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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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NASA NTRS1990

Operational testing of a figure of merit for overall task performance

An overall indicator or figure of merit (FOM), for the quality of pilot performance is needed to define optimal workload levels, predict system failure, measure the impact of new automation in the cockpit, and define the relative contributions of subtasks to overall task performance. A normative FOM was developed based on the calculation of a standard score for each component of a complex task. It reflected some effects, detailed in an earlier study, of the introduction of new data link technology into the cockpit. Since the technique showed promise, further testing was done. A new set of data was obtained using the recently developed Multi-Attribute Task Battery. This is a complex battery consisting of four tasks which can be varied in task demand, and on which performance measures can be obtained. This battery was presented to 12 subjects in a 20 minute trial at each of three levels of workload or task demand, and performance measures collected on all four tasks. The NASA-TLX workload rating scale was presented at minutes 6, 12, and 18, of each trial. A figure of merit was then obtained for each run of the battery by calculating a mean, SD, and standard score for each task. Each task contributed its own proportion to the overall FOM, and relative contributions changed with increasing workload. Thus, the FOM shows the effect of task changes, not only on the individual task that is changed, but also on the performance of other tasks and of the whole task. The cost to other tasks of maintaining constant performance on an individual task can be quantified.

Lemay, Moira↗
NASA NTRSDate not supplied

A Natural Language Understanding Approach for Digitizing Aircraft Ground Taxi Instructions

Advancements in natural language processing (NLP) technologies offer a unique opportunity to furnish aircraft crews, primarily pilots, with digital instructions for taxiing operations. Digital taxi instructions, delivered either as text or graphics, can streamline taxiing procedures, thereby reducing radio congestion, minimizing communication errors, and enhancing aircraft monitoring. Techniques used for natural language understanding (NLU), a subset of NLP focused on machine comprehension of natural language, can extract taxi instructions directly from verbal radio communications. This capability paves the way for implementing a digital taxi communication framework with minimal adjustments to the existing air traffic controller operations. This paper delves into a novel application of NLU: the automated generation of digital taxi instructions from air traffic controller speech. We detail the development of an annotation scheme to represent aircraft ground traffic communications within the US National Airspace System (NAS), employing intent classification (IC) and slot filling (SF) to extract taxi instructions using NLU models. Several neural network models were trained on a dataset annotated with our scheme, achieving notable accuracy and F1 scores. Our research demonstrates the feasibility of using NLU to automatically generate digital taxi instructions, showcasing its potential to streamline the implementation of digital taxi communications.

LSTM↗
NASA NTRSDate not supplied

A Natural Language Understanding Approach for Digitizing Aircraft Ground Taxi Instructions

Advancements in natural language processing (NLP) technologies offer a unique opportunity to furnish aircraft crews, primarily pilots, with digital instructions for taxiing operations. Digital taxi instructions, delivered either as text or graphics, can streamline taxiing procedures, thereby reducing radio congestion, minimizing communication errors, and enhancing aircraft monitoring. Techniques used for natural language understanding (NLU), a subset of NLP focused on machine comprehension of natural language, can extract taxi instructions directly from verbal radio communications. This capability paves the way for implementing a digital taxi communication framework with minimal adjustments to the existing air traffic controller operations. This paper delves into a novel application of NLU: the automated generation of digital taxi instructions from air traffic controller speech. We detail the development of an annotation scheme to represent aircraft ground traffic communications within the US National Airspace System (NAS), employing intent classification (IC) and slot filling (SF) to extract taxi instructions using NLU models. Several neural network models were trained on a dataset annotated with our scheme, achieving notable accuracy and 𝐹1 scores. Our research demonstrates the feasibility of using NLU to automatically generate digital taxi instructions, showcasing its potential to streamline the implementation of digital taxi communications.

