Abstract:SMS automatic 3D animation generating system is based on sender message content, by information extraction, semantic analysis and a series of steps, eventually generating a 3D animation which matches the content of the text message. Information extraction in the 3D animation generating system is primary and key, the purpose is to provide animated information for follow-up links to 3D animation automatically generating system. This paper introduces the path feature to realize the extraction of the Chinese text message. It mainly uses the LTP-Cloud platform of Harbin Institute of Technology to preprocess the short message. The path is extracted from the processing result and the path of feature is obtained. Getting rules by combinating the path of features by the first-order inductive learner, and then predicting the relation of messages by matching rules. Finally, Extracting the type of relationship and relation combination in the text message.

Abstract:Document System plays an important role in information dissemination and utilization. However, with the emergence of information overload, the utilization rate of data would greatly decrease. To solve this problem, a document recommendation system based on multi-granularity features and Hybrid Algorithms is proposed. User interest and document feature models are established on both phrase and term granularities. Then, the system generates recommendation lists for users based on the combination of content-based and collaborative-filtering algorithms. The tests based on authentic data demonstrate that the document recommendation system has a better performance on precision, recall rate, coverage rate and novelty. The recommendation lists are more in line with users' interests. This helps to increase the utilization rate of data and improves user experience with better performance.

Abstract:With the rapid development and application of high-speed railway EMU, its safety and reliability have attracted wide attention. In order to estimate the health status of axle box bearing of high-speed railway EMU accurately (hereinafter referred to as axle box bearing), this study proposes a classification algorithm based on Decision Tree and Support Vector Machine, and utilizes Principal Component Analysis (PCA) to reduce the feature dimension simultaneously. In addition, the performance of the classification can be further improved by collecting the temperature data of axle box bearing and various components on the drive side and non-drive side of which either in the same bogie or in different bogie. And the Analytic Hierarchy Process (AHP) has been used to distribute the weight of vectors. Extensive experiments demonstrate the effectiveness of the classification model, and the accuracy has been increased by about 5%. Furthermore, the judgment ability of health status of axle box bearing and the precision of the operation and maintenance policy can be enhanced if we establish a health assessment model through dividing the health status of axle box bearing into four parts including health, temperature rising, strong temperature, and irritative temperature.

Abstract:In order to solve the problem of heterogeneous data sources and data sharing in information systems, a virtual integration framework of multi-source heterogeneous data is proposed. Since GAV (Global-As-View) pattern mapping method in data integration system is less efficient when faced with uneven distribution of information, GAV method is improved, and the pattern mapping method based on HGAV (Hierarchical-Global-As-View) is proposed. By introducing the intermediate data source pattern, a hierarchical global view is formed, which greatly reduces the mapping space. In this way, the mapping set is simplified, and the query is easier to rewrite and optimize. The proposed algorithm is verified by the five main types of data in the Ningdong Intelligent Environment Protection project. The experimental results show that the pattern mapping algorithm based on HGAV improves the efficiency of data integration and shortens the query time, compared to the GAV schema mapping algorithm.

Abstract:Based on the WorldView-2 high resolution remote sensing image in 2011, this study uses object-based classification method and four traditional pixel-based classification methods to extract study area land use information respectively. Then, Visual interpretation map is functioned as reference map to acquire each classification methods overall accuracy and to assess the each classification result and each class type from the aspects of quantity disagreement and allocation disagreement. The result shows that: (1) The average overall classification accuracy is 75.00%. Among all the classification methods, the object-based classification method acquires the highest accuracy, 84.25%. The maximum likelihood classification method gets the lowest accuracy, 62.00%. (2) In all classification methods, the object-based classification method has obtained the lowest quantity disagreement, 4.25%. The others in sequence are as follows: neural net classification method < support vector machine method < mahalanobis distance method < maximum likelihood method. As to allocation disagreement, the support vector machine method has acquired the lowest value, 5.75%. The others in sequence are maximum likelihood method < neural net classification < mahalanobis distance method < object-based classification method. (3) As to separate class type, farmland does great influence on image's overall classification accuracy, whose quantity disagreement sequence is the maximum likelihood method(28.75%) > mahalanobis distance method(21.50%) > support vector machine method(14.75%) > neural net method(11.00%) > object-based method(3.00%). As for allocation disagreement, the sequence is object-based method(10.50%) > neural net method(5.00%) > support vector machine method(1.50%) > maximum likelihood method(0.50%) > mahalanobis distance method(0.00%).

