A novel nonlinear combination process monitoring method was proposed based on techniques with memo- ry effect （multivariate exponentially weighted moving average （MEWMA）） and kernel independent component analysis ...A novel nonlinear combination process monitoring method was proposed based on techniques with memo- ry effect （multivariate exponentially weighted moving average （MEWMA）） and kernel independent component analysis （KICA）. The method was developed for dealing with nonlinear issues and detecting small or moderate drifts in one or more process variables with autocorrelation. MEWMA charts use additional information from the past history of the process for keeping the memory effect of the process behavior trend. KICA is a recently devel- oped statistical technique for revealing hidden, nonlinear statistically independent factors that underlie sets of mea- surements and it is a two-phase algorithm., whitened kernel principal component analysis （KPCA） plus indepen- dent component analysis （ICA）. The application to the fluid catalytic cracking unit （FCCU） simulated process in- dicates that the proposed combined method based on MEWMA and KICA can effectively capture the nonlinear rela- tionship and detect small drifts in process variables. Its performance significantly outperforms monitoring method based on ICA, MEWMA-ICA and KICA, especially for lonu-term performance deterioration.展开更多
In a distributed system, one of the most important things is to establish an assignment method for distributing tasks. It is assumed that a dis tributed system does not have a central administrator, all independent pr...In a distributed system, one of the most important things is to establish an assignment method for distributing tasks. It is assumed that a dis tributed system does not have a central administrator, all independent processing units in this system want to cooperate for the best results, but they cannot know the conditions of one another. So in order to undertake the tasks in admirable pro portions, they have to adjust their undertaking tasks only by self-learning. In this paper, the performance of this system is analyzed by Markov chains, and a robust method of self-learning for independent processing units in this kind of systems is presented. This method can lead the tasks of the system to be distributed very well among all the independent processing units, and can also be used to solve the general assignment problem.展开更多
基金The National Natural Science Foundation ofChina(No60504033)
文摘A novel nonlinear combination process monitoring method was proposed based on techniques with memo- ry effect （multivariate exponentially weighted moving average （MEWMA）） and kernel independent component analysis （KICA）. The method was developed for dealing with nonlinear issues and detecting small or moderate drifts in one or more process variables with autocorrelation. MEWMA charts use additional information from the past history of the process for keeping the memory effect of the process behavior trend. KICA is a recently devel- oped statistical technique for revealing hidden, nonlinear statistically independent factors that underlie sets of mea- surements and it is a two-phase algorithm., whitened kernel principal component analysis （KPCA） plus indepen- dent component analysis （ICA）. The application to the fluid catalytic cracking unit （FCCU） simulated process in- dicates that the proposed combined method based on MEWMA and KICA can effectively capture the nonlinear rela- tionship and detect small drifts in process variables. Its performance significantly outperforms monitoring method based on ICA, MEWMA-ICA and KICA, especially for lonu-term performance deterioration.
文摘In a distributed system, one of the most important things is to establish an assignment method for distributing tasks. It is assumed that a dis tributed system does not have a central administrator, all independent processing units in this system want to cooperate for the best results, but they cannot know the conditions of one another. So in order to undertake the tasks in admirable pro portions, they have to adjust their undertaking tasks only by self-learning. In this paper, the performance of this system is analyzed by Markov chains, and a robust method of self-learning for independent processing units in this kind of systems is presented. This method can lead the tasks of the system to be distributed very well among all the independent processing units, and can also be used to solve the general assignment problem.
