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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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At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Medical impact analysis for the Space Station

In this study, Space Station medical care priorities were determined by a medical impact analysis of two analog populations, U.S. Army and U.S. Navy personnel. Diseases and injuries in the International Classification of Disease, 9th Revision, Clinical Modification (ICD-9-CM) were ranked, using a Medical Impact Score (MIS) combining modified incidence rate and a function of disease outcome. The validity of the analysis method was tested by measuring rank order correlation between the two analog populations. Despite virtually identical age and sex distributions, Army and Navy incidence rates differed significantly for half of the ICD-9-CM categories, p less than 0.05. Disability rates differed for 76 percent, p less than 0.05. Nevertheless, Army and Navy MIS rank orders for categories and sections were not significantly different, p less than 0.001. In critical ways, the Space Station will be a safer environment than earth. Cardiac events, musculoskeletal injuries, affective psychoses, and renal calculi were among the highest scoring categories.

Nelson, Brent D.↗
NASA NTRS2011

Feature Acquisition with Imbalanced Training Data

This work considers cost-sensitive feature acquisition that attempts to classify a candidate datapoint from incomplete information. In this task, an agent acquires features of the datapoint using one or more costly diagnostic tests, and eventually ascribes a classification label. A cost function describes both the penalties for feature acquisition, as well as misclassification errors. A common solution is a Cost Sensitive Decision Tree (CSDT), a branching sequence of tests with features acquired at interior decision points and class assignment at the leaves. CSDT's can incorporate a wide range of diagnostic tests and can reflect arbitrary cost structures. They are particularly useful for online applications due to their low computational overhead. In this innovation, CSDT's are applied to cost-sensitive feature acquisition where the goal is to recognize very rare or unique phenomena in real time. Example applications from this domain include four areas. In stream processing, one seeks unique events in a real time data stream that is too large to store. In fault protection, a system must adapt quickly to react to anticipated errors by triggering repair activities or follow- up diagnostics. With real-time sensor networks, one seeks to classify unique, new events as they occur. With observational sciences, a new generation of instrumentation seeks unique events through online analysis of large observational datasets. This work presents a solution based on transfer learning principles that permits principled CSDT learning while exploiting any prior knowledge of the designer to correct both between-class and withinclass imbalance. Training examples are adaptively reweighted based on a decomposition of the data attributes. The result is a new, nonparametric representation that matches the anticipated attribute distribution for the target events.

Thompson, David R.↗
DOE OSTI2025

Regime Characterization of Offshore Wind Resource Using Unsupervised Learning

Predictability of wind resource conditions is critical for offshore wind design and operations. While many studies of extreme wind conditions focus on specific events such as low-level jets or ramps, these rely on threshold definitions that limit generality. Here we present a data-driven framework that combines principal component analysis (PCA), self-organizing maps (SOM), and k-means clustering to classify wind resource conditions as typical and anomalous from climatological data. Anomalies are defined not by fixed thresholds but by flagging samples located far from SOM node centers inside the baseline SOM structure. This reframes extremes as rare ebents and hence, likely difficult to anticipate by numerical weather prediction models. We applied this approach to 23 years (2000–2022) of hourly profiles from the NOW-23 hindcast model at the Humboldt Wind Energy Area. Classification is conducted on a feature space consisting of 10 m wind speed and direction, bulk shear and veer across 30–270 m, and a low-level jet index. Dimensionality reduction is achieved through PC. A 2 × 3 OM lattice trained on the PCA vectors identified six baseline regimes spanning weak to strong flow states. High quantization-error profiles are identified and re-clustered into four anomalous regimes. The baseline regimes exhibited clear seasonal and diurnal cycles. Meanwhile, the anomalous regimes represented <10 % of all hours but showed distinct combinations of speed, shear, and veer, when compared to the baseline regimes. Anomalous regimes are typically short-lived (~few hours), yet their transitions can lead to hub-height wind changes of −18 to +9 m s -1 . For a representative 15 MW turbine, these shifts imply rapid swings in capacity factor from near-full output to negligible generation. Validation with lidar buoy data showed 51% agreement in SOM labels across ~6,000 overlapping hours, with most mismatches confined to adjacent speed classes. HRRR comparisons further revealed that anomalous regimes were disproportionately associated with forecast biases exceeding 5 m s -1 . Together, these results reframe extremes in offshore wind from absolute maxima or minima to weather states that are difficult to anticipate from models.