ATC↗
NASA NTRSDate not supplied

Spacesuit Sizing by Multivariate Simulation: Case Study for Liquid Cooling and Ventilation Garment

A technique was developed to assess the fit of spacesuit garment liquid cooling and ventilation garment (LCVG). A fit scoring scheme was built from the spacesuit sizing requirements and guidelines, and the accommodated proportion of the wearer population was estimated from statistically synthesized pseudo population data. The method evaluated different scenarios of LCVG sizing systems including 1) baseline 7-size system, 2) 7-sizes with target body dimensions iteratively adjusted for maximum fit, and 3) 9-sizes with iterative adjustments. Overall, the baseline sizing system showed 81% accommodation rate, while the hypothetical new systems in-tegrating iterative adjustments for 7- and 9-sizes predictively indicated that accommodation could improve up to 92 and 97%. The proposed method provided a structured metric to quantify fit performance of a garment sizing system, which can be further applied to different spacesuit component, PPE and garment designs.

Han Kim↗
NASA NTRS2017

NASA's EOSDIS, Trust and Certification

NASA's Earth Observing System Data and Information System (EOSDIS) has been in operation since August 1994, managing most of NASA's Earth science data from satellites, airborne sensors, filed campaigns and other activities. Having been designated by the Federal Government as a project responsible for production, archiving and distribution of these data through its Distributed Active Archive Centers (DAACs), the Earth Science Data and Information System Project (ESDIS) is responsible for EOSDIS, and is legally bound by the Office of Management and Budgets circular A-130, the Federal Records Act. It must follow the regulations of the National Institute of Standards and Technologies (NIST) and National Archive and Records Administration (NARA). It must also follow the NASA Procedural Requirement 7120.5 (NASA Space Flight Program and Project Management). All these ensure that the data centers managed by ESDIS are trustworthy from the point of view of efficient and effective operations as well as preservation of valuable data from NASA's missions. Additional factors contributing to this trust are an extensive set of internal and external reviews throughout the history of EOSDIS starting in the early 1990s. Many of these reviews have involved external groups of scientific and technological experts. Also, independent annual surveys of user satisfaction that measure and publish the American Customer Satisfaction Index (ACSI), where EOSDIS has scored consistently high marks since 2004, provide an additional measure of trustworthiness. In addition, through an effort initiated in 2012 at the request of NASA HQ, the ESDIS Project and 10 of 12 DAACs have been certified by the International Council for Science (ICSU) World Data System (WDS) and are members of the ICSUWDS. This presentation addresses questions such as pros and cons of the certification process, key outcomes and next steps regarding certification. Recently, the ICSUWDS and Data Seal of Approval (DSA) organizations merged their Core Trustworthy Data Repositories Requirements and require that members be recertified every three years. Given the rigor with which NASA manages the ESDIS Project and the DAACs, the recertification through WDSDSA, while involving some additional work, is a relatively simple process.

Certification↗
NASA NTRS1984

The 60 GHz antenna system analyses for intersatellite links, phase A

A tradeoff study for 60 GHz antenna systems applicable to an advanced Tracking and Data Acquisition System is also discussed. A conceptual design of a preferred antenna system is also discussed. The tradeoff results for four types of antenna systems are presented: (1) Reflector/fixed feed, (2) Mechanical scan, (3) Electronic scan; and (4) Hybrid mechanical/electronic scan. The 12 candidate antennas were assessed on the basis of a preliminary design and a performance analysis then were scored against 15 weighted parameters. This process resulted in the ranking of the 12 candidates for the two applications, namely, for the geostationary TDAS only with a narrow field of view and for low orbit user satellites with a wide field of view.

Source record↗
NASA NTRS1986

Multiperspective analysis and testing of expert systems

The paper describes a technique which the author developed for testing expert systems. The technique, which he calls multiperspective testing, can be applied during both the knowledge engineering phase and the acceptance testing phase of developing an exppert system. The first step in multiperspective testing is to define a group of performance measures ('perspectives') that focus on the behavior of the knowledge base. For each such measure, the results of testing are summarized in four scores, which the author calls 'expansion,' 'detection,' 'discrimination,' and 'comprehension.' These scores have the advantage of providing more specific information about how the knowledge base should be updated or corrected.

