Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “training data quality”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Implementation of Insight Responsibilities in Process Engineering

This report describes an approach for evaluating flight readiness (COFR) and contractor performance evaluation (award fee) as part of the insight role of NASA Process Engineering at Kennedy Space Center. Several evaluation methods are presented, including systems engineering evaluations and use of systems performance data. The transition from an oversight function to the insight function is described. The types of analytical tools appropriate for achieving the flight readiness and contractor performance evaluation goals are described and examples are provided. Special emphasis is placed upon short and small run statistical quality control techniques. Training requirements for system engineers are delineated. The approach described herein would be equally appropriate in other directorates at Kennedy Space Center.

Osborne, Deborah M.↗
NASA NTRS2012

Global Characterization and Monitoring of Forest Cover Using Landsat Data: Opportunities and Challenges

The compilation of global Landsat data-sets and the ever-lowering costs of computing now make it feasible to monitor the Earth's land cover at Landsat resolutions of 30 m. In this article, we describe the methods to create global products of forest cover and cover change at Landsat resolutions. Nevertheless, there are many challenges in ensuring the creation of high-quality products. And we propose various ways in which the challenges can be overcome. Among the challenges are the need for atmospheric correction, incorrect calibration coefficients in some of the data-sets, the different phenologies between compilations, the need for terrain correction, the lack of consistent reference data for training and accuracy assessment, and the need for highly automated characterization and change detection. We propose and evaluate the creation and use of surface reflectance products, improved selection of scenes to reduce phenological differences, terrain illumination correction, automated training selection, and the use of information extraction procedures robust to errors in training data along with several other issues. At several stages we use Moderate Resolution Spectroradiometer data and products to assist our analysis. A global working prototype product of forest cover and forest cover change is included.

Global↗
NASA NTRS2012

Estimation and Bias Correction of Aerosol Abundance using Data-driven Machine Learning and Remote Sensing

Air quality information is increasingly becoming a public health concern, since some of the aerosol particles pose harmful effects to peoples health. One widely available metric of aerosol abundance is the aerosol optical depth (AOD). The AOD is the integrated light extinction coefficient over a vertical atmospheric column of unit cross section, which represents the extent to which the aerosols in that vertical profile prevent the transmission of light by absorption or scattering. The comparison between the AOD measured from the ground-based Aerosol Robotic Network (AERONET) system and the satellite MODIS instruments at 550 nm shows that there is a bias between the two data products. We performed a comprehensive analysis exploring possible factors which may be contributing to the inter-instrumental bias between MODIS and AERONET. The analysis used several measured variables, including the MODIS AOD, as input in order to train a neural network in regression mode to predict the AERONET AOD values. This not only allowed us to obtain an estimate, but also allowed us to infer the optimal sets of variables that played an important role in the prediction. In addition, we applied machine learning to infer the global abundance of ground level PM2.5 from the AOD data and other ancillary satellite and meteorology products. This research is part of our goal to provide air quality information, which can also be useful for global epidemiology studies.

Malakar, Nabin K.↗
NASA NTRS2011

Growth of Three Lettuce Cultivars in NASA's HDU PEM During the 2010 DRATS Test

NASA's 2010 Desert Research and Technology Studies (DRATS) of the VEGGIE Food Production System in the Habitat Demonstration Unit (HDU) Pressurized Excursion Module (PEM) was the first operational evaluation of salad crop production technology in a NASA analog test. Rooting media and slow release fertilizers were evaluated for three lettuce cultivars that had shown promise as candidates for a surface based food production system. These tests involved comparing growth, color and quality of the lettuce cultivars grown under VEGGIE LED array (Orbitec, Madison, WI) or Biomass Production System for Education ((BSEe), Orbitec, Madison, WI) compact fluorescent lamps using a gravity feed water delivery system. Mission relevant conditions of CO2, temperature and RH were maintained using controlled environment chambers (EGC, Chagrin Falls, OH). Growth data was obtained for the two red leaf lettuce cultivars, Outredgeous and Firecracker, and the green Bibb lettuce cultivar, Flandria. Growth and quality was evaluated using different concentrations (7.5 g/L and 15 g/L) of commercial slow release fertilizer (Osmocote Plus 15-9-12, Scotts, Maryville, OH) and Nutricote 18-6-8 (Florikan, Sarasota, FL) in either a peat/vermiculite media (sunshine LP5 MiX, Sungro, Bellview, WA) or calcined montmorillonite clay [(arcillite,)Turface Proleague, Profile LLC, Buffalo Grove, IL]. The commercial peat/vermiculite mix generally resulted in larger plants than those grown in arcillite. Increasing the concentration of Osmocote from 7.5 to 15 g/L increased the height, dry mass, and leaf area of lettuce cultivars. In contrast, there was a decrease in growth parameters when concentration of Nutricote was increased from 7.5 to 15 g/L. The best growth was obtained with the 7.5 g/L Nutricote using a commercial peat/vermiculite mixture. This media was used for field testing VEGGIE plant system in the 2010 DRAT test. The VEGGIE nutrient delivery system worked well, was able to be maintained by multiple operators with a minimum of training, and supported excellent lettuce growth for the duration of the 14-day test. The operational DRAT field testing in the HDU identified light quality issues related to morphology and pigment development that will need to be addressed through additional testing. Feedback from the crew, ground support personnel, and human factors leads was uniformly positive on the psychological value of having the crop production system in the pressurized excursion module. Data are being used to design a plant atrium with LED lighting to evaluate salad crop growth during NASA's 2011 DRATS test.

