National Library of Energy BETA

Sample records for understood 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. 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...

  4. 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...

  5. 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...

  6. 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...

  7. 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:...

  8. 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...

  9. 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:...

  10. 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...

  11. 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,...

  12. 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 ...

  13. 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...

  14. 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...

  15. 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...

  16. 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...

  17. 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...

  18. 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...

  19. 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...

  20. 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...

  1. 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...

  2. 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...

  3. 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...

  4. 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

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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

  11. 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.

  12. 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

  13. 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...

  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 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.

  15. 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.

  16. 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.

  17. 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...

  18. 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 & ...

  19. 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.

  20. 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

  1. 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.

  2. 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

  3. 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, §

  4. 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.

  5. 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.

  6. 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)

  7. 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...

  8. 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...

  9. 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 ...

  10. 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...

  11. 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

  12. 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.

  13. 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.

  14. 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 ...

  15. 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

  16. 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.

  17. 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.

  18. 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

  19. 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

  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. 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 ...

  2. 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, ...

  3. 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.

  4. 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. ...

  5. 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

  6. 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 ...

  7. 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

  8. 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.

  9. 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

  10. 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.

  11. 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

  12. 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

  13. 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.

  14. 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.

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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.

  1. 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

  2. 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,

  3. 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 ...

  4. 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...

  5. 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...

  6. 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.

  7. 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:...

  8. 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

  9. 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.

  10. 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

  11. 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

  12. 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.

  13. 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.

  14. 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

  15. 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

  16. 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

  17. 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.

  18. 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.

  19. 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.

  20. 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

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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).

  6. 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.

  7. 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).

  8. 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

  9. 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;

  10. 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

  11. 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.

  12. 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.

  13. 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

  14. 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.

  15. 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.

  16. 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.

  17. 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 ...

  18. 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.

  19. 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...

  20. 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...

  1. 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 ...

  2. 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).

  3. 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.

  4. 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

  5. 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.

  6. 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

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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

  15. 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

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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)

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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: ...

  7. 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

  8. 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.

  9. 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.

  10. 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.

  11. 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

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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

  3. 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

  4. 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.

  5. 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%.

  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. 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.

  8. 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.

  9. 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.

  10. 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

  11. 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.

  12. An Improved Cloud Classification Algorithm Based on the SGP CART Site Observations

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

    Improved Cloud Classification Algorithm Based on the SGP CART Site Observations Z. Wang Goddard Earth Sciences and Technology Center University of Maryland Greenbelt, Maryland K. Sassen University of Alaska Fairbanks, Alaska Introduction Different types of clouds are usually governed by different cloud dynamics processes and have different microphysical properties, which results in different cloud radiative forcings (Hartmann et al. 1992; Chen et al. 2000). Climate changes can result in changing

  13. Momotombo Geothermal Area | Open Energy Information

    Open Energy Info (EERE)

    World Geothermal Power Generation 2001-2005. Proceedings of World Geothermal Congress; Turkey: World Geothermal Congress. Tom Harding-Newman, James Morrow, Subir Sanyal,...

  14. Context-based automated defect classification system using multiple morphological masks

    DOE Patents [OSTI]

    Gleason, Shaun S.; Hunt, Martin A.; Sari-Sarraf, Hamed

    2002-01-01

    Automatic detection of defects during the fabrication of semiconductor wafers is largely automated, but the classification of those defects is still performed manually by technicians. This invention includes novel digital image analysis techniques that generate unique feature vector descriptions of semiconductor defects as well as classifiers that use these descriptions to automatically categorize the defects into one of a set of pre-defined classes. Feature extraction techniques based on multiple-focus images, multiple-defect mask images, and segmented semiconductor wafer images are used to create unique feature-based descriptions of the semiconductor defects. These feature-based defect descriptions are subsequently classified by a defect classifier into categories that depend on defect characteristics and defect contextual information, that is, the semiconductor process layer(s) with which the defect comes in contact. At the heart of the system is a knowledge database that stores and distributes historical semiconductor wafer and defect data to guide the feature extraction and classification processes. In summary, this invention takes as its input a set of images containing semiconductor defect information, and generates as its output a classification for the defect that describes not only the defect itself, but also the location of that defect with respect to the semiconductor process layers.

