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NASA NTRS1987

System implications of aperture-shade design for the SIRTF Observatory

The 1-m-aperture Space Infrared Telescope Facility (SIRTF) will operate with a sensitivity limited only by the zodiacal background. This sensitivity requirement places severe restrictions on the amount of stray light which can reach the focal plane from off-axis sources such as the sun or earth limb. In addition, radiation from these sources can degrade the lifetime of the telescope and instrument cryogenic system which is now planned for two years before the first servicing. Since the aperture of the telescope represents a break in the telescope insulation system and is effectively the first element in the optical train, the aperture shade is a key system component. The mass, length, and temperature of the shade should be minimized to reduce system cost while maximizing the telescope lifetime and stray light performance. The independent geometric parameters that characterize an asymmetrical shade for a 600 km, 28 deg orbit were identified, and the system sensitivity to the three most important shade parameters were explored. Despite the higher heat loads compared to previously studied polar orbit missions, the analysis determined that passive radiators of a reasonable size are sufficient to meet the system requirements. An optimized design for the SIRTF mission, based on the sensitivity analysis, is proposed.

Lee, J. H.↗
NASA NTRS2004

Desert Research and Technology Study 2003 Trip Report/ICES Paper

The Advanced Extra-vehicular Activity (EVA) team of the National Aeronautics and Space Administration (NASA) Johnson Space Center (JSC) Crew and Thermal Systems Division (CTSD) participated in the Desert Research and Technology Study (RATS) in September 2003, at Meteor Crater, AZ. The Desert RATS is an integrated remote field site te t with team members from several NASA centers (Johnson Space Center; Glenn and Ames Research Centers) and universities (Bowling Green State University, University of Cincinnati, Massachusetts Institute of Technology) participating. Each week of the two-week field test had a primary focus. The primary test hardware for the first week was the I-Gravity Lunar Rover Training Vehicle, or Grover, which was on loan to NASA from the United States Geological Survey (USGS) Astrogeology Research Program. The 2003 Grover driving test results serve as a rover performance characterization baseline for the Science, Crew, Operation and Utility Testbed (SCOUT) project team, which will be designing and fabricating a next generation roving vehicle prototype in Fiscal Year (FY) 2004. The second week of testing focused on EVA geologic traverses that utilized a geologic sample field analysis science trailer and also focused on human-robotic interaction between the suited subjects and the EVA Robotic Assistant (ERA). This paper will review the Advanced EVA team's role in the context of the overall Desert RATS, as well as the EVA team results and lessons learned. For information regarding other test participants' results, the authors can refer interested parties to the test reports produced by those Desert RATS teams.

Ross, Amy↗
NASA NTRS2010

Operational Dynamic Configuration Analysis

Sectors may combine or split within areas of specialization in response to changing traffic patterns. This method of managing capacity and controller workload could be made more flexible by dynamically modifying sector boundaries. Much work has been done on methods for dynamically creating new sector boundaries [1-5]. Many assessments of dynamic configuration methods assume the current day baseline configuration remains fixed [6-7]. A challenging question is how to select a dynamic configuration baseline to assess potential benefits of proposed dynamic configuration concepts. Bloem used operational sector reconfigurations as a baseline [8]. The main difficulty is that operational reconfiguration data is noisy. Reconfigurations often occur frequently to accommodate staff training or breaks, or to complete a more complicated reconfiguration through a rapid sequence of simpler reconfigurations. Gupta quantified a few aspects of airspace boundary changes from this data [9]. Most of these metrics are unique to sector combining operations and not applicable to more flexible dynamic configuration concepts. To better understand what sort of reconfigurations are acceptable or beneficial, more configuration change metrics should be developed and their distribution in current practice should be computed. This paper proposes a method to select a simple sequence of configurations among operational configurations to serve as a dynamic configuration baseline for future dynamic configuration concept assessments. New configuration change metrics are applied to the operational data to establish current day thresholds for these metrics. These thresholds are then corroborated, refined, or dismissed based on airspace practitioner feedback. The dynamic configuration baseline selection method uses a k-means clustering algorithm to select the sequence of configurations and trigger times from a given day of operational sector combination data. The clustering algorithm selects a simplified schedule containing k configurations based on stability score of the sector combinations among the raw operational configurations. In addition, the number of the selected configurations is determined based on balance between accuracy and assessment complexity.

