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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Hybrid Approaches to Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Systems Health Management↗
NASA NTRS2021

Achieving Breakthroughs in Global Hydrologic Science by Unlocking the Power of Multisensor, Multidisciplinary Earth Observations

Over the last half century, remote sensing has transformed hydrologic science. Whereas early efforts were devoted to observation of discrete variables, we now consider spaceborne missions dedicated to interlinked global hydrologic processes.Furthermore, cloud computing and computational techniquesare accelerating analyses of these data. How will the hydrologic community use these new resources to better understand the world’s water and relatedchallenges facing society? In this Commentary, we suggest that optimizing the benefits of remote sensing for advancing hydrologic research will happen byintegratingmultidisciplinary and multisensor data, leveraging commercial satellite measurements, and employingdata assimilation, cloud computing, and machine learning.We provide several recommendations to these ends. Plain Language Summary Observations from satellites have transformed hydrologic science. Early efforts, five decades ago, mapped attributes like snow cover, rainfall, topography, and vegetation, but now we consider new missions specifically designed to study global hydrologic processes. We also take advantageof new technologies like cloud computing and artificial intelligence. We describe strategiesfor maximizing the benefits of remote sensing for hydrology, encouraging research across disciplines using multiple sensors, using new commercially available satellites, and combining remote sensing measurements with hydrologic models.

Michael Durand↗
NASA NTRSDate not supplied

Infusion of AI/ML Technology into Operational NASA Data Systems

NASA has been developing a variety of Artificial Intelligence / Machine Learning technologies related to Earth Observations. In most cases, the full value of such a technology is realized when it is infused into an operational system. NASA’s Earth Science Data Systems program has been formulating repeatable methods to execute technology infusion. These efforts include the Advancing Collaborative Connections for Earth System Science (ACCESS) program, a Technology Infusion Playbook, and an assemblage of working groups investigating methods for infusion collaboration, community development, and capacity building. ESDS has also been executing a pathfinder activity to infuse a machine-learning-driven recommender of science keywords for Earth Observation datasets, which is intended to be used for metadata curation in the Earth Observation System Data and Information System.

C Lynnes↗
NASA NTRSDate not supplied

Merging Analytic Collaborative Frameworks with New Observing Strategies Toward a Digital Twin: Earth – Episodic Pulse Event Impacts on Ocean Carbon Cycle as an Example

Virtual representations of the Earth will allow us to address some of the most critical environmental issues of our time. Here, we show the first steps toward representation of riverine, estuarine, and coastal carbon processes to enable scenario driven “what-if” analyses of the carbon system and human footprint. Excess sediment and nutrient runoff from land-based human activities impact water quality and can pose serious threats to coastal and marine ecosystems. Episodic pulse events, such as extreme precipitation events, can increase the amount of nutrients entering estuaries and coastal regions, potentially leading to large phytoplankton blooms followed by anoxic conditions. Consequences of coastal runoff are predicted to increase with the higher intensity and frequency of extreme events. Beyond the threat to coastal ecosystems, recent findings suggest these episodic pulses might play a significant role for biological production influencing regional and global carbon fluxes and budgets. An improved understanding of these events through optimal, dynamic observing strategies will increase our knowledge of the land-ocean continuum and how regional events and nutrient fluxes affect the carbon cycle and ocean ecosystem. This conceptual framework enables focused science investigations by pairing data analytics and artificial intelligence tools (otherwise termed an Analytic Center Framework, ACF) with targeted measurement acquisition through distributed sensing and intelligent asset tasking (or New Observing Strategies, NOS). This NOS and ACF iterative approach acquires and integrates complementary and coincident satellite, in-situ and model data to build a more complete and in-depth picture of science phenomena. Specifically, Apache Science Data Analytic Platform (SDAP) is extended to incorporate relevant datasets for data access, harmonized analysis, and anomaly detection. When conditions are met for a likely pulse event, NASA’s D-SHIELD (Distributed Spacecraft with Heuristic Intelligence to Enable Logistical Decisions) tool is triggered to optimize asset overpass frequency and schedule observations for persistent monitoring. Targeted data is ingested by SDAP for enhanced investigation via iterative analysis until the trigger criteria is no longer met - steps toward a digital twin.

