Virtual digital currency provides a breeding ground for terrorist financing， money laundering， drug trafficking and other criminal activities. As a representative emerging digital currency， Monero has a universally acknowledged high anonymity. Aiming at the problem of using Monroe anonymity to commit crimes， Monero anonymity technology and tracking technology were explored as well as the research progresses were reviewed in recent years， so as to provide technical supports for effectively tackling the crimes based on blockchain technology. In specific， the evolution of Monero anonymity technology was summarized， and the tracking strategies of Monero anonymity technology in academic circles were sorted out. Firstly， in the anonymity technologies， ring signature， guaranteed unlinkability （one-off public key）， guaranteed untraceability， and the important version upgrading for improving anonymity were introduced. Then， in tracking technologies， the attacks such as zero mixin attack， output merging attack， guess-newest attack， closed set attack， transaction flooding attack， tracing attacks from remote nodes and Monero ring attack were introduced. Finally， based on the analysis of anonymity technologies and tracking strategies， four conclusions were obtained： the development of anonymity technology and the development of tracking technology of Monero promote each other； the application of Ring Confidential Transactions （RingCT） is a two-edged sword， which makes the passive attack methods based on currency value ineffective， and also makes the active attack methods easier to succeed； output merging attack and zero mixin attack complement each other； Monero’s system security chain still needs to be sorted out.

Focusing on the large decryption overhead of the resource limited edge devices and the lack of effective user tracking and revocation in attribute-based encryption, a traceable and revocable Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme supporting cloud-fog computing was proposed. Firstly, through the introduction of fog nodes, the ciphertext storage and outsourcing decryption were able to be carried out on fog nodes near the users, which not only effectively protected users' private data, but also reduced users' computing overhead. Then, in response to the behaviors such as user permission changes, users intentionally or unintentionally leaking their own keys in the attribute-based encryption system, user tracking and revocation functions were added. Finally, after the identity of malicious user with the above behaviors was tracked through the algorithm, the user would be added to the revocation list, so that user's access right was cancelled. The performance analysis shows that the decryption overhead at the user end is reduced to one multiplication and one exponential operation, which can save large bandwidth and decryption time for users; at the same time, the proposed scheme supports the tracking and revocation of malicious users. Therefore, the proposed scheme is suitable for data sharing of devices with limited computing resources in cloud-fog environment.

To solve the problems of secondary injury, muscle dysfunction, stiffness of the affected limb, damage to the blood supply, and poor dynamic performance of postoperative correction in traditional reduction methods (such as manual reduction, traction reduction, and surgical reduction) of fracture treatment, a Cyber-Physical and Human System (CPHS) was proposed to guide the reduction movement of the robot. First of all, the composition of the cyber-physical system of the parallel robot was illustrated from the aspects of CPHS system such as digital twin, information perception, system integration, surgical procedure, and simulated reduction. The high positioning accuracy and repeatability of the robot were combined with minimally invasive methods effectively to guide doctors to complete a series of operations such as simulation planning and intraoperative monitoring. Secondly, according to the clinical fracture reduction process, the reduction experiments were performed on simulated fracture cases of 5 groups of different fracture postures under robot operation. Finally, the remaining placements and angle errors in each experimental group were calculated after reduction operation and were compared with the corresponding data of the traditional reduction methods. Experimental results show that the CPHS fracture reduction robot has obvious advantages in fracture reduction and patient postoperative rehabilitation compared with the traditional reduction methods.

To overcome low pathfinding efficiency and slow convergence speed of Rapid-exploring Random Tree star(RRT ^*) in high-dimensional and complex environment, an Unmanned Aerial Vehicle(UAV) path planning algorithm with self-adjusting sampling space based on RRT ^* named Adjust Sampling space-RRT ^*(AS-RRT ^*) was proposed. In this algorithm, by adjusting the sampling space adaptively, the tree was guided to grow more efficiently, which was realized through three strategies including:biased sampling, node selection and node learning. Firstly, the light and dark areas in the sampling space were defined to performing biased sampling, and the probability weights of the light and dark areas were determined by the current expansion failure rate, so as to ensure that the algorithm was both exploratory and directional when searching for the initial path. Then, once the initial path was found,the nodes were periodically filter,and the high-quality nodes were used as learning samples to generate the new sampling distribution, the lowest-quality nodes were replaced by new nodes after the algorithm reaching the maximum number of nodes. Simulation experiments for comparison were conducted in multiple types of environments. The results show that the proposed algorithm improves the inherent randomness of the sampling algorithm to a certain extent, and compared with the traditional RRT ^* algorithms, it has less pathfinding time used in the same environment, lower cost path generated in the same time, and the improvements are more obvious in three-dimensional space.

