the method of the automatic generation technology of the river boundary with the water level is studied. Two methods of obtaining the river boundary vectors corresponding to different water levels and two kinds of water level release methods are proposed, and the proposed methods are verified, which is of practical value.

Abstract:In the the Internet era of resource access, it is of great significance how to do a good job of the access control of resources. In this paper, we propose a resource access control protocol based on Bell state by using the entanglement properties of two particles. This protocol combines the quantum key distribution (Key Distribution-QKD Quantum) technology with unequal a key (Key Inadvertently) to achieve a quantum resource access control protocol, and realizes the authentication of the resource request Party in the protocol. At the same time, the security of the protocol is analyzed in this paper, which ensures that the resources are not accessed by unauthorized users and the authorized users can only access specific resources.

Abstract:Aiming at the serious hidden danger of data fusion in wireless sensor networks, this paper proposes a data fusion and encryption scheme based on iris signature key. This scheme does not only solve the problem of key memory and storage during data fusion, it also avoids the attack of the intermediate nodes on the data fusion result. Simulation results show that the scheme is more reasonable than the traditional data fusion scheme, which reduces the node energy consumption and prolongs the network life cycle.

Abstract:The garments industry faces the paradox situation: high inventory and meanwhile serious out of stock because of various consumption demands and low accuracy of forecast. As the source of the whole supply chain, the traditional model of impulsive decision making is responsible for the inaccuracy and is changed to a new model of closed-loop, bicirculating with hypothesis and verification. For the new merchandising method, GeneXus is used to build up the information support system fast and conveniently. It shows that after deployed in enterprises, the application of the system can remarkably improve the prediction accuracy, reduce the inventory level and promote the sales performance. The feasibility of the new method and system is verified.

Abstract:In view of the complexity of urban drainage facilities and the lack of effective management measures for supervision, planning and social services, this paper proposed a design scheme of intelligent drainage pipe network system based on sensor networks and GIS technology. The proposed design scheme achieved the management, planning and monitoring of the drainage pipe network as well as the controlling of the running state of the pipeline in real-time. In this study, the ultimate goal is to realize the “dynamic management, early warning and prevention, intelligent decision” of the drainage pipe networks, therefore to improve the efficiency, quality and management level of water logging drainage.

Abstract:This article introduces Rodgers innovative theory (Diffusion of Innovations) and the AHP algorithm, and probes into parents’ thoughts and attitudes towards the critical factors of their children using electronic bags, and indicates the behaviors about affecting uses of electronic bags. Based on Rodgers’ innovative theory, this article makes twenty-seven factor attribute as quantitative analysis on innovations, uses AHP algorithm to devise electronic Bags’ evaluation system and makes explicit analysis on the data which the users have submitted.

Abstract:The Android mobile terminal App applies the socket technology to the communication with the server. The server program sends commands to the ZigBee coordinator by USB serial communication, which has solved the fusion problem between TCP/IP protocol and ZigBee protocol stack. The ZigBee coordinator analyzes the commands, and transmits the corresponding control signal in the form of broadcasting to all ZigBee terminal nodes.The terminal nodes drive the HV9910B PWM dimming circuit, and implement a variety of LED lighting effects according to the control signal received. Experiments show that the system has good practicability, scalable and robustness.

Abstract:With the rapid development of the computer and software technology, a variety of monitoring systems have been used, but the current monitoring System cannot realize a comprehensive monitoring due to the particularity of meteorological services and equipment. On the basis of open-source Nagios monitoring software, this paper studies the research methods and the monitoring software for special equipment according to features of meteorological service and equipment, to offer an efficient implementation of fast and accurate monitoring servers.

Abstract:In view of the universal problems that the material management of the biomass power plants is loose, and they lack standardization and real-time interaction, this paper proposes a system based on the method of intensive management to control the whole process of power plant material. By analyzing the whole process of power plant material management and applying the Internet of Things, the method can simplify the management process. By making use of the resources to maximize and data mining, material utilization, the circulation rate and quality control management can be improved. This system has been applied in Shandong Gaotang power plant, which raised the level of materials management and economic effectiveness greatly. It is of great significance for safe, cost-effective and highly efficient operation of the plant.

