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A novel hierarchical clustering method in signed social network

Journal of Computational Information systems 2015年第21期11卷 7865-7872页

作者： Guo, Jingfeng Chen, Xiao Fan, Chaozhi Hu, Xinzhuan 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 Qian'an College North China University of Science and Technology Qian'an China

Based on a new feature that macro network structure and micro game have mutual inuence on the formation of the community, to mining communities in signed network, a novel algorithm SNCGHC is proposed. Firstly, according to the signed attributes and the node degree, the Shapley Value is improved, to avoid ignoring the effect of external environment on the Shapley Value and reduce the calculation complexity. Secondly, considering the inuence of network density on the formation of the coalition (community), we find the calculation method of average coalition payoff and create the signed network coalitional game model. Thirdly, SNCGHC depends on average payoff maximization of coalition to achieve hierarchical clustering of signed network. Finally, experiments show that our method can detect better community structures as compared to other existing methods. Copyright © 2015 Binary Information Press.

关键词： Game theory

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A PTV method based on SIFT feature points matching for velocimetry measurement of oil-water two-phase flow in horizontal pipelines

International Journal of Signal Processing, Image Processing...

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International Journal of Signal Processing, Image Processing and Pattern Recognition 2015年第12期8卷 195-206页

作者： Kong, Weihang Li, Lei Kong, Lingfu Li, Yingwei Xie, Na School of Information Science and Engineering Yanshan University QinhuangdaoHebei China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Hebei China Information Department of Library Yanshan University QinhuangdaoHebei China

Due to the advantages of scale invariant feature transform (SIFT) feature points on the invariant to image scale, brightness, rotation, occlusion, noise and so on, this paper proposes a Particle Tracking Velocimetry (PTV) method, based on SIFT feature points matching for velocity measurement of oil-water two-phase flow in horizontal pipelines. The oil-water two-phase flow with large droplet diameter, oil droplets overlap, ununiform lighting, and the centroid position of oil droplets can’t be obtained only by using traditional PTV methods through morphological processing. However, in this paper, the algorithm can directly achieve the average velocity of the flow field according to the positions of correctly matched SIFT feature points, and there is no need to extract the centroid coordinates of each oil droplet. The experimental results show that the proposed algorithm not only can be used in the average velocity measurement of oil-water two- phase flow in horizontal pipelines, but also can reach 95% in the measuring accuracy when the matching feature points are enough sufficient. © 2015 SERSC.

关键词： Velocity measurement

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A novel approach to identify influential functions in complex software network based on complex network

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ICIC Express Letters 2016年第2期10卷 485-492页

作者： Huang, Guoyan Chen, Xiaojuan Wu, Hongfei Zhang, Peng Ren, Jiadong College of Information Science and Engineering No. 438 West Hebei Ave Qinhuangdao China Computer Virtual Technology and System Integration Laboratory of Hebei Province Yanshan University No. 438 West Hebei Ave Qinhuangdao China

Identifying influential nodes is an important issue in understanding the process of information diffusion in complex software networks. Researchers generally define functions as nodes, and relationship of function calls as edges to map software into complex network. This paper proposes a novel approach to identify the influential nodes in complex software network based on complex network. Firstly, we map the function and relationship of function calling or dependency to a directed weighted call network (DWCN). Secondly, we define NodeScore (NS) to evaluate the importance of nodes in network. It has higher influence when the node has lager value of NS. The NS of each node is calculated by two parts in our opinion, Direct Dependency Nodes (DDN) and Indirect Dependency Nodes (IDN). Thirdly, algorithm ComputeNodeScore is proposed to calculate NS of each node. Finally, we mine top-k nodes based on the NS. Experimental results of various versions about software Tar and cflow show the approach is effective for identifying the influential nodes. © 2016, ICIC Express Letters ICIC International.

关键词： Complex networks

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Angle historgam model of directional relationship in 3D based on Double-projection and Rounded-subdivision

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Journal of Information and Computational Science 2014年第18期11卷 6471-6478页

作者： Kong, Dehan Liu, Yongshan Ma, Junlei Department of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao China Data Industry Research Lab Qinhuangdao China

The formal model of spatial directional relations is one of the most important parts in spatial relation research. The most of models are based on Minimum Bounding Rectangle (MBR), and they are not compliant with the regular pattern of human cognition. In order to get a closer conclusion to human cognition on directional relationship, Angle Histogram model based on Double-projection and Rounded-subdivision (AHDPRS) is proposed in this paper. The model uses the maximum inscribed circles to find out the maximum parts of the object, and calculates the directional relationship between the centers of the circles. This model ignores the inessential details to ensure the result which will be closer to human cognition. The experiments show that this model is feasible.

