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A macro–meso–residual expert ensemble network (MMR-EENet) that combines multiscale convolutional feature extraction with a dual-path mixture-of-expert decision mechanism, enhancing decision robustness and feature diversity is proposed.
Extracting weak and compound fault features from rotating machinery signals remains challenging because strong noise and multi-source modulation severely degrade the reliability of conventional decomposition methods. This study proposes an ordinal structure-guided adaptive ordinal pattern (OP) based mode decomposition...
An efficient diagnostic framework based on a field-programmable gate array (FPGA)-accelerated SNN integrating local spike-timing-dependent plasticity and global backpropagation through time is proposed, outperforming existing lightweight convolutional neural network-FPGA acceleration schemes.
MAML is enhanced by incorporating a Sample Relationship Exploration module that learns intra-class similarity to improve class separability and replaces the fixed inner-loop update scheme in MAML with a trapezoidal gradient descent scheduler that adapts the number of inner-loop update steps across training.
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