Sample records for geothermometry sanyal classification

  1. Sanyal Temperature Classification | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to:Ezfeedflag JumpID-f < RAPID‎ |Rippey Jump to:WY)ProjectValley,Isabel Jump to:Teresa,

  2. Mineral Selection for Multicomponent Equilibrium Geothermometry

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Plamer, C. D.; Ohly, S. R.; Smith, R. W.; Neupane, G.; McLing, T.; Mattson, E.

    2015-04-01T23:59:59.000Z

    Muliticomponent geothermometry requires knowledge of the mineral phases in the reservoir with which the geothermal fluids may be equilibrated.

  3. Integrated Chemical Geothermometry System for Geothermal Exploration

    Broader source: Energy.gov (indexed) [DOE]

    interpretations) * Reduce exploration and development costs Innovation * Numerical optimization of multicomponent chemical geothermometry at multiple locations * Integration with...

  4. Improvements in geothermometry. Final technical report

    SciTech Connect (OSTI)

    Potter, J.; Dibble, W.; Parks, G.; Nur, A.

    1982-07-01T23:59:59.000Z

    The following are covered: the basis of the Na-K-Ca geothermometer, geothermometry via model calculations, non ideality and complexing, and experimental calibration.

  5. Geothermometry At Central Nevada Seismic Zone Region (Shevenell...

    Open Energy Info (EERE)

    ENERGYGeothermal Home Exploration Activity: Geothermometry At Central Nevada Seismic Zone Region (Shevenell & De Rocher, 2005) Exploration Activity Details Location...

  6. Geothermometry At Walker-Lane Transitional Zone Region (Shevenell...

    Open Energy Info (EERE)

    (Shevenell & De Rocher, 2005) Exploration Activity Details Location Walker-Lane Transition Zone Geothermal Region Exploration Technique Geothermometry Activity Date Usefulness...

  7. Geothermometry At Nevada Test And Training Range Area (Sabin...

    Open Energy Info (EERE)

    Nevada Test And Training Range Area (Sabin, Et Al., 2004) Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: Geothermometry At Nevada Test And...

  8. Geothermometry At Long Valley Caldera Geothermal Area (Farrar...

    Open Energy Info (EERE)

    Home Exploration Activity: Geothermometry At Long Valley Caldera Geothermal Area (Farrar, Et Al., 2003) Exploration Activity Details Location Long Valley Caldera Geothermal...

  9. Category:Sanyal Temperature Classification | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand JumpConceptual Model, click here. Category:ConceptualGeothermal Regulatory Roadmap.sourceSARSWIR

  10. Multicomponent Equilibrium Models for Testing Geothermometry Approaches

    SciTech Connect (OSTI)

    Cooper, D. Craig; Carl D. Palmer; Robert W. Smith; Travis L. McLing

    2013-02-01T23:59:59.000Z

    Geothermometry is an important tool for estimating deep reservoir temperature from the geochemical composition of shallower and cooler waters. The underlying assumption of geothermometry is that the waters collected from shallow wells and seeps maintain a chemical signature that reflects equilibrium in the deeper reservoir. Many of the geothermometers used in practice are based on correlation between water temperatures and composition or using thermodynamic calculations based a subset (typically silica, cations or cation ratios) of the dissolved constituents. An alternative approach is to use complete water compositions and equilibrium geochemical modeling to calculate the degree of disequilibrium (saturation index) for large number of potential reservoir minerals as a function of temperature. We have constructed several “forward” geochemical models using The Geochemist’s Workbench to simulate the change in chemical composition of reservoir fluids as they migrate toward the surface. These models explicitly account for the formation (mass and composition) of a steam phase and equilibrium partitioning of volatile components (e.g., CO2, H2S, and H2) into the steam as a result of pressure decreases associated with upward fluid migration from depth. We use the synthetic data generated from these simulations to determine the advantages and limitations of various geothermometry and optimization approaches for estimating the likely conditions (e.g., temperature, pCO2) to which the water was exposed in the deep subsurface. We demonstrate the magnitude of errors that can result from boiling, loss of volatiles, and analytical error from sampling and instrumental analysis. The estimated reservoir temperatures for these scenarios are also compared to conventional geothermometers. These results can help improve estimation of geothermal resource temperature during exploration and early development.

  11. Geothermometry At Mt Princeton Hot Springs Geothermal Area (Pearl...

    Open Energy Info (EERE)

    Basis Temperature estimation of valley-fill hydrothermal reservoir Notes Si, Na-K, & Na-K-Ca geothermometry estimates yielded a reservoir temperature range of 97 to 188...

  12. Microbial impacts on geothermometry temperature predictions

    SciTech Connect (OSTI)

    Yoshiko Fujita; David W. Reed; Kaitlyn R. Nowak; Vicki S. Thompson; Travis L. McLing; Robert W. Smith

    2013-02-01T23:59:59.000Z

    Conventional geothermometry approaches assume that the composition of a collected water sample originating in a deep geothermal reservoir still reflects chemical equilibration of the water with the deep reservoir rocks. However, for geothermal prospecting samples whose temperatures have dropped to <120°C, temperature predictions may be skewed by the activity of microorganisms; microbial metabolism can drastically and rapidly change the water’s chemistry. We hypothesize that knowledge of microbial impacts on exploration sample geochemistry can be used to constrain input into geothermometry models and thereby improve the reliability of reservoir temperature predictions. To evaluate this hypothesis we have chosen to focus on sulfur cycling, because of the significant changes in redox state and pH associated with sulfur chemistry. Redox and pH are critical factors in defining the mineral-fluid equilibria that form the basis of solute geothermometry approaches. Initially we are developing assays to detect the process of sulfate reduction, using knowledge of genes specific to sulfate reducing microorganisms. The assays rely on a common molecular biological technique known as quantitative polymerase chain reaction (qPCR), which allows estimation of the number of target organisms in a particular sample by enumerating genes specific to the organisms rather than actually retrieving and characterizing the organisms themselves. For quantitation of sulfate reducing genes using qPCR, we constructed a plasmid (a piece of DNA) containing portions of two genes (known as dsrA and dsrB) that are directly involved with sulfate reduction and unique to sulfate reducing microorganisms. Using the plasmid as well as DNA from other microorganisms known to be sulfate reducers or non-sulfate reducers, we developed qPCR protocols and showed the assay’s specificity to sulfate reducers and that a qPCR standard curve using the plasmid was linear over >5 orders of magnitude. As a first test with actual field samples, the assay was applied to DNA extracted from water collected at springs located in and around the town of Soda Springs, Idaho. Soda Springs is located in the fold and thrust belt on the eastern boundary of the track of the Yellowstone Hotspot, where a deep carbon dioxide source believed to originate from Mississippian limestone contacts acidic hydrothermal fluids at depth. Both sulfate and sulfide have been measured in samples collected previously at Soda Springs. Preliminary results indicate that sulfate reducing genes were present in each of the samples tested. Our work supports evaluation of the potential for microbial processes to have altered water chemistry in geothermal exploration samples.

  13. Zachary Hensley, Jibonananda Sanyal, Joshua New Energy and Transportation Sciences Division

    E-Print Network [OSTI]

    Wang, Xiaorui "Ray"

    modified and evaluated using different energy models, including DOE's EnergyPlus and multiple programsZachary Hensley, Jibonananda Sanyal, Joshua New Energy and Transportation Sciences Division@ornl.gov Provenance In the scientific world, it is important for researchers to know where their data came from

  14. An experimental investigation into the effects of fluid composition on certain geothermometry methods

    E-Print Network [OSTI]

    Pope, Leslie Anne

    1985-01-01T23:59:59.000Z

    at the lower temperatures, and 5) thermal waters do not mix with shallower, cooler ground water. Silica geothermometer The dissolved silica content of geothermal water is used in geothermometry because silica is present in most geologic settings... This presents a real problem in evaluation of the geo- thermal potential of the area; one method gives reservoir temperatures high enough for potential geothermal energy use but another gives temperatures that are too low. The waters studied by Henry can...

  15. Geothermometry | Open Energy Information

    Open Energy Info (EERE)

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  16. Stabilization of kerogen thermal maturation: Evidence from geothermometry and burial history reconstruction, Niobrara Limestone, Berthoud oil field, western Denver Basin, Colorado

    SciTech Connect (OSTI)

    Barker, C.E.; Crysdale, B.L. (Geological Survey, Denver, CO (USA))

    1990-05-01T23:59:59.000Z

    The burial history of this fractured Niobrara Limestone reservoir and source rock offers a setting for studying the stabilization of thermal maturity because soon after peak temperature of approximately 100{degree}C was reached, exhumation lowered temperature to about 60-70{degree}C. Vitrinite reflectance (Rm = 0.6-0.7%) and published clay mineralogy data from the Niobrara Limestone indicate that peak paleotemperature was approximately 100{degree}C. Fluid inclusion data also indicate oil migration occurred at 100{degree}C. Burial history reconstruction indicates 100{degree}C was reached in the Niobrara Limestone only during minimum burial, which occurred at 70 Ma and 8000 ft depth. However, erosion beginning at 70 Ma and continuing until 50 Ma removed over 3,000 ft of rock. This depth of erosion agrees with an Rm of 0.4% measured in surface samples of the Pierre Shale. The exhumation of the reservoir decreased temperature by about 30{degree}C to near the corrected bottom-hole temperature of 50-70{degree}C. Lopatin time-temperature index (TTI) analysis suggests the Niobrara Limestone as a source rock matured to the oil generation stage (TTI = 10) about 25 Ma, significantly later than maximum burial, and after exhumation caused cooling. The Lopatin TTI method in this case seems to overestimate the influence of heating time. If time is an important factor, thermal maturity should continue to increase after peak burial and temperature so that vitrinite reflectance will not be comparable to peak paleotemperatures estimated from geothermometers set at near-peak temperature and those estimated from burial history reconstruction. The agreement between geothermometry and the burial history reconstruction in Berthoud State 4 suggests that the influence of heating time must be small. The elapsed time available at near peak temperatures was sufficient to allow stabilization of thermal maturation in this case.

  17. Geology, hydrothermal petrology, stable isotope geochemistry, and fluid inclusion geothermometry of LASL geothermal test well C/T-1 (Mesa 31-1), East Mesa, Imperial Valley, California, USA

    SciTech Connect (OSTI)

    Miller, K.R.; Elders, W.A.

    1980-08-01T23:59:59.000Z

    Borehole Mesa 31-1 (LASL C/T-1) is an 1899-m (6231-ft) deep well located in the northwestern part of the East Mesa Geothermal Field. Mesa 31-1 is the first Calibration/Test Well (C/T-1) in the Los Alamos Scientific Laboratory (LASL), Geothermal Log Interpretation Program. The purpose of this study is to provide a compilation of drillhole data, drill cuttings, well lithology, and formation petrology that will serve to support the use of well LASL C/T-1 as a calibration/test well for geothermal logging. In addition, reviews of fluid chemistry, stable isotope studies, isotopic and fluid inclusion geothermometry, and the temperature log data are presented. This study provides the basic data on the geology and hydrothermal alteration of the rocks in LASL C/T-1 as background for the interpretation of wireline logs.

  18. Liquid Geothermometry | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal Pwer Plant Jump to:Landowners and WindLightingLinthicum, Maryland: Energy

  19. Gas Geothermometry | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnualPropertyd8c-a9ae-f8521cbb8489InformationFrenchtown, NewG22 Jump to:Garnet Wind

  20. Isotope Geothermometry | Open Energy Information

    Open Energy Info (EERE)

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  1. Classification Documents and Publications

    Broader source: Energy.gov [DOE]

    Certain documents and publications created or issued by the Office of Classification are available from this page.

  2. National Geothermal Resource Assessment and Classification |...

    Office of Environmental Management (EM)

    Resource Assessment and Classification National Geothermal Resource Assessment and Classification National Geothermal Resource Assessment and Classification presentation at the...

  3. Security classification of information

    SciTech Connect (OSTI)

    Quist, A.S.

    1993-04-01T23:59:59.000Z

    This document is the second of a planned four-volume work that comprehensively discusses the security classification of information. The main focus of Volume 2 is on the principles for classification of information. Included herein are descriptions of the two major types of information that governments classify for national security reasons (subjective and objective information), guidance to use when determining whether information under consideration for classification is controlled by the government (a necessary requirement for classification to be effective), information disclosure risks and benefits (the benefits and costs of classification), standards to use when balancing information disclosure risks and benefits, guidance for assigning classification levels (Top Secret, Secret, or Confidential) to classified information, guidance for determining how long information should be classified (classification duration), classification of associations of information, classification of compilations of information, and principles for declassifying and downgrading information. Rules or principles of certain areas of our legal system (e.g., trade secret law) are sometimes mentioned to .provide added support to some of those classification principles.

  4. Standard Subject Classification System

    Broader source: Directives, Delegations, and Requirements [Office of Management (MA)]

    1979-08-14T23:59:59.000Z

    The order establishes the DOE Standard Subject Classification System for classifying documents and records by subject, including correspondence, directives, and forms.Cancels DOE O 0000.1.

  5. DUTY STATEMENT CLASSIFICATION

    E-Print Network [OSTI]

    studies, environmental impact reports, and Commission reports. (E) #12;CLASSIFICATION: Planner I - EFS of Environmental Impact Reports submitted to the Commission and prepares assessments of those sections. (M) 5 TITLE: Biologist DIVISION: Siting, Transmission & Environmental Protection DATE PREPARED: October 3

  6. Classification | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative FuelsNovember 13, 2014 Building AmericaEnergyandClassification Classification

  7. Commercial Vehicle Classification using Vehicle Signature Data

    E-Print Network [OSTI]

    Liu, Hang; Jeng, Shin-Ting; Andre Tok, Yeow Chern; Ritchie, Stephen G.

    2008-01-01T23:59:59.000Z

    Traffic Measurement and Vehicle Classification with SingleG. Ritchie. Real-time Vehicle Classification using InductiveReijmers, J.J. , "On-line vehicle classification," Vehicular

  8. Infinite dimensional discrimination and classification

    E-Print Network [OSTI]

    Shin, Hyejin

    2007-09-17T23:59:59.000Z

    classification methodologies for such data have been suggested and supporting theory is quite limited. The focus of this dissertation is on discrimination and classification in this infinite dimensional setting. The methodology and theory we develop are based...

  9. Classification of Information Manual

    Broader source: Directives, Delegations, and Requirements [Office of Management (MA)]

    1985-05-08T23:59:59.000Z

    To specify responsibilities, authorities, policy, and procedures for the management of the Department of Energy (DOE) classification system. Cancels DOE O 5650.2, dated 12-12-1978. Canceled by DOE O 5650.2B, dated 12-31-1991.

  10. Position Management and Classification

    Broader source: Directives, Delegations, and Requirements [Office of Management (MA)]

    2015-04-01T23:59:59.000Z

    The order establishes departmental requirements and responsibilities for classifying positions using general schedule (GS) and federal wage system (FWS) standards and for developing and administering a sound position management and classification program within the Department. Cancels Chapter VII of DOE O 320.1.

  11. Property:SanyalTempReservoir | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to:Ezfeedflag Jump to:ID8/Organization RAPID/Contact/ID8/Positionmaterial JumpSalinityHIgh

  12. Property:SanyalTempWellhead | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to:Ezfeedflag Jump to:ID8/Organization RAPID/Contact/ID8/Positionmaterial

  13. Classification of Information

    Broader source: Directives, Delegations, and Requirements [Office of Management (MA)]

    1978-12-12T23:59:59.000Z

    To provide specific responsibilities, standards, and procedures for the management of the Department of Energy (DOE) classification system. Cancels DOE O 5650.1, dated 7-18-78; DOE N 5650.1, dated 8-7-78; DOE N 5650.2, dated 8-7-78; DOE N 5650.3, dated 8-7-78. Canceled by DOE O 5650.2A, dated 5-8-95

  14. Seismic event classification system

    DOE Patents [OSTI]

    Dowla, Farid U. (Castro Valley, CA); Jarpe, Stephen P. (Brentwood, CA); Maurer, William (Livermore, CA)

    1994-01-01T23:59:59.000Z

    In the computer interpretation of seismic data, the critical first step is to identify the general class of an unknown event. For example, the classification might be: teleseismic, regional, local, vehicular, or noise. Self-organizing neural networks (SONNs) can be used for classifying such events. Both Kohonen and Adaptive Resonance Theory (ART) SONNs are useful for this purpose. Given the detection of a seismic event and the corresponding signal, computation is made of: the time-frequency distribution, its binary representation, and finally a shift-invariant representation, which is the magnitude of the two-dimensional Fourier transform (2-D FFT) of the binary time-frequency distribution. This pre-processed input is fed into the SONNs. These neural networks are able to group events that look similar. The ART SONN has an advantage in classifying the event because the types of cluster groups do not need to be pre-defined. The results from the SONNs together with an expert seismologist's classification are then used to derive event classification probabilities.

  15. Seismic event classification system

    DOE Patents [OSTI]

    Dowla, F.U.; Jarpe, S.P.; Maurer, W.

    1994-12-13T23:59:59.000Z

    In the computer interpretation of seismic data, the critical first step is to identify the general class of an unknown event. For example, the classification might be: teleseismic, regional, local, vehicular, or noise. Self-organizing neural networks (SONNs) can be used for classifying such events. Both Kohonen and Adaptive Resonance Theory (ART) SONNs are useful for this purpose. Given the detection of a seismic event and the corresponding signal, computation is made of: the time-frequency distribution, its binary representation, and finally a shift-invariant representation, which is the magnitude of the two-dimensional Fourier transform (2-D FFT) of the binary time-frequency distribution. This pre-processed input is fed into the SONNs. These neural networks are able to group events that look similar. The ART SONN has an advantage in classifying the event because the types of cluster groups do not need to be pre-defined. The results from the SONNs together with an expert seismologist's classification are then used to derive event classification probabilities. 21 figures.

  16. Improvements in geothermometry. Final technical report. Rev

    SciTech Connect (OSTI)

    Potter, J.; Dibble, W.; Parks, G.; Nur, A.

    1982-08-01T23:59:59.000Z

    Alkali and alkaline earth geothermometers are useful for estimating geothermal reservoir temperatures, though a general theoretical basis has yet to be established and experimental calibration needs improvement. Equilibrium cation exchange between feldspars provided the original basis for the Na-K and Na-K-Ca geothermometers (Fournier and Truesdell, 1973), but theoretical, field and experimental evidence prove that neither equilibrium nor feldspars are necessary. Here, evidence is summarized in support of these observations, concluding that these geothermometers can be expected to have a surprisingly wide range of applicability, but that the reasons behind such broad applicability are not yet understood. Early experimental work proved that water-rock interactions are slow at low temperatures, so experimental calibration at temperatures below 150/sup 0/ is impractical. Theoretical methods and field data were used instead for all work at low temperatures. Experimental methods were emphasized for temperatures above 150/sup 0/C, and the simplest possible solid and solution compositions were used to permit investigation of one process or question at a time. Unexpected results in experimental work prevented complete integration of the various portions of the investigation.

  17. Cation geothermometry in oil field waters

    SciTech Connect (OSTI)

    Smith, L.K.; Dunn, T.L.; Surdam, R.C. (Univ. of Wyoming, Laramie, WY (United States))

    1992-01-01T23:59:59.000Z

    The assumptions used in the development of cation ratio geothermometers are: (1) the ratios of the cations are controlled by cation exchange between solid silicate phases, (2) aluminum is conserved in the solid phases, and (3) neither hydrogen ions nor CO[sub 2] enter into the net reactions. These assumptions do not apply to oilfield waters where organic species are present and commonly abundant. Nine different published cation geothermometers of Na/K, Na-K-Ca, Na-K-Ca-Mg, and Mg/Li were applied to 309 water samples from both oilfield and geothermal wells. None of the cation geothermometers predicted consistent or accurate temperatures for the oilfield waters. Plots of measured v. predicted temperature for oilfield water samples gave correlation coefficients of less than 0.35. In contrast, those same plots for geothermal water samples within the same temperature range gave correlation coefficients between 0.45 and 0.95. This analysis suggests that the presence of organic species exerts a strong control on the cation ratios. Organic species form complexes of varying stability with the cations. This, in turn, changes the relative concentrations of the cations in solution over that which is expected when cation exchange between silicate phases controls the ratios. Organic complexes also strongly affect pH and P[sub CO[sub 2

  18. Geothermal: Sponsored by OSTI -- Improved Geothermometry Through...

    Office of Scientific and Technical Information (OSTI)

    of Geomicrobiological Influences on Geochemical Temperature Indicators: Final Report Geothermal Technologies Legacy Collection HelpFAQ | Site Map | Contact Us HomeBasic Search...

  19. Integrated Chemical Geothermometry System for Geothermal Exploration

    Broader source: Energy.gov [DOE]

    DOE Geothermal Peer Review 2010 - Presentation. Develop practical and reliable system to predict geothermal reservoir temperatures from integrated chemical analyses of spring and well fluids.

  20. Category:Gas Geothermometry | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand JumpConceptual Model, click here. Category:Conceptual Model Add.pngpage? For detailed information

  1. Category:Geothermometry | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand JumpConceptual Model, click here. Category:Conceptual Model Add.pngpage?source History

  2. Category:Isotope Geothermometry | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand JumpConceptual Model, click here. Category:Conceptual Model Add.pngpage?sourcehelp

  3. Category:Liquid Geothermometry | Open Energy Information

    Open Energy Info (EERE)

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  4. Geothermometry (Klein, 2007) | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH und Co KG Jump to:

  5. MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION

    E-Print Network [OSTI]

    Endres. William J.

    MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION JOB TITLE: CUSTODIAN (Regular; Twelve and ledges and clean fixtures. Maintain building entrances according to conditions by removing snow and ice

  6. MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION

    E-Print Network [OSTI]

    MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION JOB TITLE: CUSTODIAN (9 month, part according to conditions by removing snow and ice, applying sand and salt, and removing debris. Adhere

  7. MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION

    E-Print Network [OSTI]

    MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION JOB TITLE: CUSTODIAN (9 month, full according to conditions by removing snow and ice, applying sand and salt, and removing debris. Adhere

  8. MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION

    E-Print Network [OSTI]

    MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION JOB TITLE: CUSTODIAN (9 month, full and clean fixtures. Maintain building entrances according to conditions by removing snow and ice, applying

  9. MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION

    E-Print Network [OSTI]

    MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION JOB TITLE: CUSTODIAN (Regular; Twelve according to conditions by removing snow and ice, applying sand and salt, and removing debris. Adhere

  10. MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION

    E-Print Network [OSTI]

    MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION JOB TITLE: CUSTODIAN (12-month, full according to conditions by removing snow and ice, applying sand and salt, and removing debris. Adhere

  11. MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION

    E-Print Network [OSTI]

    MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION JOB TITLE: BUILDING MECHANIC II (Pay, parking lots, elevators, snow conditions, HVAC equipment temperature control systems, pool systems, ice

  12. MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION

    E-Print Network [OSTI]

    MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION JOB TITLE: CUSTODIAN/EVENT ASSOCIATE entrances according to conditions by removing snow and ice, applying sand and salt, and removing debris

  13. MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION

    E-Print Network [OSTI]

    MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION JOB TITLE: CUSTODIAN (12-month, full and clean fixtures. Maintain building entrances according to conditions by removing snow and ice, applying

  14. MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION

    E-Print Network [OSTI]

    MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION JOB TITLE: CUSTODIAN (12 month, part according to conditions by removing snow and ice, applying sand and salt, and removing debris. Adhere

  15. Waste classification sampling plan

    SciTech Connect (OSTI)

    Landsman, S.D.

    1998-05-27T23:59:59.000Z

    The purpose of this sampling is to explain the method used to collect and analyze data necessary to verify and/or determine the radionuclide content of the B-Cell decontamination and decommissioning waste stream so that the correct waste classification for the waste stream can be made, and to collect samples for studies of decontamination methods that could be used to remove fixed contamination present on the waste. The scope of this plan is to establish the technical basis for collecting samples and compiling quantitative data on the radioactive constituents present in waste generated during deactivation activities in B-Cell. Sampling and radioisotopic analysis will be performed on the fixed layers of contamination present on structural material and internal surfaces of process piping and tanks. In addition, dose rate measurements on existing waste material will be performed to determine the fraction of dose rate attributable to both removable and fixed contamination. Samples will also be collected to support studies of decontamination methods that are effective in removing the fixed contamination present on the waste. Sampling performed under this plan will meet criteria established in BNF-2596, Data Quality Objectives for the B-Cell Waste Stream Classification Sampling, J. M. Barnett, May 1998.

  16. Incremental Accelerated Gradient Methods for SVM Classification ...

    E-Print Network [OSTI]

    2013-05-02T23:59:59.000Z

    SVM Classification: Study of the Constrained ... Vector Machines (SVM) for online classification tasks. ...... A Library for Support Vector Machines [8]. 11.

  17. Benefits Summary - Temporary Job Classification | Argonne National...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Temporary Job Classification Download a summary of benefits offered to employees in the temporary job classification (at least 6 months term and 20 hoursweek). 2015 Long Term...

  18. Burning Down the Shelf: Standardized Classification, Folksonomies, and Ontological Politics

    E-Print Network [OSTI]

    Lau, Andrew J.

    2008-01-01T23:59:59.000Z

    of Dewey Decimal Classification (DDC), Library of Congressstudies in critical classification. Library Resources and

  19. On the algebraic classification of spacetimes

    E-Print Network [OSTI]

    V. Pravda

    2005-12-15T23:59:59.000Z

    We briefly overview the Petrov classification in four dimensions and its generalization to higher dimensions.

  20. MultiCriteria Clustering & Classification

    E-Print Network [OSTI]

    Libre de Bruxelles, Université

    An Extension of the K-Means' algorithm MultiCriteria Ordered Clustering 6 MCDA Classification : Sorting MCDACriteria Comparison : motivation A MCDA approach for grouping problems 5 MCDA Clustering Multicriteria Clustering

  1. Lecture Ch. 8 Cloud Classification

    E-Print Network [OSTI]

    Russell, Lynn

    clouds Middle clouds Grayish, block the sun, sometimes patchy Sharp outlines, rising, bright white1 Lecture Ch. 8 · Cloud Classification ­ Descriptive approach to clouds · Drop Growth and Precipitation Processes ­ Microphysical characterization of clouds · Complex (i.e. Real) Clouds ­ Examples

  2. MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION

    E-Print Network [OSTI]

    Endres. William J.

    MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION JOB TITLE: CUSTODIAN/EVENT ASSOCIATE to conditions by removing snow and ice, applying sand and salt, and removing debris. Adhere to current uniform

  3. MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION

    E-Print Network [OSTI]

    MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION JOB TITLE: CUSTODIAN (12 month/40 and clean fixtures. Maintain building entrances according to conditions by removing snow and ice, applying be exercised over seasonal/temporary university employees and student assistants. QUALIFICATION REQUIREMENTS

  4. MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION

    E-Print Network [OSTI]

    MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION JOB TITLE: CUSTODIAN (12 month and ice, applying sand and salt, and removing debris. Adhere to current department uniform policy supervision may be exercised over seasonal/temporary university employees and student assistants

  5. MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION

    E-Print Network [OSTI]

    MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION JOB TITLE: CUSTODIAN (12 mos and clean fixtures. Maintain building entrances according to conditions by removing snow and ice, applying be exercised over seasonal/temporary university employees and student assistants. QUALIFICATION REQUIREMENTS

  6. MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION

    E-Print Network [OSTI]

    MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION JOB TITLE: CUSTODIAN (9 month/20 hours and clean fixtures. Maintain building entrances according to conditions by removing snow and ice, applying be exercised over seasonal/temporary university employees and student assistants. QUALIFICATION REQUIREMENTS

  7. Hierarchical classification of modulation signals

    E-Print Network [OSTI]

    Kim, Nam Jin

    2002-01-01T23:59:59.000Z

    This thesis addresses the problem of classifying both analog and digital modulation signals using different kinds of classifiers. The classification of modulation signals has both civilian and military applications. A total of 31 statistical signal...

  8. HIV classification using coalescent theory

    SciTech Connect (OSTI)

    Zhang, Ming [Los Alamos National Laboratory; Letiner, Thomas K [Los Alamos National Laboratory; Korber, Bette T [Los Alamos National Laboratory

    2008-01-01T23:59:59.000Z

    Algorithms for subtype classification and breakpoint detection of HIV-I sequences are based on a classification system of HIV-l. Hence, their quality highly depend on this system. Due to the history of creation of the current HIV-I nomenclature, the current one contains inconsistencies like: The phylogenetic distance between the subtype B and D is remarkably small compared with other pairs of subtypes. In fact, it is more like the distance of a pair of subsubtypes Robertson et al. (2000); Subtypes E and I do not exist any more since they were discovered to be composed of recombinants Robertson et al. (2000); It is currently discussed whether -- instead of CRF02 being a recombinant of subtype A and G -- subtype G should be designated as a circulating recombination form (CRF) nd CRF02 as a subtype Abecasis et al. (2007); There are 8 complete and over 400 partial HIV genomes in the LANL-database which belong neither to a subtype nor to a CRF (denoted by U). Moreover, the current classification system is somehow arbitrary like all complex classification systems that were created manually. To this end, it is desirable to deduce the classification system of HIV systematically by an algorithm. Of course, this problem is not restricted to HIV, but applies to all fast mutating and recombining viruses. Our work addresses the simpler subproblem to score classifications of given input sequences of some virus species (classification denotes a partition of the input sequences in several subtypes and CRFs). To this end, we reconstruct ancestral recombination graphs (ARG) of the input sequences under restrictions determined by the given classification. These restritions are imposed in order to ensure that the reconstructed ARGs do not contradict the classification under consideration. Then, we find the ARG with maximal probability by means of Markov Chain Monte Carlo methods. The probability of the most probable ARG is interpreted as a score for the classification. To our knowledge, this particular problem was not addressed up to now. The software package Lamarc Kuhner et al. (2000) allows for sampling ARGs, but it assumes that recombination events only involve one breakpoint. However, in HIV recombinants usually have more than one breakpoint. Moreover, Lamarc does not perform an explicit breakpoint detection, but tries to find them by chance. Although this approach is suitable for most situations, it will not lead to satisfying results in case of highly recombining viruses with multiple breakpoints.