关键词： Graphic methods

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Improved BP classifier via distance for sample reduction

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Journal of computers (Taiwan) 2019年第2期30卷 1-13页

作者： Wen, Jia Deng, Jia Liu, Peng-Fei Wang, Hong-Jun Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao China School of Information Science and Engineering Yanshan University Qinhuangdao China State Key Laboratory of Software Engineering of Hebei Province Yanshan University Qinhuangdao China

The BP neural network algorithm is a commonly used technique in the classification field of pattern recognition, which iterates training samples continuously to obtain the decision boundary, until the error reaches the convergence precision. However, due to the extensive redundancy of training samples, this carpet-like training method can easily lead to slow network convergence and low classification performance. In this paper, from the view of sample, a novel BP training method called improved BP Classifier algorithm using Distance for Sample Reduction (DRBP) is proposed. Through the sample weight to get the new training sample, which are the near of the classification interface like support vector, and then iterative training BP classifier to solve the classification issue. The main function of this method is to keep the original sample information, realize the sample reduction, and improve the generalization ability of the BP neural network. First, point sets is used to visualize the training process. And then use 10 sets of the University of California Irvine (UCI) to verify the effectiveness and superiority of our experiments. Finally through Cross-validation, the classification error rate of 10 sets of UCI datasets tested by DRBP training method is lower than that of standard BP neural network (STBP). So the result of the experiment showed that the method of the combined distance sample reduce with BP network can significantly enhance the generalization ability of BP network. © 2019 computer Society of the Republic of China. All rights reserved.

关键词： Classification (of information)

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The analysis to gamma oscillations induced in high dose DHPG and spike-gamma oscillation relationship

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International Journal of Advancements in Computing technology 2012年第20期4卷 553-560页

作者： Wen, Dong Lu, Chengbiao Key Laboratory for Computer Virtual Technology and System Integration of HeBei Province Yanshan University QinhuangdaoHebei 066004 China School of Information Science and Engineering Yanshan University QinhuangdaoHebei 066004 China Institue of Electrical Engineering Yanshan University Qinhuangdao Hebei 066004 China

Neuronal oscillations in the gamma frequency range and spikes have been reported in many cortical areas in the information processing, but the role of spikes play in cortical processing remains unclear. The aim of this study was to examine the role of high dose DHPG in the generation of oscillatory field activity at gamma frequency in the medical septal diagonal band of Broca (MSDB) slice prepared from rat brain, and to determine the phase relationship between spikes and gamma oscillations. The high dose DHPG induced large amplitude field population spiking activity, which is correlated linearly with the field gamma oscillations. The relationship between timing of spikes and the phase of gamma oscillation was determined by an analysis of circular statistics. Further analysis with Rayleigh test revealed that the relationship of phase-locking between spikes and gamma rhythm is of statistical significance. These data demonstrate that DHPG-sensitive neuronal networks within medial septum are able to generate gamma oscillations which are modulated by large amplitude population spikes and phase-locking of spikes to the gamma rhythm suggest the role of memory enhancement in the presence of mGluR1 activation.

关键词： Locks (fasteners)

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Point cloud coarse registration based on 3DSIFT keypoint detection

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Journal of Computational Information systems 2014年第23期10卷 10121-10128页

作者： Gu, Xiaoying Liu, Yongshan Department of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of HeBei Province Yanshan University Qinhuangdao China Data Industry Research Lab Qinhuangdao China

Since the SIFT feature point extraction algorithm with scale changes, rotation transformation invariance, is widely used in image registration. In this paper, the SIFT algorithm is applied to three-dimensional point cloud coarse registration, the proposed 3DSIFT extraction algorithm is suitable for three-dimensional point cloud data, then point coordinates, curvature, the nearest neighbor distance mean and other information compose fourteen-dimensional vector to conduct correspondence match, use the interior point rate of Ransac to obtain optimal transformation, and finally transform the coordinates for source point clouds using the optimal transform, complete the point cloud data coarse registration. Experimental results show that our coarse registration algorithm can effectively extract feature points, and it is robust for the point cloud with noisy point, it can provide accurate and effective initial value for the precise registration such as ICP.