Bollinger, Terry B.↗
NASA NTRSDate not supplied

Pathfinder Networks for Measuring Operator Mental Model Structure with a Simple Autopilot System

Pathfinder networks are a method to represent mental models from empirically generated pairwise relatedness ratings. This study examined the effects of training exposure on mental model structures based on relatedness ratings collected using the Target Rating method. Forty-eight participants read instruction slides with or without explicit information on the functionality of an autopilot system (Advanced Mental Model or Basic Mental Model groups, respectively). Participants provided relatedness ratings and completed a comprehension test. The Advanced Mental Model group had more common links with the expected model, higher within-group network similarity scores, and higher mental model assessment questionnaire scores than the Basic Mental Model group. Both groups had coherence scores above the minimum threshold for internal consistency. Pathfinder network analysis was sensitive to changes produced by a simple exposure training intervention. In practice, a simple training program may effectively influence operator mental models in novel technological environments such as Advanced Air Mobility.

Mental Models↗
NASA NTRSDate not supplied

Machine-learning Solution for Automatic Spacesuit Motion Recognition and Measurement from Conventional Video

Extravehicular Activity (EVA) spacesuits exhibit unique movement patterns due to their design characteristics. Mobility assessments using traditional motion capture systems are cost prohibitive and not feasible for some training conditions (e.g., simulated lunar outdoor terrain). This paper aims to present the ongoing development of machine learning solutions to quantify suit motions from conventional videos without special sensors or hardware. Given the fast growth in deep/machine learning technologies, external expertise was sought from open-source communities. This was expected to accelerate development and provide more cost-effective, time-saving solutions. This work was selected for a NASA Crowdsourcing project through an agency-wide solicitation. Partnerships were formed with the NASA JSC Center of Excellence for Collaborative Innovation and an execution crowdsourcing platform partner to solicit framework developments from external contenders. NASA provided contenders with video clips of spacesuits and simultaneously measured motion capture data during EVA simulation tasks. The contenders used this data to train and develop generalized algorithms to predict motions. At the end of the crowdsourcing event, five solutions were selected from 250 submissions. Each submission was tested and scored using video clips not previously disclosed to the contenders. The scoring metrics measured how well the algorithm detected the suit shape, the 2D suit joint detection accuracy, and 3D joint detection accuracy. The winning solution was able to achieve roughly 85% prediction accuracy (weighted combination of scoring metrics). Overall, the algorithms could efficiently detect various types of spacesuits and motions across different EVA simulation environments such as the Neutral Buoyancy Lab (NBL). However, 3D joint identification is less reliable when parts of the suit were obstructed in the image. After continued improvements and validation, the fully developed system will enable EVA stakeholders to quantify suit kinematic patterns, which can help optimize suit, hardware, and task designs.

Linh Vu↗
NASA NTRSDate not supplied

Objective Structured Clinical Evaluation (OSCE) of an Artificial Intelligence (AI) Clinical Decision Support System (CDSS) Tool