Abstract:The traditional monitoring tools in grid comping have been unable to meet the virtual resource monitoring requirements in cloud computing platform. To solve this problem, this paper designs and implements a complete scheme of cloud host resource monitoring in private cloud platform based on OpenStack. Experimental results show that the proposed scheme can effectively monitor the virtual resources, and meet the requirements of the enterprise private cloud monitoring of virtual resources, and the system has good scalability.

Abstract:In order to improve the development efficiency, shorten the development cycle and reduce the development cost of Java technology in enterprise application, by researching Struts2 framework, JDBC protocol and Ajax asynchronous refresh mechanism, this paper proposes an enterprise Web development framework based on Struts2+Ajax+JDBC in accordance with the idea of MVC design pattern. According to the information release module of production statistics management system, it describes the implementation process of presentation layer, control layer, business logic layer and persistence layer in this architecture. Practice shows that the architecture has good portability, expansibility and maintainability, and it reduces the complexity and coupling of the application, and improves the development efficiency and user experience.

Abstract:As the real-time embedded systems are more and more complex, the existing RTOS design method, such as hardware and software separation,hardware and software coordination and so on, is unable to meet the requirements of its implementation. Combined with the core idea of MDA and MIC, this paper proposes a method based on model-driven for the RTOS design, which combines the temporal semantics with the Servant / Exe-Flow Model. Firstly, the paper gives the abstract semantics of the meta model expressing SEFM, and describes the concrete syntax of SEFM using XML language and block diagram language. If a different specific syntax can express the same abstract syntax, then each of them can be transformed into the other. Combined with XML parsing technology, the code generation of SEFM can be realized. Finally, the experiments of the following vehicle system show that the method of system design is feasible and correct.

Abstract:In security scenarios, most of the sensor systems (video, infrared, smoke sensor) can only achieve simple single data acquisition and processing. A mobile monitoring system based on video analysis and multi-sensor fusion is proposed with car as a carrier. The collected video is analyzed and processed with proposed algorithms (including the background-difference-based the intrusion detection algorithm and the improved TLD algorithm). With collaborative monitoring from multi-sensor, it can achieve intrusion detection, mobile tracking and monitoring.Experiments show that the system can realize the real intelligence security and user friendly interaction.

Abstract:The vehicle detection system in current use widely uses host to centrally process images passed back to the server, and it thus has shortcomings, such as large output data, long processing time and so on. This paper proposes a constructing plan using embedded hardware platform according to the composition of ITS system. The video image is emphatically analyzed, and the tracking algorithm is also optimized. The basic image processing tool OpenCV is used to select the preferable edge algorithm by comparing the canny operator and sobel operator. Considering smearing, the background is extracted with the improved averaging method (take interval frames). Finally the nuclear track is chosen for dynamic target tracking, and it is applied in the system for getting the stability of dynamic target tracking.

Abstract:Aiming at the problem that the traditional SLIC algorithm has poor quality in segmenting high resolution remote sensing images, this paper proposes an improved SLIC based on dimensionality reduction and region merging to segment the buildings. Firstly,it simplifies the dimensionality of the traditional SLIC, and the color information is replaced by the gray feature information to reduce the redundancy of the five-dimensional feature vector of the LAB color space. Secondly, the over-segmentation images are combined by using the region adjacency graph. Finally, the main parameters of the improved SLIC are analyzed and compared, namely, the number of super-pixels ‘k’, the compactness ‘m’ and the number of iterations ‘p’. The experiments show that this method can not only separate most of the building information, but also improve the operation efficiency and space efficiency. The running time efficiency is 25.5% higher than the traditional SLIC, and the segmentation precision of the building can achieve 97.6%.