Lai, Chok Fung↗
NASA NTRS1976

Joint aircraft loading/structure response statistics of time to service crack initiation

A reliability analysis for predicting the statistical distribution of time to fatigue crack initiation for aircraft structures in service is presented. The present analysis utilizes the statistical data of the specimen fatigue tests, the full-scale structure tests, and the statistical dispersion of aircraft service loads. The statistical distribution of the time to fatigue crack initiation of the full-scale structure under laboratory loading spectrum is assumed to be Weibull. The service loads for gust turbulences are modeled as Poisson processes for transport-type aircraft, while the maneuver loads are modeled as compound Poisson processes for fighter and training aircraft. It is found that the statistical distribution of time to fatigue crack initiation for aircraft structures in service is not Weibull and that the prediction on the basis of the Weibull distribution is unconservative, in particular in the early service time.

Yang, J.-N.↗
NASA NTRS1990

Launching AI in NASA ground systems

This paper will discuss recent operational successes in implementing expert systems to support the complex functions of NASA mission control systems at the Goddard Space Flight Center, including fault detection and diagnosis for real time and engineering analysis functions in the Cosmic Background Explorer and Gamma Ray Observatory missions and automation of resource planning and scheduling functions for various missions. It will also discuss ongoing developments and prototypes that will lead to increasingly sophisticated applications of artificial intelligence. These include the use of neural networks to perform telemetry monitoring functions, the implementation of generic expert system shells that can be customized to telemetry handling functions specific to NASA control centers, the applications of AI in training and user support, the long-term potential of implementing systems based around distributed, cooperative problem solving, and the use of AI to control and assist system development activities.

Perkins, Dorothy C.↗
NASA NTRS1992

Evolution of a NASTRAN trainer

The development is traced of a NASTRAN training system. The design and organization of the program is examined, including the static and dynamic modules. A discussion of how user feedback, in the form of questionnaire responses, was used to evaluate and improve the trainer is included. The trainer was used by a number of engineers, who found it to be a versatile low cost tool. It is particularly helpful in bridging the gap from theory to practical application of the finite element method for structural analysis. The program, along with documentation, is available through COSMIC.

Grooms, H. R.↗
NASA NTRS1994

A distributed computing model for telemetry data processing

We present a new approach to distributing processed telemetry data among spacecraft flight controllers within the control centers at NASA's Johnson Space Center. This approach facilitates the development of application programs which integrate spacecraft-telemetered data and ground-based synthesized data, then distributes this information to flight controllers for analysis and decision-making. The new approach combines various distributed computing models into one hybrid distributed computing model. The model employs both client-server and peer-to-peer distributed computing models cooperating to provide users with information throughout a diverse operations environment. Specifically, it provides an attractive foundation upon which we are building critical real-time monitoring and control applications, while simultaneously lending itself to peripheral applications in playback operations, mission preparations, flight controller training, and program development and verification. We have realized the hybrid distributed computing model through an information sharing protocol. We shall describe the motivations that inspired us to create this protocol, along with a brief conceptual description of the distributed computing models it employs. We describe the protocol design in more detail, discussing many of the program design considerations and techniques we have adopted. Finally, we describe how this model is especially suitable for supporting the implementation of distributed expert system applications.

Barry, Matthew R.↗
NASA NTRS2004

Rocket and Laboratory Studies in Astronomy

This report covers the period from March 1, 2001 to August 31, 2004. This grant was the continuation of NASA grant NAG5-5122 and supported the Johns Hopkins sounding rocket program that had its roots in the 1960s. The emphasis of this program has been the development of instrumentation for far-ultraviolet astronomy, the training of graduate students in all aspects of a space mission, and the application of these techniques to timely scientific problems. During this period we completed the fabrication of our new long-slit dual-order spectrograph (LIDOS), and successfully flew it on a Black Brant sounding rocket on December 16, 2003 (36.208 UG). The targets were the bright star gamma-Cassiopeiae and its surrounding reflection nebulae, IC 59 and IC 63. We also continued the analysis of the data from our previous flight to study the reflection nebula IC 405 (36.198 UG), which revealed a far-ultraviolet nebular scatter to stellar flux ratio that, contrary to expectations, rises steeply toward the blue. Verifying this result required extensive post-flight analysis and calibration of the Faint Object Telescope FOT) payload, which entailed measuring the telescope mirror reflectivities, the absolute efficiency of the spectrograph, and the telescope point spread function, using a new vacuum collimator developed as part of former graduate student Eric Burgh's Ph.D. dissertation. This work, being done with graduate student Kevin France, has been completed and a paper describing the results has been accepted for publication by the Astrophysical Journal. We have also continued a number of laboratory calibration studies and design efforts.

