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At least 37 records · Page 2

Supervised Learning Applied to Air Traffic Trajectory Classification

Given the recent increase of interest in introducing new vehicle types and missions into the National Airspace System, a transition towards a more autonomous air traffic control system is required in order to enable and handle increased density and complexity. This paper presents an exploratory effort of the needed autonomous capabilities by exploring supervised learning techniques in the context of aircraft trajectories. In particular, it focuses on the application of machine learning algorithms and neural network models to a runway recognition trajectory-classification study. It investigates the applicability and effectiveness of various classifiers using datasets containing trajectory records for a month of air traffic. A feature importance and sensitivity analysis are conducted to challenge the chosen time-based datasets and the ten selected features. The study demonstrates that classification accuracy levels of 90% and above can be reached in less than 40 seconds of training for most machine learning classifiers when one track data point, described by the ten selected features at a particular time step, per trajectory is used as input. It also shows that neural network models can achieve similar accuracy levels but at higher training time costs.

Bosson, Christabelle↗

Meteorological regimes for the classification of aerospace air quality predictions for NASA-Kennedy Space Center

A method is described for developing a statistical air quality assessment for the launch of an aerospace vehicle from the Kennedy Space Center in terms of existing climatological data sets. The procedure can be refined as developing meteorological conditions are identified for use with the NASA-Marshall Space Flight Center Rocket Exhaust Effluent Diffusion (REED) description. Classical climatological regimes for the long range analysis can be narrowed as the synoptic and mesoscale structure is identified. Only broad synoptic regimes are identified at this stage of analysis. As the statistical data matrix is developed, synoptic regimes will be refined in terms of the resulting eigenvectors as applicable to aerospace air quality predictions.

Stephens, J. B.↗

TOMCAT5G: A Configuration and Trust Analysis Tool for over-the-air Feature and Core Classification in 5G

Because surveillance and tracking are common in next generation wireless protocols, a user may want to have extra information about a cellular network before connecting to it. The thrust of this research answers the question: how much information can a user device get about a 5G cellular core network as a function of the amount of information the user device provides to the network?

42 ENGINEERING↗

A Discussion of Aerodynamic Control Effectors (ACEs) for Unmanned Air Vehicles (UAVs)

A Reynolds number based, unmanned air vehicle classification structure has been developed which identifies four classes of unmanned air vehicle concepts. The four unmanned air vehicle (UAV) classes are; Micro UAV, Meso UAV, Macro UAV, and Mega UAV. In a similar fashion a labeling scheme for aerodynamic control effectors (ACE) was developed and eleven types of ACE concepts were identified. These eleven types of ACEs were laid out in a five (5) layer scheme. The final section of the paper correlated the various ACE concepts to the four UAV classes and ACE recommendations are offered for future design activities.

Wood, Richard M.↗

Particle classification by image analysis improves understanding of corn stover degradation mechanisms during deconstruction

Biomass feedstock heterogeneity is a principal roadblock to implementation of the biorefinery concept. Even within an identical cultivar of corn stover, different bales contain not only varying abundance moisture, ash, glucan, and other chemical compounds, but also varying abundance of tissue anatomies (e.g., leaf, husk, cob, or stalk). These different anatomical components not only differ in their response to pretreatment and enzymatic hydrolysis to glucose, but also vary in their mechanical and conveyance properties. Although this heterogeneous nature of corn stover feedstock has been identified as a challenge, a fundamental knowledge gap of how these tissues behave during biorefining processing remains. Here, we demonstrate the use of a commercial fiber image analyzer typically used for wood fiber characterization to monitor the particle size and shapes of non-woody feedstock during milling, pretreatment, and hydrolysis. Additionally, we present novel use of Gaussian process classification to distinguish bundle, parenchyma, and fiber particles to an accuracy of 96.4%. Quantitative probability distribution plots for characteristics such as length and roundness allow elucidation of particle morphology as pretreatment and enzymatic hydrolysis progress. In both stalk pith and stalk rind, particles peel into individual cells whose walls are subsequently fragmented during enzymatic hydrolysis.