17 WIND ENERGY↗
DOE OSTI2011

Drain Tank Information for Developing Design Basis of the Preliminary Design - R00

Tokamak Cooling Water System (TCWS) drain tanks (DTs) serve two functions: normal operation and safety operation. Normal DTs are used for regular maintenance operations when draining is necessary. Safety DTs are used to receive the water leaked into the Vacuum Vessel (VV) after an in-vessel loss of cooling accident (LOCA) event. The preliminary design of the DTs shall be based on the information provided by this document. The capacity of the normal DTs is estimated based on the internal volume of in-vessel components [e.g., First Wall/Blanket (FW/BLK) and Divertor (DIV)], Neutral Beam Injector (NBI) components, and TCWS piping, heat exchangers, electric heaters, pump casing, pressurizers, and valves. Water volumes have been updated based on 2004 design information, changes adopted because of approved Project Change Requests (PCRs), and data verification by U.S. ITER. Two tanks will store water from normal draining operations of the FW/BLK and DIV Primary Heat Transfer Systems (PHTSs). One tank will store water from normal draining operations of the NBI PHTS. The capacity of the safety DTs is based on analysis of a design-basis accident:1 a large leak from in-vessel components. There are two safety DTs that will receive water from a VV LOCA event and drainage from the VV, as needed. In addition, there is one sump tank for the DIV that will be used for collecting drain water from the draining and drying processes and specifically for draining the DIV system as the DIV cassettes lines are at a lower elevation than the DT connection point. Information documented in this report must be refined and verified during the preliminary design of the DTs, and there are several aspects to be considered to complete the preliminary design. Input to these design considerations is discussed in this report and includes, but is not limited to, water inventory; operating procedures/maintenance; Failure Modes and Effects Analysis (FMEA); tank layout anddimensions, including design margin; classification under French Nuclear Pressure Directives, Equipements Sous Pression Nucleaires (ESPN); and adaptations for construction.

Cooling Water↗
NASA NTRS1992

The adult literacy evaluator: An intelligent computer-aided training system for diagnosing adult illiterates

An important part of NASA's mission involves the secondary application of its technologies in the public and private sectors. One current application being developed is The Adult Literacy Evaluator, a simulation-based diagnostic tool designed to assess the operant literacy abilities of adults having difficulties in learning to read and write. Using ICAT system technology in addition to speech recognition, closed-captioned television (CCTV), live video and other state-of-the art graphics and storage capabilities, this project attempts to overcome the negative effects of adult literacy assessment by allowing the client to interact with an intelligent computer system which simulates real-life literacy activities and materials and which measures literacy performance in the actual context of its use. The specific objectives of the project are as follows: (1) To develop a simulation-based diagnostic tool to assess adults' prior knowledge about reading and writing processes in actual contexts of application; (2) to provide a profile of readers' strengths and weaknesses; and (3) to suggest instructional strategies and materials which can be used as a beginning point for remediation. In the first and developmental phase of the project, descriptions of literacy events and environments are being written and functional literacy documents analyzed for their components. Examples of literacy events and situations being considered included interactions with environmental print (e.g., billboards, street signs, commercial marquees, storefront logos, etc.), functional literacy materials (e.g., newspapers, magazines, telephone books, bills, receipts, etc.) and employment related communication (i.e., job descriptions, application forms, technical manuals, memorandums, newsletters, etc.). Each of these situations and materials is being analyzed for its literacy requirements in terms of written display (i.e., knowledge of printed forms and conventions), meaning demands (i.e., comprehension and word knowledge) and social situation. From these descriptions, scripts are being generated which define the interaction between the student, an on-screen guide and the simulated literacy environment. The proposed outcome of the Evaluator is a diagnostic profile which will present broad classifications of literacy behaviors across the major areas of metacognitive abilities, word recognition, vocabulary knowledge, comprehension and writing. From these classifications, suggestions for materials and strategies for instruction with which to begin corrective action will be made. The focus of the Literacy Evaluator will be essentially to provide an expert diagnosis and an interpretation of that assessment which then can be used by a human tutor to further design and individualize a remedial program as needed through the use of an authoring system.

