Abstract:In the existing RPKI system, the open source tool Rsync is used to synchronize data between the RPKI databases and the RP servers. However, due to the particularity of the certificate data structure in the RPKI system, data synchronization using Rsync not only is inefficient, but also consumes too much system resources, so that the entire RPKI system suffers potential security risks. Therefore, the IETF proposes an incremental synchronization Delta protocol to replace Rsync's role in RPKI. This paper introduces the working logic and mechanism of Delta protocol in detail, compares it with Rsync in terms of security and efficiency, constructs the Delta protocol model by using Promela language, and verifies the model with formal verification tool SPIN. It proves that the protocol has high protocol security and stability. Finally, this paper presents the Delta protocol implementation structure, in order to offer reference and communication.

Abstract:This study takes the product review texts on the e-commerce platform as the mining object, and focuses on the identification of feature words and opinion words in reviews. First, we build bipartite network with feature-opinion words, and give the sorting algorithm of node importance in this network. At last, the algorithm is applied to the actual review text data to verify the effectiveness of the algorithm.

Abstract:Based on Application Behavior Declaration (ABD), we proposed a new method for credibility calculation. It is based on the idea of "words and deeds". In the software development phase, tracking module is implanted to extract the action path of the behavior. Then, we compare the behavior declaration of the software with the action path to achieve the purpose of evaluating whether the behavior is credible. Explicit and implicit indicators are proposed for judgment, as well as an implicit indicators model based on K-means clustering. This model is applied to the credible calculation of single behavior and the credible discrimination of similar behaviors. Experiments prove that this solution is feasible for providing new ideas for credibility testing.

Abstract:Industrial enterprises have accumulated a large amount of production data. Massive industrial data contain valuable information. By analyzing and mining these industrial data, enterprises can enhance the ability of digital management and quality data analysis. This paper analyzes the demand and data characteristics of big data in tyre industry. First, the multi-source and heterogeneous data in every link of tyre production is integrated. After analyzing the data pre-processing process, we build the analysis data set of structured manufacturing and quality inspection. According to the low performance of the traditional ADTree algorithm, this study uses bottom induction method to make full use of the known data and reduce the amount of calculation. The experiment shows that the improved algorithm is more suitable for a large amount of data. After sorting out the results of ADTree, the important factors that affect the quality of the tires can be found.

Abstract:Text recognition is an important task in computer vision. The recognition of Chinese texts is challenging because of its wide range, complicated structure, and similar classes. In order to improve this problem, an end-to-end recognition model of text is used. The proposed model uses Dense convolutional Network (DenseNet) to extract features of text images, avoiding artificial design and statistics features. Then, the features are sent to Bidirectional Long Short-Term Memory model (BLSTM) for correlation analysis of local data. This step avoids the character segmentation. Finally, the Connectionist Temporal Classifier (CTC) is used to decode the text information. Experiments show that the proposed model can effectively recognize text images, and has strong robustness to various deformed images.

Abstract:The paper analyzes the development of collaborative research platform, summarizes the three common ways of building collaborative research platform, e.g., Web2.0-based collaborative research platform, collaborative research platform based on management process, and collaborative research platform based on document management. Although each common collaborative research platform has its own characteristics, the collaborative support for the core activities of scientific research is insufficient. Then it discusses the concept and types of collaborative research, designs the process of research project management and collaborative research, puts forward the overall architecture of collaborative research platform based on private cloud disk, and designs its function structure. Finally, the realization and application of collaborative research platform is discussed. The collaborative research platform based on private cloud disk will be a good reference for the informationalization of scientific research in universities.

Abstract:Selenium automated testing tools have been widely used in the field of testing. In the scenario, they need to rely on other compilers or interpreters to perform tests. On the one hand, testers need sophisticated programming skills to use the tools, and on the other hand, test development is too difficult. In order to reduce the automation test threshold, an interpreter based on Selenium is designed for Web automated testing. Firstly, it analyzes the testing principle of Selenium, and builds dependencies on the main test classes based on the usage of the test interface, and then constructs the modules of the interpreter according to the testing requirements of the Web program. For detailed design of each module's syntax, we referred to the programmer's coding conventions, Python, JQuery, and other syntax. Finally, a specific test case is designed to evaluate the interpreter. The experimental results show that the interpreter overcomes the disadvantages of using Selenium's high threshold, makes the written test script more concise and cleaner, and the test performance has also been significantly improved.

