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NASA NTRS2005

Experiments on Supervised Learning Algorithms for Text Categorization

Modern information society is facing the challenge of handling massive volume of online documents, news, intelligence reports, and so on. How to use the information accurately and in a timely manner becomes a major concern in many areas. While the general information may also include images and voice, we focus on the categorization of text data in this paper. We provide a brief overview of the information processing flow for text categorization, and discuss two supervised learning algorithms, viz., support vector machines (SVM) and partial least squares (PLS), which have been successfully applied in other domains, e.g., fault diagnosis [9]. While SVM has been well explored for binary classification and was reported as an efficient algorithm for text categorization, PLS has not yet been applied to text categorization. Our experiments are conducted on three data sets: Reuter's- 21578 dataset about corporate mergers and data acquisitions (ACQ), WebKB and the 20-Newsgroups. Results show that the performance of PLS is comparable to SVM in text categorization. A major drawback of SVM for multi-class categorization is that it requires a voting scheme based on the results of pair-wise classification. PLS does not have this drawback and could be a better candidate for multi-class text categorization.

Namburu, Setu Madhavi↗

NASA NTRSDate not supplied

What You Need to Know about Planetary Protection Categorization for Your Mission

Planetary protection mission categorization helps to define the risk posture for requirements definition and implementation practices on space flight projects with planetary protection considerations. NASA has recently updated it’s planetary protection policy (NPR 8715.24), and with the update comes a major change in how the planetary protection categorization process is performed for NASA missions. Previously, projects would request the NASA Planetary Protection Officer (PPO) provide a categorization letter documenting the planetary protection category assigned to the proposed mission. Under the new policy the project manager (PM) proposes the planetary protection category for their mission. Depending on the level of the category, the PPO either concurs or recommends concurrence to the Chief of Safety and Mission Assurance (SMA) before forwarding to the Mission Directorate Associate Administrator (MDAA) for final approval prior to the system requirements review (SRR). But what topics must be considered for categorization? This presentation will provide key considerations for planetary protection mission categorization including the target solar system body to be explored, the trajectory to get to the target body, and the operations to be performed at the target body. Additional unique scenarios such as secondary and auxiliary payloads, encountering other solar system bodies on the way to the target body, and sample return missions will also be discussed to share lessons learned from missions that have been through the new categorization process.

Elaine Seasly↗

NASA NTRS2017

Interpretable Categorization of Heterogeneous Time Series Data

We analyze data from simulated aircraft encounters to validate and inform the development of a prototype aircraft collision avoidance system. The high-dimensional and heterogeneous time series dataset is analyzed to discover properties of near mid-air collisions (NMACs) and categorize the NMAC encounters. Domain experts use these properties to better organize and understand NMAC occurrences. Existing solutions either are not capable of handling high-dimensional and heterogeneous time series datasets or do not provide explanations that are interpretable by a domain expert. The latter is critical to the acceptance and deployment of safety-critical systems. To address this gap, we propose grammar-based decision trees along with a learning algorithm. Our approach extends decision trees with a grammar framework for classifying heterogeneous time series data. A context-free grammar is used to derive decision expressions that are interpretable, application-specific, and support heterogeneous data types. In addition to classification, we show how grammar-based decision trees can also be used for categorization, which is a combination of clustering and generating interpretable explanations for each cluster. We apply grammar-based decision trees to a simulated aircraft encounter dataset and evaluate the performance of four variants of our learning algorithm. The best algorithm is used to analyze and categorize near mid-air collisions in the aircraft encounter dataset. We describe each discovered category in detail and discuss its relevance to aircraft collision avoidance.

Drones↗

NASA NTRS1977

Variability of wetland reflectance and its effect on automatic categorization of satellite imagery

The author has identified the following significant results. Land cover categorization of data from the same overpass in four test wetland areas was carried out using a four category classification system. The tests indicate that training data based on in situ reflectance measurements and atmospheric correction of LANDSAT data can produce comparable accuracy of categorization to that achieved using more than four wetlands cover categories (salt marsh cordgrass, salt hay, unvegetated, and water tidal flat) produced overall classification accuracies of 85% by conventional and relative radiance training and 81% by use of in situ measurements. Overall mapping accuracies were 76% and 72% respectively.

Klemas, V.↗

NASA NTRS2008

Defect Categorization: Making Use of a Decade of Widely Varying Historical Data

This paper describes our experience in aggregating a number of historical datasets containing inspection defect data using different categorizing schemes. Our goal was to make use of the historical data by creating models to guide future development projects. We describe our approach to reconciling the different choices used in the historical datasets to categorize defects, and the challenges we faced. We also present a set of recommendations for others involved in classifying defects.