Laura Rogers↗
NASA NTRSDate not supplied

Toward Design Assurance of Machine-Learning Airborne Systems

In recent years, Artificial Intelligence (AI) systems, enabled by Machine Learning (ML)technology, have demonstrated impressive progress and provides historic opportunities for the aviation industry. However, several key aspects of ML technology are not compatible with existing design assurance standards and make certification problematic. In this paper, we present a case study of a visual system with a Deep Neural Network (DNN) intended to detect and identify airport runway signs. Different use cases and variants of this system exhibit different levels of criticality ranging from design assurance level (DAL) D to B. We use the case study to illustrate the challenges of certification according to the current standards, such asDO-178C. We present the system design, data generation, training, and verification in detail and describe how the design assurance objectives can be met for a DAL D variant of the system. We also discuss gaps and potential approaches for the higher design assurance levels.

Avionics↗
NASA NTRS2022

Run Time Assurance for Electric Vertical Takeoff and Landing Aircraft

NASA is conducting research to demonstrate and evaluate the application of Run Time Assurance (RTA) as a means to assure safety in Electric Vertical Takeoff and Landing (eVTOL) aircraft with highly automated or autonomous flight capability supervised by a single onboard pilot. The work described in this report demonstrates an application of RTA and examines the implications for design and analysis of aircraft functions and systems; aircraft safety hazards; safety assurance; development assurance; and pilot tasks and performance. This research effort also seeks to assess the efficacy of the combined application of traditional Functional Hazard Analysis (FHA) and the more modern System Theoretic Process Analysis (STPA) techniques to perform hazard analyses on aircraft with complex automated and autonomous systems and an onboard pilot. During the research effort we developed architectural designs of two alternate eVTOL aircraft, generally following the process characterized in the SAE standards ARP4754 and ARP4761. The design has focused on the control architectures of these aircraft, which are identical except that one incorporates RTA techniques to reduce the criticality of some key software components. Artifacts of this process include a taxonomy of aircraft-level functions, aircraft-level architecture diagrams, aircraft-level functional hazard assessments (AFHA), function allocations onto aircraft systems and subsystems, functional block diagrams for a select set of control-related functions, and system-level functional hazard assessments (SFHA) for those functions. This project has highlighted the notion that DAL D is something of a sweet spot for low-confidence controllers in an RTA-based design. Among the many activities described in DO-178C, the activities related to requirement verifiability, algorithmic accuracy, and test coverage can be the most challenging for the kinds of advanced control techniques that may be desirable in novel UAM designs, such as adaptive control, machine-learning, artificial intelligence, numerical search, and Monte Carlo based algorithms. Moreover, the standard requires that development teams demonstrate that errors leading to unacceptable failure conditions have been removed from the software. The RTA architecture, which cordons off the low-confidence function, makes it much easier to show this for these kinds of algorithms. With regard to the use of STPA and FHA as complementary hazard analysis techniques, our research effort led us to the conclusion that STPA should be used to derive requirements for hardware and software systems and/or components. Also, STPA is a natural complement to other processes in ARP4754A involving design studies and iteration.