Abstract:In the field of machine learning, the practicality and effectiveness of the Adaboost algorithm has already been demonstrated. However, since this algorithm is originally designed for classification problems, it cannot be applied directly to rating prediction problems in recommender system field. Thus the research in this area is limited. In this paper, we improve the Adaboost algorithm. By introducing the threshold value, we transform rating prediction into classification. By updating weights in the training process, we propose a framework for the rating prediction, which can integrate the multiple training models. The final rating is obtained through the integrated model. We select the Matrix Factorization model as an instance, and the experimental results show that the framework can effectively improve the prediction accuracy.

Abstract:In recent years, the K-SVD algorithm has gained more and more attention in the studies of functional magnetic resonance imaging (fMRI) data analysis. In this research, we propose a new method of blind source separation based on non-negative constrained K-SVD (NK-SVD). Firstly, we initialize a dictionary matrix randomly, and use orthogonal matching pursuit (OMP) to obtain a sparse vector matrix. Then, we use NK-SVD to update the dictionary matrix and sparse vector matrix. Furthermore, we solve the dictionary matrix pseudo inverse to obtain the brain functional activation areas by multiplying by the original data. Finally, we apply the proposed method to both simulated data and real fMRI data, where the correspondingly experimental results demonstrate the effectiveness of the proposed one, having better performance in comparison with the conventional algorithms.

Abstract:In view of the instability and poor time effectiveness of data in the city intersection vehicles queuing system, this paper puts forward a method to simulate intersections vehicles queuing system by using Flexsim. Firstly, the rules and service time of vehicles queuing System are obtained based on the field data. Then, the vehicles arrival interval functions of the queuing system are fitted by the software of ExpertFit and a simulation through using the functions was carried out. Finally, the optimized solution is proposed according to the result of the simulation. In conclusion, this study proves that the Flexsim simulation can solve problems of intersection traffic jams effectively, which presents a applicable prospect of the system.

Abstract:This paper proposes a RRT path planning algorithm based on the guiding-area which is generated with the A* algorithm. This algorithm can benefit the domain from the following aspects: the applications of RRT algorithm to the field of path planning for the intelligent vehicle can be improved significantly. The performance of the traditional RRT algorithm can be enhanced by solving some inherent issues, such as low searching efficiency, irrational nearest neighbour searching functions etc. The novel algorithm combines A* and RRT effectively. Based on low resolution grid map, A* algorithm is applied to construct the guiding area, which is used to improve the sampling efficiency. To enhance the reasonableness of the selection of searching tree node, the vehicle’s constraints are considered in the design of the nearest neighbour searching function. Finally, the superiority, validity and practicability of the proposed algorithm is verified in simulations and experiments with the real vehicle

Abstract:A method for offline hand-written Chinese character recognition is proposed based on partial cascade feature classification, which is of much research value and highly innovative. Two feature extracting algorithms are proposed as follows: weighted Low Threshold Hough Space Sampling(wHHS) and Histogram of Local Binary Distribution(HLBD). These algorithms can map images of various sizes into vectors with fixed dimension, but eliminate the disadvantages of existing algorithms, which has high sensitivity of the distribution of strokes destiny, and demand uniformization. A strategy of classification based on partial cascade feature is proposed and the relationship between number of category for classification and accuracy is put forward with the corresponding mathematical proof.

Abstract:With the development of big data, distributed support vector machine (SVM) has become a hot research topic in this field. The process of finding the global optimal support vector in the Hadoop platform is long under the traditional hierarchical Cascade SVM algorithm. This paper presents an improved method by firstly combining the traditional grid method and the particle swarm optimization(PSO) algorithm to improve the PSO algorithm. And a new satellite parallel PSO algorithm is realized by combining the single machine PSO algorithm and the Hadoop platform (NPP-PSO). The experimental results show that compared with the single SVM algorithm, the distributed SVM algorithm cannot only ensure the accuracy but can also greatly boost the computation speed. With the wide use of NPP-PSO distributed SVM, the classification accuracy has improved significantly.