Abstract:To address the large data processing problem on the vehicle networking platform in which the data throughput is small, and its poor real-time feature, this paper proposes a new real-time analysis system based on the big data stream processing technology. The proposed system consists of 5 layers including data acquisition, data forwarding, real-time data analysis, data cache and storage, and visual display. Specifically, it introduces Storm real-time computing system to real-time data processing, which is beneficial to the high concurrent access and can meet real-time requirements of the system. Furthermore, aiming at the problem that the access to the database is expensive, Redis cache strategy is used to improve the query efficiency. Experiments show that the system has low latency, high throughput, and scalability compared with the conventional multithreaded processing platform, which is able to satisfy the requirements of vehicle network data stream processing.

Abstract:In order to assist developers to set and revise PID control parameters for intelligent vehicle, a simulation platform for vehicle motion composed of three modules is designed. The shapes and parameters for tracks are stored in track module; the motion model and parameters for intelligent vehicle are built in the vehicle module; the motion simulation module is used to mimic the real physical environment with coordinate system. During emulation process, the simulation module receives parameters for vehicle and PID, with the aid of vehicle module, it then figures out the position and error information for developers. The result shows that, compared with the traditional way, this method is not constrained by certain sensors. Meanwhile, its simulation data could actually reflect vehicle motion. Importantly, it helps developers to enhance efficiency for control parameters revision.

Abstract:In view of the low level of the present cardres online education information and the low efficiency of the APP client cardres learning platform, a mobile learning platform based on WeChat enterprise account number is designed and implemented. In this study, taking the WeChat learning platform of the Chongqing E-Learning Academy Leadership as an example, the feasibility of the platform is verified. By implementing WeChat enterprise account number open API and connecting with the PC platform and the APP client, and applying the PHP, HTML5 and JSON, and other Web development technology, this study designs and implements the daily lesson, personal center and E-book, and other function for cadres' learning platform. Cadres can conveniently use the learning platform's services and resources to accomplish the learning task via command interaction in the social network environment. It provides a new way for the majority of cadres to learn, and effectively improves the convenience, and the efficiency of the cadres' E-Learning.

Abstract:In recent years, multimedia technology and data communications technology has developed rapidly. Multi-party audio and video call is becoming popular. Multi-party audio and video call can solve the problem of long-distance communications. However, the existing call system does not combine the centralized and distributed media flow control modes effectively, which fails to give full play to the advantages of the two control methods. This paper presents a multi-party call dynamic control technology. It takes the client network bandwidth and the number of users as the transformation condition. And through the combination of star topology and mesh topology, the multi-call system can adaptively adjust the distribution method of media flow. Based on SIP conference control the system uses WebRTC technology to improve call quality. The experimental results show that the technology can effectively show the congestion and resource occupancy rate of each client in the system, and adjust the system media flow control in a timely manner, so that the media stream can be transmitted smoothly and call quality can be improved.

Abstract:Nowadays, many campuses have been using electric vehicles and other new energy vehicles as a public transport to provide convenient transportation for teachers and students. This paper presents a system which uses the smart phone carried by the driver of the campus bus as the collection terminal to collect the latitude and longitude position data of the driver through the mobile APP as the location information of the campus bus, and then transmits the location information to the backstage by using the mobile Internet. By means of map server the backstage sends the location information passed by different drivers to users in Web form. The system directly uses the driver's smart phone as a campus bus location terminal, saving the cost of buying intelligent terminal equipment for dedicated bus terminal data. The school can use Wi-Fi network to save mobile communication traffic. The system has the advantages of low cost, fast deployment, low cost of use, simple maintenance and so on.