  9. Deterministic Model for Acute Myelogenous Leukemia Classification

    E-Print Network [OSTI]

    Chronopoulos, Anthony T.

    Deterministic Model for Acute Myelogenous Leukemia Classification Monica Madhukar that the proposed system robustly segments and classifies Acute Myelogenous Leukemia based on complete microscopic/backup service to the physician Keywords- Classification; Segmentation; Acute Myelogenous leukemia; Feature

  10. Reflectance Function Approximation for Material Classification

    E-Print Network [OSTI]

    Dyer, Charles R.

    Reflectance Function Approximation for Material Classification Edward Wild CS 766 Final Project This report summarizes the results of a project to approximate reflectance functions and classify materials to classify materials. Classification algorithms are proposed to deal with unseen materials. Experimental

  11. Brain MRI Classification using the Expectation Maximization

    E-Print Network [OSTI]

    Chen, Tsuhan

    Brain MRI Classification using the Expectation Maximization made a brain magnetic resonance image (MRI) classification algorithm that uses a twostage applied to a set of normal brain MR images for further testing. We accomplished a working

  12. Maturing Software Engineering Knowledge through Classifications

    E-Print Network [OSTI]

    Basili, Victor R.

    Maturing Software Engineering Knowledge through Classifications: A Case Study on Unit Testing contribution to advancing knowledge in both science and engineering. It is a way of investigating Engineering knowledge, as classifications constitute an organized structure of knowledge items. Till date

  13. Comparison: Meningioma Classification using Wavelet Packets and Normal Texture based Classification

    E-Print Network [OSTI]

    Qureshi, Hammad

    Energy Based Classification Accuracy 0 20 40 60 80 100 120 F M P T Overall Meningiomas %Accuracy RawComparison: Meningioma Classification using Wavelet Packets and Normal Texture based Classification performed better were obtained from each · Classification using k-nn (leave one out). Introduction

  14. Optimization Online - Classification with Guaranteed Probability of ...

    E-Print Network [OSTI]

    Marco C. Campi

    2009-03-18T23:59:59.000Z

    Mar 18, 2009 ... Classification with Guaranteed Probability of Error. Marco C. ... Category 3: Applications -- Science and Engineering (Statistics ). Citation:.

  15. Updating the Classification of Geothermal Resources- Presentation

    Broader source: Energy.gov [DOE]

    USGS is working with DOE, the geothermal industry, and academic partners to develop a new geothermal resource classification system.

  16. Updating the Classification of Geothermal Resources

    Broader source: Energy.gov [DOE]

    USGS is working with DOE, the geothermal industry, and academic partners to develop a new geothermal resource classification system.

  17. Multicriteria Inventory Classification Using A Genetic Algorithm

    E-Print Network [OSTI]

    Güvenir, H. Altay

    Multicriteria Inventory Classification Using A Genetic Algorithm H. Altay Guvenir a;1 , and Erdal. The new crossover technique is applied to the prob­ lem of multicriteria inventory classification. The results are compared with the classical inventory classification technique using Analytical Hierarchy

  18. Hierarchical Classification of Documents with Error Control

    E-Print Network [OSTI]

    Fu, Ada Waichee

    Hierarchical Classification of Documents with Error Control Chun­hung Cheng 1 , Jian Tang 2 , Ada. Classification is a function that matches a new object with one of the predefined classes. Document classification is characterized by the large number of attributes involved in the objects (documents

  19. Remote Sensing Ayman F. Habib Image Classification

    E-Print Network [OSTI]

    Habib, Ayman

    1 Remote Sensing Ayman F. Habib 1 Chapter 6 Image Classification Remote Sensing Ayman F. Habib 2. ­ Unsupervised classification. · Accuracy assessment. #12;2 Remote Sensing Ayman F. Habib 3 Image Classification of image pixels is based on their digital numbers/grey values in one or more spectral bands. Remote Sensing

  20. THE UNIVERSITY OF CLASSIFICATION DESCRIPTION

    E-Print Network [OSTI]

    Portman, Douglas

    1 THE UNIVERSITY OF ROCHESTER CLASSIFICATION DESCRIPTION TITLE: Web Specialist and Analyst DATE: 01 ­ Advancement Service and provide assistance in the development and management of web-based assets, direct solicitations support ­ webpages for print and e-mail marketing support, web development, Share

  1. MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION

    E-Print Network [OSTI]

    MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION Job Title: STAFF ASSISTANT (N6) Department: J. ROBERT VAN PELT LIBRARY Hourly Rate: MINIMUM $12.65 ­ MAXIMUM $16.20 Supervisor: STRATEGIC INITIATIVES LIBRARIAN SUMMARY: This position participates in a variety of digital library and service

  2. MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION

    E-Print Network [OSTI]

    Endres. William J.

    MICHIGAN TECHNOLOGICAL UNIVERSITY CLASSIFICATION DESCRIPTION JOB TITLE: BUILDING MECHANIC II (pay: Accountable for supervision and maintenance of all operating units in the SDC, MacInnes Ice Arena, Child Care, maintenance, sanitation, customer services and enforcement of SDC, MacInnes Ice Arena, Child Care Center

  3. Featureless Classification of Light Curves

    E-Print Network [OSTI]

    Kügler, Sven Dennis; Polsterer, Kai Lars

    2015-01-01T23:59:59.000Z

    In the era of rapidly increasing amounts of time series data, classification of variable objects has become the main objective of time-domain astronomy. Classification of irregularly sampled time series is particularly difficult because the data can not be represented naturally as a plain vector which directly can be fed into a classifier. In the literature, various statistical features derived from time series serve as a representation. Typically, the usefulness of the derived features is judged in an empirical fashion according to their predictive power. In this work, an alternative to the feature-based approach is investigated. In this new representation the time series is described by a density model. Similarity between each pair of time series is quantified by the distance between their respective models. The density model captures all the information available, also including measurement errors. Hence, we view this model as a generalisation to the static features which directly can be derived, e.g., as ...

  4. Class Discovery in Galaxy Classification

    E-Print Network [OSTI]

    David Bazell; David J. Miller

    2004-06-14T23:59:59.000Z

    In recent years, automated, supervised classification techniques have been fruitfully applied to labeling and organizing large astronomical databases. These methods require off-line classifier training, based on labeled examples from each of the (known) object classes. In practice, only a small batch of labeled examples, hand-labeled by a human expert, may be available for training. Moreover, there may be no labeled examples for some classes present in the data, i.e. the database may contain several unknown classes. Unknown classes may be present due to 1) uncertainty in or lack of knowledge of the measurement process, 2) an inability to adequately ``survey'' a massive database to assess its content (classes), and/or 3) an incomplete scientific hypothesis. In recent work, new class discovery in mixed labeled/unlabeled data was formally posed, with a proposed solution based on mixture models. In this work we investigate this approach, propose a competing technique suitable for class discovery in neural networks, and evaluate both methods for classification and class discovery on several astronomical data sets. Our results demonstrate up to a 57% reduction in classification error compared to a standard neural network classifier that uses only labeled data.

  5. Genetic classification of petroleum basins

    SciTech Connect (OSTI)

    Demaison, G.; Huizinga, B.J.

    1989-03-01T23:59:59.000Z

    Rather than relying on a descriptive geologic approach, this genetic classification is based on the universal laws that control processes of petroleum formation, migration, and entrapment. Petroleum basins or systems are defined as dynamic petroleum-generating and concentrating physico-chemical systems functioning on a geologic space and time scale. A petroleum system results from the combination of a generative subsystem (or hydrocarbon kitchen), essentially controlled by chemical processes, and a migration-entrapment subsystem, controlled by physical processes. The generative subsystem provides a certain supply of petroleum to the basin during a given geologic time span. The migration-entrapment subsystem receives petroleum and distributes it in a manner that can lead either to dispersion and loss or to concentration of the regional charge into economic accumulations. The authors classification scheme for petroleum basins rests on a simple working nomenclature consisting of the following qualifiers: (1) charge factor: undercharged, normally charged, or supercharged, (2) migration drainage factor: vertically drained or laterally drained, and (3) entrapment factor: low impedance or high impedance. Examples chosen from an extensive roster of documented petroleum basins are reviewed to explain the proposed classification.

  6. Discriminant forest classification method and system

    DOE Patents [OSTI]

    Chen, Barry Y.; Hanley, William G.; Lemmond, Tracy D.; Hiller, Lawrence J.; Knapp, David A.; Mugge, Marshall J.

    2012-11-06T23:59:59.000Z

    A hybrid machine learning methodology and system for classification that combines classical random forest (RF) methodology with discriminant analysis (DA) techniques to provide enhanced classification capability. A DA technique which uses feature measurements of an object to predict its class membership, such as linear discriminant analysis (LDA) or Andersen-Bahadur linear discriminant technique (AB), is used to split the data at each node in each of its classification trees to train and grow the trees and the forest. When training is finished, a set of n DA-based decision trees of a discriminant forest is produced for use in predicting the classification of new samples of unknown class.

  7. University Policy No.: AD2530 Classification: Administration

    E-Print Network [OSTI]

    Victoria, University of

    University Policy No.: AD2530 Classification: Administration PHOTOCOPY AND FACSIMILE (FAX may be obtained through library managed machines or through the use of personal accounts. 1

  8. Geomorphic Stream Classification "A Classification of Natural Rivers", Rosgen, D.L.

    E-Print Network [OSTI]

    and animals are constrained by natural channel physics #12;Stream Corridor Restoration: Principles, ProcessesGeomorphic Stream Classification "A Classification of Natural Rivers", Rosgen, D.L. #12;Why is Stream Classification Essential? Napeequa River · Physical stream channel evolution ·Similar stream types

  9. Hierarchical Classification of Documents with Error Control

    E-Print Network [OSTI]

    King, Kuo Chin Irwin

    Hierarchical Classification of Documents with Error Control Chun-hung Cheng1 , Jian Tang2 , Ada Wai is a function that matches a new object with one of the predefined classes. Document classification is characterized by the large number of attributes involved in the objects (documents). The traditional method

  10. Oil Classification with Fluorescence Spectroscopy Engineering Physics

    E-Print Network [OSTI]

    Oldenburg, Carl von Ossietzky Universität

    detected by these channels. The investigation used three methods to examine crude oil, heavy oil, sludge1 Oil Classification with Fluorescence Spectroscopy Engineering Physics Master of Engineering and classification of oil spills on water surfaces. It is an overview of the laser remote sensor technique

  11. Classification of Unexploded Ordnance Laurens Sander Beran

    E-Print Network [OSTI]

    Oldenburg, Douglas W.

    Classification of Unexploded Ordnance by Laurens Sander Beran B.Sc., The University of Victoria degree at the University of British Columbia, I agree that the Library shall make it freely available and clutter. In the statistical classification framework, model parameters are basis vectors within a multi

  12. A Thermodynamic Classification of Real Numbers

    E-Print Network [OSTI]

    Thomas Garrity

    2009-03-15T23:59:59.000Z

    A new classification scheme for real numbers is given, motivated by ideas from statistical mechanics in general and work of Knauf and of Fiala and Kleban in particular. Critical for this classification of a real number will be the Diophantine properties of its continued fraction expansion.

  13. Approaching Real-time Network Traffic Classification

    E-Print Network [OSTI]

    Haddadi, Hamed

    and studies. It serves as the input for Intrusion Detection Systems, provides Class-of-Service (CoS) mapping22 Approaching Real-time Network Traffic Classification ISSN 1470-5559 RR-06-12 October 2006-time Network Traffic Classification Wei Li, Kaysar Abdin, Robert Dann and Andrew Moore Department of Computer

  14. Tags vs Shelves: From Social Tagging to Social Classification

    E-Print Network [OSTI]

    Hammerton, James

    mechanism and (iii) existing classification systems such as the Library of Congress Classification System libraries and librarians have performed the task of classification for centuries, the process of man- uallyTags vs Shelves: From Social Tagging to Social Classification Arkaitz Zubiaga NLP & IR Group

  15. University Policy No.: AD2215 Classification: Administration

    E-Print Network [OSTI]

    Victoria, University of

    University Policy No.: AD2215 Classification: Administration Approving Authority: Board of Governors LICENSING PROGRAM POLICY Effective Date: June/90 Supersedes: Last Editorial Change: Mandated Review: 1. This statement applies to the policies and administration of trademarks registered

  16. University Policy No.: AD2210 Classification: Administration

    E-Print Network [OSTI]

    Victoria, University of

    University Policy No.: AD2210 Classification: Administration FIELDWORK AND INTERNATIONAL Approving Authority: President TRAVEL RISK MANAGEMENT POLICY Effective Date: November/07 Supersedes: New Last Editorial Change: Mandated Review: November/14 Purpose 1. The purpose of the policy

  17. IDENTIFYING ROOF FALL PREDICTORS USING FUZZY CLASSIFICATION

    SciTech Connect (OSTI)

    Bertoncini, C. A.; Hinders, M. K. [NDE Lab, College of William and Mary, Williamsburg, VA, 23187-8795 (United States)

    2010-02-22T23:59:59.000Z

    Microseismic monitoring involves placing geophones on the rock surfaces of a mine to record seismic activity. Classification of microseismic mine data can be used to predict seismic events in a mine to mitigate mining hazards, such as roof falls, where properly bolting and bracing the roof is often an insufficient method of preventing weak roofs from destabilizing. In this study, six months of recorded acoustic waveforms from microseismic monitoring in a Pennsylvania limestone mine were analyzed using classification techniques to predict roof falls. Fuzzy classification using features selected for computational ease was applied on the mine data. Both large roof fall events could be predicted using a Roof Fall Index (RFI) metric calculated from the results of the fuzzy classification. RFI was successfully used to resolve the two significant roof fall events and predicted both events by at least 15 hours before visual signs of the roof falls were evident.

  18. Audio classification from time-frequency texture

    E-Print Network [OSTI]

    Slotine, Jean-Jacques E.

    Time-frequency representations of audio signals often resemble texture images. This paper derives a simple audio classification algorithm based on treating sound spectrograms as texture images. The algorithm is inspired ...

  19. Inclination-Independent Galaxy Classification

    E-Print Network [OSTI]

    Jeremy Bailin; William E. Harris

    2008-03-09T23:59:59.000Z

    We present a new method to classify galaxies from large surveys like the Sloan Digital Sky Survey using inclination-corrected concentration, inclination-corrected location on the color-magnitude diagram, and apparent axis ratio. Explicitly accounting for inclination tightens the distribution of each of these parameters and enables simple boundaries to be drawn that delineate three different galaxy populations: Early-type galaxies, which are red, highly concentrated, and round; Late-type galaxies, which are blue, have low concentrations, and are disk dominated; and Intermediate-type galaxies, which are red, have intermediate concentrations, and have disks. We have validated our method by comparing to visual classifications of high-quality imaging data from the Millennium Galaxy Catalogue. The inclination correction is crucial to unveiling the previously unrecognized Intermediate class. Intermediate-type galaxies, roughly corresponding to lenticulars and early spirals, lie on the red sequence. The red sequence is therefore composed of two distinct morphological types, suggesting that there are two distinct mechanisms for transiting to the red sequence. We propose that Intermediate-type galaxies are those that have lost their cold gas via strangulation, while Early-type galaxies are those that have experienced a major merger that either consumed their cold gas, or whose merger progenitors were already devoid of cold gas (the ``dry merger'' scenario).

  20. Genetic classification of petroleum systems

    SciTech Connect (OSTI)

    Huizinga, B.J. (Chevron Oil Field Research Co., Richmond, CA (United States)); Demaison, G.

    1991-03-01T23:59:59.000Z

    The authors genetic classification of petroleum basins is founded on a working nomenclature that consists of combining qualifiers from each of the following three categories: (1) the charge factor (supercharged, normally charged, or undercharged), (2) the migration drainage style (vertically drained or laterally drained), and (3) the entrapment style (high impedance or low impedance). The charge factor is estimated on the basis of the richness and volumetrics of mature source rocks. The source potential index (SPI), which combines source-rock richness and thickness into a single parameter, is a convenient shortcut for comparing the petroleum potential of different source rocks containing dissimilar kerogen types and for rapidly estimating a basin's regional charging capacity. On a global scale, a general correlation exists between the magnitude of SPI and basinwide petroleum reserves. The dominant migration drainage style can be predicted from the structural and stratigraphic framework of a basin. Recognition of the dominant migration style helps to predict the location of zones of petroleum occurrence in relation to the 'hydrocarbon kitchens.' The entrapment style, which is also dependent on the structural framework and the presence of seals, describes the degree of resistance (i.e. impedance) working against dispersion of the petroleum charge. Application of these working concepts should help significantly reduce geologic risk, particularly in new ventures-type exploration.

  1. Genetic classification of petroleum systems

    SciTech Connect (OSTI)

    Demaison, G. (Stanford Univ., CA (United States)); Huizinga, B.J. (Chevron Overseas Petroleum Inc., San Ramon, CA (United States))

    1991-10-01T23:59:59.000Z

    The authors classification so petroleum systems is founded on a simple working nomenclature that consists of combining qualifiers from each of the following three categories: (1) charge factor, (2) migration drainage style, and (3) entrapment style. The charge factor is estimated on the basis of the richness and volumetrics of mature source rocks. The source potential index (SPI), which combines source-rock richness and thickness into a single parameter, is a convenient shortcut for comparing the petroleum potential of diverse source rocks containing dissimilar kerogen types and for rapidly estimating regional charging capacity. The migration drainage style is determined from the structural and stratigraphic framework of a basin. Vertical-migration drainage, which occurs mainly through faults and fracture systems breaching a seal, is characteristic of petroleum systems contained within rift basins, deltaic sequences, salt-dome provinces, wrench basins, and fold-and-thrust belts. In contrast, lateral-migration drainage sequences, salt-dome provinces, wrench basins, and fold-and-thrust belts. In contrast, lateral-migration drainage is dominant wherever stratigraphically continuous seal-reservoir doublets extend over a very large area in tectonically stable province. The entrapment style, which is also dependent on the structural framework and the presence and effectiveness of seals, describes the degree of resistance working against dispersion of the petroleum charge. Application of these working concepts should help to significantly reduce geologic risk, particularly in news ventures-type exploration.

  2. Materials and methods Evolutionary classification of C. elegans proteins. For classification of C. ele-

    E-Print Network [OSTI]

    Yu, Haiyuan

    Materials and methods Evolutionary classification of C. elegans proteins. For classification of C the ORFeome library (1) and transformed into E. coli strain DH5 in 96 well format. Up to 12 single colonies- propriate concentration. AD-ORFeome 1.0 and AD-wrmcDNA libraries. The two Gal4 activation do- main (AD

  3. Geothermometry At Lassen Volcanic National Park Area (Janik ...

    Open Energy Info (EERE)

    temperatures of 223 degrees C (Muffler et al., 1982) and 229 degrees C (Thompson, 1985) reported for Growler Hot Spring water, but are within the 220-240 degrees C...

  4. Geothermometry At Long Valley Caldera Geothermal Area (Mariner...

    Open Energy Info (EERE)

    System In Long Valley Caldera, California, From Wells, Fluid Sampling, Electrical Geophysics, And Age Determinations Of Hot-Spring Deposits Additional References Retrieved from...

  5. Geothermometry At Roosevelt Hot Springs Geothermal Area (Ward...

    Open Energy Info (EERE)

    Area. References S. H. Ward, W. T. Parry, W. P. Nash, W. R. Sill, K. L. Cook, R. B. Smith, D. S. Chapman, F. H. Brown, J. A. Whelan, J. R. Bowman (1978) A Summary of the...

  6. Geothermometry At Long Valley Caldera Geothermal Area (Sorey...

    Open Energy Info (EERE)

    studies, and seem to prove useful in most cases (Flexser, 1991; Goff et al., 1991; Smith and Suemnicht, 1991). Results from these studies are also summarized in Sorey et al....

  7. Geothermometry At Long Valley Caldera Geothermal Area (McKenzie...

    Open Energy Info (EERE)

    methods, dried, and then weighed. The extracted barium sulfate was converted to carbon dioxide by the graphite reduction method (Rafter, 1976) modified for internal...

  8. Geochemistry And Geothermometry Of Spring Water From The Blackfoot...

    Open Energy Info (EERE)

    a direct result of high potassium concentrations in the water. A correction for carbon dioxide applied to the Na-K-Ca geothermometer lowers the estimated temperatures of the...

  9. Validation of Multicomponent Equilibrium Geothermometry at Four Geothermal Power Plants

    SciTech Connect (OSTI)

    Ghanashyam Neupane; Jeffrey S Baum; Earl D Mattson; Gregory L Mines; Carl D Palmer; Robert W Smith

    2001-01-01T23:59:59.000Z

    This paper evaluates our ability to predict geothermal reservoir temperatures using water compositions measured from surface hot springs or shallow subsurface wells at four geothermal sites prior to the startup of geothermal energy production using RTEst, a multicomponent equilibrium geothermometer we have developed and are testing. The estimated reservoir temperatures of these thermal expressions are compared to measured bottom-hole temperatures of production wells at Raft River, ID; Neal Hot Springs, OR; Roosevelt Hot Springs, UT; and Steamboat Springs, NV geothermal sites. In general, temperatures of the producing reservoir estimated from the composition of water from surface expressions/shallow wells using RTEst are similar to the measured bottom-hole temperatures. For example, estimates for the Neal Hot Springs system are within ±10 ºC of the production temperatures. However, some caution must be exercised in evaluating RTEst predictions. Estimated temperature for a shallow Raft River well (Frazier well) is found to be slightly lower (ca. 15 ºC) than the bottom-hole temperatures from the geothermal plant production wells. For the Raft River system, local geology and fluid mixing model indicate that the fluid source for this shallow well may not have originated from the production reservoir. Similarly, RTEst results for Roosevelt Hot springs and Steamboat Springs geothermal areas were found consistent with the reservoir temperatures obtained from deep wells. These results suggest that the RTEst could be a valuable tool for estimating temperatures and evaluation geothermal resources.

  10. Geochemistry And Geothermometry Of Spring Water From The Blackfoot

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnualPropertyd8c-a9ae-f8521cbb8489InformationFrenchtown,Jump to:Locations In TheReservoir

  11. Geothermometry At Alum Area (DOE GTP) | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation, search OpenEI Reference LibraryAdd toWell TestingGeothermal/Power PlantUse)Alum

  12. Geothermometry At Coso Geothermal Area (1980) | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation, search OpenEI Reference LibraryAdd toWell TestingGeothermal/Power

  13. Geothermometry At Raft River Geothermal Area (1980) | Open Energy

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation, search OpenEI Reference LibraryAdd toWell2008) | Open Energy Information

  14. Geothermometry At Silver Peak Area (DOE GTP) | Open Energy Information

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation, search OpenEI Reference LibraryAdd toWell2008) | OpenSilver Peak Area (DOE GTP)

  15. Isotope Geothermometry At Lightning Dock Geothermal Area (Witcher, 2006) |

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdfGetecGtelInterias Solar Energy JumpIrem GeothermalIselin,Isofoton SAOpen Energy

  16. Geothermometry At Akutan Fumaroles Area (Kolker, Et Al., 2010) | Open

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH und Co KG Jump to:Energy

  17. Geothermometry At Blackfoot Reservoir Area (Hutsinpiller & Parry, 1985) |

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH und Co KG Jump to:EnergyOpen

  18. Geothermometry At Blue Mountain Geothermal Area (Casteel, Et Al., 2010) |

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH und Co KG Jump to:EnergyOpenOpen

  19. Geothermometry At Buffalo Valley Hot Springs Area (Laney, 2005) | Open

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH und Co KG Jump

  20. Geothermometry At Central Nevada Seismic Zone Region (Laney, 2005) | Open

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH und Co KG JumpEnergy

  1. Geothermometry At Central Nevada Seismic Zone Region (Shevenell & De

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH und Co KG JumpEnergyRocher, 2005)

  2. Geothermometry At Chena Geothermal Area (Kolker, 2008) | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH und Co KG JumpEnergyRocher,

  3. Geothermometry At Coso Geothermal Area (1978) | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH und Co KGEnergy

  4. Geothermometry At Fort Bliss Area (DOE GTP) | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH und Co KGEnergyFish Lake

  5. Geothermometry At Hot Springs Ranch Area (Szybinski, 2006) | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH und Co KGEnergyFish

  6. Geothermometry At Kawaihae Area (Thomas, 1986) | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH und Co KGEnergyFishKawaihae Area

  7. Geothermometry At Kilauea East Rift Geothermal Area (Thomas, 1986) | Open

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH und Co KGEnergyFishKawaihae

  8. Geothermometry At Lahaina-Kaanapali Area (Thomas, 1986) | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH und Co

  9. Geothermometry At Lassen Volcanic National Park Area (Janik & Mclaren,

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH und Co2010) | Open Energy

  10. Geothermometry At Lassen Volcanic National Park Area (Thompson, 1985) |

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH und Co2010) | Open EnergyOpen

  11. Geothermometry At Long Valley Caldera Geothermal Area (Farrar, Et Al.,

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH und Co2010) | Open EnergyOpen2003)

  12. Geothermometry At Long Valley Caldera Geothermal Area (Mariner & Willey,

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH und Co2010) | Open

  13. Geothermometry At Lualualei Valley Area (Thomas, 1986) | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH und Co2010) |

  14. Geothermometry At New River Area (DOE GTP) | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH und Co2010) |Information|

  15. Geothermometry At Roosevelt Hot Springs Geothermal Area (Ward, Et Al.,

    Open Energy Info (EERE)

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  16. Geothermometry At The Needles Area (DOE GTP) | Open Energy Information

    Open Energy Info (EERE)

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  17. Geothermometry At Yellowstone Region (Fournier, 1979) | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH

  18. Colorado thermal spring water geothermometry (public dataset) | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnualProperty EditCalifornia:PowerCER.png El CERCollier TechnologiesColoradoColoradoCourts

  19. Improved Geothermometry Through Multivariate Reaction Path Modeling and

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels DataDepartment of Energy Your Density Isn't YourTransport(Fact Sheet),Energy PetroleumEnergyImplementingImprove MotorEvaluation of

  20. A Dynamical Classification of the Cosmic Web

    E-Print Network [OSTI]

    J. E. Forero-Romero; Y. Hoffman; S. Gottloeber; A. Klypin; G. Yepes

    2008-09-24T23:59:59.000Z

    A dynamical classification of the cosmic web is proposed. The large scale environment is classified into four web types: voids, sheets, filaments and knots. The classification is based on the evaluation of the deformation tensor, i.e. the Hessian of the gravitational potential, on a grid. The classification is based on counting the number of eigenvalues above a certain threshold, lambda_th at each grid point, where the case of zero, one, two or three such eigenvalues corresponds to void, sheet, filament or a knot grid point. The collection of neighboring grid points, friends-of-friends, of the same web attribute constitutes voids, sheets, filaments and knots as web objects. A simple dynamical consideration suggests that lambda_th should be approximately unity, upon an appropriate scaling of the deformation tensor. The algorithm has been applied and tested against a suite of (dark matter only) cosmological N-body simulations. In particular, the dependence of the volume and mass filling fractions on lambda_th and on the resolution has been calculated for the four web types. Also, the percolation properties of voids and filaments have been studied. Our main findings are: (a) Already at lambda_th = 0.1 the resulting web classification reproduces the visual impression of the cosmic web. (b) Between 0.2 web. (c) The dynamical nature of the suggested classification provides a robust framework for incorporating environmental information into galaxy formation models, and in particular the semi-analytical ones.

  1. The Classification of M1-78

    E-Print Network [OSTI]

    G. T. Gussie

    1994-09-15T23:59:59.000Z

    The published properties of M1-78 are discussed with the purpose to resolve the object's classification as either a planetary nebula or an ultracompact HII region. A classification as a planetary nebula is rejected primarily because of the high luminosity of the object, but because of the chemical composition and expansion velocity of the nebula, a novel classification is proposed instead: that of an ultracompact HII region with a post-main sequence central star (possibly a WN star). It must therefore follow that observable ultracompact HII regions persist beyond the main sequence lifetimes of at least some massive stars, and so cannot be transient phenomena that are seen only during pre-main sequence or early main sequence evolution.

  2. New Decision Support Tool for Acute Lymphoblastic Leukemia Classification

    E-Print Network [OSTI]

    Chronopoulos, Anthony T.

    New Decision Support Tool for Acute Lymphoblastic Leukemia Classification Monica Madhukar affected by Acute Lymphoblastic Leukemia. The results show that the proposed system robustly segments and classifies acute lymphoblastic leukemia based on complete microscopic blood images. Keywords: Classification

  3. Seismic Facies Classification And Identification By Competitive Neural Networks

    E-Print Network [OSTI]

    Saggaf, Muhammad M.