Yaden, David B., Jr.↗
DOE OSTI2011

Drain Tank Information for Developing Design Basis of the Preliminary Design

Tokamak Cooling Water System (TCWS) drain tanks (DTs) serve two functions: normal operation and safety operation. Normal DTs are used for regular maintenance operations when draining is necessary. Safety DTs are used to receive the water leaked into the Vacuum Vessel (VV) after an in-vessel loss of coolant accident (LOCA) event. The preliminary design of the DTs shall be based on the information provided by this document. The capacity of the normal DTs is estimated based on the internal volume of in-vessel components [e.g., First Wall/Blanket (FW/BLK) and Divertor (DIV)]; Neutral Beam Injector (NBI) components; and TCWS piping, heat exchangers, electric heaters, pump casing, pressurizers, and valves. Water volumes have been updated based on 2004-design information, changes adopted because of approved Project Change Requests (PCRs), and data verification by US ITER and AREVA Federal Services, the US ITER A and E Company. Two tanks will store water from normal draining operations of the FW/BLK and DIV Primary Heat Transfer Systems (PHTSs). One tank will store water from normal draining operations of the NBI PHTS. The capacity of the safety DTs is based on analysis of a design basis accident: a large leak from in-vessel components. There are two safety DTs that will receive water from a VV LOCA event and drainage from the VV, as needed. In addition, there is one sump tank for the DIV that will be used for collecting drain water from the draining and drying processes and specifically for draining the DIV system as the DIV cassette lines are at a lower elevation than the DT connection point. Information documented in this report must be refined and verified during the preliminary design of the DTs, and there are several aspects to be considered to complete the preliminary design. Input to these design considerations is discussed in this report and includes, but is not limited to, water inventory; operating procedures/maintenance; Failure Modes and Effects Analysis (FMEA); tank layout and dimensions, including design margin; classification under French Nuclear Pressure Directives, Equipements Sous Pression Nucleaires (ESPN); and adaptations for construction.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2006

Northwest Africa 428: Impact-induced Annealing of an L6 Chondrite Breccia

Northwest Africa (NWA) 428 is an L chondrite that was successively thermally metamorphosed to petrologic type-6, shocked to stage S4-S5, brecciated, and annealed to approximately petrologic type-4. Its thermal and shock history resembles that of the previously studied LL6 chondrite, Miller Range (MIL) 99301, which formed on a different asteroid. The petrologic type-6 classification of NWA 428 is based on its highly recrystallized texture, coarse metal (150 +/- 150 micron), troilite (100 +/- 170 micron), and plagioclase (20-60 micron) grains, and relatively homogeneous olivine (Fa(sub 24.4 +/- 0.6)), low-Ca pyroxene (FS(sub 2.5+/- 0~,4) , and plagioclase (Ab(sub 84.2 +/- 0.4) compositions. The petrographic criteria that indicate shock stage S4-S5 include the presence of chromite veinlets, chromite-plagioclase assemblages, numerous occurrences of metallic Cu, irregular troilite grains within metallic Fe-Ni, polycrystalline troilite, duplex plessite, metal and troilite veins, large troilite nodules, and low-Ca clinopyroxene with polysynthetic twins. If the rock had been shocked before thermal metamorphism, low-Ca clinopyroxene produced by the shock event would have transformed into orthopyroxene. Post-shock brecciation is indicated by the presence of recrystallized clasts and highly shocked clasts that form sharp boundaries with the host. Post-shock annealing is indicated by the sharp optical extinction of the olivine grains; during annealing, the damaged olivine crystal lattices healed. If temperatures exceeded those approximating petrologic type-4 (approximately 600-700 C) during annealing, the low-Ca clinopyroxene would have transformed into orthopyroxene. The other shock indicators, likewise, survived the mild annealing. An impact event is the most plausible source of post-metamorphic, post-shock annealing because any A1-26 that may have been present when the asteroid accreted would have decayed away by the time NWA 428 was annealed. The similar inferred histories of NWA 428 (L6) and MIL 99301 (LL6) indicate that impact heating affected more than 1 ordinary chondrite parent body.

