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The ˜Fuser' is the name of the SWIM data integration service that has been built, utilized and evolved on ATD-2 to synchronize trajectory predictions from multiple decision support tools. NASA is researching ways to make this data stream available for broader consumption as an example implementation that would help reduce the burden for consumers while enabling access to data that would help industry prepare and innovate for TFDM (e.g. integrates TTP feed).
This presentation covers the fuser databases, naming conventions, and examples of how the databases have been used to support ATD-2 (Airspace Technology Demonstration-2).
This presentation gets into a detailed level of how things are processed in the Fuser, providing some specific challenges and solutions.
This presentation covers how a high level overview of the Fuser used on ATD-2.
The properties of deployed network fiber are affected by environmental factors due to their exposure to the elements. Particularly for quantum networks, the resultant delay variations may have significant impacts due to the extreme sensitivity of synchronization, coincidence counting, and other critical operations. In this paper, the delays of 15 km aerial-inground fiber connections are measured, and effects due to temperature, humidity and wind speed are analyzed over multiple periods spanning four seasons of a year. Machine learning methods are first utilized to reveal surprisingly pronounced effects of humidity on the delay, in addition to the expected temperature and its seasonal variations. Estimator and fusion methods are developed to estimate the delay using temperature, humidity and wind speed measurements, by utilizing smooth Gaussian Process Regression (GPR) and nonsmooth Ensemble of Trees (EOT) methods. Measurements from winter and summer periods are temporally fused using twelve different methods, and eight methods provide estimates for the delay throughout the year with median test errors under 1.28%. The results reveal distinct temperature-humidity trends across the seasons, and the ability of estimator and temporal fusion methods to exploit them for estimating the delay. These results constitute a case study of machine learning analytical results, wherein generalization equations explain the performance of various estimator and fuser methods.
We consider a monitoring scenario of phenomenon using three different streams of measurements whose quality is proportional to their constant inter-arrival times. Each measurement of a stream needs to be binary-classified to reflect the state of interest of the phenomenon. A set of classifiers is separately trained and fused for each stream at its time resolution using measurements collected under known states. We present a machine learning method to fuse the outputs of these fusers to provide a final classification at the finest time resolution. We show that this fused-fusers method provides decisions with likely superior classification probability compared to the best individual classifiers and fused-classifiers. We derive generalization equations that guarantee a superior classification probability of fused-fusers with a confidence probability specified by the classifiers’ generalization equations. We apply these results to study a practical problem of classifying Pu/Np target dissolution events at a radiochemical processing facility using gamma spectral measurements of effluent flows.
This workshop will cover DIP architecture and data integration services. Participants will get a look at how the DIP architecture is set-up as well as how data integration services are planned to be hosted on the platform. The DIP architecture review is intended to cover how DIP was envisioned and how DIP is being developed to address data needs across the industry. Participants will have a chance to provide feedback on the DIP architecture and gain insight into how one might interface with the DIP to send or receive data. The data integration services portion is intended to cover DIP’s technical approach to data integration. As an example implementation, there will be a first look at possible data fusion on the platform, including utilizing NASA’s Fuser, and tailoring for industry data consumers. Descriptions, at a high-level, of input to and output of the Fuser will also be discussed.
This workshop will cover DIP architecture and data integration services to obtain feedback from America for Airlines (A4A) Air Traffic Management Council (ATMC). Participants will get a look at how the DIP architecture is set-up as well as how data integration services are planned to be hosted on the platform. The DIP architecture review is intended to cover how DIP was envisioned and how DIP is being developed to address data needs across the industry. Participants will have a chance to provide feedback on the DIP architecture and gain insight into how one might interface with the DIP to send or receive data. The data integration services portion is intended to cover DIP’s technical approach to data integration. As an example implementation, there will be a first look at possible data fusion on the platform, including utilizing NASA’s Fuser, and tailoring for industry data consumers. Descriptions, at a high-level, of input to and output of the Fuser will also be discussed.
Electrochemistry workflows utilize various instruments and computing systems to execute workflows consisting of electrocatalyst synthesis, testing and evaluation tasks. The heterogeneity of the software and hardware of these ecosystems makes it challenging to orchestrate a complete workflow from production to characterization by automating its tasks. We propose an autonomous electrochemistry computing platform for a multi-site ecosystem that provides the services for remote experiment steering, real-time measurement transfer, and AI/ML-driven analytics. We describe the integration of a mobile robot and synthesis workstation into the ecosystem by developing custom hub-networks and software modules to support remote operations over the ecosystem’s wireless and wired networks. We describe a workflow task for generating I-V voltammetry measurements using a potentiostat, and a machine learning framework to ensure their normality by detecting abnormal conditions such as disconnected electrodes. We study a number of machine learning methods for the underlying detection problem, including smooth, non-smooth, structural and statistical methods, and their fusers. We present experimental results to illustrate the effectiveness of this platform, and also validate the proposed ML method by deriving its rigorous generalization equations.
In this session, we demonstrate two recently developed deep learning models for anomaly detection in flight operational data by the Data Sciences Group at NASA Ames Research Center. The first model is Convolutional Variational Auto-Encoder (CVAE) [1], which is an unsupervised deep encoder-decoder model, designed specifically for finding anomalies in heterogeneous multivariate time series data. We will demonstrate its application to finding anomalies in streaming data from NASA’s Digital Information Platform’s Fuser source. CVAE identifies data instances that are not representative of expected nominal behavior as anomalous. Since it is an unsupervised approach, the flagged anomalies will need to be reviewed by the subject matter experts (SMEs) for validation and labeling and is designed to assist with vulnerability discovery within Safety Monitoring System programs. The second model is Robust and Explainable Semi-supervised Anomaly Detection (RESAD) model [2], which builds on CVAE to allow learning from both minimally labeled data (previously reviewed by the SMEs) as well as majority unlabeled data. RESAD takes advantage of graph theoretic techniques to propagate the labels from the labeled data to the unlabeled data based on a pre-defined similarity metric and structures the learned feature space from flight time-series so that data of the same class would cluster tightly together. This model characteristic is enabled by training with an augmented loss function and allows learning of a more informative feature space for down-stream tasks such as search and active learning. We demonstrate RESAD using data from the NASA DASHlink project [3].
This presentation describes the manner in which the ATD-2 began consuming data from SWIM and gradually built new services to satisfy in its mission. This lessons learned from this work indicate that additional data-rich services will be required in the future. This also led to the development of data pre-processing and mediation services that are now of much interest to the community. The presentation mentions some of the barriers to progress that exist for those seeking to use SWIM flight data, and NASA's desire to share its lessons learned with the aviation community.
This presentation describes on-going work to quantify accuracy of different sources of landing time predictions as actual arrival event approaches.