Use of Bayesian Networks for Qualification Planning: Early Results of Factor Analysis.
Abstract not provided.
Rizzo, Davinia↗
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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.
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Appalachian Basin Play Fairway Analysis: Revised 2016 Combined Risk Factor Analysis
This submission contains information used to compute the combined risk factors for deep geothermal energy opportunities in the Appalachian Basin, in the context of a the Play Fairway Analysis project. The risk factors are sedimentary rock reservoir quality, thermal resource quality, potential for induced seismicity, and utilization for direct-use heating of neighborhoods. The methods used to combine the risk factors included taking the average, the geometric mean, and the minimum of the four risk factors. Combined risk maps are provided for three different sedimentary rock reservoir metrics. Combined risk maps are also provided for the three geologic risk factors alone (thermal, reservoir, and seismic), and for the three risk factors that exclude reservoir quality (utilization, seismicity, and thermal qualities). The 2015 data submission should be visited to obtain associated shapefiles, which include: 1) definition of the High and Medium priority play fairways (Inner_Fairway, and Outer_Fairway), 2) definition of the US Census Places (usCensusPlaces), 3) places (cities) of interest in the region (Places_of_Interest) identified as geothermal play fairways, 4) the point centers of the raster cells (Raster_Center_Locations), and 5) locations of industries and special-use communities (e.g., colleges and military bases) identified as low temperature heat users (Industries). The 2015 submission also includes: 1) a methodology memo that explains how the risk factors were combined (GPFA-AB_combining_risk_factors.pdf), 2) the earthquake-based seismic risk map, and 3) supporting information with details of the calculations or processing used in generating these data files. More details on each file are given in the spreadsheet "list_of_contents.xlsx" in the folder "Supporting_Information". Code used to calculate values is available at https://github.com/calvinwhealton/geothermal_pfa under the folder "combining_metrics". Note that the 2016 code is currently under the branch named "combining_metrics_2016" in the folder called "combining_metrics". This branch may be merged with the master branch in the future. Many files contained within this submission update and replace the indicated files contained in: Cornell University. (2015). Risk Factor Analysis in Low-Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB) [data set]. Retrieved from https://gdr.openei.org/submissions/622. doi:10.15121/1261942
15 GEOTHERMAL ENERGY↗
Using factor analysis to identify neuromuscular synergies during treadmill walking
Neuroscientists are often interested in grouping variables to facilitate understanding of a particular phenomenon. Factor analysis is a powerful statistical technique that groups variables into conceptually meaningful clusters, but remains underutilized by neuroscience researchers presumably due to its complicated concepts and procedures. This paper illustrates an application of factor analysis to identify coordinated patterns of whole-body muscle activation during treadmill walking. Ten male subjects walked on a treadmill (6.4 km/h) for 20 s during which surface electromyographic (EMG) activity was obtained from the left side sternocleidomastoid, neck extensors, erector spinae, and right side biceps femoris, rectus femoris, tibialis anterior, and medial gastrocnemius. Factor analysis revealed 65% of the variance of seven muscles sampled aligned with two orthogonal factors, labeled 'transition control' and 'loading'. These two factors describe coordinated patterns of muscular activity across body segments that would not be evident by evaluating individual muscle patterns. The results show that factor analysis can be effectively used to explore relationships among muscle patterns across all body segments to increase understanding of the complex coordination necessary for smooth and efficient locomotion. We encourage neuroscientists to consider using factor analysis to identify coordinated patterns of neuromuscular activation that would be obscured using more traditional EMG analyses.
NASA Center JSC↗
ERBS Human Factors Analysis: A Case Study
The incorporation of human factors into the system development process and the benefits derived are discussed. The human factors analysis task for the Earth radiation budget satellite (ERBS) payload operations control center (POCC) is a pathfinder in the new applications approach to this discipline within the mission and data operations directorate. The topics covered include: discussions of the motivation for human factors analysis; the involvement of the human factors research group (HFRG) with project and system developers, and some examples of human factors issues addressed in the ERBS analysis task.
Karen L Moe↗
Factor Analysis Applied the VFY-218 RCS Data
Present statistical factor analysis of computer simulations and measurement data for the VFY-218 configuration. Factor analysis try to quantify the statistical grouping of measurements and simulations.
Woo, Alex↗
Data structure characterization of miltispectral data using principal component and principal factor analysis
Both principal component analysis (PCA) and principal factor analysis (PFA) were used to analyze an experimental multispectral data structure in terms of common and unique variance. Only the common variance of the multispectral data was associated with the principal factor, while higher-order principal components were associated with both common and unique variance. The unique variance was found to represent small spectral variations within each cover type as well as noise vectors, and was most abundant in the lower-order principal components. The lower-order principal components can be useful in research designed to discriminate minor physical variations within features, and to highlight localized change when using multitemporal-multispectral data. Conversely, PFA of the multispectral data provided an insight into a great potential for discriminating basic land-cover types by excluding the unique variance which was related to the noise and minor spectral variations.