Abstract:In order to solve the instability of gesture data among the current gesture control technologies, a robot gesture control system based on RFID backscatter communication is proposed in this study. Firstly, we add windows to the data for handling the discontinuity of the reflected signal in the time domain, and use the idea of relative entropy to extract the feature segmentation of dynamic gestures. Secondly, based on the Dynamic Time Warping (DTW) algorithm, we calculate the matching degree between the current segment and one-dimensional components in the training set, and we achieve gesture classification combining with the k-nearest algorithm. Finally, we complete this human-computer interaction application through the wireless Bluetooth serial port. Experimental results show that the system can perform actions of forward, backward, leftward, rightward, clockwise rotation, and stop. The correct feedback rate of the robot for gesture commands is higher than 84%, which demonstrates that the system has sound feasibility and robustness in actual deployment.

Abstract:With the continuous development of information technology and aerospace technology, the amount of remote sensing image data has shown an explosive growth. The problem of large storage requirements, slow reading process, and long image processing time has been faced with the management of image data by a surveying and mapping facility in Shaanxi. Cache algorithm is designed with a three-level cache module used for cache image data. At the same time, combined with Hadoop open source technology, a remote sensing image business management system is designed. The system has high cohesion, low coupling, and strong scalability features. Testing shows that the system can effectively solve the image storage bottleneck, while improving the image reading speed and shorten the image processing time.

Abstract:Face recognition in surveillance videos is an essential technology in public security and has gotten more and more attention. But it is a little hard for the face recognition systems to be integrated into real application due to the low recognition rate caused partly by low face image quality. This study proposes a method of face image quality assessment using CNN. The proposed net, modified from the Alexnet, connects intermediate convolution layers to fully connect layer, to get multiple image features. Then, face image quality scores can be gotten from proposed net which is trained by end to end. In addition, a face image quality metric is used to relate the quality with the face recognition algorithm. Experiments on Color FERET datasets show that the proposed algorithm is able to elevate the face image quality exactly. Further experiments on a video surveillance dataset (collected by ourselves) show that the proposed method can select high quality face image for face recognition, leading to significant improvements in recognition accuracy.

Abstract:In case that public safety has already caused extensive social concern in recent years, how to use surveillance video to detect abnormal pedestrians and prevent dangerous events becomes a hot topic. Abnormal pedestrians are those who are distinctly different from ordinary pedestrians in appearance, for example, using helmet to cover the face or ducking from the camera. Considering that the characteristics of abnormal pedestrians are mainly concentrated in head and face, this study proposes a fast detection method for abnormal pedestrians based on multi-task Convolutional Neural Network (CNN) and one-class Support Vector Machine (SVM) for head-facial features. First, we detect head-facial regions in surveillance video, then we use the multi-task CNN to extract features of these regions, and then we use one-class SVM to judge whether it is a normal pedestrian or not. In addition, this study designs a convolution kernel splitting method for CNN to accelerate the feature extraction speed. Finally, the experiment shows that the algorithm proposed in this study can effectively and quickly detect abnormal pedestrians in surveillance video.

Abstract:Thanks to hardware upgrades and extensive WiFi coverage, the business and services of smart mobile terminals and Web-side have been developing in parallel. Therefore, it is a hot issue today to support cross-platform R&D technologies those are adaptive to Web and mobile terminals. Ionic invokes system's native interface to complete development of application software; AngularJS framework technology enhances the dynamic application of HTML; MongoDB database technology realizes single server deployment and multiple data center architecture with database, collection, document as storage unit. This study focuses on how to combine Ionic frame and AngularJS technology to realize the design of adaptive front page. It also studies the principle of storage of MongoDB, how to store the file by its built-in file system GridFS, and how to fragment storage data by its built-in slicing sharding system. Based on the research results, an adaptive application software between Web end and mobile terminal is designed, which is convenient for users to switch between Web end and mobile end, and realizes a good user experience.

