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The synthesis of dynamic junction over-constrained parallel-parallel mechanism based on the weighted directed graph theory

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ICIC Express Letters 2013年第11期7卷 3057-3065页

作者： Guo, Xijuan Zhang, Dayu Geng, Qingjia Zhang, Wenyuan The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University No. 438 Hebei Ave. Qinhuangdao 066004 China

Several dynamic junction over-constrained parallel-parallel mechanisms are proposed in this paper with a new method based on graph theory synthesis. This kind of mechanism is the pivotal part of the equivalent mechanism in helicopter automatic tilting device. The purpose of synthesis is that the synthesis result is helpful to optimize the structure and optimal design for helicopter automatic tilting device, and the reason why a graph method is chosen is that it can be programmed easily. The characteristic of "Dynamic Junction" is proposed relative to fixed junction. Because the joint of connecting two parallel mechanisms is dynamic, these two mechanisms can motion relatively. A mount of permutation and combination calculating processes are needed because of dynamic characteristic. In this view, choosing a synthesis method which can program easily in computer is appropriate. The graph method is the best choice. However, classic graph method is difficult to discriminate the graph isomorphism and limited to apply in spatial mechanism. Therefore, this paper proposed a new graph way. First, mechanism is modeled in weighted directed graph, then based on equal freedom of degree, many other graph models are got which have the same motion characteristic. Finally many target mechanisms can be derived by those models. The new method avoids the problem of classic graph method and is easy to program and run in computer, it is proved effectively because several dynamic junction over-constrained parallel-parallel mechanisms are successfully synthesized by it. © 2013 ISSN 1881-803X.

关键词： Dynamics

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Automatic web page data extraction through MD5 trigeminal tree and improved BIRCH

Automatic web page data extraction through MD5 trigeminal tr...

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2022 International Conference on Electronic Information Engineering, Big Data, and computer technology, EIBDCT 2022

作者： Gong, Jibing Kou, Xiaomeng Zhang, Hanyun Peng, Jiquan Gong, Shishan Wang, Shuli School of Information Science and Engineering Yanshan University Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao066004 China Key Laboratory for Software Engineering of Hebei Province Yanshan University Qinhuangdao066004 China School of Information Science Yanshan University Qinhuangdao066004 China

ISBN: (纸本)9781510655133

This paper proposes an automatic data extraction algorithm for web pages based on noise reduction and visualization blocks' construction. In this algorithm, we first build an MD5 trigeminal tree of the web page's source files using a Message-Digest Algorithm. And then we arrange some noise reduction. Finally, we construct visualization blocks by an optimized cluster algorithm named BIRCH A-CF which could create an area clustering feature forest and complete the clustering by dynamically changing the circle's radius according to the correction factor. We perform experiments on eight different datasets to compare our method with eight baseline methods. The experimental results show that our approach outperforms current methods by providing more accuracy and robustness, it also accelerates noise reduction and reduces the number of nodes effectively. © 2022 SPIE.

关键词： Visualization

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A virtualized data center energy-saving mechanism based on switching operating mode of physical servers and reserving virtual machines

A virtualized data center energy-saving mechanism based on s...

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作者： Yin, Chunxia Liu, Jian Jin, Shunfu College of Information Science and Engineering Yanshan University Qinhuangdao China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao China Ocean College of Hebei Agricultural University Qinhuangdao China Institute of Computing Technology Chinese Academy of Sciences Beijing China

The energy consumption of virtualized data centers has grown very fast in last several years. Because a large number of hosts are running in an idle state, virtualized data centers waste a large amount of electric energy. To save more energy for virtualized data centers, an energy-saving mechanism is proposed based on switching operating mode of physical servers and reserving virtual machines (VMs). The main idea is that when the amount of idle VMs reaches twice of a specified threshold, half of these idle VMs are reserved to process the new task that is about to arrive, and the physical server which hosts the other half of the idle VMs is switched to the sleep mode. From the perspective of task arrival rate and sleep parameters, we use two-dimensional Markov processes to analyze the proposed energy-saving mechanisms. By using matrix geometry solutions, we theoretically estimate energy consumption and response performance. According to the numerical experiments, the proposed energy-saving mechanism obviously cut down electric energy consumption and ensures response performance. Finally, the specified threshold for the amount of VMs is optimized by building a cost function. © 2020 John Wiley & Sons, Ltd.

关键词： Cost functions

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Outlier detection based on cluster outlier factor and mutual density 10th

Outlier detection based on cluster outlier factor and mutual...

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10th International Symposium on Intelligence Computation and Applications, ISICA 2018

作者： Zhang, Zhongping Zhu, Mengfan Qiu, Jingyang Liu, Cong Zhang, Debin Qi, Jie School of Information Science and Engineering Yanshan University QinhuangdaoHebei066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province QinhuangdaoHebei066004 China Hebei Education Examinations Authority ShijiazhuangHebei050000 China The First Middle School of Qian An Country Qian’anJilin131400 China

ISBN: (纸本)9789811364723

Outlier detection is an important task in data mining with numerous applications. Recent years, the study on outlier detection is very active, many algorithms were proposed including that based on clustering. However, most outlier detection algorithms based on clustering often need parameters, and it is very difficult to select a suitable parameter for different data set. In order to solve this problem, an outlier detection algorithm called outlier detection based on cluster outlier factor and mutual density is proposed in this paper which combining the natural neighbor search algorithm of the Natural Outlier Factor (NOF) algorithm and based on the Density and Distance Cluster (DDC) algorithm. The mutual density and γ density is used to construct decision graph. The data points with γ density anomalously large in decision graph are treated as cluster centers. This algorithm detect the boundary of outlier cluster using cluster outlier factor called Cluster Outlier Factor (COF), it can automatic find the parameter. This method can achieve good performance in clustering and outlier detection which be shown in the experiments. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Data mining