09 BIOMASS FUELS↗

Evaluation of a Process to Separate Ensiled Corn Grain and Stover for Improved Utilization as a Biomass Feedstock

Corn grain and chopped stover were stored anaerobically at 37% to 50% (w.b.) aggregate moisture content to create a unique biomass feedstock. However, after storage, the two fractions must be separated to accommodate different conversion pathways. In this work, a modified cleaning system from a grain combine harvester was evaluated to investigate its effectiveness in separating the grain from the stover. While this system has been purposefully built for this task, there is no previous literature on separating grain at high material other than grain (MOG) to grain ratios. Using this system, material was separated into four fractions: Grain, Tailings, Heavy MOG, and Light MOG. Subsamples were collected, oven dried, and then hydrodynamically separated to quantify the grain content in the four fractions. Several different configurations of air velocities, mass flow rate, material MOG: grain ratios, and sieve types and openings were investigated. Grain capture was defined as the fraction of the total grain dry mass collected in the grain and tailings fractions. In five experiments, the grain capture effectiveness varied from 89.2% to 99.4% on a dry basis. The dry basis foreign matter (FM) in the clean grain was 0.6% to 10.8%. As a result, the use of a modified combine cleaning system has the potential to be one step in a system to fractionate corn grain from stover (i.e., MOG) in a biorefinery that uses both starch and cellulose as biomass feedstocks.

09 BIOMASS FUELS↗

Impact of anatomical fractionation of corn stover on hammer mill throughput and energy consumption

The goal of this Case Study was to quantify the impacts of variable moisture and ash on hammer mill throughput and energy consumption and on loss of very wet stover that causes failures in the first stage grinder and that are not able to be fed to conversion, as compared to a status quo Base Case system. Also considered was convertible carbohydrate content (minimum total carbohydrate specification) and maximum ash content and the delivered feedstock cost impacts of not being able to feed stover not meeting the total carbohydrate specification to the conversion reactor. Laboratory data on the impacts of moisture content and tissue fraction on throughput and energy consumption in a stage 2 hammer mill were received from FCIC Subtask 5.1. Additional air classifier throughput, energy consumption and separation efficiency data were obtained from FCIC Subtask 5.1 for the new air classifier, which has three exit streams (lights, middle and heavies). These data were utilized to develop the necessary response surface equations to perform throughput analysis using discrete event simulation. Because the ash contents and particle sizes had not been analyzed in the laboratory at the time of the model runs, we assumed that the ash distributed proportionally with total mass into the lights and heavies in an air classifier having two exit streams (lights and heavies) and that the lights fraction from the air classifier was not removed. Key takeaways from this Case Study are that due to lower energy consumption, it is more cost effective to hammer mill fractionated corn stover tissues than whole stover. Reduction of grinding energy was significant and may possibly be connected to particle-particle interactions in the grinder that lead to increased residence time of leaves and husks, resulting in decreased throughput and higher generation of fines when milling whole stover. While we did not see significant impacts to throughput, this was due to moisture failures of the first stage grinder in each system dominating failures and downtime. The operating cost savings of reduced grinding energy savings in the second stage hammer mills alone was high enough to offset the added capital cost of the air classifier and extra grinding line.

09 BIOMASS FUELS↗

Microbial Monitoring of New Cleanrooms Used to Curate Astrobiologically Relevant Asteroid Samples from Bennu and Ryugu

