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Sample records for geothermometry sanyal classification

  1. Category:Sanyal Temperature Classification | Open Energy Information

    Open Energy Info (EERE)

    Sanyal Temperature Classification Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Category:Sanyal Temperature Classification Geothermalpower.jpg Looking for the Sanyal...

  2. Sanyal Temperature Classification | Open Energy Information

    Open Energy Info (EERE)

    and (e) unusual operational problems that impact power cost (such as scaling, corrosion, high content of non-condensable gases, etc.). Table 1. A Possible Classification...

  3. Hot Pot Geothermal Area | Open Energy Information

    Open Energy Info (EERE)

    of Replacement Wells: Average Temperature of Geofluid: Sanyal Classification (Wellhead): Reservoir Temp (Geothermometry): Reservoir Temp (Measured): Sanyal Classification...

  4. Tungsten Mountain Geothermal Area | Open Energy Information

    Open Energy Info (EERE)

    of Replacement Wells: Average Temperature of Geofluid: Sanyal Classification (Wellhead): Reservoir Temp (Geothermometry): Reservoir Temp (Measured): Sanyal Classification...

  5. McGuinness Hills Geothermal Area | Open Energy Information

    Open Energy Info (EERE)

    of Replacement Wells: Average Temperature of Geofluid: Sanyal Classification (Wellhead): Reservoir Temp (Geothermometry): Reservoir Temp (Measured): Sanyal Classification...

  6. Dixie Meadows Geothermal Area | Open Energy Information

    Open Energy Info (EERE)

    of Geofluid: Sanyal Classification (Wellhead): Reservoir Temp (Geothermometry): Reservoir Temp (Measured): Sanyal Classification (Reservoir): Depth to Top of Reservoir:...

  7. Jersey Valley Geothermal Area | Open Energy Information

    Open Energy Info (EERE)

    of Replacement Wells: Average Temperature of Geofluid: Sanyal Classification (Wellhead): Reservoir Temp (Geothermometry): Reservoir Temp (Measured): Sanyal Classification...

  8. Drum Mountain Geothermal Area | Open Energy Information

    Open Energy Info (EERE)

    of Geofluid: Sanyal Classification (Wellhead): Reservoir Temp (Geothermometry): Reservoir Temp (Measured): Sanyal Classification (Reservoir): Depth to Top of Reservoir:...

  9. Geothermal Literature Review At General Us Region (Sanyal, Et...

    Open Energy Info (EERE)

    navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: Geothermal Literature Review At General Us Region (Sanyal, Et Al., 2004) Exploration Activity Details...

  10. Geothermometry | Open Energy Information

    Open Energy Info (EERE)

    In The Past 20 Years- Geochemistry In Geothermal Exploration Resource Evaluation And Reservoir Management Geothermometry (Powell and Cumming, 2010) Any Spreadsheets for...

  11. Reese River Geothermal Area | Open Energy Information

    Open Energy Info (EERE)

    Reservoir Temp (Geothermometry): Reservoir Temp (Measured): Sanyal Classification (Reservoir): Depth to Top of Reservoir: Depth to Bottom of Reservoir: Average Depth to...

  12. Liquid Geothermometry | Open Energy Information

    Open Energy Info (EERE)

    Exploration Group: Geochemical Techniques Exploration Sub Group: Geochemical Data Analysis Parent Exploration Technique: Geothermometry Information Provided by Technique...

  13. Gas Geothermometry | Open Energy Information

    Open Energy Info (EERE)

    Exploration Group: Geochemical Techniques Exploration Sub Group: Geochemical Data Analysis Parent Exploration Technique: Geothermometry Information Provided by Technique...

  14. Isotope Geothermometry | Open Energy Information

    Open Energy Info (EERE)

    Exploration Group: Geochemical Techniques Exploration Sub Group: Geochemical Data Analysis Parent Exploration Technique: Geothermometry Information Provided by Technique...

  15. Category:Geothermometry | Open Energy Information

    Open Energy Info (EERE)

    Technique Subcategories This category has the following 2 subcategories, out of 2 total. G Gas Geothermometry 1 pages I Isotope Geothermometry 1 pages Pages in...

  16. Category:Gas Geothermometry | Open Energy Information

    Open Energy Info (EERE)

    Gas Geothermometry Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Geothermalpower.jpg Looking for the Gas Geothermometry page? For detailed information on Gas...

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

    Open Energy Info (EERE)

    Geothermal Region Exploration Technique Geothermometry Activity Date Usefulness useful DOE-funding Unknown Notes Enthalpy-Chloride digram. Not exactly cation geothermometry...

  18. Integrated Chemical Geothermometry System for Geothermal Exploration |

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

    Department of Energy Chemical Geothermometry System for Geothermal Exploration Integrated Chemical Geothermometry System for Geothermal Exploration 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. tracers_spycher_integrated_chemical.pdf (272.32 KB) More Documents & Publications Integrated Chemical Geothermometry System for Geothermal Exploration

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

    Open Energy Info (EERE)

    Geothermometry At Central Nevada Seismic Zone Region (Shevenell & De Rocher, 2005) Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: Geothermometry...

  20. Geothermometry At Nw Basin & Range Region (Shevenell & De Rocher...

    Open Energy Info (EERE)

    Geothermometry At Nw Basin & Range Region (Shevenell & De Rocher, 2005) Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: Geothermometry At Nw...

  1. Geothermometry At Blackfoot Reservoir Area (Hutsinpiller & Parry...

    Open Energy Info (EERE)

    Activity Details Location Blackfoot Reservoir Area Exploration Technique Geothermometry Activity Date Usefulness useful DOE-funding Unknown References Amy Hutsinpiller, W. T....

  2. Improvements in geothermometry. Final technical report

    SciTech Connect (OSTI)

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

    1982-07-01

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

  3. Geothermometry At Mt St Helens Area (Shevenell & Goff, 1995)...

    Open Energy Info (EERE)

    St Helens Area (Shevenell & Goff, 1995) Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: Geothermometry At Mt St Helens Area (Shevenell & Goff,...

  4. 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...

  5. Geothermometry At Walker-Lane Transitional Zone Region (Laney...

    Open Energy Info (EERE)

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

  6. Geothermometry At Blue Mountain Geothermal Area (Casteel, Et...

    Open Energy Info (EERE)

    Details Location Blue Mountain Geothermal Area Exploration Technique Geothermometry Activity Date 2010 - 2010 Usefulness useful DOE-funding Unknown Exploration Basis A water...

  7. Geothermometry At Upper Hot Creek Ranch Area (Benoit & Blackwell...

    Open Energy Info (EERE)

    Activity Details Location Upper Hot Creek Ranch Area Exploration Technique Geothermometry Activity Date Usefulness useful DOE-funding Unknown Notes Ten water samples were collected...

  8. Geothermometry At Clear Lake Area (Thompson, Et Al., 1992) |...

    Open Energy Info (EERE)

    Activity Details Location Clear Lake Area Exploration Technique Geothermometry Activity Date Usefulness useful DOE-funding Unknown Notes Based on the above discussion,...

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

    Open Energy Info (EERE)

    Silver Peak Area (DOE GTP) Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: Geothermometry At Silver Peak Area (DOE GTP) Exploration Activity...

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

    Open Energy Info (EERE)

    Alum Area (DOE GTP) Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: Geothermometry At Alum Area (DOE GTP) Exploration Activity Details Location...

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

    Open Energy Info (EERE)

    The Needles Area (DOE GTP) Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: Geothermometry At The Needles Area (DOE GTP) Exploration Activity...

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

    Open Energy Info (EERE)

    New River Area (DOE GTP) Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: Geothermometry At New River Area (DOE GTP) Exploration Activity Details...

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

    Open Energy Info (EERE)

    Fort Bliss Area (DOE GTP) Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: Geothermometry At Fort Bliss Area (DOE GTP) Exploration Activity...

  14. Geothermometry At Lassen Volcanic National Park Area (Thompson...

    Open Energy Info (EERE)

    Thompson, 1985) Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: Geothermometry At Lassen Volcanic National Park Area (Thompson, 1985) Exploration...

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

    Open Energy Info (EERE)

    1973 - 1974 Usefulness not indicated DOE-funding Unknown Exploration Basis Masters thesis Norma Biggar, Geophysical Institute University of Alaska Notes Geothermometry...

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

    Open Energy Info (EERE)

    Fish Lake Valley Area (DOE GTP) Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: Geothermometry At Fish Lake Valley Area (DOE GTP) Exploration...

  17. Geothermometry At Akutan Fumaroles Area (Kolker, Et Al., 2010...

    Open Energy Info (EERE)

    Geothermometry Activity Date Usefulness useful DOE-funding Unknown Notes The chemistry of the hot springs strongly suggests the existence of a neutral chloride reservoir...

  18. Geothermometry At Desert Queen Area (Garchar & Arehart, 2008...

    Open Energy Info (EERE)

    Desert Queen Area (Garchar & Arehart, 2008) Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Exploration Activity: Geothermometry At Desert Queen Area (Garchar &...

  19. 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...

  20. Multicomponent Equilibrium Models for Testing Geothermometry Approaches

    SciTech Connect (OSTI)

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

    2013-02-01

    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.

  1. New Mexico conservative ion water chemistry data and chalcedony geothermometry

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Shari Kelley

    2015-10-21

    Compilation of boron, lithium, bromine, and silica data from wells and springs throughout New Mexico from a wide variety of sources. The chalcedony geothermometry calculation is included in this file.

  2. 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

    2013-02-01

    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

  3. 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.

    2015-04-01

    Multicomponent geothermometry requires knowledge of the mineral phases in the reservoir with which the geothermal fluids may be equilibrated. These minerals phases are most often alteration products rather than primary minerals. We have reviewed the literature on geothermal systems representing most major geologic environments typically associated with geothermal activity and identified potential alteration products in various environments. We have included this information in RTEst, a code we have developed to estimate reservoir conditions (temperature, CO2 fugacity) from the geochemistry of near-surface geothermal waters. The information has been included in RTEst through the addition of filters that decrease the potential numbermore » of minerals from all possibilities based on the basis species to those that are more relevant to the particular conditions in which the user is interested. The three groups of filters include host rock type (tholeiitic, calc-alkaline, silicic, siliciclastic, carbonate), water type (acidic, neutral), and the temperature range over which the alteration minerals were formed (low, medium, high). The user-chosen mineral assemblage is checked to make sure that it does not violate the Gibbs phase rule. The user can select one of three mineral saturation weighting schemes that decrease the chance the optimization from being skewed by reaction stoichiometry or analytical uncertainty.« less

  4. Mineral Selection for Multicomponent Equilibrium Geothermometry

    SciTech Connect (OSTI)

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

    2015-04-01

    Multicomponent geothermometry requires knowledge of the mineral phases in the reservoir with which the geothermal fluids may be equilibrated. These minerals phases are most often alteration products rather than primary minerals. We have reviewed the literature on geothermal systems representing most major geologic environments typically associated with geothermal activity and identified potential alteration products in various environments. We have included this information in RTEst, a code we have developed to estimate reservoir conditions (temperature, CO2 fugacity) from the geochemistry of near-surface geothermal waters. The information has been included in RTEst through the addition of filters that decrease the potential number of minerals from all possibilities based on the basis species to those that are more relevant to the particular conditions in which the user is interested. The three groups of filters include host rock type (tholeiitic, calc-alkaline, silicic, siliciclastic, carbonate), water type (acidic, neutral), and the temperature range over which the alteration minerals were formed (low, medium, high). The user-chosen mineral assemblage is checked to make sure that it does not violate the Gibbs phase rule. The user can select one of three mineral saturation weighting schemes that decrease the chance the optimization from being skewed by reaction stoichiometry or analytical uncertainty.

  5. Improved Geothermometry Through Multivariate Reaction Path Modeling and Evaluation of Geomicrobiological Influences on Geochemical Temperature Indicators

    Broader source: Energy.gov [DOE]

    Improved Geothermometry Through Multivariate Reaction Path Modeling and Evaluation of Geomicrobiological Influences on Geochemical Temperature Indicators presentation at the April 2013 peer review meeting held in Denver, Colorado.

  6. Improved Geothermometry Through Multivariate Reaction Path Modeling and Evaluation of Geomicrobiological Influences on Geochemical Temperature Indicators

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

    Improved Geothermometry Through Multivariate Reaction Path Modeling and Evaluation of Geomicrobiological Influences on Geochemical Temperature Indicators Project Officer: Eric Hass Total Project Funding: $999,000 April 24, 2013 Craig Cooper Larry Hull Idaho National Laboratory This presentation does not contain any proprietary confidential, or otherwise restricted information. 2 | US DOE Geothermal Program eere.energy.gov Relevance/Impact of Research Geothermometry enables estimation of

  7. Extremely Low Temperature | Open Energy Information

    Open Energy Info (EERE)

    Extremely Low Temperature: No definition has been provided for this term. Add a Definition Sanyal Temp Classification This temperature scheme was developed by Sanyal in...

  8. Geochemistry Sampling for Traditional and Multicomponent Equilibrium Geothermometry in Southeast Idaho

    SciTech Connect (OSTI)

    Cannon, Cody; Wood, Thomas; Neupane, Ghanashyam; McLing, Travis; Mattson, Earl; Dobson, Patrick; Conrad, Mark

    2014-10-01

    The Eastern Snake River Plain (ESRP) is an area of high regional heat flux due the movement of the North American Plate over the Yellowstone Hotspot beginning ca.16 Ma. Temperature gradients between 45-60 °C/km (up to double the global average) have been calculated from deep wells that penetrate the upper aquifer system (Blackwell 1989). Despite the high geothermal potential, thermal signatures from hot springs and wells are effectively masked by the rapid flow of cold groundwater through the highly permeable basalts of the Eastern Snake River Plain aquifer (ESRPA) (up to 500+ m thick). This preliminary study is part of an effort to more accurately predict temperatures of the ESRP deep thermal reservoir while accounting for the effects of the prolific cold water aquifer system above. This study combines the use of traditional geothermometry, mixing models, and a multicomponent equilibrium geothermometry (MEG) tool to investigate the geothermal potential of the ESRP. In March, 2014, a collaborative team including members of the University of Idaho, the Idaho National Laboratory, and the Lawrence Berkeley National Laboratory collected 14 thermal water samples from and adjacent to the Eastern Snake River Plain. The preliminary results of chemical analyses and geothermometry applied to these samples are presented herein.

