An effective nonlinear model reduction approach, empirical Gramians balanced reduction approach, is studied, to reduce the computation complexity in nonlinear power system model application. The realization procedure is: firstly, computing the empirical controllable and observable Gramians matrices of nonlinear power system model, secondly, by these two matrices, computing the balance transformation matrix to obtain the balanced system model of the original model, then, computing the controllable and observable matrices of the balanced system to obtain the diagonal Hankel singular matrix. Finally, deciding the lower-order subspace to obtain the reduced power system model. A 15-machine power system model is taken as an example to perform the reduction simulation analysis.
Sparse coding is currently an active topic in signal processing and pattern recognition. Meta Face Learning (MFL) isatypical sparse coding method and exhibits promising performance for classification. Unfortunately, due to using the l1-norm minimization, MFLis expensive to compute and is not robust enough. To address these issues, this paper proposesafaster and more robust version of MFLwith the l2-norm regularization constraint on coding coefficients. The proposed method is used to learnaclass-specific dictionary for facial expression recognition. Extensive experiments on two popular facial expression databases, i.e., the JAFFEdatabase and the Cohn-Kanade database, demonstrate that our method shows promising computational efficiency and robustness on facial expression recognition tasks.