Pub Date : 2010-11-01DOI: 10.1109/ISKE.2010.5680843
Jiaxuan Huang, Hongsen Huang
When we are dealing with community structure detecting in the blogosphere, we have come to face some obstacles. The data in a blog may be updated frequently by its owner, making the whole blogosphere become very large during a short period of time. It can be very expensive to deal with such huge amount of data using those traditional methods. Meanwhile, few blogs in the blogosphere can be identified as members of a specify community clearly from their own characters, while we have to judge most blogs depending on the relationship with other neighboring blogs using centrality metrics. Recently, a new method that combines active learning and semi-supervised learning gives quite a good performance on improving the speed and accuracy of machine learning on large scale of data. In this paper, we employ this method to solve the community clustering problem with a vast and complex data set. We try to show that this method really does a better job on labeling and clustering large scale of data by comparing the result with the one achieved in the traditional way. Afterward, we may make some improvements and use it to deal with community detecting in the blogosphere.
{"title":"Using empirical risk minimization to detect community structure in the blogosphere","authors":"Jiaxuan Huang, Hongsen Huang","doi":"10.1109/ISKE.2010.5680843","DOIUrl":"https://doi.org/10.1109/ISKE.2010.5680843","url":null,"abstract":"When we are dealing with community structure detecting in the blogosphere, we have come to face some obstacles. The data in a blog may be updated frequently by its owner, making the whole blogosphere become very large during a short period of time. It can be very expensive to deal with such huge amount of data using those traditional methods. Meanwhile, few blogs in the blogosphere can be identified as members of a specify community clearly from their own characters, while we have to judge most blogs depending on the relationship with other neighboring blogs using centrality metrics. Recently, a new method that combines active learning and semi-supervised learning gives quite a good performance on improving the speed and accuracy of machine learning on large scale of data. In this paper, we employ this method to solve the community clustering problem with a vast and complex data set. We try to show that this method really does a better job on labeling and clustering large scale of data by comparing the result with the one achieved in the traditional way. Afterward, we may make some improvements and use it to deal with community detecting in the blogosphere.","PeriodicalId":6417,"journal":{"name":"2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering","volume":"15 1","pages":"418-421"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72556426","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 : 2010-11-01DOI: 10.1109/ISKE.2010.5680842
Shaojie Qiao, Tianrui Li, Hong Li, Yan Zhu, Jing Peng, Jiangtao Qiu
As the Web contains rich and convenient information, Web search engine is increasingly becoming the dominant information retrieving approach. In order to rank the query results of web pages in an effective and efficient fashion, we propose a new page rank algorithm based on similarity measure from the vector space model, called SimRank, to score web pages. Firstly, we propose a new similarity measure to compute the similarity of pages and apply it to partition a web database into several web social networks (WSNs). Secondly, we improve the traditional PageRank algorithm by taking into account the relevance of page to a given query. Thirdly, we design an efficient web crawler to download the web data. And finally, we perform experimental studies to evaluate the time efficiency and scoring accuracy of SimRank with other approaches.
{"title":"SimRank: A Page Rank approach based on similarity measure","authors":"Shaojie Qiao, Tianrui Li, Hong Li, Yan Zhu, Jing Peng, Jiangtao Qiu","doi":"10.1109/ISKE.2010.5680842","DOIUrl":"https://doi.org/10.1109/ISKE.2010.5680842","url":null,"abstract":"As the Web contains rich and convenient information, Web search engine is increasingly becoming the dominant information retrieving approach. In order to rank the query results of web pages in an effective and efficient fashion, we propose a new page rank algorithm based on similarity measure from the vector space model, called SimRank, to score web pages. Firstly, we propose a new similarity measure to compute the similarity of pages and apply it to partition a web database into several web social networks (WSNs). Secondly, we improve the traditional PageRank algorithm by taking into account the relevance of page to a given query. Thirdly, we design an efficient web crawler to download the web data. And finally, we perform experimental studies to evaluate the time efficiency and scoring accuracy of SimRank with other approaches.","PeriodicalId":6417,"journal":{"name":"2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering","volume":"47 1","pages":"390-395"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86544438","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 : 2010-11-01DOI: 10.1109/ISKE.2010.5680762
F. A. Zotes, M. Peñas
One of the most challenging issues in astrodynamic is the control of the satellites, in order to achieve a certain trajectory or to maintain some specific position with respect to a planet or to other spacecrafts. An interesting and quite innovative problem is the control of satellites around the five Lagrangian points of a binary system, using continuous thrust. In this work, using a complex and realistic model of the binary system, intelligent systems based on fuzzy logic are used to control the satellites in the Earth-Moon Libration points. The fuzzy controllers have been compared to conventional ones and it has been proved how this intelligent strategy reduces the control effort and therefore the fuel budget.