  9. Classification of Geothermal Systems: A Possible Scheme | Open...

    Open Energy Info (EERE)

    of Geothermal Systems: A Possible Scheme Abstract Abstract unavailable. Author Subir K. Sanyal Conference Thirtieth Workshop on Geothermal Reservoir Engineering; Stanford,...

  10. Improved Geothermometry Through Multivariate Reaction-path Modeling and Evaluation of Geomicrobiological Influences on Geochemical Temperature Indicators: Final Report

    SciTech Connect (OSTI)

    Mattson, Earl; Smith, Robert; Fujita, Yoshiko; McLing, Travis; Neupane, Ghanashyam; Palmer, Carl; Reed, David; Thompson, Vicki

    2015-03-01

    The project was aimed at demonstrating that the geothermometric predictions can be improved through the application of multi-element reaction path modeling that accounts for lithologic and tectonic settings, while also accounting for biological influences on geochemical temperature indicators. The limited utilization of chemical signatures by individual traditional geothermometer in the development of reservoir temperature estimates may have been constraining their reliability for evaluation of potential geothermal resources. This project, however, was intended to build a geothermometry tool which can integrate multi-component reaction path modeling with process-optimization capability that can be applied to dilute, low-temperature water samples to consistently predict reservoir temperature within ±30 °C. The project was also intended to evaluate the extent to which microbiological processes can modulate the geochemical signals in some thermal waters and influence the geothermometric predictions.

  11. Understanding Classification

    Energy Savers [EERE]

    CONFIDENTIAL SECRET TOP SECRET 2 (3) Submit any formal challenges to the classification of ... classification uses three levels (Confidential, Secret, Top Secret) to define the ...

  12. Gabbs Valley Geothermal Area | Open Energy Information

    Open Energy Info (EERE)

    Reservoir Temp (Measured): Sanyal Classification (Reservoir): Depth to Top of Reservoir: Depth to Bottom of Reservoir: Average Depth to Reservoir: Use the "Edit with...

  13. Coyote Canyon Geothermal Area | Open Energy Information

    Open Energy Info (EERE)

    Sanyal Classification (Reservoir): Depth to Top of Reservoir: Depth to Bottom of Reservoir: Average Depth to Reservoir: Use the "Edit with Form" button at the top of the...

  14. Carson Lake Corral Geothermal Area | Open Energy Information

    Open Energy Info (EERE)

    Sanyal Classification (Reservoir): Depth to Top of Reservoir: Depth to Bottom of Reservoir: Average Depth to Reservoir: Use the "Edit with Form" button at the top of the...

  15. Gabbs Valley Geothermal Area | Open Energy Information

    Open Energy Info (EERE)

    Sanyal Classification (Reservoir): Depth to Top of Reservoir: Depth to Bottom of Reservoir: Average Depth to Reservoir: Use the "Edit with Form" button at the top of the...

  16. McCoy Geothermal Area | Open Energy Information

    Open Energy Info (EERE)

    Sanyal Classification (Reservoir): Depth to Top of Reservoir: Depth to Bottom of Reservoir: Average Depth to Reservoir: Use the "Edit with Form" button at the top of the...

  17. Silver Peak Geothermal Area | Open Energy Information

    Open Energy Info (EERE)

    Sanyal Classification (Reservoir): Depth to Top of Reservoir: Depth to Bottom of Reservoir: Average Depth to Reservoir: Use the "Edit with Form" button at the top of the...

  18. Steam Field | Open Energy Information

    Open Energy Info (EERE)

    Steam Field Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Print PDF Sanyal Temperature Classification: Steam Field Dictionary.png Steam Field: No definition has been...

  19. Ultra High Temperature | Open Energy Information

    Open Energy Info (EERE)

    Ultra High Temperature Jump to: navigation, search GEOTHERMAL ENERGYGeothermal Home Print PDF Sanyal Temperature Classification: Ultra High Temperature Dictionary.png Ultra High...

  20. Property:SanyalTempWellhead | Open Energy Information

    Open Energy Info (EERE)

    Area + Moderate Temperature + Blue Mountain Geothermal Area + Moderate Temperature + Brady Hot Springs Geothermal Area + Low Temperature + C Chena Geothermal Area + Extremely...

  1. Property:SanyalTempReservoir | Open Energy Information

    Open Energy Info (EERE)

    Area + High Temperature + C Chena Geothermal Area + Very Low Temperature + D Desert Peak Geothermal Area + Moderate Temperature + F Fenton Hill HDR Geothermal Area + High...

  2. 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.

    1980-08-01

    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.

  3. Classification Training Institute Catalog | Department of Energy

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

    Classification Training Institute Catalog Classification Training Institute Catalog Classification Training Institute (CTI) Catalog

  4. 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.

  5. Security classification of information

    SciTech Connect (OSTI)

    Quist, A.S.

    1993-04-01

    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.

  6. Security classification of information

    SciTech Connect (OSTI)

    Quist, A.S.

    1989-09-01

    Certain governmental information must be classified for national security reasons. However, the national security benefits from classifying information are usually accompanied by significant costs -- those due to a citizenry not fully informed on governmental activities, the extra costs of operating classified programs and procuring classified materials (e.g., weapons), the losses to our nation when advances made in classified programs cannot be utilized in unclassified programs. The goal of a classification system should be to clearly identify that information which must be protected for national security reasons and to ensure that information not needing such protection is not classified. This document was prepared to help attain that goal. This document is the first of a planned four-volume work that comprehensively discusses the security classification of information. Volume 1 broadly describes the need for classification, the basis for classification, and the history of classification in the United States from colonial times until World War 2. Classification of information since World War 2, under Executive Orders and the Atomic Energy Acts of 1946 and 1954, is discussed in more detail, with particular emphasis on the classification of atomic energy information. Adverse impacts of classification are also described. Subsequent volumes will discuss classification principles, classification management, and the control of certain unclassified scientific and technical information. 340 refs., 6 tabs.

  7. Standard Subject Classification System

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

    1979-08-14

    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.

  8. Engineering rock mass classifications

    SciTech Connect (OSTI)

    Bieniawski, Z.T.

    1989-01-01

    This book is a reference on rock mass classification, consolidating into one handy source information widely scattered through the literature. Includes new, unpublished material and case histories. Presents the fundamental concepts of classification schemes and critically appraises their practical application in industrial projects such as tunneling and mining.

  9. 2-Stage Classification Modeling

    Energy Science and Technology Software Center (OSTI)

    1994-11-01

    CIRCUIT2.4 is used to design optimum two-stage classification configurations and operating conditions for energy conservation. It permits simulation of five basic grinding-classification circuits, including one single-stage and four two-stage classification arrangements. Hydrocyclones, spiral classifiers, and sieve band screens can be simulated, and the user may choose the combination of devices for the flowsheet simulation. In addition, the user may select from four classification modeling methods to achieve the goals of a simulation project using themore » most familiar concepts. Circuit performance is modeled based on classification parameters or equipment operating conditions. A modular approach was taken in designing the program, which allows future addition of other models with relatively minor changes.« less

  10. Standard Subject Classification System

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

    1978-07-19

    The order establishes the Department of Energy (DOE) Standard Subject Classification System for classifying documents and records by subject, including correspondence, directives, and forms. Canceled by DOE O 0000.1A.

  11. Classification | Department of Energy

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

    Classification Classification PREVENTING THE PROLIFERATION OF NUCLEAR WEAPONS Since the advent of the nuclear age, the United States has been dedicated to preventing the proliferation of nuclear weapons. In order to stop the spread of nuclear weapons-related technology, Congress gave the Atomic Energy Commission (now the Department of Energy [DOE]), authority to control nuclear weapons-related information. This task has gained even greater importance in recent years with an increasing number of

  12. Position Management and Classification - DOE Directives, Delegations...

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

    O 325.2, Position Management and Classification by Bruce Murray Functional areas: Position Classification, Federal Wage System Standards, Position Management and Classification The...

  13. Position Management and Classification

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

    2015-04-01

    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. Canceled by DOE O 325.2 Chg 1 (Admin Chg), 9-1-15.

  14. Position Management and Classification

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

    2015-04-01

    The order establishes departmental requirements and responsibilities for classifying positions using the general schedule (GS) and federal wage system (FWS) standards and to develop and administer a sound position management and classification program. Supersedes DOE O 325.2, dated 4-1-15.

  15. Classification of Information Manual

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

    1985-05-08

    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.

  16. Brochure, Understanding Classification - June 2012 | Department...

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

    Brochure, Understanding Classification - June 2012 Brochure, Understanding Classification - June 2012 June 2012 This booklet highlights your responsibilities identified in DOE...

  17. Materials Classification & Accelerated Property Predictions using...

    Office of Scientific and Technical Information (OSTI)

    Technical Report: Materials Classification & Accelerated Property Predictions using Machine Learning Citation Details In-Document Search Title: Materials Classification & ...

  18. Catalog, Classification Training Institute | Department of Energy

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

    Catalog, Classification Training Institute Catalog, Classification Training Institute 2016 Classification Training Course Catalog. To ensure that all classification and declassification decisions are based on these principles, the Office of Classification has undertaken the establishment and maintenance of a comprehensive classification and declassification education program. The training and education program is perpetually evolving with new courses and special briefings as events dictate.

  19. Automated Defect Classification (ADC)

    Energy Science and Technology Software Center (OSTI)

    1998-01-01

    The ADC Software System is designed to provide semiconductor defect feature analysis and defect classification capabilities. Defect classification is an important software method used by semiconductor wafer manufacturers to automate the analysis of defect data collected by a wide range of microscopy techniques in semiconductor wafer manufacturing today. These microscopies (e.g., optical bright and dark field, scanning electron microscopy, atomic force microscopy, etc.) generate images of anomalies that are induced or otherwise appear on wafermore » surfaces as a result of errant manufacturing processes or simple atmospheric contamination (e.g., airborne particles). This software provides methods for analyzing these images, extracting statistical features from the anomalous regions, and applying supervised classifiers to label the anomalies into user-defined categories.« less

  20. Office of Classification

    Office of Energy Efficiency and Renewable Energy (EERE)

    The Office of Classification develops and interprets Government-wide and Department-wide policies, procedures and guidance, performs document reviews, and conducts training to ensure the accurate identification of information and documents that must be classified or controlled under statute or Executive order to protect the National Security, and controlled unclassified information (Official Use Only) to protect commercial and private interests and to provide for the effective operation of the Government.

  1. Classification of Information

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

    1978-12-12

    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

  2. Automatic Fault Classification

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

    Automatic Fault Classification of Photovoltaic Strings Based on an In Situ IV Characterization System and a Gaussian Process Algorithm. C. Birk Jones ∗ , Manel Mart´ ınez-Ram´ on ‡ , § Ryan Smith † , Craig K. Carmignani ∗ , Olga Lavrova ∗ , Charles Robinson ∗ , and Joshua S. Stein ∗ ∗ Sandia National Laboratories Solar PV & Grid Integration, Albuquerque, NM, USA. ‡ Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA, §

  3. Seismic event classification system

    DOE Patents [OSTI]

    Dowla, Farid U.; Jarpe, Stephen P.; Maurer, William

    1994-01-01

    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.

  4. Seismic event classification system

    DOE Patents [OSTI]

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

    1994-12-13

    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.

  5. Improved Geothermometry Through Multivariate Reaction Path Modeling...

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

    at the April 2013 peer review meeting held in Denver, Colorado. geothermometrycooperpeer2013.pdf (922.86 KB) More Documents & Publications track 4: enhanced geothermal ...

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

    Open Energy Info (EERE)

    In The Past 20 Years- Geochemistry In Geothermal Exploration Resource Evaluation And Reservoir Management Additional References Retrieved from "http:en.openei.orgw...

  7. Improvements in geothermometry. Final technical report. Rev

    SciTech Connect (OSTI)

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

    1982-08-01

    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.

  8. Colorado thermal spring water geothermometry (public dataset...

    Open Energy Info (EERE)

    chemical geothermometers for Colorado thermal springs. Data citations include Barrett, J. K. and Pearl, R. H. (1976), George, R. D., Curtis, H. A., Lester, O. C., Crook, J. K.,...

  9. 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.

  10. Position Classification | Department of Energy

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

    Position Classification Position Classification Documents Available for Download June 5, 2014 POLICY GUIDANCE MEMORANDUM #03 Addressing Missclassified Positions This memorandum provides policy guidance on how to consistently address misclassified positions within the Department and is effective immediately. There are several different circumstances that affect how a misclassified position will be addressed. April 27, 2010 POLICY GUIDANCE MEMORANDUM #08 DOE Fair Labor Standards Act (FLSA)

  11. 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). PDF icon 2015 Long...

  12. EPA - UIC Well Classifications | Open Energy Information

    Open Energy Info (EERE)

    Well Classifications Jump to: navigation, search OpenEI Reference LibraryAdd to library Web Site: EPA - UIC Well Classifications Author Environmental Protection Agency Published...

  13. Universal Membrane Classification Scheme: Maximizing the Return...

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

    Universal Membrane Classification Scheme: Maximizing the Return on High Temperature PEM Membrane Research Universal Membrane Classification Scheme: Maximizing the Return on High ...

  14. Position Management and Classification - DOE Directives, Delegations...

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

    CURRENT DOE O 325.2 Chg 1 (AdminChg), Position Management and Classification by Bruce Murray Functional areas: Administrative Change, Position Classification, Federal Wage System...

  15. HIV classification using coalescent theory

    SciTech Connect (OSTI)

    Zhang, Ming; Letiner, Thomas K; Korber, Bette T

    2008-01-01

    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

  16. ADP computer security classification program

    SciTech Connect (OSTI)

    Augustson, S.J.

    1984-01-01

    CG-ADP-1, the Automatic Data Processing Security Classification Guide, provides for classification guidance (for security information) concerning the protection of Department of Energy (DOE) and DOE contractor Automatic Data Processing (ADP) systems which handle classified information. Within the DOE, ADP facilities that process classified information provide potentially lucrative targets for compromise. In conjunction with the security measures required by DOE regulations, necessary precautions must be taken to protect details of those ADP security measures which could aid in their own subversion. Accordingly, the basic principle underlying ADP security classification policy is to protect information which could be of significant assistance in gaining unauthorized access to classified information being processed at an ADP facility. Given this policy, classification topics and guidelines are approved for implementation. The basic program guide, CG-ADP-1 is broad in scope and based upon it, more detailed local guides are sometimes developed and approved for specific sites. Classification topics are provided for system features, system and security management, and passwords. Site-specific topics can be addressed in local guides if needed.