Rubin, Alan E.↗
NASA NTRS2007

Real-Time Application of Multi-Satellite Precipitation Analysis for Floods and Landslides

Satellite data acquired and processed in real time now have the potential to provide the spacetime information on rainfall needed to monitor flood and landslide events around the world. This can be achieved by integrating the satellite-derived forcing data with hydrological models and landslide algorithms. Progress in using the TRMM Multi-satellite Precipitation Analysis (TMPA) as input to flood and landslide forecasts is outlined, with a focus on understanding limitations of the rainfall data and impacts of those limitations on flood/landslide analyses. Case studies of both successes and failures will be shown, as well as comparison with ground comparison data sets-- both in terms of rainfall and in terms of flood/landslide events. In addition to potential uses in real-time, the nearly ten years of TMPA data allow retrospective running of the models to examine variations in extreme events. The flood determination algorithm consists of four major components: 1) multi-satellite precipitation estimation; 2) characterization of land surface including digital elevation from NASA SRTM (Shuttle Radar Terrain Mission), topography-derived hydrologic parameters such as flow direction, flow accumulation, basin, and river network etc.; 3) a hydrological model to infiltrate rainfall and route overland runoff; and 4) an implementation interface to relay the input data to the models and display the flood inundation results to potential users and decision-makers, In terms of landslides, the satellite rainfall information is combined with a global landslide susceptibility map, derived from a combination of global surface characteristics (digital elevation topography, slope, soil types, soil texture, and land cover classification etc.) using a weighted linear combination approach. In those areas identified as "susceptible" (based on the surface characteristics), landslides are forecast where and when a rainfall intensity/duration threshold is exceeded. Results are described indicating general agreement with landslide occurrences.

Adler, Robert↗
DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy's National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real-time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI's Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: Run in real-time on the edge (≥ 12 Hz), Classification: Achieve less than 5% false positive detection, Classification: Achieve ≥ 95% methane plume detection rate, Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time. In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗
DOE OSTI2021

“Thought I’d Share First” and Other Conspiracy Theory Tweets from the COVID-19 Infodemic: Exploratory Study

Background: The COVID-19 outbreak has left many people isolated within their homes; these people are turning to social media for news and social connection, which leaves them vulnerable to believing and sharing misinformation. Health-related misinformation threatens adherence to public health messaging, and monitoring its spread on social media is critical to understanding the evolution of ideas that have potentially negative public health impacts. Objective: The aim of this study is to use Twitter data to explore methods to characterize and classify four COVID-19 conspiracy theories and to provide context for each of these conspiracy theories through the first 5 months of the pandemic. Methods: We began with a corpus of COVID-19 tweets (approximately 120 million) spanning late January to early May 2020. We first filtered tweets using regular expressions (n=1.8 million) and used random forest classification models to identify tweets related to four conspiracy theories. Our classified data sets were then used in downstream sentiment analysis and dynamic topic modeling to characterize the linguistic features of COVID-19 conspiracy theories as they evolve over time. Results: Analysis using model-labeled data was beneficial for increasing the proportion of data matching misinformation indicators. Random forest classifier metrics varied across the four conspiracy theories considered (F1 scores between 0.347 and 0.857); this performance increased as the given conspiracy theory was more narrowly defined. We showed that misinformation tweets demonstrate more negative sentiment when compared to non-misinformation tweets and that theories evolve over time, incorporating details from unrelated conspiracy theories as well as real-world events. Conclusions: Although we focus here on health-related misinformation, this combination of approaches is not specific to public health and is valuable for characterizing misinformation in general, which is an important first step in creating targeted messaging to counteract its spread. Initial messaging should aim to preempt generalized misinformation before it becomes widespread, while later messaging will

5g↗
DOE OSTI2019

Quantifying Transmission Fault Voltage Influence and its Potential Impact on Distributed Energy Resources