Abstract:There are no teachers to evaluate the learning state of the learners in the online learning system, so most of the learners need to set their own learning strategies and adjust their learning steps, which lead to the lack of individualized guidance for the learners and the poor efficiency of the learners. In order to solve this problem, a diagnostic evaluation model was proposed in the College English Diagnostic Test System. The model analyzed the data collected in the database from the four aspects of learners' learning condition, question types association analysis, knowledge point association analysis, and CET-4 grade prediction, and it used Student-Problem Chart analysis method, data mining, and machine learning to build models respectively. Finally, the diagnostic evaluation model was obtained by merging these four sub models and it was implanted to improve the College English Diagnostic Test System. The experimental results showed that the diagnostic evaluation can effectively help learners to practice and improve their grades of CET-4.

Abstract:Aimed at virtual machine disaster backup and recovery problem faced by OpenStack, the authors design and carry out a virtual machine backup system based on Ceph storage snapshot. During backup process, generate a snapshot of the virtual machine disk stored in Ceph, and according to the backup requirement, calculate disk valid data or modified data, save the config information of the virtual machine and disk data. During recovery process, automatically create the same configured virtual machine, and restore the data of current snapshot point to the corresponding disk. The experimental results show that proposed system can effectively save backup time and storage space compared with OpenStack's snapshot backup method, and implement the functions of incremental backup and multi-disk backup that the latter does not have.

Abstract:Human-computer interaction is inseparable from emotion recognition. At present, there is a problem of low recognition rate and poor robustness both in single-modality emotion recognition and multi-physiological parameters fusion emotion recognition. Therefore, a fusion emotion recognition system based on two different types of signals is proposed, that is, a dual-modality emotion recognition system that integrates physiological parameters of skin electrical signals and text information. Firstly, by collecting and analyzing the characteristic parameters of the corresponding emotional skin electrical signals and the emotional keyword features of the textual information, the artificial neural network algorithm and the Gaussian mixture model algorithm are designed as a single mode emotion classifier, respectively. The Gaussian mixture model weights the decision layers. Experimental results show that this kind of fusion system has higher accuracy than multi-modality emotion recognition combined with single mode and multiple physiological parameters. Therefore, based on the two different types of emotional characteristics of skin electrical signals and text information, an emotion recognition system with high recognition rate and sound robustness can be constructed.

Abstract:In order to help users to choose the logistics distribution service that meets their personalized preferences as much as possible, combined with the multi-criteria rating characteristics of the distribution service, this study constructs a recommendation algorithm based on multi-criteria rating collaborative filtering and extends and improves the traditional collaborative filtering algorithm. The target user's rating of each criterion of the candidate service is reduced by the introduction of a personalized feature of the service to reduce the error of the hot service to user similarity calculation. Considering that the user's service criterion rating is fluctuating, the information entropy is used to average the user's history rating. It is combined with the predictive value obtained through collaborative filtering. Then based on the difference in the volatility of different criteria of the same user, the score of the user's rating for all criteria of the service is calculated, and the predicted rating value of each criterion is weighted with the corresponding weight to make service recommendations. The experimental data of the rating data sample of the distribution service transaction is verified by experiments. The accuracy and average absolute error index have better performance. The algorithm is applied to the logistics and distribution service platform to build a recommendation system, which can improve the platform's personalized service capabilities.

Abstract:Inside the Plasma Control System (PCS), Operating System (OS) noises usually bring latency measured in microseconds, which make the PCS hard to satisfy the increasing complexity of advanced plasma control requirements. Analysis shows that schedule and interrupts are the main cause of latency. In order to avoid scheduling, real-time priorities are applied in control processes, and related system adjustments are done. In order to decrease the frequency of interrupts, device interrupts are migrated and a revised kernel with fewer ticks is adopted. These work turn out to be efficient to reduce latency, and sufficient time is reserved for data acquisition, computation, and transmission.

Abstract:A new method of classifying and identifying sub-carriers is proposed for Orthogonal Frequency Division Multiplexing (OFDM) signals in non-cooperative communication systems, because there are many sub-carrier modulation types and some sub-carrier modulation types are difficult to be identified in OFDM signals. This method combined and improved the constellation cluster projection method and Logarithmic Likelihood Function (LLF) algorithm. The method first performed constellation clustering projection on different subcarrier modulation signals to recognize the common subcarrier modulation, then calculated the LLF to recognize Offset QAM (OQAM) subcarrier signal and common subcarrier signals. On the basis of the previous step, the LLF of subcarrier group was derived to make the LLF calculation easier to be classified by the decision threshold. Theoretical deduction and computer simulation results showed that the method could recognize the subcarrier modulation when the SNR is 15 dB.

