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MOEA/D with adaptive IWO for synthesizing phase-only reconfigurable linear arrays

Open Chemical Engineering Journal 2015年第1期9卷 125-133页

作者： Tan, Yanyan Li, Shengtao Fang, Xiaonan School of Information Science and Engineering Shandong Normal University Jinan250014 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Shandong Normal University Jinan250014 China

In order to well solve the phase-only reconfigurable arrays synthesis problems, we introduce an adaptive strategy in invasive weed optimization (IWO), and integrate the adaptive IWO (AIWO) into the framework of MOEA/D, a popular multi-objective algorithm. Then, a new version of MOEA/D with adaptive IWO, named MOEA/D-AIWO is proposed in this paper for solving the synthesis problems. In MOEA/D-AIWO, the proposed adaptive strategy is adopted for improving search ability and balancing diversity and convergence. We introduce an adaptive standard deviation, which changes not only with the increase of evolution generations, but also exponentially with the fitness function value of each individual. This strategy improves the convergence rate and helps the seeds escape from local optimum. Taking advantage of the powerful searching ability of invasive weeds and well framework of MOEA/D, the overall performance of the proposed MOEA/D-AIWO is illustrated in solving two sets of phase-only reconfigurable arrays synthesis problems. Comparing results with MOEA/D-IWO (MOEA/D with original IWO) and MOEA/D-DE are also provided in this paper. © Tan et al.

关键词： Invasive weed optimization

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A Naive Bayesian Network Intrusion Detection Algorithm Based on Principal Component Analysis

A Naive Bayesian Network Intrusion Detection Algorithm Based...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Xiaoyan Han Liancheng Xu Min Ren Weiping Gu School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology School of Mathematics and Quantitative Economics Shandong University of Finance and Economics Jinan China

Traditional Naive Bayesian classification model does not consider the feature redundancy in intrusion forensics and neglects the difference between data attributes in different intrusion actions. This paper proposed a Naive Bayesian network intrusion detection algorithm based on the principal component analysis, it calculate the characteristic value of the original network attack data, then extract the main properties through the principal component analysis. Take the main properties as the new attribute set and the corresponding principal component contribution rate as weights to improve traditional Naive Bayesian classification algorithm. The experimental results showed that the algorithm can effectively reduce the data dimension and improve the efficiency of detection.

关键词： Bayes methods Principal component analysis Intrusion detection Algorithm design and analysis Feature extraction Data mining

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Design of sub-optimal controller for discrete-time systems with multiple control input delays

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ICIC Express Letters, Part B: Applications 2015年第9期6卷 2531-2538页

作者： Han, Shiyuan Wu, Xiaoyu Zheng, Xiangwei Chen, Yuehui Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Shandong Normal University No. 88 East Wenhua Road Lixia District Jinan China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan No. 336 West Road of Nanxinzhuang Jinan China Shandong Computer Science Center No. 2008 Xinle Street Jinan China

This note presents the sub-optimal linear-quadratic controller for a discretetime system with multiple control input delays. Sub-optimality of the solution is designed in two steps. First, a delay-free transformed system is introduced by using a discretized vector, and the quadratic performance index is reformed into a corresponding form. Then, the sub-optimal controller is derived from the maximum principle. Also, the sufficiencies of the existence and uniqueness of designed controller are proved. Finally, simulation results demonstrate the effectiveness of the sub-optimal controller. © 2015 ISSN 2185-2766

关键词： Discrete time control systems

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Imbalanced web spam classification based on nested rotation forest

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ICIC Express Letters 2015年第3期9卷 937-944页

作者： Fang, Xiaonan Zhang, Huaxiang Gao, Shuang Tan, Yanyan Department of Information Science and Engineering Shandong Normal University No. 88 Wenhua East Road Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Shandong Normal University No. 88 Wenhua East Road Jinan China

Web spam has emerged to be a critical problem in web search. Unfortunately, single classifiers always perform poorly on imbalanced web spam data sets. For better solving these problems, a nested Rotation Forest structure is proposed, i.e., ensemble classifiers instead of single decision trees are used as the base classifiers of Rotation Forest. Meanwhile, SMOTE is adopted to change the imbalanced ratio of the training set. Rotation Forest and SMOTE method are combined to improve the classification effect in the paper. Experimental results on WEBSPAM UK2007 data set show that our proposed methods can markedly boost the performance of web spam classification. © 2015, ICIC International.

关键词： Classification (of information)

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Double filtration of topic tracking based on KL divergence

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Journal of Computational Information Systems 2014年第19期10卷 8543-8552页

作者： Lu, Ran Wei, Jingxuan Xun, Jing School of Information Science and Engineering Shandong Normal University Ji'nan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Ji'nan China

In order to overcome the shortcoming of the static threshold and the problem of the false reports, the method of double filtering topic tracking based on Kullback-Leibler divergence is put forward. In this method, it uses the KL divergence to obtain the initial threshold and the candidate reports. Then, depending on the time characteristics of reports, it studies dynamic threshold method. Finally, it extracts named entities of the reports to calculate similarity of named entities, which completes topic tracking. The experiments prove the feasibility of the algorithm and improves the efficiency of the topic tracking to a certain extent. In addition, it reduces the false negative rate and the false alarm rate.