Abstract:The optimization of short-term load forecasting simulation for the Grid power system can improve prediction accuracy and robustness of the results. Although the existing prediction models can meet the requirements of prediction speed, the accuracy and stability of the predicted results are always difficult to guarantee. In order to get more accurate and stable forecast results, this paper puts forward the bacterial foraging algorithm to optimize the new predicting model of the extreme learning machine. First, the training sample and forecast sample set are formed in the power load sampling data set. The bacteria foraging optimization algorithm is used to optimize the uncertain parameters in the prediction model of extreme learning machine algorithm. Then, the improved model for power load forecasting is used. Through the optimization of the new model simulation, the results show that the use of bacterial foraging algorithm optimization model to predict extreme learning machine precision and stability are superior to the traditional forecasting model prediction results, and the algorithm has good practicability.

Abstract:The study of fuzzy cluster analysis is mainly the classification of samples. In this paper, the fuzzy clustering method is used to classify the defects of software, and the method of attribute weight calculation is introduced. The similarity of defect data is analyzed with the method of attribute proximity in data mining. According to the category of attributes, it does not only reflect the degree of similarity between the attributes of the defect data, but also reflects the distance between the attributes. In this paper, the software defect data are analyzed and compared with the measurement results. The experimental results show that the improved fuzzy clustering similarity measurement method has somehow improved in classification accuracy.

Abstract:It is becoming increasingly important to make timely and accurate prediction of human blood pressure changes in order to prevent the exacerbation caused by the instable human blood pressure .This paper proposes a prediction model of human blood pressure based on the combination of wavelet analysis and BP neural networks. This model uses the wavelet decomposition and reconstruction method to decompose and reconstruct non-stationary human blood pressure sequence, separating the high frequency components and the low frequency components in the original sequence, then the BP neural network prediction algorithm is used to establish the prediction model for each layer. Finally, the predicted values of the two models are added to obtain the predicted values of the original series. The results show that the prediction accuracy of the combined forecasting model is obviously higher than that of the traditional BP neural network prediction model, which provides an effective and reliable combination forecasting method for human blood pressure prediction.

Abstract:Among existing DOA estimation methods, the Weighted Subspace Fitting (WSF) algorithm is well-known for its high resolution of DOA estimation. However, its computational complexity is extremely high and cannot meet the real-time requirements. In this paper, we propose a Joint-PSO algorithm for WSF with less complexity. This algorithm has the following key steps: firstly we use the solution of Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) which can get the DOA estimation with extremely low complexity and stochastic Cramer-Rao bound (CRB) to determine a novel initialization space in the whole search space. Then, we randomly initiate a small number of particle in that small area. Finally, we let the particles “fly” to the solution with a suitable speed. Additionally, we also discuss and optimize the inertia factor of PSO algorithm. The simulation results find that for the same Root-Mean-Square-Error (RMSE), the particles and iteration number of the proposed algorithm are much less than that of the original PSO algorithm. As a result, the computational complexity can be greatly reduced.

Abstract:Similarity calculation is a key step in the user-based collaborative filtering algorithm. As the number of users increases, the similarity computing space will become increasingly tremendous. At the same time, the accuracy is relatively low when it is applied to the agricultural personalized recommendation system. According to the feature of agricultural materials which are strongly influenced by seasons and locations, TA-ACF(Agricultural collaborative filtering algorithm based on both time and area) is proposed based on time and area size, which improves the original similarity calculation method. In this way, these above-mentioned problems could be solved. The main idea is to establish the matrix of time and size according to the existing research of the agricultural demands, and establish a rating matrix within the time and size. As the result shows, compared to the user-based collaborative filtering algorithm, TA-ACF is able to improve the quality of recommendations without losing time efficiency.

Abstract:It is very important to reduce the candidate features in the machine learning such as classification and clustering. Most of the existing methods are based on a single feature on the target T or the association between the feature and the feature on the Y. However, these methods do not take into the combined features, such as attributes A, B contains a little amount of information in Y, and even completely independent of Y, but A & B can provide information on Y lot of information, or even completely determine the Y. Based on this, we can extract an algorithm to find single and combined features from the feature set, firstly combination of non-significant features in accordance with the conditional probability distribution table to generate new candidate features Then, the single feature and the combined features are chosen based on the criterion of the maximum correlation and the minimum redundancy. Finally, the experiment is carried out on the virtual and real data sets respectively, and the experimental results show that the feature selection algorithm can mine the dataset better, Which improves the accuracy of the corresponding machine learning algorithm to a certain extent.