It is of great significance to apply the blockchain technology to the field of digital music copyright management in view of the difficulties in copyright confirmation, infringement monitoring, right protection and evidence collection, royalty settlement and other problems in the traditional music copyright industry. A digital music copyright management system was designed and constructed by using the VNT Chain blockchain platform. In the system, the blockchain technology was used to provide the proof of music copyright and realize the evidence solidification, the Shazam algorithm was used to provide the proof of originality for music copyright, and the smart contract was used to guarantee the security and reliability of transactions. This system included six function modules:user management, copyright registration, copyright trading, infringement monitoring, evidence solidification and music ecology, covering the main parts of copyright management. According to different needs of business data, blockchain, InterPlanetary File System(IPFS) and MySQL were adopted as storage engines respectively for the system. Experimental results show that the copyright registration time of each music increases by about 1.9 s, and the storage of music feature fingerprint data of one song on IPFS costs about 8 MB averagely, which meets the expected system performance requirements.

Aspect-based sentiment analysis tries to estimate different emotional tendencies expressed in different aspects of a sentence. Aiming at the problem that the existing network model based on Recurrent Neural Network (RNN) combined with attention mechanism has too many training parameters and lacks explanation of related syntax constraints and long distance word dependence mechanism, a self-attention gated graph convolutional network was proposed, namely MSAGCN. First, the multi-headed self-attention mechanism was used to encode context words and targets, thus capturing semantic associations within the sentence. Then, a graph convolutional network was established on the sentence's dependency tree to obtain syntactic information and word dependencies. Finally, the sentiment of the specific target was obtained through the GTRU (Gated Tanh-ReLU Unit). Compared with the baseline model, the proposed model has the accuracy and F1 improved by 1%-3.3% and 1.4%-6.3% respectively. At the same time, the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model was also applied to the current task to further improve the model effect. Experimental results verify that the proposed model can better grasp the emotional tendencies of user reviews.

Outlier detection algorithms are widely used in various fields such as network intrusion detection, and medical aided diagnosis. Local Distance-Based Outlier Factor (LDOF), Cohesiveness-Based Outlier Factor (CBOF) and Local Outlier Factor (LOF) algorithms are classic algorithms for outlier detection with long execution time and low detection rate on large-scale datasets and high dimensional datasets. Aiming at these problems, an outlier detection algorithm Based on Graph Random Walk (BGRW) was proposed. Firstly, the iterations, damping factor and outlier degree for every object in the dataset were initialized. Then, the transition probability of the rambler between objects was deduced based on the Euclidean distance between the objects. And the outlier degree of every object in the dataset was calculated by iteration. Finally, the objects with highest outlier degree were output as outliers. On UCI （University of California， Irvine） real datasets and synthetic datasets with complex distribution, comparison between BGRW and LDOF, CBOF, LOF algorithms about detection rate, execution time and false positive rate were carried out. The experimental results show that BGRW is able to decrease execution time and false positive rate, and has higher detection rate.

Morphological reconstruction is a fundamental and critical operation in medical image processing, in which dilation operations are repeatedly carried out on the marker image based on the characteristics of mask image, until no change occurs on the pixels of the marker image. Concerning the problem that traditional CPU-based morphological reconstruction system has low computational efficiency, using Graphics Processing Unit (GPU) to quicken the morphological reconstruction was proposed. Firstly, a GPU-friendly data structure:parallel heap cluster was proposed. Then, based on the parallel heap cluster, a GPU-based morphological reconstruction system was designed and implemented. The experimental results show that compared with traditional CPU-based morphological reconstruction system, the proposed GPU-based morphological reconstruction system can achieve speedup ratio over 20 times. The proposed system demonstrates how to efficiently port complex data structure-based software system onto GPU.

Existing entity and relation extraction methods that rely on distant supervision suffer from noisy labeling problem. A model for joint entity and relation extraction from noisy data based on reinforcement learning was proposed to reduce the impact of noise data. There were two modules in the model:an sentence selector module and a sequence labeling module. Firstly, high-quality sentences without labeling noise were selected by instance selector module and the selected sentences were input into sequence labeling module. Secondly, predictions were made by sequence labeling module and the rewards were provided to sentence selector module to help the module select high-quality sentences. Finally, two modules were trained jointly to optimize instance selection and sequence labeling processes. The experimental results show that the F1 value of the proposed model is 47.3% in the joint entity and relation extraction, which is 1% higher than those of joint extraction models represented by CoType and 14% higher than those of serial models represented by LINE(Large-scale Information Network Embedding). The results show that the joint entity and relation extraction model in combination with reinforcement learning can effectively improve F1 value of sequential labeling model, in which the sentence selector can effectively deal with the noise of data.

