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2017 2nd International Conference on Communications, Information Management and Network Security (CIMNS2017)

作者： Ya-li SI Fu-zhi ZHANG Wen-yuan LIU School of Information Science and Engineering Yanshan University School of Liren Yanshan University The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province

Point-of-interest(POI) recommendation becomes an important research for location-based social networks, since it helps modern citizens to explore new locations in unvisited cites effectively according to their preferences. However, the current POI recommendation methods are lack of a deep mining in all time slots features and their effects on recommendation. To this end, in this paper we propose a POI recommendation method(called UPT) by combining time slot features, user-based collaborative filtering and spatial influence. Firstly, we extract time interval feature and time slot based popularity feature from history check-in datasets on LBSNs using probability statistical analysis method. Then, we devise a POI recommendation method based on the proposed temporal features to achieve better performance. In UPT, user-based collaborative filtering and smoothing technique are used by adding each time slot influence, and the overall popularity of a location is combined with each time slot feature. Our experimental results on Foursquare and Gowalla datasets show that UPT outperforms baseline POI recommendation methods in precision and recall.

关键词： Location-based social networks Point-of-interest recommendation User-based collaborative filtering Location popularity Temporal feature

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Robust visual tracking via combinative deep learning

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Journal of Physics: Conference Series 2018年第1期1074卷

作者： Jia Wen JiWei Zhao PengFei Liu Hongjun Wang The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao P.R.China Information Science and Engineering College Yanshan University Qinhuangdao P.R.China State Key Laboratory of Software Engineering of Hebei Province Yanshan University Qinhuangdao P.R.China

Object tracking is a hot topic in computer vision. In recent years, a large number of trackers has been proposed, in which the deep learning tracker has achieved excellent performance. The real-time capability of the deep learning tracker is not good enough due to the high-complexity of the network structures. This paper proposed an innovative tracking method to solve this problem. There are three important differences between this tracker and the other deep learning trackers. Firstly, the overcomplete basis in the deep learning tracker results in heavy computational cost. In order to reduce the complexity of the network, fewer units are used in the first hidden layer to replace the overcomplete basis. Secondly, a training method combining two observation models is used in the tracking process. The denoising automatic encoder is used in the first layer and the backpropagation is used in the other layers. This can avoid the diffusion of gradients which is caused by BP and adapt to the change of the targets easier. Thirdly, this tracker using adaptive particle filter to track targets. The number of particles is dynamic changes in tracking process. In this paper, we use different kinds of unlabelled datum to train network and initialize observation model. The observation model uses the samples collected in the tracking to adjust dynamically so as to adapt to the target appearance and complex environment. Compared with the existing methods, the results of experiments in different video sequences show that this tracker has a higher speed and the similar accuracy compared.

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A survey of occlusion detection method for visual object

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High technology Letters 2016年第3期22卷 256-265页

作者：张世辉 He Huan Liu Jianxin Zhang Yucheng Pang Yunchong Sang Yu School of Information Science and Engineering Yanshan University Qinhuangdao 066004 P. R. China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao 066004 P. R. China

Occlusion problem is one of the challenging issues in vision field for a long time,and the occlusion phenomenon of visual object will be involved in many vision research fields. Once the occlusion occurs in a visual system,it will affect the effects of object recognition,tracking,observation and operation,so detecting occlusion autonomously should be one of the abilities for an intelligent vision system. The research on occlusion detection method for visual object has increasingly attracted attentions of scholars. First,the definition and classification of the occlusion problem are ***,the characteristics and deficiencies of the occlusion detection methods based on the intensity image and the depth image are analyzed respectively,and the existing occlusion detection methods are compared. Finally,the problems of existing occlusion detection methods and possible research directions are pointed out.

关键词： visual object occlusion detection intensity image depth image

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Analysis of a new type islanding detection method for micro-grid based on the calculation of variance 8

Analysis of a new type islanding detection method for micro-...

