The extended dual-envelope NMPC enables smoother drifting convergence and cuts steady-state tracking errors in speed, sideslip angle, and yaw rate by 33%, 71%, and 31% respectively in hardware tests.
Split Learning in 6G Edge Networks,
2 Pith papers cite this work. Polarity classification is still indexing.
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SL-FAC reduces communication in split learning via frequency-aware compression of activations and gradients while aiming to preserve training-critical information.
citing papers explorer
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Dual-Envelope Constrained Nonlinear MPC for Distributed Drive Electric Vehicles Drifting Under Bounded Steering and Direct Yaw-Moment Control
The extended dual-envelope NMPC enables smoother drifting convergence and cuts steady-state tracking errors in speed, sideslip angle, and yaw rate by 33%, 71%, and 31% respectively in hardware tests.
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SL-FAC: A Communication-Efficient Split Learning Framework with Frequency-Aware Compression
SL-FAC reduces communication in split learning via frequency-aware compression of activations and gradients while aiming to preserve training-critical information.