Abstract:Time and space are the basis of the “strong smart grid”, which makes both the grid state and the accident closely related to them. Actually, there are more demands on emergency communication of different regions and parts. Focused on difficulties of situation fusion and weak presentation in the grid communication network, this paper proposes a distributed emergency communication system for electric power based on spatial, time, and situation fused big data. It designs an adaptive framework without relationships to services, proposes a heter-element reference for spatial and time big data. It achieves the transformation from different data into the same geo-coordinate. The classification basis of situation and plotting of grid emergency communication system is also formulated. The tests and simulations on ArcGIS and Linux demonstrate better performance in accuracy and lower data consumption.

Abstract:In the current enterprise environment microbiological detection process, it is cumbersome for the operators to manually process the data, which also bring a variety of accidental errors. In the testing process, the paper form of data flow has many problems like unreasonable, non-standard, and other issues. In particular, the sharing of project information cannot be achieved between departmental operators, so that customers and operators fail to timely and accurately access to all projects progress and results. Thus, by combining JAVA language framework mature and stable advantages, this study develops a workflow-based microbiological detection system for regulating processes and information sharing. First, it designs a set of workflows that conform to the enterprise microbiological testing process in this system. Then, it uses the Bootstrap framework to design front-end pages and limit the key data. Finally, by combining Struts and the Spring framework, it develops background business processes. The whole system is rigorous and stable in structure, and the operators can easily obtain all projects' information, which greatly improves the efficiency and accuracy of microbiological detection and brings economic benefits.

Abstract:To address the high cost, imperfect function, poor content, and unfitted buying habits of overseas customers in developing international trade on-line stores, based on the open source software Magento, this study develops an international trade on-line store that is of low cost, full-featured, content-rich, and in line with buying habits of overseas customers, through the design (including the choice of virtual hosts, Magento versions and expansion modules) and the implementation (including the installation and configuration of the chosen version and modules and the webpage customizations). It can give helpful reference and guidance to the design and implementation of international trade on-line stores based on Magento.

Abstract:The system, based on garbage-collection algorithm, orients to help the three-dimensional graphics engine to reclaim memory. The system is provided by library, without any other adapted source or compiler. With the objects constructed from interfaces of the system, the system would manage the memory automatically. The tests indicate that the system regain the circular reference memory with the affordable performance overhead.

Abstract:With the upgrade of hardware equipments in image area, more and more devices are already using megapixel images, and the device that transmits images is also a basic and important part. In allusion to those problems, this study uses simple and easy UDP/IP protocol and flexible, real-time FPGA as the hardware platform. This system adds image data protocol to UDP/IP, using hardware to implement the protocol and image display in real time. The results show that the system cannot only realize the image transmission which achieves a speed of 865.19 Mbit/s and real-time display, but it can also meet the demands of excellent transplantation, high-speed transmission, and high integration.

Abstract:In software testing field, test case reduction has been a research hotspot for a long time. Some researches currently use the complex relationship between test requirements for the test suites of test case, which can optimize the corresponding test suites on this basis. But the corresponding test case of a single test requirement may be a collection of density distribution in large quantities. This paper does an in-depth research and exploration on how to rationally optimize test case for the corresponding test suits of a single test requirement in the premise of test case. It proposes two classes based on black box testing equivalence partitioning and boundary value analysis strategy. Based on DBSCAN algorithm, it proposes a scientific and reasonable parameter selection method, and improves the adaptation degree and efficiency of algorithm. Combined with optimization algorithm and two strategies, it gets the optimal reduction set of test cases.

Abstract:According to the characteristics of the UAV aerial image, this study proposes a method for splicing information based on the Position and Orientation System (POS). First of all, the four corners of the image are calculated according to the POS parameter. Meanwhile, the SURF features of adjacent image overlap regions are extracted. The geographic coordinates of the back image are corrected according to the position relation of feature points. Then, they are stitched according to the geographic coordinates, and the image is fused by using the progressive fading out adaptive weighting strategy. Finally, a panoramic image with good visual effect is obtained.