    2000-01-01T23:59:59.000Z

    We present an approach based on competitive networks for the classification and identification of reservoir facies from seismic data. This approach can be adapted to perform either classification of the seismic facies based ...

  4. Automatic Fish Classification for Underwater Species Behavior Understanding

    E-Print Network [OSTI]

    Fisher, Bob

    Automatic Fish Classification for Underwater Species Behavior Understanding Concetto Spampinato an automatic fish classi- fication system that operates in the natural underwater en- vironment to assist marine biologists in understanding fish behavior. Fish classification is performed by combining two types

  5. Classification and Feature Extraction in Man and Machine

    E-Print Network [OSTI]

    to the SH. A piecewise linear extension as in the K-means classifier seems however less adapted to model classification. Furthermore, the comparison of the classification algorithms indicates that the Support Vector

  6. Ris-R-1556(EN) ACCUWIND -Classification of

    E-Print Network [OSTI]

    Risø-R-1556(EN) ACCUWIND - Classification of Five Cup Anemometers According to IEC61400-12-1 T generators to make classifications according to annex I and J of the standard IEC 61400-12-1 on power

  7. 163 SDSU Curriculum Guide 2010 COURSE CLASSIFICATION SYSTEM --EXAMPLES

    E-Print Network [OSTI]

    Ponce, V. Miguel

    163 SDSU Curriculum Guide 2010 COURSE CLASSIFICATION SYSTEM -- EXAMPLES Course Classification of business and other machines; accounting, geography, foreign languages, home economics, psychology, library, cartography, audiovisual, mathematics, library science, police science. C-16 Science laboratories Laboratories

  8. automatic vehicle classification: Topics by E-print Network

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Keywords: Document Classification, Document Structure, Technical Document, Support Vector Machine, Vector in the electronic format, needs an automatic...

  9. A framework for a coarse aggregate classification system

    E-Print Network [OSTI]

    Peapully, Srikrishna

    1994-01-01T23:59:59.000Z

    System 1 . 4 . . 5 . . 6 II OVERVIEW OF EXISTING CLASSIFICATION SYSTEMS . . . . 8 8 14 Soil Classification Systems Rock Classification Systems Previous Efforts in Developing an Aggregate Classification System... for Rocks . . 19 3. 1 Characterization of Aggregate Shape Based on Form Factor Proposed by Folk . . . . . . . . . . . . . . . 40 3. 2 Four Shape Categories as Described by Zingg . . 41 3. 3 Surface Texture Characterization Proposed by Kummer . . 42 4. 1...

  10. BAYESIAN INFERENCE AND OPTIMIZATION STRATEGIES FOR SOME DETECTION AND CLASSIFICATION

    E-Print Network [OSTI]

    Mignotte, Max

    energy function to be minimized. These segmentation and classification schemes can be used separatelyBAYESIAN INFERENCE AND OPTIMIZATION STRATEGIES FOR SOME DETECTION AND CLASSIFICATION PROBLEMS and classification problems of great importance in sonar imagery. More precisely this paper is concerned

  11. Competitive Mixture of Deformable Models for Pattern Classification \\Lambda

    E-Print Network [OSTI]

    Yeung, Dit-Yan

    Competitive Mixture of Deformable Models for Pattern Classification \\Lambda Kwok­Wai Cheung Dit to pattern classification. Recently, we have cast a deformable model under a Bayesian framework for classification, giving promising results. However, deformable model methods are computation­ ally expensive due

  12. Distance Metric Learning for Large Margin Nearest Neighbor Classification

    E-Print Network [OSTI]

    Weinberger, Kilian

    Distance Metric Learning for Large Margin Nearest Neighbor Classification Kilian Q. Weinberger}@cis.upenn.edu Abstract We show how to learn a Mahanalobis distance metric for k-nearest neigh- bor (kNN) classification in kNN classification--for example, achieving a test error rate of 1.3% on the MNIST handwritten digits

  13. TEXTURE CLASSIFICATION USING DISCRIMINANT WAVELET PACKET SUBBANDS Nasir Rajpoot

    E-Print Network [OSTI]

    Rajpoot, Nasir

    TEXTURE CLASSIFICATION USING DISCRIMINANT WAVELET PACKET SUBBANDS Nasir Rajpoot Department decomposition that are most useful for texture classification in an image. A functional measure based]. The classification of image data into different classes of texture is a challenging problem in image analysis [2

  14. University Policy No.: GV0225 Classification: Governance

    E-Print Network [OSTI]

    Victoria, University of

    University Policy No.: GV0225 Classification: Governance Approving Authority: Board of Governors RISK MANAGEMENT POLICY Effective Date: March/07 Supersedes: April/04 Last Editorial Change: Mandated Review: Purpose 1.00 The main purpose of this policy is to provide direction to the members

  15. University Policy No.: GV0215 Classification: Governance

    E-Print Network [OSTI]

    Victoria, University of

    University Policy No.: GV0215 Classification: Governance POLICY ON INTELLECTUAL Approving Authority Review: This policy now forms part of the Faculty Framework Agreement as Appendix D APPLICATION This policy applies to intellectual property (IP) created by members of the University in their University

  16. University Policy No.: RH8100 Classification: Research

    E-Print Network [OSTI]

    Victoria, University of

    1 University Policy No.: RH8100 Classification: Research Approving Authority: Board of Governors RESEARCH POLICY Effective Date: January 2010 Supersedes: June 2002 Last Editorial Change: Mandated Review: January 2017 PURPOSE 1.00 The purpose of this policy is to set out the manner in which research

  17. University Policy No.: AD2300 Classification: Administration

    E-Print Network [OSTI]

    Victoria, University of

    University Policy No.: AD2300 Classification: Administration Approving Authority: President FLAG DISPLAY POLICY Effective Date: March/08 Supersedes: June/99 Last Editorial Change: Mandated Review: March/15 PURPOSE 1.00 The purpose of this policy is to provide guidance on the display of flags

  18. University Policy No.: GV0230 Classification: Governance

    E-Print Network [OSTI]

    Victoria, University of

    University Policy No.: GV0230 Classification: Governance POLICY ON AUDITOR INDEPENDENCE: Approving AND OTHER NON- Supersedes: May/05 AUDIT SERVICES Last Editorial Change: Mandated Review: 1. POLICY PURPOSE The main purpose of this policy is to ensure procurement of audit, tax and other non- audit services does

  19. University Policy No.: GV0200 Classification: Governance

    E-Print Network [OSTI]

    Victoria, University of

    University Policy No.: GV0200 Classification: Governance POLICY ON HUMAN RIGHTS, EQUITY Approving: Mandated Review: 1. POLICY PURPOSE The University of Victoria's vision is to be a university of choice an active commitment to human rights, equity, fairness, and enhanced diversity. This policy responds

  20. Large margin classification in infinite neural networks

    E-Print Network [OSTI]

    Saul, Lawrence K.

    Large margin classification in infinite neural networks Youngmin Cho and Lawrence K. Saul, CA 92093-0404 Abstract We introduce a new family of positive-definite kernels for large margin classi- fication in support vector machines (SVMs). These kernels mimic the computation in large neural networks

  1. Text Classification for Intelligent Portfolio Management

    E-Print Network [OSTI]

    , earnings summaries, and Beta value (risk) associated with the individual holdings in their stock portfolioText Classification for Intelligent Portfolio Management Young-Woo Seo Joseph Giampapa Katia Sycara management, software agents that eval- uate the risks associated with the individual companies of a portfolio

  2. Classification and Utilization of Abstractions for Optimization

    E-Print Network [OSTI]

    Yi, Qing

    Classification and Utilization of Abstractions for Optimization Dan Quinlan1 , Markus Schordan2.sabjornsen@fys.uio.no Abstract. We define a novel approach to optimize the use of libraries within applications. We propose that library-defined abstractions be clas- sified to support their automated optimization and by leveraging

  3. Workload State Classification With Automation During Simulated

    E-Print Network [OSTI]

    Kaber, David B.

    Workload State Classification With Automation During Simulated Air Traffic Control David B. Kaber and Carlene M. Perry Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State to dynamically apply automation to information pro- cessing functions in aviation systems. This research examined

  4. HIERARCHICAL BAYESIAN LEARNING FOR ELECTRICAL TRANSIENT CLASSIFICATION

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    . An application to electrical transient classifi- cation for non-intrusive load monitoring is introduced, supervised classification, curve fitting, smooth transition regression model, non intrusive appliance load of electrical transients are useful in the context of non intrusive load monitoring (NILM). Ma- chine learning

  5. HEGIS CLASSIFICATIONS (THIS DOCUMENT IS KEYWORD SEARCHABLE

    E-Print Network [OSTI]

    Segraves, Kari A.

    HEGIS CLASSIFICATIONS (THIS DOCUMENT IS KEYWORD SEARCHABLE USING THE "FIND" TOOL IN YOUR ADOBE, research projects, etc. having to do with the production of food and management of natural fiber, plant, forest, and wildlife resources. 0101 Agriculture, general 0102 Agronomy (field crops, crop management

  6. AUTOTUNE E+ BUILDING ENERGY MODELS Joshua New, Jibonananda Sanyal, Mahabir Bhandari, and Som Shrestha

    E-Print Network [OSTI]

    Wang, Xiaorui "Ray"

    EnergyPlus (E+) simulations has been made publicly available. Autotune is currently being applied buildings sector. A central challenge in the domain of energy efficiency is being able to realistically of US Primary Energy Consumption (U.S. Dept. of Energy, 2010) and Production (U.S. EIA, 2009). et al

  7. BROWNIAN MOTION INDEXED BY A TIME SCALE DAVID GROW AND SUMAN SANYAL

    E-Print Network [OSTI]

    Sanyal, Suman

    on the proba- bility space (C0[0, ), P) and the increments Wti - Wti-1 are normally distributed with mean zero

  8. Geothermal Literature Review At General Us Region (Sanyal, Et Al., 2004) |

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005) |InformationInfraredInformationAl.,|

  9. Common occupational classification system - revision 3

    SciTech Connect (OSTI)

    Stahlman, E.J.; Lewis, R.E.

    1996-05-01T23:59:59.000Z

    Workforce planning has become an increasing concern within the DOE community as the Office of Environmental Restoration and Waste Management (ER/WM or EM) seeks to consolidate and refocus its activities and the Office of Defense Programs (DP) closes production sites. Attempts to manage the growth and skills mix of the EM workforce while retaining the critical skills of the DP workforce have been difficult due to the lack of a consistent set of occupational titles and definitions across the complex. Two reasons for this difficulty may be cited. First, classification systems commonly used in industry often fail to cover in sufficient depth the unique demands of DOE`s nuclear energy and research community. Second, the government practice of contracting the operation of government facilities to the private sector has introduced numerous contractor-specific classification schemes to the DOE complex. As a result, sites/contractors report their workforce needs using unique classification systems. It becomes difficult, therefore, to roll these data up to the national level necessary to support strategic planning and analysis. The Common Occupational Classification System (COCS) is designed to overcome these workforce planning barriers. The COCS is based on earlier workforce planning activities and the input of technical, workforce planning, and human resource managers from across the DOE complex. It provides a set of mutually-exclusive occupation titles and definitions that cover the broad range of activities present in the DOE complex. The COCS is not a required record-keeping or data management guide. Neither is it intended to replace contractor/DOE-specific classification systems. Instead, the system provides a consistent, high- level, functional structure of occupations to which contractors can crosswalk (map) their job titles.

  10. North American Industry Classification System (NAICS) Search Tool

    Broader source: Energy.gov [DOE]

    The North American Industry Classification System (NAICS) is the standard used by Federal statistical agencies in classifying business establishments for the purpose of collecting, analyzing, and...

  11. ClusterSculptor: Software for Expert-Steered Classification of...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    and intuitive framework to aid scientists in data classification. ClusterSculptor uses k-means as the overall clustering engine, but allows tuning its parameters interactively,...

  12. Alternating local search based VNS for linear classification

    E-Print Network [OSTI]

    2007-06-29T23:59:59.000Z

    We consider the linear classification method consisting of separating two sets of points in d-space ..... code called. CPLEX 9 by way of the CPLEXINT library [4].

  13. University Policy No.: AC1145 Classification: Academic and Students

    E-Print Network [OSTI]

    Victoria, University of

    University Policy No.: AC1145 Classification: Academic and Students Approving Authority, interdisciplinary programs, divisions, and the library) will undergo a review every five to seven years

  14. University Policy No.: IM7400 Classification: Information Management

    E-Print Network [OSTI]

    Victoria, University of

    University Policy No.: IM7400 Classification: Information Management POLICY ON THE DISTRIBUTIONPherson Library · Petch · Sedgewick · Student Union · Theatre · University Centre · Visual Arts 1.2 Off

  15. University Policy No.: AC1150 Classification: Academic and Students

    E-Print Network [OSTI]

    Victoria, University of

    University Policy No.: AC1150 Classification: Academic and Students TEACHING, portfolios, references, library resources, laboratories and equipment) related to the subject matter

  16. Artificial Neural Networks as a Tool for Galaxy Classification

    E-Print Network [OSTI]

    Ofer Lahav

    1996-12-10T23:59:59.000Z

    We describe an Artificial Neural Network (ANN) approach to classification of galaxy images and spectra. ANNs can replicate the classification of galaxy images by a human expert to the same degree of agreement as that between two human experts, to within 2 T-type units. Similar methods are applied to classification of galaxy spectra. In particular, Principal Component Analysis of galaxy spectra can be used to compress the data, to suppress noise and to provide input to the ANNs. These and other classification methods will soon be applied to the Anglo-Australian 2-degree-Field (2dF) redshift survey of 250,000 galaxies.

  17. accurate molecular classification: Topics by E-print Network

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    dynamic range selection Fernandez, Thomas 124 Proc. 12 International Conference on Pattern Recognition, Jerusalem, Israel, 1994, 467-469. A Two-Level Classification Scheme...

  18. ON THE CLASSIFICATION OF NUCLEAR C-ALGEBRAS

    E-Print Network [OSTI]

    2002-05-28T23:59:59.000Z

    allow us to reprove, from a handful of basic results, the classification of purely infinite nuclear C?-algebras of Kirchberg and Phillips. Received 1 October 1999;

  19. Turbulence, orbit equivalence, and the classification of nuclear C

    E-Print Network [OSTI]

    2012-04-24T23:59:59.000Z

    We bound the Borel cardinality of the isomorphism relation for nuclear ... These results depend intimately on the classification theory of nuclear simple C?- ...

  20. automatic text classification: Topics by E-print Network

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    California at Santa Barbara, University of 19 Arabic Text Classification Using Maximum Entropy CiteSeer Summary: Abstract: In organizations, a large amount of...

  1. automatic document classification: Topics by E-print Network

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Page Last Page Topic Index 1 Automatic construction of a concept hierarchy to assist Web document classification Woo Chul Cho Computer Technologies and Information Sciences...

  2. Hazard Classification for Fuel Supply Shutdown Facility

    SciTech Connect (OSTI)

    BENECKE, M.W.

    2000-09-07T23:59:59.000Z

    Final hazard classification for the 300 Area N Reactor fuel storage facility resulted in the assignment of Nuclear Facility Hazard Category 3 for the uranium metal fuel and feed material storage buildings (303-A, 303-B, 303-G, 3712, and 3716). Radiological for the residual uranium and thorium oxide storage building and an empty former fuel storage building that may be used for limited radioactive material storage in the future (303-K/3707-G, and 303-E), and Industrial for the remainder of the Fuel Supply Shutdown buildings (303-F/311 Tank Farm, 303-M, 313-S, 333, 334 and Tank Farm, 334-A, and MO-052).

  3. Classification of multifluid CP world models

    E-Print Network [OSTI]

    Jens Thomas; Hartmut Schulz

    2000-12-20T23:59:59.000Z

    Various classification schemes exist for homogeneous and isotropic (CP) world models, which include pressureless matter (so-called dust) and Einstein's cosmological constant Lambda. We here classify the solutions of more general world models consisting of up to four non-interacting fluids, each with pressure P, energy density epsilon and an equation of state P = (gamma - 1) epsilon with 0 0) tends to yield the smoothest fits of the Supernova Ia data from Perlmutter et al. (1999). Adopting the SN Ia constraints, exotic negative energy density components can be fittingly included only if the universe consists of four or more fluids.

  4. Classification of Fermi Gamma-RAY Bursts

    E-Print Network [OSTI]

    Horvath, I; Hakkila, J; Bagoly, Z; Preece, R D

    2015-01-01T23:59:59.000Z

    The Fermi GBM Catalog has been recently published. Previous classification analyses of the BATSE, RHESSI, BeppoSAX, and Swift databases found three types of gamma-ray bursts. Now we analyzed the GBM catalog to classify the GRBs. PCA and Multiclustering analysis revealed three groups. Validation of these groups, in terms of the observed variables, shows that one of the groups coincides with the short GRBs. The other two groups split the long class into a bright and dim part, as defined by the peak flux. Additional analysis is needed to determine whether this splitting is only a mathematical byproduct of the analysis or has some real physical meaning.

  5. Office of Classification | Department of Energy

    Office of Environmental Management (EM)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122Commercial602 1,39732onMake YourDepartment ofC T O B EOff-Grid or/2012Classification

  6. Hybrid Genetic Optimization and Statistical Model-Based Approach for the Classification

    E-Print Network [OSTI]

    Mignotte, Max

    Hybrid Genetic Optimization and Statistical Model-Based Approach for the Classification of Shadow original statistical classification method using a deformable template model separate natural objects man template, along admissible linear transformations, to take account shape variability. classification

  7. Using Unlabelled Data To Update Classification Rules With Applications In Food Authenticity Studies

    E-Print Network [OSTI]

    Washington at Seattle, University of

    library . . . . . . . . . . . 3 2 Average correct classification rates for the five meat groupsUsing Unlabelled Data To Update Classification Rules With Applications In Food Authenticity Studies programme. #12;Abstract A classification method is developed to classify samples when both labelled

  8. Classification of integrable super-systems using the SsTools environment ?

    E-Print Network [OSTI]

    Wolf, Thomas

    Program Library, Queen's University of Belfast, N. Ireland; see also [1] _______ ? Subject classification Classification of integrable super-systems using the SsTools environment. ____________________________________________________________________________ Abstract A classification problem is proposed for supersymmetric evolutionary PDE that s* *at- isfy

  9. Explosives Classifications Tracking System User Manual

    SciTech Connect (OSTI)

    Genoni, R.P.

    1993-10-01T23:59:59.000Z

    The Explosives Classification Tracking System (ECTS) presents information and data for U.S. Department of Energy (DOE) explosives classifications of interest to EM-561, Transportation Management Division, other DOE facilities, and contractors. It is intended to be useful to the scientist, engineer, and transportation professional, who needs to classify or transport explosives. This release of the ECTS reflects upgrading of the software which provides the user with an environment that makes comprehensive retrieval of explosives related information quick and easy. Quarterly updates will be provided to the ECTS throughout its development in FY 1993 and thereafter. The ECTS is a stand alone, single user system that contains unclassified, publicly available information, and administrative information (contractor names, product descriptions, transmittal dates, EX-Numbers, etc.) information from many sources for non-decisional engineering and shipping activities. The data is the most up-to-date and accurate available to the knowledge of the system developer. The system is designed to permit easy revision and updating as new information and data become available. These, additions and corrections are welcomed by the developer. This user manual is intended to help the user install, understand, and operate the system so that the desired information may be readily obtained, reviewed, and reported.

  10. Multivariate classification of infrared spectra of cell and tissue samples

    DOE Patents [OSTI]

    Haaland, David M. (Albuquerque, NM); Jones, Howland D. T. (Albuquerque, NM); Thomas, Edward V. (Albuquerque, NM)

    1997-01-01T23:59:59.000Z

    Multivariate classification techniques are applied to spectra from cell and tissue samples irradiated with infrared radiation to determine if the samples are normal or abnormal (cancerous). Mid and near infrared radiation can be used for in vivo and in vitro classifications using at least different wavelengths.

  11. Multistage Methods for Freight Train Classification Riko Jacob1

    E-Print Network [OSTI]

    Riko Jacob

    Multistage Methods for Freight Train Classification Riko Jacob1 , Peter M´arton2 , Jens Maue3 , and Marc Nunkesser3 1 Computer Science Department, TU M¨unchen, Germany jacob@in.tum.de 2 Faculty, train classification 1 Introduction In real-world railways, a freight train consists of an engine

  12. Compressive Video Classification for Decision Systems with Limited Resources

    E-Print Network [OSTI]

    Tsakalides, Panagiotis

    by the introduction of efficient computational models is video classification. With the advent of digital TVCompressive Video Classification for Decision Systems with Limited Resources George Tzagkarakis, Pavlos Charalampidis, Grigorios Tsagkatakis, Jean-Luc Starck, and Panagiotis Tsakalides Commissariat `a l'´Energie

  13. Program Transformation Mechanics A Classification of Mechanisms for Program Transformation

    E-Print Network [OSTI]

    Utrecht, Universiteit

    Program Transformation Mechanics A Classification of Mechanisms for Program Transformation with a Survey of Existing Transformation Systems Jonne van Wijngaarden Eelco Visser UU-CS-2003-048 Institute Transformation Mechanics A Classification of Mechanisms for Program Transformation with a Survey of Existing

  14. Business Plans Classification with Locally Pruned Lazy Learning Models

    E-Print Network [OSTI]

    Verleysen, Michel

    1 Business Plans Classification with Locally Pruned Lazy Learning Models Antti Sorjamaa1 , Amaury linéaire par morceaux connue sous le nom de Locally Pruned Lazy Learning Model. Mots clefs : Lazy Learning, Classification, Leave-one-Out, Elagage. Abstract : A business plan is a document presenting in a concise form

  15. A Unified Framework for MR Based Disease Classification

    E-Print Network [OSTI]

    Pohl, Kilian M.

    A Unified Framework for MR Based Disease Classification Kilian M. Pohl1,2 and Mert R. Sabuncu2 1-hippocampal gyrus. On this small size data set, our approach, which performs classification based on the MR images with the accuracy achieved by state-of-the-art techniques in schizophrenia MRI research. 1 Introduction Thanks to in

  16. SEMI-AUTOMATIC WEB SERVICE GENERATION AND CLASSIFICATION

    E-Print Network [OSTI]

    SEMI-AUTOMATIC WEB SERVICE GENERATION AND CLASSIFICATION Automated assistance to the web service of semantic web techniques with web service technologies has enabled the emergence of so-called semantic web and classification of web services. Key words: semantic web services; web service generation; web service

  17. University Policy No.: FM5205 Classification: Financial Management

    E-Print Network [OSTI]

    Victoria, University of

    resources of the University. 2. CLASSIFICATION OF CAPITAL EXPENDITURES 2.1 Building Projects - Capital or room; 3.3.5 equipment and furniture requirements for the project; 3.3.6 an estimated project cost basedUniversity Policy No.: FM5205 Classification: Financial Management CAPITAL EXPENDITURES

  18. ON FEATURE BASED AUTOMATIC CLASSIFICATION OF SINGLE AND MULTITONE SIGNALS

    E-Print Network [OSTI]

    Arabshahi, Payman

    ON FEATURE BASED AUTOMATIC CLASSIFICATION OF SINGLE AND MULTITONE SIGNALS Arindam K. Das, Payman of interest has not been previ- ously observed; it is not part of a library of known signals; and no automated demonstrating the feasibility of the above features for au- tomatic classification purposes of single

  19. Multi-Evidence, Multi-Criteria, Lazy Associative Document Classification

    E-Print Network [OSTI]

    Zaki, Mohammed Javeed

    Multi-Evidence, Multi-Criteria, Lazy Associative Document Classification Adriano Velosoa , Wagner to be applied at classification time. Our method is able to perform evidence enhancement by link forwarding approach using documents from the ACM Digital Library and from a Brazilian Web directory. Our approach

  20. DISCRIMINATION AND CLASSIFICATION OF UXO USING MAGNETOMETRY: INVERSION AND ERROR

    E-Print Network [OSTI]

    Sambridge, Malcolm

    DISCRIMINATION AND CLASSIFICATION OF UXO USING MAGNETOMETRY: INVERSION AND ERROR ANALYSIS USING for the different solutions didn't even overlap. Introduction A discrimination and classification strategy ambiguity and possible remanent magnetization the recovered dipole moment is compared to a library

  1. Insights from Machine Learning Applied to Human Visual Classification

    E-Print Network [OSTI]

    Insights from Machine Learning Applied to Human Visual Classification Arnulf B. A. Graf and Felix A to understand visual classification in humans using both psy- chophysical and machine learning techniques). On an algorithmic level, however, methods and understanding of brain processes are still limited. Here we report

  2. Statistical traffic classification by Boosting Support Vector Machines

    E-Print Network [OSTI]

    Statistical traffic classification by Boosting Support Vector Machines Gabriel Gómez Sena Facultad years, traffic classification based on the statistical properties of flows has become an important topic. In this paper we statistically analyze the data length of the first few segments exchanged by a transport flow

  3. Automatic Tissue Classification for the Human Head from Multispectral MRI

    E-Print Network [OSTI]

    Utah, University of

    1 Automatic Tissue Classification for the Human Head from Multispectral MRI Tolga Tasdizen, David for classifying multispectral MR scans of the human head into nine tissue classes. User initialization is adopted. #12;Chapter 1 Introduction Classification of head magnetic resonance imaging (MRI) data

  4. Novel Artificial Neural Networks For Remote-Sensing Data Classification

    E-Print Network [OSTI]

    Michel, Howard E.

    Novel Artificial Neural Networks For Remote-Sensing Data Classification Xiaoli Tao* and Howard E artificial neural network architectures applied to multi-class classification problems of remote-sensing data. These approaches are 1) a spiking-neural-network model for the partitioning of data into clusters, and 2) a neuron

  5. Support Vector Learning for Fuzzy Rule-Based Classification Systems

    E-Print Network [OSTI]

    Chen, Yixin

    . As a powerful machine learning approach for pattern recognition problems, support vector machine (SVM) is known1 Support Vector Learning for Fuzzy Rule-Based Classification Systems Yixin Chen, Student Member DRAFT #12;2 Abstract To design a fuzzy rule-based classification system (fuzzy classifier) with good

  6. Network sampling and classification: An investigation of network model representations

    E-Print Network [OSTI]

    Needleman, Daniel

    only one or two connectivity patterns of an observed network--such as degree distribution, or diameterNetwork sampling and classification: An investigation of network model representations Edoardo M: Connectivity pattern Network type Network metrics Network sampling Network classification Methods

  7. Protein Sequence Classification Using Probabilistic Motifs and Neural Networks

    E-Print Network [OSTI]

    Blekas, Konstantinos

    Protein Sequence Classification Using Probabilistic Motifs and Neural Networks Konstantinos Blekas classification is the sequence encoding scheme that must be used in order to feed the network. To deal with this prob- lem we propose a method that maps a protein sequence into a numerical feature space using

  8. MARS TERRAIN IMAGE CLASSIFICATION USING CARTESIAN GENETIC PROGRAMMING

    E-Print Network [OSTI]

    Fernandez, Thomas

    MARS TERRAIN IMAGE CLASSIFICATION USING CARTESIAN GENETIC PROGRAMMING J. Leitner, S. Harding, A. F to human designed approaches, a great deal of progress has been made using machine learning techniques to perform classification from images. In this work, we demonstrate the first known use of Cartesian Genetic

  9. Nonparametric Transient Classification using Adaptive Wavelets

    E-Print Network [OSTI]

    Varughese, Melvin M; Stephanou, Michael; Bassett, Bruce A

    2015-01-01T23:59:59.000Z

    Classifying transients based on multi band light curves is a challenging but crucial problem in the era of GAIA and LSST since the sheer volume of transients will make spectroscopic classification unfeasible. Here we present a nonparametric classifier that uses the transient's light curve measurements to predict its class given training data. It implements two novel components: the first is the use of the BAGIDIS wavelet methodology - a characterization of functional data using hierarchical wavelet coefficients. The second novelty is the introduction of a ranked probability classifier on the wavelet coefficients that handles both the heteroscedasticity of the data in addition to the potential non-representativity of the training set. The ranked classifier is simple and quick to implement while a major advantage of the BAGIDIS wavelets is that they are translation invariant, hence they do not need the light curves to be aligned to extract features. Further, BAGIDIS is nonparametric so it can be used for blind ...

  10. Using QA classification to guide design and manage risk

    SciTech Connect (OSTI)

    Lathrop, J. [Strategic Insights, Los Altos, CA (United States); DeKlever, R. [Raytheon Services Nevada, Las Vegas, NV (United States); Petrie, E.H. [USDOE Nevada Field Office, Las Vegas, NV (United States)

    1993-01-28T23:59:59.000Z

    Raytheon Services Nevada has developed a classification process based on probabilistic risk assessment, using accident/impact scenarios for each system classified. Initial classification analyses were performed for the 20 systems of Package IA of the Exploratory Studies Facility (ESF). The analyses demonstrated a solid, defensible methodological basis for classification which minimizes the use of direct engineering judgment. They provide guidance for ESF design and risk management through the identification of: The critical characteristics of each system that need to be controlled; and the parts of the information base that most need to be further developed through performance assessment or other efforts.

