Search NASA⌕ Search

SEARCH · Search NASA

Results for “TRANSFER OF TRAINING”

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 145 records · Page 8

The transfer of remote sensing technology in the developing nations: An observation

The cooperation and assistance of industrialized nations and the United Nations and its agencies in promoting the transfer of remote sensing technology in developing nations was discussed. Training programs, workshops, and seminars as well as on-going globally scattered demonstration projects were evaluated and it was suggested that emphasis should shift from centralized training to scheduled regional training programs, resulting in larger local participation and on-the-spot application of the technology to solve local problems.

Abiodun, A. A.↗

Training effectiveness assessment: Methodological problems and issues

The U.S. military uses a large number of simulators to train and sustain the flying skills of helicopter pilots. Despite the enormous resources required to purchase, maintain, and use those simulators, little effort has been expended in assessing their training effectiveness. One reason for this is the lack of an evaluation methodology that yields comprehensive and valid data at a practical cost. Some of these methodological problems and issues that arise in assessing simulator training effectiveness, as well as problems with the classical transfer-of-learning paradigm were discussed.

Cross, Kenneth D.↗

A Gold Medal Finish

During the summer of 1999, Darryl Mitchell of Goddard Space Flight Center's Technology Commercialization Office (TCO) met with the U.S. Olympic Committee at the official training facility in Colorado Springs, Colorado, to offer assistance in transferring NASA technologies applicable to Olympic sports. Following the meeting with the Olympic committee, Mitchell was approached by U.S. Speedskating Long Track Program Director Finn Halvorsen, who eagerly voiced his interest in working with NASA to identify a means of improving performance for his team. According to Halvorsen, 'If (NASA) can put a man on the moon, surely they can help our skaters.' Mitchell and Halvorsen went to work uncovering NASA technologies that could boost the U.S. team's skating capabilities. Mitchell received a crash course in speedskating, and as a result, generated a lengthy list of promising NASA developments that could benefit the sport. From this list, he and his Goddard TCO partner, Joe Famiglietti, deliberated over whether a NASA mirror-polishing technique could possibly be adapted to the athletes speedskates. The polishing technique, developed by Jim Lyons, a 16-year optical engineering veteran of Goddard, was derived from the same principles used to create the optics for NASA's science observatories, such as the Hubble Space Telescope (highly polished optics are required by NASA to obtain sharp, clear images in space).

Source record↗

(abstract) Formal Inspection Technology Transfer Program

A Formal Inspection Technology Transfer Program, based on the inspection process developed by Michael Fagan at IBM, has been developed at JPL. The goal of this program is to support organizations wishing to use Formal Inspections to improve the quality of software and system level engineering products. The Technology Transfer Program provides start-up materials and assistance to help organizations establish their own Formal Inspection program. The course materials and certified instructors associated with the Technology Transfer Program have proven to be effective in classes taught at other NASA centers as well as at JPL. Formal Inspections (NASA tailored Fagan Inspections) are a set of technical reviews whose objective is to increase quality and reduce the cost of software development by detecting and correcting errors early. A primary feature of inspections is the removal of engineering errors before they amplify into larger and more costly problems downstream in the development process. Note that the word 'inspection' is used differently in software than in a manufacturing context. A Formal Inspection is a front-end quality enhancement technique, rather than a task conducted just prior to product shipment for the purpose of sorting defective systems (manufacturing usage). Formal Inspections are supporting and in agreement with the 'total quality' approach being adopted by many NASA centers.

certified instructors Formal Inspections↗

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/BLM Applications Pilot Test (APT), phase 2. Volume 3: Technology transfer

Techniques used and materials presented at a planning session and two workshops held to provide hands-on training in the integration of quantitatively based remote sensing data are described as well as methods used to enhance understanding of approaches to inventories that integrate multiple data sources given various resource information objectives. Significant results from each of the technology transfer sessions are examined.

Source record↗

Communication Research in Aviation and Space Operations: Symptoms and Strategies of Crew Coordination