  17. Hazard classification process at LLNL

    SciTech Connect (OSTI)

    Hildum, J. S., LLNL

    1998-05-01

    An essential part of Integrated Safety Management is the identification of hazards in the workplace and the assessment of possible consequences of accidents involving those hazards. The process of hazard classification suggested by the DOE orders on Safety Analysis is the formalization of this identification and assessment for hazards that might cause harm to the public or workers external to the operation. Possible injury to workers in the facility who are exposed to the hazard is not considered in the designation of the hazard classification for facilities at LLNL, although worker safety is discussed in facility Safety Basis documentation.

  18. Brochure, Classification Overview of RD and FRD - September 2010...

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

    Brochure, Classification Overview of RD and FRD - September 2010 Brochure, Classification Overview of RD and FRD - September 2010 September 2010 A Classification Overview of ...

  19. Vapor Retarder Classification - Building America Top Innovation |

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

    Department of Energy Vapor Retarder Classification - Building America Top Innovation Vapor Retarder Classification - Building America Top Innovation Photo of a vapor retarder classification. Air-tight and well-insulated homes have little or no tolerance for drying if they get wet; moisture control is critical. This Top Innovation profile describes Building America research that established vapor retarder classifications and appropriate applications that has been instrumental in the market

  20. Updating the Classification of Geothermal Resources- Presentation

    Office of Energy Efficiency and Renewable Energy (EERE)

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

  1. 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.

  2. Statutes, Regulations, and Directives for Classification Program |

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

    Department of Energy Statutes, Regulations, and Directives for Classification Program Statutes, Regulations, and Directives for Classification Program Classification Atomic Energy Act of 1954 - Establishes Government-wide policies for classifying, safeguarding, and declassifying Restricted Data information. 10 CFR Part 1045, Nuclear Classification and Declassification - Establishes the Government-wide policies and procedures for implementing sections 141 and 142 of the Atomic Energy Act of

  3. Geothermal Resource Classification | Department of Energy

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

    Resource Classification Geothermal Resource Classification Geothermal Resource Classification.PDF (869.18 KB) More Documents & Publications Water Use in the Development and Operations of Geothermal Power Plants Water Use in the Development and Operations of Geothermal Power Plants Water Use in the Development and Operation of Geothermal Power Plants

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

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

    National Geothermal Resource Assessment and Classification track 2: hydrothermal | geothermal 2015 peer review National Geothermal Data System Architecture Design, Testing and ...

  5. Identification of Export Control Classification Number - ITER

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

    of Export Control Classification Number - ITER (April 2012) As the "Shipper of Record" ... be shipped from the United States to the ITER International Organization in Cadarache, ...

  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.

    2012-11-06

    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. National Security Information Classification Guidance Fundamental...

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

    ... Isotopes Separation by the Atomic Vapor Laser Isotope Separation Process (CG-UAV-2) . ... classification is based on an assessment of the damage done by releasing this information. ...

  8. Classification CommuniQué - Year: 2015 | Department of Energy

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

    5 Classification CommuniQué - Year: 2015 Classification newsletters for the year 2015, consisting of the following issues: CommuniQue 2015-1 - Spring 2015 (784.22 KB) More Documents & Publications Classification CommuniQué - Year: 2014 Classification CommuniQué - Year: 2012 Classification CommuniQué - Year: 2013

  9. Microsoft Word - Data Classification Security Framework V5.doc

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

    2007 Security Framework for Control System Data Classification and Protection Bryan T. ... Security Framework for Control System Data Classification and Protection 2 Issued by ...

  10. IDENTIFYING ROOF FALL PREDICTORS USING FUZZY CLASSIFICATION

    SciTech Connect (OSTI)

    Bertoncini, C. A.; Hinders, M. K.

    2010-02-22

    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.

  11. National Geothermal Resource Assessment and Classification

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

    National Geothermal Resource Assessment and Classification Colin F. Williams US Geological Survey Data Systems and Analysis (Resource Assessment) April 24, 2013 This presentation does not contain any proprietary confidential, or otherwise restricted information. 2 | US DOE Geothermal Office eere.energy.gov Relevance/Impact of Research * Overall Summary - Major Project Goals * Develop new Geothermal Resource Classification standards * Expand Resource Assessment scope across all 50 states

  12. Laser-guidance systems, security classification. Instruction

    SciTech Connect (OSTI)

    Flickinger, A.

    1982-12-03

    The Instruction reissues Department of Defense (DoD) Instruction 5210.62, April 25, 1980, and prescribes policies, standards, and criteria governing the security classification of information pertaining to any laser-guidance system that is developed in whole or in part with information or knowledge obtained from or developed for the Department of Defense; and provides guidance to DoD Components responsible for issuing security classification guides for individual systems and equipment under their control.

  13. Identification of Export Control Classification Number - ITER

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

    of Export Control Classification Number - ITER (April 2012) As the "Shipper of Record" please provide the appropriate Export Control Classification Number (ECCN) for the products (equipment, components and/or materials) and if applicable the nonproprietary associated installation/maintenance documentation that will be shipped from the United States to the ITER International Organization in Cadarache, France or to ITER Members worldwide on behalf of the Company. In rare instances an

  14. Brochure, Understanding Classification - June 2012 | Department of Energy

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

    Brochure, Understanding Classification - June 2012 Brochure, Understanding Classification - June 2012 June 2012 This booklet highlights your responsibilities identified in DOE Order 475.2A, Identifying Classified Information. Classification is how certain information is identified that needs to be protected in the interest of national security. DOE has a formal process for classifying and declassifying information, documents, and materials. Brochure, Understanding Classification - June 2012

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

    Open Energy Info (EERE)

    Celcius References R.H. Pearl, J.K. Barrett (1976) Geothermal resources of the Upper San Luis and Arkansas valleys, Colorado Additional References Retrieved from "http:...

  16. Geothermometry At Salt Wells Area (Coolbaugh, Et Al., 2006) ...

    Open Energy Info (EERE)

    and ICP emission for anions. The hottest sampled spring appears to match the location and temperature of the Borax Spring, first described in 1885, but reportedly inactive in 1981...

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

    Open Energy Info (EERE)

    were carried out in conjunction with geologic mapping to test the application of these ground-based techniques to geothermal exploration at three prospects in Nevada by Henkle...

  18. Geothermometry At Hot Springs Ranch Area (Szybinski, 2006) |...

    Open Energy Info (EERE)

    of two distinct waters in this group of samples (Tom Powell of Thermochem Inc., personal communication, 2005). Powell found that MDH, TRS-1 and TRS-6 are the most prospective...

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

    Open Energy Info (EERE)

    however, the data that are available strongly substantiate the presence of a thermal resource. A measured water temperature of 31 degrees C in one well is clearly above normal...

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

    Open Energy Info (EERE)

    Activity Date Usefulness useful DOE-funding Unknown Notes Michigan "The silica heat flow estimator does provide estimates of surface heat flow which appear to be in good...

  1. Geothermal Reservoir Temperatures in Southeastern Idaho using Multicomponent Geothermometry

    SciTech Connect (OSTI)

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

    2015-03-01

    Southeastern Idaho exhibits numerous warm springs, warm water from shallow wells, and hot water within oil and gas test wells that indicate a potential for geothermal development in the area. Although the area exhibits several thermal expressions, the measured geothermal gradients vary substantially (19 – 61 ºC/km) within this area, potentially suggesting a redistribution of heat in the overlying ground water from deeper geothermal reservoirs. We have estimated reservoir temperatures from measured water compositions using an inverse modeling technique (Reservoir Temperature Estimator, 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. Compositions of a selected group of thermal waters representing southeastern Idaho hot/warm springs and wells were used for the development of temperature estimates. The temperature estimates in the the region varied from moderately warm (59 ºC) to over 175 ºC. Specifically, hot springs near Preston, Idaho resulted in the highest temperature estimates in the region.

  2. Geothermometry At Northern Basin & Range Region (Cole, 1983)...

    Open Energy Info (EERE)

    Fish (Wilson), Twin Peak, Cudahy, Laverkin, Grantsville, Crystal Prison, Arrowhead, Red Hill, Monroe, Joseph, Castilla, Saratoga, Thermo, Crater, Wasatch, Beck, Deseret, Big...

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

    Open Energy Info (EERE)

    DOE-funding Unknown Notes Four formation water samples were collected from well 56-4, during an airlift test which took place between November 11 and November 14, 2007....

  4. 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...

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

    Open Energy Info (EERE)

    River geothermal system, Cassia County, Idaho Urban, T.C.; Diment, W.H.; Nathenson, M.; Smith, E.P.; Ziagos, J.P.; Shaeffer, M.H. (1 January 1986) Temperature,...

  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 Teels Marsh Area (Coolbaugh, Et Al., 2006)...

    Open Energy Info (EERE)

    useful DOE-funding Unknown Notes Follow up (to ASTER satellite imaging) analysis of spring and well waters yielded geothermometer reservoir estimates up to 192C References...

  8. Geothermometry At Rhodes Marsh Area (Coolbaugh, Et Al., 2006...

    Open Energy Info (EERE)

    useful DOE-funding Unknown Notes Follow up (to ASTER satellite imaging) analysis of spring and well waters yielded geothermometer reservoir estimates up to 162C References...

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

    Open Energy Info (EERE)

    sample taken had a pH of 8.35 and contained 2100 ppm Cl and 0.55 ppm NH3. Ratios of Na+K+ and Na+Cl remained nearly constant throughout the flow test. Cation geothermometers...

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

    Open Energy Info (EERE)

    for eight springs along the Corral Creek drainage. The springs along Corral Creek have Na-K-Ca temperatures that average 354C, a direct result of high potassium concentrations in...

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

    Open Energy Info (EERE)

    have been commonly used in Hawaii for identifying geothermal potential (i.e. silica concentration and chloride to magnesium ion ratios) were anomalous in the groundwater of this...

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

    Open Energy Info (EERE)

    published in 1976 (Mariner and Willey, 1976). Details of sampling practices and field treatment are detailed in the text. Water samples were passed through a 0.7x4 cm column...

  13. Geothermometry At Socorro Mountain Area (Owens, Et Al., 2005...

    Open Energy Info (EERE)

    thermal waters with a minimum of 82oC at depth References Lara Owens, Richard Baars, David Norman, Harold Tobin (2005) New Methods In Exploration At The Socorro Peak Kgra- A...

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

    Open Energy Info (EERE)

    Date Usefulness not indicated DOE-funding Unknown Notes Groundwater temperature and chemistry surveys were similarly unable to identify any detectable thermal influence on...

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

    Open Energy Info (EERE)

    California R.O. Fournier, Michael L. Sorey, Robert H. Mariner, Alfred H. Truesdell (1979) Chemical and Isotopic Prediction of Aquifer Temperatures in the Geothermal System at Long...

  16. Geothermometry At Coso Geothermal Area (1980) | Open Energy Informatio...

    Open Energy Info (EERE)

    DOE-funding Unknown Exploration Basis Fluid temperature of feed water Notes Cation and sulfate isotope geothermometers indicate that the reservoir feeding water to the Coso Hot...

  17. Geothermometry At Coso Geothermal Area (1978) | Open Energy Informatio...

    Open Energy Info (EERE)

    analysis to determine fluid origin. The surface expression of fumarole and acid sulfate pools and shallow steam wells gives a false indication of an extensive vapor...

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

    Open Energy Info (EERE)

    stages of hydrothermal activity, flow, and recharge in the Long Valley caldera groundwater system. Fluids were sampled from LVEW during flow testing in May 2000, July 2000,...

  19. Geothermometry At Salt Wells Area (Edmiston & Benoit, 1984) ...

    Open Energy Info (EERE)

    and drilled during the early 1980's that had not been documented previously in the literature, (2) summarize and compare chemical and temperature data from known moderate- to...

  20. Geothermometry At Gabbs Alkali Flat Area (Kratt, Et Al., 2008...

    Open Energy Info (EERE)

    Mark Coolbaugh, Chris Sladek, Rick Zehner, Robin Penfield, Ben Delwiche (2008) A New Gold Pan For The West- Discovering Blind Geothermal Systems With Shallow Temperature Surveys...

  1. Geothermometry At Columbus Salt Marsh Area (Shevenell, Et Al...

    Open Energy Info (EERE)

    References Lisa Shevenell, Mark Coolbaugh, Chris Sladek, Rick Zehner, Chris Kratt, James Faulds, Robin Penfield (2008) Our Evolving Knowledge Of Nevada'S Geothermal Resource...

  2. Geothermometry At Teels Marsh Area (Shevenell, Et Al., 2008)...

    Open Energy Info (EERE)

    References Lisa Shevenell, Mark Coolbaugh, Chris Sladek, Rick Zehner, Chris Kratt, James Faulds, Robin Penfield (2008) Our Evolving Knowledge Of Nevada'S Geothermal Resource...

  3. Geothermometry At Rhodes Marsh Area (Shevenell, Et Al., 2008...

    Open Energy Info (EERE)

    References Lisa Shevenell, Mark Coolbaugh, Chris Sladek, Rick Zehner, Chris Kratt, James Faulds, Robin Penfield (2008) Our Evolving Knowledge Of Nevada'S Geothermal Resource...

  4. Isotope Geothermometry At Lightning Dock Geothermal Area (Witcher...

    Open Energy Info (EERE)

    Usefulness useful DOE-funding Unknown Exploration Basis Part of the Geothermal Resource Evaluation and Definition (GRED) Program administered by DOE-AAO under Cooperative...

  5. Geothermometry At Northern Basin & Range Region (Laney, 2005...

    Open Energy Info (EERE)

    Nevada, Shevenell and Garside. The objective of this project is to obtain geochemical data from springs (and some wells) for which data are not publicly available, or for which...

  6. Geothermometry At Buffalo Valley Hot Springs Area (Laney, 2005...

    Open Energy Info (EERE)

    Nevada, Shevenell and Garside. The objective of this project is to obtain geochemical data from springs (and some wells) for which data are not publicly available, or for which...

