Deceptive Meta Planning (DeMP) uses two-level optimization to sustain deception against learning observers by combining short-term adaptation with meta-level learning of observer updates.
Classification of Partial Discharges Originating From Multilevel PWM Using Machine Learning
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
AWA patterns from PD pulse amplitude, width, and area enable CNNs to classify single and mixed partial discharge sources under switching voltage with over 96% test accuracy.
citing papers explorer
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Repeated Deceptive Path Planning against Learnable Observer
Deceptive Meta Planning (DeMP) uses two-level optimization to sustain deception against learning observers by combining short-term adaptation with meta-level learning of observer updates.
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Classification of Single and Mixed Partial Discharges under Switching Voltage Using an AWA-CNN Framework
AWA patterns from PD pulse amplitude, width, and area enable CNNs to classify single and mixed partial discharge sources under switching voltage with over 96% test accuracy.