Run-time assurance↗
NASA NTRSDate not supplied

INCREASING THE TRANSPARENCY AND REPRODUCIBILITY OF SPACE RADIATION SCIENCE: THE RADIATION BIOLOGY ONTOLOGY

Among the primary objectives of the Open/Open-Source Science paradigm are making scientific investigation data transparent and results reproducible [1], objectives shared by the FAIR principles [2]. To accomplish this, the conceptual framework that includes all the investigation objects needs to be accurately captured and communicated to all data consumers. A large part of this requires using metadata standards to annotate data collected. These standards should be readily accessible, informed by scientific community consensus and sufficiently specific to encompass all of the important aspects of the investigation. Starting in 2020 we have been co-leading an open consortium to develop a new metadata standard, the Radiation Biology Ontology (RBO), through the Open Biological and Biomedical Ontologies (OBO) Foundry [3]. We began by transforming many of the terms from the National Council on Radiation Protection and Measurement into concepts that can be formally related to existing OBO Foundry classes or attributes. We then identified and imported into the RBO existing OBO Foundry classes that have obvious relevance for radiation biomedicine (for example, concepts from the Environment Ontology that describe radiative processes, and concepts from the Gene Ontology dealing with molecular and cellular responses to radiation). Finally, we scrutinized datasets from investigations of radiation effects held in NASA GeneLab and LSDA repositories and added additional classes, instances, and attributes into the RBO that should be used to annotate these data. We developed the RBO using the open-source tools of GitHub and publish the RBO periodically through the NIH/NCBI BioPortal website, so systems worldwide can leverage the knowledge it contains [4]. This initial phase of concept modeling has yielded an RBO that at present has more than 300 declared concepts, with more than 3500 additional concepts imported from other OBO Foundry ontologies. While this first phase has focused on concepts for annotating samples, environments, exposures, and measurements, the next phase will center on supporting annotation of results and findings, such as concept models of molecular, cellular and tissue effects. The value of the RBO will be determined in part by our ability to engage the community in its development, and we have established a Radiobiology Informatics Consortium with unrestricted membership as the owner of the RBO in order to encourage investigators, system owners and other to join in this effort. Anyone can report issues or request new concept modeling or other features directly on GitHub. By using the BioPortal application programming interface, systems can pose dynamic queries to the latest version of the RBO for information on individual classes or entire hierarchies; this design eliminates the need for systems to be updated in order to use newer versions of the RBO. We hope to contribute to the advancement of open radiobiological science through the continued, open development of the RBO, that will provide more precise, machine-interpretable descriptions of investigations, as well as support data meta-analysis through machine learning or other artificial intelligence methods.

knowledge↗
NASA NTRS2020

Leveraging Space and Ground Assets in a Sensorweb for Scientific Monitoring: Early Results and Opportunities for the Future

Increased space and ground sensing is enabling dramaticnew measurements of a wide range of Earth Science andApplied Earth Science phenomena. New space sensors tomonitor volcanism, flooding, wildfires, weather, and manyother phenomena abound.New challenges exist to rapidly assimilate available dataand to optimize measurements (e.g. direct assets) to bestobserve these complex and dynamic spatiotemporalphenomena. Artificial Intelligence offers the potential toassist in data interpretation and resource allocation to bestallocate sensing assets. We describe some efforts to buildand experiment with such “sensorweb” systems as well asoffer some direction for the future sensorweb observationsystems.

Chien, Steve↗
NASA NTRS2020

Space Ground Sensorwebs for Volcano Monitoring

Increased space and ground sensing is enabling dramatic new measurements of a wide range of Earth Science and Applied Earth Science phenomena, including: volcanism, flooding, wildfires, weather, and many other phenomena. New Space ventures have produced significantly greater access to data and miniaturization of sensing has enabled cubesats and smallsats to deliver data of outstanding resolution. The advent of the internet of things has produced incredible amounts of relevant terrestrial data as well. Artificial Intelligence offers the potential to automate both data interpretation and resource allocation to best allocate sensing assets. We describe efforts to build and experiment with such “sensorweb” systems and offer some direction for the future sensorweb observation systems.

