Optimize process data and final 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. …”

Device parameter list. 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 process of IHWGWO. 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 computational results of the case. 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. …”

Working time of different processes (<i>h</i>). 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. …”

Code For Use in a Bookកូដសម្រាប់ប្រើប្រាស់ក្នុងសៀវភៅ by Sarun Horn (20899271)

Pseudo live-stream of EEG data is processed through the flow chart: after channel selection, each epoch undergoes Kaiser windowing (length = 250), followed by re-referencing to the... by Gursimran Singh (575288)

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PyNoetic’s online mode in action. Data is streamed from an Emotiv EPOC headset. by Gursimran Singh (575288)

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Illustration of PyNoetic’s 2D and 3D simulation module with visual feedback. by Gursimran Singh (575288)

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PyNoetic’s unique pick-place configurable flowchart that offers a no-code option for non-programmers. by Gursimran Singh (575288)

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PyNoetic’s feature extraction module that supports time domain features, frequency domain features, time-frequency domain features, spatial features as well as Brain Connectivity m... by Gursimran Singh (575288)

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Pseudo live-stream of EEG data is generated, and a simple pick-and-place flowchart is designed for channel selection and filtering. by Gursimran Singh (575288)

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PyNoetic’s channel selection module, which supports various channel selection criteria including CSP, correlation, Mutual Information, and Chi-squared. by Gursimran Singh (575288)

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Tunable parameters in stimuli generation module. ^<i>a</i>The fixation cross is displayed once before an experiment starts to help subjects concentrate. <sup><i>b</i></su... by Gursimran Singh (575288)

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PyNoetic’s pre-processing module, which supports filtering and artifact removal, including ICA. by Gursimran Singh (575288)

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Performance of various deep learning models from PyNoetic’s classification module on the Motor Imagery decoding task. by Gursimran Singh (575288)

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Illustration of recording paradigm with PyNoetic’s Stimuli generation and recording module. by Gursimran Singh (575288)

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A typical BCI system depicting the flow and processing of EEG data. by Gursimran Singh (575288)

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MEDOC: A Fast, Scalable, and Mathematically Exact Algorithm for the Site-Specific Prediction of the Protonation Degree in Large Disordered Proteins by Martin J. Fossat (3714079)

“…We show that we can drastically reduce the number of parameters necessary to determine the full, analytical Boltzmann partition function of the charge landscape at both global and site-specific levels. …”