A parametric data-driven model built with p-AAA reduces relative force estimation error by nearly 38% versus the best non-parametric model while generalizing across load amplitudes and input waveforms.
Transfer function sy nthesis as a ratio of two complex polynomials,
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DB-FGA-Net fuses VGG16 and Xception backbones with a new Frequency-Gated Attention module to reach 99.24% accuracy on 4-class brain tumor classification without augmentation and generalizes to 95.77% on an independent dataset.
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Load Identification in Bistable Spacecraft Booms via Parametric Data-Driven Modeling
A parametric data-driven model built with p-AAA reduces relative force estimation error by nearly 38% versus the best non-parametric model while generalizing across load amplitudes and input waveforms.
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DB-FGA-Net: Dual Backbone Frequency Gated Attention Network for Multi-Class Brain Tumor Classification with Grad-CAM Interpretability
DB-FGA-Net fuses VGG16 and Xception backbones with a new Frequency-Gated Attention module to reach 99.24% accuracy on 4-class brain tumor classification without augmentation and generalizes to 95.77% on an independent dataset.