Two congestion control mechanism for real-time streaming media communications based on UDP are analyzed in this paper, one is based on packet loss rate, and another is based on RTT (round-trip time), also an algorithm of adaptive one way delay congestion prediction (AAOWDCP) is proposed in the paper. AAOWDCP can shorten time interval of congestion feedback to the great extent and enhance real-time performance of congestion judgment. At last, a simulation of congestion controls, one is based on packet loss rate and another is based on RTT (round-trip time), and the performance of AAOWDCP is carried out by using the NS simulator, the simulation validates the superiority of AAOWDCP.
{"title":"Research on Adaptive Congestion Control Based on One Way Delay","authors":"Min Li, Shaobo Deng, Chunhua Zhou","doi":"10.1109/GrC.2007.48","DOIUrl":"https://doi.org/10.1109/GrC.2007.48","url":null,"abstract":"Two congestion control mechanism for real-time streaming media communications based on UDP are analyzed in this paper, one is based on packet loss rate, and another is based on RTT (round-trip time), also an algorithm of adaptive one way delay congestion prediction (AAOWDCP) is proposed in the paper. AAOWDCP can shorten time interval of congestion feedback to the great extent and enhance real-time performance of congestion judgment. At last, a simulation of congestion controls, one is based on packet loss rate and another is based on RTT (round-trip time), and the performance of AAOWDCP is carried out by using the NS simulator, the simulation validates the superiority of AAOWDCP.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121179288","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}
Statistical relational learning constructs statistical models from relational databases, combining the powers of relational learning and statistical learning. Its strong ability and special property make statistical relational learning become one of the important areas in machine learning. In this paper, the general concepts and characteristics of statistical relational learning are presented firstly. Then some major branches of this newly emerging field are discussed, including logic and rule-based approaches, frame and object-oriented approaches, and several other important approaches. After that some methods of applying rough set in statistical relational learning are described, such as gRS-ILP and VPRSILP. Finally applications of statistical relational learning are briefly introduced and some future directions of statistical relational learning and the prospects of rough set in this area are pointed out.
{"title":"Research on Statistical Relational Learning and Rough Set in SRL","authors":"Fei Chen","doi":"10.1109/GrC.2007.137","DOIUrl":"https://doi.org/10.1109/GrC.2007.137","url":null,"abstract":"Statistical relational learning constructs statistical models from relational databases, combining the powers of relational learning and statistical learning. Its strong ability and special property make statistical relational learning become one of the important areas in machine learning. In this paper, the general concepts and characteristics of statistical relational learning are presented firstly. Then some major branches of this newly emerging field are discussed, including logic and rule-based approaches, frame and object-oriented approaches, and several other important approaches. After that some methods of applying rough set in statistical relational learning are described, such as gRS-ILP and VPRSILP. Finally applications of statistical relational learning are briefly introduced and some future directions of statistical relational learning and the prospects of rough set in this area are pointed out.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124829987","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}
To conduct data mining, we often need to collect data from various data owners. Privacy concerns may prevent the parties from directly sharing the data and some types of information about the data. To conduct data mining without compromising data privacy, we propose a verification scheme to ensure that the collected data follow the requirements of data miners, which is one of the important issues in privacy-preserving data mining systems.
{"title":"A Verification Scheme for Data Aggregation in Data Mining","authors":"K. Shin, J. Zhan","doi":"10.1109/GrC.2007.121","DOIUrl":"https://doi.org/10.1109/GrC.2007.121","url":null,"abstract":"To conduct data mining, we often need to collect data from various data owners. Privacy concerns may prevent the parties from directly sharing the data and some types of information about the data. To conduct data mining without compromising data privacy, we propose a verification scheme to ensure that the collected data follow the requirements of data miners, which is one of the important issues in privacy-preserving data mining systems.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"217 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115586948","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}
It is shown that not only the qualitative criterion [alpha,beta] of qualitative mapping is a bridge between expert system and artificial neural network, the qualitative mapping and the artificial neuron can be defined each other, but support vector machine can be also induced by the granular transformation of qualitative criterion, the qualitative mapping is a mathematical model by which some of artificial intelligent methods can be fused and unified together, and a kind of artificial fused model: attribute computing network induced by qualitative mapping is presented.
