Pub Date : 2011-07-26DOI: 10.1109/ICNC.2011.6022200
Xinyuan An, Jun Chen, Yi Liu, Xunsi Wei, Tianye Xu
Because of lacking secure strategy, there appear a lot of security leaks and increasing security threats in SS7 network. Based on the analysis of security leaks in message handling of the network and features of those threats, the paper proposes a defense method against the counterfeit signaling network management messages. After adding service discrimination in MTP3 layer message handling, this method can discriminate the counterfeit management messages and stop the messages form passing through signal nodes, and thus protect SS7 network. The simulation experiment establishes a signaling network model to simulate the message handling of the defenseless network and the defended network. Experiment results demonstrate that this method is valid and feasible.
{"title":"A defense method based on improved MTP3 message discrimination in SS7 network","authors":"Xinyuan An, Jun Chen, Yi Liu, Xunsi Wei, Tianye Xu","doi":"10.1109/ICNC.2011.6022200","DOIUrl":"https://doi.org/10.1109/ICNC.2011.6022200","url":null,"abstract":"Because of lacking secure strategy, there appear a lot of security leaks and increasing security threats in SS7 network. Based on the analysis of security leaks in message handling of the network and features of those threats, the paper proposes a defense method against the counterfeit signaling network management messages. After adding service discrimination in MTP3 layer message handling, this method can discriminate the counterfeit management messages and stop the messages form passing through signal nodes, and thus protect SS7 network. The simulation experiment establishes a signaling network model to simulate the message handling of the defenseless network and the defended network. Experiment results demonstrate that this method is valid and feasible.","PeriodicalId":299503,"journal":{"name":"2011 Seventh International Conference on Natural Computation","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121600949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-07-26DOI: 10.1109/ICNC.2011.6022167
Junhong Si, Kaiyan Chen, Sen Zhang, Yipeng Guo, Baohua Zhang
In order to improve the local convergence of differential evolution algorithm, we puts forward the greedy evolution (GE) algorithm based on the greedy search strategy. According to the fitness value and the selection probability, the population of a generation is classed best vectors, better vectors and poor vectors. The best vectors is retained in the child population, the better vectors is replaced if the newly generated vector in its neighborhood is better than objective vector, and the poor vectors is regenerated until the new vector is not worse than the objective vector. Improving the locally search ability and ensuring the diversity of the population, the convergence of GE increases obviously. Analysis of 3 test problems, the reasonable range of controlling parameters is determined: NPS is 1𢈼 2 times than NP, δ is 0.05𢈼 0.3, and SP is 0.4𢈼 0.8. Comparing the optimum solution of GE algorithm with differential evolution and particle swarm optimization, the result shows that GE is better than others.
{"title":"Solving the constrained nonlinear optimization based on greedy evolution algorithm","authors":"Junhong Si, Kaiyan Chen, Sen Zhang, Yipeng Guo, Baohua Zhang","doi":"10.1109/ICNC.2011.6022167","DOIUrl":"https://doi.org/10.1109/ICNC.2011.6022167","url":null,"abstract":"In order to improve the local convergence of differential evolution algorithm, we puts forward the greedy evolution (GE) algorithm based on the greedy search strategy. According to the fitness value and the selection probability, the population of a generation is classed best vectors, better vectors and poor vectors. The best vectors is retained in the child population, the better vectors is replaced if the newly generated vector in its neighborhood is better than objective vector, and the poor vectors is regenerated until the new vector is not worse than the objective vector. Improving the locally search ability and ensuring the diversity of the population, the convergence of GE increases obviously. Analysis of 3 test problems, the reasonable range of controlling parameters is determined: NPS is 1𢈼 2 times than NP, δ is 0.05𢈼 0.3, and SP is 0.4𢈼 0.8. Comparing the optimum solution of GE algorithm with differential evolution and particle swarm optimization, the result shows that GE is better than others.","PeriodicalId":299503,"journal":{"name":"2011 Seventh International Conference on Natural Computation","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116569741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-07-26DOI: 10.1109/ICNC.2011.6022102
Ya Li, Lina Zhang, Liqin Jia
In this paper, we proposed a robust timing synchronization method for OFDM systems using an antenna array, where the effect of carrier frequency offset (CFO) was eliminated by the crossed product of the array signal vector with a single conjugate element signal of the array. The synchronization timing estimation was realized under the least square criterion. Simulation results demonstrate that the proposed method is robust in the presence of CFO and the co-channel interference signals.
