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International Conference on Electrical and Control Engineering (ICECE)

作者： Jiajia Miao Dongge Zhang Aiping Li Guoyou Chen Institute of Command Automation People''s Liberation Army University of Science and Technology Nanjing China School of Computer National University of Defense Technology Changsha China

In the dynamic distributed environment, the data sources trend to suffer changes that invalidate the mappings. We put forward a Fuzzy-based Broken Schema Mapping Detecting Approach (BSMD) to process the run time broken mapping detecting problem. The core of BSMD is a set of computationally inexpensive modules called sensors, which capture salient characteristics of data sources. There are two novel improvements: Disjunction-Weighted Average Operators are leveraged to calculate the score, which implies whether the mapping is broken; Change Weight Operators is introduced combine artificial data with real data in the training phase. The experiments over the real-world sources demonstrate the effectiveness of our fuzzy-based approach over existing solutions, as well as the utility of our improvements.

关键词： computers Programmable logic arrays Presses Maintenance engineering Sensors Peer to peer computing Machine learning

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StarEAI+: A Heterogeneous Data Integration Platform Based on Automatic Mapping Technology

StarEAI+: A Heterogeneous Data Integration Platform Based on...

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International Conference on Internet Technology and Applications (iTAP)

作者： Jiajia Miao Dongge Zhang Aiping Li Jia Yan Siyu Jiang Institute of Command Automation People''s Liberation Army University of Science and Technology Nanjing China School of Computer National University of Defense Technology Changsha China Xiamen China

Data integration is the basis of the information integration technology. With the continuous increasing of the information utilization, the large-scale heterogeneous data integration has become a hot issue in the information research. The mapping technology is the key to establish the consistency among the heterogeneous data, including the consistency of data model, the consistency of data instance and so on. By introducing the technologies of machine learning, natural language processing, as well as the theory of fuzzy model, we improve the schema mapping approach and the instance matching approach while optimize the broken mapping detecting algorithm. We extend this platform with tree modules: the automatic schema mapping module, the instance matching module, as well as the broken mapping detecting module. The StarEAI+ system has been successfully deployed in the projects of armed forces and network monitoring.

关键词： XML Bandwidth Manifolds Monitoring Data models Programmable logic arrays Information systems

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HIMA: A holistic data instance matching approach

HIMA: A holistic data instance matching approach

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International Conference on Electrical and Control Engineering, ICECE 2010

作者： Miao, Jiajia Chen, Guoyou Li, Aiping Yan, Jia Jiang, Siyu Institute of Command Automation PLA University of Science and Technology Nanjing China 613 School of Computer National University of Defense Technology Changsha China Post-Doctoral Workstation of the 73111 Army Xiamen China

ISBN: (纸本)9780769540313

Considering the consistency of instance level, we come up with a Holistic Data Instance Matching Approach (HIMA). Firstly, we measure the similarity of instances with the algorithm of string distances. HIMA makes use of the clustering algorithm, which it can handle, a large scale of data source holistically. In addition, we use the keyword extracting method, which is based on the maximum entropy model, to get rid of the useless information. The experimental results show that the keyword extracting algorithm can get 70% precision, and the condition probabilistic based algorithm is more precise than the token-based algorithm. HIMA method can achieve 83% accuracy. © 2010 IEEE.

关键词： Clustering algorithms

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StarEAI+: A heterogeneous data integration platform based on automatic mapping technology

StarEAI+: A heterogeneous data integration platform based on...

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International Conference on Internet Technology and Applications, ITAP 2010

作者： Miao, Jiajia Zhang, Dongge Li, Aiping Yan, Jia Jiang, Siyu Institute of Command Automation PLA University of Science and Technology Nanjing China 613# School of Computer National University of Defense Technology Changsha China Post-doctoral Workstation of the 73111 Army Xiamen China

ISBN: (纸本)9781424451432

关键词： Mapping

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HIMA: A Holistic Data Instance Matching Approach

HIMA: A Holistic Data Instance Matching Approach

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International Conference on Electrical and Control Engineering (ICECE)

作者： Jiajia Miao Guoyou Chen Aiping Li Jia Yan Siyu Jiang Institute of Command Automation People''s Liberation Army University of Science and Technology Nanjing China School of Computer National University of Defense Technology Changsha China The Post-Doctoral Workstation of the 73111 army Xiamen China

关键词： Clustering algorithms Entropy Data mining computers Couplings Computational modeling Programmable logic arrays

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Path capacity of multi-channel multi-hop IEEE 802.11 wireless networks with channel switching

Path capacity of multi-channel multi-hop IEEE 802.11 wireles...

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International Conference on computer science and Information Technology (CSIT)

作者： Pingshi He Ziping Xu Wangdong Qi Xia Yang Department of Computer Application Technology Institute of Command Automation People''s Liberation Army University of Science and Technology Nanjing China Department of Telecommunication Engineering Institute of Communication Engineering People''s Liberation Army University of Science and Technology Nanjing China China Electronics Systems Engineering Company China

The capacity problem is one of the main issues in researching the multi-channel IEEE 802.11 wireless networks. In this paper, we analyze the path capacity of the multi-channel wireless networks by estimating the RTS, CTS and the packet loss rate of the wireless links and calculating the average service time of the packet sending. We also analyze the channel assignment and channel switching problems and present the Neighborhood Channel Concentration Degree (NCCD) mechanism which can distribute network load into the available channels and increase the total throughput of the network. We use ns-2 as the simulator to simulate the network throughput using our NCCD and compare with the throughput of the MCR mechanism. The results show that NCCD can significantly increase the network throughput.

