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At least 109 records · Page 6

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NASA NTRS1993

Using machine learning techniques to automate sky survey catalog generation

We describe the application of machine classification techniques to the development of an automated tool for the reduction of a large scientific data set. The 2nd Palomar Observatory Sky Survey provides comprehensive photographic coverage of the northern celestial hemisphere. The photographic plates are being digitized into images containing on the order of 10(exp 7) galaxies and 10(exp 8) stars. Since the size of this data set precludes manual analysis and classification of objects, our approach is to develop a software system which integrates independently developed techniques for image processing and data classification. Image processing routines are applied to identify and measure features of sky objects. Selected features are used to determine the classification of each object. GID3* and O-BTree, two inductive learning techniques, are used to automatically learn classification decision trees from examples. We describe the techniques used, the details of our specific application, and the initial encouraging results which indicate that our approach is well-suited to the problem. The benefits of the approach are increased data reduction throughput, consistency of classification, and the automated derivation of classification rules that will form an objective, examinable basis for classifying sky objects. Furthermore, astronomers will be freed from the tedium of an intensely visual task to pursue more challenging analysis and interpretation problems given automatically cataloged data.

Fayyad, Usama M.↗

NASA NTRS1992

Self-growing neural network architecture using crisp and fuzzy entropy

The paper briefly describes the self-growing neural network algorithm, CID3, which makes decision trees equivalent to hidden layers of a neural network. The algorithm generates a feedforward architecture using crisp and fuzzy entropy measures. The results for a real-life recognition problem of distinguishing defects in a glass ribbon, and for a benchmark problen of telling two spirals apart are shown and discussed.

Cios, Krzysztof J.↗

NASA NTRS1992

SkICAT: A cataloging and analysis tool for wide field imaging surveys

We describe an integrated system, SkICAT (Sky Image Cataloging and Analysis Tool), for the automated reduction and analysis of the Palomar Observatory-ST ScI Digitized Sky Survey. The Survey will consist of the complete digitization of the photographic Second Palomar Observatory Sky Survey (POSS-II) in three bands, comprising nearly three Terabytes of pixel data. SkICAT applies a combination of existing packages, including FOCAS for basic image detection and measurement and SAS for database management, as well as custom software, to the task of managing this wealth of data. One of the most novel aspects of the system is its method of object classification. Using state-of-theart machine learning classification techniques (GID3* and O-BTree), we have developed a powerful method for automatically distinguishing point sources from non-point sources and artifacts, achieving comparably accurate discrimination a full magnitude fainter than in previous Schmidt plate surveys. The learning algorithms produce decision trees for classification by examining instances of objects classified by eye on both plate and higher quality CCD data. The same techniques will be applied to perform higher-level object classification (e.g., of galaxy morphology) in the near future. Another key feature of the system is the facility to integrate the catalogs from multiple plates (and portions thereof) to construct a single catalog of uniform calibration and quality down to the faintest limits of the survey. SkICAT also provides a variety of data analysis and exploration tools for the scientific utilization of the resulting catalogs. We include initial results of applying this system to measure the counts and distribution of galaxies in two bands down to Bj is approximately 21 mag over an approximate 70 square degree multi-plate field from POSS-II. SkICAT is constructed in a modular and general fashion and should be readily adaptable to other large-scale imaging surveys.

Weir, N.↗

NASA NTRS1992

Intelligent alarming

This talk discusses the importance of providing a process operator with concise information about a process fault including a root cause diagnosis of the problem, a suggested best action for correcting the fault, and prioritization of the problem set. A decision tree approach is used to illustrate one type of approach for determining the root cause of a problem. Fault detection in several different types of scenarios is addressed, including pump malfunctions and pipeline leaks. The talk stresses the need for a good data rectification strategy and good process models along with a method for presenting the findings to the process operator in a focused and understandable way. A real time expert system is discussed as an effective tool to help provide operators with this type of information. The use of expert systems in the analysis of actual versus predicted results from neural networks and other types of process models is discussed.

