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— This paper concerns the optimization and coordination of the conventional FACTS (Flexible AC Transmission Systems) damping controllers in multi-machine power system. Firstly, the parameters of FACTS controller are optimized. Then, a hybrid fuzzy logic controller for the coordination of FACTS controllers is presented. This coordination method is well(More)
Gene selection based on microarray data, is highly important for classifying tumors accurately. Existing gene selection schemes are mainly based on ranking statistics. From manifold learning standpoint, local geometrical structure is more essential to characterize features compared with global information. In this study, we propose a supervised gene(More)
Identifying protein complexes in protein-protein interaction (PPI) networks is a fundamental problem in computational biology. High-throughput experimental techniques have generated large, experimentally detected PPI datasets. These interactions represent a rich source of data that can be used to detect protein complexes; however, such interactions contain(More)
Currently, there are lots of methods to select informative SNPs for haplotype reconstruction. However, there are still some challenges that render them ineffective for large data sets. First, some traditional methods belong to wrappers which are of high computational complexity. Second, some methods ignore linkage disequilibrium that it is hard to interpret(More)
Current research community on data streams mining focuses on mining balanced data streams. However, the skewed class distribution appears in many data streams applications. In this paper, we introduce the method of discovering concept drifting on skewed data streams and propose an algorithm for classifying skewed data streams based on reusing data, RDFCSDS(More)
Current research on data stream classification mainly focuses on supervised learning, in which a fully labeled data stream is needed for training. However, fully labeled data streams are expensive to obtain, which makes the supervised learning approach difficult to be applied to real-life applications. In this paper, we consider the problem of one-class(More)
Sparse representation classification (SRC) is one of the most promising classification methods for supervised learning. This method can effectively exploit discriminating information by introducing a [Symbol: see text]1 regularization terms to the data. With the desirable property of sparisty, SRC is robust to both noise and outliers. In this study, we(More)
In this paper, we develop a rotation-invariant (RI) texture descriptor, termed generalized local binary pattern (GLBP). The proposed GLBP captures more detailed information by integrating local features in inter-directions and relative distribution among intra-directions. GLBP is characterized by extracting LBP-sign and LBP-magnitude to describe local(More)