Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Training Analysis”

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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
NASA NTRS1974

Planning applications in East Central Florida

The author has identified the following significant results. This is a study of applications of ERTS data to planning problems, especially as applicable to East Central Florida. The primary method has been computer analysis of digital data, with visual analysis of images serving to supplement the digital analysis. The principal method of analysis was supervised maximum likelihood classification, supplemented by density slicing and mapping of ratios of band intensities. Land-use maps have been prepared for several urban and non-urban sectors. Thematic maps have been found to be a useful form of the land-use maps. Change-monitoring has been found to be an appropriate and useful application. Mapping of marsh regions has been found effective and useful in this region. Local planners have participated in selecting training samples and in the checking and interpretation of results.

Hannah, J. W.↗
NASA NTRS1988

Cluster analysis of multiple planetary flow regimes

A modified cluster analysis method developed for the classification of quasi-stationary events into a few planetary flow regimes and for the examination of transitions between these regimes is described. The method was applied first to a simple deterministic model and then to a 500-mbar data set for Northern Hemisphere (NH), for which cluster analysis was carried out in the subspace of the first seven empirical orthogonal functions (EOFs). Stationary clusters were found in the low-frequency band of more than 10 days, while transient clusters were found in the band-pass frequency window between 2.5 and 6 days. In the low-frequency band, three pairs of clusters determined EOFs 1, 2, and 3, respectively; they exhibited well-known regional features, such as blocking, the Pacific/North American pattern, and wave trains. Both model and low-pass data exhibited strong bimodality.

Mo, Kingtse↗
NASA NTRS1999

Feature extraction of event-related potentials using wavelets: an application to human performance monitoring

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many free parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance. Copyright 1999 Academic Press.

Evoked Potentials↗
NASA NTRS2010

Uterine Contraction Modeling and Simulation

Building a training system for medical personnel to properly interpret fetal heart rate tracing requires developing accurate models that can relate various signal patterns to certain pathologies. In addition to modeling the fetal heart rate signal itself, the change of uterine pressure that bears strong relation to fetal heart rate and provides indications of maternal and fetal status should also be considered. In this work, we have developed a group of parametric models to simulate uterine contractions during labor and delivery. Through analysis of real patient records, we propose to model uterine contraction signals by three major components: regular contractions, impulsive noise caused by fetal movements, and low amplitude noise invoked by maternal breathing and measuring apparatus. The regular contractions are modeled by an asymmetric generalized Gaussian function and least squares estimation is used to compute the parameter values of the asymmetric generalized Gaussian function based on uterine contractions of real patients. Regular contractions are detected based on thresholding and derivative analysis of uterine contractions. Impulsive noise caused by fetal movements and low amplitude noise by maternal breathing and measuring apparatus are modeled by rational polynomial functions and Perlin noise, respectively. Experiment results show the synthesized uterine contractions can mimic the real uterine contractions realistically, demonstrating the effectiveness of the proposed algorithm.

Liu, Miao↗
NASA NTRS2017

Using the General Mission Analysis Tool (GMAT)

This is a software tutorial and presentation demonstrating the application of the General Mission Analysis Tool (GMAT). These slides will be used to accompany the demonstration. The demonstration discusses GMAT basics, then presents a detailed example of GMAT application to the Transiting Exoplanet Survey Satellite (TESS) mission. This talk is a combination of existing presentations and material; system user guide and technical documentation; a GMAT basics and overview, and technical presentations from the TESS projects on their application of GMAT to critical mission design. The GMAT basics slides are taken from the open source training material. The TESS slides are a streamlined version of the CDR package provided by the project with SBU and ITAR data removed by the TESS project. Slides for navigation and optimal control are borrowed from system documentation and training material.

trajectory↗
NASA NTRS1985

Visual scanning behavior

This report summarizes the results and knowledge of scan behavior gained in various simulation and laboratory studies. Results were obtained through various analysis techniques such as real-time viewing of the pilot's scanning behavior and quantitative analysis of scan behavior performance parameters (average dwell time, dwell percentages, instrument transition paths, dwell histograms, and entropy rate measures). Pilot scan behavior is discussed in the following areas; scanning is a subconscious conditioned activity, scanning is situation dependent, pilots' scanning pattern is centered around a home base. Scanning behavior data have been shown to be useful in determining pilot's workload, evaluating pilot's strategy and role, determining the rate of information transfer of various displays, and aiding in pilot training.

Harris, R. L., Sr.↗
NASA NTRS1998

Feature Extraction of Event-Related Potentials Using Wavelets: An Application to Human Performance Monitoring

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many f ree parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation,-, algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance.