Abstract:Studying the linear parallel drawing based on Bresenham, this paper calculates the average number of pixels in each scan line when the slope k belongs to (0,1/2) based on Probability, and finds that three-quarters of the drawing time can be saved with this method. According to the theoretical analysis, combined with classical Bresenham algorithm for generating line, it realizes a parallel Bresenham algorithm for generating line, and the results with this method are the same as the windows drawing program and the classic Bresenham drawing linear algorithm. It is very important to multi-point parallel rendering for scan line and is easy to design hardware which can enhance the response to real-time drawing.

Abstract:This paper uses Ajax without refreshing technology to eliminate the waiting time. At the same time, it uses the Oracle 11g database server in PL/SQL programming to create a stored procedure realization of data paging query. With these two technologies combined together, by querying or computing and transmitting the necessary part of the data to the WEB server, the client can use the asynchronous Ajax communication technology to obtain WEB server without refreshing the data, which shortens the waiting time of the users, and improves the application performance.

Abstract:To solve the problem of hard employment of graduates who expect for the impractical emolument, the paper builds a model for emolument prediction. On the basis of an classification algorithm with natural neighbor(NaN)，it analyzes the employment data of graduates majoring in Information Engineering in past three years. The paper uses factor analysis method to fetch the latency of employment emolument level determinants. Classification predicts the emolument by applying the latency as a variable based on the classification algorithm. This algorithm avoids the difficulty of parameter selection in K-nearest neighbor(KNN). The neighbors of each node can also be acquired as the topography of data set. According to the experiments, the prediction accuracy is 80.16%. The paper can guide graduates to build a reasonable emolument prediction or improve employment.

Abstract:With the rapid development of mobile internet technology, new requirements have been set for the mobile terminal positioning method. In view of the problem that we cannot take into account of energy consumption, accuracy and versatility, we propose a Hidden Markov Model-based and personal habits-based network location algorithm. The algorithm divides the roads into sections in the area covered by the base stations according to the map, and records the personal walking trajectory, which helps build the hidden markov model of GSM signals with the statistical law. Thus, with only a station’s signal sequence, we can get an accurate estimate of the user’s current position. Experiments show that the algorithm can guarantee a good positioning accuracy with lower energy consumption.

Abstract:Considering the strict requirements for data and limited application for the traditional method of the object extraction from Remotely Sensed Imagery (RSI), a building extraction algorithm based on the nonlinear scale-space filtering is proposed. Firstly, nonlinear scale-space of each band in the multispectral RSI is constructed, and the iterative filtering is done. Then, the first valley point in standard deviation curve of the global image is searched to stop the iteration process. Finally, the binarization of filtering results using the Otsu method for each band is made. To verify the validity of the proposed method, an aerial image covering Fuzhou, China is chosen to test and compare with the similar method. Experimental results show that the proposed method can smooth the noise while preserving the building edges information, and has better effect for extraction of the closely spaced buildings. Moreover, the recall of the proposed method increases more than 5% in the premise of ensuring the precision.

Abstract:With the development of regional health information sharing services, an increasing number of patient records are released. However, the adversary can infer the patient’s privacy information through the patient’s attributes, thereby causing the patient’s privacy leakage. Based on the above requirements, a privacy protection data publishing algorithm based on KD tree is proposed. By the properties of KD-tree, the generalized value of each attribute is decomposed until the generalized value of all attributes cannot be decomposed to ensure that the generalized value of all attributes of each leaf node is minimized to reduce the information loss. During the decomposition of equivalent tuple attributes, the number of sensitive attribute values for each node is made to be a diversity constraint to reduce the risk of privacy leakage. The experimental results show that this scheme can reduce the risk of leakage of privacy, and information loss.