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Opnet: Deep Occlusion Perception Network with Boundary Awareness for Amodal Instance Segmentation

Opnet: Deep Occlusion Perception Network with Boundary Aware...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shihui Zhang Ziteng Xue Yuhong Jiang Houlin Wang School of Information Science and Engineering Yanshan University Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province

The Amodal Instance Segmentation (AIS) task aims to infer the visible and occluded regions of an object instance. Existing AIS methods typically focus on directly predicting visible and occluded regions or leveraging prior knowledge to guide predictions. However, these methods often ignore the perception of occluded views, leading to inaccurate results. To address this issue and achieve high-quality AIS, we propose a boundary-aware Occlusion Perception Network (OPNet). OPNet consists of three main components: the Dynamic Feature Augmentation Pyramid (DFAP), the Dual-path Boundary Aware Module (DBAM), and the Shape-guide Refinement Module (SRM). Specifically, DBAM employs an occlusion-perception strategy to learn discriminative features with boundary information, enabling it to distinguish occlusion from multiple views. Additionally, DFAP and SRM optimize the results by enhancing feature aggregation and imposing geometric constraints. Experiments on the D2SA, KINS, and CWALT datasets show that OPNet significantly outperforms state-of-the-art AIS methods that without prior knowledge. Code is available at https://***/ZitengXue/OPNet.

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Robust Recommendation Method Based on Shilling Attack Detection and Matrix Factorization Model

Robust Recommendation Method Based on Shilling Attack Detect...

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

作者： Yu-qi HU Kai LIU Fu-zhi ZHANG School of Information Science and Engineering Yanshan University The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province

The existing robust collaborative recommendation algorithms have low robustness against PIA and Ao P attacks. Aiming at the problem, we propose a robust recommendation method based on shilling attack detection and matrix factorization model. Firstly, the type of shilling attack is identified based on statistical characteristics of attack profiles. Secondly, we devise corresponding unsupervised detection algorithms for standard attack, Ao P and PIA, and the suspicious users and items are flagged. Finally, we devise a robust recommendation algorithm by combining the proposed shilling attack detection algorithm with matrix factorization model, and conduct experiments on the Movie Lens dataset to demonstrate its effectiveness. Experimental results show that the proposed method exhibits good recommendation precision and excellent robustness for shilling attacks of multiple types.

关键词： Collaborative recommendation Shilling attack Attack type identification Attack detection Matrix factorization model

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Research on the Influence of Improved K-shell Algorithm on Commodity Profit

Research on the Influence of Improved K-shell Algorithm on C...

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作者： Jing Chen Jincheng Huang College of Information Science and Engineering Yanshan University Key Laboratory for Computer Virtual Technology and system Integration of Hebei Province Software Engineering Laboratory in Hebei Province

In order to maximize the influence of commodity profits in e-commerce platforms, designing and improving the K-shell algorithm to select the more influential seed node sets in this paper. The new algorithm improves the number of active nodes by setting node threshold and edge weight attributes. To obtain more commodity profits, a strategy IRDSN(Strategy for Improving Repeat Degree of Seed Nodes) is proposed to select initial seed nodes and improve the repeat degree of seed nodes. The profit maximization based on linear threshold model is realized by setting different propagation modes. The improved algorithm and strategy IRDSN are analysed and verified in real data set and e-commerce platform. The results show that the algorithm effectively improves the profit of commodities.

关键词： Influence threshold K-shell influence maximization profit

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Research on a Novel Influence Maximization Algorithm Based on Community Structure

Research on a Novel Influence Maximization Algorithm Based o...

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作者： Jing Chen Jiangchuan Liu College of Information Science and Engineering Yanshan University Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Software Engineering Laboratory in Hebei Province

With the research of influence maximization algorithm, many researchers have found that the existing algorithm has the problem of overlapping influence of seed nodes. In order to solve the problem of overlapping influence of seed nodes, this paper proposes an IMCS algorithm based on community structure. Firstly, we divide the community through the central node, and the quality of community division is ensured by defining community fitness and node contribution. Then through the analysis of the community division results, the seed node selects the one with the largest degree. Since most of the nodes activated by seed nodes of different communities also belong to different communities, this method solves the problem of overlapping influence to a certain extent. The experimental results show that the effectiveness of the IMCS algorithm is verified under the real network, cooperative network and artificial network, and the IMCS algorithm has a better effect than IEIR and Degree algorithms in most networks under the IC model.

关键词： Seed node influence maximization community division overlapping influence community structure independent cascade model

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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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Community detection algorithm in signed social networks based on statistics and merger

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Journal of Information and Computational Science 2015年第15期12卷 5589-5599页

作者： Guo, Jingfeng Zhao, Yue Hu, Xinzhuan Liu, Yuanying College of Information Science and Engineering Yanshan University Qinhuangdao China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

Researches on community detection in signed social networks focus on the assignment of positive and negative edges. However, the community detection approaches that positive and negative edges are handled separately in two phases cannot make full use of negative edges, which seriously affects the accuracy of community detection. To solve the problem, a new algorithm called CDA SM (Community Detection Algorithm Based on Statistics and Merger) is proposed: merge into the two phases as an unified process;in the process of community detection, the node shall be added to the community in which the node has the maximum contribution as well as the minimum interference degree. The accuracy and time efficiency of CDA SM have been demonstrated through a set of experiments including both un-weighted common networks and un-weighted signed social networks. © 2015 by Binary Information Press

关键词： Signal detection

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