The <i>D</i>_<i>98</i> in the CTV and the for the nominal scenario optimized using the WC algorithm in homogeneous and heterogeneous phantom. by Masashi Yagi (3186909)

The <i>D</i>_<i>98</i> in the CTV and the for the nominal scenario optimized using the SC algorithm in homogeneous and heterogeneous phantoms. by Masashi Yagi (3186909)

Training process. by Ning Zhao (84707)

The <i>D</i>_<i>98</i> in the CTV and the for the nominal scenario in the patient case. by Masashi Yagi (3186909)

Transient response of different algorithms-based 2-DOF PIDA controllers. by Erdal Eker (19251018)

Steady-state response of different algorithms based 2-DOF PIDA controllers. by Erdal Eker (19251018)

Time domain performance metrics of different algorithms based 2-DOF PIDA controllers. by Erdal Eker (19251018)

Best convergence curves of the algorithms with respect to iteration number. by Erdal Eker (19251018)

Adopted parameter ranges and the obtained best parameters via different algorithms. by Erdal Eker (19251018)

Statistical performance evaluation of algorithms and p-values obtained from Wilcoxon’s test. by Erdal Eker (19251018)

Quantitative experimental results of the present algorithm and other methods. by Tingting Liu (267387)

Fusion results of the proposed algorithm and other methods on Lytro-05. by Tingting Liu (267387)

Fusion results of the proposed algorithm and other methods on Lytro-05. by Tingting Liu (267387)

Fusion results of the proposed algorithm and other methods on Lytro-05. by Tingting Liu (267387)

Fusion results of the proposed algorithm and other methods on Lytro-01. by Tingting Liu (267387)

A Twin Agent Reinforcement Learning Framework by Integrating Deterministic and Stochastic Policies by Nikita Gupta (3386030)

“…The studies are conducted in the presence of parametric uncertainties, measurement noise, and nominal conditions. It is observed that for case study 1, the root-mean-square error (RMSE) value for the proposed algorithm is reduced by 40.9% when compared with TD3 and 27.57% when compared with PPO for the simulations. …”

Flow chart of particle swarm algorithm. by Nour Eldeen Mahmoud Khalifa (19259450)

“…The third phase is the training and testing phase. Finally, the best-performing model was selected and compared with the currently established models (Alexnet, Squeezenet, Googlenet, Resnet50).…”

Nominal model solution results. by Yicheng Liu (2179626)

Pseudo-color results of our algorithm versus other methods on Lytro-05. by Tingting Liu (267387)

Pseudo-color results of our algorithm versus other methods on Lytro-05. by Tingting Liu (267387)