BACKGROUND Objective Structured Clinical Evaluations (OSCEs) have long been established as a robust methodology for summative assessment of clinical skills and decision-making during medical education. The recent integration of Artificial Intelligence (AI) into clinical decision-making processes has prompted the need for novel evaluation frameworks to assess the efficacy and reliability of AI clinical decision support system (CDSS) tools. This abstract outlines the process of quantitatively evaluating a novel CDSS (“Doc in a Box” Google 2024) trained on curated medical spaceflight data in the psychomotor domain as it interfaces with a human volunteer acting as the crew medical officer (CMO). PURPOSE The AI CDSS under review was developed as part of the Lunar Command and Control Interoperability (LuCCI) project, which is intended to address a gap in how Lunar Surface Systems (LSS) would interoperate across multiple programs, commercial partners, and international partners. The project objective is to define, prototype, integrate, and evaluate an interoperable lunar command, control, data, and software reference architecture to enable autonomy and informatics capability through common standards across LSS. A multi-modal AI-based CDSS compatible with Federated LSS will assist clinicians in diagnosing and managing complex medical conditions by providing evidence-based recommendations through predictive analytics. Given the critical role of decision-support as NASA continues to evolve its Earth-independent medical operations (EIMO), it is imperative to ensure that such AI tools perform reliably and align with clinical standards during progressive lunar and Martian exploration class missions. METHODS The OSCE framework, traditionally used for evaluating human clinicians, was adapted to assess the AI tool's decision-making capabilities in simulated clinical scenarios. In this adapted OSCE, the AI CDSS was tested across a series of structured clinical scenarios designed to mimic real-life spaceflight patient cases. These scenarios included a range of conditions and complexities, allowing for comprehensive assessment of the tool's performance. Key evaluation metrics included accuracy of diagnosis, timeliness of decision-making, and appropriate recommendations for therapies. The OSCE was scored by human physician evaluators who assessed the AI's recommendations in comparison with expert clinicians' medical decision making to ensure alignment with best practices and the standard of care. RESULTS Preliminary results indicate that the AI CDSS demonstrated high accuracy in diagnostic recommendations and decision support across various scenarios. However, certain limitations were noted, such as occasional discrepancies in handling complex or nuanced cases that required a more contextual understanding. Additionally, the tool scored higher on the diagnostic portion of the rubric, with lower scores in the therapeutic recommendations. These findings highlight the importance of continuous refinement and validation of AI tools through rigorous evaluation frameworks like the OSCE. The adaptation of OSCEs for AI tools presents several advantages, including a structured and reproducible approach to evaluation, the ability to test AI systems in diverse clinical scenarios, and the opportunity to benchmark AI performance against established clinical standards to permit charting of future progress as aerospace medicine evolves as a discipline. Remaining challenges include ensuring that these evaluations capture the full spectrum of clinical decision-making scenarios that will be confronted by CMOs during missions and adequately reflecting real-world variability of the austere spaceflight environment. CONCLUSION Employing OSCEs to evaluate AI clinical decision support tools offers a promising approach to validating their clinical utility and efficacy. This methodology not only provides insights into the tool's performance but also fosters ongoing improvement and alignment with standard of care practices. Future research should focus on refining these evaluation processes and addressing limitations to enhance the integration of AI tools in clinical spaceflight settings. REFERENCES Scott S, Hearns V, Barker MA. Testing Clinical Skills: A Look at the OSCE and USMLE Clinical Skills Exams. S D Med. 2019 Oct;72(10):451-453. Majumder MAA, Kumar A, Krishnamurthy K, Ojeh N, Adams OP, Sa B. An evaluative study of objective structured clinical examination (OSCE): students and examiners perspectives. Adv Med Educ Pract. 2019 Jun 5;10:387-397. Karam VY, Park YS, Tekian A, Youssef N. Evaluating the validity evidence of an OSCE: results from a new medical school. BMC Med Educ. 2018 Dec 20;18(1):313.

Ariana M Nelson↗
NASA NTRS2025

Assessing the Added Value of Miniature X-Ray in the Setting of Portable Ultrasound in Spaceflight

