MoCo Makers.| Chasers of the Lost Data
Project Details
The Challenge | Chasers of the Lost Data
Help find ways to improve the performance of machine learning and predictive models by filling in gaps in the datasets prior to model training. This entails finding methods to computationally recover or approximate data that is missing due to sensor issues or signal noise that compromises experimental data collection. This work is inspired by data collection during additive manufacturing (AM) processes where sensors capture build characteristics in-situ, but it has applications across many NASA domains.
Machine Learning for Machine Learning - Ouroboros software
Proposal: Software simulation of (ML for ML)
Exploration of ML for ML to resolve data gaps in the datasets prior to model training. Propose developing simulation software for Fispy II FPGA breakout board being developed.