Pub Date : 2012-05-29DOI: 10.1109/ICNC.2012.6234724
Jihong Song, W. Yi
Particle swarm optimization (PSO) algorithm is an optimization algorithm in the filed of Evolutionary Computation, which has been applied widely in function optimization, artificial neural networks' training, pattern recognition, fuzzy control and some other fields. Original PSO algorithm could be trapped in the local minima easily, so in this paper we improved the original PSO algorithm using the idea of simulated annealing algorithm, which makes the PSO algorithm jump out of local minima. In this paper, two improved strategies was proposed, and after testing and comparing the two improved algorithms with the original PSO algorithm again and again, we conclude at last that efficiency of searching global about the two improved strategies is better than the original PSO.
{"title":"Improvement of original particle swarm optimization algorithm based on simulated annealing algorithm","authors":"Jihong Song, W. Yi","doi":"10.1109/ICNC.2012.6234724","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234724","url":null,"abstract":"Particle swarm optimization (PSO) algorithm is an optimization algorithm in the filed of Evolutionary Computation, which has been applied widely in function optimization, artificial neural networks' training, pattern recognition, fuzzy control and some other fields. Original PSO algorithm could be trapped in the local minima easily, so in this paper we improved the original PSO algorithm using the idea of simulated annealing algorithm, which makes the PSO algorithm jump out of local minima. In this paper, two improved strategies was proposed, and after testing and comparing the two improved algorithms with the original PSO algorithm again and again, we conclude at last that efficiency of searching global about the two improved strategies is better than the original PSO.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123455588","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}
The purpose of image steganalysis is to detect the presence of hidden messages in cover images. Steganalysis can be considered as a pattern recognition process to decide which class a test image belongs to: the cover photographic images or the stego-image. We present harmony search algorithm for feature selection for image steganalysis. Experiment show that the proposed hybrid algorithm for feature selection increases the testing accuracy of classifying result. The combination of the feature set extracted is likely to improve the performance of general steganalysis methods which have more real value for deterring covert communications and the uncorrelated features extracted contain more discriminatory information when distinguish different kinds of steganography.
{"title":"On optimal feature selection using harmony search for image steganalysis","authors":"Guoming Chen, Dong Zhang, Weiheng Zhu, Q. Tao, Chaoxia Zhang, Jinxin Ruan","doi":"10.1109/ICNC.2012.6234730","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234730","url":null,"abstract":"The purpose of image steganalysis is to detect the presence of hidden messages in cover images. Steganalysis can be considered as a pattern recognition process to decide which class a test image belongs to: the cover photographic images or the stego-image. We present harmony search algorithm for feature selection for image steganalysis. Experiment show that the proposed hybrid algorithm for feature selection increases the testing accuracy of classifying result. The combination of the feature set extracted is likely to improve the performance of general steganalysis methods which have more real value for deterring covert communications and the uncorrelated features extracted contain more discriminatory information when distinguish different kinds of steganography.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114080401","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 : 2012-05-29DOI: 10.1109/ICNC.2012.6234710
Xuan Li, Wuneng Zhou, Siming Ma, Shicao Luo, R. Chen
This Letter focuses on the function projective synchronization (FPS) of hyperchaotic Liu system and the hyperchaotic New Lorenz system. Within the two systems, we achieved the FPS at the first place through a proper control scheme. Furthermore, by designing the parameter update law, the adaptive function projective synchronization (AFPS) is also achieved. Several numerical simulations are presented to show the feasibility and effectiveness of the method.
{"title":"Function projective synchronization of Liu system and the new Lorenz system with known and unknown parameters","authors":"Xuan Li, Wuneng Zhou, Siming Ma, Shicao Luo, R. Chen","doi":"10.1109/ICNC.2012.6234710","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234710","url":null,"abstract":"This Letter focuses on the function projective synchronization (FPS) of hyperchaotic Liu system and the hyperchaotic New Lorenz system. Within the two systems, we achieved the FPS at the first place through a proper control scheme. Furthermore, by designing the parameter update law, the adaptive function projective synchronization (AFPS) is also achieved. Several numerical simulations are presented to show the feasibility and effectiveness of the method.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"197 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124415169","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 : 2012-05-29DOI: 10.1109/ICNC.2012.6234616
Li Zhang, Zhenping Pang, Yaowu Shi, L. Ren
Extraction of machine fault signals from background machine noises is of great help in improving the accuracy of machine fault diagnosis. In this paper, a prediction model of time series based on RBF neural network (RBFNN) is proposed to learn the priori knowledge of background machine noise that obscure in a template noise which is tailored from the historical samples of background machine noises. By defining the mean square error of prediction to candidate independent component with the proposed RBFNN model as the contrast function, a new ICA-R algorithm is proposed to extract the `pure' background machine noise which is then taken as reference input of a Volterra Adaptive Noise Cancellation (VANC) system. The simulation shows that the combination of ICA-R and VANC system prevails over a standard VANC system. The new VANC system is easier to be implemented in engineering applications due to its sensor-position independent characteristics.