INTRODUCTION: Point-of-care ultrasound (POCUS) has become the standard of care for imaging diagnosis and management in low-Earth Orbit (LEO) spaceflight and it has long been hypothesized that POCUS will also be the standard of care for exploration spaceflight. However, like the trajectory for which ultrasound became more portable and user-friendly, the mass, volume, and power requirements of radiography devices for both diagnostic and therapeutic applications have also been dramatically reduced. This study seeks to determine the clinical utility and added value of miniature x-ray (XR) for the diagnosis and management of each of the 119 conditions within NASA Exploration Medical Capability’s IMPACT Condition List (ICL) given that the medical system is presumed to already be carrying a handheld portable POCUS device. METHODS: For each condition, a team of reviewers performed a rapid systematic literature review seeking sensitivity and specificity data for both handheld portable ultrasound and miniature XR. When there was a paucity of data, subject matter expertise and clinical experience was added to semi-quantitatively score the added value of miniature XR, given an US was already available for both diagnosis and management. Diagnostic utility of a modality for a condition was evaluated in the setting of both the best- and worst-case scenario definitions included and defined by the ICL. Evidence tracing and quality of evidence scores were also recorded. RESULTS/DISCUSSION: Conditions for which it was determined that miniature XR added diagnostic or therapeutic value are provided in this presentation. Previously presented work by our team has demonstrated that XR provides diagnostic and management capabilities that are hypothesized to complement or surpass ultrasound for over one-third of medical conditions that may arise during exploration spaceflight (i.e., diagnosis of injuries to the axial skeleton, teeth, and lungs as well as management of orthopedic reductions, endotracheal tube placement, and drain placement confirmation). In the setting of known inclusion of a handheld portable POCUS device, there remains significant added value of portable miniature XR. Whether or not this added clinical benefit is worth the mass, volume, and power requirements of the radiography system remains yet unknown and is the focus of future work. LEARNING OBJECTIVES: 1) Understand the value of ultrasound and radiography in the diagnosis and management of medical comorbidities that may arise in exploration spaceflight; 2) Understand the medical conditions of highest concern on exploration class missions for which miniature x-ray may provide added value to portable ultrasound.

J G Steller↗
NASA NTRSDate not supplied

Assessing the Added Value of Miniature X-Ray in the Setting of US in Spaceflight

INTRODUCTION: Point-of-care ultrasound (POCUS) has become the standard of care for imaging diagnosis and management in lowEarth Orbit (LEO) spaceflight and it has long been hypothesized that POCUS will also be the standard of care for exploration spaceflight. However, like the trajectory for which ultrasound became more portable and user-friendly, the mass, volume, and power requirements of radiography devices for both diagnostic and therapeutic applications have also been dramatically reduced. This study seeks to determine the clinical utility and added value of miniature x-ray (XR) for the diagnosis and management of each of the 119 conditions within NASA Exploration Medical Capability’s IMPACT Condition List (ICL) given that the medical system is presumed to already be carrying a handheld portable POCUS device. METHODS: For each condition, a team of reviewers performed a rapid systematic literature review seeking sensitivity and specificity data for both handheld portable ultrasound and miniature XR. When there was a paucity of data, subject matter expertise and clinical experience was added to semi-quantitatively score the added value of miniature XR, given an US was already available for both diagnosis and management. Diagnostic utility of a modality for a condition was evaluated in the setting of both the best- and worst-case scenario definitions included and defined by the ICL. Evidence tracing and quality of evidence scores were also recorded. RESULTS/DISCUSSION: Conditions for which it was determined that miniature XR added diagnostic or therapeutic value are provided in this presentation. Previously presented work by our team has demonstrated that XR provides diagnostic and management capabilities that are hypothesized to complement or surpass ultrasound for over one-third of medical conditions that may arise during exploration spaceflight (i.e., diagnosis of injuries to the axial skeleton, teeth, and lungs as well as management of orthopedic reductions, endotracheal tube placement, and drain placement confirmation). In the setting of known inclusion of a handheld portable POCUS device, there remains significant added value of portable miniature XR. Whether or not this added clinical benefit is worth the mass, volume, and power requirements of the radiography system remains yet unknown and is the focus of future work. LEARNING OBJECTIVES: 1) Understand the value of ultrasound and radiography in the diagnosis and management of medical comorbidities that may arise in exploration spaceflight; 2) Understand the medical conditions of highest concern on exploration class missions for which miniature x-ray may provide added value to portable ultrasound.