Abstract:This paper puts forward a recommendation algorithm of various factors based on the labels. Users can define the factors and sort them with priority according to their own requirements., This algorithm will select the resources according to the initial information of users, then update users’ groups and users’ preferences, then recommend the useful resources for users based on the similarity calculation between users and project and the correlation calculation of the project. The algorithm model adopts classification of combination to get the results, and reduces the complexity of similarity calculation. The algorithm was applied to the personalized learning platform of remote training platform in enterprise. The results show that this algorithm has much improved the recommendation effects of user's personalized learning resources.

Abstract:With the increasing demand for medical treatment, public security, anti-terrorism and other aspects of urban life, the vital signs detection of non-contact radar is gradually getting the attention. In this paper, an algorithm for radar vital signs detection based on EMD and neural networks is presented. Due to the non-linear and non-stationary characteristics of UWB radar echo signal, this paper utilizes the space and time scales characteristics of the EMD to decompose the signal and obtain a series of IMF. By combining the BP and IGA neural networks, it optimizes the signal and obtains the heart and respiratory signals. The experimental results show that the proposed algorithm is more accurate than the direct EMD decomposition and reconstruction of the signal, which makes up for the end effect of EMD decomposition, and has broad application prospects and research value.

Abstract:To improve the performance of Support Vector Machine classifier for imbalanced data, an imbalanced data classification algorithm based on split and classifier ensemble is introduced. The majority class sample is divided into several sub sets by clustering, and each subset is combined with minority class sample to produce a training subset. Then the training subsets are learned and multiple classifiers are obtained. Finally the multiple classifiers are integrated and the ensemble classifier is obtained. Experimental results show the algorithm is effective for imbalanced dataset, especially for the minority class samples.

Abstract:In recent years, the urban public security poses a new problem to the sustainable development of social economy. Therefore, how to effectively monitor the abnormal situation of the crowd has become a hot issue. Due to the large number of moving targets and the changing of the crowd, it is difficult to study the abnormalities of the crowd by tracking the moving objects. The study shows that when the crowd is abnormal, the most obvious change is the movement speed of the crowd and the direction of the movement will suddenly change. For example, from static state or slow walking to fast running, and the sudden change of the motion direction. The corresponding motion vector of the video frame will undergo the same change. Thus, we propose the fast detection algorithm for the crowd based on motion vector. The experimental results show that the algorithm proposed can detect the abnormal movement of human beings in real time and effectively.

Abstract:This paper investigates the problem of information crawling in Web site in penetration testing. In order to meet the requirement of high efficiency and comprehensiveness of information fetching in Web penetration test, in this paper, we researched and analyzed the Web site, and proposed a web crawler strategy based on navigation link. Besides, to optimize the efficiency of URL de-emphasis, we improved the traditional MD5 de-emphasis algorithm by reducing the number of iterations. The experimental results show that the coverage of information fetching and web page download efficiency are raised with the Strategy of Web crawler.

Abstract:To establish the three-dimensional model of dormitories in higher institutions, the CFD software is employed to simulate numerically the indoor airflow distribution conducted so that the wind speed and temperature surrounding the human body as well as the PMV-PPD and Mean age of air distribution of the cross section around the human’s head and sciatic nerves can be measured. Specifically, the above-mentioned two indexes PMV-PPD and Mean age of air are supposed to be used in the analysis of the differences caused by the varied positions of two air supply grilles on the human thermal comfort. The statistics collected in this analysis show that with the same wind speed, the air supply grille 2 which is farther away from the air outlet can provide better air quality and higher thermal comfort as well as relatively lower indoor temperature (lower than 1-2 °C) than the supply grille 1 does. The result obtained from this research is able to provide theatrical foundation for the air supply grille installation in dormitories of higher institutions to some extent.

Abstract:Linux firewall provides a scalable mechanism for developers. After a thorough research of SVM principle, this paper, proposes the design and implementation of Linux firewall system based on SVM. The Netfilter framework is used to capture network packets. In the users’ space, anomaly network traffic is classified by support vector machine algorithm module and the rules of Iptables are added dynamically. Thus, the function of defending network attacks is realized. The experimental results demonstrate that the proposed system model has high detection accuracy for the classification of abnormal traffic. It proves that the SVM algorithm is feasible and effective in Linux firewall.