  11. Using QA classification to guide design and manage risk

    SciTech Connect (OSTI)

    Lathrop, J. [Strategic Insights, Los Altos, CA (United States); DeKlever, R. [Raytheon Services Nevada, Las Vegas, NV (United States); Petrie, E.H. [DOE, Las Vegas, NV (United States)

    1993-12-31T23:59:59.000Z

    Raytheon Services Nevada has developed a classification process based on probabilistic risk assessment, using accident/impact scenarios for each system classified. Initial classification analyses were performed for the 20 systems of Package 1A of the Exploratory Studies Facility (ESF). The analyses demonstrated a solid, defensible methodological basis for classification which minimizes the use of direct engineering judgment. They provide guidance for ESF design and risk management through the identification of: the critical characteristics of each system that need to be controlled; and the parts of the information base that most need to be further developed through performance assessment or other efforts.

  12. CLASSIFICATION USING EFFICIENT LU DECOMPOSITION IN Zille Huma Kamal, Ajay Gupta, Leszek Lilien,

    E-Print Network [OSTI]

    Gupta, Ajay

    1 CLASSIFICATION USING EFFICIENT LU DECOMPOSITION IN SENSORNETS Zille Huma Kamal, Ajay Gupta of sensornet nodes will be consuming energy to identify the category (classification) that relates to the event applications, such as classification, need to be extremely energy-aware. The classification process is also

  13. Invariant classification of orthogonally separable Hamiltonian systems in Euclidean space

    E-Print Network [OSTI]

    Joshua T. Horwood; Raymond G. McLenaghan; Roman G. Smirnov

    2006-05-07T23:59:59.000Z

    The problem of the invariant classification of the orthogonal coordinate webs defined in Euclidean space is solved within the framework of Felix Klein's Erlangen Program. The results are applied to the problem of integrability of the Calogero-Moser model.

  14. CLASSIFICATION OF QUADRATIC FORMS OVER SKEW FIELDS OF CHARACTERISTIC 2

    E-Print Network [OSTI]

    CLASSIFICATION OF QUADRATIC FORMS OVER SKEW FIELDS OF CHARACTERISTIC 2 MOHAMED ABDOU ELOMARY groups." 1 #12;2 MOHAMED ABDOU ELOMARY AND JEAN-PIERRE TIGNOL Theorem A. Let F be a local or global field

  15. CLASSIFICATION OF QUADRATIC FORMS OVER SKEW FIELDS OF CHARACTERISTIC 2

    E-Print Network [OSTI]

    CLASSIFICATION OF QUADRATIC FORMS OVER SKEW FIELDS OF CHARACTERISTIC 2 MOHAMED ABDOU ELOMARY groups.'' 1 #12; 2 MOHAMED ABDOU ELOMARY AND JEAN­PIERRE TIGNOL Theorem A. Let F be a local or global

  16. A PROPOSAL FOR THE PIRSF (PIR SUPERFAMILY) CLASSIFICATION SYSTEM

    E-Print Network [OSTI]

    A PROPOSAL FOR THE PIRSF (PIR SUPERFAMILY) CLASSIFICATION SYSTEM May 30, 2003 Protein Information................................................................................. 4 D. PIR Superfamily Redefined domains might be present. PIR responded to this dilemma in 1993 by distinguishing two types

  17. On Equivalence Relationships Between Classification and Ranking Algorithms

    E-Print Network [OSTI]

    Rudin, Cynthia

    We demonstrate that there are machine learning algorithms that can achieve success for two separate tasks simultaneously, namely the tasks of classification and bipartite ranking. This means that advantages gained from ...

  18. Dynamic Bayesian networks for the classification of spinning discs

    E-Print Network [OSTI]

    Schmidt, Aurora Clare, 1981-

    2004-01-01T23:59:59.000Z

    This thesis considers issues for the application of particle filters to a class of nonlinear filtering and classification problems. Specifically, we study a prototype system of spinning discs. The system combines linear ...

  19. Toward a Classification Approach to Design Douglas R. Smith

    E-Print Network [OSTI]

    Smith, Douglas R.

    Toward a Classification Approach to Design Douglas R. Smith Kestrel Institute, 3260 Hillview Avenue, Palo Alto, California 94304, USA smith@kestrel.edu 18 March 1996 Abstract. This paper addresses

  20. Machine Learning and Rule-based Approaches to Assertion Classification

    E-Print Network [OSTI]

    Uzuner, Ozlem

    2009-01-01T23:59:59.000Z

    Objectives The authors study two approaches to assertion classification. One of these approaches, Extended NegEx (ENegEx), extends the rule-based NegEx algorithm to cover alter-association assertions; the other, Statistical ...

  1. ISpace: Interactive Volume Data Classification Techniques Using Independent Component Analysis

    E-Print Network [OSTI]

    Ma, Kwan-Liu

    , multivariate data analysis, multimodality data, scientific visualization, seg- mentation, volume rendering 1ISpace: Interactive Volume Data Classification Techniques Using Independent Component Analysis, which uses Independent Component Analysis (ICA) and a multi- dimensional histogram of the volume data

  2. Classification of Two-Phase Flow Patterns by Ultrasonic Sensing

    E-Print Network [OSTI]

    Ray, Asok

    (e.g., petrochemical processes and nuclear power plants). This concept of flow pattern classification are critical for design, analysis, and operation in a variety of industries such as petrochemical processes

  3. Classification of Certain Compact Riemannian Manifolds with Harmonic Curvature a...

    E-Print Network [OSTI]

    Derdzinski, Andrzej

    Classification of Certain Compact Riemannian Manifolds with Harmonic Curvature a... Derdzinski and University Library provides access to digitized documents strictly for noncommercial educational, research) requires prior written permission from the Goettingen State- and University Library. Each copy of any part

  4. Galaxy Classification by Human Eyes and by Artificial Neural Networks

    E-Print Network [OSTI]

    Ofer Lahav

    1995-05-19T23:59:59.000Z

    The rapid increase in data on galaxy images at low and high redshift calls for re-examination of the classification schemes and for new automatic objective methods. Here we present a classification method by Artificial Neural Networks. We also show results from a comparative study we carried out using a new sample of 830 APM digitised galaxy images. These galaxy images were classified by 6 experts independently. It is shown that the ANNs can replicate the classification by a human expert almost to the same degree of agreement as that between two human experts, to within 2 $T$-type units. Similar methods can be applied to automatic classification of galaxy spectra. We illustrate it by Principal Component Analysis of galaxy spectra, and discuss future large surveys.

  5. Machine learning for patient-adaptive ectopic beat classification

    E-Print Network [OSTI]

    Wiens, Jenna Marleau

    2010-01-01T23:59:59.000Z

    Physicians require automated techniques to accurately analyze the vast amount of physiological data collected by continuous monitoring devices. In this thesis, we consider one analysis task in particular, the classification ...

  6. Multi-Class Classification with Maximum Margin Multiple Kernel

    E-Print Network [OSTI]

    Tomkins, Andrew

    (named OBSCURE and UFO-MKL, respectively) are used to optimize primal versions of equivalent problems), the OBSCURE and UFO-MKL algorithms are compared against MCMKL #12;Multi-Class Classification with Maximum

  7. Pattern Classification Using a Quantum System Dan Ventura

    E-Print Network [OSTI]

    Martinez, Tony R.

    Pattern Classification Using a Quantum System Dan Ventura Brigham Young University Department of Computer Science Provo, UT 84602 USA ventura@cs.byu.edu http://axon.cs.byu.edu/Dan We consider and compare

  8. Classification of London's public transport users using smart card data

    E-Print Network [OSTI]

    Ortega-Tong, Meisy A. (Meisy Andrea)

    2013-01-01T23:59:59.000Z

    Understanding transit users in terms of their travel patterns can support the planning and design of better services. User classification can improve market research through more targeted access to groups of interest. It ...

  9. Removal of testa from food grade copra by air classification

    E-Print Network [OSTI]

    Lopitakwong, Rommanee

    1975-01-01T23:59:59.000Z

    REMOVAL OF TESTA FROM FOOD GRADE COPRA BY AIR CLASSIFICATION A Thesi. s by ROMMANEE LOPITAKWONG Submitted to the Graduate College of Texas A&M University in partial fulfillment of the requirement for the degree of MASTER OF SCIENCE December... 1975 Major Subject: Food Technology REMOVAL OF TESTA FROM FOOD GRADE COPRA BY AIR CLASSIFICATION A Thesis by ROMMANEE LOPITAKWONG Approved as to style and content by: (Ch irman of Comm'ttee) ad of Dep tment) Member) (Member) December 1975...

  10. Atomic Classification of 6D SCFTs

    E-Print Network [OSTI]

    Jonathan J. Heckman; David R. Morrison; Tom Rudelius; Cumrun Vafa

    2015-05-04T23:59:59.000Z

    We use F-theory to classify possibly all six-dimensional superconformal field theories (SCFTs). This involves a two step process: We first classify all possible tensor branches allowed in F-theory (which correspond to allowed collections of contractible spheres) and then classify all possible configurations of seven-branes wrapped over them. We describe the first step in terms of "atoms" joined into "radicals" and "molecules," using an analogy from chemistry. The second step has an interpretation via quiver-type gauge theories constrained by anomaly cancellation. A very surprising outcome of our analysis is that all of these tensor branches have the structure of a linear chain of intersecting spheres with a small amount of possible decoration at the two ends. The resulting structure of these SCFTs takes the form of a generalized quiver consisting of ADE-type nodes joined by conformal matter. A collection of highly non-trivial examples involving E8 small instantons probing an ADE singularity is shown to have an F-theory realization. This yields a classification of homomorphisms from ADE subgroups of SU(2) into E8 in purely geometric terms, largely matching results obtained in the mathematics literature from an intricate group theory analysis.

  11. Active Classification: Theory and Application to Underwater Inspection

    E-Print Network [OSTI]

    Hollinger, Geoffrey A; Sukhatme, Gaurav S

    2011-01-01T23:59:59.000Z

    We discuss the problem in which an autonomous vehicle must classify an object based on multiple views. We focus on the active classification setting, where the vehicle controls which views to select to best perform the classification. The problem is formulated as an extension to Bayesian active learning, and we show connections to recent theoretical guarantees in this area. We formally analyze the benefit of acting adaptively as new information becomes available. The analysis leads to a probabilistic algorithm for determining the best views to observe based on information theoretic costs. We validate our approach in two ways, both related to underwater inspection: 3D polyhedra recognition in synthetic depth maps and ship hull inspection with imaging sonar. These tasks encompass both the planning and recognition aspects of the active classification problem. The results demonstrate that actively planning for informative views can reduce the number of necessary views by up to 80% when compared to passive methods...

  12. Classification of lepton mixing matrices from finite residual symmetries

    E-Print Network [OSTI]

    Renato M. Fonseca; Walter Grimus

    2014-08-19T23:59:59.000Z

    Assuming that neutrinos are Majorana particles, we perform a complete classification of all possible mixing matrices which are fully determined by residual symmetries in the charged-lepton and neutrino mass matrices. The classification is based on the assumption that the residual symmetries originate from a finite flavour symmetry group. The mathematical tools which allow us to accomplish this classification are theorems on sums of roots of unity. We find 17 sporadic cases plus one infinite series of mixing matrices associated with three-flavour mixing, all of which have already been discussed in the literature. Only the infinite series contains mixing matrices which are compatible with the data at the 3 sigma level.

  13. Topological classification of crystalline insulators with space group symmetry

    SciTech Connect (OSTI)

    Jadaun, Priyamvada [University of Texas at Austin; Xiao, Di [Carnegie Mellon University (CMU); Niu, Q. [University of Texas at Austin; Banerjee, Sanjay K. [University of Texas at Austin

    2013-01-01T23:59:59.000Z

    We show that in crystalline insulators, space group symmetry alone gives rise to a topological classification based on the discretization of electric polarization. Using C3 rotational symmetry as an example, we first prove that the polarization is discretized into three distinct classes, i.e., it can only take three inequivalent values. We then prove that these classes are topologically distinct. Therefore, a Z3 topological classification exists, with polarization as a topological class index. A concrete tight-binding model is derived to demonstrate the Z3 topological phase transition. Using first-principles calculations, we identify graphene on a BN substrate as a possible candidate to realize these Z3 topological states. To complete our analysis, we extend the classification of band structures to all 17 two-dimensional space groups. This work will contribute to a complete theory of symmetry-conserved topological phases and also elucidate topological properties of graphenelike systems.

  14. Classification of fossil fuels according to structural-chemical characteristics

    SciTech Connect (OSTI)

    A.M. Gyul'maliev; G.S. Golovin; S.G. Gagarin [Institute for Fossil Fuels, Moscow (Russian Federation)

    2007-10-15T23:59:59.000Z

    On the basis of a set of linear equations that relate the amount of major elements n{sub E} (E = C, H, O, N, S) in the organic matter of fossil fuels to structural characteristics, such as the number of cycles R, the number of atoms n{sub E}, the number of mutual chemical bonds, the degree of unsaturation of the structure {delta}, and the extent of its reduction B, a structural-chemical classification of fossil coals that is closely related to the parameters of the industrial-genetic classification (GOST 25543-88) is proposed. Structural-chemical classification diagrams are constructed for power-generating coals of Russia; coking coals; and coals designed for nonfuel purposes including the manufacture of adsorbents, synthetic liquid fuel, ion exchangers, thermal graphite, and carbon-graphite materials.

  15. Significance of Classification Techniques in Prediction of Learning Disabilities

    E-Print Network [OSTI]

    Balakrishnan, Julie M David And Kannan

    2010-01-01T23:59:59.000Z

    The aim of this study is to show the importance of two classification techniques, viz. decision tree and clustering, in prediction of learning disabilities (LD) of school-age children. LDs affect about 10 percent of all children enrolled in schools. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Decision trees and clustering are powerful and popular tools used for classification and prediction in Data mining. Different rules extracted from the decision tree are used for prediction of learning disabilities. Clustering is the assignment of a set of observations into subsets, called clusters, which are useful in finding the different signs and symptoms (attributes) present in the LD affected child. In this paper, J48 algorithm is used for constructing the decision tree and K-means algorithm is used for creating the clusters. By applying these classification techniques, LD in any child can be identified.

  16. ALGORITHMIC CLASSIFICATION OF DRAINAGE NETWORKS ON MARS AND ITS RELATION TO MARTIAN GEOLOGICAL UNITS. T. F. Stepinski1

    E-Print Network [OSTI]

    Vilalta, Ricardo

    ALGORITHMIC CLASSIFICATION OF DRAINAGE NETWORKS ON MARS AND ITS RELATION TO MARTIAN GEOLOGICAL locations covering 16 major geological units. The classification is quantitative and objective with an existing division into geological units. A morphological interpretation for this emergent classification

  17. Nonlinear Observer for 3D Rigid Body Motion Sergio Bras, Maziar Izadi, Carlos Silvestre, Amit Sanyal and Paulo Oliveira

    E-Print Network [OSTI]

    Instituto de Sistemas e Robotica

    has important applications to unmanned or manned vehicles operating in air, underwater, or in space the configuration and velocity states of a rigid body. Since most unmanned and manned vehicles can be accurately, underwater, and in space. In particular, such vehicles when operated in uncertain or poorly known

  18. Classification concepts from object oriented software design applied to engineering design

    E-Print Network [OSTI]

    Krishnamurthy, Ritesh

    2002-01-01T23:59:59.000Z

    classification charts, so as to make them more useful to both neophyte and experienced engineering designers. Engineering components have evolved from their basic forms into families of components and sub-components. Classification charts are often used...

  19. Rock Classification in Organic Shale Based on Petrophysical and Elastic Rock Properties Calculated from Well Logs

    E-Print Network [OSTI]

    Aranibar Fernandez, Alvaro A

    2015-01-05T23:59:59.000Z

    classification method was then applied to the field examples from the Haynesville shale and Woodford shales for rock classification. The estimates of porosity, TOC, bulk modulus, shear modulus, and volumetric concentrations of minerals were obtained...

  20. Surface Myoelectric Signal Classification Using the AR-GARCH Model Ghulam Rasoolb,

    E-Print Network [OSTI]

    Bouaynaya, Nidhal

    signals. In the pattern classification paradigm for controlling myoelectric prosthesis, the autoregressive, it is normally assumed that the necessary control information can be extracted from the surface myoelectric, myoelectric control. 1. Introduction The pattern classification for myoelectric control is based

  1. Classification of power quality disturbances using time-frequency ambiguity plane and

    E-Print Network [OSTI]

    Mamishev, Alexander

    Classification of power quality disturbances using time-frequency ambiguity plane and neural disturbances in power systems is an important task in power system monitoring and protection, This paper discussed. Keywords -- Power Quality Disturbances, Classification, Ambiguity Plane, Modified Fisher

  2. Text classification Naive Bayes NB theory Evaluation of TC Web Search and Text Mining

    E-Print Network [OSTI]

    Gray, Alexander

    Bayes NB theory Evaluation of TC Take-away today Text classification: definition & relevance theory Evaluation of TC Take-away today Text classification: definition & relevance to information: definition & relevance to information retrieval Naive Bayes: simple baseline text classifier Theory

  3. Performance comparison of feature extraction algorithms for target detection and classification q

    E-Print Network [OSTI]

    Ray, Asok

    Pattern classification Unattended ground sensors: border control a b s t r a c t This paper addresses

  4. Integrating Kinect Depth Data with a Stochastic Object Classification Framework for Forestry Robots

    E-Print Network [OSTI]

    Hellström, Thomas

    Integrating Kinect Depth Data with a Stochastic Object Classification Framework for Forestry Robots camera for a stochastic classification system for forestry robots. The images are classified as bush- ject classification system that uses only RGB camera. The system is aimed for a forestry robot

  5. 1202 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 46, NO. 5, MAY 1998 Classification of Underwater Mammals

    E-Print Network [OSTI]

    Intrator, Nathan

    1202 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 46, NO. 5, MAY 1998 Classification of Underwater Cooper, Nathan Intrator, and Harel Shouval Abstract--Underwater mammal sound classification is demon and different wavelet representations are stud- ied. The system achieves outstanding classification performance

  6. Building Local Terrain Maps Using SpatioTemporal Classification for Semantic Robot Localization

    E-Print Network [OSTI]

    Zell, Andreas

    of the environment. We describe how to efficiently integrate the classification results of each time stepBuilding Local Terrain Maps Using Spatio­Temporal Classification for Semantic Robot Localization Stefan Laible1 and Andreas Zell1 Abstract-- The correct classification of the surrounding ter- rain

  7. A Robust Audio Classification and Segmentation Method Lie Lu, Hao Jiang and HongJiang Zhang

    E-Print Network [OSTI]

    Jiang, Hao

    A Robust Audio Classification and Segmentation Method Lie Lu, Hao Jiang and HongJiang Zhang for audio classification that is capable of segmenting and classifying an audio stream into speech, music, environment sound and silence. Audio classification is processed in two steps, which makes it suitable

  8. Hybrid Genetic Optimization and Statistical Model-Based Approach for the Classification

    E-Print Network [OSTI]

    Mignotte, Max

    Hybrid Genetic Optimization and Statistical Model-Based Approach for the Classification of ShadowÐWe present an original statistical classification method using a deformable template model to separate transformations, to take into account the shape variability. Then, the classification problem is defined as a two

  9. Audio Classification by Search of Primary Components Julien PINQUIER, Jos ARIAS and Rgine ANDRE-OBRECHT

    E-Print Network [OSTI]

    Pinquier, Julien

    Audio Classification by Search of Primary Components Julien PINQUIER, José ARIAS and Régine ANDRE broadcasts. We present three different audio classification tools that we have developed. The first one, a speech/music classification tool, is based on three original features: entropy modulation, stationary

  10. What Those Call Numbers Mean: The Library of Congress Classification System

    E-Print Network [OSTI]

    Hutcheon, James M.

    What Those Call Numbers Mean: The Library of Congress Classification System The call number system that we use in this library, which is called the Library of Congress Classification System subject areas. If you know what letter indicates the classification for the subject you need, you can find

  11. Effective Graph Classification Based on Topological and Label Attributes , Murat Semerci2

    E-Print Network [OSTI]

    Zaki, Mohammed Javeed

    online 12 June 2012 in Wiley Online Library (wileyonlinelibrary.com). Abstract: Graph classificationEffective Graph Classification Based on Topological and Label Attributes Geng Li1 , Murat Semerci2 propose an alternative approach to graph classification that is based on feature vectors constructed from

  12. Predicting Library of Congress Classifications From Library of Congress Subject Headings

    E-Print Network [OSTI]

    Frank, Eibe

    Predicting Library of Congress Classifications From Library of Congress Subject Headings Eibe Frank This paper addresses the problem of automatically assigning a Library of Congress Classification (LCC and training data from a large library catalog to learn a model which maps from sets of LCSH to classifications

  13. U.S. Geological Survey Library Classification By R. Scott Sasscer

    E-Print Network [OSTI]

    Torgersen, Christian

    U.S. Geological Survey Library Classification System By R. Scott Sasscer U.S. Geological Survey classification system is a tool for classifying and retrieving geoscience library materials. The index promotes.S. Geological Survey Library classification system / by R. Scott Sasscer. p. cm. ­ (U.S. Geological Survey

  14. Automatic Model Classification of Measured Internet Traffic Yi Zeng and Thomas M. Chen

    E-Print Network [OSTI]

    Chen, Thomas M.

    issues for future work. II. MODEL-LIBRARY TRAFFIC CLASSIFICATION Many traffic models have been developed Model 2 module ... Model library Fig. 1. General model classification system In the general case with NAutomatic Model Classification of Measured Internet Traffic Yi Zeng and Thomas M. Chen Department

  15. LETTER Communicated by Christopher Williams Prototype Classification: Insights from Machine Learning

    E-Print Network [OSTI]

    LETTER Communicated by Christopher Williams Prototype Classification: Insights from Machine-of-class prototype classification using algorithms from machine learning that satisfy a set of invariance properties. We report a simple yet general approach to express different types of linear classification

  16. Stream classification using hierarchical artificial neural networks: A fluvial hazard management tool

    E-Print Network [OSTI]

    Vermont, University of

    Stream classification using hierarchical artificial neural networks: A fluvial hazard management-in-Chief Keywords: Stream classification Artificial neural networks Kohonen self-organizing maps Counterpropagation. In this research, we apply non-parametric, clus- tering and classification artificial neural networks to assimilate

  17. SOUND CLASSIFICATION IN A SMART ROOM ENVIRONMENT: AN APPROACH USING GMM AND HMM METHODS

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    be hospitalized at home and smart information systems would be needed in order to assist human operatorsSOUND CLASSIFICATION IN A SMART ROOM ENVIRONMENT: AN APPROACH USING GMM AND HMM METHODS Michel suited for sound classification. Until now, GMMs are frequently used for sound classification in smart

  18. University Policy No.: AC1110 Classification: Academic and Students

    E-Print Network [OSTI]

    Victoria, University of

    other than the university community. DEFINITIONS 2.00 EDUCATIONAL SERVICE CONTRACT means a contract://web.uvic.ca/univsec/pol_pro/pol1000/1002SA.html) 8.00 Approval of an Educational Service Contract will be granted by the Dean andUniversity Policy No.: AC1110 Classification: Academic and Students EDUCATIONAL SERVICE

  19. Group classification of heat conductivity equations with a nonlinear source

    E-Print Network [OSTI]

    Zhdanov, Renat

    Group classification of heat conductivity equations with a nonlinear source R.Z. Zhdanov Institute. It is shown that there are three, seven, twenty eight and twelve inequivalent classes of partial differential to the class under study and admitting symmetry group of the dimension higher than four is locally equivalent

  20. Exploiting Monotonicity Constraints in Active Learning for Ordinal Classification

    E-Print Network [OSTI]

    Utrecht, Universiteit

    Utrecht University, Utrecht, The Netherlands www.cs.uu.nl #12;ISSN: 0924-3275 Department of Information and Computing Sciences Utrecht University P.O. Box 80.089 3508 TB Utrecht The Netherlands #12;Exploiting Monotonicity Constraints in Active Learning for Ordinal Classification Pieter Soons Universiteit Utrecht

  1. Geometrical classification of Killing tensors on bidimensional flat manifolds

    E-Print Network [OSTI]

    C. Chanu; L. Degiovanni; R. G. McLenaghan

    2006-08-31T23:59:59.000Z

    Valence two Killing tensors in the Euclidean and Minkowski planes are classified under the action of the group which preserves the type of the corresponding Killing web. The classification is based on an analysis of the system of determining partial differential equations for the group invariants and is entirely algebraic. The approach allows to classify both characteristic and non characteristic Killing tensors.

  2. Comprehensive Superfamily and Function Classification of Protein Sequences

    E-Print Network [OSTI]

    on the basis of end-to-end similarity and domain architecture. The protein superfamily organization of the PIR-International Protein Sequence Database (PIR-PSD) is the only comprehensive protein classification system that is based are facilitated by the PIR Annotation and Similarity Database, which includes a pre- computed FASTA Database

  3. University Policy No.: BP3400 Classification: Buildings and Properties

    E-Print Network [OSTI]

    Victoria, University of

    University Policy No.: BP3400 Classification: Buildings and Properties POLICY ON EXTERNAL BOOKINGS Change: Mandated Review: 1.0 Purpose The purpose of this policy is to provide guidelines for the making the proposed use is compatible with the mission and policies of the University and does not interfere

  4. University Policy No.: BP3200 Classification: Buildings and Properties

    E-Print Network [OSTI]

    Victoria, University of

    University Policy No.: BP3200 Classification: Buildings and Properties POLICY ON THE USE OF VICTORIA CAMPUS Supersedes: Last Editorial Change: Mandated Review: 1. The authority to establish policy: 2.1 fundamental and material policy changes respecting the use of vehicles and parking; and, 2

  5. University Policy No.: AC1130 Classification: Academic and Students

    E-Print Network [OSTI]

    Victoria, University of

    University Policy No.: AC1130 Classification: Academic and Students Approving Authority: Board Last Editorial Change: Mandated Review: 2018 PURPOSE 1.00 The purpose of this policy is to provide guidance on the establishment and governance of student awards by the university. POLICY 2.00 Student

  6. University Policy No.: FM5105 Classification: Financial Management

    E-Print Network [OSTI]

    Victoria, University of

    University Policy No.: FM5105 Classification: Financial Management Approving Authority: Board of Governors PURCHASING SERVICES POLICY Effective Date: April/07 Supersedes: September/92 Last Editorial Change: September/09 Mandated Review: 1.0 PURPOSE AND SCOPE OF PURCHASING SERVICES POLICY: The purpose

  7. University Policy No.: FM5110 Classification: Financial Management

    E-Print Network [OSTI]

    Victoria, University of

    University Policy No.: FM5110 Classification: Financial Management Approving Authority: Board of Governors POLICY ON STRATEGIC ALLIANCES Effective Date: April/03 Supersedes: September/00 Last Editorial Change: Mandated Review: 1. PURPOSE The purpose of this policy is to set out the principles

  8. University Policy No.: SS9300 Classification: Safety and Security

    E-Print Network [OSTI]

    Victoria, University of

    University Policy No.: SS9300 Classification: Safety and Security Approving Authority: Board of Governors COMMUNICABLE DISEASES POLICY Effective Date: April/04 Supersedes: November/94 Last Editorial Change: Mandated Review: 1. POLICY PURPOSE The purpose of the policy is to provide direction

  9. University Policy No.: HR6105 Classification: Human Resources

    E-Print Network [OSTI]

    Victoria, University of

    University Policy No.: HR6105 Classification: Human Resources EQUITY POLICY FOR FEMALE Approving in pension policies have been remedied and the introduction of a parental leave policy has greatly benefited be recognized in hiring and other personnel decisions. 5. Ensure that University policies encourage gender

  10. Group classification of a nonlinear sound wave model

    E-Print Network [OSTI]

    J. C. Ndogmo

    2008-06-26T23:59:59.000Z

    Based on a recent classification of subalgebras of the symmetry algebra of the Zabolotskaya-Khokhlov equation, all similarity reductions of this equation into ordinary differential equations are obtained. Large classes of group invariant solutions of the equation are also determined, and some properties of these solutions are discussed.

  11. Storage Device Performance Prediction with Selective Bagging Classification and Regression

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    Storage Device Performance Prediction with Selective Bagging Classification and Regression Tree Lei}@eng.wayne.edu, cheneh@ustc.edu.cn Abstract. Storage device performance prediction is a key element of self-managed storage systems and application planning tasks, such as data assignment and configuration. Based

  12. Occupant Classification System for Automotive Airbag Suppression Michael E. Farmer

    E-Print Network [OSTI]

    Occupant Classification System for Automotive Airbag Suppression Michael E. Farmer§ and Anil K@cse.msu.edu Abstract The introduction of airbags into automobiles has significantly improved the safety of the occupants. Unfortunately, airbags can also cause fatal injuries if the occupant is a child smaller (in

  13. Wind speed PDF classification using Dirichlet mixtures Rudy CALIF1

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    Wind speed PDF classification using Dirichlet mixtures Rudy CALIF1 , Richard EMILION2 , Ted'Orléans), UMR CNRS 6628 Université d'Orléans, France. Abstract: Wind energy production is very sensitive to instantaneous wind speed fluctuations. Thus rapid variation of wind speed due to changes in the local

  14. Hazard classification criteria for non-nuclear facilities

    SciTech Connect (OSTI)

    Mahn, J.A.; Walker, S.A.