Jon Steller↗
NASA NTRS2006

The Effect of Sensory Noise Created by Compliant and Sway-Referenced Support Surfaces on Postural Stability

The purpose of the present experiment was to compare in normal human subjects the differential effects on postural stability of introducing somatosensory noise via compliant and/or sway-referenced support surfaces during quiet standing. The use of foam surfaces (two thicknesses: thin (0.95cm) and thick (7.62cm)) and sway-referenced support allowed comparison between two different types of destabilizing factors that increased ankle/foot somatosensory noise. Under some conditions neck extensions were used to increase sensory noise by deviating the vestibular system from its optimal orientation for balance control. The impact of these conditions on postural control was assessed through objective measures of instability. Thick foam and sway-referenced support conditions generated comparable instability in subjects, as measured by equilibrium score and minimum time-to-contact. However, simultaneous application of the conditions resulted in greater instability, suggesting a higher level of generated sensory noise and thus, different receptor types affected during each manipulation. Indeed, sway-referenced support generated greater anterior-posterior center-of-mass (COM) sway, while thick foam generated greater medio-lateral COM sway and velocity. Neck extension had minimal effect on postural stability until combined with simultaneous thick foam and sway-referenced support. Thin foam never generated enough sensory noise to affect postural stability even with noise added by sway-reference support or neck extension. These results provide an interesting window into the central integration of redundant sensory information and indicate the postural impact of sensory inputs is not solely based on their existence, but also their level of noise.

Forth, Katharine E.↗
NASA NTRS1979

Materials for helicopter gears

Some of the power train transmission gears in helicopter drive systems can become critical components as performance requirements are increased; accordingly, increasing attention must be paid to new alloys in order to obtain required performance reliability and survivability. Candidate advanced alloys, with improved high temperature properties, while increasing the resistance to scoring and scuffing, tend to have lower ductility and fracture toughness. An attempt is made to identify design materials, and process problems and requirements. In addition, it is recommended that the characterization of candidate steels be accelerated; preliminary investigation indicates that new alloys may provide improved capability against surface distress.

Source record↗
NASA NTRS2021

Geomagnetically Induced Currents at Middle Latitudes: 1. Quiet-Time Variability

Geomagnetically induced currents (GICs) at middle latitudes have received increased attention after reported power grid disruptions due to geomagnetic disturbances. However, quantifying the risk to the electric power grid at middle latitudes is difficult without understanding how the GIC sensors respond to geomagnetic activity on a daily basis. Therefore, in this study the question “Do measured GICs have distinguishable and quantifiable long-period and short-period characteristics?” is addressed. The study focuses on the long-term variability of measured GIC, and establishes the extent to which the variability relates to quiet-time geomagnetic activity. GIC quiet-day curves (QDCs) are computed from measured data for each GIC node, covering all four seasons, and then compared with the seasonal variability of thermosphere-ionosphere-electrodynamics general circulation model (TIE- GCM)-simulated neutral wind and height-integrated current density. The results show strong evidence that the middle-latitude nodes routinely respond to the tidal-driven Sq variation, with a local time and seasonal dependence on the direction of the ionospheric currents, which is specific to each node. The strong dependence of GICs on the Sq currents demonstrates that the GIC QDCs may be employed as a robust baseline from which to quantify the significance of GICs during geomagnetically active times and to isolate those variations to study independently. The QDC-based significance score computed in this study provides power utilities with a node-specific measure of the geomagnetic significance of a given GIC observation. Finally, this study shows that the power grid acts as a giant sensor that may detect ionospheric current systems.