Trejo, Leonard J.↗
NASA NTRS2016

General Mission Analysis Tool (GMAT)

This is a software tutorial and presentation demonstrating the application of the General Mission Analysis Tool (GMAT) to the critical design phase of NASA missions. The demonstration discusses GMAT basics, then presents a detailed example of GMAT application to the Transiting Exoplanet Survey Satellite (TESS) mission. Other examples include OSIRIS-Rex. This talk is a combination of existing presentations; a GMAT basics and overview, and technical presentations from the TESS and OSIRIS-REx projects on their application of GMAT to critical mission design. The GMAT basics slides are taken from the open source training material. The OSIRIS-REx slides are from a previous conference presentation. The TESS slides are a streamlined version of the CDR package provided by the project with SBU and ITAR data removed by the TESS project.

optimization↗
NASA NTRSDate not supplied

Power Cable Mass Estimation for Electric Aircraft Propulsion

Electric aircraft propulsion relies on power transmission cables to carry energy through the vehicle's electrical power train. The mass of these cables is an important consideration in overall aircraft weight, and hence cable design methods that can offer lower weight cables offer a significant opportunity.This paper compares three strategies for cable design: selection of cables from off-the-shelf options, design based on steady-state thermal limits, and design based on transient thermal limits. The thermal models consider both conductor diameter and insulation thickness, as well as a comparison between multiple conductor materials. The thermal analysis evaluates the transfer of waste heat from the conductor to the insulation and from the insulation to the air via convection. Optimizations to minimize cable mass by changing conductor radius and material properties were performed using all three modeling approaches and the results show that both custom sized cables using both steady-state and transient based model offer potential mass savings.

Electrical↗
NASA NTRS1986

A technique for incorporating the NASA spacelab payload dedicated experiment processor software into the simulation system for the payload crew training complex

The feasibility of some off-the-shelf microprocessors and state-of-art software is assessed (1) as a development system for the principle investigator (pi) in the design of the experiment model, (2) as an example of available technology application for future PI's experiments, (3) as a system capable of being interactive in the PCTC's simulation of the dedicated experiment processor (DEP), preferably by bringing the PI's DEP software directly into the simulation model, (4) as a system having bus compatibility with host VAX simulation computers, (5) as a system readily interfaced with mock-up panels and information displays, and (6) as a functional system for post mission data analysis.

Bremmer, D. A.↗
NASA NTRS1980

FAME, a microprocessor based front-end analysis and modeling environment

Higher order software (HOS) is a methodology for the specification and verification of large scale, complex, real time systems. The HOS methodology was implemented as FAME (front end analysis and modeling environment), a microprocessor based system for interactively developing, analyzing, and displaying system models in a low cost user-friendly environment. The nature of the model is such that when completed it can be the basis for projection to a variety of forms such as structured design diagrams, Petri-nets, data flow diagrams, and PSL/PSA source code. The user's interface with the analyzer is easily recognized by any current user of a structured modeling approach; therefore extensive training is unnecessary. Furthermore, when all the system capabilities are used one can check on proper usage of data types, functions, and control structures thereby adding a new dimension to the design process that will lead to better and more easily verified software designs.

Rosenbaum, J. D.↗
NASA NTRS1983

Linguistic methodology for the analysis of aviation accidents

A linguistic method for the analysis of small group discourse, was developed and the use of this method on transcripts of commercial air transpot accidents is demonstrated. The method identifies the discourse types that occur and determine their linguistic structure; it identifies significant linguistic variables based upon these structures or other linguistic concepts such as speech act and topic; it tests hypotheses that support significance and reliability of these variables; and it indicates the implications of the validated hypotheses. These implications fall into three categories: (1) to train crews to use more nearly optimal communication patterns; (2) to use linguistic variables as indices for aspects of crew performance such as attention; and (3) to provide guidelines for the design of aviation procedures and equipment, especially those that involve speech.

Goguen, J. A.↗
NASA NTRS2010

Virtual VMASC: A 3D Game Environment

The advantages of creating interactive 3D simulations that allow viewing, exploring, and interacting with land improvements, such as buildings, in digital form are manifold and range from allowing individuals from anywhere in the world to explore those virtual land improvements online, to training military personnel in dealing with war-time environments, and to making those land improvements available in virtual worlds such as Second Life. While we haven't fully explored the true potential of such simulations, we have identified a requirement within our organization to use simulations like those to replace our front-desk personnel and allow visitors to query, naVigate, and communicate virtually with various entities within the building. We implemented the Virtual VMASC 3D simulation of the Virginia Modeling Analysis and Simulation Center (VMASC) office building to not only meet our front-desk requirement but also to evaluate the effort required in designing such a simulation and, thereby, leverage the experience we gained in future projects of this kind. This paper describes the goals we set for our implementation, the software approach taken, the modeling contribution made, and the technologies used such as XNA Game Studio, .NET framework, Autodesk software packages, and, finally, the applicability of our implementation on a variety of architectures including Xbox 360 and PC. This paper also summarizes the result of our evaluation and the lessons learned from our effort.

