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Point cloud coarse matching method for similar objects

ICIC Express Letters 2016年第9期10卷 2283-2289页

作者： Zhao, Qiang Guo, Xijuan Zhang, Buying Bai, Xinyu Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University No. 438 Hebei Ave. Qinhuangdao066004 China College of Information Science and Engineering Yanshan University No. 438 Hebei Ave. Qinhuangdao066004 China Technical Center Shenyang Aircraft Corporation Shenyang110850 China

Point cloud matching is much more difficult when an initial estimate of the relative pose is not known. In order to reduce the effect of relative pose on matching operation and improve the computing speed, this article presents a method to fast match the pose of two similar objects through the covariance of their point cloud coordinates. This paper contains three parts. In the first part, a matrix is constructed through the values of point coordinates of one object and then a set of orthogonal vectors will be obtained through eigenvector of the matrix. Rotation matrix will be obtained by comparing the vectors of two similar objects. In the second part, a description of procedures is given and the complexity of the algorithm is analyzed. In the last part, the experiment shows the proposed method has high computing efficiency and the matching results are acceptable. © 2016 ICIC International.

关键词： Covariance matrix

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Skin panel design for a morphing mechanism

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ICIC Express Letters, Part B: Applications 2016年第10期7卷 2161-2167页

作者： Zhao, Qiang Guo, Xliuan Xi, Fengfeng Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province College of Information Science and Engineering Yanshan University No. 438 Hebei Avenue Qinhuangdao066004 China Department of Aerospace Engineering Ryerson University TorontoONM5B 2K3 Canada

A segmentation method is proposed to aid rigid skin panel design for an existing modular morphing wing mechanism. The method divides the surface of the modular morphing mechanism into panels through geometry discretization, and then the skin which is made up of these rigid panels will have better rigidity and it will morph more free without any gap. The method comprises three basic morphing types based on the motion of upper platform. Wherein, the mechanism side is firstly divided into a plurality of panels and each of them is approximated to flat, and then shape changes of one panel before and after morphing are analyzed. Therefore, panels can be designed as moveable and fixed patches to achieve seamless morphing. At last, computer simulation is set up to further verify the correctness of the method. © 2016 ICIC International.

关键词： Morphing mechanism Seamless morphing Skin panel Surface segmentation

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An improved community discovery algorithm in weighted social networks

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ICIC Express Letters 2016年第1期10卷 35-41页

作者： Guo, Jingfeng Liu, Miaomiao Liu, Linlin Chen, Xiao College of Information Science and Engineering Yanshan University No. 438 West Hebei Ave Qinhuangdao China College of Information Science and Engineering Northeast Petroleum University No. 199 Fazhan Road Daqing China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province No. 438 West Hebei Ave Qinhuangdao China

The algorithm AGMA (Automatic Graph Mining Algorithm) is improved and a novel community discovery algorithm, namely CRMA (Clustering Re-clustering Merging Algorithm) is proposed which can realize more reasonable community division for weighted social networks. Firstly, in the re-clustering stage, AGMA neglected the clustering situation of neighbors of current node and it did not take the weights of edges connecting the current node and its unclustered neighbors into account, which led to that some nodes cannot be clustered or their clustering was unreasonable. Aiming to this, the concept of the connection compactness between the node and the community is introduced, and then through clustering and re-clustering each node would be divided into the community that has the largest connection compactness with the node according to the node weight, the edge weight and the friend coecient between the node and the community. Secondly, for community division in some unweighted networks and signed networks, if there are many nodes whose clustered neighbors are less than its unclustered neighbors, the division results of AGMA tend to result in the lower modularity. In view of this problem, the concepts of positive edge weight density, the cluster density and the connection coecient between clusters are proposed. Lastly, communities are merged based on the above three concepts after re-clustering and the modularity is eectively enhanced. The higher correctness and better generality of the improved algorithm are veried through experiments. © 2016 ICIC International.