Zuleta, Ignacio↗
NASA NTRSDate not supplied

Human Interfaces and Management of Information (HIMI) Challenges for “In-time” Aviation Safety Management Systems (IASMS)

The envisioned transformation of the National Airspace System to integrate an In-time Aviation Safety Management System(IASMS)to assure safety in Advanced Air Mobility(AAM)brings unprecedented challenges to the design of human interfaces and management of safety information. Safety in design and operational safety assurance are critical factors for how humans will interact with increasingly autonomous systems. The IASMS Concept of Operations builds from traditional commercial operator safety management and scales in complexity to AAM. The transformative changes in future aviation systems pose potential new critical safety risks with novel types of aircraft and other vehicles having different performance capabilities, flying in increasingly complex airspace, and using adaptive contingencies to manage normal and non-normal operations. These changes compel development of new and emerging capabilities that enable innovative ways for humans to interact with data and manage information. In-creasing complexity of AAM corresponds with use of predictive modeling, data analytics, machine learning, and artificial intelligence to effectively address known hazards and emergent risks. The roles of humans will dynamically evolve in increments with this technological and operational evolution. The interfaces for how humans will interact with increasingly complex and assured systems designed to operate autonomously and how information will need to be presented are important challenges to be resolved.

Lawrence J Prinzel↗
NASA NTRSDate not supplied

Onboard Hyperspectral Image Classification via Transfer Learning for Communication-Limited Spacecraft

Employing deep-learning and artificial-intelligence (AI) techniques onboard spacecraft can dramatically improve priority data selection to ensure more effective use of the available downlink. However, deployment of effective deep-learning models requires significant training on the ground, which may not be feasible, due to limited data available in an unexplored environment. Therefore, this research explores building robust classification models for onboard data processing where training data is highly limited using transfer-learning techniques. In this paper, we focus on the use case of hyperspectral imaging for remote sensing, a domain where the high dimensionality of the data from the sensor can rapidly saturate the downlink bandwidth. With this bottleneck, there is an impending need to autonomously and robustly classify data onboard to optimize downlink of high-impact measurements, thus maximizing the scientific utility per bit transmitted to the ground. This paper examines the use of deep neural networks onboard for hyperspectral image classification in a communication-limited scenario to analyze how the models perform with limited training data. The use of transfer learning can ameliorate the issue of poor generalization by transferring features learned from training on a large source dataset for one classification task to the target classification task with limited training data. For two deep-learning models from literature, we compare the accuracy of the models trained using transfer learning to models trained from scratch using a random weight initialization with varying amounts of training data. We demonstrate the feasibility and performance of running inference of the deep-learning models on representative flight-like hardware.

Advanced Avionics, Machine Learning, Data Processi↗
NASA NTRSDate not supplied

Open Science for Life in Space: Data Sharing and Tools for Knowledge Discovery

The next era in human space exploration is rapidly approaching and will require the use of countermeasures to deep space health hazards. The development of countermeasures (or, the re-purposing of existing agents) will be highly dependent on our understanding of basic biological responses to space stressors (e.g. ionizing radiation, altered gravitational fields, altered day-night cycles, confinement, isolation, hostile-closed environments, distance-duration from Earth, exposure to celestial regolith, etc.). The fast-growing array of space biological data, which in the past was simply archived after minimal analysis, holds great potential if it can be reorganized and formatted for Open Science. Organizing the data for such analysis is a challenge because of its diverse nature (molecular, cellular, tissue, imaging, whole organism and behavior). We will discuss here several strategies that NASA’s Biological and Physical Science Division has put in place to maximize the return on investment for spaceflight bioscience data. Open Science, as a scientific philosophy, is the concept that the more people who have access to the data, the more knowledge will be gained from it. This guiding principle led NASA to develop GeneLab in 2015. GeneLab houses spaceflight and relevant ground-based multi-omics data, and has grown to ~400 transcriptomic, proteomic, metabolomic and epigenomic datasets from plant, rodent, small animal, and microbial space experiments. GeneLab provides users with various tools for data analysis and a visualization portal that allows users to interact with gene expression data from space-related ‘omics experiments. Open Science is also about building scientific communities, and with this spirit in mind, GeneLab has spawned several Analysis Working Groups (AWGs), comprised of more than 200 volunteer scientists. The AWGs initially provided feedback on the processing pipeline and metadata ‘omics standards for GeneLab. Over the last few years, they have become a community-driven science enterprise, engaging in large meta-analysis of GeneLab datasets, resulting in 10 publications (beyond the originally submitted research). Overall, the Open Science nature of GeneLab has resulted in a high degree of data re-use, resulting in 38 additional publications derived from the original 67 publication over the past four years. The enormous success and knowledge gained from GeneLab has led to a collection of sister NASA “Open Science Data Repositories (OSDR)” and research support groups. These include the NASA Ames Life Sciences Data Archive (ALSDA), the NASA Biological Institutional Scientific Collection (NBISC), and the Biospecimen Sharing Program (BSP). All are adopting the GeneLab data architecture system to maximize open-access, find-ability, accessibility, interoperability, and reusability (FAIR). ALSDA collects and curates phenotypic-physiological bioimaging-behavioral data from space and space-relevant non-human experiments, oftentimes coming from the same omics-associated experimental datasets found in GeneLab. Since 2021, a community of ~100 researchers have rallied around ALSDA, to provide feedback in a new ALSDA AWG focused on phenotypic-physiological investigation-sample-assay metadata standards (e.g., Micro-Computed Tomography, Light/Fluorescence Microscopy, Western Blot, Flow Cytometry, Novel Object Recognition, Elevated Plus Maze, etc. of ~50 assays collected). These standards are part of a new single point-of-entry data submission portal for all non-human Space Biology and Human Research Program principal investigators, to submit, curate, and share their research data. With open-access space biological data now collected and curated together with rich metadata, and with the potential for linkage to “big data” from the international biological and medical communities (NIH, EBI, etc.), the artificial intelligence and machine learning (AI/ML) era has started for Space Biology. Several other talks will cover these topics in this conference.