Feldman, Paul D.↗
NASA NTRS2009

Benefits Assessment of Algorithmically Combining Generic High Altitude Airspace Sectors

In today's air traffic control operations, sectors that have traffic demand below capacity are combined so that fewer controller teams are required to manage air traffic. Controllers in current operations are certified to control a group of six to eight sectors, known as an area of specialization. Sector combinations are restricted to occur within areas of specialization. Since there are few sector combination possibilities in each area of specialization, human supervisors can effectively make sector combination decisions. In the future, automation and procedures will allow any appropriately trained controller to control any of a large set of generic sectors. The primary benefit of this will be increased controller staffing flexibility. Generic sectors will also allow more options for combining sectors, making sector combination decisions difficult for human supervisors. A sector-combining algorithm can assist supervisors as they make generic sector combination decisions. A heuristic algorithm for combining under-utilized air space sectors to conserve air traffic control resources has been described and analyzed. Analysis of the algorithm and comparisons with operational sector combinations indicate that this algorithm could more efficiently utilize air traffic control resources than current sector combinations. This paper investigates the benefits of using the sector-combining algorithm proposed in previous research to combine high altitude generic airspace sectors. Simulations are conducted in which all the high altitude sectors in a center are allowed to combine, as will be possible in generic high altitude airspace. Furthermore, the algorithm is adjusted to use a version of the simplified dynamic density (SDD) workload metric that has been modified to account for workload reductions due to automatic handoffs and Automatic Dependent Surveillance Broadcast (ADS-B). This modified metric is referred to here as future simplified dynamic density (FSDD). Finally, traffic demand sets with increased air traffic demand are used in the simulations to capture the expected growth in air traffic demand by the mid-term.

Bloem, Michael↗
NASA NTRS2010

Multi-Stage System for Automatic Target Recognition

A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feedforward back-propagation neural network (NN) is then trained to classify each feature vector and to remove false positives. The system parameter optimizations process has been developed to adapt to various targets and datasets. The objective was to design an efficient computer vision system that can learn to detect multiple targets in large images with unknown backgrounds. Because the target size is small relative to the image size in this problem, there are many regions of the image that could potentially contain the target. A cursory analysis of every region can be computationally efficient, but may yield too many false positives. On the other hand, a detailed analysis of every region can yield better results, but may be computationally inefficient. The multi-stage ATR system was designed to achieve an optimal balance between accuracy and computational efficiency by incorporating both models. The detection stage first identifies potential ROIs where the target may be present by performing a fast Fourier domain OT-MACH filter-based correlation. Because threshold for this stage is chosen with the goal of detecting all true positives, a number of false positives are also detected as ROIs. The verification stage then transforms the regions of interest into feature space, and eliminates false positives using an artificial neural network classifier. The multi-stage system allows tuning the detection sensitivity and the identification specificity individually in each stage. It is easier to achieve optimized ATR operation based on its specific goal. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar and video image datasets.

Chao, Tien-Hsin↗
NASA NTRS2014

Human Reliability and the Cost of Doing Business

Most businesses recognize that people will make mistakes and assume errors are just part of the cost of doing business, but does it need to be? Companies with high risk, or major consequences, should consider the effect of human error. In a variety of industries, Human Errors have caused costly failures and workplace injuries. These have included: airline mishaps, medical malpractice, administration of medication and major oil spills have all been blamed on human error. A technique to mitigate or even eliminate some of these costly human errors is the use of Human Reliability Analysis (HRA). Various methodologies are available to perform Human Reliability Assessments that range from identifying the most likely areas for concern to detailed assessments with human error failure probabilities calculated. Which methodology to use would be based on a variety of factors that would include: 1) how people react and act in different industries, and differing expectations based on industries standards, 2) factors that influence how the human errors could occur such as tasks, tools, environment, workplace, support, training and procedure, 3) type and availability of data and 4) how the industry views risk & reliability influences ( types of emergencies, contingencies and routine tasks versus cost based concerns). The Human Reliability Assessments should be the first step to reduce, mitigate or eliminate the costly mistakes or catastrophic failures. Using Human Reliability techniques to identify and classify human error risks allows a company more opportunities to mitigate or eliminate these risks and prevent costly failures.