Abstract:Considering the sparsity of traditional collaborative filtering recommendation algorithms in electronic commerce systems, a new collaborative filtering algorithms based on enhanced similarity and implicit trust is presented. Firstly, a new method based on JMSD and user's preference to compute the similarity measure is presented. Secondly, a method to compute the direct trust fused with the interactive experience is proposed. Then, a method to compute the implicit trust based on direct trust and trust propagation is presented. Finally, this paper presents a model to compute the rating predictions based on the enhanced similarity and implicit trust. The experimental results in Movielens and Epinions show that the new algorithm improves recommendation quality in MAE and coverage.

Abstract:The traditional attribute reduction algorithm based on discriminant matrix can only deal with discrete data, and most of the data contains both discrete and continuous attributes. In response to this problem, this study uses a method that allows discrete data and continuous data to be processed uniformly. This method replaces the original indistinguishable relation with the flexible logic equivalence relation, simplifying the discretization process in the traditional algorithm and improving the efficiency of the algorithm. Experiments show that compared with the traditional algorithm, the improved algorithm omits the process of discretization, and can deal with discrete data and continuous data uniformly.

Abstract:This study proposes a method for automatic recognition and cutting of bank bills using windowed Hough transform: by scanning each pixel to compute the Hough transform of the image and extract the peaks of the Hough transform (which correspond to line segments). A rectangle is detected when four extracted peaks satisfy certain geometric conditions (which correspond the border of bills). Threshold is used to segment the source image and perform fitting correction for the segment result and the extracted Hough transform rectangles. The integration test results of on different image backgrounds and illumination environment indicate that the proposed strategy has a good ability to suppress the interference caused by different natural illumination and shooting angles. In addition, thumbnails view is used to extract feature, reducing the time complexity of pixel-by-pixel operation.

Abstract:In view of the problem that the collaborative filtering algorithm ignores the learners' knowledge domain (learning state), this study improves the collaborative filtering algorithm used in the recommendation of personalized education. The recommendation algorithm is divided into three steps. (1) Based on cognitive diagnosis model, the study builds up a model construction analysis of the learner's knowledge domain based on learner's response matrix. (2) It Uses the collaborative filtering algorithm, combined with the knowledge domain of the target learners to analyze the learners with similar behaviors. (3) According to the similar learner's historical behaviors and the target learner's knowledge domain, the system would recommend testing questions (items) for the target learners. This recommendation method not only draws lessons from the generality of the similar learners of the same group, but also takes into account the uniqueness of the individual learners. The study combines the two to recommend the individualized items for the target learners, which ensures the accuracy and performance of the recommendation method. In the individualized education system, the recommendation method combined with cognitive diagnosis and collaborative filtering algorithm is an improved application.

Abstract:In order to improve the basic bat algorithm's premature convergence and low solving accuracy, an improved algorithm is proposed to enhance the diversity of the swarm. Firstly, the velocity weighting factor is introduced into the bat algorithm to make it decrease linearly during the iteration. Then the position of the bat is perturbed by the random number of Cauchy distribution when the local new solution does not satisfy the acceptance condition and the nonlinear programming function is called at intervals between algorithm runs. The improved algorithm can maintain the diversity of the swarm and enhance the ability of global and local search in the optimization process. The standard function test and its application in fuzzy hierarchical analysis show that the performance of the improved bat algorithm is much better than that of the basic bat algorithm, and has better practical value.

Abstract:A novel method of unsupervised feature selection UFS-IR based on improved ReliefF is proposed to solve the problem of lack of category information in feature selection. As the ReliefF algorithm has a small sampling probability of small class samples, it cannot delete the defects of redundant features. This method uses DBSCAN clustering algorithm to guide the classification. By improving the sampling strategy, it uses the adjusted cosine similarity to measure the correlation between features as a de-redundancy credential. Experiments show that UFS-IR can effectively reduce the data dimension while ensuring the maximum correlation redundancy of the feature subset, and with good performance.