Stutte, Gary W.↗
NASA NTRS1975

The application of remote sensing to resource management and environmental quality programs in Kansas

Specific assistance to state agencies and public bodies on over 15 remote sensing projects concerned with (1) urban and regional analysis, (2) rural development, and (3) habitat management and environmental analysis is discussed. Specific problems of officials are considered and a basis for communication by demonstration is provided. In addition to data products in support of specific agency projects; consultation and training in use of satellite and aircraft imagery is provided to personnel from several state, regional, and county agencies. Effective communication and confidence is established through these efforts and users now routinely seek information and advice about the application of remote sensing technology to solution of their agency problems.

Barr, B. G.↗
NASA NTRS2026

PERSIANN-Unet: A Global Deep Learning Framework for Near-Real-Time Precipitation Estimation Using Infrared Data

Access to high-quality, high-resolution, near-real-time precipitation data is essential for hydrological and meteorological research and disaster mitigation. Traditional tools such as rain gauges and radar networks, though effective, have limitations, including sparse coverage in remote areas and high operational costs. Satellite data, with its global coverage and high spatial and temporal resolutions, mitigates limitations in coverage. Satellite precipitation products like Hydro Estimator (HE), Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) utilize both geosynchronous thermal infrared (IR) and passive microwave (PMW) data in their operation. PMW sensors offer detailed atmospheric profiles but suffer from higher latency, whereas IR sensors provide lower latency but only capture cloud-top information. Despite this constraint, IR data remains attractive for low-latency precipitation estimation. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have further improved satellite precipitation retrievals. This study introduces PERSIANN-Unet (PUnet or PERSIANN V3), a quasi-global algorithm covering 60°N–60°S that combines IR data, monthly climatology, and the UNet architecture to produce half-hourly precipitation estimates at 0.04° resolution. The product is evaluated against HE, IMERG, and PDIR-Now for 2022–2023. Results show that PUnet closely matches its training target, IMERG V07 Final, at the global scale, and performance is further evaluated against Stage IV as a reference over CONUS. Training PUnet on IMERG (2016–2021) leverages a high-quality, integrated PMW IR-gauge precipitation product while developing an IR-based framework not reliant on PMW availability. By operating on a single global image, PUnet avoids tile partitioning and blending steps, reducing edge discontinuities, and produces more spatially consistent precipitation fields across hemispheres.

Phu Nguyen↗
NASA NTRS2012

NASA OBPG Field Program and SeaBASS Updates

Calibrating ocean color satellite instruments and validating their data products requires temporal and spatial abundances of high quality in situ oceanographic data. To this end, the Ocean Ecology Laboratory (OEL) maintains two entities that are engaged in field data collection and archival. First, the OEL houses a Field Support Group to collect in situ oceanographic measurements, execute laboratory analyses, revise community-vetted protocols for conducting these exercises, and host community training events. Second, the OEL maintains the SeaWiFS Bio-optical Archive and Storage System (SeaBASS) as the permanent archive for all in situ data collected under the auspices of the NASA Ocean Biology and Biogeochemistry Program (OBB; Dr. Paula Bontempi, Program Manager). This talk provides the OBB community and interested researchers their annual update on both the Field Support Group and SeaBASS.

