Therapeutic target database 2020: enriched resource for facilitating research and early development of targeted therapeutics Y Wang, S Zhang, F Li, Y Zhou, Y Zhang, Z Wang, R Zhang, J Zhu, Y Ren, ... Nucleic acids research 48 (D1), D1031-D1041, 2020 | 798 | 2020 |
Linear and nonlinear iterative learning control JX Xu, Y Tan Springer, 2003 | 670 | 2003 |
On non-local stability properties of extremum seeking control Y Tan, D Nešić, I Mareels Automatica 42 (6), 889-903, 2006 | 659 | 2006 |
Extremum seeking from 1922 to 2010 Y Tan, WH Moase, C Manzie, D Nešić, IMY Mareels Proceedings of the 29th Chinese control conference, 14-26, 2010 | 533 | 2010 |
A composite energy function-based learning control approach for nonlinear systems with time-varying parametric uncertainties JX Xu, Y Tan IEEE Transactions on Automatic Control 47 (11), 1940-1945, 2002 | 268 | 2002 |
On global extremum seeking in the presence of local extrema Y Tan, D Nešić, IMY Mareels, A Astolfi Automatica 45 (1), 245-251, 2009 | 248 | 2009 |
NPASS: natural product activity and species source database for natural product research, discovery and tool development X Zeng, P Zhang, W He, C Qin, S Chen, L Tao, Y Wang, Y Tan, D Gao, ... Nucleic acids research 46 (D1), D1217-D1222, 2018 | 211 | 2018 |
Iterative learning control with mixed constraints for point-to-point tracking CT Freeman, Y Tan IEEE Transactions on Control Systems Technology 21 (3), 604-616, 2012 | 189 | 2012 |
On the choice of dither in extremum seeking systems: A case study Y Tan, D Nešić, I Mareels Automatica 44 (5), 1446-1450, 2008 | 187 | 2008 |
Iterative learning control design based on composite energy function with input saturation JX Xu, Y Tan, TH Lee Automatica 40 (8), 1371-1377, 2004 | 179 | 2004 |
A unifying approach to extremum seeking: Adaptive schemes based on estimation of derivatives D Nesić, Y Tan, WH Moase, C Manzie 49th IEEE conference on decision and control (CDC), 4625-4630, 2010 | 175 | 2010 |
Robust optimal design and convergence properties analysis of iterative learning control approaches JX Xu, Y Tan Automatica 38 (11), 1867-1880, 2002 | 153 | 2002 |
Unified frameworks for sampled-data extremum seeking control: Global optimisation and multi-unit systems SZ Khong, D Nešić, Y Tan, C Manzie Automatica 49 (9), 2720-2733, 2013 | 114 | 2013 |
A practical 3D-printed soft robotic prosthetic hand with multi-articulating capabilities A Mohammadi, J Lavranos, H Zhou, R Mutlu, G Alici, Y Tan, P Choong, ... PloS one 15 (5), e0232766, 2020 | 112 | 2020 |
Optimal iterative learning control design for multi-agent systems consensus tracking S Yang, JX Xu, D Huang, Y Tan Systems & Control Letters 69, 80-89, 2014 | 112 | 2014 |
Distributed deception attack detection in platoon-based connected vehicle systems Z Ju, H Zhang, Y Tan IEEE transactions on vehicular technology 69 (5), 4609-4620, 2020 | 104 | 2020 |
NEAT1 regulates neuroglial cell mediating Aβ clearance via the epigenetic regulation of endocytosis-related genes expression Z Wang, Y Zhao, N Xu, S Zhang, S Wang, Y Mao, Y Zhu, B Li, Y Jiang, ... Cellular and molecular life sciences 76, 3005-3018, 2019 | 95 | 2019 |
Host cell transcriptome profile during wild-type and attenuated dengue virus infection OM Sessions, Y Tan, KC Goh, Y Liu, P Tan, S Rozen, EE Ooi PLoS neglected tropical diseases 7 (3), e2107, 2013 | 92 | 2013 |
On the P-type and Newton-type ILC schemes for dynamic systems with non-affine-in-input factors J Xu, Y Tan Automatica 38 (7), 1237-1242, 2002 | 90 | 2002 |
Improved prediction of aqueous solubility of novel compounds by going deeper with deep learning Q Cui, S Lu, B Ni, X Zeng, Y Tan, YD Chen, H Zhao Frontiers in oncology 10, 121, 2020 | 80 | 2020 |