Abstract:Through the analysis of Docker Remote API Unauthorized Access Vulnerability, this paper proposes a vulnerability exploitation tool. The tool fills the gap of vulnerability detection tools and provides the batch testing function. It achieves high efficiency of vulnerability detection and lays the foundation for web security work such as bug fixes. It is effective and has low cost for the improvement of web security．

Abstract:Giant panda population data are important bases for knowing the population dynamics of giant pandas. Thus, it is significant to model giant panda population data for conservation. To solve the problems that the existing methods for modeling population dynamics were not able to capture the randomness in a complex ecological system and have bad extensibility, a probability membrane system for modeling the giant panda ecosystem is proposed based on the data from Chengdu Research Base of Giant Panda Breeding. On the basis of giant panda pedigree, a probability membrane system is designed with a membrane structure consisting of two nested membranes, a series of objects and evolution rules for representing the giant panda ecosystem. The simulation results show that the model can reflect the dynamic changes of giant panda population and therefore can provide reference for managers.

Abstract:With the large-scale cloud computing data center server globally deployed, its advantages such as low cost,strong expansibility etc. provide convenience for those companies or business units with large amount of data,and save the cost of capital on the construction of the IT environment. But in this data outsourcing environment, this will involve information security and privacy protection. On the premise of guaranteeing the safety and accuracy, this paper presents an outsourcing data storage model based on the protection of privacy .Combined with data privacy protection of mining algorithm, it puts forward the data integrity detection protocol based on data communication. The protocol uses data security technology, starting from the angles of safety, including the security of data transmission, data mining safety, integrity, security etc, making the third party server trustful.

Abstract:With the development of the Internet and Web technology, HTML5 specification is supported and recommended by the W3C and the mainstream Internet in the world as the new standards of the Web application development of the next generation. The function of HTML5 is also refined and stabilized, at the same time HTML5 has many new attractive features. With the canvas tags, HTML5 can dynamically generate kinds of graphics and animation, and has been fully equipped with the basic conditions designing games. In this paper, the design scheme of a little WeChat game based on HTML5 called telepathy is provided, which has combined the function of canvas and the algorithm of the polygon area.

Abstract:In recent years, most location-based service privacy protection turns to peer-to-peer based on subscriber cooperation. However, the biggest drawback of that mode lies in the integrity of collaborative users. If there are vicious users in ambient collaborative users, the information of collaborative users may be accessed illegally. So this paper proposes a method of preventing the location privacy from being revealed to vicious users via using third-party trust institutions to detect the abnormal network behaviors for mobile users. In the meanwhile, we broaden the zone of collaborative users in the mode of peer to peer through binary tree. The experimental result shows the method can effectively prevent unfaithful users from joining in the anonymous group and reduce the probability of the information leakage.

Abstract:With the expansion and increasing complexity of networks, SNMP protocol cannot meet the requirement of the network configuration management. The Netconf protocol is designed based on XML. IETF recommends it to be the standard protocol for network configuration management. It provides installation, operation, and deleting mechanism to network device configurations. This paper introduces the design and implementation of a LTE XMS network management system and analyzes the application of Netconf protocol in eNodeB configuration management.

Abstract:Cloud computing has become an important infrastructure of the Internet, and more and more applications are deployed into cloud computing to provide the online service. Virtualization technology is the key element of cloud computing and hot research field, and it provides the computation, storage and network resource virtualization. KVM whose kernel is virtualization has become the one of the main virtualization technologies. This paper analyzes the structure of KVM, and introduces the working process of KVM. Finally, we also test the performance of KVM and get the useful results. These results, which are useful for technology improvement, can be used to improve the virtualization technology.

Abstract:The Kernel function to support vector machines can be divided into two types: the local kernel function and the global kernel function. Because the local kernel function has excellent learning ability, but its generalization ability is limited, we structure a joint kernel function with two kinds of functions, so that it can combine the advantages of the two kinds of kernel functions. The experiment proves that the joint kernel function can adapt to the incremental learning process and it has better performance.