Braden, W. B.↗

NASA NTRS1994

Multivariate statistical analysis software technologies for astrophysical research involving large data bases

We developed a package to process and analyze the data from the digital version of the Second Palomar Sky Survey. This system, called SKICAT, incorporates the latest in machine learning and expert systems software technology, in order to classify the detected objects objectively and uniformly, and facilitate handling of the enormous data sets from digital sky surveys and other sources. The system provides a powerful, integrated environment for the manipulation and scientific investigation of catalogs from virtually any source. It serves three principal functions: image catalog construction, catalog management, and catalog analysis. Through use of the GID3* Decision Tree artificial induction software, SKICAT automates the process of classifying objects within CCD and digitized plate images. To exploit these catalogs, the system also provides tools to merge them into a large, complete database which may be easily queried and modified when new data or better methods of calibrating or classifying become available. The most innovative feature of SKICAT is the facility it provides to experiment with and apply the latest in machine learning technology to the tasks of catalog construction and analysis. SKICAT provides a unique environment for implementing these tools for any number of future scientific purposes. Initial scientific verification and performance tests have been made using galaxy counts and measurements of galaxy clustering from small subsets of the survey data, and a search for very high redshift quasars. All of the tests were successful, and produced new and interesting scientific results. Attachments to this report give detailed accounts of the technical aspects for multivariate statistical analysis of small and moderate-size data sets, called STATPROG. The package was tested extensively on a number of real scientific applications, and has produced real, published results.

Djorgovski, S. George↗

NASA NTRS1994

Multivariate Statistical Analysis Software Technologies for Astrophysical Research Involving Large Data Bases

We developed a package to process and analyze the data from the digital version of the Second Palomar Sky Survey. This system, called SKICAT, incorporates the latest in machine learning and expert systems software technology, in order to classify the detected objects objectively and uniformly, and facilitate handling of the enormous data sets from digital sky surveys and other sources. The system provides a powerful, integrated environment for the manipulation and scientific investigation of catalogs from virtually any source. It serves three principal functions: image catalog construction, catalog management, and catalog analysis. Through use of the GID3* Decision Tree artificial induction software, SKICAT automates the process of classifying objects within CCD and digitized plate images. To exploit these catalogs, the system also provides tools to merge them into a large, complex database which may be easily queried and modified when new data or better methods of calibrating or classifying become available. The most innovative feature of SKICAT is the facility it provides to experiment with and apply the latest in machine learning technology to the tasks of catalog construction and analysis. SKICAT provides a unique environment for implementing these tools for any number of future scientific purposes. Initial scientific verification and performance tests have been made using galaxy counts and measurements of galaxy clustering from small subsets of the survey data, and a search for very high redshift quasars. All of the tests were successful and produced new and interesting scientific results. Attachments to this report give detailed accounts of the technical aspects of the SKICAT system, and of some of the scientific results achieved to date. We also developed a user-friendly package for multivariate statistical analysis of small and moderate-size data sets, called STATPROG. The package was tested extensively on a number of real scientific applications and has produced real, published results.

Djorgovski, S. G.↗

NASA NTRS1990

GENEX: A knowledge-based expert assistant for Genbank data analysis

We describe a knowledge-based expert assistant, GENEX (Gene Explorer), that simplifies some analysis of Genbank data. GENEX is written in CLIPS (C Language Integrated Production System), and expert system tool, developed at the NASA Johnson Space Center. The main purpose of the system is to look for gene start site annotations, unusual DNA sequence composition, and regulatory protein patterns. application where determinations are made via a decision tree.

Batra, Sajeev↗

NASA NTRS1997

Optimization of 2005 Sample Return for Mars Exopaleontology

If we wish to retain the search for past/present life as an objective in the program, exobiological milestones need to be addressed at each opportunity. At this stage of Mars exploration, these milestones are complimentary to those of other disciplines. Achieving the goals of the exobiological community will require a long-term view of the problem and a phased plan of exploration. Decision trees are a realistic way to assess present needs and to benefit from future missions. Timely progress in achieving milestones identified will ensure we are moving ahead at each opportunity in meeting what the Agency (and public!) regards as an essential part of the program. It is clear that life-related issues must be handled carefully as they are presented to NASA and the public not to create an imbalance, or more importantly, misunderstanding about where we are in the exploration plan.