    1997-03-01T23:59:59.000Z

    Sandia National Laboratories` Integrated Risk Management Department has developed a process for establishing the appropriate hazard classification of a new facility or operation, and thus the level of rigor required for the associated authorization basis safety documentation. This process is referred to as the Preliminary Hazard Screen. DOE Order 5481.1B contains the following hazard classification for non-nuclear facilities: high--having the potential for onsite or offsite impacts to large numbers of persons or for major impacts to the environment; moderate--having the potential for considerable onsite impacts but only minor offsite impacts to people or the environment; low--having the potential for only minor onsite and negligible offsite impacts to people or the environment. It is apparent that the application of such generic criteria is more than likely to be fraught with subjective judgment. One way to remove the subjectivity is to define health and safety classification thresholds for specific hazards that are based on the magnitude of the hazard, rather than on a qualitative assessment of possible accident consequences. This paper presents the results of such an approach to establishing a readily usable set of non-nuclear facility hazard classifications.

  15. Gender classification by combining clothing, hair and facial component classifiers

    E-Print Network [OSTI]

    Lu, Bao-Liang

    facial images is widely used in applications like human­computer interface, demographics, and customer difficulties. The psychological experi- ment in our previous work showed that hair provides discrimi- native of these images. They get 96.3% classification accuracy on color images, and 93.5% on gray images on average. From

  16. Improved Classification of Medical Data Using Abductive Network Committees

    E-Print Network [OSTI]

    Abdel-Aal, Radwan E.

    Improved Classification of Medical Data Using Abductive Network Committees Trained on Different 3 860 4281 #12;Summary This paper demonstrates the use of abductive network classifier committees the features and forming subsets of uniform predictive quality for training individual members. The abductive

  17. Morphological Image Analysis and Its Application to Sunspot Classification

    E-Print Network [OSTI]

    van Dyk, David

    magnetic polarity. The classification and tracking of sunspots is an active undertaking of solar both of their future evo- lution and of explosive associated events higher in the solar atmosphere, such as solar flares and coronal mass ejections. To aid in this prediction, sunspot groups are manually

  18. A Visual Analytics Approach for Correlation, Classification, and

    E-Print Network [OSTI]

    Swan II, J. Edward

    are combined into a parallel coordinates based framework for enhanced multivariate visual analysis. Figure 1A Visual Analytics Approach for Correlation, Classification, and Regression Analysis Chad A. Steed Mississippi State University 1021 Balch Blvd. Stennis Space Center, MS 39529 USA T.J. Jankun-Kelly tjk

  19. A Visual Analytics Approach for Correlation, Classification, and Regression Analysis

    E-Print Network [OSTI]

    Swan II, J. Edward

    multivariate visual analysis. The current work features an expanded version of MDX that builds on recentA Visual Analytics Approach for Correlation, Classification, and Regression Analysis Chad A. Steeda, Mississippi State University, Stennis Space Center, MS, 39529; cDepartment of Computer Science and Engineering

  20. Classification of Sweden's Forest and Alpine Vegetation Using Optical

    E-Print Network [OSTI]

    Classification of Sweden's Forest and Alpine Vegetation Using Optical Satellite and Inventory Data of Sweden's Forest and Alpine Vegetation Using Optical Satellite and Inventory Data. Abstract Creation of accurate vegetation maps from optical satellite data requires use of reference data to aid

  1. Classification of two dimensional fixed sun angle solar sail trajectories

    E-Print Network [OSTI]

    Roberts, Mark

    Classification of two dimensional fixed sun angle solar sail trajectories Stephen Wokes, Phil heliocentric trajectories for fixed sun angle solar sails are examined. The objective of this work (lightness factor) and Sun angle this phase space shows all possible solar sail trajectories. This phase

  2. Automatic learning for the classification of primary frequency control behaviour

    E-Print Network [OSTI]

    Wehenkel, Louis

    types of expected or unexpected behaviours. The proposed approach is based on automatic learning whichAutomatic learning for the classification of primary frequency control behaviour Bertrand. Abstract-- In this paper we propose a methodology based on supervised automatic learning in order

  3. Industrial Steam Power Cycles Final End-Use Classification

    E-Print Network [OSTI]

    Waterland, A. F.

    1983-01-01T23:59:59.000Z

    Final end uses of steam include two major classifications: those uses that condense the steam against heat transfer surfaces to provide heat to an item of process or service equipment; and those that require a mass flow of steam for stripping...

  4. Examining the Evolution and Distribution of Patent Classifications

    E-Print Network [OSTI]

    Börner, Katy

    Examining the Evolution and Distribution of Patent Classifications Daniel O. Kutz School of Library@indiana.edu ABSTRACT Today, more so then ever, patents play an important role in helping inventors and organizations protect their intellectual property. With a 150% increase in the number of patents granted over the last

  5. Classification and forecasting of load curves Nolwen Huet

    E-Print Network [OSTI]

    Cuesta, Juan Antonio

    Classification and forecasting of load curves Nolwen Huet Abstract The load curve, which gives of electricity customer uses. This load curve is only available for customers with automated meter reading. For the others, EDF must estimate this curve. Usually a clustering of the load curves is performed, followed

  6. Archives Research Assistant Classification: Student Assistant 3 (LSA 3)

    E-Print Network [OSTI]

    Archives Research Assistant Classification: Student Assistant 3 (LSA 3) Salary: $9.50 - $9.69 Hours: 15-20 per week The University of Oregon Libraries invites application for a part-time, temporary Archives Research Assistant in Knight Library's Special Collections and University Archives. The student

  7. University Policy No.: IM7305 Classification: Information Management

    E-Print Network [OSTI]

    Victoria, University of

    University Policy No.: IM7305 Classification: Information Management COPYRIGHT AND THE USE OF VIDEO of Victoria Libraries, McPherson Library Loan Policy (Policy 2550, section 2.1.2); "Fair Dealing" means a fair as it has in the University of Victoria Libraries, McPherson Library Loan Policy (Policy 2550, section 2

  8. University Policy No.: IM7300 Classification: Information Management

    E-Print Network [OSTI]

    Victoria, University of

    University Policy No.: IM7300 Classification: Information Management POLICY ON COPYRIGHT. The Act is available for you to read at the reference desks of the McPherson Library, the Diana Priestly Law Library, and the Curriculum Laboratory. Computer programs are usually licensed rather than

  9. Insights from Machine Learning Applied to Human Visual Classification

    E-Print Network [OSTI]

    Insights from Machine Learning Applied to Human Visual Classification Arnulf B. A. Graf and Felix A both psy- chophysical and machine learning techniques. Frontal views of human faces were used processes are still limited. Here we report on a study combining psychophysical and ma- chine learning

  10. Robust Neuroimaging-Based Classification Techniques of Autistic vs. Typically

    E-Print Network [OSTI]

    Farag, Aly A.

    abnormalities in several brain regions. Increased head size was the first observed characteristic in children1 Robust Neuroimaging-Based Classification Techniques of Autistic vs. Typically Developing Brain with autism. According to the published studies, different anatomical structures of the brain have been

  11. Morphological Classification of Galaxies Using Artificial Neural Networks

    E-Print Network [OSTI]

    Nicholas M. Ball

    2001-10-22T23:59:59.000Z

    The results of morphological galaxy classifications performed by humans and by automated methods are compared. In particular, a comparison is made between the eyeball classifications of 454 galaxies in the Sloan Digital Sky Survey (SDSS) commissioning data (Shimasaku et al. 2001) with those of supervised artificial neural network programs constructed using the MATLAB Neural Network Toolbox package. Networks in this package have not previously been used for galaxy classification. It is found that simple neural networks are able to improve on the results of linear classifiers, giving correlation coefficients of the order of 0.8 +/- 0.1, compared with those of around 0.7 +/- 0.1 for linear classifiers. The networks are trained using the resilient backpropagation algorithm, which, to the author's knowledge, has not been specifically used in the galaxy classification literature. The galaxy parameters used and the network architecture are both important, and in particular the galaxy concentration index, a measure of the concentration of light towards the centre of the galaxy, is the most significant parameter. Simple networks are briefly applied to 29,429 galaxies with redshifts from the SDSS Early Data Release. They give an approximate ratio of types E/S0:Sp:Irr of 14 +/- 5 : 86 +/- 12 : 0 +/- 0.1, which broadly agrees with the well known approximate ratios of 20:80:1 observed at low redshift.

  12. Classification of the Visual Landscape for Transmission Planning1

    E-Print Network [OSTI]

    Standiford, Richard B.

    . The third concept is that the specific design of transmission towers can result in a negative re- sponseClassification of the Visual Landscape for Transmission Planning1 Curtis Miller2/ , Nargis Jetha3 can be made of the visual change to the landscape due to the introduction of transmission facilities

  13. Test-Cost Sensitive Classification on Data with Missing Values

    E-Print Network [OSTI]

    Yang, Qiang

    Test-Cost Sensitive Classification on Data with Missing Values Qiang Yang, Senior Member, IEEE values on several data sets. Index Terms--Cost-sensitive learning, decision trees, naive Bayes. æ 1, Charles Ling, Xiaoyong Chai, and Rong Pan Abstract--In the area of cost-sensitive learning, inductive

  14. Fuzzy Rough Positive Region based Nearest Neighbour Classification

    E-Print Network [OSTI]

    Gent, Universiteit

    of the main subjects in machine learning and pattern recognition, with applications in fields like spam theory to improve the FNN classifier. Fuzzy rough set theory was designed to model imperfect knowledgeFuzzy Rough Positive Region based Nearest Neighbour Classification Nele Verbiest, Chris Cornelis

  15. THE CLASSIFICATION OF EXCEPTIONAL CDQL WEBS ON COMPACT COMPLEX SURFACES

    E-Print Network [OSTI]

    Pereira, Jorge Vitório

    THE CLASSIFICATION OF EXCEPTIONAL CDQL WEBS ON COMPACT COMPLEX SURFACES J. V. PEREIRA AND L. PIRIO Abstract. Codimension one webs are configurations of finitely many codi- mension one foliations in general equation among the first integrals of the foliations defining the web reminiscent of Abel's addition the

  16. Knowledge Transformation for Cross-Domain Sentiment Classification

    E-Print Network [OSTI]

    Li, Tao

    Knowledge Transformation for Cross-Domain Sentiment Classification Tao Li School of Computer With the explosion of user-generated web2.0 content in the form of blogs, wikis and discussion forums, the Internet domain, thereby build- ing high-quality sentiment models without manual effort? We outline a novel

  17. Context-Aware Query Classification Huanhuan Cao1

    E-Print Network [OSTI]

    Yang, Qiang

    Context-Aware Query Classification Huanhuan Cao1 Derek Hao Hu2 Dou Shen3 Daxin Jiang4 Jian-Tao Sun {derekhh, qyang}@cse.ust.hk 3,4 {doushen, djiang, jtsun}@microsoft.com ABSTRACT Understanding users' search become one of the most popular tools for Web users to find their desired information. As a result

  18. SIC (MUltiple SIgnal Classification) CSP (Cross-power Spectrum Phase)

    E-Print Network [OSTI]

    Takiguchi, Tetsuya

    2ch CSP ( ) 1 MU- SIC (MUltiple SIgnal Classification) CSP (Cross- power Spectrum Phase) [1, 2, 3, 4] [5, 6] [7, 8, 9, 10] [7] CSP CSP [8] [9] CSP [10] Estimation of talker's head orientation based (Kobe univ.) [11] 2ch CSP CSP CSP CSP 2 CSP GCC-PHAT (Generalized Cross- Correlation PHAse Transform

  19. CLASSIFICATION AND REACTIVITY OF SECONDARY ALUMINUM PRODUCTION WASTE

    E-Print Network [OSTI]

    environment.14 Keywords: Landfills, aluminum, hydrogen, salt cake, dross, calorimeter, waste disposal15 16 17CLASSIFICATION AND REACTIVITY OF SECONDARY ALUMINUM PRODUCTION WASTE Navid H. Jafari Student Member and Reactivity of Secondary Aluminum Production Waste1 Navid H. Jafari1 , Timothy D. Stark2 and Ralph Roper3 2 3

  20. Integration of Local and Global Shape Analysis for Logo Classification

    E-Print Network [OSTI]

    Samet, Hanan

    Integration of Local and Global Shape Analysis for Logo Classification Jan Neumann, Hanan Samet@il.ibm.com Abstract. A comparison is made of global and local methods for the shape analysis of logos in an image metric on the logos. As representatives for the two classes of methods, we use the negative shape method

  1. AUTOMATIC TV LOGO DETECTION AND CLASSIFICATION IN BROADCAST VIDEOS

    E-Print Network [OSTI]

    AUTOMATIC TV LOGO DETECTION AND CLASSIFICATION IN BROADCAST VIDEOS Nedret ¨OZAY, B¨ulent SANKUR In this study1 , we present a fully automatic TV logo iden- tification system. TV logos are detected in static of a logo candidate is established, TV logos are recognized via their subspace fea- tures. Comparative

  2. TOWARDS VERTICAL VELOCITY AND HYDROMETEOR CLASSIFICATION FROM ARM WIND PROFILERS

    E-Print Network [OSTI]

    - 98CH10886 with the U.S. Department of Energy. The publisher by accepting the manuscriptTOWARDS VERTICAL VELOCITY AND HYDROMETEOR CLASSIFICATION FROM ARM WIND PROFILERS Scott Giangrande Department/Atmospheric Sciences Division Brookhaven National Laboratory U.S. Department of Energy Office

  3. Husnjak et al., 2004. Soil inventory and soil classification in Croatia ISRIC World Soil Information Country Series

    E-Print Network [OSTI]

    Rossiter, D G "David"

    Husnjak et al., 2004. Soil inventory and soil classification in Croatia Page 1 ISRIC World Soil Information Country Series Soil inventory and soil classification in Croatia: historical review, current classification in Croatia Page 2 Summary An historical overview of soil survey and soil classification activities

  4. Semi-supervised Learning for Photometric Supernova Classification

    E-Print Network [OSTI]

    Richards, Joseph W; Freeman, Peter E; Schafer, Chad M; Poznanski, Dovi

    2011-01-01T23:59:59.000Z

    We present a semi-supervised method for photometric supernova typing. Our approach is to first use the nonlinear dimension reduction technique diffusion map to detect structure in a database of supernova light curves and subsequently employ random forest classification on a spectroscopically confirmed training set to learn a model that can predict the type of each newly observed supernova. We demonstrate that this is an effective method for supernova typing. As supernova numbers increase, our semi-supervised method efficiently utilizes this information to improve classification, a property not enjoyed by template based methods. Applied to supernova data simulated by Kessler et al. (2010b) to mimic those of the Dark Energy Survey, our methods achieve (cross-validated) 96% Type Ia purity and 86% Type Ia efficiency on the spectroscopic sample, but only 56% Type Ia purity and 48% efficiency on the photometric sample due to their spectroscopic followup strategy. To improve the performance on the photometric sample...

  5. A Multi-Dimensional Classification Model for Scientific Workflow Characteristics

    SciTech Connect (OSTI)

    Ramakrishnan, Lavanya; Plale, Beth

    2010-04-05T23:59:59.000Z

    Workflows have been used to model repeatable tasks or operations in manufacturing, business process, and software. In recent years, workflows are increasingly used for orchestration of science discovery tasks that use distributed resources and web services environments through resource models such as grid and cloud computing. Workflows have disparate re uirements and constraints that affects how they might be managed in distributed environments. In this paper, we present a multi-dimensional classification model illustrated by workflow examples obtained through a survey of scientists from different domains including bioinformatics and biomedical, weather and ocean modeling, astronomy detailing their data and computational requirements. The survey results and classification model contribute to the high level understandingof scientific workflows.

  6. Classification of birth weights based on dichotomous variables

    E-Print Network [OSTI]

    Perry, Lynn McIver

    1980-01-01T23:59:59.000Z

    in contructing classification/ discrimination models has been to treat qualitative data as if they were continuous and app'Iy techniques such as Fisher's Linear Discrimi'nant Function (LDF), some authors recently have advocated the reverse procedure ? convert... of Fishers' Linear Discriminant Function (LDF). Although the coasnon practice is clearly to treat qualitative data as if they were continuous and use Fishers' LDF, some authors have advocated the reverse procedure, which consists of converting continuous...

  7. Classification Policy, Guidance & Reports | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative FuelsNovember 13, 2014 Building AmericaEnergyand AssurancesChristineClassification

  8. Classification/Declassification of Government Documents | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative FuelsNovember 13, 2014 Building AmericaEnergyandClassification

  9. Classification of Flipped SU(5) Heterotic-String Vacua

    E-Print Network [OSTI]

    Alon E. Faraggi; John Rizos; Hasan Sonmez

    2014-03-31T23:59:59.000Z

    We extend the classification of the free fermionic heterotic-string vacua to models in which the SO(10) GUT symmetry at the string scale is broken to the flipped SU(5) subgroup. In our classification method, the set of basis vectors defined by the boundary conditions which are assigned to the free fermions is fixed and the enumeration of the string vacua is obtained in terms of the Generalised GSO (GGSO) projection coefficients entering the one-loop partition function. We derive algebraic expressions for the GGSO projections for all the physical states appearing in the sectors generated by the set of basis vectors. This enables the analysis of the entire string spectrum to be programmed in to a computer code therefore, we performed a statistical sampling in the space of 2^{44} (approximately 10^{13}) flipped $SU(5)$ vacua and scanned up to 10^{12} GGSO configurations. For that purpose, two independent codes were developed based on JAVA and FORTRAN95. All the results presented here are confirmed by the two independent routines. Contrary to the corresponding Pati-Salam classification, we do not find exophobic flipped SU(5) vacua with an odd number of generations. We study the structure of exotic states appearing in the three generation models that additionally contain a viable Higgs spectrum. Moreover, we demonstrate the existence of models in which all the exotic states are confined by a hidden sector non-Abelian gauge symmetry as well as models that may admit the racetrack mechanism.

  10. Spectral classification of stars based on LAMOST spectra

    E-Print Network [OSTI]

    Liu, Chao; Zhang, Bo; Wan, Jun-Chen; Deng, Li-Cai; Hou, Yonghui; Wang, Yuefei; Yang, Ming; Zhang, Yong

    2015-01-01T23:59:59.000Z

    In this work, we select the high signal-to-noise ratio spectra of stars from the LAMOST data andmap theirMK classes to the spectral features. The equivalentwidths of the prominent spectral lines, playing the similar role as the multi-color photometry, form a clean stellar locus well ordered by MK classes. The advantage of the stellar locus in line indices is that it gives a natural and continuous classification of stars consistent with either the broadly used MK classes or the stellar astrophysical parameters. We also employ a SVM-based classification algorithm to assignMK classes to the LAMOST stellar spectra. We find that the completenesses of the classification are up to 90% for A and G type stars, while it is down to about 50% for OB and K type stars. About 40% of the OB and K type stars are mis-classified as A and G type stars, respectively. This is likely owe to the difference of the spectral features between the late B type and early A type stars or between the late G and early K type stars are very we...

  11. LibShortText: A Library for Short-text Classification and Analysis LibShortText: A Library for Short-text Classification and

    E-Print Network [OSTI]

    Lin, Chih-Jen

    LibShortText: A Library for Short-text Classification and Analysis LibShortText: A Library for Short-text Classification and Analysis Hsiang-Fu Yu rofuyu@cs.utexas.edu Department of Computer Science University, Taipei 106, Taiwan Editor: Editor name Abstract LibShortText is an open source library for short

  12. Kinematic classifications of local interacting galaxies: implications for the merger/disk classifications at high-z

    E-Print Network [OSTI]

    Hung, Chao-Ling; Yuan, Tiantian; Larson, Kirsten L; Casey, Caitlin M; Smith, Howard A; Sanders, D B; Kewley, Lisa J; Hayward, Christopher C

    2015-01-01T23:59:59.000Z

    The classification of galaxy mergers and isolated disks is key for understanding the relative importance of galaxy interactions and secular evolution during the assembly of galaxies. The kinematic properties of galaxies as traced by emission lines have been used to suggest the existence of a significant population of high-z star-forming galaxies consistent with isolated rotating disks. However, recent studies have cautioned that post-coalescence mergers may also display disk-like kinematics. To further investigate the robustness of merger/disk classifications based on kinematic properties, we carry out a systematic classification of 24 local (U)LIRGs spanning a range of galaxy morphologies: from isolated spiral galaxies, ongoing interacting systems, to fully merged remnants. We artificially redshift the WiFeS observations of these local (U)LIRGs to z=1.5 to make a realistic comparison with observations at high-z, and also to ensure that all galaxies have the same spatial sampling of ~900 pc. Using both kineme...

  13. Deep Geothermal Reservoir Temperatures in the Eastern Snake River Plain, Idaho using Multicomponent Geothermometry

    SciTech Connect (OSTI)

    Ghanashyam Neupane; Earl D. Mattson; Travis L. McLing; Carl D. Palmer; Robert W. Smith; Thomas R. Wood

    2014-02-01T23:59:59.000Z

    The U.S. Geological survey has estimated that there are up to 4,900 MWe of undiscovered geothermal resources and 92,000 MWe of enhanced geothermal potential within the state of Idaho. Of particular interest are the resources of the Eastern Snake River Plain (ESRP) which was formed by volcanic activity associated with the relative movement of the Yellowstone Hot Spot across the state of Idaho. This region is characterized by a high geothermal gradient and thermal springs occurring along the margins of the ESRP. Masking much of the deep thermal potential of the ESRP is a regionally extensive and productive cold-water aquifer. We have undertaken a study to infer the temperature of the geothermal system hidden beneath the cold-water aquifer of the ESRP. Our approach is to estimate reservoir temperatures from measured water compositions using an inverse modeling technique (RTEst) that calculates the temperature at which multiple minerals are simultaneously at equilibrium while explicitly accounting for the possible loss of volatile constituents (e.g., CO2), boiling and/or water mixing. In the initial stages of this study, we apply the RTEst model to water compositions measured from a limited number of wells and thermal springs to estimate the regionally extensive geothermal system in the ESRP.

  14. Problems of trace element ratios and geothermometry in a gravel geothermal-aquifer system

    SciTech Connect (OSTI)

    Sonderegger, J.L.; Donovan, J.J.; Ruscetta, C.A.; Foley, D. (eds.)

    1981-05-01T23:59:59.000Z

    A Tertiary-age, block-faulted basin in which a Pleistocene gravel bed acts as a confined aquifer and permits the lateral dispersion of the geothermal fluids is studied. Basic data on geology and trace element holes presented previously are reproduced along with fluoride data. Evaluation of the phenomena in this system was attempted using a dissolved silica-enthalpy graph. A chalcedomy curve is also plotted. An enthalpy versus chloride plot suggests that either conductive cooling occurs before mixing or that higher chloride content background waters are available for mixing. (MHR)

  15. Geothermometry At Desert Queen Area (Garchar & Arehart, 2008) | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation, search OpenEI Reference LibraryAdd toWell TestingGeothermal/PowerInformation

  16. Geothermometry At Mt Princeton Hot Springs Geothermal Area (Pearl, Et Al.,

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation, search OpenEI Reference LibraryAdd toWell

  17. Geothermometry At Neal Hot Springs Geothermal Area (U.S. Geothermal Inc.,

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation, search OpenEI Reference LibraryAdd toWell2008) | Open Energy Information Neal

  18. Geothermometry At Rhodes Marsh Area (Coolbaugh, Et Al., 2006) | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation, search OpenEI Reference LibraryAdd toWell2008) | Open Energy

  19. Geothermometry At Rhodes Marsh Area (Shevenell, Et Al., 2008) | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation, search OpenEI Reference LibraryAdd toWell2008) | Open EnergyInformation Et

  20. Geothermometry At Salt Wells Area (Coolbaugh, Et Al., 2006) | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation, search OpenEI Reference LibraryAdd toWell2008) | Open EnergyInformation

  1. Geothermometry At Salt Wells Area (Edmiston & Benoit, 1984) | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation, search OpenEI Reference LibraryAdd toWell2008) | Open

  2. Geothermometry At Clear Lake Area (Thompson, Et Al., 1992) | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH und Co KG

  3. Geothermometry At Columbus Salt Marsh Area (Shevenell, Et Al., 2008) | Open

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH und Co KGEnergy Information

  4. Geothermometry At Fish Lake Valley Area (DOE GTP) | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH und Co KGEnergyFish Lake Valley

  5. Geothermometry At Gabbs Alkali Flat Area (Kratt, Et Al., 2008) | Open

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH und Co KGEnergyFish LakeEnergy

  6. Geothermometry At Long Valley Caldera Geothermal Area (McKenzie &

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH und Co2010) | OpenTruesdell, 1977)

  7. Geothermometry At Long Valley Caldera Geothermal Area (Sorey, Et Al., 1991)

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH und Co2010) | OpenTruesdell,

  8. Geothermometry At Mt St Helens Area (Shevenell & Goff, 1995) | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH und Co2010) |Information

  9. Geothermometry At Nevada Test And Training Range Area (Sabin, Et Al., 2004)

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH und Co2010) |Information| Open

  10. Geothermometry At Northern Basin & Range Region (Cole, 1983) | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH und Co2010)

  11. Geothermometry At Northern Basin & Range Region (Laney, 2005) | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH und Co2010)Information

  12. Geothermometry At Nw Basin & Range Region (Laney, 2005) | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH und

  13. Geothermometry At Nw Basin & Range Region (Shevenell & De Rocher, 2005) |

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH undOpen Energy Information

  14. Geothermometry At Reese River Area (Henkle & Ronne, 2008) | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH undOpen Energy

  15. Geothermometry At Salt Wells Area (Henkle, Et Al., 2005) | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH undOpen Energy1978) | Open

  16. Geothermometry At Socorro Mountain Area (Owens, Et Al., 2005) | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH undOpen Energy1978) |

  17. Geothermometry At Teels Marsh Area (Coolbaugh, Et Al., 2006) | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH undOpen Energy1978)

  18. Geothermometry At Teels Marsh Area (Shevenell, Et Al., 2008) | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH undOpen Energy1978)Information

  19. Geothermometry At U.S. Midwest Region (Vugrinovich, 1987) | Open Energy

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH undOpenInformation Region

  20. Geothermometry At Upper Hot Creek Ranch Area (Benoit & Blackwell, 2006) |

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH undOpenInformation RegionOpen

  1. Geothermometry At Walker-Lane Transitional Zone Region (Laney, 2005) | Open

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH undOpenInformation

  2. Geothermometry At Walker-Lane Transitional Zone Region (Shevenell & De

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump1946865°,Park,2005)EnergyAmatitlanGmbH undOpenInformationRocher,

  3. Neural frame classification 1 Walter J Freeman Origin, structure, and role of background EEG activity

    E-Print Network [OSTI]

    Freeman, Walter J.

    -1244 from the National Aeronautics and Space Administration, and EIA- 0130352 from the National Science International Database and Brain Dynamics Centre #12;Neural frame classification

  4. Tip sheet: Expanded Library of Congress Call Number Classification system Call Number Subject Matter

    E-Print Network [OSTI]

    Kambhampati, Patanjali

    Tip sheet: Expanded Library of Congress Call Number Classification system Call Number Subject R: Medicine T: Technology U: Military Science Z: Bibliography. Library Science. Information

  5. THE PHOTOMETRIC CLASSIFICATION SERVER FOR Pan-STARRS1

    SciTech Connect (OSTI)

    Saglia, R. P.; Bender, R.; Seitz, S.; Senger, R.; Snigula, J.; Phleps, S.; Wilman, D. [Max Planck Institute for Extraterrestrial Physics, Giessenbachstrasse, Postfach 1312, 85741 Garching (Germany); Tonry, J. L.; Burgett, W. S.; Chambers, K. C.; Heasley, J. N.; Kaiser, N.; Magnier, E. A.; Morgan, J. S. [Institute for Astronomy, University of Hawaii, 2680 Woodlawn Drive, Honolulu, HI 96822 (United States); Greisel, N. [University Observatory Munich, Ludwig-Maximilians Universitaet, Scheinerstrasse 1, 81679 Munich (Germany); Bailer-Jones, C. A. L.; Klement, R. J.; Rix, H.-W.; Smith, K. [Max Planck Institute for Astronomy, Koenigstuhl 17, D-69117 Heidelberg (Germany); Green, P. J. [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); and others

    2012-02-20T23:59:59.000Z

    The Pan-STARRS1 survey is obtaining multi-epoch imaging in five bands (g{sub P1} r{sub P1} i{sub P1} z{sub P1} y{sub P1}) over the entire sky north of declination -30 deg. We describe here the implementation of the Photometric Classification Server (PCS) for Pan-STARRS1. PCS will allow the automatic classification of objects into star/galaxy/quasar classes based on colors and the measurement of photometric redshifts for extragalactic objects, and will constrain stellar parameters for stellar objects, working at the catalog level. We present tests of the system based on high signal-to-noise photometry derived from the Medium-Deep Fields of Pan-STARRS1, using available spectroscopic surveys as training and/or verification sets. We show that the Pan-STARRS1 photometry delivers classifications and photometric redshifts as good as the Sloan Digital Sky Survey (SDSS) photometry to the same magnitude limits. In particular, our preliminary results, based on this relatively limited data set down to the SDSS spectroscopic limits, and therefore potentially improvable, show that stars are correctly classified as such in 85% of cases, galaxies in 97%, and QSOs in 84%. False positives are less than 1% for galaxies, Almost-Equal-To 19% for stars, and Almost-Equal-To 28% for QSOs. Moreover, photometric redshifts for 1000 luminous red galaxies up to redshift 0.5 are determined to 2.4% precision (defined as 1.48 Multiplication-Sign Median|z{sub phot} - z{sub spec}|/(1 + z)) with just 0.4% catastrophic outliers and small (-0.5%) residual bias. For bluer galaxies up to the same redshift, the residual bias (on average -0.5%) trend, percentage of catastrophic failures (1.2%), and precision (4.2%) are higher, but still interestingly small for many science applications. Good photometric redshifts (to 5%) can be obtained for at most 60% of the QSOs of the sample. PCS will create a value-added catalog with classifications and photometric redshifts for eventually many millions of sources.