Abstract:Flash memory is a pure electronic equipment and has the advantages of smaller volume, faster reading speed, lower power consumption and strong vibration resistance, so it is used to partly replace the disk to improve the performance of storage system. But the existing design and optimization of buffer replacement algorithms are based on the physical characteristics of mechanical hard disk. Therefore, it is necessary to redesign a new buffer replacement algorithm which contrapose the physical characteristics of flash memory. This study presents a new buffer replacement algorithm named CF-ARC. A new type of page mechanism replacement is designed, which means the access frequency should be considered when the clean or dirty pages are replaced. The clean pages less visit should replace the buffer to improve the hit rate in hotspot and achieve a better performance. The experimental results show that CF-ARC has better performance than other buffer replacement algorithms in most cases.

Abstract:Fetal weight is an important indicator of fetal development and maternal safety, but fetal weight cannot be measured directly and can only be predicted according to the examination data of pregnant women. This study proposes a model of fetal weight prediction based on the Genetic Algorithm to optimize BP Neural Network (GA-BPNN). First, the model of continuous weight change in pregnant women is established by using regression model and feature normalization preprocessing. Then, the genetic algorithm is used to optimize the initial weights and thresholds of BP neural network and establish a fetal weight prediction model. 3000 pregnant women data are randomly sampled from a hospital in the eastern part of China in 2016. The proposed model is compared with the prediction model based on the traditional BP neural network. The results show that the GA-BPNN fetal weight prediction model proposed in this paper not only accelerates the convergence of the model, but also improves the prediction accuracy of fetal weight by 14%.

Abstract:The existing healthy development evaluation scheme has defects like a long life cycle, subjective sense, single evaluation index and many others, which has restrictions on the children's long-term development and preschool education informatization. Therefore, this paper introduces the evaluation method of AHP-BPNN. According to the physiological and psychological characteristics of children's development, it uses the AHP analytic hierarchy process to establish scientific and multidimensional evaluation system. Meanwhile, the initial weights is initialized. Then it makes the improved BPNN analysis to optimize the weight, to get a more optimal parameter solution. Based on the continuous practice of 214 children in a kindergarten in Shenyang for 90 days of observation, it shows that the proposed method greatly reduces the subjectivity of teacher evaluation, makes the evaluation system more scientific, reasonable and perfect, and gives a comprehensive guide.

Abstract:The safety of industrial control network is becoming more prominent. Electric power is an important national infrastructure, so the safety protection of smart grid industrial control system is extremely important. In smart grid industrial control system, according to the status quo of the low internal protection level of the control network and the lack of internal network of anomaly traffic detection, this paper analyzes the composition of the industrial control system, the network security demand, and the threats faced by the smart grid industrial control system. It proposes to apply traffic anomaly detection technology to the security protection of smart grid industrial control system, which forms the two-level security protection. Then, the classification and characteristics of traffic anomaly detection methods and the characteristics of network traffic of smart grid industrial control system are studied. And it proposes a dynamic semi-supervised K-means algorithm based on entropy and OCSVM to improve the semi-supervised K-means algorithm for improving the internal protection level of the smart grid industrial control system.

Abstract:A fault diagnosis model is proposed by using improved SOM neural network for the purpose of improving fault and safety monitoring, especially when it lacks accurate positioning and correlation analysis in power dispatching automation system. Firstly, based on the analysis of the historical data of the dispatching system, the feature vector of the fault is extracted and the learning sample is established. And then the connection with input and output for the subsequent test is trained for verification through the algorithm. Finally, the experiment which tests the data and verifies the effectiveness of its fault diagnosis is in the network with the inherent mapping of the data. The final results show that this model is an effective artificial intelligence diagnosis method for different types of fault recognition and diagnosis.

Abstract:In order to improve the low efficiency of data mining with high density and complex heterogeneous data network, a data mining algorithm based on multi dimension association structure is proposed. Firstly, on the basis of the data personality characteristics and the differences of the data storage, forwarding and processing in the heterogeneous large data network, the data definition and multi-dimensional correlation model of the heterogeneous data network are given. Then, based on the large data network, the paper proposes a multidimensional association fine-grained data mining algorithm based on the reconstruction of the large data units, the dimension replacement, the granularity and the granularity. Finally, the efficiency of the algorithm is compared with the coarse grained algorithm and the linear structured data mining algorithm. The experimental results show that the proposed algorithm has better performance.