Farmer, Jack D.↗

NASA NTRS1999

Total Lightning Activity Associated with Tornadic Storms

Severe storms often have high flash rates (in excess of one flash per second) and are dominated by intracloud lightning activity. In addition to the extraordinary flash rates, there is a second distinguishing lightning characteristic of severe storms that seems to be important. When the total lightning history is examined, one finds sudden increases in the lightning rate, which we refer to as lightning "jumps," that precede the occurrence of severe weather by ten or more minutes. These jumps are typically 30-60 flashes/min, and are easily identified as anomalously large derivatives in the flash rate. This relationship is associated with updraft intensification and updraft strength is an important factor in storm severity (through the accumulation of condensate aloft and the stretching of vorticity). In several cases, evidence for diminishment of midlevel rotation and the descent of angular momentum from aloft is present prior to the appearance of the surface tornado. Based on our experience with severe and tornadic storms in Central Florida, we believe the total lightning may augment the more traditional use of NEXRAD radars and storm spotters. However, a more rigorous relation of these jumps to storm kinematics is needed if we are to apply total lightning in a decision tree that leads to improved warning lead times and decreased false alarm rates.

Goodman, Steven J.↗

NASA NTRS1999

X-Ray Fingerprinting Techniques for Recognizing A Hydrological Role in the Formation of Minerals on the Surface of Mars

Previous work has demonstrated the ability of a miniaturized XRD-XRF instrument to perform in-situ analyses without sample preparation or acquisition. Deployment of this instrument on a Martian rover will allow a large number of rapid qualitative analyses, which will maximize the diversity of samples studied and selected for possible return. As a first step in designing a decision tree for recognizing minerals in complex mixtures, d spacings were plotted against intensity for several mineral groups comprising rock and soil types inferred for the surface of Mars (weathered basalt, playa and hydrothermal deposits, clay-rich soils). In all groups, d spacings cluster in a range from about 1-4 angstroms, which can under certain circumstances obscure patterns for individual phases. However, within the silicate family, minerals containing either bound OH- or molecules of H20 (clays, micas, amphiboles, zeolites) are characterized by a shift of peaks to higher d spacings. Large d spacings (greater than about 7 angstroms) thus act as a first-order filter for distinguishing hydrous from anhydrous silicates. The ability to quickly verify the presence of silicates that have interacted with water has important implications for using mineral chemistry and structure to help decipher the hydrologic and atmospheric history of Mars. This represents a beginning for developing more sophisticated methods of pattern recognition. These will combine XRD and XRF analyses with optical data to rapidly7 discern environmentally diagnostic assemblages without the necessity of identifying every peak individual mineral phase.

Metzger, Ellen P.↗

NASA NTRS2000

Mitigating Adverse Effects of a Human Mission On Possible Martian Indigenous Ecosystems

Although human beings are, by most standards, the most capable agents to search for and detect extraterrestrial life, we are also potentially the most harmful. While there has been substantial work regarding forward contamination with respect to robotic missions, the issue of potential adverse effects on possible indigenous Martian ecosystems, such as biological contamination, due to a human mission has remained relatively unexplored and may require our attention now as this presentation will try to demonstrate by exploring some of the relevant scientific questions, mission planning challenges, and policy issues. An informal, high-level mission planning decision tree will be discussed and is included as the next page of this abstract. Some of the questions to be considered are: To what extent could contamination due to a human presence compromise possible indigenous life forms? To what extent can we control contamination? For example, will it be local or global? What are the criteria for assessing the biological status of Mars, both regionally and globally? For example, can we adequately extrapolate from a few strategic missions such as sample return missions? What should our policies be regarding our mission planning and possible interaction with what are likely to be microbial forms of extraterrestrial life? Central to the science and mission planning issues is the role and applicability of terrestrial analogs, such as Lake Vostok for assessing drilling issues, and modeling techniques. Central to many of the policy aspects are scientific value, international law, public concern, and ethics. Exploring this overall issue responsibly requires an examination of all these aspects and how they interrelate.