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8th IEEE International Power Electronics and Motion Control Conference, IPEMC-ECCE Asia 2016

作者： Ying, Zhang Wang, Xiaohuan Zhang, Chunjiang Feng, Jianzhou Yanshan University Key Lab of Power Electronics for Energy Conservation and Motor Drive of HeBei Province Qinhuangdao China Yanshan University Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

ISBN: (纸本)9781509012107

UL1741 rules that to realize the islanding detection, the inverter must stop running within 2s after the main grid failure occurs. However, with the increasing of the capacity of distributed generation system, in order to guarantee the stability and reliability of power supply simultaneously, IEEE Std.1547 proposed the concept that DG runs off-grid after islanding occurs. The concepts of the traditional islanding protection were attacked, the original islanding detection methods are no longer suitable for the flexible micro power grid operation system. This paper presents a new active islanding detection method which could suitable for micro-grid operation and control. The method, which under the premise of without destroying islanding operation conditions, fine-tunes the PWM modulation wave, real-time acquisition the voltage of point of common coupling, using the mathematical statistics method to extract the quadratic differential of the output voltage, according to the changes of the mathematical characteristic value in the grid connected mode and the islanding mode to judge if there was islanding occurred, and the theory basis of quantitative analysis of the disturbance quantity selection. Simulation and experiment results verify the correctness and feasibility of the proposed method. © 2016 IEEE.

关键词： Distributed power generation

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Human Movement Detection and Gait Periodicity Analysis Using Channel State Information 12

Human Movement Detection and Gait Periodicity Analysis Using...

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12th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2016

作者： Liu, Zijuan Wang, Lin Liu, Wenyuan Li, Binbin School of Information Science and Engineering Yanshan University China Key Laboratory for Computer Virtual Technology and System Integration of HeBei Province China School of Economics and Management Yanshan University Qinhuangdao HeBei China

ISBN: (纸本)9781509056965

Under the influence of multipath effects and small scale fading, the robustness and reliability of the existing human detection methods based on radio frequency signals are easy to be impaired. In this paper, we propose a novel design that using the multi-layer filtering of channel state information (CSI) to identify moving targets in dynamic environments and analyze the gait periodicity of human. We employ an efficient CSI subcarrier feature difference to the multi-layer filtering method leveraging principal component analysis (PCA) and discrete wavelet transform (DWT) to eliminate the noises. Furthermore, we propose a profile matching mechanism for human detection and a periodicity analysis mechanism for human gait taking advantage of the above design. We evaluated it with the commodity Wi-Fi infrastructures in different environments. Experimental results indicate that our approach performs identification of human with an average accuracy of 94%. © 2016 IEEE.

关键词： Principal component analysis

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A study on the influence propagation model in topic attention networks

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International Journal of Performability Engineering 2017年第5期13卷 721-730页

作者： Chen, Xiao Guo, Jingfeng Tian, Kelun Fan, Chaozhi Pan, Xiao College of Information Science and Engineering YanShan University Qinhuangdao066004 China Qian'An College North China University of Science and Technology Qian'an064400 China College of Economic and Management Shijiazhuang Tiedao University Shijiazhuang050043 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao066004 China

The social networks with the complex user relations and huge amount of data and hidden information, bring new opportunities and challenges for the study of information diffusion and influence maximization. In recent years, there are more and more researches on the influence maximization of topic preference. However, most of the existing researches only take the topic as an attribute of the users, and the importance of the topic in network structure is not considered. In view of this situation, firstly, this paper constructed a new topic attention network model fusing the social relation and the topic preference. Secondly, based on connected degree of set pair and Markov random walk model, we propose the calculated method of the topic preference for users, and then mining the seed set with influence by the greedy strategy. Thirdly, we propose the calculated method of the activation probability of the user based on the user relation and the topic preference, and propose the influence maximization algorithm TAN-CELF in topic attention networks. Finally, on Dou-ban network dataset, from three metrics ISST, ISRT and ISRNT, compare with algorithm L-GAUP and CELF, the experimental results show that algorithm TAN-CELF that is proposed by this paper has a higher performance on influence scope. © 2017 Totem Publisher, Inc. All rights reserved.

关键词： Data mining

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Research of Signed Networks Community Detection Based on the Tightness of Common Neighbors 6

Research of Signed Networks Community Detection Based on the...