Lee, Jae K.↗
A Human Factor Analysis to Mitigate Fall Risk Factors in an Aerospace Environment
This slide presentation reviews the study done to quanitfy the risks from falls from three locations (i.e., Shuttle Landing Facility Launch Complex Payloads and Vehicle Assembly Building) at the Kennedy Space Center. The Analytical Hierarchy Process (AHP) is reviewed and the mathematical model developed is detailed.
Ware, Joylene H.↗
Two programs for the factor analysis of geologic and remote-sensing data
Fortran IV programs for factor analysis of geologic and remote sensing data
FACTOR ANALYSIS↗
Tensor-Dictionary Learning with Deep Kruskal-Factor Analysis
We introduce new dictionary learning methods for tensor-variate data of any order. We represent each data item as a sum of Kruskal decomposed dictionary atoms within the framework of beta-process factor analysis (BPFA). Our model is nonparametric and can infer the tensor-rank of each dictionary atom. This Kruskal-Factor Analysis (KFA) is a natural generalization of BPFA. We also extend KFA to a deep convolutional setting and develop online learning methods. We test our approach on image processing and classification tasks achieving state of the art results for 2D & 3D inpainting and Caltech 101. The experiments also show that atom-rank impacts both overcompleteness and sparsity.
Stevens, Andrew J.↗
Distal Edge Stenosis After Stent Placement for Isolated Superior Mesenteric Artery Dissection: Mechanisms and Risk Factor Analysis
Purpose: To analyze factors potentially associated with the occurrence of distal edge stenosis after stent placement for isolated superior mesenteric artery dissection (ISMAD). Materials and Methods: Cases of consecutive patients who were diagnosed with spontaneous ISMAD between February 2010 and July 2018 were retrospectively identified. Of the 123 cases identified, 45 patients (42 men; three women) underwent endovascular stent placement and were included in the study. Univariate and multivariate analyses were used to assess factors potentially associated with distal edge stenosis. Results: The technical success rate among study patients was 100%. During 26.7 ± 17.3 months of follow-up, CT angiography demonstrated good distal edge patency in 25 patients (55.6%) and evidence of distal edge stenosis in 20 patients (44.4%). In univariate analysis, stent length (odds radio [OR] 1.03; 95% confidence interval [CI] 1.01, 1.06; P = .02), stent-to-vessel (S/V) diameter ratio (OR 2.27; 95% CI 1.35, 3.82; P < .01), and angulation at the distal edge (OR 1.05; 95% CI 1.00, 1.10; P =.03) were significantly associated with distal edge stenosis; only S/V diameter ratio (OR 3.36; 95% CI 1.41, 7.99; P < .01) and angulation at the distal edge (OR 1.12; 95% CI 1.01, 1.23; P =.03) retained this significance in multivariate analysis. Conclusions: Distal edge stenosis after stent placement for ISMAD is common. S/V diameter ratio and angulation at the distal edge are independent risk factors for distal edge stenosis in patients with ISMAD who undergo stent placement.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Human factors analysis of workstation design: Earth Radiation Budget Satellite Mission Operations Room
A human factors analysis addressed three related yet distinct issues within the area of workstation design for the Earth Radiation Budget Satellite (ERBS) mission operation room (MOR). The first issue, physical layout of the MOR, received the most intensive effort. It involved the positioning of clusters of equipment within the physical dimensions of the ERBS MOR. The second issue for analysis was comprised of several environmental concerns, such as lighting, furniture, and heating and ventilation systems. The third issue was component arrangement, involving the physical arrangement of individual components within clusters of consoles, e.g., a communications panel.
Stewart, L. J.↗
Application of homomorphic signal processing to stress wave factor analysis
The stress wave factor (SWF) signal, which is the output of an ultrasonic testing system where the transmitting and receiving transducers are coupled to the same face of the test structure, is analyzed in the frequency domain. The SWF signal generated in an isotropic elastic plate is modelled as the superposition of successive reflections. The reflection which is generated by the stress waves which travel p times as a longitudinal (P) wave and s times as a shear (S) wave through the plate while reflecting back and forth between the bottom and top faces of the plate is designated as the reflection with p, s. Short-time portions of the SWF signal are considered for obtaining spectral information on individual reflections. If the significant reflections are not overlapped, the short-time Fourier analysis is used. A summary of the elevant points of homomorphic signal processing, which is also called cepstrum analysis, is given. Homomorphic signal processing is applied to short-time SWF signals to obtain estimates of the log spectra of individual reflections for cases in which the reflections are overlapped. Two typical SWF signals generated in aluminum plates (overlapping and non-overlapping reflections) are analyzed.