Lupisella, Mark L.↗

NASA NTRS2001

Analysis of the Westland Data Set

The "Westland" set of empirical accelerometer helicopter data with seeded and labeled faults is analyzed with the aim of condition monitoring. The autoregressive (AR) coefficients from a simple linear model encapsulate a great deal of information in a relatively few measurements; and it has also been found that augmentation of these by harmonic and other parameters call improve classification significantly. Several techniques have been explored, among these restricted Coulomb energy (RCE) networks, learning vector quantization (LVQ), Gaussian mixture classifiers and decision trees. A problem with these approaches, and in common with many classification paradigms, is that augmentation of the feature dimension can degrade classification ability. Thus, we also introduce the Bayesian data reduction algorithm (BDRA), which imposes a Dirichlet prior oil training data and is thus able to quantify probability of error in all exact manner, such that features may be discarded or coarsened appropriately.

Wen, Fang↗

NASA NTRS2000

Condition Monitoring for Helicopter Data

In this paper the classical "Westland" set of empirical accelerometer helicopter data is analyzed with the aim of condition monitoring for diagnostic purposes. The goal is to determine features for failure events from these data, via a proprietary signal processing toolbox, and to weigh these according to a variety of classification algorithms. As regards signal processing, it appears that the autoregressive (AR) coefficients from a simple linear model encapsulate a great deal of information in a relatively few measurements; it has also been found that augmentation of these by harmonic and other parameters can improve classification significantly. As regards classification, several techniques have been explored, among these restricted Coulomb energy (RCE) networks, learning vector quantization (LVQ), Gaussian mixture classifiers and decision trees. A problem with these approaches, and in common with many classification paradigms, is that augmentation of the feature dimension can degrade classification ability. Thus, we also introduce the Bayesian data reduction algorithm (BDRA), which imposes a Dirichlet prior on training data and is thus able to quantify probability of error in an exact manner, such that features may be discarded or coarsened appropriately.

Wen, Fang↗

NASA NTRS2001

Human Mars Mission Contamination Issues

A potential challenge for a human Mars mission is that while humans are by most measures the obvious best way to search for life on Mars, we may also be the most problematic in that we could unduly compromise the search for life by contaminating relevant environments and/or possibly adversely and irreversibly affecting indigenous life. Perhaps more problematic is the fundamental epistemic challenge of the "one data point" limitation which could decrease confidence in applying terrestrially based research to extraterrestrial life issues in general. An informal decision tree is presented as one way to begin thinking about contamination issues. There are many sub-questions and distinctions not shown such as biological vs. nonbiological (but biologically relevant) contamination, viable vs. dead organisms, masking indigenous organisms vs. merely making the search more difficult, and independent origin vs. panspermia distinctions. While it may be unlikely that terrestrial microbes could survive on Mars, let alone reproduce and unduly compromise the search for life, the unpredictable potential for microbial life to survive, grow exponentially, evolve and modify (and sometimes destroy) environments, warrants focusing carefully on biologically relevant contamination as we prepare to send humans to the first planet that may have indigenous life-forms.

Lupisella, M. L.↗

NASA NTRS2001

Multi-Sensor Characterization of the Boreal Forest: Initial Findings

Results are presented in an initial apriori knowledge approach toward using complementary multi-sensor multi-temporal imagery in characterizing vegetated landscapes over a site in the Boreal Ecosystem-Atmosphere Study (BOREAS). Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Airborne Synthetic Aperture Radar (AIRSAR) data were segmented using multiple endmember spectral mixture analysis and binary decision tree approaches. Individual date/sensor land cover maps had overall accuracies between 55.0% - 69.8%. The best eight land cover layers from all dates and sensors correctly characterized 79.3% of the cover types. An overlay approach was used to create a final land cover map. An overall accuracy of 71.3% was achieved in this multi-sensor approach, a 1.5% improvement over our most accurate single scene technique, but 8% less than the original input. Black spruce was evaluated to be particularly undermapped in the final map possibly because it was also contained within jack pine and muskeg land coverages.