The day-to-day operators of today's aerospace systems work under increasing pressures to accomplish more with less. They work in operational systems which are complex, technology-based, and high-risk; in which incidents and accidents have far-reaching and costly consequences. For these and other reasons, there is concern that the safety net formerly built upon redundant systems and abundant resources may become overburdened. Although we know that human ingenuity can overcome incredible odds, human nature can also fail in unpredictable ways. Over the last 20 years, a large percentage of aviation accidents and incidents have been attributed to human errors rather than hardware or environmental factors alone. A class of errors have been identified which are not due to a lack of individual, technical competencies. Rather, they are due to the failure of teams to utilize readily available resources or information in a timely fashion. These insights began a training revolution in the aviation industry called Cockpit Resource Management, which later became known as Crew Resource Management (CRM) as its concepts and applications extended to teams beyond the flightdeck. Then, as now, communication has been a cornerstone in CRM training since crew coordination and resource management largely resides within information transfer processes--both within flightcrews, and between flightcrews and the ground operations teams that support them. The research I will describe takes its roots in CRM history as we began to study communication processes in order to discover symptoms of crew coordination problems, as well as strategies of effective crew management. On the one hand, communication is often the means or the tool by which team members manage their resources, solve problems, maintain situational awareness and procedural discipline. Conversely, it is the lack of planning and resource management, loss of vigilance and situational awareness, and non-standard communications that are implicated in accidents and incidents. NASA/Ames Crew Factors researchers have been developing a model of effective crew coordination in order to understand the sources of performance breakdowns, and to develop effective solutions and interventions. Because communication is a primary mechanism by which information is received and transmitted, and because it is observable behavior, we focus on these group processes in order to identify patterns of communication that distinguish effective from less effective crew performance. Since a prime objective is to develop training recommendations for enhancing communication skills, we interpret our findings in the context of relevant task and environmental conditions, role and procedural constraints, and the normal real-time parameters of flight operations. Another research objective is to consider how communication and coordination can be enhanced through design. For example, flight deck and hardware design as well as procedural and software design may greatly influence the efficiency with which crews communicate and coordinate their work. In addition, teams and tasks may be designed, organized, and trained so that team interactions with each other are based upon appropriately shared knowledge, procedures and situation awareness. In short, we are interested in enhancing communication practices through (1) the training of specific communication skills, and (2) the design of equipment, tasks, procedures, and teams that optimize smooth, unambiguous communication processes. Two examples of communication research will be described; one in aviation and one in space operations. The first example is a high-fidelity full mission simulation study which investigates the affect of flightdeck automation on crew coordination and communication (contrasting crew performance in the DC-9 vs. MD88). Additional information is contained in the original extended abstract.

Kanki, Barbara G.↗

An evaluation of training effectiveness of an intelligent tutoring system

The study evaluated the training effectiveness of an intelligent tutoring system (ITS) for the Remote Manipulator System (RMS). The study examined how well individuals learn the training content and skills from the RMS ITS and to what extent the content and skills learned using the ITS transfer to RMS task performance in the SES, a high fidelity simulator. Three astronauts completed 8 2-hour ITS sessions addressing movement in three coordinate systems, grapple, ungrapple, berth, and unberth procedures, and singularities and reach limits. Their performance was also observed in an SES training session. Performance data were collected using multiple measures: ITS task performance, transfer performance on the SES, a conceptual knowledge test, an opinion survey completed by astronauts, and comments and observations from astronauts and trainers. Results indicated the RMS ITS to be moderately effective and provided evidence of the efficacy of ITS's, in general. Comments and suggestions are provided relating to how the ITS could be improved and to enable decision makers to judge the effectiveness of the RMS ITS.

Johnson, Debra Steele↗

Helicopter training simulators: Key market factors

Simulators will gain an increasingly important role in training helicopter pilots only if the simulators are of sufficient fidelity to provide positive transfer of skills to the aircraft. This must be done within an economic model of return on investment. Although rotor pilot demand is still only a small percentage of overall pilot requirements, it will grow in significance. This presentation described the salient factors influencing the use of helicopter training simulators.

Mcintosh, John↗

Conference on General Aviation-Business Flying, University of Tennessee, Tullahoma, Tenn., August 17-19, 1972, Proceedings.

The papers deal with safety measures, commuter airlines, charter and lease flying, pleasure flying and private travel, technology for the next decade, avionics, and the airport network. Attention is given to transfer between general and commercial terminals, airport standards, traffic control, increasing training effectiveness through simulation, proposed revision of Part 61 of FAA regulations, and accident prevention. Individual items are announced in this issue.

Source record↗

Flight Planning Branch NASA Co-op Tour

This semester I worked with the Flight Planning Branch at the NASA Johnson Space Center. I learned about the different aspects of flight planning for the International Space Station as well as the software that is used internally and ISSLive! which is used to help educate the public on the space program. I had the opportunity to do on the job training in the Mission Control Center with the planning team. I transferred old timeline records from the planning team's old software to the new software in order to preserve the data for the future when the software is retired. I learned about the operations of the International Space Station, the importance of good communication between the different parts of the planning team, and enrolled in professional development classes as well as technical classes to learn about the space station.