  15. KEPLER ECLIPSING BINARY STARS. III. CLASSIFICATION OF KEPLER ECLIPSING BINARY LIGHT CURVES WITH LOCALLY LINEAR EMBEDDING

    SciTech Connect (OSTI)

    Matijevic, Gal; Prsa, Andrej; Orosz, Jerome A.; Welsh, William F.; Bloemen, Steven; Barclay, Thomas E-mail: andrej.prsa@villanova.edu

    2012-05-15

    We present an automated classification of 2165 Kepler eclipsing binary (EB) light curves that accompanied the second Kepler data release. The light curves are classified using locally linear embedding, a general nonlinear dimensionality reduction tool, into morphology types (detached, semi-detached, overcontact, ellipsoidal). The method, related to a more widely used principal component analysis, produces a lower-dimensional representation of the input data while preserving local geometry and, consequently, the similarity between neighboring data points. We use this property to reduce the dimensionality in a series of steps to a one-dimensional manifold and classify light curves with a single parameter that is a measure of 'detachedness' of the system. This fully automated classification correlates well with the manual determination of morphology from the data release, and also efficiently highlights any misclassified objects. Once a lower-dimensional projection space is defined, the classification of additional light curves runs in a negligible time and the method can therefore be used as a fully automated classifier in pipeline structures. The classifier forms a tier of the Kepler EB pipeline that pre-processes light curves for the artificial intelligence based parameter estimator.

  16. Performance analysis of distributed applications using automatic classification of communication inefficiencies

    DOE Patents [OSTI]

    Vetter, Jeffrey S.

    2005-02-01

    The method and system described herein presents a technique for performance analysis that helps users understand the communication behavior of their message passing applications. The method and system described herein may automatically classifies individual communication operations and reveal the cause of communication inefficiencies in the application. This classification allows the developer to quickly focus on the culprits of truly inefficient behavior, rather than manually foraging through massive amounts of performance data. Specifically, the method and system described herein trace the message operations of Message Passing Interface (MPI) applications and then classify each individual communication event using a supervised learning technique: decision tree classification. The decision tree may be trained using microbenchmarks that demonstrate both efficient and inefficient communication. Since the method and system described herein adapt to the target system's configuration through these microbenchmarks, they simultaneously automate the performance analysis process and improve classification accuracy. The method and system described herein may improve the accuracy of performance analysis and dramatically reduce the amount of data that users must encounter.

  17. YOUNG PLANETARY NEBULAE: HUBBLE SPACE TELESCOPE IMAGING AND A NEW MORPHOLOGICAL CLASSIFICATION SYSTEM

    SciTech Connect (OSTI)

    Sahai, Raghvendra; Villar, Gregory G.; Morris, Mark R.

    2011-04-15

    Using Hubble Space Telescope images of 119 young planetary nebulae (PNs), most of which have not previously been published, we have devised a comprehensive morphological classification system for these objects. This system generalizes a recently devised system for pre-planetary nebulae, which are the immediate progenitors of PNs. Unlike previous classification studies, we have focused primarily on young PNs rather than all PNs, because the former best show the influences or symmetries imposed on them by the dominant physical processes operating at the first and primary stage of the shaping process. Older PNs develop instabilities, interact with the ambient interstellar medium, and are subject to the passage of photoionization fronts, all of which obscure the underlying symmetries and geometries imposed early on. Our classification system is designed to suffer minimal prejudice regarding the underlying physical causes of the different shapes and structures seen in our PN sample, however, in many cases, physical causes are readily suggested by the geometry, along with the kinematics that have been measured in some systems. Secondary characteristics in our system, such as ansae, indicate the impact of a jet upon a slower-moving, prior wind; a waist is the signature of a strong equatorial concentration of matter, whether it be outflowing or in a bound Keplerian disk, and point symmetry indicates a secular trend, presumably precession, in the orientation of the central driver of a rapid, collimated outflow.

  18. Resolving Radiological Classification and Release Issues for Many DOE Solid Wastes and Salvageable Materials

    SciTech Connect (OSTI)

    Hochel, R.C.

    1999-06-14

    The cost effective radiological classification and disposal of solid materials with potential volume contamination, in accordance with applicable U.S. Department of Energy (DOE) Orders, suffers from an inability to unambiguously distinguish among transuranic waste, low-level waste, and unconditional-release materials. Depending on the classification, disposal costs can vary by a hundred-fold. But in many cases, the issues can be easily resolved by a combination of process information, some simple measurements, and calculational predictions from a computer model for radiation shielding.The proper classification and disposal of many solid wastes requires a measurement regime that is able to show compliance with a variety of institutional and regulatory contamination limits. Although this is not possible for all solid wastes, there are many that do lend themselves to such measures. Several examples are discussed which demonstrate the possibilities, including one which was successfully applied to bulk contamination.The only barriers to such broader uses are the slow-to-change institutional perceptions and procedures. For many issues and materials, the measurement tools are available; they need only be applied.