  6. On the Classification of Low-Rank Braided Fusion Categories

    E-Print Network [OSTI]

    Bruillard, Paul Joseph

    2013-05-23T23:59:59.000Z

    ON THE CLASSIFICATION OF LOW-RANK BRAIDED FUSION CATEGORIES A Dissertation by PAUL JOSEPH BRUILLARD Submitted to the O ce of Graduate Studies of Texas A&M University in partial ful llment of the requirements for the degree of DOCTOR...+=p . BFC Braided Fusion Category. C0 The M uger center of the category C. Cad The adjoint subcategory. Cpt The pointed subcategory. Cop Opposite (mirror) category to C. coevX Coevaluation I! X X . C2 (G;K ) 2-cochains of G with coe cients in K . C...

  7. Complete Classification of 1+1 Gravity Solutions

    E-Print Network [OSTI]

    T. Kloesch; T. Strobl

    1997-11-25T23:59:59.000Z

    A classification of the maximally extended solutions for 1+1 gravity models (comprising e.g. generalized dilaton gravity as well as models with non-trivial torsion) is presented. No restrictions are placed on the topology of the arising solutions, and indeed it is found that for generic models solutions on non-compact surfaces of arbitrary genus with an arbitrary non-zero number of holes can be obtained. The moduli space of classical solutions (solutions of the field equations with fixed topology modulo gauge transformations) is parametrized explicitly.

  8. Automatic Fault Characterization via Abnormality-Enhanced Classification

    SciTech Connect (OSTI)

    Bronevetsky, G; Laguna, I; de Supinski, B R

    2010-12-20T23:59:59.000Z

    Enterprise and high-performance computing systems are growing extremely large and complex, employing hundreds to hundreds of thousands of processors and software/hardware stacks built by many people across many organizations. As the growing scale of these machines increases the frequency of faults, system complexity makes these faults difficult to detect and to diagnose. Current system management techniques, which focus primarily on efficient data access and query mechanisms, require system administrators to examine the behavior of various system services manually. Growing system complexity is making this manual process unmanageable: administrators require more effective management tools that can detect faults and help to identify their root causes. System administrators need timely notification when a fault is manifested that includes the type of fault, the time period in which it occurred and the processor on which it originated. Statistical modeling approaches can accurately characterize system behavior. However, the complex effects of system faults make these tools difficult to apply effectively. This paper investigates the application of classification and clustering algorithms to fault detection and characterization. We show experimentally that naively applying these methods achieves poor accuracy. Further, we design novel techniques that combine classification algorithms with information on the abnormality of application behavior to improve detection and characterization accuracy. Our experiments demonstrate that these techniques can detect and characterize faults with 65% accuracy, compared to just 5% accuracy for naive approaches.

  9. Hazard categorization and classification for the sodium storage facility

    SciTech Connect (OSTI)

    Van Keuren, J.C.

    1994-08-30T23:59:59.000Z

    The Sodium Storage Facility is planned to be constructed in the 400 area for long term storage of sodium from the Fast Flux Test Facility (FFTF). It will contain four large sodium storage tanks. Three of the tanks have a capacity of 80,000 gallons of sodium each, and the fourth will hold 52,500 gallons. The tanks will be connected by piping with each other and to the FFTF. Sodium from the FFTF primary and secondary Heat Transport Systems (HTS), Interim Decay Storage (IDS), and the Fuel Storage Facility (FSF) will be transferred to the facility, and stored there in a frozen state pending final disposition. A Hazard Classification has been performed in order to evaluate the potential toxic consequences of a sodium fire according to the provisions of DOE Order 5481.1B. The conclusion of these evaluations is that the Sodium Storage Facility meets the requirements of the lowest Hazard Category, i.e., radiological facility, and the Hazard Classification is recommended to be moderate.

  10. Hyperspectral landcover classification for the Yakima Training Center, Yakima, Washington

    SciTech Connect (OSTI)

    Steinmaus, K.L.; Perry, E.M.; Petrie, G.M.; Irwin, D.E.; Foote, H.P.; Wurstner, S.K.; Stephen, A.J.

    1998-04-01T23:59:59.000Z

    The US Department of Energy`s (DOE`s) Pacific Northwest National Laboratory (PNNL) was tasked in FY97-98 to conduct a multisensor feature extraction project for the Terrain Modeling Project Office (TMPO) of the National Imagery and Mapping Agency (NIMA). The goal of this research is the development of near-autonomous methods to remotely classify and characterize regions of military interest, in support of the TMPO of NIMA. These methods exploit remotely sensed datasets including hyperspectral (HYDICE) imagery, near-infrared and thermal infrared (Daedalus 3600), radar, and terrain datasets. The study site for this project is the US Army`s Yakima Training Center (YTC), a 326,741-acre training area located near Yakima, Washington. Two study areas at the YTC were selected to conduct and demonstrate multisensor feature extraction, the 2-km x 2-km Cantonment Area and the 3-km x 3-km Choke Point area. Classification of the Cantonment area afforded a comparison of classification results at different scales.

  11. Classification Based Mode Decisions for Video over Networks Deepak S. Turaga and Tsuhan Chen

    E-Print Network [OSTI]

    Chen, Tsuhan

    classification based approach to making such mode decisions accurately and efficiently. We first illustrateClassification Based Mode Decisions for Video over Networks Deepak S. Turaga and Tsuhan Chen Engineering Carnegie Mellon University Pittsburgh, PA 15213 Inter-Intra Decision Regions mrMAD Energy 0 10 20

  12. COMPRESSIVE VIDEO CLASSIFICATION IN A LOW-DIMENSIONAL MANIFOLD WITH LEARNED DISTANCE METRIC

    E-Print Network [OSTI]

    Tsakalides, Panagiotis

    COMPRESSIVE VIDEO CLASSIFICATION IN A LOW-DIMENSIONAL MANIFOLD WITH LEARNED DISTANCE METRIC George Tzagkarakis1 , Grigorios Tsagkatakis2 , Jean-Luc Starck1 and Panagiotis Tsakalides2 1 Commissariat `a l'´Energie of video classification based on a set of com- pressed features, without the need of accessing the original

  13. Hardware-Based Support Vector Machine Classification in Logarithmic Number Systems

    E-Print Network [OSTI]

    Pottenger, William M.

    for its energy-efficient properties [3,9]. Successful deployment of logarithmic functionality in neuralHardware-Based Support Vector Machine Classification in Logarithmic Number Systems Faisal M. Khan loss in classification accuracy. I. INTRODUCTION Cognitive systems capable of gathering information

  14. Energy-Efficient Multi-Pipeline Architecture for Terabit Packet Classification

    E-Print Network [OSTI]

    Prasanna, Viktor K.

    Energy-Efficient Multi-Pipeline Architecture for Terabit Packet Classification Weirong Jiang-pipeline architecture for energy-efficient packet classification. We optimize the HyperCuts algorithm, which Angeles, CA 90089, USA Email: {weirongj, prasanna}@usc.edu Abstract--Energy efficiency has become

  15. Wavelets and Support Vector Machines for Texture Classification Kashif Mahmood Rajpoot Nasir Mahmood Rajpoot

    E-Print Network [OSTI]

    Rajpoot, Nasir

    Wavelets and Support Vector Machines for Texture Classification Kashif Mahmood Rajpoot Nasir@dcs.warwick.ac.uk Abstract We present a novel texture classification algorithm using 2-D discrete wavelet transform (DWT, and a local energy function is computed corresponding to each pixel of the feature images. This feature vector

  16. High-performance Architecture for Dynamically Updatable Packet Classification on FPGA

    E-Print Network [OSTI]

    Prasanna, Viktor K.

    . Our architecture demonstrates 4Ã? energy efficiency while achieving 2Ã? throughput compared to TCAM. 1High-performance Architecture for Dynamically Updatable Packet Classification on FPGA Yun R. Qu and FPGA based implementations for packet classification have been studied over the past decade. Al

  17. Classification using Intersection Kernel Support Vector Machines is Efficient Subhransu Maji

    E-Print Network [OSTI]

    O'Brien, James F.

    Classification using Intersection Kernel Support Vector Machines is Efficient Subhransu Maji EECS classification using kernelized SVMs re- quires evaluating the kernel for a test vector and each of the support vectors. For a class of kernels we show that one can do this much more efficiently. In particular we show

  18. EPA`s program for risk assessment guidelines: Cancer classification issues

    SciTech Connect (OSTI)

    Wiltse, J. [Environmental Protection Agency, Washington, DC (United States)

    1990-12-31T23:59:59.000Z

    Issues presented are related to classification of weight of evidence in cancer risk assessments. The focus in this paper is on lines of evidence used in constructing a conclusion about potential human carcinogenicity. The paper also discusses issues that are mistakenly addressed as classification issues but are really part of the risk assessment process. 2 figs.

  19. G-Structures Local Equivalence and Classification of Co-frames

    E-Print Network [OSTI]

    Patrick, George

    Motivation G-Structures Local Equivalence and Classification of Co-frames Solution Summary and Further Results Local Equivalence and Classification of Finite Type G-Structures Ivan Struchiner1 1 and Infinite Dimensional Ivan Struchiner Equivalence of G-Structures #12;Motivation G-Structures Local

  20. CLASSIFICATION OF BIOMEDICAL HIGH-RESOLUTION MICRO-CT IMAGES FOR DIRECT VOLUME RENDERING

    E-Print Network [OSTI]

    López-Sánchez, Maite

    CLASSIFICATION OF BIOMEDICAL HIGH-RESOLUTION MICRO-CT IMAGES FOR DIRECT VOLUME RENDERING Maite L,cerquide,davidm,anna}@maia.ub.es ABSTRACT This paper introduces a machine learning approach into the process of direct volume rendering that generates the classification func- tion within the optical property function used for rendering. Briefly

  1. Statistics & Gene Expression Data Analysis Note 8: Binary Regression Outcomes and classification probabilities

    E-Print Network [OSTI]

    West, Mike

    classification, validation, prognosis Binary regression models · Linear regression model based on regression ­ Standard statistical models transform from real-value to (0, 1) using a specified non-linear functionStatistics & Gene Expression Data Analysis Note 8: Binary Regression Outcomes and classification

  2. Hash-SVM: Scalable Kernel Machines for Large-Scale Visual Classification , Shih-Fu Chang

    E-Print Network [OSTI]

    Chang, Shih-Fu

    Hash-SVM: Scalable Kernel Machines for Large-Scale Visual Classification Yadong Mu , Gang Hua , Wei the efficiency of non-linear kernel SVM in very large scale visual classification prob- lems. Our key idea be transformed into solving a linear SVM over the hash bits. The proposed Hash-SVM enjoys dramatic storage cost

  3. PROGRESSIVE CLASSIFICATION IN THE COMPRESSED DOMAIN FOR LARGE EOS SATELLITE DATABASES 1

    E-Print Network [OSTI]

    Kontoyiannis, Ioannis

    PROGRESSIVE CLASSIFICATION IN THE COMPRESSED DOMAIN FOR LARGE EOS SATELLITE DATABASES 1 Vittorio for classifying large images that is more accurate and less computationally expensive than the classical pixel­by­pixel approach. This approach, called progressive classification, is well suited for analyzing large images

  4. Using Temporal Information in an Automated Classification of Summer, Marginal Ice Zone Imagery*

    E-Print Network [OSTI]

    Kansas, University of

    Using Temporal Information in an Automated Classification of Summer, Marginal Ice Zone Imagery, even with the human eye. BackScatter instability causu the intensities of the fiistyear ice, multiyear ice, and open water classes to intermix, thus making an intensity-based classification invalid

  5. CLASSIFICATION OF NON-HEAT GENERATING OUTDOOR OBJECTS IN THERMAL SCENES FOR AUTONOMOUS ROBOTS

    E-Print Network [OSTI]

    Shaw, Leah B.

    describes a physics-based adaptive Bayesian pattern classification model that uses a passive thermal as a result of the diurnal cycle of solar energy. The model that we present will allow bots to "see beyond by the classes of objects and design our Adaptive Bayesian Classification Model. We demonstrate that our novel

  6. Large-Scale Patent Classification with Min-Max Modular Support Vector Machines

    E-Print Network [OSTI]

    Lu, Bao-Liang

    Large-Scale Patent Classification with Min-Max Modular Support Vector Machines Xiao-Lei Chu, Chao Ma, Jing Li, Bao-Liang Lu Senior Member, IEEE, Masao Utiyama, and Hitoshi Isahara Abstract-- Patent-world patent classification typically exceeds one million, and this number increases every year. An effective

  7. Early Classification of Multivariate Time Series Using a Hybrid HMM/SVM model

    E-Print Network [OSTI]

    Obradovic, Zoran

    Early Classification of Multivariate Time Series Using a Hybrid HMM/SVM model Mohamed F. Ghalwash to use a shorter time interval for classification is often more favorable than having a slightly more with other models that use full time series both in training and testing. Analysis of biomedical data has

  8. STRUCTURAL DAMAGE CLASSIFICATION COMPARISON USING SUPPORT VECTOR MACHINE AND BAYESIAN MODEL SELECTION

    E-Print Network [OSTI]

    Boyer, Edmond

    STRUCTURAL DAMAGE CLASSIFICATION COMPARISON USING SUPPORT VECTOR MACHINE AND BAYESIAN MODEL, CA, USA 92093-0085 mdtodd@ucsd.edu ABSTRACT Since all damage identification strategies inevitably in the decision-making process of damage detection, classification, and prognosis, which employs training data (or

  9. Classification of Melanoma Lesions Using Wavelet-based Texture Analysis Rahil Garnavi, Mohammad Aldeen

    E-Print Network [OSTI]

    Bailey, James

    Classification of Melanoma Lesions Using Wavelet-based Texture Analysis Rahil Garnavi, Mohammad for classification of melanoma. The method applies tree-structured wavelet transform on different color chan- nels and hidden naive bayes to classify melanoma in a test set of 102 images, which resulted in an accuracy of 88

  10. Classification of melanoma using tree structured wavelet Sachin V. Patwardhan a

    E-Print Network [OSTI]

    Relue, Patricia

    Classification of melanoma using tree structured wavelet transforms Sachin V. Patwardhan a , Atam P developed and evaluated for the classification of skin lesion images into melanoma and dysplastic nevus in discriminating melanoma from dysplastic nevus. The results are also compared with those obtained using another

  11. A Source Classification Algorithm for Astronomical X-ray Imagery of Stellar Clusters

    E-Print Network [OSTI]

    Salvaggio, Carl

    A Source Classification Algorithm for Astronomical X-ray Imagery of Stellar Clusters by Susan M of Dissertation: A Source Classification Algorithm for Astronomical X-ray Imagery of Stellar Clusters I, Susan M. Hojnacki, hereby grant permission to Wallace Memorial Library of R.I.T. to reproduce my dissertation

  12. On the synergy between texture classification and deformation process sequence selection for

    E-Print Network [OSTI]

    Zabaras, Nicholas J.

    On the synergy between texture classification and deformation process sequence selection properties. This paper demonstrates the synergy between classification of FCC polycrystal tex- ture and multi are adaptively selected from the database is employed. Key words: Texture library, materials-by-design, data

  13. VEGETATION CLASSIFICATION USING SEASONAL VARIATIONS OF SCATTEROMETER DATA AT C-BAND AND

    E-Print Network [OSTI]

    Long, David G.

    VEGETATION CLASSIFICATION USING SEASONAL VARIATIONS OF SCATTEROMETER DATA AT C-BAND AND KU for submission to the university library. Date Dr. David Long Chair, Graduate Committee Accepted of Engineering and Technology #12;ABSTRACT VEGETATION CLASSIFICATION USING SEASONAL VARIATIONS OF SCATTEROMETER

  14. Computational analysis of microarray gene expression profiles: clustering, classification, and beyond

    E-Print Network [OSTI]

    Dai, Yang

    Computational analysis of microarray gene expression profiles: clustering, classification) the discovery of gene clusters, and (3) the classification of biological samples. In addition, we discuss how inch, and a library of thousands of genes is placed on a single chip. To probe the global gene

  15. Enhancing the detection and classification of coral reef and associated benthic habitats

    E-Print Network [OSTI]

    Rundquist, Donald C.

    Enhancing the detection and classification of coral reef and associated benthic habitats. Rundquist, M. Lawson, and R. Perk (2007), Enhancing the detection and classification of coral reef and Atkinson, 2000]. Holden and LeDrew [1999] have shown that a high-resolution in situ spectral library can

  16. Simultaneous Segmentation and Grading of Hippocampus for Patient Classification with Alzheimer's

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    Simultaneous Segmentation and Grading of Hippocampus for Patient Classification with Alzheimer library used during our experiments was composed by 2 populations, 50 Cognitively Normal subjects (CN of the HC at the same time resulted in an efficient patient classification with a success rate of 90

  17. REMOTE SENSING TECHNIQUES FOR LAND USE CLASSIFICATION OF RIO JAUCA WATERSHED USING IKONOS IMAGES

    E-Print Network [OSTI]

    Gilbes, Fernando

    REMOTE SENSING TECHNIQUES FOR LAND USE CLASSIFICATION OF RIO JAUCA WATERSHED USING IKONOS IMAGES-Mayagüez E-mail: edwinmm80@yahoo.com Key words: GIS, remote sensing, land use, supervised classification resource and supplies water to the metropolitan area. Remote sensing techniques can be used to assess

  18. kenmerk 369.075 1 RULES FOR CLASSIFICATION AND RANKING OF STAFF

    E-Print Network [OSTI]

    Twente, Universiteit

    , a new job classification system (UFO) came into force. As a result of the introduction of UFO, old job jobs are ranked. At several levels the introduction of UFO has consequences for the classification for the application of UFO is the job. This is the `cluster of tasks to be performed by the employee based

  19. Machine Learning for Seismic Signal Processing: Seismic Phase Classification on a Manifold

    E-Print Network [OSTI]

    Meyer, Francois

    Machine Learning for Seismic Signal Processing: Seismic Phase Classification on a Manifold Juan--In this research, we consider the supervised learning problem of seismic phase classification. In seismology, knowledge of the seismic activity arrival time and phase leads to epicenter localization and surface

  20. Data-driven classification of ventilated lung tissues using electrical impedance tomography

    E-Print Network [OSTI]

    Adler, Andy

    Data-driven classification of ventilated lung tissues using electrical impedance tomography Camille for identifying ventilated lung regions utilizing electrical impedance tomography (EIT) images rely on dividing of a data-driven classification method to identify ventilated lung ROI based on forming k clusters from

  1. A complete electrical hazard classification system and its application

    SciTech Connect (OSTI)

    Gordon, Lloyd B [Los Alamos National Laboratory; Cartelli, Laura [Los Alamos National Laboratory

    2009-01-01T23:59:59.000Z

    The Standard for Electrical Safety in the Workplace, NFPA 70E, and relevant OSHA electrical safety standards evolved to address the hazards of 60-Hz power that are faced primarily by electricians, linemen, and others performing facility and utility work. This leaves a substantial gap in the management of electrical hazards in Research and Development (R&D) and specialized high voltage and high power equipment. Examples include lasers, accelerators, capacitor banks, electroplating systems, induction and dielectric heating systems, etc. Although all such systems are fed by 50/60 Hz alternating current (ac) power, we find substantial use of direct current (dc) electrical energy, and the use of capacitors, inductors, batteries, and radiofrequency (RF) power. The electrical hazards of these forms of electricity and their systems are different than for 50160 Hz power. Over the past 10 years there has been an effort to develop a method of classifying all of the electrical hazards found in all types of R&D and utilization equipment. Examples of the variation of these hazards from NFPA 70E include (a) high voltage can be harmless, if the available current is sufficiently low, (b) low voltage can be harmful if the available current/power is high, (c) high voltage capacitor hazards are unique and include severe reflex action, affects on the heart, and tissue damage, and (d) arc flash hazard analysis for dc and capacitor systems are not provided in existing standards. This work has led to a comprehensive electrical hazard classification system that is based on various research conducted over the past 100 years, on analysis of such systems in R&D, and on decades of experience. Initially, national electrical safety codes required the qualified worker only to know the source voltage to determine the shock hazard. Later, as arc flash hazards were understood, the fault current and clearing time were needed. These items are still insufficient to fully characterize all types of electrical hazards. The new comprehensive electrical hazard classification system uses a combination of voltage, shock current available, fault current available, power, energy, and waveform to classify all forms of electrical hazards. Based on this electrical hazard classification system, many new tools have been developed, including (a) work controls for these hazards, (b) better selection of PPE for R&D work, (c) improved training, and (d) a new Severity Ranking Tool that is used to rank electrical accidents and incidents with various forms of electrical energy.

  2. MUSIC GENRE CLASSIFICATION USING NOVEL FEATURES AND A WEIGHTED VOTING Dalwon Jang, Minho Jin, and Chang D. Yoo

    E-Print Network [OSTI]

    Yoo, Chang D.

    MUSIC GENRE CLASSIFICATION USING NOVEL FEATURES AND A WEIGHTED VOTING METHOD Dalwon Jang, Minho Jin classification system based on two novel features and a weighted voting method. The proposed features, modulation classification system. 1. INTRODUCTION With the recent proliferation of digital music, there is increas- ing

  3. On the use of MapReduce to build Linguistic Fuzzy Rule Based Classification Systems for Big Data

    E-Print Network [OSTI]

    Granada, Universidad de

    fuzzy rules it is able to provide an interpretable and effective classification model. This method of Education. Fuzzy Rule Based Classification Systems (FRBCSs) [4] are potent and popular tools for patternOn the use of MapReduce to build Linguistic Fuzzy Rule Based Classification Systems for Big Data

  4. A Visual Analytics Approach for Correlation, Classification, and Regression Analysis

    SciTech Connect (OSTI)

    Steed, Chad A [ORNL; SwanII, J. Edward [Mississippi State University (MSU); Fitzpatrick, Patrick J. [Mississippi State University (MSU); Jankun-Kelly, T.J. [Mississippi State University (MSU)

    2013-01-01T23:59:59.000Z

    New approaches that combine the strengths of humans and machines are necessary to equip analysts with the proper tools for exploring today s increasing complex, multivariate data sets. In this paper, a visual data mining framework, called the Multidimensional Data eXplorer (MDX), is described that addresses the challenges of today s data by combining automated statistical analytics with a highly interactive parallel coordinates based canvas. In addition to several intuitive interaction capabilities, this framework offers a rich set of graphical statistical indicators, interactive regression analysis, visual correlation mining, automated axis arrangements and filtering, and data classification techniques. This chapter provides a detailed description of the system as well as a discussion of key design aspects and critical feedback from domain experts.

  5. A Visual Analytics Approach for Correlation, Classification, and Regression Analysis

    SciTech Connect (OSTI)

    Steed, Chad A [ORNL; SwanII, J. Edward [Mississippi State University (MSU); Fitzpatrick, Patrick J. [Mississippi State University (MSU); Jankun-Kelly, T.J. [Mississippi State University (MSU)

    2012-02-01T23:59:59.000Z

    New approaches that combine the strengths of humans and machines are necessary to equip analysts with the proper tools for exploring today's increasing complex, multivariate data sets. In this paper, a novel visual data mining framework, called the Multidimensional Data eXplorer (MDX), is described that addresses the challenges of today's data by combining automated statistical analytics with a highly interactive parallel coordinates based canvas. In addition to several intuitive interaction capabilities, this framework offers a rich set of graphical statistical indicators, interactive regression analysis, visual correlation mining, automated axis arrangements and filtering, and data classification techniques. The current work provides a detailed description of the system as well as a discussion of key design aspects and critical feedback from domain experts.

  6. Cosmic web-type classification using decision theory

    E-Print Network [OSTI]

    Leclercq, Florent; Wandelt, Benjamin

    2015-01-01T23:59:59.000Z

    We propose a decision criterion for segmenting the cosmic web into different structure types (voids, sheets, filaments and clusters) on the basis of their respective probabilities and the strength of data constraints. Our approach is inspired by an analysis of games of chance where the gambler only plays if a positive expected net gain can be achieved based on some degree of privileged information. The result is a general solution for classification problems in the face of uncertainty, including the option of not committing to a class for a candidate object. As an illustration, we produce high-resolution maps of web-type constituents in the nearby Universe as probed by the Sloan Digital Sky Survey main galaxy sample. Other possible applications include the selection and labeling of objects in catalogs derived from astronomical survey data.

  7. Complexity Classifications for Propositional Abduction in Post's Framework

    E-Print Network [OSTI]

    Creignou, Nadia; Thomas, Michael

    2010-01-01T23:59:59.000Z

    In this paper we investigate the complexity of abduction, a fundamental and important form of non-monotonic reasoning. Given a knowledge base explaining the world's behavior it aims at finding an explanation for some observed manifestation. In this paper we consider propositional abduction, where the knowledge base and the manifestation are represented by propositional formulae. The problem of deciding whether there exists an explanation has been shown to be \\SigPtwo-complete in general. We focus on formulae in which the allowed connectives are taken from certain sets of Boolean functions. We consider different variants of the abduction problem in restricting both the manifestations and the hypotheses. For all these variants we obtain a complexity classification for all possible sets of Boolean functions. In this way, we identify easier cases, namely \\NP-complete, \\coNP-complete and polynomial cases. Thus, we get a detailed picture of the complexity of the propositional abduction problem, hence highlighting s...

  8. Classification of groundwater at the Nevada Test Site

    SciTech Connect (OSTI)

    Chapman, J.B.

    1994-08-01T23:59:59.000Z

    Groundwater occurring at the Nevada Test Site (NTS) has been classified according to the ``Guidelines for Ground-Water Classification Under the US Environmental Protection Agency (EPA) Ground-Water Protection Strategy`` (June 1988). All of the groundwater units at the NTS are Class II, groundwater currently (IIA) or potentially (IIB) a source of drinking water. The Classification Review Area (CRA) for the NTS is defined as the standard two-mile distance from the facility boundary recommended by EPA. The possibility of expanding the CRA was evaluated, but the two-mile distance encompasses the area expected to be impacted by contaminant transport during a 10-year period (EPA,s suggested limit), should a release occur. The CRA is very large as a consequence of the large size of the NTS and the decision to classify the entire site, not individual areas of activity. Because most activities are located many miles hydraulically upgradient of the NTS boundary, the CRA generally provides much more than the usual two-mile buffer required by EPA. The CRA is considered sufficiently large to allow confident determination of the use and value of groundwater and identification of potentially affected users. The size and complex hydrogeology of the NTS are inconsistent with the EPA guideline assumption of a high degree of hydrologic interconnection throughout the review area. To more realistically depict the site hydrogeology, the CRA is subdivided into eight groundwater units. Two main aquifer systems are recognized: the lower carbonate aquifer system and the Cenozoic aquifer system (consisting of aquifers in Quaternary valley fill and Tertiary volcanics). These aquifer systems are further divided geographically based on the location of low permeability boundaries.

  9. Automatic classification of documents with an in-depth analysis of information extraction and automatic summarization

    E-Print Network [OSTI]

    Hohm, Joseph Brandon, 1982-

    2004-01-01T23:59:59.000Z

    Today, annual information fabrication per capita exceeds two hundred and fifty megabytes. As the amount of data increases, classification and retrieval methods become more necessary to find relevant information. This thesis ...

  10. Solar wind and geomagnetism: toward a standard classification of geomagnetic activity from 1868 to 2009

    E-Print Network [OSTI]

    Zerbo, J. L.

    We examined solar activity with a large series of geomagnetic data from 1868 to 2009. We have revisited the geomagnetic activity classification scheme of Legrand and Simon (1989) and improve their scheme by lowering the ...

  11. Approches Statistique et Linguistique Pour la Classification de Textes d'Opinion Portant sur les Films

    E-Print Network [OSTI]

    collection textuelle les textes porteurs d'opinion, ou encore à localiser les passages porteurs d'opi- nion baissé...) ; ­ la classification d'opinion, qui a pour but d'attribuer une étiquette au texte selon l'opi

  12. Classification with Artificial Neural Networks and Support Vector Machines: application to oil fluorescence spectra

    E-Print Network [OSTI]

    Oldenburg, Carl von Ossietzky Universität

    methods to examine crude oils, heavy refined oils, and sludge oils: the channels relationships method (CRMClassification with Artificial Neural Networks and Support Vector Machines: application to oil, and Oil fluorescence ABSTRACT: This paper reports on oil classification with fluorescence spectroscopy

  13. Comparing and Combining Semantic Verb Classifications Oliver Culo, Katrin Erk, Sebastian Pado+,

    E-Print Network [OSTI]

    Padó, Sebastian

    Comparing and Combining Semantic Verb Classifications Oliver Culo, Katrin Erk, Sebastian Pad version of FrameNet (Erk et al., 2003); however, while Schulte im Walde classifies the manner of motion

  14. Automated unsupervised classification of the Sloan Digital Sky Survey stellar spectra using k-means clustering

    E-Print Network [OSTI]

    Almeida, J Sanchez

    2012-01-01T23:59:59.000Z

    (Abridged) This paper explores the use of k-means clustering as a tool for automated unsupervised classification of massive stellar spectral catalogs. The classification criteria are defined by the data and the algorithm, with no prior physical framework. We work with a representative set of stellar spectra associated with the SDSS SEGUE and SEGUE-2 programs. We classify the original spectra as well as the spectra with the continuum removed. The second set only contains spectral lines, and it is less dependent on uncertainties of the flux calibration. The classification of the spectra with continuum renders 16 major classes. Roughly speaking, stars are split according to their colors, with enough finesse to distinguish dwarfs from giants of the same effective temperature, but with difficulties to separate stars with different metallicities. Overall, there is no one-to-one correspondence between the classes we derive and the MK types. The classification of spectra without continuum renders 13 classes, the colo...