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

    Open Energy Info (EERE)

    Nevada, Shevenell and Garside. The objective of this project is to obtain geochemical data from springs (and some wells) for which data are not publicly available, or for which...

  8. Geothermometry At Central Nevada Seismic Zone Region (Laney,...

    Open Energy Info (EERE)

    Nevada, Shevenell and Garside. The objective of this project is to obtain geochemical data from springs (and some wells) for which data are not publicly available, or for which...

  9. Light-water reactor accident classification

    SciTech Connect (OSTI)

    Washburn, B.W.

    1980-02-01

    The evolution of existing classifications and definitions of light-water reactor accidents is considered. Licensing practice and licensing trends are examined with respect to terms of art such as Class 8 and Class 9 accidents. Interim definitions, consistent with current licensing practice and the regulations, are proposed for these terms of art.

  10. Common occupational classification system - revision 3

    SciTech Connect (OSTI)

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

    1996-05-01

    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.

  11. Security Framework for Control System Data Classification and Protection |

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

    Department of Energy Framework for Control System Data Classification and Protection Security Framework for Control System Data Classification and Protection This document presents a data classification process that gives utility administrators, control engineers, and IT personnel a cohesive approach to deploying efficient and effective process control security. Security Framework for Control System Data Classification and Protection (230.98 KB) More Documents & Publications Essential

  12. National Geothermal Resource Assessment and Classification | Department of

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

    Energy Resource Assessment and Classification National Geothermal Resource Assessment and Classification National Geothermal Resource Assessment and Classification presentation at the April 2013 peer review meeting held in Denver, Colorado. gs_resource_assessment_peer2013.pdf (2.37 MB) More Documents & Publications National Geothermal Resource Assessment and Classification track 2: hydrothermal | geothermal 2015 peer review National Geothermal Data System Architecture Design, Testing and

  13. Classification CommuniQué - Year: 2013 | Department of Energy

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

    3 Classification CommuniQué - Year: 2013 Classification newsletters for the year 2013, consisting of the following issues: CommuniQué 2013-1 - Spring 2013 (910.28 KB) CommuniQué 2013-2 - Fall 2013 (724.57 KB) More Documents & Publications Briefing, Transclassified Foreign Nuclear Information - June 2014 Classification CommuniQué - Year: 2012

  14. Microsoft Word - Global Harmonization Classifications.docx

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

    Harmonization Classifications: The following is prepared for your understanding of the new Global Harmonization System Physical hazards  H200: Unstable explosive  H201: Explosive; mass explosion hazard  H202: Explosive; severe projection hazard  H203: Explosive; fire, blast or projection hazard  H204: Fire or projection hazard  H205: May mass explode in fire  H220: Extremely flammable gas  H221: Flammable gas  H222: Extremely flammable aerosol  H223: Flammable

  15. A classification scheme for risk assessment methods.

    SciTech Connect (OSTI)

    Stamp, Jason Edwin; Campbell, Philip LaRoche

    2004-08-01

    This report presents a classification scheme for risk assessment methods. This scheme, like all classification schemes, provides meaning by imposing a structure that identifies relationships. Our scheme is based on two orthogonal aspects--level of detail, and approach. The resulting structure is shown in Table 1 and is explained in the body of the report. Each cell in the Table represent a different arrangement of strengths and weaknesses. Those arrangements shift gradually as one moves through the table, each cell optimal for a particular situation. The intention of this report is to enable informed use of the methods so that a method chosen is optimal for a situation given. This report imposes structure on the set of risk assessment methods in order to reveal their relationships and thus optimize their usage.We present a two-dimensional structure in the form of a matrix, using three abstraction levels for the rows and three approaches for the columns. For each of the nine cells in the matrix we identify the method type by name and example. The matrix helps the user understand: (1) what to expect from a given method, (2) how it relates to other methods, and (3) how best to use it. Each cell in the matrix represent a different arrangement of strengths and weaknesses. Those arrangements shift gradually as one moves through the table, each cell optimal for a particular situation. The intention of this report is to enable informed use of the methods so that a method chosen is optimal for a situation given. The matrix, with type names in the cells, is introduced in Table 2 on page 13 below. Unless otherwise stated we use the word 'method' in this report to refer to a 'risk assessment method', though often times we use the full phrase. The use of the terms 'risk assessment' and 'risk management' are close enough that we do not attempt to distinguish them in this report. The remainder of this report is organized as follows. In Section 2 we provide context for this report

  16. Cross-ontological analytics for alignment of different classification schemes

    DOE Patents [OSTI]

    Posse, Christian; Sanfilippo, Antonio P; Gopalan, Banu; Riensche, Roderick M; Baddeley, Robert L

    2010-09-28

    Quantification of the similarity between nodes in multiple electronic classification schemes is provided by automatically identifying relationships and similarities between nodes within and across the electronic classification schemes. Quantifying the similarity between a first node in a first electronic classification scheme and a second node in a second electronic classification scheme involves finding a third node in the first electronic classification scheme, wherein a first product value of an inter-scheme similarity value between the second and third nodes and an intra-scheme similarity value between the first and third nodes is a maximum. A fourth node in the second electronic classification scheme can be found, wherein a second product value of an inter-scheme similarity value between the first and fourth nodes and an intra-scheme similarity value between the second and fourth nodes is a maximum. The maximum between the first and second product values represents a measure of similarity between the first and second nodes.

  17. Classification CommuniQué - Year: 2014 | Department of Energy

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

    4 Classification CommuniQué - Year: 2014 Classification newsletters for the year 2014, consisting of the following issues: CommuniQue Spring 2014 (967.62 KB) CommuniQue Fall 2014 (1.04 MB) More Documents & Publications Briefing, DOE Order 475.2B, Identifying Classified Information, What Derivative Classifiers Should Know Brochure, Understanding Classification - June 2012 Declassification Instruction Guide, March 2014

  18. Classification/Declassification of Government Documents | Department of

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

    Energy Services » Classification » Classification/Declassification of Government Documents Classification/Declassification of Government Documents BALANCING NATIONAL SECURITY WITH OPENESS DOE promotes the release of information assets to the maximum extent possible consistent at all times with our paramount concern for the security of our nation. Programs are established to review historical records scheduled for declassification, documents requested under statute or Executive Order,

  19. Briefing, Classification Bulletin GEN-16 - October 2014 | Department...

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

    No Comment Policy on Classified Information in the Open Literature This briefing provides information on Classification Bulletin GEN-16, No Comment Policy on Classified Information ...

  20. 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...

  1. Hawaii Land Study Bureau's Land Classification Finder | Open...

    Open Energy Info (EERE)

    Not Provided DOI Not Provided Check for DOI availability: http:crossref.org Online Internet link for Hawaii Land Study Bureau's Land Classification Finder Citation Hawaii State...

  2. Special nuclear material information, security classification guidance. Instruction

    SciTech Connect (OSTI)

    Flickinger, A.

    1982-12-03

    The Instruction reissues DoD Instruction 5210.67, July 5, 1979, and provides security classification guidance for information concerning significant quantities of special nuclear material, other than that contained in nuclear weapons and that used in the production of energy in the reactor plant of nuclear-powered ships. Security classification guidance for these data in the latter two applications is contained in Joint DoE/DoD Nuclear Weapons Classification Guide and Joint DoE/DoD Classification Guide for the Naval Nuclear Propulsion Program.

  3. A Brief Classification of Geothermal Systems | Open Energy Information

    Open Energy Info (EERE)

    LibraryAdd to library General: A Brief Classification of Geothermal Systems Author Paul Brophy Published GRC Annual Meeting, 2007 DOI Not Provided Check for DOI availability:...

  4. Classification CommuniQué - Year: 2012 | Department of Energy

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

    2 Classification CommuniQué - Year: 2012 Classification newsletters for the year 2012 consisting of the following issues: CommuniQué 2012-1 - March/April 2012 (626.89 KB) CommuniQué 2012-2 - September/October 2012 (716.99 KB) CommuniQué 2012-2 - September/October 2012, Crossword Puzzle Solution (36.28 KB) More Documents & Publications Classification CommuniQué - Year: 2013 Classification CommuniQué - Year: 2014

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

    DOE Patents [OSTI]

    Haaland, David M.; Jones, Howland D. T.; Thomas, Edward V.

    1997-01-01

    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.

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

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

    Classification Policy, Guidance & Reports Classification Policy, Guidance & Reports Statutes, Regulations, Executive Orders, DOE Directives and Bulletins Atomic Energy Act of 1954 - Establishes Government-wide policies for classifying, safeguarding, and declassifying Restricted Data information. 10 CFR Part 1045 - establishes responsiblities and requirements for classifying and declassifying RD and FRD. Executive Order 13526, Classified National Security Information - Prescribes the

  7. WNClASSIflfO CLASSIFICATION CANCELLED

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

    WNClASSIflfO ^ CLASSIFICATION CANCELLED DATE FEB 1 6 1957 For The Atomic Cnargy Commission *74/^ Cl<JlMAJUL Chief, Declftuifloatlon Branch ^VAA ARGOfraS NATION f V L j..ABORATOKy C o n t r a c t Mo. W°31»109-Sng-3S Wc Ho Z.irm, D i r e c t o r ; ( t ^ * * 3 j ! * ) ) ! 4 ^ RADIOCARBON .FROM P I L S GRAPHIfB^ CHEMICAL LiETiiODii FOR ITS GOKCKNTRATION JAMES R« ARNOLD AND Wc Fo LIBB3f Photostat P r i c e ' T " - - ^ ^ J y O Microfilm Price $_ Available from the Office of Technical

  8. Hazard classification assessment for the High Voltage Initiator

    SciTech Connect (OSTI)

    Cogan, J.D.

    1994-04-19

    An investigation was conducted to determine whether the High Voltage Initiator (Sandia p number 395710; Navy NAVSEA No. 6237177) could be assigned a Department of Transportation (DOT) hazard classification of ``IGNITERS, 1.4G, UN0325`` under Code of Federal Regulations, 49 CFR 173.101, when packaged per Mound drawing NXB911442. A hazard classification test was performed, and the test data led to a recommended hazard classification of ``IGNITERS, 1.4G, UN0325,`` based on guidance outlined in DOE Order 1540.2 and 49 CFR 173.56.

  9. NEW- DOE O 325.2, Position Management and Classification

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

    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.

  10. Universal Membrane Classification Scheme: Maximizing the Return on High

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

    Temperature PEM Membrane Research | Department of Energy Universal Membrane Classification Scheme: Maximizing the Return on High Temperature PEM Membrane Research Universal Membrane Classification Scheme: Maximizing the Return on High Temperature PEM Membrane Research This presentation on maximizing the return of high temperature PEM membrane research was given at the High Temperature Membrane Working Group Meeting in May 2007. htmwg_kopasz.pdf (1010.95 KB) More Documents & Publications

  11. National Geothermal Resource Assessment and Classification | Department of

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

    Energy Resource Assessment and Classification National Geothermal Resource Assessment and Classification This work will enable lower risk/cost deployment of conventional and EGS geothermal power. USGS is also supporting GTP input to DOE National Energy Modeling by providing resource assessment data by geothermal region as input to GTP supply curves. analysis_williams_resource_assessment.pdf (661.92 KB) More Documents & Publications National Geothermal Resource Assessment and

  12. Classification Bulletin GEN-16 Revision 2 | Department of Energy

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

    Bulletin GEN-16 Revision 2 Classification Bulletin GEN-16 Revision 2 Provides guidance to DOE Federal and contractor employees with access to classified information on appropriate actions when classified information (i.e., Restricted Data (RD), Formerly Restricted Data (FRD), Transclassified Foreign Nuclear Information (IFNI), National Security Information (NSI)) appears in the open literature and to clarify the circumstances that constitute comment. Classification Bulletin GEN-16 Revision 2

  13. Automatic classification of time-variable X-ray sources

    SciTech Connect (OSTI)

    Lo, Kitty K.; Farrell, Sean; Murphy, Tara; Gaensler, B. M.

    2014-05-01

    To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of supervised classification is suitable for this problem. Here, we present a supervised learning method to automatically classify variable X-ray sources in the Second XMM-Newton Serendipitous Source Catalog (2XMMi-DR2). Random Forest is our classifier of choice since it is one of the most accurate learning algorithms available. Our training set consists of 873 variable sources and their features are derived from time series, spectra, and other multi-wavelength contextual information. The 10 fold cross validation accuracy of the training data is ∼97% on a 7 class data set. We applied the trained classification model to 411 unknown variable 2XMM sources to produce a probabilistically classified catalog. Using the classification margin and the Random Forest derived outlier measure, we identified 12 anomalous sources, of which 2XMM J180658.7–500250 appears to be the most unusual source in the sample. Its X-ray spectra is suggestive of a ultraluminous X-ray source but its variability makes it highly unusual. Machine-learned classification and anomaly detection will facilitate scientific discoveries in the era of all-sky surveys.

  14. Evaluating multimedia chemical persistence: Classification and regression tree analysis

    SciTech Connect (OSTI)

    Bennett, D.H.; McKone, T.E.; Kastenberg, W.E.

    2000-04-01

    For the thousands of chemicals continuously released into the environment, it is desirable to make prospective assessments of those likely to be persistent. Widely distributed persistent chemicals are impossible to remove from the environment and remediation by natural processes may take decades, which is problematic if adverse health or ecological effects are discovered after prolonged release into the environment. A tiered approach using a classification scheme and a multimedia model for determining persistence is presented. Using specific criteria for persistence, a classification tree is developed to classify a chemical as persistent or nonpersistent based on the chemical properties. In this approach, the classification is derived from the results of a standardized unit world multimedia model. Thus, the classifications are more robust for multimedia pollutants than classifications using a single medium half-life. The method can be readily implemented and provides insight without requiring extensive and often unavailable data. This method can be used to classify chemicals when only a few properties are known and can be used to direct further data collection. Case studies are presented to demonstrate the advantages of the approach.