Marr, Aja M.↗

Establishing and Monitoring an Aseptic Workspace

When are aseptic operations necessary? In order to meet certain bioburden requirements, some components must undergo dry heat microbial reduction (DHMR) or other sterilizing procedures. If sensitive surfaces must be re-exposed after DHMR, this could compromise the bioburden levels. Recontaminating sterilized surfaces could be costly both in time by requiring repeated DHMR and risk to the hardware, which may not be compatible with repeated high temperature bakes. In order to prevent recontamination of the sensitive surfaces, an aseptic environment and sterile technique must be employed. Aseptic environments mean working in a space with almost no detectable bioburden in the air or on surfaces. Ideally, DHMR happens as late as possible to avoid requiring aseptic operations, as it can be considered a high-risk operations. Preparing the cleanroom for aseptic operations Establishing an ISO (International Organization for Standardization) class 5 space to minimize airborne particles. Maintain low bioburden in the cleanroom by using biocidal cleaners. Using multiple biocidal techniques decreases the likelihood of selecting for resistant microorganisms. 70% Isopropyl Alcohol (IPA) denatures the proteins in a microorganism (note: 70% IPA is better at killing microorganisms than 100% IPA) 7% hydrogen peroxide: damages DNA and proteins through oxygen radical damage. Ultraviolet-C (UV-C) lamps: causes crosslinking in DNA which prevents replication. Monitor cleanroom regularly for bioburden trending: Standard bioassay: Swab or wipe samples of cleanroom surfaces processed for colony forming unity (viable or spore selected); Rapid bioassay: Adenosine triphosphate (ATP) or Limulus amebocyte lysate (LAL) for a bioburden snapshot. High levels can signal an immediate re-cleaning before standard bioassay samples are taken. Airborne monitoring: Active (pulling air through a filter) or passive (particle fallout) for bioburden. Verify bioburden levels just before aseptic operation. Test hardware and cleanroom surfaces and air 3 days before the planned aseptic operation. Rapid bioburden just before aseptic operation to ensure room was not re-contaminated. Preparing personnel and tools: Personnel training. Everyone in the cleanroom: Standard cleanroom certification Everyone on the team: 1 day Planetary Protection overview. Aseptic operators only: Half-day aseptic operations training. Covers sterile garmenting/gloves, Sterile handling with a focus on contact transfer risk, tool/GSE preparation, and two-operator system for opening sterilized tools/components. Tool sterilization: All tools to be used during an aseptic operation need to be identified. Compatible tools are sterilized by DHMR or Autoclave. Double wrapped so that the exterior bag can be handled by a non-sterile operator, and the sterile. Tools that are not compatible with high heat do not come in contact with sensitive surfaces: either substitutes are found, or tools are isolated by wrapping in sterile foil. During an aseptic operation. Pre-task to make sure everyone understands the operations, who is handling what, and when the most critical surfaces will be exposed. Monitoring during the operation. Bioburden: active and passive airborne bioburden sampling, glove-tip dabs onto a plate after completion of operation (3 days for results). Particles: real time particle counter constantly running, with alarm for exceeding ISO 5 conditions.

aseptic processing↗

Observation-Based Cloud Radiative Kernels from A-Train

We develop a method to empirically derive broadband and spectral cloud radiative kernels by cloud type from pixel-scale collocated A-Train observations and reanalysis, which does not require additional cloudy radiative transfer calculations nor cloud properties. This method is able to estimate the cloud feedback by maintaining the consistency between CRKs and cloud responses.

Yue, Qing↗

Reusing Data and Metadata to Create New Metadata Through Machine-Learning & Other Programmatic Methods

Recent improvements in natural language processing (NLP) enable metadata to be created programmatically from reused original metadata or even the dataset itself. Transfer-learning applied to NLP has greatly improved performance and reduced training data requirements. In this talk, we’ll compare machine-generated metadata to human-generated metadata and discuss characteristics of metadata and data archives that affect suitability for machine-learning reuse of metadata. Where as human-generated metadata is often populated once, populated from the perspective of data supplier, populated by many individuals with different words for the same thing, and limited in length, machine-generated metadata can be updated any number of times, generated from the perspective of any user, constrained to a standardized set of terms that can be evolved over time, and be any length required. Machine-learning generated metadata offers benefits but also additional needs in terms of version control, process transparency, human-computer interaction, and IT requirements. As a successful example, we’ll discuss how a dataset of abstracts and associated human-tagged keywords from a standardized list of several thousand keywords were used to create a machine-learning model that predicted keyword metadata for open-source code projects on code.nasa.gov. We’ll also discuss a less successful example from data.nasa.gov to show how data archive architecture and characteristics of initial metadata can be strong controls on how easy it is to leverage programmatic methods to reuse metadata to create additional metadata.

