30
Deep Learning-Based Detection of Electrical Faults and Power Quality Disturbances in Aerospace Power Systems
Compact ResNet detects electrical faults in 400 Hz aircraft power systems with 95.87 percent accuracy after deployment on Xilinx Zynq MPSoC.
The study targets multiclass fault and power quality disturbance detection in 400 Hz aerospace networks using a high-fidelity simulation model inspired by the Boeing 787 electrical architecture, covering 21 normal, disturbance, switching, open-circuit, and short-circuit conditions. Two 73,500-sample datasets are built from 1D waveforms and STFT time-frequency representations, augmented with domain randomization and class-specific GANs, and the time-series dataset is released via IEEE DataPort. A compact ResNet with 175,685 parameters achieved 96.94 percent software test accuracy, and 95.87 percent after 8-bit quantization on a Xilinx Zynq UltraScale Plus ZCU102 with 6.90 ms mean accelerator latency per record.
30
Sound Debloating of Redundant Checks in Zero-Knowledge Machine-Learning Circuits
Automated framework soundly removes up to 48.7% of redundant constraints in ezkl and zkml ZK-ML circuits, cutting prover time by up to 72.8%.
The framework uses whole-circuit abstract interpretation and a provenance graph to verify that each removed redundant check (range proofs, sign lookups, bit decompositions) remains entailed by the rest of the circuit, provably preserving soundness. It was evaluated on MLP, CNN, RNN, and transformer circuits generated by ezkl and zkml, with up to 25.3 million constraints. It removes up to 48.7% of constraints and reduces prover time by up to 72.8% without weakening security. Under-constrained circuits in deployed ZK systems have previously enabled attackers to forge transactions and bypass verification.
35
30
30