| 1 |
Convergence analysis of feedback-based iterative learning control with input saturation
Sebastian G, Tan Y, Oetomo D
Automatica, 101, 44, 2019 |
| 2 |
Thermodynamic properties for calcium molybdate, molybdenum tri-oxide and aqueous molybdate ion
Morishita M, Kinoshita Y, Houshiyama H, Nozaki A, Yamamoto H
Journal of Chemical Thermodynamics, 114, 30, 2017 |
| 3 |
Physical origins of remarkable thermostabilization by an octuple mutation for the adenosine A(2a) receptor
Kajiwara Y, Ogino T, Yasuda S, Takamuku Y, Murata T, Kinoshita M
Chemical Physics Letters, 657, 119, 2016 |
| 4 |
Molecular dynamics simulation of fluid sodium
Hasheminasab F, Mehdipour N
Fluid Phase Equilibria, 427, 161, 2016 |
| 5 |
Proteins of well-defined structures can be designed without backbone readjustment by a statistical model
Zhou XQ, Xiong P, Wang M, Ma RS, Zhang JH, Chen Q, Liu HY
Journal of Structural Biology, 196(3), 350, 2016 |
| 6 |
Kinetic Monte Carlo simulation of the initial growth of Ag thin films
Zhu YG, Wang TL
Applied Surface Science, 324, 831, 2015 |
| 7 |
Adaptive iterative learning control for nonlinearly parameterised systems with unknown time-varying delays and input saturations
Zhang RK, Hou ZS, Chi RH, Ji HH
International Journal of Control, 88(6), 1133, 2015 |
| 8 |
Kinetic Monte Carlo simulation of 3-D growth of NiTi alloy thin films
Zhu YG, Pan X
Applied Surface Science, 321, 24, 2014 |
| 9 |
New inversion and ab initio intermolecular potentials for supercritical fluorine: Calculation of some properties and MD simulation
Salemi S, Abbaspour M, Ghabdian M
Journal of Supercritical Fluids, 89, 119, 2014 |
| 10 |
Iterative learning control for output-constrained systems with both parametric and nonparametric uncertainties
Jin X, Xu JX
Automatica, 49(8), 2508, 2013 |