关键词： Clustering algorithms

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Semantic analysis and retrieval of spatial data based on the uncertain ontology model in Digital Earth

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International Journal of Digital Earth 2015年第1期8卷 3-16页

作者： Shengtao Sun Lizhe Wang Rajiv Ranjan Aizhi Wu The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province School of Information Science and EngineeringYanshan UniversityQinhuangdaoChina Institute of Remote Sensing and Digital Earth Chinese Academy of SciencesBeijingChina Computational Informatics CSIROCanberraACTAustralia College of Vehicles and Energy Yanshan UniversityQinhuangdaoChina

Metadata are the information about and description of *** Digital Earth,metadata become variant and heterogeneous with many *** paper studies uncertain features in the generation and application of metadata,and two types of uncertainties(incomplete and imprecise)are described based on semantic quantitative measurement method semantic relationship quantitative measurement based on possibilistic logic and probability statistic(SRQ-PP).Moreover,in the case study,we apply two types of quantitative measurements based on SRQ-PP to describe incomplete(uncertain)knowledge and imprecise(vague)information separately in spatial data service retrieval,which in turn is helpful to identify additional potential data resources and provide a quantitative analysis of the results.

关键词： metadata semantic ontology uncertainty measurement spatial data digital earth

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A novel collaborative recommendation algorithm integrating probabilistic matrix factorization and neighbor model

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Journal of Information and Computational Science 2015年第5期12卷 2011-2019页

作者： Yu, Hongtao Dou, Lisha Zhang, Fuzhi The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province School of Information Science and Engineering Yanshan University Qinhuangdao China

The existing collaborative recommendation algorithms suffer from lower recommendation precision due to the problem of data sparsity. To solve this problem, we propose a novel collaborative recommendation algorithm which integrates the probabilistic matrix factorization and neighbor models. We first propose a method to calculate the similarity between users or items based on the probabilistic matrix factorization model and construct a natural exponential function to compute the weighted similarity. Then we devise a collaborative recommendation algorithm to make recommendations for the target user, which dynamically adjusts the recommendation results for user-and item-based models by the balance adjustment factor. The experimental results on the MovieLens dataset show that the proposed algorithm outperforms the existing algorithms in terms of prediction accuracy. Copyright © 2015 Binary Information Press.

关键词： Exponential functions

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Research on Router Algorithm Based on Trust Primary Node in the Environment of P2P Network

Research on Router Algorithm Based on Trust Primary Node in ...

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2015 International Conference on Informatics, Control and Automation(ICA 2015)

作者： Jing CHEN Yun WAN Department of Computer College of Information Science and EngineeringYanshan University Key Laboratory of Computer Virtual Technology and System Integration of Hebei Province

The distributed, heterogeneous and anonymous attribute of P2 P networks make the node information sharing facing security problems. In order to prevent malicious and false service of nodes, and improve the retrieval efficiency, the primary node selection algorithm based on trust mechanism is proposed in this paper. Firstly, the network topology based on virtual community is described, and the function of primary node is discussed. Secondly, the primary node is selected according to direct trust value and recommendation trust value of each node in the virtual community. Finally, resource retrieval algorithm is designed according to the selected trust primary node, and the algorithm is experimentally verified. In some certain extent, the results show that the method is able to reduce the fraud of primary node and improve the retrieval efficiency.

关键词： P2P network virtual community Primary node Trust mechanism Router algorithm

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A novel approach to efficiently mine structural patterns from software execution sequence

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Journal of Computational Information Systems 2015年第3期11卷 1109-1119页

作者： Huang, Guoyan Zhang, Bing Ren, Rong Ren, Jiadong College of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

Analyzing structure of a software system is not sufficient for understanding its functionality. Certain characteristics of software are often hidden in its structure and can only be discovered when the various execution sequence of the software are identified. Analysis of execution sequence, therefore, becomes an indispensable aid in software comprehension. In this paper, a novel approach to efficiently mine structural patterns from software execution sequence is proposed. First, software execution traces are modeled as sequential patterns. Due to loops, patterns can occur multiple times within a trace, which leads to high cost of time and super complexity of the research. Then, an algorithm is designed to eliminate repetitive patterns (ERP) in software and software sequential pattern mining (SSPM) algorithm is presented to divide software unique sequential patterns into different patterns in order to mine structural characteristics of the software. At last, by the comparative analysis of similarity between different pattern collections, it draws the conclusion that software system can be compartmentalized into n fixed patterns. Experimental results also prove the conclusion well. ©, 2015, Binary Information Press. All right reserved.