Abstract:In recent years, object detection has been applied to many fields. However, retraining using large amount of bounding-box labeled data is needed. This study improves the Faster RCNN method and solves the problem of detecting multi-object in images using few-shot single object training data without bounding-box annotation. We propose a non-classwise bounding-box regression layer, which is only trained by public dataset and used for product training image labeling and testing image detection. Combined with Grabcut method, a data augmentation method is proposed to generate multi-object product training image. The improved faster RCNN model is re-trained by these images. In addition, a re-identification layer is added to the Faster RCNN architecture and improves the detection performance.

Abstract:In order to solve the problem of the long-term survival of the astronauts in the space station, the research of space plants becomes more and more important. At present, there are some problems in image recognition field, such as the method of the shallow images recognition is difficult to extract hierarchical features of space plant images, and deep convolution neural network has fixed size input and long recognition time. To deal with these problems, a method based on fully convolutional networks is proposed in this study, and the networks have the ability to extract features from the shallow to deep, deep fusion spectrum features, and spatial features to achieve an efficient and accurate representation of the space plants image, so as to achieve fast and accurate recognition of the space plants image.

Abstract:As a security measure, CAPTCHA is widely used in Internet. This study proposes a CAPTCHA identification method based on convolutional neural network. Through convolutional layer concatenation, residual learning, global pool, and other technical means, under the premise of ensuring the recognition accuracy rate is not affected, it greatly reduces the amount of network parameters. This study uses the CAPTCHA in the railway ticket website and the CAPTCHA in the educational system as examples to test the performance of the model. For the CAPTCHA in railway ticket website, the experimental results show that this method has the least amount of parameters, and the recognition accuracy of this method is 98.76% for image and the recognition accuracy of the Chinese phrases is 99.14%. For the CAPTCHA in educational system, it has the least amount of parameters and the accuracy is 87.30%.

Abstract:In a complex electromagnetic environment, communication signals and interference overlap in the time-frequency domain, and they are difficult to be separated. To solve this problem, a method of interference suppression and communication signal reconstruction based on signal sparse representation is proposed. Firstly, the K-Singular Value Decomposition (K-SVD) algorithm is used to construct the over-complete sub-dictionaries of communication signals and interferences. Then, we build a joint dictionary by over-complete sub-dictionaries, and use the Orthogonal Matching Pursuit (OMP) algorithm to separate and reconstruct the signals. Finally, we simulate the interference suppression and reconstruction process of time-frequency overlapping 2ASK signal and 2PSK signal by computer, and verify the process of interference suppression and signal reconstruction of OFDM signal by experiments. The simulation experimental results show that this method can realize the interference suppression and signal reconstruction of communication signals under the condition of time-frequency overlapping.

Abstract:With the continuous development of video satellite technology, quick and accurate target detection in satellite video data has gradually become a research hotspot. This study improves the single-stage target detection framework from two aspects. In view of the features of small target size and low resolution in satellite images, the deconvolution operation is used to enrich the context information of the target, and the convolution features of the corresponding scales are combined into the super parameter features to enrich the details of the target. In addition, the image feature multilevel meshing is put forward, and the results of different meshes are fused to improve the detection accuracy of the model. According to the characteristics of satellite gaze imaging and the slow motion of the scene, we designed a content consistency discriminant network. Through the discriminant result, some redundant detection steps can be omitted to improve the overall detection efficiency. Through the concrete analysis of the experimental results of the "Jilin-1" Satellite, the accuracy and speed of target detection in satellite video were achieved by the detection system.

Abstract:To perform fast collision detection on the Finite Element Model (FEM) of deformable components in engineering design, an algorithm of collision detection based on the Axis-Aligned Bounding Box (AABB) tree is developed. For the FEMs to be checked, the facial surfaces are triangulated at first. AABBs are then generated for the triangles, and optimized AABB trees are built to divide the space. The AABBs and AABB tree are used to exclude disjoint graphics, and the Devillers & Guigue algorithm are applied to perform fast triangle intersection test. Parallel computing is also applied to accelerate the calculation. This proposed algorithm is applied for testing models, and the result shows that the efficient collision detection algorithm could give reliable result for complex FEMs.