{"title":"Intelligent satellites control based on fuzzy logic in the Earth-Moon Libration points","authors":"F. A. Zotes, M. Peñas","doi":"10.1109/ISKE.2010.5680762","DOIUrl":"https://doi.org/10.1109/ISKE.2010.5680762","url":null,"abstract":"One of the most challenging issues in astrodynamic is the control of the satellites, in order to achieve a certain trajectory or to maintain some specific position with respect to a planet or to other spacecrafts. An interesting and quite innovative problem is the control of satellites around the five Lagrangian points of a binary system, using continuous thrust. In this work, using a complex and realistic model of the binary system, intelligent systems based on fuzzy logic are used to control the satellites in the Earth-Moon Libration points. The fuzzy controllers have been compared to conventional ones and it has been proved how this intelligent strategy reduces the control effort and therefore the fuel budget.","PeriodicalId":6417,"journal":{"name":"2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering","volume":"32 1","pages":"605-610"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87699724","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 : 2010-11-01DOI: 10.1109/ISKE.2010.5680830
Changliang Liu, Ming-yang Yu
The mathematical model of the object in power plant is of extremely significance for the design and analysis of the thermal control system. There are many methods to identify the parameters of the desiring object. In this article, we adopt a modified form of a relatively effective yet simple algorithm called differential evolution algorithm (DE) which is a population based stochastic optimization approach. The differential evolution algorithm uses the difference of randomly sampled pairs of vectors in the population for its mutation operators and is applied mainly in real parameter optimization. Based on analysis of DE searching mechanism, the article proposed the improved differential evolution algorithm with self-adaptive parameters to promote its robust, optima searching capability and speed. In order to prove the effectiveness of the improved differential evolution algorithm, we work out relevant model identifying program on MATLAB and identify the mathematical models. Then we analyze the result using the method of comparing.
{"title":"Modified differential evolution algorithm and its application in thermal process model identification","authors":"Changliang Liu, Ming-yang Yu","doi":"10.1109/ISKE.2010.5680830","DOIUrl":"https://doi.org/10.1109/ISKE.2010.5680830","url":null,"abstract":"The mathematical model of the object in power plant is of extremely significance for the design and analysis of the thermal control system. There are many methods to identify the parameters of the desiring object. In this article, we adopt a modified form of a relatively effective yet simple algorithm called differential evolution algorithm (DE) which is a population based stochastic optimization approach. The differential evolution algorithm uses the difference of randomly sampled pairs of vectors in the population for its mutation operators and is applied mainly in real parameter optimization. Based on analysis of DE searching mechanism, the article proposed the improved differential evolution algorithm with self-adaptive parameters to promote its robust, optima searching capability and speed. In order to prove the effectiveness of the improved differential evolution algorithm, we work out relevant model identifying program on MATLAB and identify the mathematical models. Then we analyze the result using the method of comparing.","PeriodicalId":6417,"journal":{"name":"2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering","volume":"26 1","pages":"450-453"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84377368","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 : 2010-11-01DOI: 10.1109/ISKE.2010.5680871
Z. Tian, Rongrong Huang
From the perspective of organizational knowledge alliance this paper researches knowledge transfer and innovation issues, analysis of several existing shortage of knowledge transfer model, established knowledge integration model of knowledge transfer based on knowledge alliances. Combined with the knowledge of multinational coalition practice, proposed synergy innovation model of knowledge transfer to achieve inter-organizational knowledge sharing and complementary resources and enhance organizational capacity of knowledge innovation.