{"title":"A robust timing synchronization method for OFDM system","authors":"Ya Li, Lina Zhang, Liqin Jia","doi":"10.1109/ICNC.2011.6022102","DOIUrl":"https://doi.org/10.1109/ICNC.2011.6022102","url":null,"abstract":"In this paper, we proposed a robust timing synchronization method for OFDM systems using an antenna array, where the effect of carrier frequency offset (CFO) was eliminated by the crossed product of the array signal vector with a single conjugate element signal of the array. The synchronization timing estimation was realized under the least square criterion. Simulation results demonstrate that the proposed method is robust in the presence of CFO and the co-channel interference signals.","PeriodicalId":299503,"journal":{"name":"2011 Seventh International Conference on Natural Computation","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131386515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-07-26DOI: 10.1109/ICNC.2011.6022511
Yuxin Zhao, Lijuan Chen, Wenjian Liu
A new method applying finite element is proposed in this paper to approximate probability density function of the state of a navigation system. The weak solution of navigation stochastic differential model is denoted by the Kolmogorov's forward equation, which it is very difficult to be obtained. The solution is approached through finite element to obtain a prior probability density function of the state, then a posterior probability density function is gained through Bayesian formula, By taking the underwater vehicle integrated navigation system as the instance and carrying out the contrastive analysis with Particle Filter, the feasibility of solving of navigation stochastic differential model with the help of finite element is confirmed through simulating experiment results.
{"title":"Solving 2-dimensional navigation stochastic differential model based on finite element","authors":"Yuxin Zhao, Lijuan Chen, Wenjian Liu","doi":"10.1109/ICNC.2011.6022511","DOIUrl":"https://doi.org/10.1109/ICNC.2011.6022511","url":null,"abstract":"A new method applying finite element is proposed in this paper to approximate probability density function of the state of a navigation system. The weak solution of navigation stochastic differential model is denoted by the Kolmogorov's forward equation, which it is very difficult to be obtained. The solution is approached through finite element to obtain a prior probability density function of the state, then a posterior probability density function is gained through Bayesian formula, By taking the underwater vehicle integrated navigation system as the instance and carrying out the contrastive analysis with Particle Filter, the feasibility of solving of navigation stochastic differential model with the help of finite element is confirmed through simulating experiment results.","PeriodicalId":299503,"journal":{"name":"2011 Seventh International Conference on Natural Computation","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131754766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-07-26DOI: 10.1109/ICNC.2011.6022537
Li-juan Kan, Ma Yunlai
In view of the disadvantage of the methods on weapon equipment research project between local and abroad, the author constructs a AHP-GCP model by the valid connection of AHP(Analytic Hierarchy Process) and GCP(Catastrophe Progression Process), and applies it in evaluation model of weapon equipment research project, and the result indicates that this method can evaluate the weapon equipment research project effectively, and can remedy the defect of the weapon equipment research project evaluation to some extent. Thus provides an effective approach for weapon equipment research project evaluation.
{"title":"Notice of RetractionApplication of AHP-GCP model in weapon equipment research project evaluation","authors":"Li-juan Kan, Ma Yunlai","doi":"10.1109/ICNC.2011.6022537","DOIUrl":"https://doi.org/10.1109/ICNC.2011.6022537","url":null,"abstract":"In view of the disadvantage of the methods on weapon equipment research project between local and abroad, the author constructs a AHP-GCP model by the valid connection of AHP(Analytic Hierarchy Process) and GCP(Catastrophe Progression Process), and applies it in evaluation model of weapon equipment research project, and the result indicates that this method can evaluate the weapon equipment research project effectively, and can remedy the defect of the weapon equipment research project evaluation to some extent. Thus provides an effective approach for weapon equipment research project evaluation.","PeriodicalId":299503,"journal":{"name":"2011 Seventh International Conference on Natural Computation","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127019750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-07-26DOI: 10.1109/ICNC.2011.6022318
Haixia Li, Youzeng Wang, Qingfeng Liu, Sun Chuanguang
In this paper, we study the problems with learning effect to minimize the total weighted completion time on a single or parallel batch machines. We provide some optimal algorithms for three special cases, where all the jobs have the same processing time.