{"title":"Attribute Computing Network Induced by Linear Transformation and Granular Transformation of Qualitative Criterion","authors":"Jia-li Feng","doi":"10.1109/GrC.2007.8","DOIUrl":"https://doi.org/10.1109/GrC.2007.8","url":null,"abstract":"It is shown that not only the qualitative criterion [alpha,beta] of qualitative mapping is a bridge between expert system and artificial neural network, the qualitative mapping and the artificial neuron can be defined each other, but support vector machine can be also induced by the granular transformation of qualitative criterion, the qualitative mapping is a mathematical model by which some of artificial intelligent methods can be fused and unified together, and a kind of artificial fused model: attribute computing network induced by qualitative mapping is presented.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128241423","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}
Type-2 fuzzy sets are used for modeling uncertainty and imprecision in a better way. These type-2 fuzzy sets were originally presented by Zadeh in 1975 and are essentially "fuzzy fuzzy" sets where the fuzzy degree of membership is a type-1 fuzzy set. The new concepts were introduced by Mendel and Liang allowing the characterization of a type-2 fuzzy set with a superior membership function and an inferior membership function; these two functions can be represented each one by a type-1 fuzzy set membership function. The interval between these two functions represents the footprint of uncertainty (FOU), which is used to characterize a type-2 fuzzy set.
二类模糊集可以更好地对不确定性和不精确性进行建模。这些2型模糊集最初由Zadeh在1975年提出，本质上是“模糊模糊”集，其中模糊隶属度是1型模糊集。Mendel和Liang引入了新的概念，允许具有一个上隶属函数和一个下隶属函数的2型模糊集的刻划;这两个函数可以分别用1型模糊集隶属函数表示。这两个函数之间的间隔表示不确定性的足迹(footprint of uncertainty, FOU)， FOU用于描述2型模糊集。
{"title":"Type-2 Fuzzy Logic: Theory and Applications","authors":"O. Castillo, P. Melin, J. Kacprzyk, W. Pedrycz","doi":"10.1109/GrC.2007.118","DOIUrl":"https://doi.org/10.1109/GrC.2007.118","url":null,"abstract":"Type-2 fuzzy sets are used for modeling uncertainty and imprecision in a better way. These type-2 fuzzy sets were originally presented by Zadeh in 1975 and are essentially \"fuzzy fuzzy\" sets where the fuzzy degree of membership is a type-1 fuzzy set. The new concepts were introduced by Mendel and Liang allowing the characterization of a type-2 fuzzy set with a superior membership function and an inferior membership function; these two functions can be represented each one by a type-1 fuzzy set membership function. The interval between these two functions represents the footprint of uncertainty (FOU), which is used to characterize a type-2 fuzzy set.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129482096","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}
An overview of several classes of association rules is given. It is shown that these classes have theoretically interesting and practically useful properties. The class of association rules with the F-property is introduced. It is shown that association rules from this class have properties similar to properties of the Fisher's test. Results concerning the association rules with the F-property are presented.
{"title":"Observational Calculi, Classes of Association Rules and F-property","authors":"J. Rauch","doi":"10.1109/GrC.2007.88","DOIUrl":"https://doi.org/10.1109/GrC.2007.88","url":null,"abstract":"An overview of several classes of association rules is given. It is shown that these classes have theoretically interesting and practically useful properties. The class of association rules with the F-property is introduced. It is shown that association rules from this class have properties similar to properties of the Fisher's test. Results concerning the association rules with the F-property are presented.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121326283","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}
Prediction of gene function is a classification problem. Given its simplicity and relatively high accuracy, K-Nearest Neighbor (KNN) classification has become a popular choice for many real life applications. However, traditional KNN approach has two drawbacks. First, it cannot identify classes that do not exist in the training data sets. Second, it treats all K neighbors in a similar way without consideration of the distance differences between the test instance and its neighbors. In this paper, exploiting the potential of fuzzy set theory to handle uncertainty in data sets, we develop a fuzzy KNN approach for gene function classification. Experiments show that integrating fuzzy set theory into original KNN approach improves the overall performance of the classification model.
{"title":"Gene Function Classification Using Fuzzy K-Nearest Neighbor Approach","authors":"Dan Li, J. Deogun, Kefei Wang","doi":"10.1109/GrC.2007.99","DOIUrl":"https://doi.org/10.1109/GrC.2007.99","url":null,"abstract":"Prediction of gene function is a classification problem. Given its simplicity and relatively high accuracy, K-Nearest Neighbor (KNN) classification has become a popular choice for many real life applications. However, traditional KNN approach has two drawbacks. First, it cannot identify classes that do not exist in the training data sets. Second, it treats all K neighbors in a similar way without consideration of the distance differences between the test instance and its neighbors. In this paper, exploiting the potential of fuzzy set theory to handle uncertainty in data sets, we develop a fuzzy KNN approach for gene function classification. Experiments show that integrating fuzzy set theory into original KNN approach improves the overall performance of the classification model.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114873779","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 the paper, we present an efficient method for pruning multiple alternatives in the OWA aggregation. The proposed method intends to identify inferior alternatives per se and diminish the number of alternatives without any efforts to exploit the OWA operator weights from decision maker. The efficacy of the proposed method is verified by simulation analysis in which different levels of alternatives and different levels of criteria are used.