To ensure the security and reliability of communication in the smart grid, more and more authentication protocols have been applied in the communication process. For the authentication protocol proposed by Mahmood et al. (MAHMOOD K, CHAUDHRY S A, NAQVI H, et al. An elliptic curve cryptography based lightweight authentication scheme for smart grid communication. Future Generation Computer Systems. 2018,81:557-565), some defects were pointed out. For example, this protocol can be easily attacked by internal privileged personnel, is lack of password replacement phase and unfriendly to users, in which unique username cannot be guaranteed, even a formula error exists. To improve this protocol, an authentication protocol based on elliptic curve was proposed. Firstly, a login phase between the user and the device was added in the improved protocol. Secondly, elliptic curve cryptography puzzle was used to realize information exchange. Finally, the password replacement phase was added. Through the formal analysis by BAN (Burrows-Abadi-Needha) logic, the improved protocol is safe and feasible, which can resist internal personnel attacks, has password replacement and unique username, and is more friendly to users.

In the open network environment, identity authentication is an important means to ensure information security. Aiming at the authentication protocol proposed by Li, et al (LI X, WU F, KHAN M K, et al. A secure chaotic map-based remote authentication scheme for telecare medicine information systems. Future Generation Computer Systems, 2017, 84:149-159.), some security defects were pointed out, such as user impersonation attacks and denial service attacks. In order to overcome those vulnerabilities, a new protocol scheme with multi-factor was proposed. In this protocol, extended chaotic mapping was adopted, dynamic identity was used to protect user anonymity, and three-way handshake was used to achieve asynchronous authentication. Security analysis result shows that the new protocol can resist impersonation attacks and denial service attacks and protect user anonymity and unique identity.

A group decision making model based on optimization model and consistency adjustment algorithm was established for the group decision problems with incomplete occurrence probability information of hesitant fuzzy elements. First of all, some new concepts were introduced, including Probability Incomplete Hesitant Fuzzy Preference Relations (PIHFPRs), the expected consistency of PIHFPRs and the acceptable additive expected consistency of PIHFPRs. Secondly, the minimization of deviations between PIHFPRs and the weight vectors was regarded as the objective function, a linear optimization model was constructed to calculate the probability information of the PIHFPRs. Then, by using the integrated operator for weighted probability incomplete hesitant fuzzy preference relations, the comprehensive PIHFPR was determined. A group consistency adjustment algorithm was further designed, which not only makes the adjusted PIHFPRs are acceptable expected consistent, but also can obtain the weight vectors for alternatives. Finally, the proposed group decision-making model was applied to a numerical example about the selection of block chain. Experimental results show that the decision-making results are reasonable and reliable, and the actual situation can be reflected.

To solve problems of population diversity and convergence speed when an Ant Colony System (ACS) is used to robot path planning, a dynamic chaos operator was introduced in the ACS. The dynamic chaotic ACS can balance population diversity and convergence speed. The core of dynamic chaotic ACS is that a Logistic chaotic operator was added to the traditional ACS to increase population diversity and improve the quality of the solutions. First, the chaotic operator was added to the pre-iteration to adjust the global pheromone value in the path to increase the population diversity of the algorithm, so as to avoid the algorithm to fall into the local optimal solution. Then, in the later stage, the ACS was used to ensure convergence speed of the dynamic chaotic ACS. The experimental results show that the dynamic chaotic ACS has better population diversity compared with the ACS for the robot path planning problem. The solution quality is higher and the convergence speed is faster. Compared with the Elitist Ant colony System (EAS) and the rank-based Ant System (ASrank), the dynamic chaotic ACS can balance the relationship between the quality of the solutions and the convergence speed. The dynamic chaotic ACS can find better optimal solutions even in the complex obstacle environment. The dynamic chaotic ACS can improve the efficiency of mobile robot path planning.