Karagulle, H.↗
Application of homomorphic signal processing to stress wave factor analysis
The stress wave factor (SWF) signal, which is the output of an ultrasonic testing system where the transmitting and receiving transducers are coupled to the same face of the test structure, is analyzed in the frequency domain. The SWF signal generated in an isotropic elastic plate is modelled as the superposition of successive reflections. The reflection which is generated by the stress waves which travel P times as a longitudinal (P) wave and s times as a shear (S) wave through the plate while reflecting back and forth between the bottom and top faces of the plate is designated as the reflection with P, s. Short-time portions of the SWF signal are considered for obtaining spectral information on individual reflections. If the significant reflections are not overlapped, the short-time Fourier analysis is used. A summary of the elevant points of homomorphic signal processing, which is also called cepstrum analysis, is given. Homomorphic signal processing is applied to short-time SWF signals to obtain estimates of the log spectra of individual reflections for cases in which the reflections are overlapped. Two typical SWF signals generated in aluminum plates (overlapping and non-overlapping reflections) are analyzed.
Williams, J. H., Jr.↗
Factor analysis and predictive validity of microcomputer-based tests
11 tests were selected from two microcomputer-based performance test batteries because previously these tests exhibited rapid stability (less than 10 min, of practice) and high retest reliability efficiencies (r greater than 0.707 for each 3 min. of testing). The battery was administered three times to each of 108 college students (48 men and 60 women) and a factor analysis was performed. Two of the three identified factors appear to be related to information processing ("encoding" and "throughput/decoding"), and the third named an "output/speed" factor. The spatial, memory, and verbal tests loaded on the "encoding" factor and included Grammatical Reasoning, Pattern Comparison, Continuous Recall, and Matrix Rotation. The "throughput/decoding" tests included perceptual/numerical tests like Math Processing, Code Substitution, and Pattern Comparison. The output speed factor was identified by Tapping and Reaction Time tests. The Wonderlic Personnel Test was group administered before the first and after the last administration of the performance tests. The multiple Rs in the total sample between combined Wonderlic as a criterion and less than 5 min. of microcomputer testing on Grammatical Reasoning and Math Processing as predictors ranged between 0.41 and 0.52 on the three test administrations. Based on these results, the authors recommend a core battery which, if time permits, would consist of two tests from each factor. Such a battery is now known to permit stable, reliable, and efficient assessment.
Non-NASA Center↗
Human Factors Analysis to Improve the Processing of Ares-1 Launch Vehicle
This slide presentation reviews the use of Human Factors analysis in improving the ground processing procedures for the Ares-1 launch vehicle. The light vehicle engineering designers for Ares-l launch vehicle had to design the flight vehicle for effective, efficient and safe ground operations in the cramped dimensions in a rocket design. The use of a mockup of the area where the technician would be required to work proved to be a very effective method to promote the collaboration between the Ares-1 designers and the ground operations personnel.
Stambolian, Damon B.↗
Human Error Assessment and Reduction Technique (HEART) and Human Factor Analysis and Classification System (HFACS)
Research results have shown that more than half of aviation, aerospace and aeronautics mishaps/incidents are attributed to human error. As a part of Safety within space exploration ground processing operations, the identification and/or classification of underlying contributors and causes of human error must be identified, in order to manage human error. This research provides a framework and methodology using the Human Error Assessment and Reduction Technique (HEART) and Human Factor Analysis and Classification System (HFACS), as an analysis tool to identify contributing factors, their impact on human error events, and predict the Human Error probabilities (HEPs) of future occurrences. This research methodology was applied (retrospectively) to six (6) NASA ground processing operations scenarios and thirty (30) years of Launch Vehicle related mishap data. This modifiable framework can be used and followed by other space and similar complex operations.
human error↗
Human Error Assessment and Reduction Technique (HEART) and Human Factor Analysis and Classification System (HFACS)
Research results have shown that more than half of aviation, aerospace and aeronautics mishaps incidents are attributed to human error. As a part of Safety within space exploration ground processing operations, the identification and/or classification of underlying contributors and causes of human error must be identified, in order to manage human error. This research provides a framework and methodology using the Human Error Assessment and Reduction Technique (HEART) and Human Factor Analysis and Classification System (HFACS), as an analysis tool to identify contributing factors, their impact on human error events, and predict the Human Error probabilities (HEPs) of future occurrences. This research methodology was applied (retrospectively) to six (6) NASA ground processing operations scenarios and thirty (30) years of Launch Vehicle related mishap data. This modifiable framework can be used and followed by other space and similar complex operations.
mishaps↗
Human Error Assessment and Reduction Technique (HEART) and Human Factor Analysis and Classification System (HFACS)
Research results have shown that more than half of aviation, aerospace and aeronautics mishaps incidents are attributed to human error. As a part of Quality within space exploration ground processing operations, the identification and or classification of underlying contributors and causes of human error must be identified, in order to manage human error.This presentation will provide a framework and methodology using the Human Error Assessment and Reduction Technique (HEART) and Human Factor Analysis and Classification System (HFACS), as an analysis tool to identify contributing factors, their impact on human error events, and predict the Human Error probabilities (HEPs) of future occurrences. This research methodology was applied (retrospectively) to six (6) NASA ground processing operations scenarios and thirty (30) years of Launch Vehicle related mishap data. This modifiable framework can be used and followed by other space and similar complex operations.
human error↗