Abstract:Given the sparse structure of Ultra Wide-Band (UWB) channels, Compressive Sensing (CS) is exploited for UWB channel estimation. Muti-Task Compressive Sensing (MTCS), as a CS implementation, has exhibited a potential for promoting signal reconstruction. The signal parameters and data sharing can be solved using the Gamma-Gaussian prior. In this paper, the Hierarchy Dirichle processing (HDP) provides the tree structure of the HDP prior for data sharing across multiple tasks. We research the channel estimation performance of HDP Hidden Markov Model based Muti-Task Compressive Sensing (HDP-HMM-MTCS) for UWB communication systems. In particular, investigate the effects of three factors. Firstly, the sparse structure of a standardized IEEE 802.15.4a channel under Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) environments is estimated. Secondly, the CS Rate (CSR) regions' effect on the HDP-HMM-MTCS channel estimation performance is calculated. Thirdly, the SNR regions are compared with the results of the MTCS, Simple-Task Compressive Sensing (STCS), Orthogonal Matching Pursuit (OMP), and the L1 magic estimations. The simulation results demonstrate that the HDP-HMM-MTCS has the minimum executable time and its channel estimation performances exceed those of the MTCS and the other algorithms, regardless of the LOS and NLOS environments. Therefore, the HDP-HMM-MTCS is an effective and efficient UWB channel estimation method for a sparse channel mode.

Abstract:Aiming at the security protection in virtual environments such as cloud computing, an automatic scheduling deployment framework of security service chain based on SDN/NFV is proposed in this paper. The ABAC strategy model is extended to describe the security requirements of users and priorities are used to solve the policy conflicts to arrange virtualized security appliance. The load of each virtualized security appliance instance and the real-time link transmission delay are quantified to dispatch network traffic. Finally, the flow table generated by SDN controller is sent to the network to complete traffic redirection and implement the process of automatically building the security service chain according to the security requirements. The entire framework is implemented in the experimental environment to achieve the automatic scheduling deployment based on floodlight, virtualized security appliance, and it has obtained anticipatory effects.

Abstract:As the traditional data assets trading platform relies on the central management of the completion of the transaction process, it cannot guarantee the security of data assets in the process of transaction. Based on the technical characteristics of block chain technology: decentration, and unforgeability, this study proposes a new digital asset security transaction method based on block chain technology. Firstly, the drawbacks of traditional digital asset trading platform are expounded, and the key technology of block chain technology in data assets security transaction is analyzed. Secondly, from the perspective of data storage, transaction information, executive scheme is put forward. Finally, the verification algorithm of verification node in data transaction process is verified. The experimental results show that the proposed method in this study can be applied to digital asset security transaction.

Abstract:Online examination is widely used in distance education. Automated test paper is the key technology of online examination. The problem of generating test paper is the solution of multi-objective expected value, and it often has multiple solutions. For solving multi-objective function, the advantage of artificial intelligence algorithm is more and more obvious. Among them, the multi-objective optimization of genetic algorithm and ant colony optimization is more efficient, and can be more competent for the automatic test paper generation of the database technology curriculum. The application of artificial intelligence algorithm in test paper generation is discussed. The index system of test paper generation is constructed, and a mathematical model of multi-objective constraint is established, and the multi-objective expectation is optimized. The experiments results demonstrate that artificial intelligence algorithm has the highest success rate, with an average of more than 98% (including 100% of ant colony optimization, 96% of genetic algorithm), while those other than the artificial intelligence algorithm have low success rate, with the random variables 62%, backtracking method 84%. The application of artificial intelligence method, especially genetic algorithm and ant colony optimization, improves the efficiency of automated test paper generation. It meets the needs of various actual test paper generation, and makes online examination very well applied.