关键词： computer software

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Research on community detection in social network based on Pairwise Merging

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Journal of Computational Information Systems 2015年第19期11卷 7079-7086页

作者： Chen, Jing Wan, Yun Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China College of Information Science and Engineering Yanshan University Qinhuangdao China

Community detection has become an important challenge during the past decade. Network is divided into some groups or communities that are densely connected to each other inside while less connected to the nodes outside. Community detection in social networks using hybrid merging of sub-communities is proposed. It is based on the idea of bottom to up. But the initial phase of algorithm resulted in many sub- communities and hybrid merging generated some weak communities. In order to resolve these problems, the algorithm CDPM (Community Detection based on Pairwise Merging of sub-communities) is proposed in this paper. The main idea of the algorithm starts with some small communities. Then, these small communities are extended by using similarity in order to decrease the number of sub-communities. Finally, these sub-communities are merged by pairwise merging which is mainly based on maximization modularity. Meanwhile, checking structure of every community is to avoid the appearance of weak community. The CDPM is proved on two real networks, and the results show that the performance of CDPM is close to real network and the quality of community detection is improved. © 2015 by Binary Information Press.

关键词： Merging

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New friend recommendation with user interest and socialization

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Journal of Information and Computational Science 2015年第11期12卷 4253-4262页

作者： Huang, Guoyan Chang, Yixin Li, Yakun College of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

Friend recommendation is popular in social site to help people make new friends and expand their social networks. However, the conventional friend recommendation method is low accuracy for the sparsity of data and cold start. To solve this problem, we propose a new personalized recommendation method with user interest and socialization. This approach incorporating user interest and social information mainly leverages both user-based collaborative filtering method and user topological structure in social networks. Firstly, user interest degree can be obtained to identify the user preferences. Secondly, get the new optimized similarity degree and the target user's nearest neighbor by using social collaborative filtering algorithm. In the end, the final recommendation set of user can be acquired from the highest predicted rating of the candidate collection. The experiments indicate that this proposed method can enhance the Effectiveness and accuracy of recommendation compared with the traditional friend recommendation algorithm. ©, 2015, Binary Information Press. All right reserved.

关键词： Collaborative filtering

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Mining maximal frequent patterns based on hierarchical bit vector and stack principle

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Journal of Computational Information Systems 2015年第1期11卷 369-376页

作者： Wang, Qian Ren, Jiadong He, Haitao Huang, Guoyan College of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

Maximal frequent pattern mining usually adopts a global pattern growth way, and a maximal frequent pattern can be obtained unless most of its subsets are checked to be frequent. An algorithm for mining maximal frequent patterns based on hierarchical bit vector and stack principle, called MaxPat HB, is proposed in this paper. Different with the traditional thinking of "test after connection", MaxPat HB adopts an individual pattern growth way and uses another thought of "testing while growing" which can efficiently gain the specified maximal frequent pattern and decrease the scale of candidate generation. The original data is converted to hierarchical bit vectors and maximal frequent patterns are mined hierarchically according to item unit. Candidate itemset generation adopts the stack principle, the push and pop operation is imitated by transforming bit values in a vector. Logic calculations between bit vectors are faster than the matches between strings, and the hierarchical structure can extremely reduce the searching space. Experiment result shows its high efficiency and good scalability. ©, 2014, Journal of Computational Information Systems. All right reserved.

关键词： Vectors

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