{"title":"Batch scheduling jobs with learning effect to minimize the total weighted completion time","authors":"Haixia Li, Youzeng Wang, Qingfeng Liu, Sun Chuanguang","doi":"10.1109/ICNC.2011.6022318","DOIUrl":"https://doi.org/10.1109/ICNC.2011.6022318","url":null,"abstract":"In this paper, we study the problems with learning effect to minimize the total weighted completion time on a single or parallel batch machines. We provide some optimal algorithms for three special cases, where all the jobs have the same processing time.","PeriodicalId":299503,"journal":{"name":"2011 Seventh International Conference on Natural Computation","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127741441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-07-26DOI: 10.1109/ICNC.2011.6022553
Aiping Xu, Qi Wang, Li Hu, Hong Shu
A large number of environmental phenomena may be regarded as the realizations of spatiotemporal random fields. In practice, these environmental phenomena are sparsely sampled generally. In order to research deeply, it is necessary to construct a continuous spatiotemporal data surface, so the prediction or interpolation must be done. The spatiotemporal variogram and covariance model is useful means of describing the spatiotemporal correlation structure. For the straightforward extension of variogram and covariance from pure spatial to spatiotemporal fields, there are a number of statistical studies about theoretical spatiotemporal model but very less research on model computing. After making some theoretical spatiotemporal statistical analysis, this paper focused mainly on the computation of spatiotemporal variogram and covariance model and implement effective variogram and covariance model. Firstly, the spatiotemporal product-sum model is deduced into the form of calculable in theory. Secondly, the most likely variogram model and its parameters of sill, nugget, and range are derived through computing the spatial and temporal variogram respectively. Thirdly, the policy of how to determine the parameters k1,k2 and k3 in the product-sum model are put forward. The objective to introduce k1,k2 and k3 is to ensure the effectiveness of variogram and covariance model. Lastly, the spatiotemporal variogram and covariance model are implemented. The results have shown the positive definite characteristics of the spatiotemporal variogram and covariance varying with the parameter k1 and reverse variation characteristics between variogram and covariance, which proves that the theoretical model chosen is effective and the computing approach about spatiotemporal variogram and covariance model is feasible. The research of this paper has laid the foundation for spatiotemporal prediction or interpolation, because prediction or interpolation can do only basing on suitable variogram or covariance model.
{"title":"Computing spatiotemporal variogram and covariance model","authors":"Aiping Xu, Qi Wang, Li Hu, Hong Shu","doi":"10.1109/ICNC.2011.6022553","DOIUrl":"https://doi.org/10.1109/ICNC.2011.6022553","url":null,"abstract":"A large number of environmental phenomena may be regarded as the realizations of spatiotemporal random fields. In practice, these environmental phenomena are sparsely sampled generally. In order to research deeply, it is necessary to construct a continuous spatiotemporal data surface, so the prediction or interpolation must be done. The spatiotemporal variogram and covariance model is useful means of describing the spatiotemporal correlation structure. For the straightforward extension of variogram and covariance from pure spatial to spatiotemporal fields, there are a number of statistical studies about theoretical spatiotemporal model but very less research on model computing. After making some theoretical spatiotemporal statistical analysis, this paper focused mainly on the computation of spatiotemporal variogram and covariance model and implement effective variogram and covariance model. Firstly, the spatiotemporal product-sum model is deduced into the form of calculable in theory. Secondly, the most likely variogram model and its parameters of sill, nugget, and range are derived through computing the spatial and temporal variogram respectively. Thirdly, the policy of how to determine the parameters k1,k2 and k3 in the product-sum model are put forward. The objective to introduce k1,k2 and k3 is to ensure the effectiveness of variogram and covariance model. Lastly, the spatiotemporal variogram and covariance model are implemented. The results have shown the positive definite characteristics of the spatiotemporal variogram and covariance varying with the parameter k1 and reverse variation characteristics between variogram and covariance, which proves that the theoretical model chosen is effective and the computing approach about spatiotemporal variogram and covariance model is feasible. The research of this paper has laid the foundation for spatiotemporal prediction or interpolation, because prediction or interpolation can do only basing on suitable variogram or covariance model.","PeriodicalId":299503,"journal":{"name":"2011 Seventh International Conference on Natural Computation","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134552423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-07-26DOI: 10.1109/ICNC.2011.6022234
Dongxiao He, Jie Liu, Da-you Liu, Di Jin, Zhen Jia
In this paper we present a new ant colony optimization for community detection in large networks, which takes modularity Q as objective function. An important difference that distinguishes our algorithm from the former ant algorithms is the manner in which the ants are used in the algorithm. Unlike those existing methods in which each ant searches for a candidate solution, each ant in our algorithm only decides whether its current vertex joins the community of its previous vertex with the aid of a simulated annealing idea, whose purpose is to locally optimize function Q. In each iteration, the ants work collectively so as to uncover the community structure of the network. Moreover, we introduce a thought of “layer and rule” into this method for further improving its performance. Our algorithm doesn't employ the pheromone, which reduces its running time and makes it well suitable for large-scale networks. Meanwhile, it still performs very well on both computer-generated benchmark and some widely used real-world networks compared with a set of competing algorithm in terms of clustering quality.