  15. Some Extensions of the K-Means Algorithm for Image Segmentation and Pattern Classification

    E-Print Network [OSTI]

    Marroquin, Jose L.

    1993-01-01T23:59:59.000Z

    In this paper we present some extensions to the k-means algorithm for vector quantization that permit its efficient use in image segmentation and pattern classification tasks. It is shown that by introducing state ...

  16. CENTROID-BASED TEXTURE CLASSIFICATION USING THE SIRV REPRESENTATION Aurelien Schutz, Lionel Bombrun and Yannick Berthoumieu

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    ) and unsupervised k-means methods assume that (i) textured images are sorted in k sub- collections of samples, i, to quality check of manufactured pieces by comparison of internal structures. Among classification methods

  17. CERIAS Tech Report 2007-04 SECURITY IN WIRELESS SENSOR NETWORKS -A LAYER BASED CLASSIFICATION

    E-Print Network [OSTI]

    Liblit, Ben

    and communicational capabilities. Secondly, there is an additional risk of physical attacks such as node captureCERIAS Tech Report 2007-04 SECURITY IN WIRELESS SENSOR NETWORKS - A LAYER BASED CLASSIFICATION

  18. A Field Demonstration of an Instrument Performing Automatic Classification of Geologic

    E-Print Network [OSTI]

    -sensitive classifications of geologic surfaces in mesoscale scenes. A series of tests at the Cima Volcanic Fields in the Mojave Desert, California demonstrate mesoscale surficial mapping at two distinct sites of geologic

  19. FUNDRAISING AND GIFT ACCEPTANCE University Policy No: ER4105 Classification: External Relations

    E-Print Network [OSTI]

    Victoria, University of

    1 FUNDRAISING AND GIFT ACCEPTANCE University Policy No: ER4105 Classification: External Relations.01 For Gifts-in-kind to the Library or to the University of Victoria Art Collection, authority may be delegated

  20. Data driven process monitoring based on neural networks and classification trees

    E-Print Network [OSTI]

    Zhou, Yifeng

    2005-11-01T23:59:59.000Z

    of this dissertation is to develop process monitoring schemes that can be applied to complex process systems. Neural networks have been a popular tool for modeling and pattern classification for monitoring of process systems. However, due to the prohibitive...

  1. Classification of subsurface objects using singular values derived from signal frames

    DOE Patents [OSTI]

    Chambers, David H; Paglieroni, David W

    2014-05-06T23:59:59.000Z

    The classification system represents a detected object with a feature vector derived from the return signals acquired by an array of N transceivers operating in multistatic mode. The classification system generates the feature vector by transforming the real-valued return signals into complex-valued spectra, using, for example, a Fast Fourier Transform. The classification system then generates a feature vector of singular values for each user-designated spectral sub-band by applying a singular value decomposition (SVD) to the N.times.N square complex-valued matrix formed from sub-band samples associated with all possible transmitter-receiver pairs. The resulting feature vector of singular values may be transformed into a feature vector of singular value likelihoods and then subjected to a multi-category linear or neural network classifier for object classification.

  2. Fuzzy Partitioning Using Real Coded Variable Length Genetic Algorithm for Pixel Classification

    E-Print Network [OSTI]

    Bandyopadhyay, Sanghamitra

    , fuzzy clustering, pattern recognition, remote sensing imagery, Department of Computer Science, KalyaniFuzzy Partitioning Using Real Coded Variable Length Genetic Algorithm for Pixel Classification space. Real-coded variable string length genetic fuzzy clustering with automatic evolution of clusters

  3. A global typology of cities : classification tree analysis of urban resource consumption

    E-Print Network [OSTI]

    Saldivar-Sali, Artessa Niccola D., 1980-

    2010-01-01T23:59:59.000Z

    A study was carried out to develop a typology of urban metabolic (or resource consumption) profiles for 155 globally representative cities. Classification tree analysis was used to develop a model for determining how certain ...

  4. Digital classification of composite format color-infrared aerial video imagery

    E-Print Network [OSTI]

    Palmer, Rebecca Anne

    1989-01-01T23:59:59.000Z

    million acres). However, the overall agreement on a township basis (down to 1000 acres) was only 634 (Bryant et al. , 1980). Landsat imagery can also be used in multitemporal analyses, usually resulting in a more accurate classification than...

  5. The Effect of OCR Errors on Stylistic Text Classification Sterling Stuart Stein

    E-Print Network [OSTI]

    The Effect of OCR Errors on Stylistic Text Classification Sterling Stuart Stein Linguistic retrieval; Taghva and Coombs [1] found that a search engine could be made to work well over OCR documents

  6. Real-time processing of remote sensor data as applied to Arctic ice classification

    E-Print Network [OSTI]

    Permenter, James Austin

    1973-01-01T23:59:59.000Z

    REAL-TIME PROCESSING OF REMOTE SENSOR DATA AS APPLIED TO ARCTIC ICE CLASSIFICATION A Thesis by JAMES AUSTIN PERMENTER partial ! Submitted to the Graduate College of Texas A)M University in fulfillment of the requirement for the degree... of MASTER OF SCIENCE December 1973 Major Subject: Electrical Engineering REAL-TIME PROCESSING OF REMOTE SENSOR DATA AS APPLIED TO ARCTIC ICE CLASSIFICATION A Thesis by James Austin Permenter Approved as to style and content by: ] ( rman of Commi...

  7. A thermodynamic classification of pairs of real numbers via the Triangle Multi-dimensional continued fraction

    E-Print Network [OSTI]

    Thomas Garrity

    2012-05-25T23:59:59.000Z

    A new classification scheme for pairs of real numbers is given, generalizing earlier work of the author that used continued fraction, which in turn was motivated by ideas from statistical mechanics in general and work of Knauf and Fiala and Kleban in particular. Critical for this classification are the number theoretic and geometric properties of the triangle map, a type of multi-dimensional continued fraction.

  8. Invariant Classification and Limits of Maximally Superintegrable Systems in 3D

    E-Print Network [OSTI]

    J. Capel; J. Kress; S. Post

    2015-01-28T23:59:59.000Z

    The invariant classification of superintegrable systems is reviewed and utilized to construct singular limits between the systems. It is shown, by construction, that all superintegrable systems on conformally flat, 3D complex Riemannian manifolds can be obtained from singular limits of a generic system on the sphere. By using the invariant classification, the limits are geometrically motivated in terms of transformations of roots of the classifying polynomials.

  9. Updating the US Hydrologic Classification: An Approach to Clustering and Stratifying Ecohydrologic Data

    SciTech Connect (OSTI)

    McManamay, Ryan A [ORNL; Bevelhimer, Mark S [ORNL; Kao, Shih-Chieh [ORNL

    2013-01-01T23:59:59.000Z

    Hydrologic classifications unveil the structure of relationships among groups of streams with differing stream flow and provide a foundation for drawing inferences about the principles that govern those relationships. Hydrologic classes provide a template to describe ecological patterns, generalize hydrologic responses to disturbance, and stratify research and management needs applicable to ecohydrology. We developed two updated hydrologic classifications for the continental US using two streamflow datasets of varying reference standards. Using only reference-quality gages, we classified 1715 stream gages into 12 classes across the US. By including more streamflow gages (n=2618) in a separate classification, we increased the dimensionality (i.e. classes) and hydrologic distinctiveness within regions at the expense of decreasing the natural flow standards (i.e. reference quality). Greater numbers of classes and higher regional affiliation within our hydrologic classifications compared to that of the previous US hydrologic classification (Poff, 1996) suggested that the level of hydrologic variation and resolution was not completely represented in smaller sample sizes. Part of the utility of classification systems rests in their ability classify new objects and stratify analyses. We constructed separate random forests to predict hydrologic class membership based on hydrologic indices or landscape variables. In addition, we provide an approach to assessing potential outliers due to hydrologic alteration based on class assignment. Departures from class membership due to disturbance take into account multiple hydrologic indices simultaneously; thus, classes can be used to determine if disturbed streams are functioning within the realm of natural hydrology.

  10. Parametrization and Classification of 20 Billion LSST Objects: Lessons from SDSS

    SciTech Connect (OSTI)

    Ivezic, Z.; /Washington U., Seattle, Astron. Dept.; Axelrod, T.; /Large Binocular Telescope, Tucson; Becker, A.C.; /Washington U., Seattle, Astron. Dept.; Becla, J.; /SLAC; Borne, K.; /George Mason U.; Burke, David L.; /SLAC; Claver, C.F.; /NOAO, Tucson; Cook, K.H.; /LLNL, Livermore; Connolly, A.; /Washington U., Seattle, Astron. Dept.; Gilmore, D.K.; /SLAC; Jones, R.L.; /Washington U., Seattle, Astron. Dept.; Juric, M.; /Princeton, Inst. Advanced Study; Kahn, Steven M.; /SLAC; Lim, K-T.; /SLAC; Lupton, R.H.; /Princeton U.; Monet, D.G.; /Naval Observ., Flagstaff; Pinto, P.A.; /Arizona U.; Sesar, B.; /Washington U., Seattle, Astron. Dept.; Stubbs, Christopher W.; /Harvard U.; Tyson, J.Anthony; /UC, Davis

    2011-11-10T23:59:59.000Z

    The Large Synoptic Survey Telescope (LSST) will be a large, wide-field ground-based system designed to obtain, starting in 2015, multiple images of the sky that is visible from Cerro Pachon in Northern Chile. About 90% of the observing time will be devoted to a deep-wide-fast survey mode which will observe a 20,000 deg{sup 2} region about 1000 times during the anticipated 10 years of operations (distributed over six bands, ugrizy). Each 30-second long visit will deliver 5{sigma} depth for point sources of r {approx} 24.5 on average. The co-added map will be about 3 magnitudes deeper, and will include 10 billion galaxies and a similar number of stars. We discuss various measurements that will be automatically performed for these 20 billion sources, and how they can be used for classification and determination of source physical and other properties. We provide a few classification examples based on SDSS data, such as color classification of stars, color-spatial proximity search for wide-angle binary stars, orbital-color classification of asteroid families, and the recognition of main Galaxy components based on the distribution of stars in the position-metallicity-kinematics space. Guided by these examples, we anticipate that two grand classification challenges for LSST will be (1) rapid and robust classification of sources detected in difference images, and (2) simultaneous treatment of diverse astrometric and photometric time series measurements for an unprecedentedly large number of objects.

  11. Classification and storage of wastewater from floor finish removal operations

    SciTech Connect (OSTI)

    Hunt, C.E.

    1996-05-01T23:59:59.000Z

    This study evaluates the wastewater generated from hard surface floor finish removal operations at Lawrence Livermore Laboratory in order to determine if this wastewater is a hazardous waste, either by statistical evaluation, or other measurable regulatory guidelines established in California Regulations. This research also comparatively evaluates the 55 gallon drum and other portable tanks, all less than 1,000 gallons in size in order to determine which is most effective for the management of this waste stream at Lawrence Livermore Laboratory. The statistical methods in SW-846 were found to be scientifically questionable in their application to hazardous waste determination. In this statistical evaluation, the different data transformations discussed in the regulatory guidance document were applied along with the log transformation to the population of 18 samples from 55 gallon drums. Although this statistical evaluation proved awkward in its application, once the data is collected and organized on a spreadsheet this statistical analysis can be an effective tool which can aid the environmental manager in the hazardous waste classification process.

  12. Historical literature review on waste classification and categorization

    SciTech Connect (OSTI)

    Croff, A.G.; Richmond, A.A.; Williams, J.P.

    1995-03-01T23:59:59.000Z

    The Staff of the Waste Management Document Library (WMDL), in cooperation with Allen Croff have been requested to provide information support for a historical search concerning waste categorization/classification. This bibliography has been compiled under the sponsorship of Oak Ridge National Laboratory`s Chemical Technology Division to help in Allen`s ongoing committee work with the NRC/NRCP. After examining the search, Allen Croff saw the value of the search being published. Permission was sought from the database providers to allow limited publication (i.e. 20--50 copies) of the search for internal distribution at the Oak Ridge National Laboratory and for Allen Croff`s associated committee. Citations from the database providers who did not grant legal permission for their material to be published have been omitted from the literature review. Some of the longer citations have been included in an abbreviated form in the search to allow the format of the published document to be shortened from approximately 1,400 pages. The bibliography contains 372 citations.

  13. System diagnostics using qualitative analysis and component functional classification

    DOE Patents [OSTI]

    Reifman, Jaques (Lisle, IL); Wei, Thomas Y. C. (Downers Grove, IL)

    1993-01-01T23:59:59.000Z

    A method for detecting and identifying faulty component candidates during off-normal operations of nuclear power plants involves the qualitative analysis of macroscopic imbalances in the conservation equations of mass, energy and momentum in thermal-hydraulic control volumes associated with one or more plant components and the functional classification of components. The qualitative analysis of mass and energy is performed through the associated equations of state, while imbalances in momentum are obtained by tracking mass flow rates which are incorporated into a first knowledge base. The plant components are functionally classified, according to their type, as sources or sinks of mass, energy and momentum, depending upon which of the three balance equations is most strongly affected by a faulty component which is incorporated into a second knowledge base. Information describing the connections among the components of the system forms a third knowledge base. The method is particularly adapted for use in a diagnostic expert system to detect and identify faulty component candidates in the presence of component failures and is not limited to use in a nuclear power plant, but may be used with virtually any type of thermal-hydraulic operating system.

  14. System diagnostics using qualitative analysis and component functional classification

    DOE Patents [OSTI]

    Reifman, J.; Wei, T.Y.C.

    1993-11-23T23:59:59.000Z

    A method for detecting and identifying faulty component candidates during off-normal operations of nuclear power plants involves the qualitative analysis of macroscopic imbalances in the conservation equations of mass, energy and momentum in thermal-hydraulic control volumes associated with one or more plant components and the functional classification of components. The qualitative analysis of mass and energy is performed through the associated equations of state, while imbalances in momentum are obtained by tracking mass flow rates which are incorporated into a first knowledge base. The plant components are functionally classified, according to their type, as sources or sinks of mass, energy and momentum, depending upon which of the three balance equations is most strongly affected by a faulty component which is incorporated into a second knowledge base. Information describing the connections among the components of the system forms a third knowledge base. The method is particularly adapted for use in a diagnostic expert system to detect and identify faulty component candidates in the presence of component failures and is not limited to use in a nuclear power plant, but may be used with virtually any type of thermal-hydraulic operating system. 5 figures.

  15. Toward the classification of the realistic free fermionic models

    SciTech Connect (OSTI)

    Faraggi, A.E.

    1997-08-01T23:59:59.000Z

    The realistic free fermionic models have had remarkable success in providing plausible explanations for various properties of the Standard Model which include the natural appearance of three generations, the explanation of the heavy top quark mass and the qualitative structure of the fermion mass spectrum in general, the stability of the proton and more. These intriguing achievements makes evident the need to understand the general space of these models. While the number of possibilities is large, general patterns can be extracted. In this paper the author presents a detailed discussion on the construction of the realistic free fermionic models with the aim of providing some insight into the basic structures and building blocks that enter the construction. The role of free phases in the determination of the phenomenology of the models is discussed in detail. The author discusses the connection between the free phases and mirror symmetry in (2,2) models and the corresponding symmetries in the case of (2,0) models. The importance of the free phases in determining the effective low energy phenomenology is illustrated in several examples. The classification of the models in terms of boundary condition selection rules, real world-sheet fermion pairings, exotic matter states and the hidden sector is discussed.

  16. Multiverse Scenarios in Cosmology: Classification, Cause, Challenge, Controversy, and Criticism

    E-Print Network [OSTI]

    Ruediger Vaas

    2010-01-05T23:59:59.000Z

    Multiverse scenarios in cosmology assume that other universes exist "beyond" our own universe. They are an exciting challenge both for empirical and theoretical research as well as for philosophy of science. They could be necessary to understand why the big bang occurred, why (some of) the laws of nature and the values of certain physical constants are the way they are, and why there is an arrow of time. This essay clarifies competing notions of "universe" and "multiverse"; it proposes a classification of different multiverse types according to various aspects how the universes are or are not separated from each other; it reviews the main reasons for assuming the existence of other universes: empirical evidence, theoretical explanation, and philosophical arguments; and, finally, it argues that some attempts to criticize multiverse scenarios as "unscientific", insisting on a narrow understanding of falsification, is neither appropriate nor convincing from a philosophy of science point of view. -- Keywords: big bang, universe, multiverse, cosmic inflation, time, quantum gravity, string theory, laws of nature, physical constants, fine-tuning, anthropic principle, philosophy of science, metaphysics, falsificationism

  17. Separation and Classification of Lipids Using Differential Ion Mobility Spectrometry

    SciTech Connect (OSTI)

    Shvartsburg, Alexandre A.; Isaac, Georgis; Leveque, Nathalie; Smith, Richard D.; Metz, Thomas O.

    2011-04-12T23:59:59.000Z

    Correlations between the dimensions of a 2-D separation create trend lines that normally depend on structural or functional characteristics of the compound class and thus facilitate classification of unknowns. This broadly applies to conventional ion mobility spectrometry (IMS)/mass spectrometry (MS), where the major biomolecular classes (e.g., lipids, peptides, nucleotides) occupy different trend line domains. However, strong correlation between the IMS and MS separations for ions of same charge has impeded finer distinctions. Differential IMS (or FAIMS) is generally much less correlated to MS and thus should better separate the trend lines and associated domains. We report the first observation of chemical class separation by trend lines using FAIMS, here for lipids. For all lipids, FAIMS is indeed more independent of MS than conventional IMS, and subclasses (such as phospho-, glycero-, or sphingolipids) form distinct, often non-overlapping domains. Even finer categories with different functional groups or degrees of unsaturation are often separated. As expected, resolution improves in He-rich gases: at ~70% He, glycerolipid isomers with different positions of fatty acid attachment can be resolved. These results open the door for lipidomics application of FAIMS, particularly shotgun lipidomics and targeted analyses of bioactive lipids.

  18. Developmental defects in zebrafish for classification of EGF pathway inhibitors

    SciTech Connect (OSTI)

    Pruvot, Benoist; Curé, Yoann; Djiotsa, Joachim; Voncken, Audrey; Muller, Marc, E-mail: m.muller@ulg.ac.be

    2014-01-15T23:59:59.000Z

    One of the major challenges when testing drug candidates targeted at a specific pathway in whole animals is the discrimination between specific effects and unwanted, off-target effects. Here we used the zebrafish to define several developmental defects caused by impairment of Egf signaling, a major pathway of interest in tumor biology. We inactivated Egf signaling by genetically blocking Egf expression or using specific inhibitors of the Egf receptor function. We show that the combined occurrence of defects in cartilage formation, disturbance of blood flow in the trunk and a decrease of myelin basic protein expression represent good indicators for impairment of Egf signaling. Finally, we present a classification of known tyrosine kinase inhibitors according to their specificity for the Egf pathway. In conclusion, we show that developmental indicators can help to discriminate between specific effects on the target pathway from off-target effects in molecularly targeted drug screening experiments in whole animal systems. - Highlights: • We analyze the functions of Egf signaling on zebrafish development. • Genetic blocking of Egf expression causes cartilage, myelin and circulatory defects. • Chemical inhibition of Egf receptor function causes similar defects. • Developmental defects can reveal the specificity of Egf pathway inhibitors.

  19. Texture and Color Distribution-basedTexture and Color Distribution-based Classification for Live Coral DetectionClassification for Live Coral Detection

    E-Print Network [OSTI]

    Stough, Joshua

    Coral DetectionClassification for Live Coral Detection Joshua V. Stough, Lisa Greer, Matt BensonJoshua V, ... · Coral benthic studiesCoral benthic studies ­ NCC/LBP with NN ­NCC/LBP with NN ­ [Marcos et al., Optics

  20. Quiz # 7, STAT 383, Prof. Suman Sanyal, April 8, 2009 (Q2, Page 354) To decide whether the pipe welds in a nuclear power plant meet

    E-Print Network [OSTI]

    Sanyal, Suman

    welds in a nuclear power plant meet specifications, a random sample of welds is to be selected : µ nuclear power plants is to determine if welds

  1. Automated Classification of ELODIE Stellar Spectral Library Using Probabilistic Artificial Neural Networks

    E-Print Network [OSTI]

    Bazarghan, Mahdi

    2008-01-01T23:59:59.000Z

    A Probabilistic Neural Network model has been used for automated classification of ELODIE stellar spectral library consisting of about 2000 spectra into 158 known spectro-luminosity classes. The full spectra with 561 flux bins and a PCA reduced set of 57, 26 and 16 components have been used for the training and test sessions. The results shows a spectral type classification accuracy of 3.2 sub-spectral type and luminosity class accuracy of 2.7 for the full spectra and an accuracy of 3.1 and 2.6 respectively with the PCA set. This technique will be useful for future upcoming large data bases and their rapid classification.

  2. Automated Classification of ELODIE Stellar Spectral Library Using Probabilistic Artificial Neural Networks

    E-Print Network [OSTI]

    Mahdi Bazarghan

    2008-04-17T23:59:59.000Z

    A Probabilistic Neural Network model has been used for automated classification of ELODIE stellar spectral library consisting of about 2000 spectra into 158 known spectro-luminosity classes. The full spectra with 561 flux bins and a PCA reduced set of 57, 26 and 16 components have been used for the training and test sessions. The results shows a spectral type classification accuracy of 3.2 sub-spectral type and luminosity class accuracy of 2.7 for the full spectra and an accuracy of 3.1 and 2.6 respectively with the PCA set. This technique will be useful for future upcoming large data bases and their rapid classification.

  3. Finding minimum gene subsets with heuristic breadth-first search algorithm for robust tumor classification

    E-Print Network [OSTI]

    Wang, Shu-Lin; Li, Xue-Ling; Fang, Jianwen

    2012-07-25T23:59:59.000Z

    (RBF) kernel or KNN classifier. Computing matrix CM is equivalent to doing the classification accuracy of all nodes in a layer shown in Figure 1. 14: Convert CM to the vector V: = (v1, v2, #1;, vw×p), and set V[(i? 1) × p+ j].subset: = Row[i] [ Column....09, respectively. And the others are set similarly. The HBSA with SVM is called HBSA-SVM. The k-fold Cross-Validation (k-fold CV) is commonly used to evaluate classification model. Here it is applied only on training set to measure Acc(T). If k is set to Trn (the...

  4. Automated Classification of Sloan Digital Sky Survey (SDSS) Stellar Spectra using Artificial Neural Networks

    E-Print Network [OSTI]

    Mahdi Bazarghan; Ranjan Gupta

    2008-04-26T23:59:59.000Z

    Automated techniques have been developed to automate the process of classification of objects or their analysis. The large datasets provided by upcoming spectroscopic surveys with dedicated telescopes urges scientists to use these automated techniques for analysis of such large datasets which are now available to the community. Sloan Digital Sky Survey (SDSS) is one of such surveys releasing massive datasets. We use Probabilistic Neural Network (PNN) for automatic classification of about 5000 SDSS spectra into 158 spectral type of a reference library ranging from O type to M type stars.

  5. NON-MELANOMA SKIN LESION CLASSIFICATION USING COLOUR IMAGE DATA IN A HIERARCHICAL K-NN CLASSIFIER

    E-Print Network [OSTI]

    Fisher, Bob

    NON-MELANOMA SKIN LESION CLASSIFICATION USING COLOUR IMAGE DATA IN A HIERARCHICAL K-NN CLASSIFIER an algorithm for classification of non- melanoma skin lesions based on a novel hierarchical K- Nearest lesions, including two non-melanoma cancer types. This is the most extensive published result on non-melanoma

  6. Taxonomic Classification of Planning Decisions in Health Care: a Review of the State of the Art in OR/MS

    E-Print Network [OSTI]

    Boucherie, Richard J.

    Taxonomic Classification of Planning Decisions in Health Care: a Review of the State of the Art Classification of Planning Decisions in Health Care: a Review of the State of the Art in OR/MS 2 1. Introduction a comprehensive bibliography on operating room management articles. `ORCHID' [181] is a reference library, which

  7. Application of artificial neural networks for damage indices classification with the use of Lamb waves for the aerospace structures.

    E-Print Network [OSTI]

    Application of artificial neural networks for damage indices classification with the use of Lamb of view. Artificial neural network has been used for the classification of fatigue cracks and artificial@agh.edu.pl, *corresponding author Keywords: NDT, Ultrasonic testing, Lamb waves, Artificial intelligence, Artificial Neural

  8. ARKTOS: A Knowledge Engineering Software Package for Satellite Sea Ice Classification Leen-Kiat Soh and Costas Tsatsoulis

    E-Print Network [OSTI]

    Kansas, University of

    ice experts as visual cues for sea ice features and classification rules and ultimately building system, and also address research issues in explicit encoding of domain expertise and capture of visual ice) research performs automated, intelligent SAR sea ice classification. Once it is deployed

  9. An Extraction Method for the Characterization of the Fuzzy Rule Based Classification Systems' Behavior using Data Complexity

    E-Print Network [OSTI]

    Granada, Universidad de

    be considered a new trend in the use of FRBCSs in pattern recognition. No data complexity metrics have beenAn Extraction Method for the Characterization of the Fuzzy Rule Based Classification Systems Abstract-- When dealing with problems using Fuzzy Rule Based Classification Systems it is difficult to know

  10. EVALUATION OF GEOMETRIC FEATURE DESCRIPTORS FOR DETECTION AND CLASSIFICATION OF LUNG NODULES IN LOW DOSE CT SCANS OF THE CHEST

    E-Print Network [OSTI]

    Farag, Aly A.

    EVALUATION OF GEOMETRIC FEATURE DESCRIPTORS FOR DETECTION AND CLASSIFICATION OF LUNG NODULES IN LOW descriptors, common in computer vision, for false positive reduction and for classification of lung nodules in low dose CT (LDCT) scans. A data-driven lung nodule modeling approach creates templates for common

  11. Journal of Machine Learning Research 11 (2010) 491-516 Submitted 3/09; Revised 8/09; Published 2/10 Classification Using Geometric Level Sets

    E-Print Network [OSTI]

    Willsky, Alan S.

    classification, an efficient scheme is developed using a logarithmic number of decision functions in the number/10 Classification Using Geometric Level Sets Kush R. Varshney KRV@MIT.EDU Alan S. Willsky WILLSKY@MIT.EDU Laboratory: Ulrike von Luxburg Abstract A variational level set method is developed for the supervised classification

  12. Dewey Decimal Classification System We are a worldwide library cooperative, owned, governed and sustained by members since 1967. Our public purpose

    E-Print Network [OSTI]

    Rodriguez, Carlos

    Dewey Decimal Classification System We are a worldwide library cooperative, owned, governed to reduce costs for libraries through collaboration. The world's most widely used classification system can used by libraries in 138 countries. The Dewey Decimal Classification® system continues to evolve

  13. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 48, NO. 9, SEPTEMBER 2010 3521 An End-to-End Error Model for Classification

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    and for the development of robust classification methods using existing SAR instruments. Index Terms--Calibration, image-to-End Error Model for Classification Methods Based on Temporal Change or Polarization Ratio of SAR Intensities a synthetic aperture radar (SAR) intensity ratio as a classification feature. The two SAR intensities involved

  14. T.T.T.Nguyen, G.Armitage, A Survey of Techniques for Internet Traffic Classification using Machine Learning A Survey of Techniques for Internet Traffic

    E-Print Network [OSTI]

    Armitage, Grenville

    of their various business goals. Traffic classification may be a core part of automated intrusion detection systemsT.T.T.Nguyen, G.Armitage, A Survey of Techniques for Internet Traffic Classification using Machine Learning A Survey of Techniques for Internet Traffic Classification using Machine Learning Thuy T.T. Nguyen

  15. Lightweight Detection and Classification for Wireless Sensor Networks in Realistic Environments

    E-Print Network [OSTI]

    Batson, Alan

    too complex for energy-and-cost-effective WSN nodes. This study explores how to design efficient sensing and classification al- gorithms that achieve reliable sensing performance on energy-and- cost network system. Moreover, a sen- sor node must be energy efficient. As a res

  16. J. Phys. III France 2 (1992) 1925-1941 OCTOBER 1992, PAGE 1925 Classification

    E-Print Network [OSTI]

    Boyer, Edmond

    J. Phys. III France 2 (1992) 1925-1941 OCTOBER 1992, PAGE 1925 Classification Physics Abstracts 05) a thermally radiative or non-radiative ambient sink and (iii) two energy converters. The first converter (RH) transforms the energy of the black-body radiation into heat, while the second one (HW) (which has a non

  17. Protein Classification Artificial Neural System: A filter program for database search

    SciTech Connect (OSTI)

    Wu, C.H.; Wang, C.C.; Yazdanpanahi, I. [Univ. of Texas Health Center, Tyler, TX (United States)

    1993-12-31T23:59:59.000Z

    A neural network classification method has been developed as an alternative approach to the large database search/organization problem. The system, termed Protein Classification Artificial Neural System (ProCANS), is implemented on a Cray Y-MP8/864 supercomputer for rapid superfamily classification of unknown proteins based on the information content of the neural interconnections. The system employs an n-gram hashing function for sequence encoding and modular back-propagation networks for classification. The system was developed with the first 2,724 entries in 690 superfamilies of the annotated PIR (Protein Identification Resource) protein sequence database. Three prediction sets were used to evaluate the system performance. The first consists of 651 annotated entries randomly chosen from the 690 superfamilies. The second set consists of 482 unclassified entries from the preliminary PIR database, whose superfamilies were identified by the fasta, blastp and sp database search methods. The third set is a subset of data set 2 with only superfamilies of more than 20 entries. At a low cut-off score of 0.01, the sensitivity is 92, 82 and 100%, respectively, for the three prediction sets. At a high cut-off score of 0.9, on the other hand, a close to 100% specificity is achieved with a reduced sensitivity.