Shull, Forrest↗

NASA NTRS2011

Asian Dust Weather Categorization with Satellite and Surface Observations

This study categorizes various dust weather types by means of satellite remote sensing over central Asia. Airborne dust particles can be identified by satellite remote sensing because of the different optical properties exhibited by coarse and fine particles (i.e. varying particle sizes). If a correlation can be established between the retrieved aerosol optical properties and surface visibility, the intensity of dust weather can be more effectively and consistently discerned using satellite rather than surface observations. In this article, datasets consisting of collocated products from Moderate Resolution Imaging Spectroradiometer Aqua and surface measurements are analysed. The results indicate an exponential relationship between the surface visibility and the satellite-retrieved aerosol optical depth, which is subsequently used to categorize the dust weather. The satellite-derived spatial frequency distributions in the dust weather types are consistent with China s weather station reports during 2003, indicating that dust weather classification using satellite data is highly feasible. Although the period during the springtime from 2004 to 2007 may be not sufficient for statistical significance, our results reveal an increasing tendency in both intensity and frequency of dust weather over central Asia during this time period.

Lin, Tang-Huang↗

NASA NTRS1967

Cleanroom air sampler counts, categorizes, and records particle data

Light scattering particle counter monitors particles in a clean room. It categorizes and records the particles according to size and functions simultaneously in three separate areas. The counter uses a transducer head to transform light signals into electric signals.

Nelson, M. B.↗

NASA NTRS1975

Automatic categorization of land-water cover types of the Green Swamp, Florida, using Skylab multispectral scanner (S-192) data

The techniques used and the results achieved in the successful application of Skylab Multispectral Scanner (EREP S-192) high-density digital tape data for the automatic categorizing and mapping of land-water cover types in the Green Swamp of Florida were summarized. Data was provided from Skylab pass number 10 on 13 June 1973. Significant results achieved included the automatic mapping of a nine-category and a three-category land-water cover map of the Green Swamp. The land-water cover map was used to make interpretations of a hydrologic condition in the Green Swamp. This type of use marks a significant breakthrough in the processing and utilization of EREP S-192 data.

Coker, A. E.↗

NASA NTRS1982

Software error data collection and categorization

Software errors detected during development of an interactive special purpose editor system were studied. This product was followed during nine months of coding, unit testing, function testing, and system testing. A new error categorization scheme was developed.

Ostrand, T. J.↗

NASA NTRS1986

Diamond like carbon coatings: Categorization by atomic number density

Dense diamond-like hydrocarbon films grown at the NASA Lewis Research Center by radio frequency self bias discharge and by direct ion beam deposition were studied. A new method for categorizing hydrocarbons based on their atomic number density and elemental composition was developed and applied to the diamond-like hydrocarbon films. It was shown that the diamond-like hydrocarbon films are an entirely new class of hydrocarbons with atomic number densities lying between those of single crystal diamond and adamantanes. In addition, a major review article on these new materials was completed in cooperation with NASA Lewis Research Center personnel.

Angus, John C.↗

NASA NTRS1994

Vector quantizer designs for joint compression and terrain categorization of multispectral imagery

Two vector quantizer designs for compression of multispectral imagery and their impact on terrain categorization performance are evaluated. The mean-squared error (MSE) and classification performance of the two quantizers are compared, and it is shown that a simple two-stage design minimizing MSE subject to a constraint on classification performance has a significantly better classification performance than a standard MSE-based tree-structured vector quantizer followed by maximum likelihood classification. This improvement in classification performance is obtained with minimal loss in MSE performance. The results show that it is advantageous to tailor compression algorithm designs to the required data exploitation tasks. Applications of joint compression/classification include compression for the archival or transmission of Landsat imagery that is later used for land utility surveys and/or radiometric analysis.

Gorman, John D.↗

NASA NTRS1993

Categorization and prioritization of flight deck information

The paper describes an experiment whose objectives were to: (1) make initial inferences about categories into which pilots place information; and (2) empirically determine how pilots mentally represent flight deck information, and how their cognitive processes of categorization and prioritization act upon those representations.

Jonsson, Jon E.↗

NASA NTRS2003

A Framework for Categorizing Important Project Variables

While substantial research has led to theories concerning the variables that affect project success, no universal set of such variables has been acknowledged as the standard. The identification of a specific set of controllable variables is needed to minimize project failure. Much has been hypothesized about the need to match project controls and management processes to individual projects in order to increase the chance for success. However, an accepted taxonomy for facilitating this matching process does not exist. This paper surveyed existing literature on classification of project variables. After an analysis of those proposals, a simplified categorization is offered to encourage further research.

Parsons, Vickie S.↗

NASA NTRS2019

Satellite Conjunction "Probability," "Possibility," and "Plausibility": A Categorization of Competing Conjunction Assessment Risk Assessment Paradigms

A number of different conjunction assessment (CA) risk analysis methods and metrics have been proposed in the critical literature, and they vary widely in purport and form. However, they tend to be proposed individually and episodically, so that it is difficult for a CA practitioner to take stock of the possibilities, under- stand their fundamental differences, and make informed choices for their particular CA risk assessment enterprise. The present study seeks to collect the major proposals for risk assessment methods and parameters and organize them categorically, under the proposed divisions of “probability,” “plausibility,” and “possibility,” as well as formulate what appears for each to be its fundamental question and, where applicable, null hypothesis. This activity can, through a bottom-up approach, provide some of the building blocks for an overarching CA philosophy, as well as establish concepts and terminology potentially useful to the broader discussion of these topics.