关键词： Metadata

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An efficient collision detection algorithm of convex polygons based on Minkowski sum

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ICIC Express Letters 2013年第2期7卷 461-464页

作者： Geng, Qingjia Guo, Xijuan Zhang, Jianfei Zhang, Buying The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province College of Information Science and Engineering Yanshan University No. 438 Hebei Ave. Qinhuangdao 066004 China College of Computer Hebei Normal University of Science and Technology No. 360 West Hebei St. Qinhuangdao 066004 China

Collision detection is a hot research topic in the field of path planning of mobile robot, virtual assembly simulation, and so on. Fast and accurate collision detection has become one of the most key technologies of restricting their development. The study method based on Minkowski sum is an effective method for accurate collision detection through accurate numerical calculations. This paper presents a new efficient collision detection algorithm of convex polygons based on Minkowski sum, which uses the minimum separation distance to detect whether two convex polygons collide or not. The algorithm's performance is also analyzed and verified by experiment in the paper. Compared with the traditional collision detection algorithms based on the Minkowski sum, the algorithm's calculation is simpler and execution performance is higher. © 2013 ISSN 1881-803X.

关键词： Motion planning

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Reinforced Heterogeneous Graphlet Design for Knowledge Graph Representation Learning

SSRN

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SSRN 2023年

作者： Gong, Jibing Lin, Yuting Zhao, Yi Lin, Tianyu Fang, Xiaohan Feng, Xinchao Peng, Jiquan School of Information Science and Engineering Yanshan University Qinhuangdao066004 China The Key Laboratory for Computer Virtual Technology System Integration of Hebei Province Yanshan University Qinhuangdao066004 China

Knowledge Graphs have been practical tools to represent and integrate plentiful structural and semantic information in mainstream industrial scenarios. While promising, the heterogeneity and complexity of KGs pose a formidable obstacle, especially for graph representation learning. Most existing KG embedding models omit the dynamic high-order connectivity patterns, and heavily rely on the handcrafted patterns to handle the complex semantic relationships, thereby limiting their ability to adaptively capture nuanced and intricate relationships of knowledge graphs in different tasks. To fill this gap, we present ReHGD — a model designed for KGs that focuses on adaptively designing typed graphlets through a cooperative Multi-agent Reinforcement Learning algorithm. This approach can learn more discriminative graph representations tailored to specific downstream tasks. Specifically, ReHGD engages in the creation of typed graphlets through a two-stage process: it (1) establishes a reinforced chain design module to generate chains without predefined rules, and (2) employs a buffer-aware sampling technique to derive episodic chains from prior experiences. Subsequently, motifs are deduced by applying commute count and Hadamard product operations to the episodic chain-based subgraphs. Finally, ReHGD utilize chain and motif aggregations to learn graph representations. Experimental results on three real-world KGs show that ReHGD outperforms baselines on entity classification. © 2023, The Authors. All rights reserved.

关键词： Knowledge graph

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SYVR virtual Equipment Manufacturing Simulation

SYVR Virtual Equipment Manufacturing Simulation

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International Conference on virtual Reality and Visualization (ICVRV)

作者： Yuhan Liu Yong Tang Qian Sun Shiny VR Yanshan University College of Information Science and Engineering Qinhuangdao China Yanshan University The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

ISBN: (纸本)9781538684986;9781538684979

SYVR is a real-time simulation graphic engine focusing on equipment manufacturing. It is based on decades of research works of physics simulations, is able to provide services like real-time simulation for key parts of devices, large-scale com-plicated situation simulation, virtual experiment, operational training, visualization of concept design and etc., which con-sist of the whole process of virtual manufacturing and simulation. SYVR can provide comprehensive technology support for industrial 3-dimensional data, accelerate applications of VR to all aspects of industry, cut down development costs of equipment manufacturing significantly, highly increase effi-ciency of equipment design, and promote development of equipment manufacturing.

关键词： Conferences virtual reality Visualization

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