life sciences↗
NASA NTRS2022

Hybrid Model Based Approaches for Systems Health Management and Prognostics

This is a previously approved and published presentation. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Present achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. In principle, data-driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data-driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety-critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Prognostics↗
NASA NTRS2022

MLtool: Universal Supervised Machine Learning Tool to Model Tabulated Data

Machine Learning (ML) is a subfield of Artificial Intelligence that gives computers the ability to learn from past data without being explicitly programmed. The predictive capabilities of ML models have already been used to facilitate several scientific breakthroughs. However, the practical application of ML is often limited due to the gaps in technical knowledge of its users. The common issue faced by many scientific researchers is the inability to choose the appropriate ML pipelines that are needed to treat real-world data, which is often sparse and noisy. To solve this problem, we have developed an automated Machine Learning tool (MLtool) that includes a set of ML algorithms and approaches to aid scientific researchers. The current version of MLtool is implemented as an object-oriented Python code that is easily extensible. It includes 44 different regression algorithms used to model data. MLtool helps users select the best model for their data, based on the scoring metrics used. Besides regression algorithms, MLtool also includes a suite of pre- and post-processing techniques such as missing value imputation, categorical variable encoding, input feature normalization, uncertainty quantification, exploratory data analysis (EDA), etc. MLtool was tested on several publicly available multi-dimensional data sets and was found capable of making accurate predictions.

Machine learning↗
NASA NTRS2022

Adaptive IV&V for Increasingly Complex Software Systems

As NASA software systems continue to innovate, becoming more complex and nondeterministic, the need for the NASA IV&V Program to become systemically adaptive to ensure mission success is paramount. To ensure adaptability within resource constraints, IV&V has developed an agile, risk-based approach to identify, characterize, scope, focus, and prioritize mission assurance activities. This risk based adaptive framework has been applied to trends such as increased reliance on data driven algorithms for safety and mission critical software behavior, use of MBSE in system design, and application of agile principles to embedded software development. The framework is enabled by continuous innovation of new approaches such as software only test beds, assurance design tools, and initiatives that augment IV&V assurance methods with artificial intelligence and machine learning techniques. This presentation will highlight the trends the NASA IV&V Program is seeing, the innovative steps it is taking to address those challenges, and how it is postured to address evolving risk and constantly changing and new technologies.

