Anomaly Detection and Precursor Identification in UAV flight data
This project is using past algorithms developed by the NASA ARC (Ames Research Center) Data Sciences Group and modifying them with application to identifying previously-unknown an…
ExoMiner discovery of ExoPlanets via data from the Kepler and TESS space telescopes
ExoMiner is being used to statistically validate exoplanets detected by the Kepler space telescope and to identify promising exoplanet candidates for the TESS space telescope
Explainable and robust deep semi-supervised model for multi-class anomaly detection in flight data
This model is a semi-supervised deep learning based anomaly detection for aircraft flight data. It is designed to work when a small subset of data is reviewed and labeled by exper…
Anomaly detection in aeronautics data with quantum-compatible discrete deep generative model
Our team developed high-performance unsupervised deep machine-learning models for the detection of flight-operations anomalies. The models’ engineered-feature (latent) spaces are…
Machine Learning Airport Surface Model: Airport Configuration Prediction
The ML-airport-configuration software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models inte…
Machine Learning Airport Surface Model: Arrival Runway Prediction
The ML-airport-arrival-runway software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models int…
Machine Learning Airport Surface Model: Departure Runway Prediction
The ML-airport-departure-runway software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models i…
Machine Learning Airport Surface Model: Estimated ON Time Prediction
The ML-airport-estimated-ON software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models inten…
Machine Learning Airport Surface Model: Taxi-in Prediction
The ML-airport-taxi-in software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended f…
Machine Learning Airport Surface Model: Taxi-out Prediction
The ML-airport-taxi-out software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended…
NextGen Advanced Methods: ATCSCC Webinar Speech2Text and Analysis
The Advanced Methods project explores the use of innovative and emerging technologies to drive post operational analysis of Traffic Management for aircraft. Technologies such as m…
NextGen Data Analytics: Letters of Agreement
Today, operation constraints are documented via Standard Operating Procedures (SOP) and Letters of Agreement (LOA) and are not made available to the public in a consistent manner.…
Unsupervised anomaly detection in flight data with deep variational autoencoders
This model is an unsupervised deep learning based anomaly detection for aircraft flight data based on variational autoencoders with convolutional architecture. The model is design…
Federated Learning Using In-Space Data (FLUID )
Enables neural net models to be trained using a combination of terrestrial data and space-borne data without the need to downlink or uplink data for consolidation and training in…
Mapping Digital Infrastructure (MADI)
The Mapping Digital Infrastructure (MADI) is a series of natural-language processing based tools that are designed to supplement the human-in-the-loop data exploration for CAS.
Use of AI for UAV power train monitoring
Use of PyTorch feedforward neural networks to monitor the health of electronic speed controllers and motors.
Biological and Physical Sciences (BPS) RNA Sequencing Benchmark Training Dataset
RNA sequencing data from spaceflown and control mouse liver samples, sourced from NASA GeneLab and augmented with generative adversarial network to provide synthetic data points.
NIR spectroscopy-based analytical tool for immediate determination of chemical signature
Goals: Develop AI/ML based tool that can identify materials composition and size distribution “on the fly”
Improved Differential Dynamic Microscopy (DDM) tool for characterization of soft matter
Goals: Develop AI/ML based tool that can improve processability of images and derive the outcome
Troupe Rover Demonstration
The Troupe Rover demonstration project is implementing an algorithm called "SafeMAP" or Safe Multi-Agent Planner. SafeMAP is used in distributed multi-rover or multi-uav system to…
Manager for Intelligent Knowledge Access (MIKA)
Manager for Intelligent Knowledge Access (MIKA) is a Python toolkit specialized for state-of-the-art knowledge discovery and information retrieval for technical documents.
AdaStress
AdaStress, a tool for finding and analyzing the likeliest failures in a simulated system under test.
Rover ML-based vision system
The rover uses deep neural networks perform perception, currently related to line following.
Autoresolver/Tailored Arrival Manager
The Autoresolver system is a tool for autonomous air traffic management. It is designed to perform many of the tasks that air-traffic controllers have historically performed inclu…