Skip to content

Towards Learning Spatio-Temporal Data Stream Relationships for Failure Detection in Avionics

Semantic Scholar · Article (Handbook of Dynamic Data Driven Applications Systems) · 870e55353e3d56bb57a981ee258829d1bf20d57d · Published 2018-01-01 · Handbook of Dynamic Data Driven Applications Systems · 4 authors

View on Semantic Scholar ↗

Abstract and citation only, verbatim from Semantic Scholar; full text lives there. All credit to the authors and Handbook of Dynamic Data Driven Applications Systems.

Abstract

Future dynamic data driven applications systems (DDDAS) using machine learning can identify complex dynamic data-driven failure models, which will in turn enable more accurate flight planning and control for emergency conditions, and is compared with synthetic data.

Authors

  • Sida Chen
  • Shigeru Imai
  • Wennan Zhu
  • Carlos A. Varela

Keywords

  • Computer Science
  • Engineering

Citation

Sida Chen, Shigeru Imai, Wennan Zhu , et al. (2018). Towards Learning Spatio-Temporal Data Stream Relationships for Failure Detection in Avionics. Handbook of Dynamic Data Driven Applications Systems. Semantic Scholar ID 870e55353e3d56bb57a981ee258829d1bf20d57d. https://doi.org/10.1007/978-3-319-95504-9_5 ↗