SBIR/STTR
Auto-Suggest Capability via Machine Learning in SMART NAS, Phase I
Project Introduction
We build machine learning capabilities that enables the Shadow Mode Assessment using Realistic Technologies for the NAS (SMART NAS) system to synthesize, optimize, and "auto-suggest" optimized Traffic Management Initiatives (TMIs). Multi Level Multi View (MLMV) machine learning is used to identify similar historical situations (days, scenarios, or airport conditions) in the NAS. TMIs used in historically similar situations are locally modified to optimize the parameters of the TMI to be used in the current day situation. SMART NAS is used to evaluate TMIs and to present fast time simulations to the end user to review the TMI and associated performance metrics before implementation.
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Primary U.S. Work Locations and Key Partners
| Organizations Performing Work | Role | Type | Location |
|---|---|---|---|
| The Innovation Laboratory, Inc. | Lead Organization | Industry | Portland, Oregon |
Ames Research Center
(ARC)
|
Supporting Organization | NASA Center | Moffett Field, California |
Primary U.S. Work Locations
-
California
-
Oregon
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Organizational Responsibility
Responsible Mission Directorate:
- Space Technology Mission Directorate (STMD)
Lead Organization:
- The Innovation Laboratory, Inc.
Responsible Program:
- SBIR/STTR
Project Management
Program Director:
Program Manager:
Principal Investigator:
Project Duration
Start: Jun 2015
End: Dec 2015
Technology Maturity (TRL)
| Start: | 1 |
| Current: | 1 |
| Estimated End: | 2 |
Applied Research
Development
Demo & Test
Technology Areas
Primary:
- TX01 Propulsion Systems
- TX01.3 Aero Propulsion
- TX01.3.2 Turbine Based Combined Cycle
Target Destinations
The Sun, Earth, The Moon, Others Inside the Solar System, Outside the Solar System, Mars
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