Focusing on the low accuracy problem of regular fingerprint matching indoor localization algorithm for Wi-Fi network confronted with signal fluctuation or jamming, the fingerprint matching indoor localization algorithm based on Dynamic Time Warping (DTW) similarity for Wi-Fi network was proposed. Firstly, the Wi-Fi signal characteristics in localization area were converted to the time-series fingerprints according to the sequence of sampling. The similarity between the locating data and sampling data was obtained by computing the fingerprint DTW distance of Wi-Fi signal. Then, according to the structural characteristics of the sampling area, the fingerprint sampling problem of Wi-Fi signal was divided into three kinds of basic sampling methods based on dynamic path. Finally, the accuracy and completeness of the fingerprint feature information were increased by the combination of multiple dynamic path sampling methods, which improved the accuracy and location precision of fingerprint matching. The extensive experimental results show that, compared with the instantaneous fingerprint matching indoor localization algorithm, within the location error of 3 m, the cumulative error frequency of the proposed localization algorithm, was 10% higher for uniform motion and 13% higher for variable motion within routing area, and 9% higher for crossed curvilinear motion and 3% higher for S-type curvilinear motion within open area. The proposed localization algorithm can improve accuracy and location precision of fingerprint matching effectively in real indoor localization applications.

To reduce the impact of Programmed I/O (PIO) write latency in PCI express (PCIe) transmission process, too many times of interaction between the host and the embedded processing system and other issues on transmission bandwidth, a Direct Memory Access (DMA) controller based on command buffering mechanism was designed to improve the transmission bandwidth utilization. Using the internal command buffer of the Field-Programmable Gate Array (FPGA), the DMA controller could cache the data transfer request of the PC. The FPGA could dynamically access the storage space of the PC according to its own requirements and enhance the transmission flexibility. At the same time, a dynamic mosaic DMA scheduling method was proposed to reduce the times of host-to-hardware interaction and interrupt generation by merging the access requests of adjacent storage areas. In the system transmission rate test, the maximum write speed of DMA was 1631 MB/s, the maximum rate of DMA read was up to 1582 MB/s, the maximum of bandwidth was up to 85.4% of the theoretical bandwidth of PCIe bus. Compared with the traditional PIO mode DMA transfer method, DMA read bandwidth increased by 58%, DMA write bandwidth increased by 36%. The experimental results show that the proposed design can effectively improve the DMA transfer efficiency, and is significantly better than PIO method.

In order to improve the performance of cache for encrypted data based on Ciphertext Policy Attribute Based Encryption (CP-ABE), an effective replacement algorithm named Minimum Attribute Value (MAV) algorithm was proposed.Combining the access mechanism of ciphertext in CP-ABE and counting the number of high frequency attribute values, the attribute similarity was calculated by using cosine similarity method and the table of high frequency attribute values; meanwhile, the attribute value of each cache file was calculated according to the attribute similarity and size of the encrypted file, then the file with the minimum attribute valuve was replaced. The experimental results prove that the MAV algorithm has better performance in increasing byte hit rate and file request hit rate than the algorithms of Least-Recently-Used (LRU), Least-Frequently-Used (LFU) and Size for encrypted data based on CP-ABE.

Since the visually impaired person could not enjoy videos and other digital media thoroughly, in order to extend tactile perception channels for video media, an electrostatic force tactile rendering method for video perception was put forward. Firstly, target pixels of the current video frame were acquired according to the location of the finger, and color information of target pixels were transformed from RGB color model to HSI color model. Then the hue parameter of target pixels was used to map stimuli frequencies of electrostatic force, the intensity and saturation parameters of target pixels were used to map stimuli amplitudes of electrostatic force, and the tactile stimuli signal was composited to render the real-time video. Finally, dynamic color perception experiments and identification of brightness perception experiments were designed. The results show that the proposed method can realize to sense the information of objects in the video, the average accuracy of dynamic identification reaches 90.6%, the average accuracy of color identification reaches 69.4%, and the average accuracy of brightness identification reaches 80.0%. The proposed method can extract the dynamic characteristics of video information effectively and enhance the real-time tactile rendering for video.

Enhancing the accuracy of link prediction is one of the fundamental problems in the research of complex networks. The existing node similarity-based prediction indexes do not make full use of the importance influences of the nodes in the network. In order to solve the above problem, a link prediction algorithm based on the node importance was proposed. The node degree centrality, closeness centrality and betweenness centrality were used on the basis of similarity indexes such as Common Neighbor (CN), Adamic-Adar (AA) and Resource Allocation (RA) of local similarity-based link prediction algorithm. The link prediction indexes of CN, AA and RA with considering the importance of nodes were proposed to calculate the node similarity. The simulation experiments were taken on four real-world networks and Area Under the receiver operation characteristic Curve (AUC) was adopted as the standard index of link prediction accuracy. The experimental results show that the link prediction accuracies of the proposed algorithm on four data sets are higher than those of the other comparison algorithms, like Common Neighbor (CN) and so on. The proposed algorithm outperforms traditional link prediction algorithm and produces more accurate prediction on the complex network.

