Search Results for author:
Found 4 papers, 2 papers with code
Deep Neural Network NMPC for Computationally Tractable Optimal Power Management of Hybrid Electric Vehicle
This study presents a method for deep neural network nonlinear model predictive control (DNN-MPC) to reduce computational complexity, and we show its practical utility through its application in optimizing the energy management of hybrid electric vehicles (HEVs).
NaturalInversion: Data-Free Image Synthesis Improving Real-World Consistency
We introduce NaturalInversion, a novel model inversion-based method to synthesize images that agrees well with the original data distribution without using real data.
CRU: A Novel Neural Architecture for Improving the Predictive Performance of Time-Series Data
However, because this approach is learned in an independent model for each component, it cannot learn the relationships between time-series components.
CoVA: Exploiting Compressed-Domain Analysis to Accelerate Video Analytics
This paper presents CoVA, a novel cascade architecture that splits the cascade computation between compressed domain and pixel domain to address the decoding bottleneck, supporting both temporal and spatial queries.