Manepalli, Suchitra↗
NASA NTRSDate not supplied

Squeezing Every Last 'Bit' of Information from Enceladus Mass Spectrometry

Potential opportunities to return to Enceladus in Discovery and Flagship class missions inspire development of next-generation instruments and creative approaches to sample collection, sample analysis, and data analysis and transmission strategies. Mass spectrometers (MS) are ideally suited to future Enceladus missions due to their analytical power in identifying a range of molecular and ionic compositions – including complex organics – and potentially astrobiologically-important features such as isotope ratios, chirality, and enantiomeric excess. However, long communication delays from Enceladus and limited bandwidth limits the data transmission from these higher-data-volume instruments, likely delaying mission-related response to new data. We explore the utility of data science and machine learning (ML) on isotope ratio (IR)MS data collected from laboratory analogs of Enceladus to: 1) process data quickly for rapid ground-based analyses, 2) understand if compositional and biosignature information could be extracted from IRMS data, and 3) evaluate whether onboard ML techniques could improve sample analysis, cadence, and transmission prioritization. Laboratory analogs analyzed isotopes of volatile CO2 that interacted with seawaters of varying composition, and include both abiotic and biotic (microbially-influenced) experiments. Enceladus’s alkaline oceans promote speciation of carbon into multiple forms (e.g., H2CO3 / CO2, HCO3-, and CO32-), each of which could be isotopically fractionated by abiotic or biotic reactions. Large (>2‰) changes in carbon isotopes (δ13C) are observed from some biotic experiments inoculated with complex microbial ecosystems relative to the abiotic seawaters. ML training and classification suggests that microbial samples can be distinguished from abiotic samples, yet that a broad range of microbial experiments are necessary to train ML models to cover a range of complexities including disequilibria, and isotopic and compositional fractionation.

geochemistry↗
NASA NTRSDate not supplied

Contextualizing Air Traffic Management Conversations using Natural Language Understanding

Efficient management of air traffic and mitigation of delays depend on extracting actionable information from unstructured data, such as dialogues from the Federal Aviation Administration’s (FAA’s) Air Traffic Control System Command Center (ATCSCC) telecons. This study presents a pipeline utilizing Natural Language Processing (NLP) methods for Intent Classification (IC) and Slot Filling (SF) to identify and extract Traffic Management Initiatives (TMIs) from aviation-specific dialogues. We leveraged DeBERTa, a pre-trained transformer model, and fine-tuned it to the nuances of the aviation domain. Despite challenges posed by annotation complexities, the IC model achieved promising results with a weighted average F1-score of 0.81. Our results are close to those of human annotators, which demonstrates the model’s strong alignment with human-level performance. The SF model also showed strong performance, achieving a weighted F1-score of 0.97, which demonstrates its effectiveness in accurately predicting key slots. Our analysis revealed limitations in handling less frequent intents and slot labels due to data sparsity, motivating future efforts to adopt joint IC-SF modeling and data augmentation strategies. This research highlights the potential of domain-specific NLP to streamline decision-making in the aviation industry and improve the management of TMIs.

Air Traffic Control Management↗
NASA NTRS1977

Pattern recognition of Landsat data based upon temporal trend analysis

The Delta Classifier defined as an agricultural crop classification scheme employing a temporal trend procedure is applied to more than 100 different Landsat data sets collected during the 1974-1975 growing season throughout the major wheat-producing regions of the United States. The classification approach stresses examination of temporal trends of the Landsat mean vectors of crops in the absence of corresponding ground truth information. It is shown that the resulting classifications compare favorably to ground truth estimates for wheat proportion in those cases where ground truth is available, and that the temporal trend procedure yields estimates of the wheat proportion that are comparable to the best results from maximum likelihood classification with photointerpreter-defined training fields.

Engvall, J. L.↗
NASA NTRS1997

Facilitating LOS Debriefings: A Training Manual

This manual is a practical guide to help airline instructors effectively facilitate debriefings of Line Oriented Simulations (LOS). It is based on a recently completed study of Line Oriented Flight Training (LOFT) debriefings at several U.S. airlines. This manual presents specific facilitation tools instructors can use to achieve debriefing objectives. The approach of the manual is to be flexible so it can be tailored to the individual needs of each airline. Part One clarifies the purpose and objectives of facilitation in the LOS setting. Part Two provides recommendations for clarifying roles and expectations and presents a model for organizing discussion. Part Tree suggests techniques for eliciting active crew participation and in-depth analysis and evaluation. Finally, in Part Four, these techniques are organized according to the facilitation model. Examples of how to effectively use the techniques are provided throughout, including strategies to try when the debriefing objectives are not being fully achieved.

McDonnell, Lori K.↗
NASA NTRS2011

Accelerated Aging Experiments for Prognostics of Damage Growth in Composite Materials

Composite structures are gaining importance for use in the aerospace industry. Compared to metallic structures their behavior is less well understood. This lack of understanding may pose constraints on their use. One possible way to deal with some of the risks associated with potential failure is to perform in-situ monitoring to detect precursors of failures. Prognostic algorithms can be used to predict impending failures. They require large amounts of training data to build and tune damage model for making useful predictions. One of the key aspects is to get confirmatory feedback from data as damage progresses. These kinds of data are rarely available from actual systems. The next possible resource to collect such data is an accelerated aging platform. To that end this paper describes a fatigue cycling experiment with the goal to stress carbon-carbon composite coupons with various layups. Piezoelectric disc sensors were used to periodically interrogate the system. Analysis showed distinct differences in the signatures of growing failures between data collected at conditions. Periodic X-radiographs were taken to assess the damage ground truth. Results after signal processing showed clear trends of damage growth that were correlated to damage assessed from the X-ray images.

composites↗