Abstract:The automatic production of wood-based panel has been realized with the development of continuous press production line, but the defect detection is still manual. As an important part of detection, defect recognition is a process of using a classifier to identify defects based on feature value. For the reason of the continuous production of wood-based panels, the defects need to be identified quickly and accurately. Therefore, a cart tree is proposed to identify the defects of the wood-based panel in this study. The defect features of shape and texture are firstly obtained using image preprocessing and image segmentation, and then the cart tree is generated by Gini exponent, at last defects are identified by using the cart tree. But it is easy to cause the problem of overfitting using cart tree without pruning, so the study obtains the optimal subtree by using the cost complexity algorithm and 10 cross-validations. The experimental results reflect that the accuracy rate of defect recognition reaches 93% with the proposed cart tree, which can satisfy the requirements of real-time and accuracy on defect identification.

Abstract:By the extension of network life cycle and the balance of network energy consumption, in order to improve the energy utilization rate of wireless sensor networks, we proposes a routing algorithm for network encoding of cluster head nodes in the dynamic clustering algorithm in wireless sensor network. In the stage of establishing a cluster, the residual energy of nodes and the received signal strength are used to complete clustering, solving the problem of premature failure of some nodes due to excessive energy consumption. In the stage of the data acquisition, the method of random linear network encoding based on cluster head effectively reduces the number of packets transmitted to the gateway node, and the cost of network energy. Simulation results show that compared with the standard protocol AODV, the proposed algorithm can effectively balance the node energy consumption, improve the energy efficiency, and improve the network throughput and end-to-end delay.

Abstract:RBAC model is a solution which defines users with different roles, and the roles are in different classes which mean the users with different roles have different permission. Usually, we can use secret keys to discriminate the different roles. However, the role in this access control model is a security class including some users. Changes will appear in this system with personnel changes frequently. Due to the keys are corresponding to the roles, how to update the keys in these frequent changes is the focus of this study. There are three kinds of model in RBAC, the linear model, the tree model, and Directed Acyclic Graph (DAG). This paper discusses the changes of users and security class from the linear and tree model. The problem in the method where the inferior keys are determined by the superior keys is also discussed. Thus, key management for changeable RBAC system is effectively realized.

Abstract:This research has proposed an adaptive ant colony algorithm and its colored Petri net approach, aiming to solve the slow operation problems existed in vehicle routing problems. The new developed method has improved the updating rule of pheromone, changed the formula of transfer probability and the constructed technique of feasible solutions, with the Petri net model conducted accordingly. The proposed method is finally verified compared to GA and some other ACOs through standard test cases, with the results demonstrated that the method developed effectively improves the convergence efficiency, refrains the searching from being trapped in local optima, and ensures the diversity of the final solutions.

Abstract:With the rapid development of digital image processing technology, image recovery has been widely used in the fields of medicine, military, public defense, and agro-meteorology. This study integrates TVL1, ROF, Squares TVL1 (STVL1), and SHI model, proposes a non-convex and non-smooth model for removing impulse noise, and uses a variable separation technique ADMM to solve the model. In general, gradient-based methods are not suitable for non-smooth optimizations. Half-quadratic and Iterative Reweighted Least Squares (IRLS) algorithms cannot be applied to non-smooth functions when the zero point is non-differentiable. For non-convex non-smooth terms, Graduated NonConvexity (GNC) algorithms track non-smooth and non-convex minimums along the potential energy of a series of approximate non-smooth energy functions and need to consider their computational time. So in order to deal with non-convex non-smooth terms of the model, the multi-step convex relaxation method is used to solve the subproblem of the model. Although this method only leads to the local optimal solution of the original nonconvex problem, the local solution is an improvement over the global solution of the initial convex relaxation. In addition, because each stage is a convex optimization problem, this method is computationally efficient. The genetic algorithm was used to select the parameters of the model. Through a large number of experiments on different pictures and different noises, the robustness, running time, ISNR and PSNR of the model were better than the other three models. And this model can maintain the local information of the image with better visual quality.

Abstract:In order to improve the prediction ability of the traditional Z-Score financial prediction model, this paper proposes a financial prediction model of Z-Score for listed companies based on improved Fruit fly Optimization Algorithm (FOA) by combining the good searching ability of improved FOA algorithm and the Z-Score financial prediction model. The Root Mean Square Error (RMSE) between the predicted value and target value is reduced by improved FOA algorithm being applied to optimize the parameters of Z-Score model. We compare the predicted value and target value of the financial data of listed companies to test the accuracy of financial prediction. The experimental results are as follows:accuracies of the traditional Z-Score financial prediction model, FOA algorithm optimized Z-Score model, and improved FOA algorithm optimized Z-Score model are 65%, 70%, and 80%, respectively. Experiments show that the improved algorithm significantly improves the predictive ability of Z-Score financial prediction model, it is also illustrated the validity of the algorithm.