Introduction: NASA has constructed two new cleanrooms to house materials from the OSRIS-REx and Hayabusa2 missions to the asteroids Ryugu (162173) and Bennu (101955), respectively. In accordance with standard astromaterials curation practices, these cleanrooms will be monitored for particulate contamination and maintained to ISO 5 equivalent standards1. Since the samples in these collections are expected to contain prebiotic organic compounds that may help explain the origin of life on Earth, these labs will also be monitored for organic and biological contamination2. Samples from Ryugu arrived on Earth in December, 2020. After basic characterization in Japan, NASA received a subset of these samples at the astromaterials curation facility in Houston in December of 2021. OSIRIS-REx is expected to return samples in September, 2023. Here we present preliminary microbial monitoring results from monthly monitoring of these new labs and the connected microtomy and staging areas that support them, as they are being commissioned. We also compare these results to baseline values for other astromaterials curation labs. We will also briefly describe additional cleaning efforts employed to reduce the bioburden in these new cleanrooms. Methods: Microbial samples were collected from surfaces using a dry macrofoam swab (Puritan Brand 2518051PFRNDFD). Swabs were also opened in the lab but not touched to any surfaces to function as negative controls. Samples and controls were processed inside a class II biosafety cabinet to avoid inadvertent cross contamination. The swabs were suspended in 15 ml of PBS (Phosphate Buffered Saline) and vortexed for 20 seconds to remove cells from the swab surface. The PBS was used to inoculate Petri dishes filled with TSA (Tryptic Soy Agar), Blood Agar, or Reasoners 2 agar to check for microbial growth. Each plate was inoculated with 0.1 ml of PBS. The TSA and blood agar plates were incubated at 35˚C and the Reasoners 2 agar plates were incubated at 25˚C for seven days. Petri dishes filled with Potato dextrose agar, Saboraud dextrose agar, or Saboraud dextrose agar with 0.1 mg/ml of chloramphenicol, an antibiotic, were used to check for fungal growth. These plates were inoculated with 0.3 ml of PBS and incubated at 30˚C. The remaining PBS was frozen at -80 ˚C for DNA sequencing. After incubation, isolates were counted and reisolated for identification. Isolates were identified using the VITEK23 system or by sequencing a portion of the 16S rRNA gene for bacteria or the ribosomal internal transcribed spacer (ITS) for fungi. Sequencing was performed with an ABI 3500 Sanger sequencer. Results: During our initial sampling, six of the seven sites sampled (86%) displayed bacterial or fungal growth. Samples collected from the staging areas and microtomy labs are not included in this calculation since those areas are maintained at a lower ISO 7 equivalent cleanliness standard. A month later, only three of the seven sites (43%) displayed bacterial growth. No fungal growth was detected in the second sampling. Since new equipment had been introduced to the Hayabusa2 lab since the first round of sampling, an additional three sampling sites were included in the second round of sampling. None of these sites displayed microbial growth. These sites will be included in all future sampling efforts. Bacterial isolates have been identified from the following genera at multiple time points: Micrococcus, Staphylococcus, and Bacillus. Isolates from the genera: Microbacterium, Nocardioides, Methylocystis, and Microvirga were identified in the initial sampling, but were not present at later time points. Identification of fungal isolates is in progress. Results are summarized in Table 1. Discussion: The recovery rate or percentage of positive samples4 was initially 86%, which is higher than the median recovery rate for comparable ISO 5 equivalent curation labs like Stardust (33%), Hayabusa (33%), and Cosmic Dust (50%). However, after a month of operation, the recovery rate for these same sites decreased to 43%, which is similar to what we observe in comparable curation cleanrooms with no microbial control requirements. Adding in the new sampling sites further decreases the recovery rate to 30%. With the reduction in recovery rate, we also observed a decrease in microbial diversity. At the first time point, we observed at least 10 different bacterial species and at least two different fungi. This is a higher diversity than the median values for comparable ISO 5 equivalent labs (2-4 isolates per sampling event). After the second sampling, we observed at least 4 bacterial species and no fungi, which is more consistent with comparable labs. We expect the recovery rate and diversity in both labs to continue to decrease as routine operation continues. We will use ultrapure hydrogen peroxide to disinfect equipment and work areas prior to opening any sample containers. Most of the bacterial and fungal isolates were detected on samples from the cleanroom floors. This is consistent with baseline results from other curation labs. Organisms from the genera Bacillus, Staphylococcus, and Micrococcus that were repeatedly detected are common in cleanrooms and on human skin5,6. These organisms are generally thought to be introduced when people enter the cleanroom. Microbacterium, Nocardioides, and Microvirga have also previously been identified in astromaterials cleanrooms, but not as frequently as Bacillus, Staphylococcus, and Micrococcus. Methylocystis is a novel genus in the astromaterials cleanrooms, but it was identified with low accuracy (93% match in the sequenced region of the 16S rRNA gene) and further work is needed to confirm this identification. Microbacterium is a diverse genus with isolates identified from terrestrial and aquatic sediments. Some species of Microbacterium are capable of degrading complex organic compounds found in crude oil. The presence of these bacteria in the OSIRIS REx and Hayabusa2 cleanrooms should be closely monitored. Methylocystis is a genus of methanotrophic bacteria capable of oxidizing methane. If this identification proves to be correct and it is detected again, it should be closely monitored as well. Under nominal operating conditions, samples should not ever encounter the cleanroom floor or other high traffic areas. If we observe an increase in the bioburden in sensitive work areas that appears to be influenced by organism transfer from high traffic areas like the floors, we can employ additional hydrogen peroxide treatments to disinfect high traffic areas. Routine microbial monitoring of these labs will ensure that NASA’s astromaterials collections remain pristine and useful for scientific study. Table 1. Sampling Locations and Colony Counts Bacterial CFUa Fungal CFU Bacterial CFU Fungal CFU Lab - Location 11/2/2021 11/2/2021 12/13/2021 12/13/2021 H2b-Floor 4 8 1 0 H2-staging pass through 3 0 0 0 H2-microtomy pass through TNTCc 0 0 0 H2 Microscope 1 NA NA 0 0 H2 Microscope 2 NA NA 0 0 H2-Table NA NA 0 0 OREXd- microtomy pass through 0 0 6 0 OREX – Anteroom pass through 0 0 0 0 OREX – Floor 1 2 0 0 OREX Witness Foil Table 3 0 1 0 Staging-Floor 16 0 15 0 Microtomy-Floor 3 0 2 0 a: CFU = Colony Forming Unit b: H2 = Hayabusa2 Lab c: TNTC = too numerous to count d: OREX = OSIRIS-REx Lab References: 1. ISO 14644-1:2015 - Cleanrooms and associated controlled environments -- Part 1: Classification of air cleanliness by particle concentration. 37 (2015). 2. McCubbin, F. M. et al. Space Sci Rev 215, (2019). 3. Pincus, D. H. Encyclopedia of Rapid Microbiological Methods (2005). 4. The United States Pharmacopeial Convention. USP General Chapter <1116> 17, 784–794 (2013). 5. Sheraba, N. S., Yassin, A. S. & Amin, M. BMC Research Notes 3, 278 (2010). 6. Utescher, C. L. de A., Franzolin, M. R., Trabulsi, L. R. & Gambale, V. Brazilian Journal of Microbiology 38, 710–716 (2007).