  15. AUTOMATIC CLASSIFICATION OF VARIABLE STARS IN CATALOGS WITH MISSING DATA

    SciTech Connect (OSTI)

    Pichara, Karim; Protopapas, Pavlos

    2013-11-10

    We present an automatic classification method for astronomical catalogs with missing data. We use Bayesian networks and a probabilistic graphical model that allows us to perform inference to predict missing values given observed data and dependency relationships between variables. To learn a Bayesian network from incomplete data, we use an iterative algorithm that utilizes sampling methods and expectation maximization to estimate the distributions and probabilistic dependencies of variables from data with missing values. To test our model, we use three catalogs with missing data (SAGE, Two Micron All Sky Survey, and UBVI) and one complete catalog (MACHO). We examine how classification accuracy changes when information from missing data catalogs is included, how our method compares to traditional missing data approaches, and at what computational cost. Integrating these catalogs with missing data, we find that classification of variable objects improves by a few percent and by 15% for quasar detection while keeping the computational cost the same.

  16. Classification of US hydropower dams by their modes of operation

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

    McManamay, Ryan A.; Oigbokie, II, Clement O.; Kao, Shih -Chieh; Bevelhimer, Mark S.

    2016-02-19

    A key challenge to understanding ecohydrologic responses to dam regulation is the absence of a universally transferable classification framework for how dams operate. In the present paper, we develop a classification system to organize the modes of operation (MOPs) for U.S. hydropower dams and powerplants. To determine the full diversity of MOPs, we mined federal documents, open-access data repositories, and internet sources. W then used CART classification trees to predict MOPs based on physical characteristics, regulation, and project generation. Finally, we evaluated how much variation MOPs explained in sub-daily discharge patterns for stream gages downstream of hydropower dams. After reviewingmore » information for 721 dams and 597 power plants, we developed a 2-tier hierarchical classification based on 1) the storage and control of flows to powerplants, and 2) the presence of a diversion around the natural stream bed. This resulted in nine tier-1 MOPs representing a continuum of operations from strictly peaking, to reregulating, to run-of-river, and two tier-2 MOPs, representing diversion and integral dam-powerhouse configurations. Although MOPs differed in physical characteristics and energy production, classification trees had low accuracies (<62%), which suggested accurate evaluations of MOPs may require individual attention. MOPs and dam storage explained 20% of the variation in downstream subdaily flow characteristics and showed consistent alterations in subdaily flow patterns from reference streams. Lastly, this standardized classification scheme is important for future research including estimating reservoir operations for large-scale hydrologic models and evaluating project economics, environmental impacts, and mitigation.« less

  17. A Hybrid Classification Scheme for Mining Multisource Geospatial Data

    SciTech Connect (OSTI)

    Vatsavai, Raju; Bhaduri, Budhendra L

    2007-01-01

    Supervised learning methods such as Maximum Likelihood (ML) are often used in land cover (thematic) classification of remote sensing imagery. ML classifier relies exclusively on spectral characteristics of thematic classes whose statistical distributions are often overlapping. The spectral response distributions of thematic classes are dependent on many factors including elevation, soil types, and atmospheric conditions present at the time of data acquisition. A second problem with statistical classifiers is the requirement of large number of accurate training samples, which are often costly and time consuming to acquire over large geographic regions. With the increasing availability of geospatial databases, it is possible to exploit the knowledge derived from these ancillary datasets to improve classification accuracies even when the class distributions are highly overlapping. Likewise newer semi-supervised techniques can be adopted to improve the parameter estimates of statistical model by utilizing a large number of easily available unlabeled training samples. Unfortunately there is no convenient multivariate statistical model that can be employed for mulitsource geospatial databases. In this paper we present a hybrid semi-supervised learning algorithm that effectively exploits freely available unlabeled training samples from multispectral remote sensing images and also incorporates ancillary geospatial databases. We have conducted several experiments on real datasets, and our new hybrid approach shows over 15% improvement in classification accuracy over conventional classification schemes.

  18. Classification of Birds and Bats Using Flight Tracks

    SciTech Connect (OSTI)

    Cullinan, Valerie I.; Matzner, Shari; Duberstein, Corey A.

    2015-05-01

    Classification of birds and bats that use areas targeted for offshore wind farm development and the inference of their behavior is essential to evaluating the potential effects of development. The current approach to assessing the number and distribution of birds at sea involves transect surveys using trained individuals in boats or airplanes or using high-resolution imagery. These approaches are costly and have safety concerns. Based on a limited annotated library extracted from a single-camera thermal video, we provide a framework for building models that classify birds and bats and their associated behaviors. As an example, we developed a discriminant model for theoretical flight paths and applied it to data (N = 64 tracks) extracted from 5-min video clips. The agreement between model- and observer-classified path types was initially only 41%, but it increased to 73% when small-scale jitter was censored and path types were combined. Classification of 46 tracks of bats, swallows, gulls, and terns on average was 82% accurate, based on a jackknife cross-validation. Model classification of bats and terns (N = 4 and 2, respectively) was 94% and 91% correct, respectively; however, the variance associated with the tracks from these targets is poorly estimated. Model classification of gulls and swallows (N ≥ 18) was on average 73% and 85% correct, respectively. The models developed here should be considered preliminary because they are based on a small data set both in terms of the numbers of species and the identified flight tracks. Future classification models would be greatly improved by including a measure of distance between the camera and the target.

  19. Department of Energy versus Department of Defense: security, classification markings, procedures, and clearance requirements

    SciTech Connect (OSTI)

    Not Available

    1985-01-01

    The differences between the Department of Energy's and the Department of Defense's system of classification and security are clarified.

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

    SciTech Connect (OSTI)

    Ramakrishnan, Lavanya; Plale, Beth

    2010-04-05

    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.

  1. CLASSIFICATION OF THE MGR NON-FUEL COMPONENTS DISPOSAL CONTAINER

    SciTech Connect (OSTI)

    J.A. Ziegler

    1999-08-31

    The purpose of this analysis is to document the Quality Assurance (QA) classification of the Monitored Geologic Repository (MGR) non-fuel components disposal container system structures, systems and components (SSCs) performed by the MGR Safety Assurance Department. This analysis also provides the basis for revision of YMP/90-55Q, Q-List (YMP 1998). The Q-List identifies those MGR SSCs subject to the requirements of DOE/RW-0333P, ''Quality Assurance Requirements and Description'' (QARD) (DOE 1998).

  2. Security classification of information concerning high-energy lasers. Instruction

    SciTech Connect (OSTI)

    MacCallum, J.

    1981-09-18

    The Instruction reissues Department of Defense (DoD) Instruction 5210.61, April 7, 1977, to update policy and guidance, and establishes uniform criteria for the security classification of information concerning DoD programs and projects involving the research, development, test and evaluation (RDT E), application, production, and operational use of high-energy lasers (HEL), and their application for military purposes, whether as weapons or in other military systems.

  3. CLASSIFICATION OF THE MGR SAFEGUARDS AND SECURITY SYSTEM

    SciTech Connect (OSTI)

    J.A. Ziegler

    1999-08-31

    The purpose of this analysis is to document the Quality Assurance (QA) classification of the Monitored Geologic Repository (MGR) safeguards and security system structures, systems and components (SSCs) performed by the MGR Safety Assurance Department. This analysis also provides the basis for revision of YMP/90-55Q, Q-List (YMP 1998). The Q-List identifies those MGR SSCs subject to the requirements of DOE/RW-0333P, ''Quality Assurance Requirements and Description'' (QARD) (DOE 1998).

  4. 3.0 UNIT IDENTIFICATION, CLASSIFICATION, AND PRIORITIZATION

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

    3-1 3.0 UNIT IDENTIFICATION, CLASSIFICATION, AND PRIORITIZATION 3.1 INTRODUCTION This section describes what constitutes a waste management unit at the Hanford Site. In addition, it describes how waste management units are classified, prioritized, and grouped for common investigation and response or corrective action. A waste management unit represents any location within the boundary of the Hanford Site that may require action to mitigate a potential environmental impact. This would include all

  5. Microsoft Word - Data Classification Security Framework V5.doc

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

    888P Unlimited Release Printed July 2007 Security Framework for Control System Data Classification and Protection Bryan T. Richardson and John Michalski Prepared by Sandia National Laboratories Albuquerque, New Mexico 87185 and Livermore, California 94550 Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy's National Nuclear Security Administration under Contract DE-AC04-94AL85000. Approved for public release;

  6. 1. INSERT ABOVE, CLASSIFICATION LEVEL, UNCLASSIFIED, OR OFFICIAL USE ONLY

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

    (03-98) (Exception to SF 14, Approved by NARS, June 1978) 1. INSERT ABOVE, CLASSIFICATION LEVEL, UNCLASSIFIED, OR OFFICIAL USE ONLY 4. PRECEDENCE DESIGNATION ("X" appropriate box): 6. FROM 9. TO COMMUNICATION CENTER ROUTING U.S. DEPARTMENT OF ENERGY TELECOMMUNICATION MESSAGE (See reverse side for Instructions) 5. TYPE OF MESSAGE FOR COMMUNICATION CENTER USE MESSAGE IDENTIFICATION NR: DTG: Z YES 2. MESSAGE CONTAINS WEAPON DATA? ("X" appropriate box. Message Center will not

  7. Various forms of indexing HDMR for modelling multivariate classification problems

    SciTech Connect (OSTI)

    Aksu, a?r?; Tunga, M. Alper

    2014-12-10

    The Indexing HDMR method was recently developed for modelling multivariate interpolation problems. The method uses the Plain HDMR philosophy in partitioning the given multivariate data set into less variate data sets and then constructing an analytical structure through these partitioned data sets to represent the given multidimensional problem. Indexing HDMR makes HDMR be applicable to classification problems having real world data. Mostly, we do not know all possible class values in the domain of the given problem, that is, we have a non-orthogonal data structure. However, Plain HDMR needs an orthogonal data structure in the given problem to be modelled. In this sense, the main idea of this work is to offer various forms of Indexing HDMR to successfully model these real life classification problems. To test these different forms, several well-known multivariate classification problems given in UCI Machine Learning Repository were used and it was observed that the accuracy results lie between 80% and 95% which are very satisfactory.

  8. A Biochar Classification System and Associated Test Methods

    SciTech Connect (OSTI)

    Camps-Arbestain, Marta; Amonette, James E.; Singh, Balwant; Wang, Tao; Schmidt, Hans-Peter

    2015-02-18

    In this chapter, a biochar classification system related to its use as soil amendment is proposed. This document builds upon previous work and constrains its scope to materials with properties that satisfy the criteria for biochar as defined by either the International Biochar Initiative (IBI) Biochar Standards or the European Biochar Community (EBC) Standards, and it is intended to minimise the need for testing in addition to those required according to the above-mentioned standards. The classification system envisions enabling stakeholders and commercial entities to (i) identify the most suitable biochar to fulfil the requirements for a particular soil and/or land-use, and (ii) distinguish the application of biochar for specific niches (e.g., soilless agriculture). It is based on the best current knowledge and the intention is to periodically review and update the document based on new data and knowledge that become available in the scientific literature. The main thrust of this classification system is based on the direct or indirect beneficial effects that biochar provides from its application to soil. We have classified the potential beneficial effects of biochar application to soils into five categories with their corresponding classes, where applicable: (i) carbon (C) storage value, (ii) fertiliser value, (iii) liming value, (iv) particle-size, and (v) use in soil-less agriculture. A summary of recommended test methods is provided at the end of the chapter.

  9. Deep Learning in Label-free Cell Classification

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

    Chen, Claire Lifan; Mahjoubfar, Ata; Tai, Li-Chia; Blaby, Ian K.; Huang, Allen; Niazi, Kayvan Reza; Jalali, Bahram

    2016-03-15

    Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individualmore » cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. In conclusion, this system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.« less

  10. UAS Detection Classification and Neutralization: Market Survey 2015

    SciTech Connect (OSTI)

    Birch, Gabriel Carisle; Griffin, John Clark; Erdman, Matthew Kelly

    2015-07-01

    The purpose of this document is to briefly frame the challenges of detecting low, slow, and small (LSS) unmanned aerial systems (UAS). The conclusion drawn from internal discussions and external reports is the following; detection of LSS UAS is a challenging problem that can- not be achieved with a single detection modality for all potential targets. Classification of LSS UAS, especially classification in the presence of background clutter (e.g., urban environment) or other non-threating targets (e.g., birds), is under-explored. Though information of avail- able technologies is sparse, many of the existing options for UAS detection appear to be in their infancy (when compared to more established ground-based air defense systems for larger and/or faster threats). Companies currently providing or developing technologies to combat the UAS safety and security problem are certainly worth investigating, however, no company has provided the statistical evidence necessary to support robust detection, identification, and/or neutralization of LSS UAS targets. The results of a market survey are included that highlights potential commercial entities that could contribute some technology that assists in the detection, classification, and neutral- ization of a LSS UAS. This survey found no clear and obvious commercial solution, though recommendations are given for further investigation of several potential systems.

  11. Unsupervised Feature Learning for High-Resolution Satellite Image Classification

    SciTech Connect (OSTI)

    Cheriyadat, Anil M

    2013-01-01

    The rich data provided by high-resolution satellite imagery allow us to directly model geospatial neighborhoods by understanding their spatial and structural patterns. In this paper we explore an unsupervised feature learning approach to model geospatial neighborhoods for classification purposes. While pixel and object based classification approaches are widely used for satellite image analysis, often these approaches exploit the high-fidelity image data in a limited way. In this paper we extract low-level features to characterize the local neighborhood patterns. We exploit the unlabeled feature measurements in a novel way to learn a set of basis functions to derive new features. The derived sparse feature representation obtained by encoding the measured features in terms of the learned basis function set yields superior classification performance. We applied our technique on two challenging image datasets: ORNL dataset representing one-meter spatial resolution satellite imagery representing five land-use categories and, UCMERCED dataset consisting of 21 different categories representing sub-meter resolution overhead imagery. Our results are highly promising and, in the case of UCMERCED dataset we outperform the best results obtained for this dataset. We show that our feature extraction and learning methods are highly effective in developing a detection system that can be used to automatically scan large-scale high-resolution satellite imagery for detecting large-facility.

  12. 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.; Tonry, J. L.; Burgett, W. S.; Chambers, K. C.; Heasley, J. N.; Kaiser, N.; Magnier, E. A.; Morgan, J. S.; Greisel, N.; Bailer-Jones, C. A. L.; Klement, R. J.; Rix, H.-W.; Smith, K.; Green, P. J.; and others

    2012-02-20

    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.

