Intra-Spacecraft RFID Localization
Presentation addresses machine learning approaches to RFID localization. Experiences with the REALM-1 system on ISS are shared, as are the expansions with REALM-2 and REALM-3.
RFID↗
SEARCH · Engineering Papers
Results for “machine learnning”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Prediction of Adverse Events in Time Series Data Using ACCEPT (Adverse Condition and Critical Event Prediction Toolbox)
Abstract:An open-source toolbox called ACCEPT (Adverse Condition and Critical Event Prediction Toolbox) that operates from within the Matlab programming environment is introduced. The toolbox provides an open source, special-purpose functionality that can be used specifically for the prediction or forecasting of adverse events in time series data. It also provides a single, unifying framework in which to compare a variety of combinations of algorithmic approaches addressing this problem. The architectural framework also offers the flexibility for expansion to accommodate the newest, latest popular regression and detection methods for this particular problem, enabling the development of an infrastructure that can act as a proving ground platform for comparing new techniques to the state of the art. As such, its intention is to act as a catalyst in advancing the state of the art in technologies related to this problem. There are a variety of other toolboxes that offer similar capabilities and features, however they are either more suitable as general purpose machine learning toolboxes or are only available commercially or through agreements. ACCEPT fills that niche by offering visibility into a specific approach for addressing the adverse event prediction problem.
programming environments↗
TPSAS-NF1676L-27237-DND
Objective - It is observed that 5.6% of all CERES Terra/Aqua data contains missing cloud cover information or insufficient imager data for a reliable scene identification (some times it can reach up to 50% of data for a specific scene type) - The unavailability of imager data lead to gaps in global radiation budget dataset. - In this study, our objective is to develop a Machine learning methodology for the improved determination of CERES scene type and subsequent clear-sky TOA flux estimation using standalone CERES TOA radiance measurements (without any MODIS/Imager data).
CERES↗
Enhancing Dataset Discovery With Knowledge Graph Link Prediction Techniques
● In the evolving landscape of open science, the ability to navigate and discover pertinent datasets is increasingly significant. This primarily hinges on the presence of detailed metadata, delineating the dataset’s content, and potential spheres of application. ● The GES DISC datasets are characterized by science keywords to enable dataset discovery in web search interfaces. ● A problem may arise where a dataset lacks a science keyword that it otherwise should have. ● Machine learning techniques such as link prediction can be used to detect these missing science keywords by estimating the probability of new links forming between dataset and keyword nodes.
machine learning↗
Automating the Process of Optimization in Spacecraft Design
Spacecraft design optimization is a difficult problem, due to the complexity of optimization cost surfaces, and human expertise in optimization that is necessary in order to achieve good results. In this paper, we propose the use of a set of generic, metaheuristic optimization algorithms (e.g., generic algorithms, simulated annealing), which is configured for a particular optimization problem by an adaptive problem solver based on artificial intelligence and machine learning techniques. We describe work in progress on OASIS, a system for adaptive problem solving based on these principles.
optimization↗
NASA ATD2/DIP SWIFT Update
This briefing includes real-time machine learning based services powered by SWIM and discussion of the opportunity to engage in NASA’s Digital Information Platform effort.
SWIFT↗
ACCEPT: Introduction of the Adverse Condition and Critical Event Prediction Toolbox
The prediction of anomalies or adverse events is a challenging task, and there are a variety of methods which can be used to address the problem. In this paper, we introduce a generic framework developed in MATLAB (sup registered mark) called ACCEPT (Adverse Condition and Critical Event Prediction Toolbox). ACCEPT is an architectural framework designed to compare and contrast the performance of a variety of machine learning and early warning algorithms, and tests the capability of these algorithms to robustly predict the onset of adverse events in any time-series data generating systems or processes.
machine learning↗
An Ensemble Neural Network Model for Predicting Rare-Earth Oxide and Silicate Heat Capacities at High Temperature
In this work, a neural network model was developed to predict the constant pressure heat capacity for materials in the rare-earth oxide—silica material space. Several model architectures were trained and tested on heat capacity data generated from first-principles density functional theory calculations. Hyperparameter optimization was performed, and the optimal model was selected for heat capacity predictions. The optimal model architecture was found to have a root-mean-squared error of 5.12 ± 3.37 J/mol-K. The optimal model architecture was then used in a bagging ensemble model trained using the leave-one-group-out method to provide error estimates for model predictions. The out-of-bag score for the ensemble model was 0.997. The predicted heat capacities agree well with the DFT and experimental results and were computed orders of magnitude faster than DFT simulations. Machine learning shows the potential to provide a suitable surrogate model for thermochemical property predictions for candidate environmental barrier coating materials but refining of input material features and model architectures could further improve accuracy for these models.