DeMott, Diana↗
NASA NTRS2024

Iona Ecological Conservation: Utilizing Earth Observations to Understand Landscape Patterns and Assist in Wildlife Management in Iona National Park, Angola

Following the end of the Angolan civil war in 2002, human and livestock populations have increased exponentially within Iona National Park. An ongoing drought since 2017 has brought these people and livestock into increasing competition with local wildlife for resources – highlighting a conservation challenge that will become more entrenched as the effects of anthropogenic climate change increase. In 2019, African Parks began co-managing Iona National Park in Angola with the Angolan government, hoping to enact scientifically grounded management strategies to meet this challenge. To accomplish this, African Parks needed contemporary and historic information on the spatial distribution of landcover types within Iona and adjacent areas. We constructed and applied a Random Forest classifier in Google Earth Engine to multispectral imagery gathered from Landsat 5, 7, 8 and Sentinel-1 and 2 to meet this need. Using the classifier, we generated a time-series of land cover maps between 1990–2023, from which landscape metrics and change detection analysis were calculated to show how certain habitats and formations had changed over time. The resulting maps have producer and user’s accuracies above 87% and show four broad landcover regions within the study area. Notably, we observed a decrease in the park’s diversity as per the Shannon Diversity Index – an index that considers the richness of classes, as well the evenness of their distribution. A lack of arid specific land cover indices and ground-truthed training data from earlier years limited the accuracy and resolution of our landcover maps. However, this project still demonstrates that Earth observations can be used to form the basis of conservation policy in arid environments, where ground-truth data may be difficult to obtain or non-existent.

remote sensing↗
NASA NTRS1987

Cloud cover determination in polar regions from satellite imagery

A definition is undertaken of the spectral and spatial characteristics of clouds and surface conditions in the polar regions, and to the creation of calibrated, geometrically correct data sets suitable for quantitative analysis. Ways are explored in which this information can be applied to cloud classifications as new methods or as extensions to existing classification schemes. A methodology is developed that uses automated techniques to merge Advanced Very High Resolution Radiometer (AVHRR) and Scanning Multichannel Microwave Radiometer (SMMR) data, and to apply first-order calibration and zenith angle corrections to the AVHRR imagery. Cloud cover and surface types are manually interpreted, and manual methods are used to define relatively pure training areas to describe the textural and multispectral characteristics of clouds over several surface conditions. The effects of viewing angle and bidirectional reflectance differences are studied for several classes, and the effectiveness of some key components of existing classification schemes is tested.

Barry, R. G.↗
NASA NTRS1990

Statistical methods and neural network approaches for classification of data from multiple sources

Statistical methods for classification of data from multiple data sources are investigated and compared to neural network models. A problem with using conventional multivariate statistical approaches for classification of data of multiple types is in general that a multivariate distribution cannot be assumed for the classes in the data sources. Another common problem with statistical classification methods is that the data sources are not equally reliable. This means that the data sources need to be weighted according to their reliability but most statistical classification methods do not have a mechanism for this. This research focuses on statistical methods which can overcome these problems: a method of statistical multisource analysis and consensus theory. Reliability measures for weighting the data sources in these methods are suggested and investigated. Secondly, this research focuses on neural network models. The neural networks are distribution free since no prior knowledge of the statistical distribution of the data is needed. This is an obvious advantage over most statistical classification methods. The neural networks also automatically take care of the problem involving how much weight each data source should have. On the other hand, their training process is iterative and can take a very long time. Methods to speed up the training procedure are introduced and investigated. Experimental results of classification using both neural network models and statistical methods are given, and the approaches are compared based on these results.