Gosses, Justin↗

Exploring Applications of Machine Learning for Wildfire Monitoring and Detection using Unmanned Aerial Vehicles

Wildfires are increasing in frequency and severity around the world, including the United States. The losses caused by wildfires could be mitigated if high-risk areas, hotspots, and flare-ups could be monitored continuously, such as through the use of Unmanned Aerial Vehicles (UAVs). This paper documents exploratory efforts using machine learning to determine efficient flight paths for UAVs and to detect wildfires using image classification. On path planning, three machine learning techniques—Genetic Algorithm, Simulated Annealing, and Dynamic Programming—were explored. Genetic Algorithm was found to be an effective approach for path planning for wildfire monitoring and surveillance by UAVs. For a scenario of 25 locations in a circular arrangement, the algorithm was able to return the optimal path. The accuracy and execution time was found to be sensitive to the algorithm hyperparameters selected, which was especially evident in scenarios with hundreds or thousands of locations. Simulated Annealing was also found to be an effective approach for UAV path planning, with a major benefit of avoiding getting trapped in local minima and being straightforward to implement. Like Genetic Algorithm, the performance of Simulated Annealing was also found to be sensitive to the algorithm hyperparameters selected. By comparison, Dynamic Programming guarantees optimality for any number of locations, but it was found to be less practical in terms of execution time for scenarios with more than about a couple dozen locations. On wildfire detection, image classification using deep learning with a convolutional neural network was explored. Transfer learning was found to be a useful technique to efficiently train deep learning models. Also, it was determined that GPU processing can increase training speed by an order of magnitude, which enables significantly faster development. For a validation test set of 500 images, there were only two false negatives and zero false positives. These results demonstrate that detecting wildfires in static cameras using machine learning is feasible and establish a baseline for using images captured by UAVs in flight for wildfire detection.

Wildfire management↗

A Machine Learning Approach for the Remote Sensing Retrieval of Oil Spills from Polarimetric Measurements

The Fresnel laws of specular reflection directly connect remote sensing observations of light polarization within the sunglint region to the ocean surface refractive index. This parameter is instrumental to identify areas affected by oil, or any other floating substance capable of modifying the refractive index of pure seawater. In preparation for the NASA Plankton, Aerosol, ocean Ecosystem (PACE) mission, we are developing an advanced retrieval scheme that exploits such measurements to deliver the refractive index as an operational product. The original method was developed based on observations of the airborne Research Scanning Polarimeter. Here we present several aspects related to the extension of this technique to the PACE HARP-2 sensor (especially regarding the projected accuracy), and discuss preliminary results obtained by applying neural-network trainings to look-up tables produced with the forward radiative transfer code, with the goal of improving the computational performance. Success in the final implementation will guarantee a version of the retrieval algorithm suitable to fast-response needs and disaster management.

machine learning↗

Increasing Cognitive Ability/Reserve Using Software – Pilot (ICARUS-Pilot)