Wedell, P. Jeremy↗
NASA NTRS1995

NASA/DOD Aerospace Knowledge Diffusion Research Project. Report 35: The use of computer networks in aerospace engineering

This research used survey research to explore and describe the use of computer networks by aerospace engineers. The study population included 2000 randomly selected U.S. aerospace engineers and scientists who subscribed to Aerospace Engineering. A total of 950 usable questionnaires were received by the cutoff date of July 1994. Study results contribute to existing knowledge about both computer network use and the nature of engineering work and communication. We found that 74 percent of mail survey respondents personally used computer networks. Electronic mail, file transfer, and remote login were the most widely used applications. Networks were used less often than face-to-face interactions in performing work tasks, but about equally with reading and telephone conversations, and more often than mail or fax. Network use was associated with a range of technical, organizational, and personal factors: lack of compatibility across systems, cost, inadequate access and training, and unwillingness to embrace new technologies and modes of work appear to discourage network use. The greatest positive impacts from networking appear to be increases in the amount of accurate and timely information available, better exchange of ideas across organizational boundaries, and enhanced work flexibility, efficiency, and quality. Involvement with classified or proprietary data and type of organizational structure did not distinguish network users from nonusers. The findings can be used by people involved in the design and implementation of networks in engineering communities to inform the development of more effective networking systems, services, and policies.

Bishop, Ann P.↗
NASA NTRSDate not supplied

Lessons Learned from NASA Goddard Space Flight Center’s Product Development Lead Training Schedule and Cost Development Workshop: Continuous Improvement

This presentation provides a status of the Goddard Space Flight Center (GSFC) effort to increase foundational knowledge of Product Development Leads (PDLs) in schedule and cost management including earned value management (EVM). In 2012, GSFC’s Engineering and Technology Directorate (ETD) implemented an in-house training program to prepare PDLs for managing the technical, cost, schedule, and risk aspects of spaceflight systems to meet their subsystem commitments. Developed in-house, the PDL training program provides an integrated approach to requirements development, risk, schedule and cost management, EVM, performance tracking, and other areas. The program has been held twice yearly since its inception with 531 participating and 451 completing the curriculum. In 2017, the program won the Robert H. Goddard award for Quality and Process Improvement. Program development and evolution were presented in the 2018 NASA Schedule and Cost Symposium. The presentation was so well received that this year we focus on one workshop within the program: Schedule and Cost Development, including EVM. We examine the on-going logic modeling process and how participant and stakeholder data influence workshop content and design, and how the disciplines of schedule and cost contribute to mission success. In this presentation we refresh you on how the approach integrates lecture, small group discussion, estimating, case study exercises, and problem solving. We update you on the data collected from participants and stakeholders, and we discuss how we use these data to measure training effectiveness. Specific topics include: • How the logic model is used as the backbone for continuous program improvement, • How feedback influences implementation and curriculum updates, • How data collection and analysis inform workshop content and development, including participant discoveries of EVM data, • How including the resource analyst and planner in the product development team supports project success.

Lessons Learned↗
NASA NTRS2017

Aerosol Data Assimilation at GMAO

This presentation presents an overview of the aerosol data assimilation work performed at GMAO. The GMAO Forward Processing system and the biomass burning emissions from QFED are first presented. Then, the current assimilation of Aerosol Optical Depth (AOD), performed by means of the analysis splitting method is briefly described, followed by some results on the quality control of observations using a Neural Network trained using AERONET AOD. Some applications are shown such as the Mount Pinatubo eruption in 1991 using the MERRA-2 aerosol dataset. Finally preliminary results on the EnKF implementation for aerosol assimilation are presented.

Splitting↗
NASA NTRSDate not supplied

Towards Gbps Downlinks from Low-Cost Active Phased Arrays

Spacecraft performing science missions use high-directivity antennas to quickly downlink large amounts of collected data. Movement required to keep gimbaled reflector antennas trained on target receivers can impart blockages or vibrations which perturb precision science instruments. Recent manufacturing advances have made Ka-band electronically-steered antenna arrays a cost-effective option to mitigate these challenges, but only if these devices can be proven to deliver comparable quality of service. In this work we present a hardware and software architecture for high-rate links using an active phased array-based terminal in a 1U (10cm cube) form factor. Over-the-air tests in an antenna range characterize two key features of the architecture: high-order (32APSK and above) modulations and dual carriers. We calculate an optimal modulation-dependent input power backoff from array saturation and perform orbital simulations of the link at this operating point. We conclude that reliable links with rates in excess of 1Gbps are possible with current-generation hardware.