Benediktsson, Jon Atli↗
NASA NTRS2016

A Parametric Model of Shoulder Articulation for Virtual Assessment of Space Suit Fit

Shoulder injury is one of the most severe risks that have the potential to impair crewmembers' performance and health in long duration space flight. Overall, 64% of crewmembers experience shoulder pain after extra-vehicular training in a space suit, and 14% of symptomatic crewmembers require surgical repair (Williams & Johnson, 2003). Suboptimal suit fit, in particular at the shoulder region, has been identified as one of the predominant risk factors. However, traditional suit fit assessments and laser scans represent only a single person's data, and thus may not be generalized across wide variations of body shapes and poses. The aim of this work is to develop a software tool based on a statistical analysis of a large dataset of crewmember body shapes. This tool can accurately predict the skin deformation and shape variations for any body size and shoulder pose for a target population, from which the geometry can be exported and evaluated against suit models in commercial CAD software. A preliminary software tool was developed by statistically analyzing 150 body shapes matched with body dimension ranges specified in the Human-Systems Integration Requirements of NASA ("baseline model"). Further, the baseline model was incorporated with shoulder joint articulation ("articulation model"), using additional subjects scanned in a variety of shoulder poses across a pre-specified range of motion. Scan data was cleaned and aligned using body landmarks. The skin deformation patterns were dimensionally reduced and the co-variation with shoulder angles was analyzed. A software tool is currently in development and will be presented in the final proceeding. This tool would allow suit engineers to parametrically generate body shapes in strategically targeted anthropometry dimensions and shoulder poses. This would also enable virtual fit assessments, with which the contact volume and clearance between the suit and body surface can be predictively quantified at reduced time and cost.

Kim, K. Han↗
NASA NTRSDate not supplied

Language Model For Earth Science: Exploring Potential Downstream Applications As Well As Current Challenges

The use of deep learning techniques to build transformer language models such as SciBERT and GPT3 have transformed the natural language technology (NLT) landscape. These new NLTs are being used in speech to text and vice versa, auto-mated text classification, sentiment analysis, topic modeling, text summarization, and cognitive assistants. While Earth science has no shortage of unstructured data such as journal and conference papers, little efforts have focused on harnessing NLTs for knowledge extraction and supporting the scientific process. This paper surveys the use of language models in different science. BERT-E, a new Earth science-specific language model, is presented. BERT-E is generated using a transfer learning solution. A language model that has already been trained for general Science (SciBERT) is fine-tuned using abstracts and full text extracted from various Earth science-related articles. A downstream keywords classification application is used for evaluation, and the use of BERT-E shows improved performance. The need to develop a robust set of benchmarks in evaluating the language model such as BERT-E is discussed. Finally, example applications are presented to inspire additional ideas for applications using domain-specific language models.

R Ramachandran↗
NASA NTRS2018

Arog Dual Purpose Habitat

Vision and Mission Statement Commonality of design between a space transit habitat and Mars surface habitat has the potential to reduce mission cost and risk by reducing crew training and increasing efficiency on arrival. Using the diverse array of talent and expertise available at the University of Michigan, BLiSS will explore the concept of commonality on every level of the design. System Definition We will design a habitation system that will house a human crew on either the Martian surface or a deep space transit. The system will be designed with a high degree of commonality between the two configurations. The team will validate the design through analysis and the construction of full scale mockups.

Nilton De Oliveira Renno↗
NASA NTRS1986

Space Station Habitability Recommendations Based on a Systematic Comparative Analysis of Analogous Conditions

Conditions analogous to the proposed NASA Space Station are systematically analyzed in order to extrapolate design guidelines and recommendations concerning habitability and crew productivity. Analogous environments studied included Skylab, Sealab, Tektite, submarines, Antarctic stations and oil drilling platforms, among others. These analogues were compared and rated for size and composition of group, social organization, preparedness for mission, duration of tour, types of tasks, physical and psychological isolation, personal motivation, perceived risk, and quality of habitat and life support conditions. One-hundred design recommendations concerning, sleep, clothing, exercise, medical support, personal hygiene, food preparation, group interaction, habitat aesthetics, outside communications, recreational opportunities, privacy and personal space, waste disposal, onboard training, simulation and task preparation, and behavioral and physiological requirements associated with a microgravity environment, are provided.

Stuster, Jack W.↗