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Accepted papers

 1.   Y. F. Wang, D. H. Wang, and T. Y. Chai, Extraction and adaptation of fuzzy rules for friction modeling and control, IEEE Transactions on Fuzzy Systems,（accepted on January 18, 2011）(ERA-A*)

2. N. K. Lee and D. H. Wang, SOMEA: self-organizing map based extraction algorithm for DNA motif identification with heterogeneous model, BMC Bioinformatics, Vol. 12, (Suppl 1): S16, 2011. (ERA-A)

3.  K. Meng, Z. Y. Dong, D. H. Wang, and K. P. Wong, Self-adaptive RBF neural network classifier for transformer fault analysis, IEEE Transactions on Power Systems, Vol. 25, No.3, pp. 1350-1360, Aug. 2010. (ERA-A*)

4. Y. F. Wang, D. H. Wang, and T. Y. Chai, Modelling and control compensation of nonlinear friction using adaptive fuzzy systems, Mechanical Systems and Signal Processing, Vol. 23, No.8, pp. 2445–2457, 2009. (ERA-A*)

5.  I. Mrazova and D. H. Wang, Improved generalization of neural classifiers with enforced internal representation, Neurocomputing, Vol.70, No.16-18, pp.2940 -2952, 2007. (ERA-B)

1. R. Yu and D. H. Wang, On impulsive mode of linear singular systems subject to decentralized output feedback, IEEE Transactions on Automatic Control, Vol. 48, No. 10, pp. 1804-1809, 2003. (ERA-A*)    

2.  R. Yu and D. H. Wang, Further study on structural properties of LTI singular systems under output feedback, Automatica, Vol.39, No.4, pp. 685-692, 2003. (ERA-A*)     

3. R. Yu and D. H. Wang, Algebraic properties of singular systems subject to decentralized output feedback, IEEE Transactions on Automatic Control, Vol. 47, No.11, pp. 1898 –1903, 2002. (ERA-A*)    

4. D. H. Wang and P. Bao, Robust impulse control of uncertain singular systems by decentralized output feedback, IEEE        Transactions on Automatic Control, Vol.45, No.3, pp.500-505, 2000. (ERA-A*)    

5.  D. H. Wang and C. B. Soh, On regularizing singular systems by decentralized output feedback, IEEE Transactions on Automatic Control, Vol.44, No.1, pp.148-152, 1999. (ERA-A*)  

Topic 1: Transcription factor binding sites and composite cis-regulatory elements discovery for large scale DNA datasets.

We aim to develop advanced computational approaches for identifying putative functional patterns in biological sequences with good reliability and scalability. Currently we focus on discovery of single transcription factor binding sites using clustering, fuzzy logic, evolutionary computation and phylogenetic footprinting techniques.