Abstract:With the development of new social media, Weibo, as an important media for the dissemination of public opinion, has become a platform for the excavation of public opinion. Natural Language Processing technology can extract effective emotional information from Weibo texts, and provide scientific decision-making basis for monitoring network public opinion, forecasting potential problems, and product analysis. In order to overcome the limitation of the existing shallow learning algorithm for complex function expression, this study attempts to integrate the idea of deep learning, and puts forward an improved recurrent neural network based on Word2Vec and long-term memory network to analyze Chinese Weibo emotion. In the more than 20 000 Chinese corpus of training experiment, the experimental data with SVM, RNN, and CNN are compared, comparison results show that the emotion analysis model proposed in this study reaches the accuracy rate of 91.96%, thus it can effectively improve the accuracy of the Weibo text sentiment classification.

Abstract:A new scheme for constructing a two steps Catmull-Clark subdivision surface with the vertex normal vector constraint interpolates the vertices of a quadrilateral mesh with arbitrary topology. Firstly, the new mesh is generated by the modified Catmull-Clark subdivision. Secondly, the new mesh is adjusted through vertex normal vector constraints. The two-phase scheme makes the limit surface interpolate all vertices and normal vector in the original mesh by applying the progressive iterative method and the Lagrange multiplier method respectively. The experimental examples are given to show that the method is effective both in interpolating initial control points and normal vector, the limit surface has good modeling effect.

Abstract:When the high altitude unmanned aerial vehicle is extinguishing, it is necessary to detect the fire point. The traditional detection method is easily disturbed by the environment, its reaction is insensitive and its real-time feature is poor. Therefore, a new method based on binocular stereo vision is proposed in this study. First, we set up the experiment platform and design the hardware circuit. Then the classical binocular stereo vision ranging algorithm is studied deeply, and a new method of distance measurement is proposed. The method mainly includes:image acquisition, region of interest extraction, image preprocessing, and dynamic template matching based on the weight fusion. Finally, the multi segment nonlinear compensation method is used to construct the distance measurement model, and a large number of experiments are carried out on the experimental platform. The experimental result show that within 50 meters, the error of the ranging model is better than 1 meter, and within 100 meters, the ranging error is better than 2 meters, the matching precision is 6.947 pixels/m. The result basically meets the requirement of precise ranging for high altitude UAV fire extinguishing, so the distance measurement accuracy of the ranging model and the engineering application are both good.

Abstract:In moving object detection process, it needs to automatically judge whether it has detected the moving object, although there is no moving object in the current scene, detection result wrongly judges that it has detected the moving object. In order to find the source of the error, optical flow disturbance effect is found through experiment. The optical flow disturbance effect detection algorithm is designed, and the effect of optical flow perturbation is clearly detected. Next, through the binarization method of image it eliminates optical flow disturbance effect. The ideal results of the moving object detection are obtained. This research proves that the optical flow perturbation effect exists in the space, which can cause interference to the detection of moving object. It also can eliminate the effect of optical flow disturbance and improve the accuracy and reliability of moving object detection and judgment.

Abstract:Efficiency of the clipping algorithm in computer graphics will directly affect the graphics computing, processing, display speed, and user experience. Around the redundant computing, reuse of objects, multithreading control, etc. in the reducing cycle, this study improved traditional vector clipping algorithms in Java environment. This improved algorithm is superior to the traditional vector graphics clipping algorithm in terms of execution efficiency and memory consumption. The application of proposed algorithm can effectively improve the efficiency of engineering graphics generation.

Abstract:In order to apply the TMS320C6678 processor into multiple network devices interaction scenarios, this study focuses on the work mechanism of the dual network card, gives the detailed realization of its driver, and makes a test on TI's EVM6678L board. The test result proves that the double card driver is successful.