For fairing requirements of the T-Bézier curve, the T-Bézier curve was smoothed by using the energy method. A control point of the T-Bézier curve was modified by using the energy method to make the T-Bézier curve smooth, while it was shown how the interference factor α influenced the smoothness of the T-Bézier curve. It was obtained a method that a fairing T-Bézier curve would be obtained by moving a control point: the α could be determined before the new control point would be found out, the new T-Bézier curve was produced by these new control points. The whole curve would be smoothed: firstly, the interference factors {α_i}_i=1ⁿ were determined; secondly, the equation system whose coefficient matrix was a real symmetric matrix tridiagonal was solved; thirdly, the new control points {P_i}_i=0ⁿ were obtained; finally, the new T-Bézier curve could be produced. Not only overall fairness of the T-Bézier curve but also C² continuity of data points was achieved. Finally, it was shown that the proposed algorithm is simple, practical and effective by three examples.

The neighboring relationship of sketch patches and photo patches on the manifold cannot always reflect their intrinsic data structure. To resolve this problem, a Locality-Constrained Neighbor Embedding (LCNE) based face sketch-photo synthesis algorithm was proposed. The Neighbor Embedding (NE) based synthesis method was first applied to estimate initial sketches or photos. Then, the weight coefficients were constrained according to the similarity between the estimated sketch patches or photo patches and the training sketch patches or training photo patches. Subsequently, alternative optimization was deployed to determine the weight coefficients, select K candidate image patches and update the target synthesis patch. Finally, the synthesized image was generated by merging all the estimated sketch patches or photo patches. In the contrast experiments, the proposed method outperformed the NE based synthesis method by 0.0503 in terms of Structural SIMilarity (SSIM) index and by 14% in terms of face recognition accuracy. The experimental results illustrate that the proposed method resolves the problem of weak compatibility among neighbor patches in the NE based method and greatly alleviates the noises and deformations in the synthetic image.

To solve the problem of location verification caused by collusion attack in Vehicular Ad Hoc NETworks (VANET), a multi-round vote location verification based on weight and difference was proposed. In the mechanism, a static frame was introduced and the Beacon messages format was redesigned to alleviate the time delay of location verification. By setting malicious vehicles filtering process, the position of the specific region was voted by the neighbors with different degrees of trust, which could obtain credible position verification. The experimental results illustrate that in the case of collusion attack, the scheme achieves a higher accuracy of 93.4% compared to Minimum Mean Square Estimation (MMSE) based location verification mechanism.

According to the feature of high-dimensional data, the number of variables is usually larger than the sample size and the data are often heterogeneous, a robust and effective feature selection method was proposed by using the dimensional reduction technique of variable selection and the modal regression based estimation method. The estimation algorithm was given by using Local Quadratic Algorithm (LQA) and Expectation-Maximum (EM) algorithm, and the selection method of the parameter adjustment was also discussed. Data analysis of the simulation shows that the proposed method is overall better than the least square and median regression based regularized method. Compared with the existing methods, the proposed method has higher prediction ability and stronger robustness especially for the non-normal error distribution.

In order to facilitate the execution of the questionnaire survey, and to improve the efficiency of the statistical data collection, this paper proposed a method to realize a questionnaire survey system based on the Android platform running on mobile phone. The survey system was C/S structured. The server included a questions design module based on Java Server Page (JSP), a result statistical module, the database and the C#-based Web service that gave interfaces to access database. The client was implemented on Android platform, which acquired information of the questions from the database, and displayed questions and their options on the screen for users to answer. When the user completed the answer to the questions, the client would write the answers back to the database. This system was first tested on Android emulator, and then on the mobile phone. The testing results indicate that questions survey function has been efficiently realized in the system. Moreover, since the client can run on a mobile device, the survey process can be carried out freely anywhere, anytime, which means that the survey process would not only be convenient and efficient, but also broaden the target clients of the survey system. The proposed system could be adopted by enterprises or organizations to carry out market or social investigations.

