Hybrid Observer and Genetic Algorithm Cut Simulated Induction Motor Speed Tracking Errors
Researchers developed a hybrid observer and multi-objective optimization framework that cut simulated motor speed tracking errors by over sixty percent.
Researchers developed a hybrid observer and multi-objective optimization framework that cut simulated motor speed tracking errors by over sixty percent.
A new study shows that small cyber-attacks on HVDC measurements can abruptly reverse bulk power transfer by switching the system's ...
A new simulation framework couples Density Functional Theory calculations with a temperature-dependent circuit model and a modified Kalman filter, cutting ...
Automated vehicles promise safer highways, but the hardest moments are often the simplest ones: a car ahead braking suddenly, a ...
A hybrid controller combining fractional calculus, neural networks and eel-inspired optimization achieved 0.0321-second settling times for BLDC motor speed control ...
Researchers at Cranfield University built a digital twin of aircraft electromechanical actuators that generated 70,000 labelled fault simulations to train ...
Researchers have developed a two-stage wavelet and swarm-optimized neural network framework that detects and classifies faults in underground transmission cables ...
© 2026 Scienmag - Science Magazine
© 2026 Scienmag - Science Magazine