A B Regberg↗

Biomass Feedstock National User Facility--Improving Bale Deconstruction and Material Flow

The project aims to reduce feedstock variability using a quality-by-design approach beginning when the biomass is introduced to the process and will continue through the size reduction process, which will yield the results of fine generation reduction, contaminant removal, control of the physical and chemical critical material attributes in the process, and management of the flowability.

09 BIOMASS FUELS↗

Climate-Induced Mortality of Spruce Stands in Belarus

The aim of this work is an analysis of the causes of spruce (Picea abies L.) decline and mortality in Belarus. The analysis was based on forest inventory and Landsat satellite (land cover classification, climate variables (air temperature, precipitation, evaporation, vapor pressure deficit, SPEI drought index)), and GRACE-derived soil moisture estimation (equivalent of water thickness anomalies, EWTA). We found a difference in spatial patterns between dead stands and all stands (i.e., before mortality). Dead stands were located preferentially on relief features with higher water stress risk (i.e., higher elevations, steeper slopes, south and southwestern exposure). Spruce mortality followed a series of repeated droughts between 1990 and 2010. Mortality was negatively correlated with air humidity (r = -0.52), and precipitation (r = -0.57), and positively correlated with the prior year vapor pressure deficit (r = 0.47), and drought increase (r = 0.57). Mortality increased with the increase in occurrence of spring frosts (r = 0.5), and decreased with an increase in winter cloud cover (r = -0.37). Spruce mortality was negatively correlated with snow water accumulation (r = -0.81) and previous year anomalies in water soil content (r = -0.8). Weakened by water stress, spruce stands were attacked by pests and phytopathogens. Overall, spruce mortality in Belarussian forests was caused by drought episodes and drought increase in synergy with pest and phytopathogen attacks. Vast Picea abies mortality in Belarus and adjacent areas of Russia and Eastern Europe is a result of low adaptation of that species to increased drought. This indicates the necessity of spruce replacement by drought-tolerant indigenous (e.g., Pinus sylvestris, Querqus robur) or introduced (e.g., Larix sp. or Pseudotsuga menzieslii) species to obtain sustainable forest growth management.

spruce decline↗

A Multi-Sensor Water Vapor, Temperature and Cloud Climate Data Record: Status of Our MEaSUREs 2012 Project

- Our primary objectives: Incorporate all A-Train water vapor, including small-scale, horizontal and vertical water vapor structure from MODIS (Moderate Resolution Imaging Spectroradiometer) and GPS (Global Positioning System); Use MODIS cloud classification that is collocated at the pixel-scale over the full AIRS/AMSU (Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit) swath; Extend summaries to PDFs (Probability Distribution Functions)/higher order moments of water vapor sorted by cloud classes; Establish robust, scale-dependent statistical relationships between cloud, temperature, and water vapor PDFs for the climate modeling community. - We will also: Update our current data record to new CloudSat/CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) cloud classification; Provide merged AIRS and MLS (Microwave Limb Sounder) water vapor profiles using averaging kernels; Include temperature and water vapor from models (ECMWF - European Centre for Medium-Range Weather Forecasts) and re-analyses (MERRA - Modern Era-Retrospective Analysis for Research and Applications); Classify METOP-A (Meteorological Operational Satellite-A) and NOAA (National Oceanic and Atmospheric Administration) satellite water vapor with AVHRR (Advanced Very High Resolution Radiometer) during SNOs (Solar Neutrino Oscillations); Compare with the NVAP-M (re-analysis and extension of the NASA Water Vapor Project (NVAP)) water vapor climatologies; Use existing data sources and mature algorithms, and document processing algorithms.

