화학공학소재연구정보센터
검색결과 : 284건
No. Article
1 Proximal Algorithms for Large-Scale Statistical Modeling and Sensor/Actuator Selection
Zare A, Mohammadi H, Dhingra NK, Georgiou TT, Jovanovic MR
IEEE Transactions on Automatic Control, 65(8), 3441, 2020
2 New dynamical observer for a batch crystallization process based on solute concentration
Brivadis L, Andrieu V, Chabanon E, Gagniere E, Lebaz N, Serres U
Journal of Process Control, 87, 17, 2020
3 Characterization of a polycrystalline photovoltaic cell using artificial neural networks
Cortes B, Sanchez RT, Flores JJ
Solar Energy, 196, 157, 2020
4 Deconvolving distribution of relaxation times, resistances and inductance from electrochemical impedance spectroscopy via statistical model selection: Exploiting structural-sparsity regularization and data-driven parameter tuning
Li X, Ahmadi M, Collins L, Kalinin SV
Electrochimica Acta, 313, 570, 2019
5 Investigating structural and occupant drivers of annual residential electricity consumption using regularization in regression models
Satre-Meloy A
Energy, 174, 148, 2019
6 Prediction of CH4 adsorption on different activated carbons by developing an optimal multilayer perceptron artificial neural network
Yang KJ, Xiong MM
Energy Sources Part A-recovery Utilization and Environmental Effects, 41(17), 2061, 2019
7 A Regularized Variable Projection Algorithm for Separable Nonlinear Least-Squares Problems
Chen GY, Gan M, Chen CLP, Li HX
IEEE Transactions on Automatic Control, 64(2), 526, 2019
8 The Proximal Augmented Lagrangian Method for Nonsmooth Composite Optimization
Dhingra NK, Khong SZ, Jovanovic MR
IEEE Transactions on Automatic Control, 64(7), 2861, 2019
9 Analytical developments and experimental validation of a thermocouple model through an experimentally acquired impulse response function
Frankel JI, Chen HC
International Journal of Heat and Mass Transfer, 141, 1301, 2019
10 Numerical simulation of turbulent gas-solid flow using an approximate deconvolution model
Schneiderbauer S, Saeedipour M
International Journal of Multiphase Flow, 114, 287, 2019