关键词： Capacity planning Computational modeling Switches Throughput

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Log integration on large scale for global networking monitoring

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Journal of Central South University 2009年第6期16卷 976-981页

作者：缪嘉嘉吴泉源贾焰 School of Computer National University of Defense Technology Institute of Command Automation PLA University of Science and Technology

Supposing that the overall situation is dug out from the distributed monitoring nodes, there should be two critical obstacles, heterogenous schema and instance, to integrating heterogeneous data from different monitoring sensors. To tackle the challenge of heterogenous schema, an instance-based approach for schema mapping, named instance-based machine-learning （IML） approach was described. And to solve the problem of heterogenous instance, a novel approach, called statistic-based clustering （SBC） approach, which utilized clustering and statistics technologies to match large scale sources holistically, was also proposed. These two algorithms utilized the machine-leaning and clustering technology to improve the accuracy. Experimental analysis shows that the IML approach is more precise than SBC approach, reaching at least precision of 81% and recall rate of 82%. Simulation studies further show that SBC can tackle large scale sources holisticalty with 85% recall rate when there are 38 data sources.

关键词： machine-learning clustering data integration schema matching instance matching

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A novel DoubleMinOver classifier based On second-order tensor

A novel DoubleMinOver classifier based On second-order tenso...

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International Conference on Natural Computation (ICNC)

作者： Zhe Wang Rui Han Zhisong Pan Xuelei Ni Department of Computer Science & Engineering East China University of Science and Technology Shanghai China Department of Information & Communication Engineering Huaian College of Information and Technology Jiangsu China Institute of Command Automation People''s Liberation Army University of Science and Technology Nanjing China Department of Information System Shanghai Automotive Industry Corporation Shanghai China

It is well-known that the DoubleMinOver classifier as an extendibility of MinOver was developed to provide a maximum margin solution with a bias. But, it can be found that the DoubleMinOver algorithm only classifies the vector pattern and fails to work in the pattern represented with the second-order tensor. In this paper, we propose a novel DoubleMinOver classifier named STDoubleMinOver that can directly classify the second-order tensor pattern. Compared with the existing DoubleMinOver with only one weight w, the proposed STDoubleMinOver induces two weight vectors u and v so as to deal with the second-order tensor. The experiments here have demonstrated that the proposed STDoubleMinOver has a superior classification to the vectorized DoubleMinOver.

关键词： Tensile stress Feature extraction Accuracy Artificial neural networks Training Lenses Iris

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SVDD regularized with area under the ROC 09

SVDD regularized with area under the ROC

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2nd International Conference on Interaction sciences: Information Technology, Culture and Human, ICIS 2009

作者： Chen, Bin Li, Bin Zhisong, Pan Dept. of Computer Yangzhou University Yangzhou China Institute of Command Automation PLA University of Science Technology Nanjing China

ISBN: (纸本)9781605587103

Support Vector Data Description (SVDD) has been successfully applied to detect outliers as points outside the minimal ball enclosing given target training objects. In its design, some rarelyhappened (labeled) outliers can be available, SVDD using Negative examples (NSVDD) can develop a more exact description than SVDD by utilizing the outliers to minimize the empirical risk and thus obtain better classification. However, the low classification error does not always mean good ROC performance when facing the problems with the unknown misclassification costs and/or the class distributions. Inspired by the characteristic of Area Under ROC (AUC) against the above problems, in this paper, we develop a SVDD regularized by the AUC (RSVDD) to optimize the empirical risk added a AUC regularizer in presence of a few available outliers. Experimental results verify the effectiveness of RSVDD in comparison with the traditional SVDD, NSVDD, ROC optimizer Support Vector Machine (ROCSVM) and SVM. Copyright © 2009 ACM.

关键词： Support vector machines

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一种无线传感器网络安全邻居发现算法

一种无线传感器网络安全邻居发现算法

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第21届全国计算机新科技与计算机教育学术大会

作者： YUAN En 袁恩 Dept of Computer Institute of Command Automation PLA University of Science & TechnologyNanjing 21 解放军理工大学指挥自动化学院计算机系南京210007

邻居发现是无线传感器网络的基础技术之一。在安全敏感的应用中，邻居发现的安全面临威胁。本文设计了一种安全的邻居发现算法，利用密钥机制完成双向认证并构建合法邻居表，同时新布设到网络中的节点可以随时加入到网络中。实验和理论... 详细信息

邻居发现是无线传感器网络的基础技术之一。在安全敏感的应用中，邻居发现的安全面临威胁。本文设计了一种安全的邻居发现算法，利用密钥机制完成双向认证并构建合法邻居表，同时新布设到网络中的节点可以随时加入到网络中。实验和理论分析表明，安全邻居发现算法能够较好地完成邻居发现任务，并且大大提高了邻居发现的安全性。

关键词：无线传感器网络密钥机制邻居发现网络节点

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