A new medium access control method is proposed over the predominant Ethernet broadcast channel. Taking advantages of intrinsic variable length characteristic of standard Ethernet frame, message-oriented dynamic priori...A new medium access control method is proposed over the predominant Ethernet broadcast channel. Taking advantages of intrinsic variable length characteristic of standard Ethernet frame, message-oriented dynamic priority mechanism is established. Prioritized medium access control operates under a so-called block mode in event of collisions. High priority messages have a chance to preempt block status incurred by low priority ones. By this means, the new MAC provides a conditional deterministic real time performance beyond a statistical one. Experiments demonstrate effectiveness and attractiveness of the proposed scheme. Moreover, this new MAC is completely compatible with IEEE802.3.展开更多
To deal with multi-source multi-class classification problems, the method of combining multiple multi-class probability support vector machines (MPSVMs) using Bayesian theory is proposed in this paper. The MPSVMs are ...To deal with multi-source multi-class classification problems, the method of combining multiple multi-class probability support vector machines (MPSVMs) using Bayesian theory is proposed in this paper. The MPSVMs are designed by mapping the output of standard support vector machines into a calibrated posterior probability by using a learned sigmoid function and then combining these learned binary-class probability SVMs. Two Bayes based methods for combining multiple MPSVMs are applied to improve the performance of classification. Our proposed methods are applied to fault diagnosis of a diesel engine. The experimental results show that the new methods can improve the accuracy and robustness of fault diagnosis.展开更多
Support vector machines (SVMs) have been introduced as effective methods for solving classification problems. However, due to some limitations in practical applications, their generalization performance is sometimes f...Support vector machines (SVMs) have been introduced as effective methods for solving classification problems. However, due to some limitations in practical applications, their generalization performance is sometimes far from the expected level. Therefore, it is meaningful to study SVM ensemble learning. In this paper, a novel genetic algorithm based ensemble learning method, namely Direct Genetic Ensemble (DGE), is proposed. DGE adopts the predictive accuracy of ensemble as the fitness function and searches a good ensemble from the ensemble space. In essence, DGE is also a selective ensemble learning method because the base classifiers of the ensemble are selected according to the solution of genetic algorithm. In comparison with other ensemble learning methods, DGE works on a higher level and is more direct. Different strategies of constructing diverse base classifiers can be utilized in DGE. Experimental results show that SVM ensembles constructed by DGE can achieve better performance than single SVMs, bagged and boosted SVM ensembles. In addition, some valuable conclusions are obtained.展开更多
基金This workis partly supported by national 863 project under grant 2002 AA412010 08
文摘A new medium access control method is proposed over the predominant Ethernet broadcast channel. Taking advantages of intrinsic variable length characteristic of standard Ethernet frame, message-oriented dynamic priority mechanism is established. Prioritized medium access control operates under a so-called block mode in event of collisions. High priority messages have a chance to preempt block status incurred by low priority ones. By this means, the new MAC provides a conditional deterministic real time performance beyond a statistical one. Experiments demonstrate effectiveness and attractiveness of the proposed scheme. Moreover, this new MAC is completely compatible with IEEE802.3.
基金This work was supported by the National Key Fundamental Research Project of China (2002cb312200) ,the National High TechnologyResearch and Development Program of China (2002AA412010) , and in part supported by the Natural Science Foundation of China(60575036)
文摘To deal with multi-source multi-class classification problems, the method of combining multiple multi-class probability support vector machines (MPSVMs) using Bayesian theory is proposed in this paper. The MPSVMs are designed by mapping the output of standard support vector machines into a calibrated posterior probability by using a learned sigmoid function and then combining these learned binary-class probability SVMs. Two Bayes based methods for combining multiple MPSVMs are applied to improve the performance of classification. Our proposed methods are applied to fault diagnosis of a diesel engine. The experimental results show that the new methods can improve the accuracy and robustness of fault diagnosis.
基金This work was supported by National Basic Research Programof China under Grant2002cb312200 01 3National Nature ScienceFoundation of China under Grant60174038.
文摘Support vector machines (SVMs) have been introduced as effective methods for solving classification problems. However, due to some limitations in practical applications, their generalization performance is sometimes far from the expected level. Therefore, it is meaningful to study SVM ensemble learning. In this paper, a novel genetic algorithm based ensemble learning method, namely Direct Genetic Ensemble (DGE), is proposed. DGE adopts the predictive accuracy of ensemble as the fitness function and searches a good ensemble from the ensemble space. In essence, DGE is also a selective ensemble learning method because the base classifiers of the ensemble are selected according to the solution of genetic algorithm. In comparison with other ensemble learning methods, DGE works on a higher level and is more direct. Different strategies of constructing diverse base classifiers can be utilized in DGE. Experimental results show that SVM ensembles constructed by DGE can achieve better performance than single SVMs, bagged and boosted SVM ensembles. In addition, some valuable conclusions are obtained.