{"title":"Ant colony optimization for community detection in large-scale complex networks","authors":"Dongxiao He, Jie Liu, Da-you Liu, Di Jin, Zhen Jia","doi":"10.1109/ICNC.2011.6022234","DOIUrl":"https://doi.org/10.1109/ICNC.2011.6022234","url":null,"abstract":"In this paper we present a new ant colony optimization for community detection in large networks, which takes modularity Q as objective function. An important difference that distinguishes our algorithm from the former ant algorithms is the manner in which the ants are used in the algorithm. Unlike those existing methods in which each ant searches for a candidate solution, each ant in our algorithm only decides whether its current vertex joins the community of its previous vertex with the aid of a simulated annealing idea, whose purpose is to locally optimize function Q. In each iteration, the ants work collectively so as to uncover the community structure of the network. Moreover, we introduce a thought of “layer and rule” into this method for further improving its performance. Our algorithm doesn't employ the pheromone, which reduces its running time and makes it well suitable for large-scale networks. Meanwhile, it still performs very well on both computer-generated benchmark and some widely used real-world networks compared with a set of competing algorithm in terms of clustering quality.","PeriodicalId":299503,"journal":{"name":"2011 Seventh International Conference on Natural Computation","volume":"25 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133061678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to eliminate the influence of Smear Effect on follow-up processing of star images, this paper researched the source and statistical model of Smear Effect. After researching the working progress of inter-line Charge Coupled Device(CCD), inter-frame CCD and full-frame CCD, this paper builds a statistical model for the background noise and then proposes an algorithm to do radiometric correction in smear images based on modeling and estimating the intensity of background noise in star image. Experimental results indicate that the algorithm in this paper can remove smear effect in star image efficiently while retaining origin information. The method in this paper can eliminate the influence of smear effect in star images while retaining origin information.
{"title":"A robust smear removal method for inter-frame Charge-Coupled Device star images","authors":"Jianwei Gao, Zhen Zhang, Rui Yao, Jinqiu Sun, Yanning Zhang","doi":"10.1109/ICNC.2011.6022512","DOIUrl":"https://doi.org/10.1109/ICNC.2011.6022512","url":null,"abstract":"In order to eliminate the influence of Smear Effect on follow-up processing of star images, this paper researched the source and statistical model of Smear Effect. After researching the working progress of inter-line Charge Coupled Device(CCD), inter-frame CCD and full-frame CCD, this paper builds a statistical model for the background noise and then proposes an algorithm to do radiometric correction in smear images based on modeling and estimating the intensity of background noise in star image. Experimental results indicate that the algorithm in this paper can remove smear effect in star image efficiently while retaining origin information. The method in this paper can eliminate the influence of smear effect in star images while retaining origin information.","PeriodicalId":299503,"journal":{"name":"2011 Seventh International Conference on Natural Computation","volume":"319 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133400125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-07-26DOI: 10.1109/ICNC.2011.6021907
Lei Jiang, L. Ding, Yunwen Lei, Ming Chen, Zhigao Zeng
Clustering is an important data analysis and data mining technique. PSO clustering is one of the popular partition algorithm. But it often suffers from the problem of premature convergence and traps in suboptimum solution. This paper uses adaptive niche particle swarm algorithm to improve clustering. And it is also studied the impact of different fitness optimization function to clustering data. The results show that the algorithm which hybrids adaptive niche to PSO clustering techniques has more competitive. It also shows that the new fitness optimization function we proposed is more promising in the fields of high dimension data set and large difference of the number samples in clusters than the popular function of Merwe introduced.
{"title":"Notice of RetractionHybrid adaptive niche to improve particle swarm optimization clustering algorithm","authors":"Lei Jiang, L. Ding, Yunwen Lei, Ming Chen, Zhigao Zeng","doi":"10.1109/ICNC.2011.6021907","DOIUrl":"https://doi.org/10.1109/ICNC.2011.6021907","url":null,"abstract":"Clustering is an important data analysis and data mining technique. PSO clustering is one of the popular partition algorithm. But it often suffers from the problem of premature convergence and traps in suboptimum solution. This paper uses adaptive niche particle swarm algorithm to improve clustering. And it is also studied the impact of different fitness optimization function to clustering data. The results show that the algorithm which hybrids adaptive niche to PSO clustering techniques has more competitive. It also shows that the new fitness optimization function we proposed is more promising in the fields of high dimension data set and large difference of the number samples in clusters than the popular function of Merwe introduced.","PeriodicalId":299503,"journal":{"name":"2011 Seventh International Conference on Natural Computation","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134086049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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