Adam Gannon↗
NASA NTRSDate not supplied

NASA’s Prototype Spectral Water Inversion Processor and Emulator (SWIPE): Towards Global Coastal and Inland Water Quality and Algal Biodiversity Monitoring

Degradation of Earth’s inland water resources due to anthropogenic perturbations and climate anomalies at both local and global scales continues to place human health at substantial risk. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This presentation will provide updates on NASA’s prototype open-source aquatic modeling platform, Spectral Water Inversion Processor and Emulator (SWIPE), which is a comprehensive, multi-faceted modeling platform for both forward and inverse modeling of diverse aquatic ecosystems from the benthos to top-of-atmosphere (TOA). SWIPE provides a cohesive application which leverages recent advancements in particle modeling, Big Data analytics, and machine learning to develop a high-fidelity synthetic training ground for sensitivity studies and algorithm development for multispectral or upcoming hyperspectral missions. Some of the prominent features of SWIPE to be discussed include: 1. Advanced hyperspectral modeling of globally diverse algal and non-algal particles using a novel two-layer coated sphere scattering model and radiative transfer modeling, 2. Massive, highly detailed synthetic spectral libraries of Analysis-Ready-Data (ARD) which include spectral libraries of particle microphysics, water biogeophysical and optical properties, as well as surface and TOA reflectances at 1 nm resolution, 3. An ensemble of pre-built analytic, machine learning, and deep learning inversion algorithms for various water quality and biodiversity related retrieval parameters and uncertainty quantification, 4. Sensor-agnostic water quality inversion at wide ranging spatial and spectral resolutions including a codebase for seamless application in the Google Earth Engine and NASA Earth Exchange (NEX) for planetary scale analysis. SWIPE will be a fully open-source platform based in python with comprehensive documentation, tutorials, and options for distributed computing on high performance computing clusters or on single, local machines. Further, we will discuss how we envision SWIPE contributing towards a global analysis of coastal and inland water quality dynamics.

top-of-atmosphere (TOA)↗
NASA NTRS2014

Space Suit Performance: Methods for Changing the Quality of Quantitative Data

NASA is currently designing a new space suit capable of working in deep space and on Mars. Designing a suit is very difficult and often requires trade‐offs between performance, cost, mass, and system complexity. To verify that new suits will enable astronauts to perform to their maximum capacity, prototype suits must be built and tested with human subjects. However, engineers and flight surgeons often have difficulty understanding and applying traditional representations of human data without training. To overcome these challenges, NASA is developing modern simulation and analysis techniques that focus on 3D visualization. Early understanding of actual performance early on in the design cycle is extremely advantageous to increase performance capabilities, reduce the risk of injury, and reduce costs. The primary objective of this project was to test modern simulation and analysis techniques for evaluating the performance of a human operating in extra‐vehicular space suits.

Cowley, Matthew↗
NASA NTRS2010

Survey of On-Orbit Sleep Quality: Short-Duration Flyers

The NASA Human Research Program (HRP) Behavioral Health and Performance Element (BHP), in conjunction with the NASA Space Medicine Division, is currently completing the largest systematic, subjective assessment of shuttle astronauts sleep behaviors and sleep quality on Earth, during training periods, and during space flight missions. Since July 2009, a total of 66 astronauts have completed a secure online survey regarding specific sleep strategies, crew policies, and mitigation effectiveness. In addition to the survey, each astronaut participant met individually with trained BHP and SD representatives for a structured, follow-up interview. Data are currently being assessed and the study s principal investigator will be providing some preliminary findings at the Investigators Workshop. Additional analyses will be conducted in the following months to examine predictors of optimal sleep in space, and to evaluate the differences in countermeasure effectiveness between groups based on their sleep experience on the ground and on orbit. A revised survey for a subsequent investigation on the experiences of long-duration flyers will be developed in the Spring and implemented in the Summer of 2010. Findings from both of these investigations will inform countermeasure strategies for astronauts, medical operations, and habitat designers for future exploration missions, as well as upcoming shuttle and ISS missions.