Wesley W Deadrick↗
NASA NTRSDate not supplied

Communication Demand in the National Airspace - A Federated Learning Approach

Within the national airspace system (NAS), efficient use of spectrum remains a challenge; as UAS and UAM missions evolve, the amount of mission-critical aircraft communications are expected to significantly grow. To accommodate the increased demand, NASA Glenn Research Center is investigating artificial intelligence approaches that could dynamically allocate spectrum; however, these solutions are driven by communication and aviation data items, many of which are not directly available. One such cornerstone data item is communication demand, parameterizing the needs within a sector in terms that may directly inform spectrum allocation, such as channel access duration, bandwidth, and modulation type. This paper considers the complexity of predicting communication demand as a function of NAS behaviors. Unlike prior prediction work in communications, this research must consider how the NAS may be impacted by external factors - such as convective weather and closures - rather than estimating demand from time-series forecasting alone. Much of this research considers a federated learning design to predict communication events in terms of the type of event occurring (sector coordination, conflict resolution, etc.). To do so, an investigation of products from Sherlock Data Warehouse is conducted, identifying the trends, sufficiency, and correlations of each product to identified events. Additionally, a preliminary discussion for inferring associations between these event types and their communication parameters (duration, bandwidth, modulation) is presented. By utilizing federated learning, imbalances in the types of events and data present throughout the NAS can inform local models without impairing global training. Furthermore, the two-stage approach proposed allows for robust and speculative communication modelling, as communication techniques continue to evolve. As a result, this model enables a generalized approach to understanding NAS communications which is able to inform long-term changes to aviation spectrum management.

Nathan Schimpf↗
NASA NTRSDate not supplied

A Federated Learning Approach to Predicting Communication Demand in the National Airspace

Within the national airspace system (NAS), efficient use of spectrum remains a challenge; as UAS and UAM missions evolve, the amount of mission-critical aircraft communications are expected to significantly grow. To accommodate the increased demand, NASA Glenn Research Center is investigating artificial intelligence approaches that could dynamically allocate spectrum; however, these solutions are driven by communication and aviation data items, many of which are not directly available. One such cornerstone data item is communication demand, parameterizing the needs within a sector in terms that may directly inform spectrum allocation, such as channel access duration, bandwidth, and modulation type. This paper considers the complexity of predicting communication demand as a function of NAS behaviors. Unlike prior prediction work in communications, this research must consider how the NAS may be impacted by external factors - such as convective weather and closures - rather than estimating demand from time-series forecasting alone. Much of this research considers a federated learning design to predict communication events in terms of the type of event occurring (sector coordination, conflict resolution, etc). To do so, an investigation of products from Sherlock Data Warehouse is conducted, identifying the trends, sufficiency, and correlations of each product to identified events. Additionally, a preliminary discussion for inferring associations between these event types and their communication parameters (duration, bandwidth, modulation) is presented. By utilizing federated learning, imbalances in the types of events and data present throughout the NAS can inform local models without impairing global training. Furthermore, the two-stage approach proposed allows for robust and speculative communication modelling, as communication techniques continue to evolve. As a result, this model enables a generalized approach to understanding NAS communications which is able to inform long-term changes to aviation spectrum management.

Nathan Schimpf↗
NASA NTRSDate not supplied

Machine-Learning for Safety Critical Airborne Applications Part II: Case Study

The exceptional progress in the field of Artificial Intelligence (AI) systems, enabled by Machine Learning (ML) technology in recent years provides historic opportunities for the aviation industry. Current certification standards for avionics were developed prior to the ML renaissance and have several fundamental incompatibilities with the ML technology. WG-114 is working hard to release a new standard as soon as possible but for now there is no recognized means of compliance for ML based systems even of low criticality. In this talk, we present the custom ML workflow that can be used comply with all objectives of the current certification standards for a low-criticality (DAL D and C) ML-based system. To illustrate the practical application of the custom ML workflow we present a case study of a system based on a Deep Neural Network (DNN) intended to detect and identify airport runway signs. We present the system design, data generation, training, and verification in detail and describe how the design assurance objectives can be met for a DAL D and DAL C systems.

Johann Schumann↗