4 accepted papers
Time series forecasting fundamentally involves learning probability distributions over future observations. However, existing loss functions rely on point-wise Euclidean metrics, neglecting the intrinsic geometric structure of probability distributions. This leads to suboptimal alignment between pre…
Accurately forecasting commodity demand remains a critical challenge due to volatile market dynamics, nonlinear dependencies, and the need for economically consistent predictions. This paper introduces PREIG, a novel deep learning framework tailored for commodity demand forecasting. The model unique…
We propose mVIL-Fusion, a three-level multisensor fusion system that is able to achieve robust state estimation and globally consistent mapping in perceptually degraded environments. First, LiDAR depth-assisted visual-inertial odometry (VIO) with LiDAR odometry (LO) synchronous prediction and distor
The pectoral fin is a major source of thrust generation for fish utilizing median or paired fins (MPF) mode. Along with the pectoral fin, spanwise flexibility plays a major role in the generation of thrust. This paper investigates the effect of spanwise flexibility on propulsion performance using th…