  13. How to Find More Supernovae with Less Work: Object ClassificationTechn...

    Office of Scientific and Technical Information (OSTI)

    Language: English Subject: 99; ASTEROIDS; CLASSIFICATION; DETECTION; EFFICIENCY; FORESTS; ... Word Cloud More Like This Full Text preview image File size NAView Full Text View Full ...

  14. Machine Learning Approaches for High-resolution Urban Land Cover Classification: A Comparative Study

    SciTech Connect (OSTI)

    Vatsavai, Raju; Chandola, Varun; Cheriyadat, Anil M; Bright, Eddie A; Bhaduri, Budhendra L; Graesser, Jordan B

    2011-01-01

    The proliferation of several machine learning approaches makes it difficult to identify a suitable classification technique for analyzing high-resolution remote sensing images. In this study, ten classification techniques were compared from five broad machine learning categories. Surprisingly, the performance of simple statistical classification schemes like maximum likelihood and Logistic regression over complex and recent techniques is very close. Given that these two classifiers require little input from the user, they should still be considered for most classification tasks. Multiple classifier systems is a good choice if the resources permit.

  15. DOE Publishes Petition of CSA Group for Classification as a Nationally...

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

    notice of petition and request for public comments regarding CSA Group for classification as a nationally recognized certification program for small electric motors. 78 FR...

  16. NEW- DOE O 325.2, Position Management and Classification - DOE...

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

    NEW- DOE O 325.2, Position Management and Classification by Website Administrator The order establishes departmental requirements and responsibilities for classifying positions...

  17. Net-Zero Energy Buildings: A Classification System Based on Renewable...

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

    Net-Zero Energy Buildings: A Classification System Based on Renewable Energy Supply Options Shanti Pless and Paul Torcellini Technical Report NRELTP-550-44586 June 2010 Technical ...

  18. Pattern classification and associative recall by neural networks

    SciTech Connect (OSTI)

    Chiueh, Tzi-Dar.

    1989-01-01

    The first part of this dissertation discusses a new classifier based on a multilayer feed-forward network architecture. The main idea is to map irregularly-distributed prototypes in a classification problem to codewords that are organized in some way. Then the pattern classification problem is transformed into a threshold decoding problem, which is easily solved using simple hard-limiter neurons. At first the author proposes the new model and introduce two families of good internal representation codes. Then some analyses and software simulation concerning the storage capacity of this new model are done. The results show that the new classifier is much better than the classifier based on the Hopfield model in terms of both the storage capacity and the ability to classify correlated prototypes. A general model for neural network associative memories with a feedback structure is proposed. Many existing neural network associative memories can be expressed as special cases of this general model. Among these models, there is a class of associative memories, called correlation associative memories, that are capable of storing a large number of memory patterns. If the function used in the evolution equation is monotonically nondecreasing, then a correlation associative memory can be proved to be asymptotically stable in both the synchronous and asynchronous updating modes. Of these correlation associative memories, one stands out because of its VLSI implementation feasibility and large storage capacity. This memory uses the exponentiation function in its evolution equation; hence it is called exponential correlation associative memory (ECAM).

  19. Hazard categorization and classification for the sodium storage facility

    SciTech Connect (OSTI)

    Van Keuren, J.C.

    1994-08-30

    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.

  20. Cloud classification using whole-sky imager data

    SciTech Connect (OSTI)

    Buch, K.A. Jr.; Sun, C.H.; Thorne, L.R.

    1996-04-01

    Clouds are one of the most important moderators of the earth radiation budget and one of the least understood. The effect that clouds have on the reflection and absorption of solar and terrestrial radiation is strongly influenced by their shape, size, and composition. Physically accurate parameterization of clouds is necessary for any general circulation model (GCM) to yield meaningful results. The work presented here is part of a larger project that is aimed at producing realistic three-dimensional (3D) volume renderings of cloud scenes based on measured data from real cloud scenes. These renderings will provide the important shape information for parameterizing GCMs. The specific goal of the current study is to develop an algorithm that automatically classifies (by cloud type) the clouds observed in the scene. This information will assist the volume rendering program in determining the shape of the cloud. Much work has been done on cloud classification using multispectral satellite images. Most of these references use some kind of texture measure to distinguish the different cloud types and some also use topological features (such as cloud/sky connectivity or total number of clouds). A wide variety of classification methods has been used, including neural networks, various types of clustering, and thresholding. The work presented here uses binary decision trees to distinguish the different cloud types based on cloud features vectors.

  1. Support Vector Machine algorithm for regression and classification

    Energy Science and Technology Software Center (OSTI)

    2001-08-01

    The software is an implementation of the Support Vector Machine (SVM) algorithm that was invented and developed by Vladimir Vapnik and his co-workers at AT&T Bell Laboratories. The specific implementation reported here is an Active Set method for solving a quadratic optimization problem that forms the major part of any SVM program. The implementation is tuned to specific constraints generated in the SVM learning. Thus, it is more efficient than general-purpose quadratic optimization programs. Amore » decomposition method has been implemented in the software that enables processing large data sets. The size of the learning data is virtually unlimited by the capacity of the computer physical memory. The software is flexible and extensible. Two upper bounds are implemented to regulate the SVM learning for classification, which allow users to adjust the false positive and false negative rates. The software can be used either as a standalone, general-purpose SVM regression or classification program, or be embedded into a larger software system.« less

  2. Automatic Fault Characterization via Abnormality-Enhanced Classification

    SciTech Connect (OSTI)

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

    2010-12-20

    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.

  3. Towards catchment classification in data-scarce regions

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

    Auerbach, Daniel A.; Buchanan, Brian P.; Alexiades, Alex V.; Anderson, Elizabeth P.; Encalada, Andrea C.; Larson, Erin I.; McManamay, Ryan A.; Poe, Gregory L.; Walter, M. Todd; Flecker, Alexander S.

    2016-01-29

    Assessing spatial variation in hydrologic processes can help to inform freshwater management and advance ecological understanding, yet many areas lack sufficient flow records on which to base classifications. Seeking to address this challenge, we apply concepts developed in data-rich settings to public, global data in order to demonstrate a broadly replicable approach to characterizing hydrologic variation. The proposed approach groups the basins associated with reaches in a river network according to key environmental drivers of hydrologic conditions. This initial study examines Colorado (USA), where long-term streamflow records permit comparison to previously distinguished flow regime types, and the Republic of Ecuador,more » where data limitations preclude such analysis. The flow regime types assigned to gages in Colorado corresponded reasonably well to the classes distinguished from environmental features. The divisions in Ecuador reflected major known biophysical gradients while also providing a higher resolution supplement to an existing depiction of freshwater ecoregions. Although freshwater policy and management decisions occur amidst uncertainty and imperfect knowledge, this classification framework offers a rigorous and transferrable means to distinguish catchments in data-scarce regions. The maps and attributes of the resulting ecohydrologic classes offer a departure point for additional study and data collection programs such as the placement of stations in under-monitored classes, and the divisions may serve as a preliminary template with which to structure conservation efforts such as environmental flow assessments.« less

  4. Chemistry Data for Geothermometry Mapping of Deep Hydrothermal Reservoirs in Southeastern Idaho

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Earl Mattson

    2016-01-18

    This dataset includes chemistry of geothermal water samples of the Eastern Snake River Plain and surrounding area. The samples included in this dataset were collected during the springs and summers of 2014 and 2015. All chemical analysis of the samples were conducted in the Analytical Laboratory at the Center of Advanced Energy Studies in Idaho Falls, Idaho. This data set supersedes #425 submission and is the final submission for AOP 3.1.2.1 for INL. Isotopic data collected by Mark Conrad will be submitted in a separate file.

  5. A complete electrical hazard classification system and its application

    SciTech Connect (OSTI)

    Gordon, Lloyd B; Cartelli, Laura

    2009-01-01

    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

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

    SciTech Connect (OSTI)

    Wiltse, J.

    1990-12-31

    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.

  7. Associations among hydrologic classifications and fish traits to support environmental flow standards

    SciTech Connect (OSTI)

    McManamay, Ryan A; Bevelhimer, Mark S; Frimpong, Dr. Emmanuel A,

    2014-01-01

    Classification systems are valuable to ecological management in that they organize information into consolidated units thereby providing efficient means to achieve conservation objectives. Of the many ways classifications benefit management, hypothesis generation has been discussed as the most important. However, in order to provide templates for developing and testing ecologically relevant hypotheses, classifications created using environmental variables must be linked to ecological patterns. Herein, we develop associations between a recent US hydrologic classification and fish traits in order to form a template for generating flow ecology hypotheses and supporting environmental flow standard development. Tradeoffs in adaptive strategies for fish were observed across a spectrum of stable, perennial flow to unstable intermittent flow. In accordance with theory, periodic strategists were associated with stable, predictable flow, whereas opportunistic strategists were more affiliated with intermittent, variable flows. We developed linkages between the uniqueness of hydrologic character and ecological distinction among classes, which may translate into predictions between losses in hydrologic uniqueness and ecological community response. Comparisons of classification strength between hydrologic classifications and other frameworks suggested that spatially contiguous classifications with higher regionalization will tend to explain more variation in ecological patterns. Despite explaining less ecological variation than other frameworks, we contend that hydrologic classifications are still useful because they provide a conceptual linkage between hydrologic variation and ecological communities to support flow ecology relationships. Mechanistic associations among fish traits and hydrologic classes support the presumption that environmental flow standards should be developed uniquely for stream classes and ecological communities, therein.

  8. Development of characterization protocol for mixed liquid radioactive waste classification

    SciTech Connect (OSTI)

    Zakaria, Norasalwa; Wafa, Syed Asraf; Wo, Yii Mei; Mahat, Sarimah

    2015-04-29

    Mixed liquid organic waste generated from health-care and research activities containing tritium, carbon-14, and other radionuclides posed specific challenges in its management. Often, these wastes become legacy waste in many nuclear facilities and being considered as problematic waste. One of the most important recommendations made by IAEA is to perform multistage processes aiming at declassification of the waste. At this moment, approximately 3000 bottles of mixed liquid waste, with estimated volume of 6000 litres are currently stored at the National Radioactive Waste Management Centre, Malaysia and some have been stored for more than 25 years. The aim of this study is to develop a characterization protocol towards reclassification of these wastes. The characterization protocol entails waste identification, waste screening and segregation, and analytical radionuclides profiling using various analytical procedures including gross alpha/ gross beta, gamma spectrometry, and LSC method. The results obtained from the characterization protocol are used to establish criteria for speedy classification of the waste.

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

    SciTech Connect (OSTI)

    Steed, Chad A; SwanII, J. Edward; Fitzpatrick, Patrick J.; Jankun-Kelly, T.J.

    2012-02-01

    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.

  10. Classification of Malaysia aromatic rice using multivariate statistical analysis

    SciTech Connect (OSTI)

    Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md; Masnan, M. J.; Zakaria, A.; Rahim, N. A.; Omar, O.

    2015-05-15

    Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC–MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties.

  11. Classification of groundwater at the Nevada Test Site

    SciTech Connect (OSTI)

    Chapman, J.B.

    1994-08-01

    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.

  12. Classification of generalized quantum statistics associated with the exceptional Lie (super)algebras

    SciTech Connect (OSTI)

    Stoilova, N. I.; Jeugt, J. van der

    2007-04-15

    Generalized quantum statistics (GQS) associated with a Lie algebra or Lie superalgebra extends the notion of para-Bose or para-Fermi statistics. Such GQS have been classified for all classical simple Lie algebras and basic classical Lie superalgebras. In the current paper we finalize this classification for all exceptional Lie algebras and superalgebras. Since the definition of GQS is closely related to a certain Z grading of the Lie (super)algebra G, our classification reproduces some known Z gradings of exceptional Lie algebras. For exceptional Lie superalgebras such a classification of Z gradings has not been given before.

  13. Classification Bulletin GEN-16, Revision 2, No Comment Poly on Classified Information in the Open Literature

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

    3, 2014 MEMORANDUM FORDISTRl ~N fJJ!}/, 'ViJ ~ FROM: A~~s?oVokdKES DIRECTOR OFFICE OF CLASSIFICATION OFFICE OF ENVIRONMENT, HEALTH, SAFETY AND SECURITY SUBJECT: Classification Bulletin GEN-16, REVISION 2, "No Comment" Policy on Classified Information in the Open Literature Classification Bulletin GEN-16, Revision 1, '"NO COMMENT' POLICY ON CLASSIFIED INFORMATION IN THE PUBLIC DOMAIN," has been updated to address concerns regarding documents marked as classified in the open

  14. Fact #623: May 17, 2010 Classification Changes in the CAFE Standards |

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

    Department of Energy 3: May 17, 2010 Classification Changes in the CAFE Standards Fact #623: May 17, 2010 Classification Changes in the CAFE Standards Beginning with model year (MY) 2011, the classification of cars or light trucks has changed for the purposes of the Corporate Average Fuel Economy (CAFE) Standards. Two-wheel-drive (2wd) sport utility vehicles of 6,000 pounds or less gross vehicle weight rating (GVWR) will no longer be classified as light trucks, though the 4wd models of these

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

    SciTech Connect (OSTI)

    Hunt, C.E.

    1996-05-01

    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.

  16. System diagnostics using qualitative analysis and component functional classification

    DOE Patents [OSTI]

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

    1993-11-23

    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.

  17. System diagnostics using qualitative analysis and component functional classification

    DOE Patents [OSTI]

    Reifman, Jaques; Wei, Thomas Y. C.

    1993-01-01

    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.

  18. Classification of heart valve condition using acoustic measurements

    SciTech Connect (OSTI)

    Clark, G.

    1994-11-15

    Prosthetic heart valves and the many great strides in valve design have been responsible for extending the life spans of many people with serious heart conditions. Even though the prosthetic valves are extremely reliable, they are eventually susceptible to long-term fatigue and structural failure effects expected from mechanical devices operating over long periods of time. The purpose of our work is to classify the condition of in vivo Bjork-Shiley Convexo-Concave (BSCC) heart valves by processing acoustic measurements of heart valve sounds. The structural failures of interest for Bscc valves is called single leg separation (SLS). SLS can occur if the outlet strut cracks and separates from the main structure of the valve. We measure acoustic opening and closing sounds (waveforms) using high sensitivity contact microphones on the patient`s thorax. For our analysis, we focus our processing and classification efforts on the opening sounds because they yield direct information about outlet strut condition with minimal distortion caused by energy radiated from the valve disc.