With increasing shares of distributed energy resources (DERs) on the electric grid, the power system is transitioning from having fewer large generators with relatively simple dynamics to many small generators with aggregate responses to system disturbances that are difficult to predict. In particular, voltage fluctuations might cause large losses of generation via DER operation/interconnection compliance. To understand orchestral DER dynamics, a first step is to determine the influence of a disturbance on the overall bulk power system. This work introduces metrics that allow for the classification and comparison of a variety of system faults with respect to their influences on voltage profiles. These metrics are applied to standardized fault simulations on the Western Interconnection Paths with a full-scale Positive Sequence Load Flow model. The results are presented with a highlight on Path 61, which has the potential for DER generation reduction exceeding the current credible contingency of a double Palo Verde loss.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Utilizing Geo-located Sensors and Social Media for Studying Population Dynamics and Land Classification

The ability to understand where, when, and why humans move across space and time has always been essential to research areas such as urban planning, transportation, population dynamics, and emergency preparedness and response. The increasing sources of activity data is generating novel opportunities to understand human dynamics that previously was not possible; Geo-located user generated content from mobile devices and sensors allow a level of spatial and temporal granularity that could possibly answer the reasons for human movement at a high-resolution. This work discusses a broad array of research agenda in human dynamics and land use by proposing an explicit model that assists in delineating and articulating the opportunities, challenges, and limitations of using new forms of unauthoritated data, such as social media in main-stream GIS research. We study mobile phone call volume and GPS locations to characterize human activity patterns and provide inference on land use. Later, we demonstrate the ability of geo-located social media posts to provide insight on population density estimates for special events and Points of Interest detection. The chapter underpins the need to utilize new forms of data collection mechanism as well as their use to augment our understanding of human dynamics research and future application of geographical information systems.

Thakur, Gautam Malviya↗
DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy’s National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real- time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI’s Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: 1. Run in real-time on the edge (≥ 12 Hz) 2. Classification: Achieve less than 5% false positive detection 3. Classification: Achieve ≥ 95% methane plume detection rate 4. Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗
NASA NTRS2014

An Automated Approach to Map the History of Forest Disturbance from Insect Mortality and Harvest with Landsat Time-Series Data

Forests contain a majority of the aboveground carbon (C) found in ecosystems, and understanding biomass lost from disturbance is essential to improve our C-cycle knowledge. Our study region in the Wisconsin and Minnesota Laurentian Forest had a strong decline in Normalized Difference Vegetation Index (NDVI) from 1982 to 2007, observed with the National Ocean and Atmospheric Administration's (NOAA) series of Advanced Very High Resolution Radiometer (AVHRR). To understand the potential role of disturbances in the terrestrial C-cycle, we developed an algorithm to map forest disturbances from either harvest or insect outbreak for Landsat time-series stacks. We merged two image analysis approaches into one algorithm to monitor forest change that included: (1) multiple disturbance index thresholds to capture clear-cut harvest; and (2) a spectral trajectory-based image analysis with multiple confidence interval thresholds to map insect outbreak. We produced 20 maps and evaluated classification accuracy with air-photos and insect air-survey data to understand the performance of our algorithm. We achieved overall accuracies ranging from 65% to 75%, with an average accuracy of 72%. The producer's and user's accuracy ranged from a maximum of 32% to 70% for insect disturbance, 60% to 76% for insect mortality and 82% to 88% for harvested forest, which was the dominant disturbance agent. Forest disturbances accounted for 22% of total forested area (7349 km2). Our algorithm provides a basic approach to map disturbance history where large impacts to forest stands have occurred and highlights the limited spectral sensitivity of Landsat time-series to outbreaks of defoliating insects. We found that only harvest and insect mortality events can be mapped with adequate accuracy with a non-annual Landsat time-series. This limited our land cover understanding of NDVI decline drivers. We demonstrate that to capture more subtle disturbances with spectral trajectories, future observations must be temporally dense to distinguish between type and frequency in heterogeneous landscapes.