{"title":"An Efficient Elimination of Input Data in the OWA Aggregation","authors":"B. Ahn","doi":"10.1109/GrC.2007.93","DOIUrl":"https://doi.org/10.1109/GrC.2007.93","url":null,"abstract":"In the paper, we present an efficient method for pruning multiple alternatives in the OWA aggregation. The proposed method intends to identify inferior alternatives per se and diminish the number of alternatives without any efforts to exploit the OWA operator weights from decision maker. The efficacy of the proposed method is verified by simulation analysis in which different levels of alternatives and different levels of criteria are used.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126483198","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 this paper a model-free optimization method is applied to the problem of unit sizing in a hybrid power system such that demand of residential area is met. Optimal sizing of two systems is considered. In the system No.l, the produced power is delivered to the load and the hydrogen produced by the reformer is stored in the tank. If the power produced by the wind turbine is more than the demand, the remainder of wind turbine's power is delivered to the electrolyzer to produce hydrogen, such that when the wind power cannot meet the demand, the fuel cell is fed by the stored hydrogen and produces enough power, together with the wind turbine's power. In the system No.2, the hydrogen produced by the reformer is delivered to the fuel cell directly. When the power produced by the wind turbine plus power produced by the fuel cell (fed by the reformer) is more than the demand, the remainder is delivered to the electrolyzer. In contrast, when the power produced by the wind turbine plus that produced by the fuel cell (fed by the reformer) is less than the demand, some more fuel cells are employed and they are fed by the stored hydrogen. Our aim is to minimize the costs of the system such that the demand is met. PSO algorithm is used for optimal sizing of system's components.
{"title":"Unit Sizing of a Stand-Alone Hybrid Power System Using Model-Free Optimization","authors":"Mehdi Hakimi, S. N. M. Tafreshi, M. Rajati","doi":"10.1109/GrC.2007.143","DOIUrl":"https://doi.org/10.1109/GrC.2007.143","url":null,"abstract":"In this paper a model-free optimization method is applied to the problem of unit sizing in a hybrid power system such that demand of residential area is met. Optimal sizing of two systems is considered. In the system No.l, the produced power is delivered to the load and the hydrogen produced by the reformer is stored in the tank. If the power produced by the wind turbine is more than the demand, the remainder of wind turbine's power is delivered to the electrolyzer to produce hydrogen, such that when the wind power cannot meet the demand, the fuel cell is fed by the stored hydrogen and produces enough power, together with the wind turbine's power. In the system No.2, the hydrogen produced by the reformer is delivered to the fuel cell directly. When the power produced by the wind turbine plus power produced by the fuel cell (fed by the reformer) is more than the demand, the remainder is delivered to the electrolyzer. In contrast, when the power produced by the wind turbine plus that produced by the fuel cell (fed by the reformer) is less than the demand, some more fuel cells are employed and they are fed by the stored hydrogen. Our aim is to minimize the costs of the system such that the demand is met. PSO algorithm is used for optimal sizing of system's components.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125495406","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}
Longjun Huang, Minghe Huang, Bin Guo, Zhiming Zhuang
One of the keys to constructing decision tree model is to choose standard for testing attribute, for the criteria of selecting test attributes influences the classification accuracy of the tree. There exists diversity choosing standards for testing attribute based on entropy, Bayesian, and so on. In this paper, the degree of dependency of decision attribute on condition attribute, based on rough set theory, is used as a heuristic for selecting the attribute that will best separate the samples into individual classes. The results of example and experiments show that compared with the entropy-based approach, our approach is a better way to select nodes for constructing decision tree.
{"title":"A New Method for Constructing Decision Tree Based on Rough Set Theory","authors":"Longjun Huang, Minghe Huang, Bin Guo, Zhiming Zhuang","doi":"10.1109/GrC.2007.13","DOIUrl":"https://doi.org/10.1109/GrC.2007.13","url":null,"abstract":"One of the keys to constructing decision tree model is to choose standard for testing attribute, for the criteria of selecting test attributes influences the classification accuracy of the tree. There exists diversity choosing standards for testing attribute based on entropy, Bayesian, and so on. In this paper, the degree of dependency of decision attribute on condition attribute, based on rough set theory, is used as a heuristic for selecting the attribute that will best separate the samples into individual classes. The results of example and experiments show that compared with the entropy-based approach, our approach is a better way to select nodes for constructing decision tree.","PeriodicalId":259430,"journal":{"name":"2007 IEEE International Conference on Granular Computing (GRC 2007)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133906377","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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