PAS method estimates extreme non-linear wave impact loads within 2-15% of brute-force Monte Carlo results while using only 1-3% of the high-fidelity simulation time.
Joint depth-segmentation learning with segment priors for non-contact seedling height and stem thickness estimation
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
A new outdoor garlic seedling dataset and adversarial augmentation policy learning improve detection AP50 to 91.6% and missing-seedling F1 to 67% under variable illumination.
citing papers explorer
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Design loads for wave impacts -- introducing the Probabilistic Adaptive Screening (PAS) method for predicting extreme non-linear loads on maritime structures
PAS method estimates extreme non-linear wave impact loads within 2-15% of brute-force Monte Carlo results while using only 1-3% of the high-fidelity simulation time.
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Learning Adversarial Augmentation Policies for Robust Garlic Seedling Detection
A new outdoor garlic seedling dataset and adversarial augmentation policy learning improve detection AP50 to 91.6% and missing-seedling F1 to 67% under variable illumination.