{"title":"The model and mechanism of knowledge transfer based on knowledge alliance","authors":"Z. Tian, Rongrong Huang","doi":"10.1109/ISKE.2010.5680871","DOIUrl":"https://doi.org/10.1109/ISKE.2010.5680871","url":null,"abstract":"From the perspective of organizational knowledge alliance this paper researches knowledge transfer and innovation issues, analysis of several existing shortage of knowledge transfer model, established knowledge integration model of knowledge transfer based on knowledge alliances. Combined with the knowledge of multinational coalition practice, proposed synergy innovation model of knowledge transfer to achieve inter-organizational knowledge sharing and complementary resources and enhance organizational capacity of knowledge innovation.","PeriodicalId":6417,"journal":{"name":"2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering","volume":"46 1","pages":"273-277"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89851252","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 : 2010-11-01DOI: 10.1109/ISKE.2010.5680750
Pengying Du, Xiaoping Luo, Shaowu Du
The high negative voltage power supply for the ECRH(Electron Cyclotron Resonance Heating) is a key component of the cyclotron. This system is the nonlinear system with the high requirements on static and dynamic performance. It is difficult to control well using traditional PID controller, so a new intelligent method is adopted to turn PID parameters base on RBF Network. Simulation results show that the system has good dynamic and static performance. The steady-state accuracy is less than 0.3%, adjustment time is less than 200µs. And the system has strong robustness and adaptive capacity based on RBF.
{"title":"Adaptive PID control of high-voltage power supply for ECRH on RBF network","authors":"Pengying Du, Xiaoping Luo, Shaowu Du","doi":"10.1109/ISKE.2010.5680750","DOIUrl":"https://doi.org/10.1109/ISKE.2010.5680750","url":null,"abstract":"The high negative voltage power supply for the ECRH(Electron Cyclotron Resonance Heating) is a key component of the cyclotron. This system is the nonlinear system with the high requirements on static and dynamic performance. It is difficult to control well using traditional PID controller, so a new intelligent method is adopted to turn PID parameters base on RBF Network. Simulation results show that the system has good dynamic and static performance. The steady-state accuracy is less than 0.3%, adjustment time is less than 200µs. And the system has strong robustness and adaptive capacity based on RBF.","PeriodicalId":6417,"journal":{"name":"2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering","volume":"172 1","pages":"556-558"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76967811","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 : 2010-11-01DOI: 10.1109/ISKE.2010.5680811
C. Franco, J. Montero
This paper examines the framework of preference structures for the general treatment of information. First some fuzzy preference structures are introduced and then, by means of ambiguity and coherence measures, certain attributes of information can be explicitly identified and studied. This investigation is also relevant for a general discussion on fuzzy preference semantics.
{"title":"Organizing information by fuzzy preference structures fuzzy preference semantics","authors":"C. Franco, J. Montero","doi":"10.1109/ISKE.2010.5680811","DOIUrl":"https://doi.org/10.1109/ISKE.2010.5680811","url":null,"abstract":"This paper examines the framework of preference structures for the general treatment of information. First some fuzzy preference structures are introduced and then, by means of ambiguity and coherence measures, certain attributes of information can be explicitly identified and studied. This investigation is also relevant for a general discussion on fuzzy preference semantics.","PeriodicalId":6417,"journal":{"name":"2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering","volume":"57 1","pages":"135-140"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77951208","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 : 2010-11-01DOI: 10.1109/ISKE.2010.5680809
Hsu-Kun Wu, J. Hsieh, K. Yu
In this paper, least trimmed squares (LTS) estimators, frequently used in robust (or resistant) linear parametric regression problems, will be generalized to nonparametric LTS-fuzzy neural networks (LTS-FNNs) for nonlinear regression problems. Emphasis is put particularly on the robustness against outliers. This provides alternative learning machines when faced with general nonlinear learning problems. Simple weight updating rules based on gradient descent and iteratively reweighted least squares (IRLS) algorithms will be provided. Some numerical examples will be provided to compare the robustness against outliers for usual fuzzy neural networks (FNNs) and the proposed LTS-FNNs. Simulation results show that the LTS-FNNs proposed in this paper have good robustness against outliers.