Abstract:By applying the Web Graphical Library (WebGL), a browser can render Three Dimensional (3D) images without installing any additive plugins. The three.js, a JavaScript library that encapsulates the low-level WebGL API, can even construct a 3D graphic more efficiently. Since existed visualization approaches for Redundant Array of Independent Disk (RAID) are non-intuitive and only with simple interactive functions, a new 3D RAID visualization framework is proposed in this work based on the three.js library. The model simulation and the interactive functions for visualizing a 3D RAID are explained step by step and implemented respectively. Compared to the traditional methods, the proposed 3D visualization framework constructs a more realizable 3D scene, thus provides the users better experiences both at visualization and interaction.

Abstract:Compared with the traditional key to open the door and start the car, the keyless Passive Entry and Passive Start (PEPS) system realizes the complex bi-directional identity authentication between the car and the key through the communication between low frequency and radio frequency. In order to guarantee two-way identity authentication, this paper introduces the security authentication method of PEPS system based on UDS protocol in detail. Through the diagnostic learning function of UDS protocol, the authentication protection mechanism of remote keys and BCM, EMS, and ESCL is realized, which ensures the safety of the keyless PEPS system and provides strong technical support for its rapid development.

Abstract:The process of landslides for mine dumps is a dynamic, large-delay, and complex situational problem. There are many factors that affect the landslide in mine dumps, and each characteristic index influences each other. However, there is no strict categorizing standard for index of landslide warning for dumping sites. This study proposes Principal Component Analysis Long-Term and Short-Term Memory network (PCA-LSTM) model, using PCA and correlation analysis, mining the first principal component among all the characteristic indicators, and the other indicators with strong correlation with the first principal component. The obtained other characteristic indexes and the first principal component are used as the main characteristic indicators to predict the dumping landslide, and the LSTM is used to combine the existing input information and the historical information when dealing with time series problems. The LSTM model predicts the displacement of the first principal component through a number of other characteristic indicators and has obtained sound results.

Abstract:In practical applications, the ownership problem of multiple tags is often encountered in one call. Most of the existing ownership transfer protocols are applicable only to the transfer of single label ownership. In order to solve the problem, a multi label transfer protocol is designed. The proposed protocol can reduce the number of communication entities in the system without relying on trusted third parties. The protocol encrypts the transmission message based on the Chinese Remainder Theorem and word Cro operation to ensure the safety and reliability of communication messages. In the process of one call, the protocol can transfer the ownership of multiple labels simultaneously. Based on the logic formalization of BAN, the mathematical proof process is given, and the security and performance of the protocol are analyzed. It shows that the protocol has the security and low computation needed for the transfer of ownership.

Abstract:Radio Frequency Identification Technology (RFID) is one of the key technologies of indoor positioning. The traditional LANDMARC location algorithms have poor positioning accuracy. To solve this problem, a novel location algorithm is proposed by combining the dynamic centroid iteration and error correction. It updates nearest neighbors in turn by employing the centroid of the neighboring area as next reference tag which takes the minimum location relationship as the criterion, and achieves the pre-positioning coordinate until the location relationship with the target tag is lower than the threshold. The correction factor is adopted to compensate error of the pre-positioning coordinate by relocating each of k-nearest neighbors. Simulation results show that the proposed algorithm performs better in terms of positioning accuracy than LANDMARC.

Abstract:Call detail records contain rich spatio-temporal information and social information, which partly reflect users' habits and social pattern. It is of great significance for the study of mobile communication user profiling. Our study is based on a monthly call detail records of 10 000 subscribers provided by a Chinese telecom operator. In this study, on the one hand, we extract the moving distance, the radius of gyration, the number of access points, and the entropy of moving directions to characterize user's mobile pattern. On the other hand, we extract the call duration, the number of contact, the ratio of calling, and the entropy of sociality to characterize user's social life. Then users are divided into groups and each user gets a word cloud card based on these features. So the portrait study of mobile communication users is completed. Our work is a promising step towards inferring user attributes and understanding user characteristics using call detail records.

Abstract:With the achievement of China's lunar exploration project, the last task of achieving lunar soil sampling unmanned on the lunar surface and return safely will be executed. To be able to simulate the status of the probe, Autodesk 3dsmax is used to create the probe 3D model and transfer the 3DS format; C++ and OSG library are combined to implement the 3D reconstruction visualization. The experimental results show that OSG library can meet the needs of visual display, and provide some references for the visualization of the lunar exploration project.