Fetzer, Eric↗

Applications of artificial intelligence 1993: Knowledge-based systems in aerospace and industry; Proceedings of the Meeting, Orlando, FL, Apr. 13-15, 1993

The present volume on applications of artificial intelligence with regard to knowledge-based systems in aerospace and industry discusses machine learning and clustering, expert systems and optimization techniques, monitoring and diagnosis, and automated design and expert systems. Attention is given to the integration of AI reasoning systems and hardware description languages, care-based reasoning, knowledge, retrieval, and training systems, and scheduling and planning. Topics addressed include the preprocessing of remotely sensed data for efficient analysis and classification, autonomous agents as air combat simulation adversaries, intelligent data presentation for real-time spacecraft monitoring, and an integrated reasoner for diagnosis in satellite control. Also discussed are a knowledge-based system for the design of heat exchangers, reuse of design information for model-based diagnosis, automatic compilation of expert systems, and a case-based approach to handling aircraft malfunctions.

Fayyad, Usama M.↗

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↗

Air support facilities

Airports are discussed in terms of the interface between the ground and air for transportation systems. The classification systems, design, facilities, administration, and operations of airports are described.

Source record↗

Building a Shared Definitional Model of Long Duration Human Spaceflight

In 1956, on the eve of human space travel Strughold first proposed a simple classification of the present and future stages of manned flight that identified key factors, risks and developmental stages for the evolutionary journey ahead. As we look to optimize the potential of the ISS as a gateway to new destinations, we need a current shared working definitional model of long duration human space flight to help guide our path. Initial search of formal and grey literature augmented by liaison with subject matter experts. Search strategy focused on both the use of term long duration mission and long duration spaceflight, and also broader related current and historical definitions and classification models of spaceflight. The related sea and air travel literature was also subsequently explored with a view to identifying analogous models or classification systems. There are multiple different definitions and classification systems for spaceflight including phase and type of mission, craft and payload and related risk management models. However the frequently used concepts of long duration mission and long duration spaceflight are infrequently operationally defined by authors, and no commonly referenced classical or gold standard definition or model of these terms emerged from the search. The categorization (Cat) system for sailing was found to be of potential analogous utility, with its focus on understanding the need for crew and craft autonomy at various levels of potential adversity and inability to gain outside support or return to a safe location, due to factors of time, distance and location.

Orr, M.↗

CALIPSO Lidar Level 3 Aerosol Profile Product: Version 3 Algorithm Design

The CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) level 3 aerosol profile product reports globally gridded, quality-screened, monthly mean aerosol extinction profiles retrieved by CALIOP (the Cloud-Aerosol Lidar with Orthogonal Polarization). This paper describes the quality screening and averaging methods used to generate the version 3 product. The fundamental input data are CALIOP level 2 aerosol extinction profiles and layer classification information (aerosol, cloud, and clear-air). Prior to aggregation, the extinction profiles are quality-screened by a series of filters to reduce the impact of layer detection errors, layer classification errors, extinction retrieval errors, and biases due to an intermittent signal anomaly at the surface. The relative influence of these filters are compared in terms of sample rejection frequency, mean extinction, and mean aerosol optical depth (AOD). The “extinction QC flag” filter is the most influential in preventing high-biases in level 3 mean extinction, while the “misclassified cirrus fringe” filter is most aggressive at rejecting cirrus misclassified as aerosol. The impact of quality screening on monthly mean aerosol extinction is investigated globally and regionally. After applying quality filters, the level 3 algorithm calculates monthly mean AOD by vertically integrating the monthly mean quality-screened aerosol extinction profile. Calculating monthly mean AOD by integrating the monthly mean extinction profile prevents a low bias that would result from alternately integrating the set of extinction profiles first and then averaging the resultant AOD values together. Ultimately, the quality filters reduce level 3 mean AOD by −24 and −31 % for global ocean and global land, respectively, indicating the importance of quality screening.

Tackett, Jason L.↗

Intelligent tutoring systems as tools for investigating individual differences in learning

The ultimate goal of this research is to build an improved model-based selection and classification system for the United States Air Force. Researchers are developing innovative approaches to ability testing. The Learning Abilities Measurement Program (LAMP) examines individual differences in learning abilities, seeking answers to the questions of why some people learn more and better than others and whether there are basic cognitive processes applicable across tasks and domains that are predictive of successful performance (or whether there are more complex problem solving behaviors involved).

Shute, Valerie J.↗