Function <i>χ</i>_<i>opt</i> superimposed with the phase-field function indicating the channel geometry. by Martina Bukač (20161460)

The phase-field function for three different network configurations considered in this work. by Martina Bukač (20161460)

Workflow of algorithm for whole brain flow simulation. by Erlend Hodneland (391541)

Geometric interpretation and data visualisation equivalent describing the DI algorithmic pipeline. by Borjan Milinkovic (21349908)

Networking Chemicals Flows: Efficiency–Value–Environment Functionalized Symbiosis Algorithms and Application by Yizheng Lyu (17346506)

Networking Chemicals Flows: Efficiency–Value–Environment Functionalized Symbiosis Algorithms and Application by Yizheng Lyu (17346506)

Networking Chemicals Flows: Efficiency–Value–Environment Functionalized Symbiosis Algorithms and Application by Yizheng Lyu (17346506)

The flow chart of IERWHO algorithm. by WanRu Zhao (18980374)

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The flow chart of WHO algorithm. by WanRu Zhao (18980374)

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Antihypertensive treatment algorithm. by Kira Bang Bove (9607123)

Details of path flows and path travel times by the proposed algorithms. by C. K. Wong (3224922)

Width of stripes as a function of time. by Pratik Mullick (12851011)

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Flow of wavelet threshold denoising. by Bingbing Li (461702)

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Completion times for different algorithms. by Jianbin Zheng (587000)

“…In the context of intelligent manufacturing, there is still significant potential for improving the productivity of riveting and welding tasks in existing H-beam riveting and welding work cells. In response to the multi-agent system of the H-beam riveting and welding work cell, a recurrent multi-agent proximal policy optimization algorithm (rMAPPO) is proposed to address the multi-agent scheduling problem in the H-beam processing. …”

The average cumulative reward of algorithms. by Jianbin Zheng (587000)

“…In the context of intelligent manufacturing, there is still significant potential for improving the productivity of riveting and welding tasks in existing H-beam riveting and welding work cells. In response to the multi-agent system of the H-beam riveting and welding work cell, a recurrent multi-agent proximal policy optimization algorithm (rMAPPO) is proposed to address the multi-agent scheduling problem in the H-beam processing. …”

(a) Flow chart for PSO algorithm (b) position and velocity update of particles (c) convergence of PSO. by Waqas Anjum (8415186)

Comparison of deconvolution and optimization algorithms on a batch of data. by Ali-Kemal Aydin (10968731)

“…Both experimental data have been resampled at 50ms and used to compute a set of TFs (in orange) either with direct deconvolution approaches (Fourier or Toeplitz methods, middle-upper panel TFs) or with 1-Γ function optimization performed by 3 different algorithms (middle-lower panel TFs). …”

Overview of algorithm. by Takayuki Niizato (162226)

Simulation settings of rMAPPO algorithm. by Jianbin Zheng (587000)

“…In the context of intelligent manufacturing, there is still significant potential for improving the productivity of riveting and welding tasks in existing H-beam riveting and welding work cells. In response to the multi-agent system of the H-beam riveting and welding work cell, a recurrent multi-agent proximal policy optimization algorithm (rMAPPO) is proposed to address the multi-agent scheduling problem in the H-beam processing. …”

The SSIM for the different algorithms. by Bingbing Li (461702)

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