  19. Historical literature review on waste classification and categorization

    SciTech Connect (OSTI)

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

    1995-03-01

    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.

  20. Classification methodology for tritiated waste requiring interim storage

    SciTech Connect (OSTI)

    Cana, D.; Dall'ava, D.

    2015-03-15

    Fusion machines like the ITER experimental research facility will use tritium as fuel. Therefore, most of the solid radioactive waste will result not only from activation by 14 MeV neutrons, but also from contamination by tritium. As a consequence, optimizing the treatment process for waste containing tritium (tritiated waste) is a major challenge. This paper summarizes the studies conducted in France within the framework of the French national plan for the management of radioactive materials and waste. The paper recommends a reference program for managing this waste based on its sorting, treatment and packaging by the producer. It also recommends setting up a 50-year temporary storage facility to allow for tritium decay and designing future disposal facilities using tritiated radwaste characteristics as input data. This paper first describes this waste program and then details an optimized classification methodology which takes into account tritium decay over a 50-year storage period. The paper also describes a specific application for purely tritiated waste and discusses the set-up expected to be implemented for ITER decommissioning waste (current assumption). Comparison between this optimized approach and other viable detritiation techniques will be drawn. (authors)

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

    SciTech Connect (OSTI)

    Pruvot, Benoist; Cur, Yoann; Djiotsa, Joachim; Voncken, Audrey; Muller, Marc

    2014-01-15

    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.

  2. Deep Spatiotemporal Feature Learning with Application to Image Classification

    SciTech Connect (OSTI)

    Karnowski, Thomas Paul; Arel, Itamar; Rose, Derek C

    2010-01-01

    Deep machine learning is an emerging framework for dealing with complex high-dimensionality data in a hierarchical fashion which draws some inspiration from biological sources. Despite the notable progress made in the field, there remains a need for an architecture that can represent temporal information with the same ease that spatial information is discovered. In this work, we present new results using a recently introduced deep learning architecture called Deep Spatio-Temporal Inference Network (DeSTIN). DeSTIN is a discriminative deep learning architecture that combines concepts from unsupervised learning for dynamic pattern representation together with Bayesian inference. In DeSTIN the spatiotemporal dependencies that exist within the observations are modeled inherently in an unguided manner. Each node models the inputs by means of clustering and simple dynamics modeling while it constructs a belief state over the distribution of sequences using Bayesian inference. We demonstrate that information from the different layers of this hierarchical system can be extracted and utilized for the purpose of pattern classification. Earlier simulation results indicated that the framework is highly promising, consequently in this work we expand DeSTIN to a popular problem, the MNIST data set of handwritten digits. The system as a preprocessor to a neural network achieves a recognition accuracy of 97.98% on this data set. We further show related experimental results pertaining to automatic cluster adaptation and termination.

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

    DOE Patents [OSTI]

    Chambers, David H; Paglieroni, David W

    2014-05-06

    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.

  4. Early F-type stars - refined classification, confrontation with Stromgren photometry, and the effects of rotation

    SciTech Connect (OSTI)

    Gray, R.O.; Garrison, R.F.

    1989-02-01

    The classification for early F-type stars in the MK spectral classification system presented by Gray and Garrison (1987) is refined. The effect of rotation on spectral classification and ubvy-beta photometry of early F-type stars is examined. It is found that the classical luminosity criterion, the 4417 A/4481 A ratio gives inconsistent results. It is shown that most of the stars in the Delta Delphini class of metallic-line stars are either normal or are indistinguishable from proto-Am stars. It is suggested that the designation Delta Delphini should be dropped. The classifications are compared with Stromgren photometry. The effects of rotation on the delta-c sub 1 index in the early-F field dwarfs is demonstrated. 55 references.

  5. Classification of Lattice Defects in the Kesterite Cu2ZnSnS4...

    Office of Scientific and Technical Information (OSTI)

    Journal Article: Classification of Lattice Defects in the Kesterite Cu2ZnSnS4 and Cu2ZnSnSe4 Earth-Abundant Solar Cell Absorbers Citation Details In-Document Search Title: ...

  6. Updating the US hydrologic classification: an approach to clustering and stratifying ecohydrologic data

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

    Updating the US hydrologic classification: an approach to clustering and stratifying ecohydrologic data Ryan A. McManamay, * Mark S. Bevelhimer and Shih-Chieh Kao Environmental Sciences Division, Oak Ridge National Lab, Oak Ridge, TN 37831, USA ABSTRACT Hydrologic classifications unveil the structure of relationships among groups of streams with differing streamflows and provide a foundation for drawing inferences about the principles that govern those relationships. Hydrologic classes provide a

  7. Stream Classification Tool User Manual: For Use in Applications in Hydropower-Related Evironmental Mitigation

    SciTech Connect (OSTI)

    McManamay, Ryan A.; Troia, Matthew J.; DeRolph, Christopher R.; Samu, Nicole M.

    2016-01-01

    Stream classifications are an inventory of different types of streams. Classifications help us explore similarities and differences among different types of streams, make inferences regarding stream ecosystem behavior, and communicate the complexities of ecosystems. We developed a nested, layered, and spatially contiguous stream classification to characterize the biophysical settings of stream reaches within the Eastern United States (~ 900,000 reaches). The classification is composed of five natural characteristics (hydrology, temperature, size, confinement, and substrate) along with several disturbance regime layers, and each was selected because of their relevance to hydropower mitigation. We developed the classification at the stream reach level using the National Hydrography Dataset Plus Version 1 (1:100k scale). The stream classification is useful to environmental mitigation for hydropower dams in multiple ways. First, it creates efficiency in the regulatory process by creating an objective and data-rich means to address meaningful mitigation actions. Secondly, the SCT addresses data gaps as it quickly provides an inventory of hydrology, temperature, morphology, and ecological communities for the immediate project area, but also surrounding streams. This includes identifying potential reference streams as those that are proximate to the hydropower facility and fall within the same class. These streams can potentially be used to identify ideal environmental conditions or identify desired ecological communities. In doing so, the stream provides some context for how streams may function, respond to dam regulation, and an overview of specific mitigation needs. Herein, we describe the methodology in developing each stream classification layer and provide a tutorial to guide applications of the classification (and associated data) in regulatory settings, such as hydropower (re)licensing.

  8. Classification of poison inhalation hazard materials into severity groups

    SciTech Connect (OSTI)

    Griego, N.R.; Weiner, R.F.

    1996-02-01

    Approximately 1.5 billion tons of hazardous materials (hazmat) are transported in the US annually, and most reach their destinations safely. However, there are infrequent transportation accidents in which hazmat is released from its packaging. These accidental releases can potentially affect the health of the exposed population and damage the surrounding environment. Although these events are rare, they cause genuine public concern. Therefore, the US Department of Transportation Research & Special Programs Administration (DOT- RSPA) has sponsored a project to evaluate the protection provided by the current bulk (defined as larger than 118 gallons) packagings used to transport materials that have been classified as Poison Inhalation Hazards (PIH) and recommend performance standards for these PIH packagings. This project was limited to evaluating bulk packagings larger than 2000 gallons. This project involved classifying the PIH into severity categories so that only one set of packaging performance criteria would be needed for each severity category rather than a separate set of performance criteria for each individual PIH. By grouping the PIH into Hazard Zones, Packaging Groups and performance standards for these Hazard Zones can be defined. Each Hazard Zone can correspond to a Packaging Group or, as in 49CFR173 for non-bulk packagings, one Packaging Group may cover more than one Hazard Zone. If the packaging groups are chosen to correspond to the classification categories presented in this report, then the maximum allowable leak rates used to define these categories could be used as the maximum allowable leak rates for the performance oriented packaging standards. The results discussed in this report are intended to provide quantitative guidance for the appropriate authorities to use in making these decisions.

  9. Authorization Basis Safety Classification of Transfer Bay Bridge Crane at the 105-K Basins

    SciTech Connect (OSTI)

    CHAFFEE, G.A.

    2000-04-06

    This supporting document provides the bases for the safety classification for the K Basin transfer bay bridge crane and the bases for the Structures, Systems, and Components (SSC) safety classification. A table is presented that delineates the safety significant components. This safety classification is based on a review of the Authorization Basis (AB). This Authorization Basis review was performed regarding AB and design baseline issues. The primary issues are: (1) What is the AB for the safety classification of the transfer bay bridge crane? (2) What does the SSC safety classification ''Safety Significant'' or ''Safety Significant for Design Only'' mean for design requirements and quality requirements for procurement, installation and maintenance (including replacement of parts) activities for the crane during its expected life time? The AB information on the crane was identified based on review of Department of Energy--Richland Office (RL) and Spent Nuclear Fuel (SNF) Project correspondence, K Basin Safety Analysis Report (SAR) and RL Safety Evaluation Reports (SERs) of SNF Project SAR submittals. The relevant correspondence, actions and activities taken and substantive directions or conclusions of these documents are provided in Appendix A.

  10. Classification of octet AB-type binary compounds using dynamical charges: A materials informatics perspective

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

    Pilania, G.; Gubernatis, J. E.; Lookman, T.

    2015-12-03

    The role of dynamical (or Born effective) charges in classification of octet AB-type binary compounds between four-fold (zincblende/wurtzite crystal structures) and six-fold (rocksalt crystal structure) coordinated systems is discussed. We show that the difference in the dynamical charges of the fourfold and sixfold coordinated structures, in combination with Harrison’s polarity, serves as an excellent feature to classify the coordination of 82 sp–bonded binary octet compounds. We use a support vector machine classifier to estimate the average classification accuracy and the associated variance in our model where a decision boundary is learned in a supervised manner. Lastly, we compare the out-of-samplemore » classification accuracy achieved by our feature pair with those reported previously.« less

  11. A modified version of the geomechanics classification for entry design in underground coal mines

    SciTech Connect (OSTI)

    Newman, D.A.; Bieniawski, Z.T.

    1985-01-01

    The Geomechanics Classification was modified for entry and roof support design in underground room-and-pillar coal mines. Adjustment multipliers were introduced to incorporate the influence of strata weatherability, high horizontal stresses, and the roof support reinforcement factor into the existing classification system. Sixty-two case histories of both standing and fallen mine roof were collected from two mines in the northern Appalachian coalfield. Twenty-seven engineering and geologic parameters were recorded for each case. A partial correlation analysis was carried out on the cases to establish which parameters have a significant impact upon the supported stand-up time of coal mine roof. Survival analysis, a statistical technique used in medical research to assess the effect of a drug or treatment on a patient's life expectancy, was conducted together with stepwise multiple regression to derive values for the adjustment multipliers. A practical example is included to illustrate the application of the modified Geomechanics Classification to underground coal mine design.

  12. Classification of octet AB-type binary compounds using dynamical charges: A materials informatics perspective

    SciTech Connect (OSTI)

    Pilania, G.; Gubernatis, J. E.; Lookman, T.

    2015-12-03

    The role of dynamical (or Born effective) charges in classification of octet AB-type binary compounds between four-fold (zincblende/wurtzite crystal structures) and six-fold (rocksalt crystal structure) coordinated systems is discussed. We show that the difference in the dynamical charges of the fourfold and sixfold coordinated structures, in combination with Harrison’s polarity, serves as an excellent feature to classify the coordination of 82 sp–bonded binary octet compounds. We use a support vector machine classifier to estimate the average classification accuracy and the associated variance in our model where a decision boundary is learned in a supervised manner. Lastly, we compare the out-of-sample classification accuracy achieved by our feature pair with those reported previously.

  13. Multi-contingency preprocessing for security assessment using physical concepts and CQR with classifications

    SciTech Connect (OSTI)

    Chen, R.H.; Malik, O.P.; Hope, G.S. ); Jingde Gao; Shiying Wang; Niande Xiang )

    1993-08-01

    A new method for preprocessing the outage lists for an Expert System for Security Assessment of power systems is presented in this paper. This method uses disturbing capability, structure and location classifications and CQR with Classifications (CQRC). Because this method avoids the traditional enumerative methods such as Automatic Contingency Selection, the number of contingencies can be reduced very sharply with heuristic common sense and rules of CQRC before numerical computing. So the double contingency problem or extremely heavy cost of computing can be tackled successfully. The proposed method along with an Automatic Contingency Analysis and Classification technique can assess the classified contingencies with basic physical concepts and without network simplification, minimize the number of missing harmful contingencies efficiently and give a clear accurate output.

  14. AFREET: HUMAN-INSPIRED SPATIO-SPECTRAL FEATURE CONSTRUCTION FOR IMAGE CLASSIFICATION WITH SUPPORT VECTOR MACHINES

    SciTech Connect (OSTI)

    S. PERKINS; N. HARVEY

    2001-02-01

    The authors examine the task of pixel-by-pixel classification of the multispectral and grayscale images typically found in remote-sensing and medical applications. Simple machine learning techniques have long been applied to remote-sensed image classification, but almost always using purely spectral information about each pixel. Humans can often outperform these systems, and make extensive use of spatial context to make classification decisions. They present AFREET: an SVM-based learning system which attempts to automatically construct and refine spatio-spectral features in a somewhat human-inspired fashion. Comparisons with traditionally used machine learning techniques show that AFREET achieves significantly higher performance. The use of spatial context is particularly useful for medical imagery, where multispectral images are still rare.

  15. A minimum spanning forest based classification method for dedicated breast CT images

    SciTech Connect (OSTI)

    Pike, Robert; Sechopoulos, Ioannis; Fei, Baowei

    2015-11-15

    Purpose: To develop and test an automated algorithm to classify different types of tissue in dedicated breast CT images. Methods: Images of a single breast of five different patients were acquired with a dedicated breast CT clinical prototype. The breast CT images were processed by a multiscale bilateral filter to reduce noise while keeping edge information and were corrected to overcome cupping artifacts. As skin and glandular tissue have similar CT values on breast CT images, morphologic processing is used to identify the skin based on its position information. A support vector machine (SVM) is trained and the resulting model used to create a pixelwise classification map of fat and glandular tissue. By combining the results of the skin mask with the SVM results, the breast tissue is classified as skin, fat, and glandular tissue. This map is then used to identify markers for a minimum spanning forest that is grown to segment the image using spatial and intensity information. To evaluate the authors’ classification method, they use DICE overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on five patient images. Results: Comparison between the automatic and the manual segmentation shows that the minimum spanning forest based classification method was able to successfully classify dedicated breast CT image with average DICE ratios of 96.9%, 89.8%, and 89.5% for fat, glandular, and skin tissue, respectively. Conclusions: A 2D minimum spanning forest based classification method was proposed and evaluated for classifying the fat, skin, and glandular tissue in dedicated breast CT images. The classification method can be used for dense breast tissue quantification, radiation dose assessment, and other applications in breast imaging.

