Dear Editor,This letter proposes an arbitrary pre-assigned time sliding mode approach to achieve distributed secondary control for microgrids with external disturbances.By constructing an effective time-varying gain f...Dear Editor,This letter proposes an arbitrary pre-assigned time sliding mode approach to achieve distributed secondary control for microgrids with external disturbances.By constructing an effective time-varying gain function,we can set the convergence time arbitrarily to stabilize the system,which is without being affected by initial conditions and other design parameters.展开更多
In many data stream mining applications, traditional density estimation methods such as kemel density estimation, reduced set density estimation can not be applied to the density estimation of data streams because of ...In many data stream mining applications, traditional density estimation methods such as kemel density estimation, reduced set density estimation can not be applied to the density estimation of data streams because of their high computational burden, processing time and intensive memory allocation requirement. In order to reduce the time and space complexity, a novel density estimation method Dm-KDE over data streams based on the proposed algorithm m-KDE which can be used to design a KDE estimator with the fixed number of kernel components for a dataset is proposed. In this method, Dm-KDE sequence entries are created by algorithm m-KDE instead of all kemels obtained from other density estimation methods. In order to further reduce the storage space, Dm-KDE sequence entries can be merged by calculating their KL divergences. Finally, the probability density functions over arbitrary time or entire time can be estimated through the obtained estimation model. In contrast to the state-of-the-art algorithm SOMKE, the distinctive advantage of the proposed algorithm Dm-KDE exists in that it can achieve the same accuracy with much less fixed number of kernel components such that it is suitable for the scenarios where higher on-line computation about the kernel density estimation over data streams is required. We compare Dm-KDE with SOMKE and M-kernel in terms of density estimation accuracy and running time for various stationary datasets. We also apply Dm-KDE to evolving data streams. Experimental results illustrate the effectiveness of the pro- posed method.展开更多
基金supported by the National Natural Science Foundation of China(62173175,61873033)the Shandong Provincial Natural Science Foundation(ZR2024MF032)。
文摘Dear Editor,This letter proposes an arbitrary pre-assigned time sliding mode approach to achieve distributed secondary control for microgrids with external disturbances.By constructing an effective time-varying gain function,we can set the convergence time arbitrarily to stabilize the system,which is without being affected by initial conditions and other design parameters.
基金This work was supported in part by the National Natural Science Foundation of China （Grants Nos. 61170122, 61272210）, by Japan Society for the Promotion of Sciences （JSPS）, by the Natural Science Foundation of Jiangsu Province （BK2011417, BK2011003）, by Jiangsu 333 Expert Engineering Grant （BRA201114-2）, and by 2011 and 2012 Postgraduate Student＇s Creative Research Funds of Jiangsu Province （CXZZ11-0483, CXZZ12-0759）.
文摘In many data stream mining applications, traditional density estimation methods such as kemel density estimation, reduced set density estimation can not be applied to the density estimation of data streams because of their high computational burden, processing time and intensive memory allocation requirement. In order to reduce the time and space complexity, a novel density estimation method Dm-KDE over data streams based on the proposed algorithm m-KDE which can be used to design a KDE estimator with the fixed number of kernel components for a dataset is proposed. In this method, Dm-KDE sequence entries are created by algorithm m-KDE instead of all kemels obtained from other density estimation methods. In order to further reduce the storage space, Dm-KDE sequence entries can be merged by calculating their KL divergences. Finally, the probability density functions over arbitrary time or entire time can be estimated through the obtained estimation model. In contrast to the state-of-the-art algorithm SOMKE, the distinctive advantage of the proposed algorithm Dm-KDE exists in that it can achieve the same accuracy with much less fixed number of kernel components such that it is suitable for the scenarios where higher on-line computation about the kernel density estimation over data streams is required. We compare Dm-KDE with SOMKE and M-kernel in terms of density estimation accuracy and running time for various stationary datasets. We also apply Dm-KDE to evolving data streams. Experimental results illustrate the effectiveness of the pro- posed method.