Adam C Kellerman↗
NASA NTRS2005

Satellite and Ground System Solutions at Your Fingertips

In the summer of 1998, the blockbuster action movie Armageddon captivated audiences with a thrilling doomsday plot about a meteor the size of Texas that was racing towards the Earth. Though the premise of the movie was purely fictional, the unfortunate reality is that near-Earth asteroids such as the one portrayed in the film do exist. On December 23, 2004, NASA announced that an asteroid it anticipated to pass near the Earth on April 13, 2029, had been assigned the highest score to date on the universally used Torino Impact Hazard Scale. At first, the flyby distance for the asteroid, dubbed MN4, was uncertain and an Earth impact could not be ruled out. The odds of impact were initially believed to be 1 in 300, high enough to merit special monitoring by astronomers around the world, but were then escalated to 1 in 37 on December 27. NASA officials noted, however, that these odds should not be of public concern, since they were likely to change on a day-to-day basis as new data were received. The officials were correct in their assertion, as any chances of an impact with Earth in 2029 were completely ruled out later that same day. Integral Systems, Inc., a leading provider of satellite ground systems and the first company to offer an integrated suite of commercial-off-the-shelf software products for satellite command and control, is helping NASA keep a careful watch for any close-encountering asteroids with its tracking technology. The company supported the first NASA Discovery mission, the Near Earth Asteroid Rendezvous (NEAR) program, back in 1996, and has expanded its business by building more ground systems for a greater variety of satellites than any other company in the world. (NASA has since launched seven more Discovery missions, with the eighth lifting off earlier this year.) The experience gained from the company s participation in developing satellite command and control ground systems for the NEAR program has bolstered its flagship product line, the EPOCH Integrated Product Suite (IPS), first featured in Spinoff 1997, and led to the creation of its latest product, the Skylight Direct Broadcast Ground Terminal.

Source record↗
NASA NTRS2005

Development of the Surface Management System Integrated with CTAS Arrival Tools

The Surface Management System (SMS) developed by NASA Ames Research Center in coordination with the Federal Aviation Administration (FAA) is a decision support tool to help tower traffic coordinators and Ground/Local controllers in managing and controlling airport surface traffic in order to increase capacity, efficiency, and flexibility. SMS provides common situation awareness to personnel at various air traffic control facilities such as airport traffic control towers (ATCT s), airline ramp towers, Terminal Radar Approach Control (TRACON), and Air Route Traffic Control Center (ARTCC). SMS also provides a traffic management tool to assist ATCT traffic management coordinators (TMCs) in making decisions such as airport configuration and runway load balancing. The Build 1 of the SMS tool was installed and successfully tested at Memphis International Airport (MEM) and received high acceptance scores from ATCT controllers and coordinators, as well as airline ramp controllers. NASA Ames Research Center continues to develop SMS under NASA s Strategic Airspace Usage (SAU) project in order to improve its prediction accuracy and robustness under various modeling uncertainties. This paper reports the recent development effort performed by the NASA Ames Research Center: 1) integration of Center TRACON Automation System (CTAS) capability with SMS and 2) an alternative approach to obtain airline gate information through a publicly available website. The preliminary analysis results performed on the air/surface traffic data at the DFW airport have shown significant improvement in predicting airport arrival demand and IN time at the gate. This paper concludes with recommendations for future research and development.

Jung, Yoon C.↗
NASA NTRSDate not supplied

Machine-learning Solution for Automatic Spacesuit Motion Recognition and Measurement from Conventional Video

Extravehicular Activity (EVA) spacesuits exhibit unique movement patterns due to their design characteristics. Mobility assessments using traditional motion capture systems are cost prohibitive and not feasible for some training conditions (e.g., simulated lunar outdoor terrain). This paper aims to present the ongoing development of machine learning solutions to quantify suit motions from conventional videos without special sensors or hardware. Preliminary work into this field was promising but given the fast growth in deep/machine learning technologies, external expertise was sought from open-source communities. Partnerships were formed with the NASA JSC Center of Excellence for Collaborative Innovation (CoCEI) and an execution crowdsourcing platform partner to solicit machine learning framework developments from external contenders. NASA provided contenders with images and video clips of spacesuits with simultaneously measured motion capture data during EVA simulation tasks. The contenders used this data to train and develop generalized algorithms to predict motions. At the end of the crowdsourcing event, the top five solutions were selected from 250 submissions. Each submission was tested and scored using video clips not previously disclosed to the contenders. The weighted scoring metrics measured how well the algorithm detected the suit shape, the 2D suit joint detection accuracy, and 3D joint detection accuracy. The winning solution was able to achieve roughly 85% prediction accuracy. Overall, the algorithms could efficiently detect various types of spacesuits and motions across different EVA environments such as the NASA Active Response Gravity Offload System (ARGOS). After continued improvements and validation, the fully developed system will enable EVA stakeholders to quantify suit kinematic patterns, which can help optimize suit, hardware, and task designs.

Linh Vu↗