Improvement of CBS to CBR process. by Pingping Yan (462509)

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Speed response curve and torque response curve after the change of moment of inertia. by Haoran Cui (7235234)

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Hardware structure diagram of PMSM speed control system driver board. by Haoran Cui (7235234)

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Sensitivity analysis of ASMC <i>σ</i> parameter. by Haoran Cui (7235234)

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Partial experimental speed waveform results under sudden load increase of three systems. by Haoran Cui (7235234)

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AP improvement rates for various targets on the NWPU VHR-10 dataset. by Pingping Yan (462509)

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EMA attention mechanism working principle. by Pingping Yan (462509)

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Ablation study on the NWPU VHR-10 dataset. by Pingping Yan (462509)

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GSConv working principle. by Pingping Yan (462509)

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Visualization comparison on NWPU VHR-10 dataset. by Pingping Yan (462509)

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Visualization comparison on RSOD dataset. by Pingping Yan (462509)

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PR comparison on NWPU VHR-10 dataset. by Pingping Yan (462509)

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Server.R file of the application for extraction and display of information from semi-structured and free-text wildlife necropsy data within the Wildbase Pathology Register. by Stefan Saverimuttu (20882325)

Overall process planning. by Dawei Wang (471687)

“…A sophisticated optimization model has been developed to simulate the optimal operation of machinery, aiming to maximize equipment utilization efficiency while addressing the challenges posed by worker fatigue. An innovative algorithm, the improved hybrid gray wolf and whale algorithm fused with a penalty function for construction machinery optimization (IHWGWO), is introduced, incorporating a penalty function to handle constraints effectively. …”

Model intrinsic parameter flow. by Dawei Wang (471687)

“…A sophisticated optimization model has been developed to simulate the optimal operation of machinery, aiming to maximize equipment utilization efficiency while addressing the challenges posed by worker fatigue. An innovative algorithm, the improved hybrid gray wolf and whale algorithm fused with a penalty function for construction machinery optimization (IHWGWO), is introduced, incorporating a penalty function to handle constraints effectively. …”

A comparison of whether to add a penalty. by Dawei Wang (471687)

“…A sophisticated optimization model has been developed to simulate the optimal operation of machinery, aiming to maximize equipment utilization efficiency while addressing the challenges posed by worker fatigue. An innovative algorithm, the improved hybrid gray wolf and whale algorithm fused with a penalty function for construction machinery optimization (IHWGWO), is introduced, incorporating a penalty function to handle constraints effectively. …”

The interleaved processes. by Dawei Wang (471687)

“…A sophisticated optimization model has been developed to simulate the optimal operation of machinery, aiming to maximize equipment utilization efficiency while addressing the challenges posed by worker fatigue. An innovative algorithm, the improved hybrid gray wolf and whale algorithm fused with a penalty function for construction machinery optimization (IHWGWO), is introduced, incorporating a penalty function to handle constraints effectively. …”

Final optimization results. by Dawei Wang (471687)

“…A sophisticated optimization model has been developed to simulate the optimal operation of machinery, aiming to maximize equipment utilization efficiency while addressing the challenges posed by worker fatigue. An innovative algorithm, the improved hybrid gray wolf and whale algorithm fused with a penalty function for construction machinery optimization (IHWGWO), is introduced, incorporating a penalty function to handle constraints effectively. …”

Vehicle-related parameters. by Dawei Wang (471687)

“…A sophisticated optimization model has been developed to simulate the optimal operation of machinery, aiming to maximize equipment utilization efficiency while addressing the challenges posed by worker fatigue. An innovative algorithm, the improved hybrid gray wolf and whale algorithm fused with a penalty function for construction machinery optimization (IHWGWO), is introduced, incorporating a penalty function to handle constraints effectively. …”

The comparison in gantt result. by Dawei Wang (471687)

“…A sophisticated optimization model has been developed to simulate the optimal operation of machinery, aiming to maximize equipment utilization efficiency while addressing the challenges posed by worker fatigue. An innovative algorithm, the improved hybrid gray wolf and whale algorithm fused with a penalty function for construction machinery optimization (IHWGWO), is introduced, incorporating a penalty function to handle constraints effectively. …”