Locke, J.↗
NASA NTRSDate not supplied

Super-Resolution from Space: Using MERRA-2 and MAIAC Satellite Imagery to Produce Daily Continuous 1 km PM2.5 Estimates

PM2.5 measurements from ground stations are the gold standard when available, but the expense and coverage of such stations limits widespread monitoring. Having accurate PM2.5 estimates outside the range of these stations is important for monitoring this crucial aspect of air quality. The goal of this project is to produce daily 1 km continuous PM2.5 estimates for the contiguous US relying primarily on satellite-derived data sources. This is important because models based on such data can be more easily expanded outside the study area and produce global estimates as well. The temporal availability of such data products is often weekly/daily, unlike land-use products with are often available at a yearly or worse temporal resolution. To achieve our goal, we use a couple of different deep neural network architectures to produce PM2.5 measurements at 10 km and 1 km resolution. We use two model architectures, a UNET-like model and a GAN-based model. We train both models using MERRA-2 data and MAIAC AOD data scaled to 10 km and 1 km for the two different prediction resolutions. MERRA-2 imagery is data rich with a wide range of geospatial variables at 50 km and has long historical availability (beginning in 1980). We’re also using higher spatial resolution MAIAC data at 1 km to provide finer resolution spatial context. This essentially leverages the spatial resolution of MAIAC data and the “wider” information of MERRA-2 data to predict PM2.5. For the target data we’re using a modeled 1 km PM2.5 dataset produced by Harvard to pre-train our models and then fine-tune our models using ground station measurements. Not only are our results comparable with the performance of the Harvard dataset, but can be generalized to any area or time where MERRA-2 and MAIAC data is available.

satellite imagery↗
NASA NTRS1975

Study of the utilization of EREP data from the Wabash River Basin

The author has identified the following significant results. Analysis of the digitized SL/4 S190A color IR photography proved very difficult. An area within Allen County, including Ft. Wayne, was studied. Eight segments of the study area were clustered separately and the cluster maps were then compared with the photography and maps available. The training areas for the land use classes were selected from the cluster maps. The separability measures (transformed divergence) of the classes indicated that many of the land use classes were not spectrally separable. The classification results bore this out. Visually the resulting classification map was poor, with 67 percent correct data. These results were of significantly lower quality than those obtained for the summertime SL/2 data near Lake Monroe, Indiana. Low contrast between land use classes during the wintertime and the limited spectral range and resolution are the major causes for the poor performance.

Silva, L. F.↗
NASA NTRS1976

Automated electroencephalography system and electroencephalographic coordinates of space motion sickness, part 1

A self-contained and portable device which permits clinical electroencephalography (EEG) to be conducted in remote locations by minimally trained, nontechnical personnel was developed and tested. The unit accomplishes semiautomatic acquisition of EEG data from the patient, simultaneous transmission of eight data channels to a central hospital facility over conventional telephone equipment, and automatic printing (at the remote site) of the EEG report generated at the central location. Consequently, this system enables the delivery of high-quality EEG diagnostic services in a geographically remote site with the accuracy and speed formerly possible only in certain large medical centers. Beside obvious potential clinical applications, this system serves as an initial prototype of a unit which could provide inflight EEG during future space missions.

Frost, J. D., Jr.↗
NASA NTRS2010

Design Considerations for Human Rating of Liquid Rocket Engines

I.Human-rating is specific to each engine; a. Context of program/project must be understood. b. Engine cannot be discussed independently from vehicle and mission. II. Utilize a logical combination of design, manufacturing, and test approaches a. Design 1) It is crucial to know the potential ways a system can fail, and how a failure can propagate; 2) Fault avoidance, fault tolerance, DFMR, caution and warning all have roles to play. b. Manufacturing and Assembly; 1) As-built vs. as-designed; 2) Review procedures for assembly and maintenance periodically; and 3) Keep personnel trained and certified. c. There is no substitute for test: 1) Analytical tools are constantly advancing, but still need test data for anchoring assumptions; 2) Demonstrate robustness and explore sensitivities; 3) Ideally, flight will be encompassed by ground test experience. III. Consistency and repeatability is key in production a. Maintain robust processes and procedures for inspection and quality control based upon development and qualification experience; b. Establish methods to "spot check" quality and consistency in parts: 1) Dedicated ground test engines; 2) Random components pulled from the line/lot to go through "enhanced" testing.

Parkinson, Douglas↗