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6th International Conference on Digital Home, ICDH 2016

作者： Xinzhuan, Hu Guo, Jingfeng Chen, Xiao Zhao, Xiaomeng School of Economics and Management Yanshan University Hebei Ave. Qinhuangdao China College of Information Science and Engineering Yanshan University Hebei Ave. Qinhuangdao China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Hebei Ave. Qinhuangdao China

ISBN: (纸本)9781509044009

According to the characteristics of positive and negative edge of signed networks, a new signed networks community detection algorithm BTCN-SNCD (Signed networks Community Detection Based on the Tightness of Common Neighbors) is proposed based on the tightness of common neighbors. Firstly, in view of the shortcomings of the traditional local similarity metrics only considering the number of common neighbors, the contribution degree and the tightness of common neighbors are defined, which can discover the initial communities with tighter structure. Secondly, for the overlapping nodes exist in initial communities, we propose overlap coefficient and community density to remerge the initial communities, thus improving community detection accuracy. Finally, the correctness and efficiency of the BTCN-SNCD algorithm are verified through a comparison with classic signed networks community detection algorithms. © 2016 IEEE.

关键词： Signal detection

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New methods of constructing quaternary sequence Pairs

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ICIC Express Letters 2016年第9期10卷 2177-2183页

作者： Jia, Yanguo Shen, Xiumin Duan, Xiaobei Song, Xiaofei School of Information Sciences and Engineering Yanshan University China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province No. 438 Hebei Ave. Qinhuangdao066004 China

In this letter, new methods of constructing quaternary sequence pairs are presented based on binary sequence pairs with two-level autocorrelation, almost perfect binary sequence pairs and cyclic shift sequences. The quaternary sequence pairs con- structed by these methods are periodic autocorrelation signals. These methods are handy and exible, and can greatly enrich the theory of quaternary sequence pairs. © 2016 ICIC International.

关键词： Autocorrelation

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A new method of identifying influential nodes in complex software network based on leaderrank

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ICIC Express Letters 2016年第12期10卷 2837-2844页

作者： Huang, Guoyan Liu, Jing Chen, Xiaojuan Ren, Jiadong College of Information Science and Engineering Yanshan University China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province No. 438 West Hebei Ave. Qinhuangdao066004 China

Identifying influential nodes is crucial for understanding and improving the stability and robustness of complex software network. This paper presents a new method based on LeaderRank information to identify top-k influential nodes in directed-weighted complex software network. Firstly, the function and relationship of function calling or dependency are mapped to a Directed function dependency network (DN). Secondly, a so-called stem node connected with every other node by a bidirectional link is introduced into the original network. Thirdly, an algorithm Constructing DWN (C-DWN) is used to transform a DN into a Directed-Weighted function dependency network (DWN). Finally, the Identifying Influential Node in Software Network (IIN-SN) algorithm is proposed to identify influential nodes. Experimental results show that the proposed method is effective for identifying influential nodes with the final score ranking following power-laws. The top-k most influential nodes are the same and the final score (NS) of each node is a little larger during the software evolution. What is more, we present that some factors such as in-degree of nodes and influence of connected nodes are related to the influence of nodes. © 2016 ICIC International.

关键词： Complex networks

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Efficient subtree results computation for XML keyword queries

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Frontiers of computer Science 2015年第2期9卷 253-264页

作者： Ziyang CHEN School of Information Science and Engineering Yanshan University Hebei 066004 China Environmental Management College of China Hebei 066004 China The Key Laboratory for Computer Virtual Technology and System Integration of HeBei Province Yanshan University Hebei 066004 China

In this paper, we focus on efficient construction of restricted subtree （RSubtree） results for XML keyword queries on a multicore system. We firstly show that the perfor- mance bottlenecks for existing methods lie in 1） computing the set of relevant keyword nodes （RKNs） for each subtree root node, 2） constructing the corresponding RSubtree, and 3） parallel execution. We then propose a two-step generic top-down subtree construction algorithm, which computes SLCA/ELCA nodes in the first step, and parallelly gets RKNs and generates RSubtree results in the second step, where generic means that 1） our method can be used to compute dif- ferent kinds of subtree results, 2） our method is independent of the query semantics; top-down means that our method con- structs each RSubtree by visiting nodes of the subtree con- structed based on an RKN set level-by-level from left to right, such that to avoid visiting as many useless nodes as possible. The experimental results show that our method is much more efficient than existing ones according to various metrics.

关键词： XML SLCA/ELCA RSubtree RKNs parallel

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