Forest↗
DOE OSTI2020

Track Seeding and Labelling with Embedded-space Graph Neural Networks

To address the unprecedented scale of HL-LHC data, the Exa.TrkX project is investigating a variety of machine learning approaches to particle track reconstruction. The most promising of these solutions, graph neural networks (GNN), process the event as a graph that connects track measurements (detector hits corresponding to nodes) with candidate line segments between the hits (corresponding to edges). Detector information can be associated with nodes and edges, enabling a GNN to propagate the embedded parameters around the graph and predict node-, edge- and graph-level observables. Previously, message-passing GNNs have shown success in predicting doublet likelihood, and we here report updates on the state-of-the-art architectures for this task. In addition, the Exa.TrkX project has investigated innovations in both graph construction, and embedded representations, in an effort to achieve fully learned end-to-end track finding. Hence, we present a suite of extensions to the original model, with encouraging results for hitgraph classification. In addition, we explore increased performance by constructing graphs from learned representations which contain non-linear metric structure, allowing for efficient clustering and neighborhood queries of data points. We demonstrate how this framework fits in with both traditional clustering pipelines, and GNN approaches. The embedded graphs feed into high-accuracy doublet and triplet classifiers, or can be used as an end-to-end track classifier by clustering in an embedded space. A set of post-processing methods improve performance with knowledge of the detector physics. Finally, we present numerical results on the TrackML particle tracking challenge dataset, where our framework shows favorable results in both seeding and track finding.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Benchmarking sensor fusion capabilities of an integrated instrumentation package

Quantifying and mitigating environmental risks introduced by marine energy conversion systems requires a variety of sensors (active acoustic, passive acoustic, and optical). The operation of these sensors must satisfy three directives to be effective: (1) do not alter the environment through operation of sensors; (2) capture rare events; and (3) do not accrue unmanageable volumes of low-value data. This requires integrating sensors into a single package, rather than operating them independently. The Adaptable Monitoring Package is an integrated instrumentation package that combines a multibeam sonar, acoustic camera, current profiler, optical cameras, and an array of hydrophones. The capabilities and limitations of the AMP sensors were benchmarked using cooperative targets, and real-time target tracking and detection was used to detect opportunistic targets (e.g., diving birds, seals). During an initial deployment, automatic detection of opportunistic targets achieved a 58% true positive rate and a 99% true negative rate (100% corresponding to an ideal system in both cases). In post-processing, target tracking data were used to evaluate automatic target classification capabilities using a k-nearest neighbor algorithm. Findings suggest that real-time target classification should be possible and enable integrated instrumentation systems to meet the monitoring needs of marine energy deployments.

16 TIDAL AND WAVE POWER↗
NASA NTRS1990

Real-time measurement of mental workload: A feasibility study

The primary goal of the study was to explore the utility of event-related brain potentials (ERP) as real-time measures of workload. To this end, subjects performed two different tasks both separately and together. One task required that subjects monitor a bank of constantly changing gauges and detect critical deviations. Difficulty was varied by changing the predictability of the gauges. The second task was mental arithmetic. Difficulty was varied by requiring subjects to perform operations on either two or three columns of numbers. Two conditions that could easily be distinguished on the basis of performance measures were selected for the real-time evaluation of ERPs. A bootstrapping approach was adopted in which one thousand samples of n trials (n = 1, 3, 5 ...65) were classified using several measures of P300 and Slow Wave amplitude. Classification accuracies of 85 percent were achieved with 25 trials. Results are discussed in terms of potential enhancements for real-time recording.

Kramer, Arthur↗
DOE OSTI2019

Hyperspectral vegetation identification at a legacy underground nuclear explosion test site

The detection, location, and identification of suspected underground nuclear explosions (UNEs) are global security priorities that rely on integrated analysis of multiple data modalities for uncertainty reduction in event analysis. Vegetation disturbances may provide complementary signatures that can confirm or build on the observables produced by prompt sensing techniques such as seismic or radionuclide monitoring networks. For instance, the emergence of non-native species in an area may be indicative of anthropogenic activity or changes in vegetation health may reflect changes in the site conditions resulting from an underground explosion. Previously, we collected high spatial resolution (10 cm) hyperspectral data from an unmanned aerial system at a legacy underground nuclear explosion test site and its surrounds. These data consist of visible and near-infrared wavebands over 4.3 km 2 of high desert terrain along with high spatial resolution (2.5 cm) RGB context imagery. In this work, we employ various spectral detection and classification algorithms to identify and map vegetation species in an area of interest containing the legacy test site. Here, we employed a frequentist framework for fusing multiple spectral detections across various reference spectra captured at different times and sampled from multiple locations. The spatial distribution of vegetation species is compared to the location of the underground nuclear explosion. We discover a difference in species abundance within a 130 m radius of the center of the test site.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