关键词： Information systems

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Mining Approximate Frequent Patterns from Noisy Databases

Mining Approximate Frequent Patterns from Noisy Databases

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International Conference on Broadband, Wireless Computing, Communication and Applications (BWCCA)

作者： Xiaomei Yu Yongqin Li Hong Wang School of Information Science and Engineering Shandong Normal University Jinan Shandong China Shandong Provincial Key Laboratory for Distributed Computer software Novel Technology School of Teacher's Education Shandong Normal University Jinan Shandong China

ISBN: (纸本)9781467383165

As an important branch in the field of frequent pattern mining, approximate frequent pattern (AFP) mining attracts much attention recently. Various algorithms have been proposed to discover long true AFPs in presence of random noise. This paper considers the key issues of AFP mining in noisy databases, and categorizes the previous approaches according to the ways they cope with missing items in the transactions. And then a study of different data models on AFP is presented, in which the merits and defects are analyzed. Finally, we draw a conclusion and propose some solutions to deal with the problems in the field of AFP mining.

关键词： Itemsets Algorithm design and analysis Approximation algorithms Data mining Noise measurement Data models

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Network Forensics Scenario Reconstruction Method Based on Hidden Markov Models

Network Forensics Scenario Reconstruction Method Based on Hi...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Weiping Gu Liancheng Xu Min Ren Xiaoyan Han School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China School of Mathematics and Quantitative Economics Shandong University of Finance and Economics Jinan China

Reconstruction Method of Network Forensics Scenario has grown into a mature and rich technology that provides advanced skills to get the chain of evidence. Using statistical methods to analyze intrusion logs in order to present evidentiary values in court are often refuted as baseless and inadmissible evidences which is not considering the input spent. These spendings is to generate the reports no matter they are well-grounded evidences or not. Thus, this paper presents the Scenario Reconstruction Method combines the Viterbi algorithm, the most likely sequence of Meta evidence which replaces the Meta evidence was acquired. With suspected evidence, thus obtaining the chain of evidence. However, the Viterbi algorithm parameters is derived from the Baum-Welch (B-W) algorithm, and the B-W algorithm is easy to fall into local optima solution. While an Adaptive Genetic Algorithm (AGA) is used to estimate parameters of the Hidden Markov model (HMM), where Chromosome coding method and genetic operation mode are designed. The experimental results show that, this method can accurately reproduce the crime scene of network intrusion, compared with the network forensic evidence fusion method which is based on the HMM. The method has been applied to forensics system, and has obtained good result.

关键词： Hidden Markov models Sociology Statistics Maximum likelihood decoding Biological cells Genetics Genetic algorithms

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Double anomaly detection algorithm based on dendritic cells

Double anomaly detection algorithm based on dendritic cells

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2014 International Conference on Materials Science and Computational Engineering, ICMSCE 2014

作者： Xu, Lian Cheng Xu, Di Zhou, Guang Gang Hou, Xiu Yan Department of Information Science and Engineering Shandong Normal University China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology China

ISBN: (纸本)9783038350996

On the problem of data quantity in anomaly detection, traditional dendritic cell algorithms should be improved by proposing an antigen data preprocessing method which introduced suspected abnormal base. The abnormal data should be judged with high efficiency in the shortest time possible by double anomaly detection. First of all, set of the original data should be preprocessed into individual antigen, and was matched with the suspected abnormal base to conduct the first anomaly detection. Then a double anomaly detection should be conducted by dendritic cell algorithm and the intensity of anomaly be judged according to the dynamic anomaly index. Experiments show that this algorithm produces better effect on both time and accuracy. © (2014) Trans Tech Publications, Switzerland.

关键词： Cells

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Hybrid collaborative filtering recommendation based on time and diversity

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ICIC Express Letters, Part B: Applications 2015年第9期6卷 2539-2545页

作者： Wang, Hong Sun, Yanshen Yu, Xiaomei Wang, Jihua School of Information Science and Engineering Shandong Normal University No. 88 East Wenhua Road Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology No. 88 East Wenhua Road Jinan China School of Earth Sciences Zhejiang University No. 388 Yuhangtang Road Hangzhou China

Many researchers have been attracted in personalized recommendation which is considered as one of the most effective methods to solve the problem of information overloading. We propose a hybrid collaborative filtering recommendation method based on time and diversity in this paper. Firstly, we construct a personalized recommendation model by using an improved frequent item mining method and an improved visibility graph, thus making us obtain user-item networks and user-user networks. Secondly, we define concepts of similarity and similarity index and then put forward a revised recommendation algorithm in the user-user networks based on link mining and diversity. Finally, we apply our methods to four classic datasets in order to evaluate the performance of our algorithms. Experimental results show that our methods do better than traditional collaborative filtering methods and content based recommendation methods in precision, recall and time. © 2015 ISSN 2185-2766

关键词： Visibility

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A visualization method in virtual educational system

A visualization method in virtual educational system

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5th International Symposium on IT in Medicine and Education, ITME 2013

作者： Zhang, Guijuan Lu, Dianjie Liu, Hong School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China

ISBN: (纸本)9789400776173

We present a visualization-based knowledge expression approach for virtual educational system in this paper. Our method allows teachers and students to understand complex algorithms and procedures more intuitively and conveniently during the process of teaching and learning. We take the decision tree and the random forest algorithm in the field of Data mining as examples in this paper. In our method, the decision tree is represented by a virtual 3D tree model that both the structure and the classification results can be showed clearly. In addition, random forest is represented by a group of virtual 3D trees and their positions denote the similarity between the decision trees. We also provide several user-interaction tools in our system. The tools help users to browse the forest, select a tree, delete a tree and even see the detail information of the decision tree. The effective and understandable results show the feasibility of applying visualization method in virtual educational system. © Springer Science+Business Media Dordrecht 2014.

关键词： Visualization

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