Abstract
The Deep Lynx Machine Learning (ML) Adapter is a generic adapter that programmatically runs the ML as continuous data is received. Then, Jupyter Notebooks can be customized according to the project for pre-processing the data, building the machine learning models, prediction analysis of incoming data using an existing model, and forecasting anomalies / failures of the physical asset.
- Developers:
-
Wilsdon, Katherine [1] ; Kunz, Matthew ; Browning, Jeren [1]
- Idaho National Lab. (INL), Idaho Falls, ID (United States)
- Release Date:
- 2021-10-05
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Programming Languages:
-
Python
R
- Licenses:
-
MIT License
- Sponsoring Org.:
-
USDOE Office of Energy Efficiency and Renewable Energy (EERE)Primary Award/Contract Number:AC07-05ID14517
- Code ID:
- 66088
- Research Org.:
- Idaho National Laboratory (INL), Idaho Falls, ID (United States)
- Country of Origin:
- United States
- Keywords:
- Deep Lynx Adapter; Machine Learning; Artificial Intelligence; Digital Twin
Citation Formats
Wilsdon, Katherine N., Kunz, Matthew R., and Browning, Jeren M.
Deep-Lynx-ML-Adapter.
Computer Software.
https://github.com/idaholab/Deep-Lynx-ML-Adapter.
USDOE Office of Energy Efficiency and Renewable Energy (EERE).
05 Oct. 2021.
Web.
doi:10.11578/dc.20211028.1.
Wilsdon, Katherine N., Kunz, Matthew R., & Browning, Jeren M.
(2021, October 05).
Deep-Lynx-ML-Adapter.
[Computer software].
https://github.com/idaholab/Deep-Lynx-ML-Adapter.
https://doi.org/10.11578/dc.20211028.1.
Wilsdon, Katherine N., Kunz, Matthew R., and Browning, Jeren M.
"Deep-Lynx-ML-Adapter." Computer software.
October 05, 2021.
https://github.com/idaholab/Deep-Lynx-ML-Adapter.
https://doi.org/10.11578/dc.20211028.1.
@misc{
doecode_66088,
title = {Deep-Lynx-ML-Adapter},
author = {Wilsdon, Katherine N. and Kunz, Matthew R. and Browning, Jeren M.},
abstractNote = {The Deep Lynx Machine Learning (ML) Adapter is a generic adapter that programmatically runs the ML as continuous data is received. Then, Jupyter Notebooks can be customized according to the project for pre-processing the data, building the machine learning models, prediction analysis of incoming data using an existing model, and forecasting anomalies / failures of the physical asset.},
doi = {10.11578/dc.20211028.1},
url = {https://doi.org/10.11578/dc.20211028.1},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20211028.1}},
year = {2021},
month = {oct}
}