environmental barrier coatings↗
Statistical Classification of Biosignature Information: Combining Elemental, Molecular, Reflectance, and Raman Data to Increase Life Detection Confidence
Planetary exploration missions seeking past or present signs of life carry not just a single instrument, but a suite. There is a need to study how these multiple data types can be combined to create “composite” biosignatures [1]. Algorithmic methods using existing data on living and non-living systems, though limited by the n = 1 of Earth, can nonetheless be informative. We assembled a database of 1277 measurements spanning 16 representative systems either indicative or non-indicative of life. Five classification (machine learning) methods were used on each individual data type, then on the entire set. This abstract summarizes the results; the data is described in more detail in [2], and methods in [3].
Biosignatures↗
In Search of Data-Driven Improvements to RANS Models Applied to Separated Flows
The goal of this work is to improve the capability of Reynolds-averaged Navier-Stokes turbulence models for separated flows using data-driven enhancements. The resulting model should be “universal” in the sense that it can be used by anyone and applied to as many flows as possible without concern for unusual or detrimental behavior. At worst, the data-driven corrections should not degrade the accuracy of the baseline model (in this case the Spalart-Allmaras one-equation model), while preserving the Galilean invariance and similar theoretical qualities of the original model. In the literature, most current data-driven improvements to turbulence models are only applicable to very similar types of cases as those used to train the model for a specific class of flows. In this work, the impact of using a wide array of cases in the machine-learning training is described. Unwanted behaviors from trained neural networks are examined, and possible mitigation strategies are proposed. However, to date, consistent and broadly applicable data-driven improvements for separated flows have not been achieved.
turbulence modeling↗
Big-data Efficient and Automated Science Transfer (BEAST): An Open-Source Software Architecture for Arc Jet Data Management, Modeling, and Automation
Big-data Efficient and Automated Science Transfer (BEAST) is a facility data management application developed for the NASA Ames arc jet facilities. The current decentralized data management practices limit statistical tracking, synchronization between video/time series, search capability, data throughput, and data processing speed/efficiency. Consequently, BEAST was developed to provide a new data infrastructure with streamlined data collection, processing, transfer, and analysis. This new framework also seeks to implement the FAIR principles of data stewardship: Findable, Accessible, Interoperable, and Reusable. The BEAST framework is based on a combination of the Python Django web framework and the Python data stack to provide a monolithic, open-source platform for data management, automation, and machine learning. This architecture was chosen for maintainability and scalability for a small, in-house development team. This paper will describe the application framework, deployment, and discuss the benefits and future plans for the system.
Data management↗
Big-data Efficient Automated Science Transfer (BEAST): an open-source software architecture for arc jet data management, modeling, and automation
Big-data Efficient and Automated Science Transfer (BEAST) was conceived to address the existing ground testing data management of the NASA Ames arc jet facilities (e.g., manually entered Excel files and USB drive data transfers). These data management practices were seen as a choke point for future thermal protection system (TPS) development as they limit statistical tracking, resolution of diagnostics, coordination between video/time series, data throughput, and data processing speed/efficiency. Consequently, BEAST was developed to provide a new data infrastructure with streamlined data collection, processing, transfer, and analysis. This new framework also seeks to implement the FAIR principles of data stewardship: Findable, Accessible, Interoperable, and Reusable. The BEAST framework is based on a combination of the Python Django web framework and the Python data stack to provide a monolithic, open-source platform for data management, automation, and machine learning. This architecture was chosen for maintainability and scalability for a small, in-house development team. This paper will describe the application framework, deployment, and discuss the benefits and future plans for the system.
Data management↗
VFR Trajectory Forecasting using Deep Generative Model for Autonomous Airspace Operations
To enable the airspace integration of autonomous operations, such as uncrewed aircraft conducting cargo deliveries, there is a need to forecast the positions of the surrounding traffic with which they may interact. This paper focuses on forecasting Visual Flight Rules traffic, a significant source of uncertainty and risk in the airspace, especially around small regional airports, due to the unplanned and often untracked nature of such flights. A deep generative model is developed, trained on historical traffic data at example towered and non-towered airports, and used to predict flight trajectories. Experimental results are presented comparing the performance of variational autoencoder and classical machine learning forecasting when applied to both the towered and non-towered airports over varying time horizons. The results show the advantages of the variational autoencoder in producing accurate probabilistic forecasts over varying time horizons.