{"title":"Adaptive cancellation of background machine noise based on combination of ICA-R and RBFNN","authors":"Li Zhang, Zhenping Pang, Yaowu Shi, L. Ren","doi":"10.1109/ICNC.2012.6234616","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234616","url":null,"abstract":"Extraction of machine fault signals from background machine noises is of great help in improving the accuracy of machine fault diagnosis. In this paper, a prediction model of time series based on RBF neural network (RBFNN) is proposed to learn the priori knowledge of background machine noise that obscure in a template noise which is tailored from the historical samples of background machine noises. By defining the mean square error of prediction to candidate independent component with the proposed RBFNN model as the contrast function, a new ICA-R algorithm is proposed to extract the `pure' background machine noise which is then taken as reference input of a Volterra Adaptive Noise Cancellation (VANC) system. The simulation shows that the combination of ICA-R and VANC system prevails over a standard VANC system. The new VANC system is easier to be implemented in engineering applications due to its sensor-position independent characteristics.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124041622","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 : 2012-05-29DOI: 10.1109/ICNC.2012.6234541
Xianghua Li, Chao Gao, Tianyang Lu, Li Tao
In the field of content-based 3D model retrieval, classifying and organizing 3D model database is an important fundamental research, which is critical for improving the retrieval performance. Clustering is one of the most effective methods to classify 3D models. However, there has been little work on it. This paper proposes a dynamic clustering algorithm based on artificial immune system for classifying 3D models, which not only can classify existing models, but can deal with new incremental models. Experimental results show that our algorithm can obtain better classification of 3D models.
{"title":"A dynamic clustering algorithm based on artificial immune system for analyzing 3D models","authors":"Xianghua Li, Chao Gao, Tianyang Lu, Li Tao","doi":"10.1109/ICNC.2012.6234541","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234541","url":null,"abstract":"In the field of content-based 3D model retrieval, classifying and organizing 3D model database is an important fundamental research, which is critical for improving the retrieval performance. Clustering is one of the most effective methods to classify 3D models. However, there has been little work on it. This paper proposes a dynamic clustering algorithm based on artificial immune system for classifying 3D models, which not only can classify existing models, but can deal with new incremental models. Experimental results show that our algorithm can obtain better classification of 3D models.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126443746","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 : 2012-05-29DOI: 10.1109/ICNC.2012.6234565
Yangzheng Zeng, Lilan Tu, Guojun Liu
This paper focuses on the global delay-dependent robust asymptotic stability of stochastic neural networks of neutral type with time-varying delays. The delay functions of networks under consideration are bounded but not necessarily differentiable. Based on the stochastic Lyapunov stability theory, itÔ's differential rule and linear matrix inequality (LMI) optimization technique, a delay-dependent asymptotic stability criterion is derived. Finally, an illustrative example is given to show the effectiveness and feasibility of the proposed method.
{"title":"Robust stability of stochastic neural networks of neutral type with time-varying delays","authors":"Yangzheng Zeng, Lilan Tu, Guojun Liu","doi":"10.1109/ICNC.2012.6234565","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234565","url":null,"abstract":"This paper focuses on the global delay-dependent robust asymptotic stability of stochastic neural networks of neutral type with time-varying delays. The delay functions of networks under consideration are bounded but not necessarily differentiable. Based on the stochastic Lyapunov stability theory, itÔ's differential rule and linear matrix inequality (LMI) optimization technique, a delay-dependent asymptotic stability criterion is derived. Finally, an illustrative example is given to show the effectiveness and feasibility of the proposed method.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125716183","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}
Floorplanning is a key step in the physical design of Very Large Scale Integrated (VLSI) circuits. It is a multi-objective combinatorial optimization and has been proved to be a NP-hard problem. To solve this problem, a coevolutionary multi-objective particle swarm optimization (CMOPSO) algorithm is proposed in this paper. The algorithm imports the concept of coevolutionary algorithm and elitist strategy into basic PSO algorithm, takes both the layout area and total interconnection wire length into consideration simultaneously. Experimental results showed the new algorithm can achieve a better performance.