Hejduk, M. D.↗

NASA NTRS2019

Satellite Conjunction “Probability,” “Plausibility,” and “Possibility”: A Categorization of Competing Satellite Conjunction Assessment Risk Analysis Paradigms

Hejduk, M. D.↗

NASA NTRS2023

Software Error Incident Categorizations in Aerospace

Software↗

NASA NTRS2024

Historical Aerospace Software Errors Categorized to Influence Fault Tolerance

Since the first use of computers in space and aircraft, software errors have occurred. These errors can manifest as loss-of-life or less catastrophically. As the demand for automation increases, software in mission or safety-critical systems should be designed to be tolerant to the most likely software faults. This paper categorizes a set of 55 historic aerospace software error incidents from 1962 to 2023 to determine trends of how and where automation is most likely to fail, behaving unexpectedly. A distinction between software producing unexpected (erroneous) output versus no output (failsilent) is introduced. Of the historical incidents analyzed, 85% were from software producing wrong output rather than simply stopping. Rebooting was found to be ineffective to clear erroneous behavior, and not reliable to recover from silent failures. Error origin was within the code/logic itself in 58% of cases, 16% from configurable data, 15% from unexpected sensor input, and 11% from command/operator input. A substantial forty percent (40%) of unexpected software behavior was indicated by the absence of code, arising from unanticipated situations and missing requirements, and 16% of incidents were subjectively deemed “unknown-unknowns”. No incidents were found to be the result of programming language, compiler, tool, or operating system; and only sixteen percent (16%) of all incidents were considered errors traditional computer science/programming in nature. These findings indicate that for fault tolerance, erroneous automation behavior must be a primary consideration especially at critical moments, and reboot recoverability may not be viable. Special care should be taken to validate configurable data and commands prior to use. “Test-like-you-fly”, including hardware-in-the-loop combined with robust off-nominal testing should be used to uncover missing logic arising from unanticipated situations not covered by requirements alone. This study uniquely focuses on manifestations of unexpected flight software behavior, independent of ultimate root cause. We characterize software error behavior and origin to improve software design, test, and operations for resilience to the most common manifestations, and provide a rich dataset for further study.

Aerospace↗

NASA NTRSDate not supplied

Categorization of planets and exoplanets for Astrobiology.

Introduction: The number of exoplanets detected is astounding –and was not predicted. Note that Bo-rucki’s foundational paper for the Kepler mission in 1984 predicted:“...a detection rate of one planet per year of observation appears possible.”[1]. Even more astounding is the enormous diversity of exoplanets [2,3]. It is already clear from the data, that our Solar System does not bound the diversity and the range of processes seen in the exoplanets [2,3,4]. As telescopes improve it is certain that the number of exoplanets will become astronomical and the diversity will increase apace. The search for habitable locations and for evidence of life is a central part of the approach and excitement of exoplanet research [5,6]. Based on our experience to date we can expect that there will be enormous diversity in the types of habitability and life on exoplanets and that the Earth and our Solar System do not bound the possibilities for either habitability and life, and may not even provide a definite guide to selecting c and i-dates for detailed study from the enormous lists of exoplanets that will emerge. How can we develop a system for categorizing exoplanets in a way that allows for selection and prioritization in the search for diverse habitats and for diverse lifeforms?The short answer is we have no idea. In this short abstract I will venture some suggested approach-es. (see also[7]). To date, our solar system provides three classes for a habitable world:1) Earth. Water worlds, represented, of course, by Earth, and Earth-like worlds Venus and Mars. 2) Europa. Ice-covered worlds represented by Europa and including Enceladus and others, and 3) Titan. Cryogenic liquid covered worlds, represented by Titan. Earth and Europa world simply life made from carbon compounds in a water medium. Interest in these“ water worlds” is rooted in our understanding of life on Earth–the only example of life we have. The primary difference between Earth and Europa worlds is access to sunlight as an energy source on Earth. Titan represents the concept of carbon-based life in a cryogenic liquid such as CH4or C2H6 [8-13]. It is unlikely that Earth, Europa, and Titan represent the full range of possible classes of habitability for the many exoplanets that have been, or will be, discovered. It is also unlikely that life as we know it on Earth represents the full range of possible life on exoplanets. I am suggesting here that we start with these three classes (Earth, Europa, Titan) for exoplanet characterization and add others based on predicted types, such as “Hycean” worlds, a hypothetical type of planet with a hot, water-covered surface with a hydrogen dominated atmosphere [14] and the many examples considered in [15,16] and even imagined habitability and life forms [7] such as Sarr -a small, hot (500°C) rocky planet with an atmosphere over liquid sulfur. And that hosts only sulfur-based life. We are probably safe in the assumption that the richness and diversity of the exoplanets will exceed that of our collective imaginations.

Christopher P Mckay↗