  16. Real-time detection and classification of anomalous events in streaming data

    DOE Patents [OSTI]

    Ferragut, Erik M.; Goodall, John R.; Iannacone, Michael D.; Laska, Jason A.; Harrison, Lane T.

    2016-04-19

    A system is described for receiving a stream of events and scoring the events based on anomalousness and maliciousness (or other classification). The events can be displayed to a user in user-defined groupings in an animated fashion. The system can include a plurality of anomaly detectors that together implement an algorithm to identify low probability events and detect atypical traffic patterns. The atypical traffic patterns can then be classified as being of interest or not. In one particular example, in a network environment, the classification can be whether the network traffic is malicious or not.

  17. NEW - DOE O 325.2 Chg 1 (Admin Chg), Position Management and Classification

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

    The order establishes departmental requirements and responsibilities for classifying positions using the general schedule (GS) and federal wage system (FWS) standards and to develop and administer a sound position management and classification program. Cancels DOE O 325.2, dated 4-1-15.

  18. The Council of Industrial Boiler Owners special project on non-utility fossil fuel ash classification

    SciTech Connect (OSTI)

    Svendsen, R.L.

    1996-12-31

    Information is outlined on the Council of Industrial Boiler Owners (CIBO) special project on non-utility fossil fuel ash classification. Data are presented on; current (1996) regulatory status of fossil-fuel combustion wastes; FBC technology identified for further study; CIBO special project methods; Bevill amendment study factors; data collection; and CIBO special project status.

  19. A Hybrid Semi-supervised Classification Scheme for Mining Multisource Geospatial Data

    SciTech Connect (OSTI)

    Vatsavai, Raju; Bhaduri, Budhendra L

    2011-01-01

    Supervised learning methods such as Maximum Likelihood (ML) are often used in land cover (thematic) classification of remote sensing imagery. ML classifier relies exclusively on spectral characteristics of thematic classes whose statistical distributions (class conditional probability densities) are often overlapping. The spectral response distributions of thematic classes are dependent on many factors including elevation, soil types, and ecological zones. A second problem with statistical classifiers is the requirement of large number of accurate training samples (10 to 30 |dimensions|), which are often costly and time consuming to acquire over large geographic regions. With the increasing availability of geospatial databases, it is possible to exploit the knowledge derived from these ancillary datasets to improve classification accuracies even when the class distributions are highly overlapping. Likewise newer semi-supervised techniques can be adopted to improve the parameter estimates of statistical model by utilizing a large number of easily available unlabeled training samples. Unfortunately there is no convenient multivariate statistical model that can be employed for mulitsource geospatial databases. In this paper we present a hybrid semi-supervised learning algorithm that effectively exploits freely available unlabeled training samples from multispectral remote sensing images and also incorporates ancillary geospatial databases. We have conducted several experiments on real datasets, and our new hybrid approach shows over 25 to 35% improvement in overall classification accuracy over conventional classification schemes.

  20. Next Generation Nuclear Plant Structures, Systems, and Components Safety Classification White Paper

    SciTech Connect (OSTI)

    Pete Jordan

    2010-09-01

    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.

  1. Land cover classification in multispectral imagery using clustering of sparse approximations over learned feature dictionaries

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

    Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.; Altmann, Garrett L.

    2014-12-09

    We present results from an ongoing effort to extend neuromimetic machine vision algorithms to multispectral data using adaptive signal processing combined with compressive sensing and machine learning techniques. Our goal is to develop a robust classification methodology that will allow for automated discretization of the landscape into distinct units based on attributes such as vegetation, surface hydrological properties, and topographic/geomorphic characteristics. We use a Hebbian learning rule to build spectral-textural dictionaries that are tailored for classification. We learn our dictionaries from millions of overlapping multispectral image patches and then use a pursuit search to generate classification features. Land cover labelsmore » are automatically generated using unsupervised clustering of sparse approximations (CoSA). We demonstrate our method on multispectral WorldView-2 data from a coastal plain ecosystem in Barrow, Alaska. We explore learning from both raw multispectral imagery and normalized band difference indices. We explore a quantitative metric to evaluate the spectral properties of the clusters in order to potentially aid in assigning land cover categories to the cluster labels. In this study, our results suggest CoSA is a promising approach to unsupervised land cover classification in high-resolution satellite imagery.« less

  2. Using Process Knowledge to Manage Disposal Classification of Ion-Exchange Resin - 13566

    SciTech Connect (OSTI)

    Bohnsack, Jonathan N.; James, David W.

    2013-07-01

    It has been previously shown by EPRI [1] that Class B and C resins represent a small portion by volume of the overall generation of radioactively contaminated resins. In fact, if all of the resins were taken together the overall classification would meet Class A disposal requirements. Lowering the classification of the ion exchange resins as they are presented for disposal provides a path for minimizing the amount of waste stored. Currently there are commercial options for blending wastes from various generators for Class A disposal in development. The NRC may have by this time introduced changes and clarifications to the Branch Technical Position (BTP) on Concentration Averaging and Encapsulation [2] that may ultimately add more flexibility to what can be done at the plant level. The BTP has always maintained that mixtures of resins that are combined for ALARA purposes or operational efficiency can be classified on the basis of the mixture. This is a point often misinterpreted and misapplied. This paper will address options that can be exercised by the generator that can limit B and C waste generation by more rigorous tracking of generation and taking advantage of the normal mix of wastes. This can be achieved through the monitoring of reactor coolant chemistry data and coupled with our knowledge of radionuclide production mechanisms. This knowledge can be used to determine the overall accumulation of activity in ion-exchange resins and provides a 'real-time' waste classification determination of the resin and thereby provide a mechanism to reduce the production of waste that exceeds class A limits. It should be noted that this alternative approach, although rarely used in a nuclear power plant setting, is acknowledged in the original BTP on classification [3] as a viable option for determining radionuclide inventories for classification of waste. Also included is a discussion of an examination performed at the Byron plant to estimate radionuclide content in the

  3. A probablistic neural network classification system for signal and image processing

    SciTech Connect (OSTI)

    Bowman, B.

    1994-11-15

    The Acoustical Heart Valve Analysis Package is a system for signal and image processing and classification. It is being developed in both Matlab and C, to provide an interactive, interpreted environment, and has been optimized for large scale matrix operations. It has been used successfully to classify acoustic signals from implanted prosthetic heart valves in human patients, and will be integrated into a commercial Heart Valve Screening Center. The system uses several standard signal processing algorithms, as well as supervised learning techniques using the probabilistic neural network (PNN). Although currently used for the acoustic heart valve application, the algorithms and modular design allow it to be used for other applications, as well. We will describe the signal classification system, and show results from a set of test valves.

  4. Classification of heart valve single leg separations from acoustic clinical measurements

    SciTech Connect (OSTI)

    Clark, G.A.; Bowman, B.C.; Boruta, N.; Thomas, G.H.; Jones, H.E.; Buhl, M.R.

    1994-05-01

    Our system classifies the condition (intact or single leg separated) of in vivo Bjork-Shiley Convexo-Concave (BSCC) heart valves by processing acoustic measurements of clinical heart valve opening sounds. We use spectral features as inputs to a two-stage classifier, which first classifies individual heart beats, then classifies valves. Performance is measured by probability of detection and probability of false alarm, and by confidence intervals on the probability of correct classification. The novelty of the work lies in the application of advanced techniques to real heart valve data, and extensions of published algorithms that enhance their applicability. We show that even when given a very small number of training samples, the classifier can achieve a probability of correct classification of 100%.

  5. Communications and control for electric power systems: Power flow classification for static security assessment

    SciTech Connect (OSTI)

    Niebur, D.; Germond, A.

    1993-02-01

    This report investigates the classification of power system states using an artificial neural network model, Kohonen's self-organizing feature map. The ultimate goal of this classification is to assess power system static security in real-time. Kohonen's self-organizing feature map is an unsupervised neural network which maps N-dimensional input vectors to an array of M neurons. After learning, the synaptic weight vectors exhibit a topological organization which represents the relationship between the vectors of the training set. This learning is unsupervised, which means that the number and size of the classes are not specified beforehand. In the application developed in the paper, the input vectors used as the training set are generated by off-line load-flow simulations. The learning algorithm and the results of the organization are discussed.

  6. Communications and control for electric power systems: Power flow classification for static security assessment

    SciTech Connect (OSTI)

    Niebur, D.; Germond, A.

    1993-02-01

    This report investigates the classification of power system states using an artificial neural network model, Kohonen`s self-organizing feature map. The ultimate goal of this classification is to assess power system static security in real-time. Kohonen`s self-organizing feature map is an unsupervised neural network which maps N-dimensional input vectors to an array of M neurons. After learning, the synaptic weight vectors exhibit a topological organization which represents the relationship between the vectors of the training set. This learning is unsupervised, which means that the number and size of the classes are not specified beforehand. In the application developed in the paper, the input vectors used as the training set are generated by off-line load-flow simulations. The learning algorithm and the results of the organization are discussed.

  7. Groenewold-Moyal product, ?*-cohomology, and classification of translation-invariant non-commutative structures

    SciTech Connect (OSTI)

    Varshovi, Amir Abbass

    2013-07-15

    The theory of ?*-cohomology is studied thoroughly and it is shown that in each cohomology class there exists a unique 2-cocycle, the harmonic form, which generates a particular Groenewold-Moyal star product. This leads to an algebraic classification of translation-invariant non-commutative structures and shows that any general translation-invariant non-commutative quantum field theory is physically equivalent to a Groenewold-Moyal non-commutative quantum field theory.

  8. Classification of Distributed Data Using Topic Modeling and Maximum Variation Sampling

    SciTech Connect (OSTI)

    Patton, Robert M; Beaver, Justin M; Potok, Thomas E

    2011-01-01

    From a management perspective, understanding the information that exists on a network and how it is distributed provides a critical advantage. This work explores the use of topic modeling as an approach to automatically determine the classes of information that exist on an organization's network, and then use the resultant topics as centroid vectors for the classification of individual documents in order to understand the distribution of information topics across the enterprise network. The approach is tested using the 20 Newsgroups dataset.

  9. Vegetation classification in southern pine mixed hardwood forests using airborne scanning laser point data.

    SciTech Connect (OSTI)

    McGaughey, Robert J.; Reutebuch, Stephen E.

    2012-09-01

    Forests of the southeastern United States are dominated by a relatively small number of conifer species. However, many of these forests also have a hardwood component composed of a wide variety of species that are found in all canopy positions. The presence or absence of hardwood species and their position in the canopy often dictates management activities such as thinning or prescribed burning. In addition, the characteristics of the under- and mid-story layers, often dominated by hardwood species, are key factors when assessing suitable habitat for threatened and endangered species such as the Red Cockaded Woodpecker (Picoides borealis) (RCW), making information describing the hardwood component important to forest managers. General classification of cover types using LIDAR data has been reported (Song et al. 2002, Brennan and Webster 2006) but most efforts focusing on the identification of individual species or species groups rely on some type of imagery to provide more complete spectral information for the study area. Brandtberg (2007) found that use of intensity data significantly improved LIDAR detection and classification of three leaf-off deciduous eastern species: oaks (Quercus spp.), red maple (Acer rubrum L.), and yellow poplar (Liriodendron tulipifera L.). Our primary objective was to determine the proportion of hardwood species present in the canopy using only the LIDAR point data and derived products. However, the presence of several hardwood species that retain their foliage through the winter months complicated our analyses. We present two classification approaches. The first identifies areas containing hardwood and softwood (conifer) species (H/S) and the second identifies vegetation with foliage absent or present (FA/FP) at the time of the LIDAR data acquisition. The classification results were used to develop predictor variables for forest inventory models. The ability to incorporate the proportion of hardwood and softwood was important to the

  10. On the construction of a new stellar classification template library for the LAMOST spectral analysis pipeline

    SciTech Connect (OSTI)

    Wei, Peng; Luo, Ali; Li, Yinbi; Tu, Liangping; Wang, Fengfei; Zhang, Jiannan; Chen, Xiaoyan; Hou, Wen; Kong, Xiao; Wu, Yue; Zuo, Fang; Yi, Zhenping; Zhao, Yongheng; Chen, Jianjun; Du, Bing; Guo, Yanxin; Ren, Juanjuan; Pan, Jingchang; Jiang, Bin; Liu, Jie E-mail: weipeng@nao.cas.cn; and others

    2014-05-01

    The LAMOST spectral analysis pipeline, called the 1D pipeline, aims to classify and measure the spectra observed in the LAMOST survey. Through this pipeline, the observed stellar spectra are classified into different subclasses by matching with template spectra. Consequently, the performance of the stellar classification greatly depends on the quality of the template spectra. In this paper, we construct a new LAMOST stellar spectral classification template library, which is supposed to improve the precision and credibility of the present LAMOST stellar classification. About one million spectra are selected from LAMOST Data Release One to construct the new stellar templates, and they are gathered in 233 groups by two criteria: (1) pseudo g r colors obtained by convolving the LAMOST spectra with the Sloan Digital Sky Survey ugriz filter response curve, and (2) the stellar subclass given by the LAMOST pipeline. In each group, the template spectra are constructed using three steps. (1) Outliers are excluded using the Local Outlier Probabilities algorithm, and then the principal component analysis method is applied to the remaining spectra of each group. About 5% of the one million spectra are ruled out as outliers. (2) All remaining spectra are reconstructed using the first principal components of each group. (3) The weighted average spectrum is used as the template spectrum in each group. Using the previous 3 steps, we initially obtain 216 stellar template spectra. We visually inspect all template spectra, and 29 spectra are abandoned due to low spectral quality. Furthermore, the MK classification for the remaining 187 template spectra is manually determined by comparing with 3 template libraries. Meanwhile, 10 template spectra whose subclass is difficult to determine are abandoned. Finally, we obtain a new template library containing 183 LAMOST template spectra with 61 different MK classes by combining it with the current library.