{"title":"A coevolutionary multi-objective PSO algorithm for VLSI floorplanning","authors":"Zhen Chen, Jinzhu Chen, Wenzhong Guo, Guolong Chen","doi":"10.1109/ICNC.2012.6234515","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234515","url":null,"abstract":"Floorplanning is a key step in the physical design of Very Large Scale Integrated (VLSI) circuits. It is a multi-objective combinatorial optimization and has been proved to be a NP-hard problem. To solve this problem, a coevolutionary multi-objective particle swarm optimization (CMOPSO) algorithm is proposed in this paper. The algorithm imports the concept of coevolutionary algorithm and elitist strategy into basic PSO algorithm, takes both the layout area and total interconnection wire length into consideration simultaneously. Experimental results showed the new algorithm can achieve a better performance.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129994178","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 : 2012-05-29DOI: 10.1109/ICNC.2012.6234748
Changsheng Zhang, Meng Zhu, Bin Zhang
Clustering is a popular data analysis and data mining technique. In this paper, an improved ant colony clustering algorithm is presented to optimally partition N objects into K clusters and a comparative study has been made to prove its high performance using four evaluation measures. This algorithm has been tested on several synthetic datasets compared with the proposed ant colony based clustering algorithm called ACA. The experimental data reveals very encouraging results in terms of the quality of clustering.
{"title":"An improved ant-based clustering algorithm","authors":"Changsheng Zhang, Meng Zhu, Bin Zhang","doi":"10.1109/ICNC.2012.6234748","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234748","url":null,"abstract":"Clustering is a popular data analysis and data mining technique. In this paper, an improved ant colony clustering algorithm is presented to optimally partition N objects into K clusters and a comparative study has been made to prove its high performance using four evaluation measures. This algorithm has been tested on several synthetic datasets compared with the proposed ant colony based clustering algorithm called ACA. The experimental data reveals very encouraging results in terms of the quality of clustering.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134196168","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 : 2012-05-29DOI: 10.1109/ICNC.2012.6234533
Caiming Liu, Yan Zhang, Jinquan Zeng, Lingxi Peng, Run Chen
The Internet of Things (IoT) confronts a complicated and changeful attack environment. It is necessary to evaluate the security risk of IoT dynamically to judge the situation of IoT. To resolve the above problem, a dynamical risk assessment method for IoT inspired by Artificial Immune System is proposed in this paper. The proposed method is made up of Detection Agent of Attack and Sub-system of Dynamical Risk Assessment. Furthermore, it adopts the technology of detector distribution. The simulation of immune principles and mechanisms in the real IoT environment is deduced by set theory in math. The attack detector evolves dynamically in the IoT immune environment. Its change forms the dynamical security risk value of IoT.
{"title":"Research on Dynamical Security Risk Assessment for the Internet of Things inspired by immunology","authors":"Caiming Liu, Yan Zhang, Jinquan Zeng, Lingxi Peng, Run Chen","doi":"10.1109/ICNC.2012.6234533","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234533","url":null,"abstract":"The Internet of Things (IoT) confronts a complicated and changeful attack environment. It is necessary to evaluate the security risk of IoT dynamically to judge the situation of IoT. To resolve the above problem, a dynamical risk assessment method for IoT inspired by Artificial Immune System is proposed in this paper. The proposed method is made up of Detection Agent of Attack and Sub-system of Dynamical Risk Assessment. Furthermore, it adopts the technology of detector distribution. The simulation of immune principles and mechanisms in the real IoT environment is deduced by set theory in math. The attack detector evolves dynamically in the IoT immune environment. Its change forms the dynamical security risk value of IoT.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"235 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131544165","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 : 2012-05-29DOI: 10.1109/ICNC.2012.6234645
Bihan Wu, Gang Wu, Mengdong Yang
Ant Colony Optimization (ACO) is a kind of meta-heuristics algorithm, which simulates the social behavior of ants and could be a good alternative to existing algorithms for NP hard combinatorial optimization problems, like the 0-1 knapsack problem and the Traveling Salesman Problem (TSP). Although ACO can get solutions that are quite near to the optimal solution, it still has its own problems. Premature bogs the system down in a locally optimal solution rather than the global optimal one. To get better solutions, it requires a larger number of ants and iterations which consume more time. Parallelization is an effective way to solve large-scale ant colony optimization algorithms over large dataset. We propose a MapReduce based ACO approach. We show how ACO algorithms can be modeled into the MapReduce framework. We describe the algorithm design and implementation of ACO on Hadoop.
{"title":"A MapReduce based Ant Colony Optimization approach to combinatorial optimization problems","authors":"Bihan Wu, Gang Wu, Mengdong Yang","doi":"10.1109/ICNC.2012.6234645","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234645","url":null,"abstract":"Ant Colony Optimization (ACO) is a kind of meta-heuristics algorithm, which simulates the social behavior of ants and could be a good alternative to existing algorithms for NP hard combinatorial optimization problems, like the 0-1 knapsack problem and the Traveling Salesman Problem (TSP). Although ACO can get solutions that are quite near to the optimal solution, it still has its own problems. Premature bogs the system down in a locally optimal solution rather than the global optimal one. To get better solutions, it requires a larger number of ants and iterations which consume more time. Parallelization is an effective way to solve large-scale ant colony optimization algorithms over large dataset. We propose a MapReduce based ACO approach. We show how ACO algorithms can be modeled into the MapReduce framework. We describe the algorithm design and implementation of ACO on Hadoop.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130695367","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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