{"title":"Study on least trimmed squares fuzzy neural networks","authors":"Hsu-Kun Wu, J. Hsieh, K. Yu","doi":"10.1109/ISKE.2010.5680809","DOIUrl":"https://doi.org/10.1109/ISKE.2010.5680809","url":null,"abstract":"In this paper, least trimmed squares (LTS) estimators, frequently used in robust (or resistant) linear parametric regression problems, will be generalized to nonparametric LTS-fuzzy neural networks (LTS-FNNs) for nonlinear regression problems. Emphasis is put particularly on the robustness against outliers. This provides alternative learning machines when faced with general nonlinear learning problems. Simple weight updating rules based on gradient descent and iteratively reweighted least squares (IRLS) algorithms will be provided. Some numerical examples will be provided to compare the robustness against outliers for usual fuzzy neural networks (FNNs) and the proposed LTS-FNNs. Simulation results show that the LTS-FNNs proposed in this paper have good robustness against outliers.","PeriodicalId":6417,"journal":{"name":"2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering","volume":"95 1","pages":"123-127"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84405942","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 : 2010-11-01DOI: 10.1109/ISKE.2010.5680821
Xuzhen Zhang, Pingui Jia, Junying Guo
This paper proposed an improved particle swarm optimization (PSO) algorithm called redistributing PSO (RPSO) for designing IIR digital filters. The proposed RPSO avoids the stagnation problem by automatically triggering particles redistributing when premature convergence is detected. Every particle is redistributed either within the whole problem space or around the mean between the global best and its current position. This mechanism helps particles escape from local convergence regions and continue progress toward true global optimum. The simulation results of low-pass and band-pass filters show that RPSO is better than PSO, quantum particle swarm optimization (QPSO), chaos particle swarm optimization (CPSO), and differential cultural (DC) algorithm with better mean performance and more stability and is an efficient method for IIR digital filter design.
{"title":"An improved particle swarm optimizer for IIR digital filter design","authors":"Xuzhen Zhang, Pingui Jia, Junying Guo","doi":"10.1109/ISKE.2010.5680821","DOIUrl":"https://doi.org/10.1109/ISKE.2010.5680821","url":null,"abstract":"This paper proposed an improved particle swarm optimization (PSO) algorithm called redistributing PSO (RPSO) for designing IIR digital filters. The proposed RPSO avoids the stagnation problem by automatically triggering particles redistributing when premature convergence is detected. Every particle is redistributed either within the whole problem space or around the mean between the global best and its current position. This mechanism helps particles escape from local convergence regions and continue progress toward true global optimum. The simulation results of low-pass and band-pass filters show that RPSO is better than PSO, quantum particle swarm optimization (QPSO), chaos particle swarm optimization (CPSO), and differential cultural (DC) algorithm with better mean performance and more stability and is an efficient method for IIR digital filter design.","PeriodicalId":6417,"journal":{"name":"2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering","volume":"57 1","pages":"191-196"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84762535","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 : 2010-11-01DOI: 10.1109/ISKE.2010.5680766
V. López, Matilde Santos, P. Esteve, D. Roldán, Juan C. de Mendoza
This paper shows the advantage of using Intelligent Systems while developing engineering projects. The paper is based on three examples and it is focus into one of them: The E-learning Migration application, developed as a teaching aid tool in different organizations where various platforms coexist; this is the case of some Spanish universities. This tool solves three basic problems, which are shown in three independent modules. The first of these ones, concerns the recording and migration of the data; the second one, refers to the storing and organization of the images. The last one is an E-learning platform recommender based on an ID3 tree-decision. We are going to focus our article in this concrete module.
{"title":"Practical applications of Intelligent Systems: An E-learning platforms recommender","authors":"V. López, Matilde Santos, P. Esteve, D. Roldán, Juan C. de Mendoza","doi":"10.1109/ISKE.2010.5680766","DOIUrl":"https://doi.org/10.1109/ISKE.2010.5680766","url":null,"abstract":"This paper shows the advantage of using Intelligent Systems while developing engineering projects. The paper is based on three examples and it is focus into one of them: The E-learning Migration application, developed as a teaching aid tool in different organizations where various platforms coexist; this is the case of some Spanish universities. This tool solves three basic problems, which are shown in three independent modules. The first of these ones, concerns the recording and migration of the data; the second one, refers to the storing and organization of the images. The last one is an E-learning platform recommender based on an ID3 tree-decision. We are going to focus our article in this concrete module.","PeriodicalId":6417,"journal":{"name":"2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering","volume":"71 1 1","pages":"628-633"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83341518","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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