Abstract:Professor Zhou Ping, et al. has proposed a scheme of forward secure proxy blind signature. This study analyzes the scheme, and points out the problems that information owners can deny the signature scheme. That is, the owner denies that the signature information is provided by themselves, which may be fake. The improvement is put forward based on the above analysis. The experiment shows that the improved scheme not only keeps the excellent qualities of the original plan, but also solves the problem of the information owner's denial of signature.

Abstract:Because the result of method used by the project group currently which is a combination of floodfill and template matching is poor, and there is also under-segmentation or over-segmentation, this paper proposes the application of fully convolutional networks in semantic segmentation for wheat images. Firstly, the output information of the second pool layer is integrated as the input of the Softmax layer. Then, the Batch Normalization layer is introduced into the network layer, and 21 classes of output of the network are changed into the output of the 2 classes because of the characteristics of wheat. And the paper uses the F-measure to evaluate the result. The experimental results show that the proposed network can improve the segmentation result.

Abstract:Building realistic and rich 3D scenes is the main task of visualization. The data management and visibility judgments of the scene play a crucial role in the quality and efficiency of subsequent rendering. In order to make up for the shortcomings of traditional scene organization in practical application, this study adopts the adaptive binary tree scene organization algorithm to manage the scene, and uses the hierarchical cutting method to cut the nodes of the scene tree. In the process of cutting, the object of the operation is the bounding sphere and bounding box in the node. Experiments show that this layered algorithm based on the bounding sphere and bounding box greatly reduces the number of nodes involved in cutting and improves the accuracy of cutting. It has better cutting efficiency and high stability.

Abstract:Wireless Sensor Network (WSN) is characterized by intensive and random distribution. The WSN with large-scale node deployment makes it difficult or even impossible for practical application and research work. On the basis of the wireless sensor nodes simulation and the application layer process model, this study takes received signal strength indication ranging positioning technology to control node transmission distance, and has deployed a large number of nodes within a relatively small region based on OPNET. After the operation of the simulation model, the performance of network application layer has been analyzed, including the network transmission radius, the relationship between transmission radius and energy consumption, the network delay and throughput. The convergence and the rationality of the network topology structure has been verified. According to the multipath attenuation model, synthetically considering the balance between the data packet transmission distance and the energy consumption, the numerical relationship has been calculated between the node transmission radius and the maximum transmission radius, as well as between the node transmission radius and the minimum transmission radius.

Abstract:The recognition of the book spine and call number is crucial for a robot working in library, which has become a hot spot of present research. In this paper, a simple projection operator is proposed and studied to improve the detection of the book spine and call number. A column vector of book is constructed on the basis of the binary image of books in bookshelf, then a project column vector is obtained through a column projection operator. The book spine can be recognized correctly by analyzing the curve of the project column vector. Similarly, the row vector and the column vector of the book spine are constructed on the basis of the binary image of the book spine with a call number. Then, a project row vector is obtained through a row projection operator. The row position information of the call number can be recognized by analyzing the curve of the row projection vector. In a similar way, a project column vector is obtained through a column projection operator, and the column position information can be recognized by analyzing the curve of the column vector. On the basis above, each image element of the call number can be cut and the matching degree between the image element and standard char template can be calculated after normalization, through which the char corresponding to the maximal match degree will be regarded as the recognition result. Experiments show that the projection operator can quickly and correctly recognize the book back as well as the call number.

Abstract:This paper studies the gas ejector, a two-dimensional trajectory mathematical model of gas ejector. It designs and develops visual programming software to complete the zero-dimensional interior ballistic mathematical model on the.Net platform and the numerical simulation of trajectory mathematical model of two-dimensional calculation integration. The model focuses on the simulation of trajectory in two-dimensional mathematics. The core is that Fluent is secondary developed by C# language. Engineers design the parameters in the custom GUI interface. The integrated software can be studied under different proportion, pressure, velocity, temperature and flow data. It saves a lot of time to master the engineering background and set the interface of Fluent software parameters, greatly improving the efficiency of researchers, which is suitable for repetitive experimental research. It provides theoretical and technical support for the prediction and measurement of the gas flow parameters.