Reith, Ernest↗

NASA NTRS1997

Learning and Tuning of Fuzzy Rules

In this chapter, we review some of the current techniques for learning and tuning fuzzy rules. For clarity, we refer to the process of generating rules from data as the learning problem and distinguish it from tuning an already existing set of fuzzy rules. For learning, we touch on unsupervised learning techniques such as fuzzy c-means, fuzzy decision tree systems, fuzzy genetic algorithms, and linear fuzzy rules generation methods. For tuning, we discuss Jang's ANFIS architecture, Berenji-Khedkar's GARIC architecture and its extensions in GARIC-Q. We show that the hybrid techniques capable of learning and tuning fuzzy rules, such as CART-ANFIS, RNN-FLCS, and GARIC-RB, are desirable in development of a number of future intelligent systems.

Berenji, Hamid R.↗

NASA NTRS2003

Visualizing Decision-making Behaviours in Agent-based Autonomous Spacecraft

The authors will report initial progress on the PIAudit project as a Research Resident Associate Program. The objective of this research is to prototype a tool for visualizing decision-making behaviours in autonomous spacecraft. This visualization will serve as an information source for human analysts. The current visualization prototype for PIAudit combines traditional Decision Trees with Weights of Evidence.

North, Steve↗

NASA NTRS2003

Enhancing and Archiving the APS Catalog of the POSS I

We have worked on two different projects: 1) Archiving the APS Catalog of the POSS I for distribution to NASA's NED at IPAC, SIMBAD in France, and individual astronomers and 2) The automated morphological classification of galaxies. We have completed archiving the Catalog into easily readable binary files. The database together with the software to read it has been distributed on DVD's to the national and international data centers and to individual astronomers. The archived Catalog contains more than 89 million objects in 632 fields in the first epoch Palomar Observatory Sky Survey. Additional image parameters not available in the original on-line version are also included in the archived version. The archived Catalog is also available and can be queried at the APS web site (URL: http://aps.umn.edu) which has been improved with a much faster and more efficient querying system. The Catalog can be downloaded as binary datafiles with the source code for reading it. It is also being integrated into the SkyQuery system which includes the Sloan Digital Sky Survey, 2MASS, and the FIRST radio sky survey. We experimented with different classification algorithms to automate the morphological classification of galaxies. This is an especially difficult problem because there are not only a large number of attributes or parameters and measurement uncertainties, but also the added complication of human disagreement about the adopted types. To solve this problem we used 837 galaxy images from nine POSS I fields at the North Galactic Pole classified by two independent astronomers for which they agree on the morphological types. The initial goal was to separate the galaxies into the three broad classes relevant to issues of large scale structure and galaxy formation and evolution: early (ellipticals and lenticulars), spirals, and late (irregulars) with an accuracy or success rate that rivals the best astronomer classifiers. We also needed to identify a set of parameters derived from the digitized images that separate the galaxies by type. The human eye can easily recognize complicated patterns in images such as spiral arms which can be spotty, blotchy affairs that are difficult for automated techniques. A galaxy image can potentially be described by hundreds of parameters, all of which may have some relation to the morphological type. In the set of initial experiments we used 624 such parameters, in two colors, blue and red. These parameters include the surface brightness and color measured at different radii, ratios of these parameters at different radii, concentration indices, Fourier transforms and wavelet decomposition coefficients. We experimented with three different classes of classification algorithms; decision trees, k-nearest neighbors, and support vector machines (SVM). A range of experiments were conducted and we eventually narrowed the parameters to 23 selected parameters. SVM consistently outperformed the other algorithms with both sets of features. By combining the results from the different algorithms in a weighted scheme we achieved an overall classification success of 86%.

Humphreys, Roberta M.↗