  18. MAS 07 Multi-Class SVM for Forestry Classification MAS 07.1 Overview

    E-Print Network [OSTI]

    California at Los Angeles, University of

    MAS 07 Multi-Class SVM for Forestry Classification MAS 07.1 Overview In this project, we propose (IFN). We will show one example on a national forest near Sedan (in France), and compare our result binary SVMs. In this project we use the One-Against-All (OAA) scheme, which consists of building one SVM

  19. Classification of instability modes in a model of aluminium reduction cells with a uniform magnetic field

    E-Print Network [OSTI]

    (sodium aluminium fluoride) by an electric current of 350-500kA, which passes vertically down fromClassification of instability modes in a model of aluminium reduction cells with a uniform magnetic reduction cells in the presence of a uniform, vertical, background magnetic field is presented

  20. Discriminative Illumination: Per-Pixel Classification of Raw Materials based on Optimal Projections of Spectral BRDF

    E-Print Network [OSTI]

    Gu, Jinwei

    Discriminative Illumination: Per-Pixel Classification of Raw Materials based on Optimal Projections training samples, after projecting to which, the spectral reflectance of different materials are maximally--is learned from training samples, after projecting to which, the spectral BRDFs of different materials can

  1. University Policy No: BP3315 POLICY ON UNIVERSITY OF Classification: External Relations

    E-Print Network [OSTI]

    Victoria, University of

    1 University Policy No: BP3315 POLICY ON UNIVERSITY OF Classification: External Relations VICTORIA Editorial Change: Mandated Review: May 27, 2020 PURPOSE 1.00 The purpose of this policy is to establish") as the University's art Museum. DEFINITIONS For the purposes of this policy: 2.00 University refers specifically

  2. M-FISH IMAGE REGISTRATION AND CLASSIFICATION Yu-Ping Wang

    E-Print Network [OSTI]

    Poirazi, Yiota

    M-FISH IMAGE REGISTRATION AND CLASSIFICATION Yu-Ping Wang School of Computing and Engineering hybridization (M-FISH) imaging is a recently developed cytogenetic technique for cancer diagnosis and research on genetic disorders. By simultaneously viewing the multiple-labeled specimens in different color channels, M-FISH

  3. Efficient Online Classification using an Ensemble of Bayesian Linear Logistic Regressors

    E-Print Network [OSTI]

    Vijayakumar, Sethu

    a linear logistic regression as the base classifier with Bayesian learning for the regression The Randomly Varying Coefficient model approximates a multivariate non-linear function using a set of localEfficient Online Classification using an Ensemble of Bayesian Linear Logistic Regressors Narayanan

  4. Using Classification to Evaluate the Output of Confidence-Based Association Rule Mining

    E-Print Network [OSTI]

    Frank, Eibe

    Using Classification to Evaluate the Output of Confidence-Based Association Rule Mining Stefan, New Zealand {mhall, eibe}@cs.waikato.ac.nz Abstract. Association rule mining is a data mining concerning both running time and size of rule sets. 1 Introduction Association rule mining is a widely

  5. Using Classification to Evaluate the Output of ConfidenceBased Association Rule Mining

    E-Print Network [OSTI]

    Frank, Eibe

    Using Classification to Evaluate the Output of Confidence­Based Association Rule Mining Stefan Hamilton, New Zealand {mhall, eibe}@cs.waikato.ac.nz Abstract. Association rule mining is a data mining concerning both running time and size of rule sets. 1 Introduction Association rule mining is a widely

  6. PROGRESSIVE CLASSIFICATION IN THE COMPRESSED DOMAIN FOR LARGE EOS SATELLITE DATABASES1

    E-Print Network [OSTI]

    Kontoyiannis, Ioannis

    PROGRESSIVE CLASSIFICATION IN THE COMPRESSED DOMAIN FOR LARGE EOS SATELLITE DATABASES1 Vittorio and Signal Processing, Atlanta, GA, May 1996. ABSTRACT We introduce a new framework for classifying large-by-pixel approach. This approach, called progressive classi cation, is well suited for analyzing large images

  7. cAnt-Miner: An Ant Colony Classification Algorithm to Cope with Continuous Attributes

    E-Print Network [OSTI]

    Heinke, Dietmar

    cAnt-Miner: An Ant Colony Classification Algorithm to Cope with Continuous Attributes Fernando E. B {febo2,A.A.Freitas,C.G.Johnson}@kent.ac.uk Abstract. This paper presents an extension to Ant-Miner, named cAnt- Miner (Ant-Miner coping with continuous attributes), which incorpo- rates an entropy

  8. Classification of Annual Great Lakes Ice Cycles: Winters of 19732002* RAYMOND A. ASSEL

    E-Print Network [OSTI]

    Classification of Annual Great Lakes Ice Cycles: Winters of 1973­2002* RAYMOND A. ASSEL National (Manuscript received 12 July 2004, in final form 13 June 2005) ABSTRACT Annual seasonal average ice cover from 1973 to 2002 and associated dates of first ice, last ice, and ice duration are presented and discussed

  9. Varits abliennes sur les corps finis: thorme de Tate et classification de Honda-Tate

    E-Print Network [OSTI]

    Wittenberg, Olivier

    Variétés abéliennes sur les corps finis: théorème de Tate et classification de Honda-Tate Olivier Wittenberg 5 décembre 2001 Résumé Le but de cet exposé est de montrer (suivant Tate et Honda) que les classes polynôme minimal sur Q. Le théorème que l'on se propose de démontrer est le suivant. Théorème 1.0.2 (Honda

  10. RECOD at ImageCLEF 2011: Medical Modality Classification using Genetic Programming

    E-Print Network [OSTI]

    Fernandez, Thomas

    of storage and processing power makes possible the use of intelligent computer systems for image manipulation of the power of different but potentially complementary descriptors. In this work we propose the use of Genetic classification using genetic programming. In Section 4 we present ex- perimental setup and results for the Image

  11. AN ALTERNATIVE APPROACH OF FINDING COMPETING HYPOTHESES FOR BETTER MINIMUM CLASSIFICATION ERROR TRAINING

    E-Print Network [OSTI]

    Mak, Brian Kan-Wing

    is a powerful dis- criminative technique to optimize any system parameters so that the ultimate classification to be more stable. We also design an approximation algorithm based on beam search to locate the nearest; for instance, optimizing the hidden Markov model (HMM) parame- ters [1, 2], discriminative feature extraction

  12. Agricultural and Related Pest Control Applicator License Classifications under the Florida Department of

    E-Print Network [OSTI]

    Watson, Craig A.

    PI-59 Agricultural and Related Pest Control Applicator License Classifications under the Florida Department of Agriculture and Consumer Services (FDACS)1 Frederick M. Fishel2 1. This document is PI-59, one Gainesville, FL 32611. The Institute of Food and Agricultural Sciences (IFAS) is an Equal Opportunity

  13. A Hierarchical Classification Scheme to Derive Interprocess Communication in Process Networks

    E-Print Network [OSTI]

    Kienhuis, Bart

    % of the cases, we still relay on integer linear programming while in the remaining 95%, the tests presented, an ILP test has still to be applied. Thus, we introduce a hierarchical classification scheme. In only 5% of the cases to classify, we still relay on integer linear programming while in the remaining

  14. Material Classification By Drilling Diana LaBelle, John Bares, Illah Nourbakhsh

    E-Print Network [OSTI]

    Nourbakhsh, Illah

    Material Classification By Drilling Diana LaBelle, John Bares, Illah Nourbakhsh Robotics Institute based on the physical parameters of a roof bolting drill. This paper presents our methodology, as well as early results based on drilling experiments conducted in the laboratory using a custom poured concrete

  15. Automated classification of A/E/C web content R. Amor & K. Xu

    E-Print Network [OSTI]

    Amor, Robert

    purely on the search terms entered by the user. This means that the web pages which are found are often search en- gine. The premise behind this approach is that it is possible to accurately identify then a user searching for content in a particular area (e.g. by specifying a classification code

  16. Integrating an automatic classification method into the medical image retrieval process

    E-Print Network [OSTI]

    Ruiz, Miguel E.

    the performance of the University at Buffalo Medical Text and Images Retrieval System (UBMedTIRS). This paper classification process was performed using the Image Retrieval for Medical Application (IRMA) codes3 employed to acquire the image such as x-ray, ultrasound, magnetic resonance measurement, nuclear medicine

  17. Classification of hyperspectral images by tensor modeling and additive morphological decomposition

    E-Print Network [OSTI]

    Boyer, Edmond

    . AMD defines a scale-space decomposition for multivariate images without any loss of information. AMD`emes, MINES Paristech, France Abstract Pixel-wise classification in high-dimensional multivariate images is modeled as a tensor structure and tensor principal components analysis is compared as dimensional

  18. Canonical Correlation Analysis for Multilabel Classification: A Least-Squares Formulation,

    E-Print Network [OSTI]

    Ye, Jieping

    --Canonical Correlation Analysis (CCA) is a well-known technique for finding the correlations between two sets1 Canonical Correlation Analysis for Multilabel Classification: A Least-Squares Formulation, Extensions, and Analysis Liang Sun, Shuiwang Ji, Student Member, IEEE, and Jieping Ye, Member, IEEE Abstract

  19. K-Centroids-Based Supervised Classification of Texture Images using the SIRV modeling

    E-Print Network [OSTI]

    Boyer, Edmond

    analysis have shown the interest to use jointly scale-space decomposi- tion and stochastic modeling its impact on classification performances, the geometry of the cluster in the feature space should on scale-space decomposition, for the modeling of spatial dependencies characterizing the texture image

  20. Classification of transportation packaging and dry spent fuel storage system components according to importance to safety

    SciTech Connect (OSTI)

    McConnell, J.W., Jr; Ayers, A.L. Jr; Tyacke, M.J. [Lockheed Idaho Technologies Co., Idaho Falls, ID (United States)

    1996-02-01T23:59:59.000Z

    This report provides a graded approach for classification of components used in transportation packaging and dry spent fuel storage systems. This approach provides a method for identifying, the classification of components according to importance to safety within transportation packagings and dry spent fuel storage systems. Record retention requirements are discussed to identify the documentation necessary to validate that the individual components were fabricated in accordance with their assigned classification. A review of the existing regulations pertaining to transportation packagings and dry storage systems was performed to identify current requirements The general types of transportation packagings and dry storage systems were identified. Discussions were held with suppliers and fabricators of packagings and storage systems to determine current practices. The methodology used in this report is based on Regulatory Guide 7.10, Establishing Quality Assurance Programs for Packaging Used in the Transport of Radioactive Material. This report also includes a list of generic components for each of the general types of transportation packagings and spent fuel storage systems. The safety importance of each component is discussed, and a classification category is assigned.

  1. Bag-of-Visual-Words Models for Adult Image Classification and Filtering Thomas Deselaers1

    E-Print Network [OSTI]

    Deselaers, Thomas

    types of images are allowed. In the literature, different porn image filtering tech- niques were features for porn image classification are pre- sented and used in a retrieval/nearest neighbour clas model to discriminate between different classes of content-type. 2 Porn Image Identification For porn

  2. PPE Certification of Hazard Assessment Dept: Area: Job Classification/Task

    E-Print Network [OSTI]

    Slatton, Clint

    PPE 7 Appendix A PPE Certification of Hazard Assessment Dept: Area: Job Classification/Task: HAZARDS (Circle Hazards) Describe Specific Hazards Identify Type of PPE Required for the Hazards Eye Hazard Impact Penetration Dust Chemical Radiation Heat Bioaerosols Projectiles Head Hazard Burn Electric

  3. CLASSIFICATION OF SYSTEMS' HEALTH CONDITION USING THE NEW ADAPTIVE FUZZY-BASED FEATURE

    E-Print Network [OSTI]

    Boyer, Edmond

    level (called hydraulic pressure data) are used and compared. The results show that based on the AE-based and also on the hydraulic pressure based AFFCA systems health state classification, the changes system and the related data to be discussed are related to the evaluation of sliding friction wear

  4. Chimera: Large-Scale Classification using Machine Learning, Rules, and Crowdsourcing

    E-Print Network [OSTI]

    Doan, AnHai

    Chimera: Large-Scale Classification using Machine Learning, Rules, and Crowdsourcing Chong Sun1 has been published on how this is done in practice. In this paper we describe Chimera, our solution solutions cease to work. We describe how Chimera employs a combination of learning, rules (created by in

  5. REGULARISED k-MEANS CLUSTERING FOR DIMENSION REDUCTION APPLIED TO SUPERVISED CLASSIFICATION

    E-Print Network [OSTI]

    McLachlan, Geoff

    REGULARISED k-MEANS CLUSTERING FOR DIMENSION REDUCTION APPLIED TO SUPERVISED CLASSIFICATION]. The most popular clustering methods are hierarchical and k-means. However, several key issues for the analysis of large datasets is limited. The procedure k-means is relatively scalable and efficient when

  6. From top-level to domain ontologies: Ecosystem classifications as a case study

    E-Print Network [OSTI]

    Bittner, Thomas

    From top-level to domain ontologies: Ecosystem classifications as a case study Thomas Bittner at Buffalo Abstract. This paper shows how to use a top-level ontology to create robust and logically coherent definitions formally using the top-level terms whose seman- tics was specified rigorously in a logic-based top

  7. Classification of Cabo Frio (Brazil) three-dimensional ocean features using single-slice acoustic observations

    E-Print Network [OSTI]

    Jesus, Sérgio M.

    Classification of Cabo Frio (Brazil) three-dimensional ocean features using single-slice acoustic-000 Arraial do Cabo, RJ, Brazil, {lcalado, ana.claudia}@ieapm.mar.mil.br Acoustic tomography is now a well for an instantaneous sound speed field constructed from dynamical predictions for Cabo Frio, Brazil. The results show

  8. AUTOMATIC DETECTION AND CLASSIFICATION OF DEFECT ON ROAD PAVEMENT USING ANISOTROPY MEASURE

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    AUTOMATIC DETECTION AND CLASSIFICATION OF DEFECT ON ROAD PAVEMENT USING ANISOTROPY MEASURE Tien Sy-sy.nguyen@etu.univ-orleans.fr ABSTRACT Existing systems for automated pavement defect detection can only identify cracking type defects for the inspectors and road users [1]. In the last few years, several automated pavement inspecting systems which use

  9. Toward a Text Classification System for the Quality Assessment of Software Requirements Written in

    E-Print Network [OSTI]

    Kosseim, Leila

    in Natural Language Olga Ormandjieva Department of Computer Science and Software Engineering ConcordiaToward a Text Classification System for the Quality Assessment of Software Requirements Written and Software Engineering Concordia University Montreal, Canada kosseim@cse.concordia.ca Ishrar Hussain

  10. The Green Sheet and Opposition to American Motion Picture Classification in the 1960s

    E-Print Network [OSTI]

    Saltz, Zachary

    2011-04-19T23:59:59.000Z

    The Green Sheet and Opposition to American Motion Picture Classification in the 1960s By Zachary Saltz University of Kansas, Copyright 2011 Submitted to the graduate degree program in Film and Media Studies and the Graduate Faculty... of the University of Kansas in partial fulfillment of the requirements for the degree of Master of Arts. ________________________________ Chairperson Dr. John Tibbetts ________________________________ Dr. Michael Baskett...

  11. Coarse-grained Classification of Web Sites by Their Structural Properties

    E-Print Network [OSTI]

    Lindemann, Christoph

    Coarse-grained Classification of Web Sites by Their Structural Properties Christoph Lindemann properties which reflect the functionality of a Web site. These structural properties consider the size, the organization, the composition of URLs, and the link structure of Web sites. Opposed to previous work, we

  12. Classification of "Quaternionic" Bloch-bundles: Topological Insulators of type AII

    E-Print Network [OSTI]

    Giuseppe De Nittis; Kiyonori Gomi

    2014-04-23T23:59:59.000Z

    We provide a classification of type AII topological insulators in dimension d=1,2,3,4. Our analysis is based on the construction of a topological invariant, the FKMM-invariant, which completely classifies "Quaternionic" vector bundles (a.k.a. "symplectic" vector bundles) in dimension dtopological invariant.

  13. CLASSIFICATION OF HUMAN ACTIONS INTO DYNAMICS BASED PRIMITIVES WITH APPLICATION TO DRAWING

    E-Print Network [OSTI]

    Murray, Richard M.

    video- games and animation where virtual human motion is based on the learning and description of realCLASSIFICATION OF HUMAN ACTIONS INTO DYNAMICS BASED PRIMITIVES WITH APPLICATION TO DRAWING TASKS D Institute of Technology Pasadena, CA 91125 Abstract: We develop the study of primitives of human motion

  14. MULTISCALE RANDOM PROJECTIONS FOR COMPRESSIVE CLASSIFICATION Marco F. Duarte,r

    E-Print Network [OSTI]

    of sam- pling and compression [1, 2]. CS enables the design of new kinds of compressive imaging systems ratio test; in the case of image classification, it exploits the fact that a set of images of a fixed- quires compressive image projections, we achieve high clas- sification rates using many fewer

  15. USING STREAM CLASSIFICATION TO PRIORITIZE RIPARIAN REHABILITATION AFTER EXTREME EVENTS1

    E-Print Network [OSTI]

    USING STREAM CLASSIFICATION TO PRIORITIZE RIPARIAN REHABILITATION AFTER EXTREME EVENTS1 2 Sherman watersheds has impaired the capacity of ripar- ian vegetation and floodplains to reduce stream energy it because it represents the greatest cumulative energy level. Larger flood events last for too short a time

  16. The evaluation of an analytical protocol for the determination of substances in waste for hazard classification

    E-Print Network [OSTI]

    Boyer, Edmond

    1 The evaluation of an analytical protocol for the determination of substances in waste for hazard The classification of waste as hazardous could soon be assessed in Europe using largely the hazard properties of its knowledge of the component constituents of a given waste will therefore be necessary. An analytical protocol

  17. Soft Classification with Gaussian Mixture Model for Clinical Dual-Energy CT Reconstructions

    E-Print Network [OSTI]

    1 Soft Classification with Gaussian Mixture Model for Clinical Dual-Energy CT Reconstructions, and Ken D. Sauer, Member, IEEE Abstract--We study the distribution of the clinical dual-energy CT (DECT material separation. Index Terms--Computed tomography (CT), dual energy, sta- tistical method, Gaussian

  18. Seismic interpretation and classification of mud volcanoes of the South Caspian Basin, offshore Azerbaijan.

    E-Print Network [OSTI]

    Yusifov, Mehdi Zahid

    2005-11-01T23:59:59.000Z

    Basin. A 2D seismic grid in southeastern offshore Azerbaijan is used to define the areal distribution of mud volcanoes and to make a classification of the mud volcanoes based on characteristic seismic features. As a result detailed database for each...

  19. A GIS and object oriented classification application to the problem of scaling ecological patterns and processes

    E-Print Network [OSTI]

    Lira-Noriega, André s

    2010-11-18T23:59:59.000Z

    and NIR at gg 1 m ground pixel resolution) – NDVI and SAVI in ERDAS Imagine 9.2 – Object oriented classification (eCognition 3) to extract exact location of host trees – Model the probability of presence using a metapopulation fkframework Road Trip...

  20. Discrimination and Classification of Nonstationary Time Series Using the SLEX Model

    E-Print Network [OSTI]

    Discrimination and Classification of Nonstationary Time Series Using the SLEX Model Hsiao-Yun HUANG a discriminant scheme based on the SLEX (smooth localized complex exponential) library. The SLEX library forms domains. Thus, the SLEX library has the ability to extract local spectral features of the time series

  1. Satellite SAR Remote Sensing of Great Lakes Ice Cover, Part 2. Ice Classification and Mapping

    E-Print Network [OSTI]

    Satellite SAR Remote Sensing of Great Lakes Ice Cover, Part 2. Ice Classification and Mapping° to 60° for all polarizations, was processed to radar cross-section to establish a library of signatures (look-up table) for different ice types. The library is used in the computer classifica- tion

  2. Discrimination and Classification of Nonstationary Time Series using the SLEX Model

    E-Print Network [OSTI]

    Discrimination and Classification of Nonstationary Time Series using the SLEX Model Hsiao-Yun Huang scheme based on the SLEX (Smooth Localized Complex EXponential) library. The SLEX library forms domains. Thus, the SLEX library has the ability to extract local spectral features of the time series

  3. JOB AND POSITION TITLE LIST VERSION 2 Classification Project 2000 Job Title Position Title EEO

    E-Print Network [OSTI]

    Cui, Yan

    JOB AND POSITION TITLE LIST VERSION 2 Classification Project 2000 Job Title Position Title EEO Administrative Supervisor II Housing Supervisor 41 Library Supervisor I Library Departmental Supervisor 41 Library Supervisor III Library Supervisor 41 Administrative Support Supervisor II Mail Services Supervisor

  4. Real Time Video Scene Detection and Classification John M. Gauch, Susan Gauch, Sylvain Bouix, Xiaolan Zhu

    E-Print Network [OSTI]

    Kansas, University of

    Real Time Video Scene Detection and Classification John M. Gauch, Susan Gauch, Sylvain Bouix (Video Indexing for Searching Over Networks) digital video library system has been developed in our and retrieval of digital video. This has motivated video library research at a number of institutions

  5. Hazardous waste Interpretation of the definition and classification of hazardous waste

    E-Print Network [OSTI]

    Siddharthan, Advaith

    Hazardous waste Interpretation of the definition and classification of hazardous waste www Scottish Environment Protection Agency Environment and Heritage Service Rio House Corporate Office Waste.environment-agency.gov.uk www.sepa.org.uk www.ehsni.gov.uk © Environment Agency 2005 ISBN: 1 84432 454 0 An electronic pdf

  6. HIDDEN CONDITIONAL RANDOM FIELDS FOR CLASSIFICATION OF IMAGINARY MOTOR TASKS FROM EEG DATA

    E-Print Network [OSTI]

    Yanikoglu, Berrin

    del Norte Barranquilla, Colombia [ email: delgado, mcetin ]@sabanciuniv.edu ABSTRACT Brain they involve learned statistical models matched to the classification problem; they do not suffer from some of multi- media and gaming have started to incorporate these technologies in recent years as well [2]. BCIs

  7. Artificial Neural Network Technology: for Classification and Cartography of Scientific and Technical Information.

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    Artificial Neural Network Technology: for Classification and Cartography of Scientific Artificial Neural Networks (ANNs) to extend NEURODOC into a neural platform for the cluster analysis with the aid of neural networks (models which are essentially non-linear and threshold-driven). In the design

  8. Automatic classification of Sleep Stages on a EEG signal by Artificial Neural Networks

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    Automatic classification of Sleep Stages on a EEG signal by Artificial Neural Networks Nizar of modeling and design to improve the performances of our tool. Key-Words: - Artificial Neural Networks data analysis tools. Concerning this latter point, we have proposed that Artificial Neural Networks

  9. Quasi-stationary states and a classification of the range of pair interactions

    SciTech Connect (OSTI)

    Gabrielli, A. [Istituto dei Sistemi Complessi (ISC), CNR, Via dei Taurini 19, Rome (Italy); Joyce, M. [Laboratoire de Physique Nucleaire et Hautes Energies, Universite Pierre et Marie Curie (France); Marcos, B. [Laboratoire J.-A. Dieudonne, Universite de Nice-Sophia Antipolis (France)

    2011-03-24T23:59:59.000Z

    Systems of long-range interacting particles present typically 'quasi-stationary' states (QSS). Investigating their lifetime for a generic pair interaction V(r{yields}{infinity}){approx}1/r{sup {gamma}} we give a classification of the range of the interactions according to the dynamical properties of the system.

  10. Next Generation Nuclear Plant Structures, Systems, and Components Safety Classification White Paper

    SciTech Connect (OSTI)

    Pete Jordan

    2010-09-01T23:59:59.000Z

    This white paper outlines the relevant regulatory policy and guidance for a risk-informed approach for establishing the safety classification of Structures, Systems, and Components (SSCs) for the Next Generation Nuclear Plant and sets forth certain facts for review and discussion in order facilitate an effective submittal leading to an NGNP Combined Operating License application under 10 CFR 52.

  11. THE UNIVERSITY OF OKLAHOMA PETITION FOR IN-STATE TUITION CLASSIFICATION

    E-Print Network [OSTI]

    Oklahoma, University of

    THE UNIVERSITY OF OKLAHOMA PETITION FOR IN-STATE TUITION CLASSIFICATION Office of Admissions 1000 from an Oklahoma high school? YES NO NAME AND LOCATION OF HIGH SCHOOL Have you attended a college or university in Oklahoma during the past two years? YES NO IF YES, COMPLETE AREA BELOW. LIST INSTITUTIONS

  12. A Fuzzy Neural Network Approach Based on Dirichlet Tesselations for Nearest Neighbor Classification of

    E-Print Network [OSTI]

    Likas, Aristidis

    fuzzy sets to pattern classification. 1 Introduction Several models have been developed during the last­mail: andreas@theseas.ntua.gr Abstract A neural network classifier using fuzzy set representation of pattern this synergistic combination to building efficient pattern classifiers [5, 7, 9], as the application of fuzzy sets

  13. Class-dependent rough-fuzzy granular space, dispersion index and classification

    E-Print Network [OSTI]

    Pal, Sankar Kumar

    granular computing Soft computing Pattern recognition Remote sensing a b s t r a c t A new rough-fuzzy model for pattern classification based on granular computing is described in the present article. In this model, we propose the formulation of class-dependent granules in fuzzy environment. Fuzzy membership

  14. Classification and volumetric analysis of temporal bone pneumatization using cone beam computed tomography

    E-Print Network [OSTI]

    Terasaki, Mark

    bone pneumatization in adults using cone beam computed tomography (CBCT) scans. Study Design. A total Oral Pathol Oral Radiol 2014;117:376-384) The advances in cone beam computed tomography (CBCT) overClassification and volumetric analysis of temporal bone pneumatization using cone beam computed

  15. LOP: A Novel SRAM-based Architecture for LOw Power Packet Classification

    E-Print Network [OSTI]

    New South Wales, University of

    LOP: A Novel SRAM-based Architecture for LOw Power Packet Classification Xin He, Jorgen Peddersen, Sri Parameswaran School of Computer Science and Engineering, National ICT Australia The University of New South Wales, Sydney, NSW 2052, Australia {xinhe,jorgenp,sridevan}@cse.unsw.edu.au UNSW-CSE-TR-0907

  16. Static Load Classification for Improving the Value Predictability DataCache Misses

    E-Print Network [OSTI]

    Hauswirth, Matthias

    performance parameters critical length cycle time), energy consumption, heat dissipation, chip in hardwareStatic Load Classification for Improving the Value Predictability Data­Cache Misses Martin double a program's execution time. better toler­ data­cache miss latency, architects have proposed

  17. Self-learning IP Traffic Classification based on Statistical Flow Characteristics

    E-Print Network [OSTI]

    Zander, Sebastian

    and intrusion detection. The most common identification technique based on the inspection of `known port numbers or encrypted traffic. The authors of [1] propose signature-based methods to classify P2P traffic. AlthoughSelf-learning IP Traffic Classification based on Statistical Flow Characteristics Sebastian Zander1

  18. Enhancing Mobile Object Classification Using Geo-referenced Maps and Evidential Grids

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    Enhancing Mobile Object Classification Using Geo-referenced Maps and Evidential Grids Marek Kurdej, geo-referenced maps, mobile perception, prior knowledge, evidential occupancy grid, au- tonomous, Julien Moras, V´eronique Cherfaoui, Philippe Bonnifait Abstract-- Evidential grids have recently shown

  19. CLASSIFICATION OF SUMMARIZED VIDEOS USING HIDDEN MARKOV MODELS ON COMPRESSED CHROMATICITY

    E-Print Network [OSTI]

    Drew, Mark S.

    1 CLASSIFICATION OF SUMMARIZED VIDEOS USING HIDDEN MARKOV MODELS ON COMPRESSED CHROMATICITY Science Simon Fraser University Vancouver, B.C., CANADA ABSTRACT As digital libraries and video databases grow, we need methods to assist us in the synthesis and analysis of digital video. Since

  20. Caragea et al. Text Message Classification for the Haiti Earthquake Proceedings of the 8th

    E-Print Network [OSTI]

    Caragea et al. Text Message Classification for the Haiti Earthquake Proceedings of the 8th International ISCRAM Conference ­ Lisbon, Portugal, May 2011 1 Classifying Text Messages for the Haiti and aggregates tweets and text messages about the Haiti disaster relief so that they can be easily accessed