Abstract:Message acknowledgement mechanism in RabbitMQ includes confirm and ack. If the message doesnot need to be persisted, when the producer receives confirmation, the message may still be lost and the producer canot know that, so consumers may not receive the message. If the message needs to be persisted, when the producer receives confirmation, the message may be on the way to consumers. This study optimizes the message acknowledgement mechanism in RabbitMQ. After optimization, confirmation is sent to the producer after receiving the ack. The producer resends the message if the message is lost. Information that needs to be recorded is reduced during the message acknowledgement process. The producer receives confirmation after consumers receive the message successfully. The reliability of transient tiny messages delivery is improved. The experimental results reveal that the method can improve the sending rate of persistent tiny messages obviously when the number of clients is small.

Abstract:With the rapid development of virtual reality technology, the way we use the computer to display 3D models of realistic scene has become a research hotspot. The system based on Unity3D platform is constructed by using 3DMax to build the scene, combining Unity3D's prominent interface interaction technology to form Virtual Roaming System of Nanchang No.1 Metro Line. Finally, through the external link about ground station panorama, the system has integrated function of roaming subway station both inside and outside. The result shows that virtual roaming system can bring users intuitive roaming experience.

Abstract:Traffic congestion is becoming an increasingly serious problem. Traffic accidents caused by risky driving behaviors are one important cause. Therefore, the accurate evaluation of driving behaviors has become a research hotspot. This study puts forward an evaluation algorithm of driving behaviors based on the combination of FCM and BP neural network. Firstly, FCM is used to make initial clusters of driving behaviors. Secondly, in accordance with the results of clusters, an algorithm that the typical samples are automatically selected as the training samples for BP neural network classifier is proposed. Finally, the trained BP neural network is used to classify the driving behaviors. The research result shows that the algorithm can eliminate subjective factors and make accurate, objective and efficient driving behavior evaluation.

Abstract:On the basis of research on map projection, this study selects Gnomonic projection to the electronic map of carrier-based early warning aircraft. By calculating and analyzing length and angle distortion of Gnomonic projection, the maximal range of the map of carrier-based early warning aircraft is established. To resolve problem of incorrect calculating length and angle with map ruler, it provides correct length and angle calculation between two points for map ruler. The automatic change of electronic map is also researched.

Abstract:With the rapid development in recent years, Virtual Reality (VR) has been one of the hottest new technologies in the world. VR hardware has become more mature, and VR apps has been widely used in various fields such as entertainment, gaming and education. But there is no good VR payment solution in current payment domain. Based on virtual reality payment research, this paper uses token technology to restore payment data into security module embedded in VR devices, and constructs immersive VR payment scene by using 3D modeling technology, to provide a safe, convenient and open VR payment solution. The results show that the design solves the problem of the bad interaction and low security in current VR payment, and it effectively increases the payment success rate.

Abstract:With the rapid development of network information technology, its security protection system is increasingly being valued by various colleges and universities. How to strengthen the safety management of network information resources of campus network has become a research hotspot and difficulty. This study proposes a design of the fortress machine network architecture based on the main and standby redundancy, and takes the fortress machine into the management work of the university network information security. We also summarize the security management problems. The paper analyzes various fortress machine core management functions, and the fortress machine is introduced to the WAF protection system between the school and the core of the data center. It gives the core link configuration and code as well as the summary in the end.

Abstract:MSF-VQ is a kind of image features for face recognition. Firstly, vector quantization histogram feature is calculated with the predetermined codebook. Then, the histogram is extended through Markov stationary feature to get MSF-VQ features. MSF-VQ features for face recognition show high recognition rate. But it can still be improved on the determination of codebook and spatial information expression. According to these two points, this study puts forward an improved method. It first calculates the codebook based on the facial data set, so as to improve face resolution capability of vector quantization histogram, and then combines several MSF features calculated from different directions sampling, to increase spatial location information contained by MSF-VQ feature. Experimental results show that the improved MSF-VQ method has higher face recognition rate.