When the tracked targets get seriously obscured, temporarily leave the tracking screen or have significant displacement variation, adjoining interval updating algorithm based on on-line Boosting will lead to the error accumulation thus producing the drift or even tracking failure. Therefore, a reformative target tracking method based on on-line Boosting was proposed. The classifier feature library was updated by using on-line Boosting algorithm, and the threshold was dynamically renewed by using Kalman filter, hence the system could automatically capture the local features and apply corresponding adjustment to the value of threshold according to the tracking confidence of the object. When the confidence of the moving target was less than the lower threshold value, Blob tracking methodology would be applied. It processed as follows: the target was segmented into many regions according to the similarity of both color and space, and each single region contained the information of region number, location and size. One of the regions would be randomly selected into an on-line Boosting tracking module for testing, and the switch to the adjacent region by applying update algorithm for tracking would not happen unless the captured confidence level reached the upper threshold. Results of tests on different video sequences show that the proposed algorithm is capable of speedily and accurately capturing the target object real-time and holding a better robustness in comparison of the traditional on-line Boosting algorithm and other tracking algorithms.

By investigating the main physical factors that may affect the high-speed Electric Multiple Unit (EMU) generalized comfort, a portable generalized comfort test system was designed, which integrated all the sensors, signal adjusting modules, embedded data detection and computer system into a simulation human model, and this model was capable to test the generalized comfort in real-time without the requirement of car wiring and would not affect the operation of the train. The characteristics and requirements of the test system were analyzed, its components and operating principles were introduced, and the feasibility of this system was verified as well. The results show that the testing system is highly stable and precise.

To remove the salt and pepper noise in images effectively while preserving the detail, a salt and pepper noise detection algorithm based on directional median was proposed after analyzing merits and drawbacks of different slat and pepper noise detection algorithms. The algorithm divided the image into doubtful noise pixels and signal pixels in the first step. At the second step, the algorithm set nine directional detection areas in the 5×5 detection window centered on the doubtful pixels, and confirmed the noise pixels after comparing gray value of doubtful noise pixels and median gray value of the detection areas. The feasibility of the leak detection in the algorithm reduced load of subsequent processing. At the same time, the algorithm possessed low false detection rate which preserved the detail. The simulation results show the validity of the algorithm.

For preventing URL （Universal Resource Locator） attacks, this paper proposed a model based on artificial immunity. Applying the self-protection mechanism of biological immune system to network security, through deploying resource safely, avoiding guessing primary key, requesting valid check and business exception check, this model prevented URL attack in different levels while responding to valid request normally and made the applications acquire the secondary immunity. This paper also proposed and analyzed the key technology of implementing the model: the algorithm of avoiding guessing primary key and an enhanced static password algorithm based on random number. This model has been applied to two practical applications. The test results and the practical applications confirm the effectiveness of the model.

In order to realize the visualization, spatial analysis, automatic modeling and dynamic updating of model, the mine spatial environment and traditional 3D spatial data models were analyzed. Then an entity-oriented spatio-temporal hybrid data model was designed. It integrated the 3D parameter skeleton model, Topology-concerned and Entity-oriented 3D Vector Data Model (TEVDM) and Octree-Block Model (OBM). The TEVDM and OBM were used to express the boundary and internal property of orebodys. The Parametric Entity-Network Data Model (PENDM) was introduced into the model to describe the skeleton of the roadway engineering and production systems. Particularly, a behavior model was introduced into the model to describe the personnel and devices with behavior characteristics. The model also included the complete spatial feature which could describe the set of semantically related entities. Finally, some samples of using this model were given to describe the mines' spatial phenomenon and carry out spatial analysis. The samples show this model has better practicability in mining than traditional models.

Cross-sectional interpolation is one of the key steps in 3D reconstruction of medical images. Considering the shortcomings of current interpolation methods, such as blurring the object boundary and low computational efficiency, an interslice interpolation method for medical images based on voxel similarity was proposed in this paper. According to the principle of voxel relativity in cross-section images and structures feature, this method calculated the relativity of voxels to classify pixels and interpolated it. The experimental results illustrate that the new method has less computational complexity and better improves the quality of image than the previous interpolation methods.

Concerning the problem that the impact of illegal nodes (including the node unable to locate) on the localization process in DV-Hop localization algorithm has not been taken into consideration, this paper proposed a secure localization mechanism based on DV-Hop. In other words, the character of message exchange between the nodes was introduced to detect the wormhole attacks in this paper. Time property and space property were used to define the valid beacon nodes, along with encryption and authentication mechanisms to resist against the node-tampering attack in the communication process. Finally, the nodes were located securely. The simulation results show that, in hostile environment, the proposed mechanism has a high probability to detect the wormhole attacks, and the relative localization error can be reduced by 63% or so.