BACKGROUND This research study was competitively awarded under the 2022 JSC Innovation Charge Account (ICA) program administered by NASA Johnson Space Center’s Joint Technology Working Group. Study period of performance was May through September 2022, with a maximum allowed procurement budget of $10K. The study sought to quantify and assess the potential benefit of using commercial-off-the-shelf (COTS) cognitive training software to improve cognitive performance in an astronaut-like terrestrial population. METHODS Five volunteer research participants were recruited from the JSC employee population to mimic certain demographic characteristics of the NASA astronaut population (age, education/discipline). Participant cognitive performance was assessed before and after executing eighteen sessions of remote cognitive training executed nominally three times per week using six exercises within an adaptive app-based COTS software package (BrainHQ, Posit Science) on study-provided tablets. Pre- and post-training cognitive performance was measured using internal assessments in BrainHQ as well as Cognition Test Battery (CTB) version ISS B01 v3 (3.0.9-201710021500), an independent software test developed specifically for NASA and used currently in research studies on astronauts. BrainHQ exercises were posited to map well or partially to several CTB sub-tests. Participants provided feedback on their study experience formally via semi-structured interview at the conclusion of testing and informally throughout the study if they encountered issues. RESULTS The enrolled ICARUS-Pilot study participants generally matched Artemis crew demographic characteristics. Four of five participants have completed study training and assessment activities as of the writing of this abstract. These test participants complied well with desired training session frequency and duration yielding an average cumulative active training duration of 15 hours over an average of 45 days; participants showed 78% average improvement in metric performance for the six trained exercises, with an associated overall 33%ile ranking increase against performance of the entire BrainHQ subscribing population for internal pre/post assessment, agreeing with post-study survey self-reported performance increases. CTB overall feedback scoring, not corrected for learning effects, showed an average of 19% performance improvement across its 10 performance measures over the training period for the completed participants. Detailed analyses will be conducted once participant data collection for the study is complete and the resulting dataset is fully populated. DISCUSSION These preliminary results provide a positive trend for the effectiveness of the training approach, but further analysis will be needed to establish significance, investigate far transfer, and suggest the needed participant pool size for subsequent efforts to achieve statistically significant outcomes given similar results. The pilot study has already been helpful by allowing the study team to learn a great deal about the capabilities and limitations of the COTS software package that will be reflected in future proposals along with revised timelines for study execution and test participant management. From participant feedback, one common thread regarding the COTS training was that it felt overly repetitive – future proposals should reassess overall training duration, available levels for each trained exercise, and the behavior of the BrainHQ internal scheduler in determining which exercises should be trained and for how long. If the final analysis of this feasibility study ultimately supports it, the study team will recommend further investigation to fully evaluate this potential countermeasure and optimize its implementation. Future proposals would cite this feasibility study’s outcome and would seek to refine the training protocol and obtain statistically significant results for cognitive performance increases as well as retention data.

cognitive training↗

Increasing Cognitive Ability/Reserve Using Software – Pilot (ICARUS-Pilot)

Background: This research study was competitively awarded under the 2022 JSC Innovation Charge Account (ICA) program administered by NASA Johnson Space Center’s Joint Technology Working Group. Study period of performance was May through September 2022, with a maximum allowed procurement budget of $10K. The study sought to quantify and assess the potential benefit of using commercial-off-the-shelf (COTS) cognitive training software to improve cognitive performance in an astronaut-like terrestrial population. Methods: Five volunteer research participants were recruited from the JSC employee population to mimic certain demographic characteristics of the NASA astronaut population (age, education/discipline). Participant cognitive performance was assessed before and after executing eighteen sessions of remote cognitive training executed nominally three times per week using six exercises within an adaptive app-based COTS software package (BrainHQ, Posit Science) on study-provided tablets. Pre- and post-training cognitive performance was measured using internal assessments in BrainHQ as well as Cognition Test Battery (CTB) version ISS B01 v3 (3.0.9-201710021500), an independent software test developed specifically for NASA and used currently in research studies on astronauts. BrainHQ exercises were posited to map well or partially to several CTB sub-tests. Participants provided feedback on their study experience formally via semi-structured interview at the conclusion of testing and informally throughout the study if they encountered issues. Results: The enrolled ICARUS-Pilot study participants generally matched Artemis crew demographic characteristics. Four of five participants have completed study training and assessment activities as of the writing of this abstract. These test participants complied well with desired training session frequency and duration yielding an average cumulative active training duration of 15 hours over an average of 45 days; participants showed 78% average improvement in metric performance for the six trained exercises, with an associated overall 33%ile ranking increase against performance of the entire BrainHQ subscribing population for internal pre/post assessment, agreeing with post-study survey self-reported performance increases. CTB overall feedback scoring, not corrected for learning effects, showed an average of 19% performance improvement across its 10 performance measures over the training period for the completed participants. Detailed analyses will be conducted once participant data collection for the study is complete and the resulting dataset is fully populated. Discussion: These preliminary results provide a positive trend for the effectiveness of the training approach, but further analysis will be needed to establish significance, investigate far transfer, and suggest the needed participant pool size for subsequent efforts to achieve statistically significant outcomes given similar results. The pilot study has already been helpful by allowing the study team to learn a great deal about the capabilities and limitations of the COTS software package that will be reflected in future proposals along with revised timelines for study execution and test participant management. From participant feedback, one common thread regarding the COTS training was that it felt overly repetitive – future proposals should reassess overall training duration, available levels for each trained exercise, and the behavior of the BrainHQ internal scheduler in determining which exercises should be trained and for how long. If the final analysis of this feasibility study ultimately supports it, the study team will recommend further investigation to fully evaluate this potential countermeasure and optimize its implementation. Future proposals would cite this feasibility study’s outcome and would seek to refine the training protocol and obtain statistically significant results for cognitive performance increases as well as retention data.

cognitive training↗