uncrewed aircraft↗
VFR Trajectory Forecasting using Deep Generative Model for Autonomous Airspace Operations
To enable the airspace integration of autonomous operations, such as uncrewed aircraft conducting cargo deliveries, there is a need to forecast the positions of the surrounding traffic with which they may interact. This paper focuses on forecasting Visual Flight Rules traffic, a significant source of uncertainty and risk in the airspace, especially around small regional airports, due to the unplanned and often untracked nature of such flights. A deep generative model is developed, trained on historical traffic data at example towered and non-towered airports, and used to predict flight trajectories. Experimental results are presented comparing the performance of variational autoencoder and classical machine learning forecasting when applied to both the towered and non-towered airports over varying time horizons. The results show the advantages of the variational autoencoder in producing accurate probabilistic forecasts over varying time horizons.
uncrewed aircraft↗
Editorial: Advanced Technologies in Remote Sensing of Aerosols and Trace Gases Special Issue
The seven papers in this special issue span a range of satellite remote-sensing applications, from characterizing near-surface aerosol pollution and SO2 concentrations (Qu et al., 2023a and Xu et al., 2023, respectively), to atmospheric column methane and cloud measurement (Karoff and Vara-Vela, 2023 and Jian et al., 2023, respectively), to the stratospheric aerosol distribution over the over Xinjiang region of China (ZiWei and Xiangling, 2023). In these efforts to advance the field, common threads are the use of various machine learning methods to extract patterns from vast amounts of satellite data, and in many cases, the integration of multiple datasets, from different sources, sometimes including models, to provide the constraints required to obtain meaningful geophysical quantities.
aerosol remote sensing↗
Big-data Efficient and Automated Science Transfer (BEAST): An Open-Source Software Architecture for Arc Jet Data Management, Modeling, and Automation
Big-data Efficient and Automated Science Transfer (BEAST) was conceived to address the existing ground testing data management of the NASA Ames arc jet facilities (e.g., manually entered Excel files and USB drive data transfers). These data management practices were seen as a choke point for future thermal protection system (TPS) development as they limit statistical tracking, resolution of diagnostics, coordination between video/time series, data throughput, and data processing speed/efficiency. Consequently, BEAST was developed to provide a new data infrastructure with streamlined data collection, processing, transfer, and analysis. This new framework also seeks to implement the FAIR principles of data stewardship: Findable, Accessible, Interoperable, and Reusable. The BEAST framework is based on a combination of the Python Django web framework and the Python data stack to provide a monolithic, open-source platform for data management, automation, and machine learning. This architecture was chosen for maintainability and scalability for a small, in-house development team. This paper will describe the application framework, deployment, and discuss the benefits and future plans for the system.
Data management↗
TPS Certification by Analysis: Model-Driven Characterization of Properties and Failure in Woven Thermal Protection Systems
Woven, ablative thermal protection system (TPS) materials provide a robust option for aggressive (re)entries and thus have been baselined for the upcoming Mars Sample Return (MSR) mission's Earth Entry System (EES). The reliability requirements for MSR-EES necessitate understanding of material property variability, which could be significant given the complex structure and anisotropic nature of properties in TPS weaves, as well as the response to potential impact with micrometeoroids or orbital debris during the EES re-entry. The TPS Certification by Analysis effort within the Entry Systems Modeling project seeks to provide computational models and analyses that support the certification against such material-based risks. For the present talk, focus will be given to the characterization of baseline woven TPS material properties and mechanical failure limits, which entails (1) use of computational techniques (e.g., machine learning) to interpret computed tomography images of the weave to generate representative structural models and (2) application of multiscale material modeling approaches to characterize thermomechanical and failure properties.
thermal protection systems↗
Climate Informatics
The impacts of present and potential future climate change will be one of the most important scientific and societal challenges in the 21st century. Given observed changes in temperature, sea ice, and sea level, improving our understanding of the climate system is an international priority. This system is characterized by complex phenomena that are imperfectly observed and even more imperfectly simulated. But with an ever-growing supply of climate data from satellites and environmental sensors, the magnitude of data and climate model output is beginning to overwhelm the relatively simple tools currently used to analyze them. A computational approach will therefore be indispensable for these analysis challenges. This chapter introduces the fledgling research discipline climate informatics: collaborations between climate scientists and machine learning researchers in order to bridge this gap between data and understanding. We hope that the study of climate informatics will accelerate discovery in answering pressing questions in climate science.
Climate change↗