  19. Recursive Partitioning Analysis for New Classification of Patients With Esophageal Cancer Treated by Chemoradiotherapy

    SciTech Connect (OSTI)

    Nomura, Motoo; Department of Clinical Oncology, Aichi Cancer Center Hospital, Nagoya; Department of Radiation Oncology, Aichi Cancer Center Hospital, Nagoya ; Shitara, Kohei; Kodaira, Takeshi; Kondoh, Chihiro; Takahari, Daisuke; Ura, Takashi; Kojima, Hiroyuki; Kamata, Minoru; Muro, Kei; Sawada, Satoshi

    2012-11-01

    Background: The 7th edition of the American Joint Committee on Cancer staging system does not include lymph node size in the guidelines for staging patients with esophageal cancer. The objectives of this study were to determine the prognostic impact of the maximum metastatic lymph node diameter (ND) on survival and to develop and validate a new staging system for patients with esophageal squamous cell cancer who were treated with definitive chemoradiotherapy (CRT). Methods: Information on 402 patients with esophageal cancer undergoing CRT at two institutions was reviewed. Univariate and multivariate analyses of data from one institution were used to assess the impact of clinical factors on survival, and recursive partitioning analysis was performed to develop the new staging classification. To assess its clinical utility, the new classification was validated using data from the second institution. Results: By multivariate analysis, gender, T, N, and ND stages were independently and significantly associated with survival (p < 0.05). The resulting new staging classification was based on the T and ND. The four new stages led to good separation of survival curves in both the developmental and validation datasets (p < 0.05). Conclusions: Our results showed that lymph node size is a strong independent prognostic factor and that the new staging system, which incorporated lymph node size, provided good prognostic power, and discriminated effectively for patients with esophageal cancer undergoing CRT.

  20. DOE Publishes Petition of CSA Group for Classification as a Nationally Recognized Certification Program for Small Electric Motors

    Broader source: Energy.gov [DOE]

    The Department of Energy has published a Federal Register notice of petition and request for public comments regarding CSA Group for classification as a nationally recognized certification program for small electric motors.

  1. Understanding Classification

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

    U U n n d d e e r r s s t t a a n n d d i i n n g g C C l l a a s s s s i i f f i i c c a a t t i i o o n n O O f f f f i i c c e e o o f f C C l l a a s s s s i i f f i i c c a a t t i i o o n n O O f f f f i i c c e e o o f f H H e e a a l l t t h h , , S S a a f f e e t t y y a a n n d d S S e e c c u u r r i i t t y y U U . . S S . . D D e e p p a a r r t t m m e e n n t t o o f f E E n n e e r r g g y y J J u u n n e e 2 2 0 0 1 1 2 2 1 Now that you have your clearance, you are likely to be

  2. Object Classification

    Office of Scientific and Technical Information (OSTI)

    ... For this analysis we used a soft- boundary SVM method called C-SVM, which handles noisy ... provide as much sig- nalbackground separation power as the decision tree based methods. ...

  3. Removal of introduced inorganic content from chipped forest residues via air classification

    SciTech Connect (OSTI)

    Lacey, Jeffrey A.; Aston, John E.; Westover, Tyler L.; Cherry, Robert S.; Thompson, David N.

    2015-08-04

    Inorganic content in biomass decreases the efficiency of conversion processes, especially thermochemical conversions. The combined concentrations of specific ash forming elements are the primary attributes that cause pine residues to be considered a degraded energy conversion feedstock, as compared to clean pine. Air classification is a potentially effective and economical tool to isolate high inorganic content biomass fractions away from primary feedstock sources to reduce their ash content. In this work, loblolly pine forest residues were air classified into 10 fractions whose ash content and composition were measured. Ash concentrations were highest in the lightest fractions (5.8–8.5 wt%), and in a heavy fraction of the fines (8.9–15.1 wt%). The removal of fractions with high inorganic content resulted in a substantial reduction in the ash content of the remaining biomass in forest thinnings (1.69–1.07 wt%) and logging residues (1.09–0.68 wt%). These high inorganic content fractions from both forest residue types represented less than 7.0 wt% of the total biomass, yet they contained greater than 40% of the ash content by mass. Elemental analysis of the air classified fractions revealed the lightest fractions were comprised of high concentrations of soil elements (silicon, aluminum, iron, sodium, and titanium). However, the elements of biological origin including calcium, potassium, magnesium, sulfur, manganese, and phosphorous were evenly distributed throughout all air classified fractions, making them more difficult to isolate into fractions with high mineral concentrations. Under the conditions reported in this study, an economic analysis revealed air classification could be used for ash removal for as little as $2.23 per ton of product biomass. As a result, this study suggests air classification is a potentially attractive technology for the removal of introduced soil minerals from pine forest residues.

  4. Classification of heart valve sounds from experiments in an anechoic water tank

    SciTech Connect (OSTI)

    Axelrod, M C; Clark, G A; Scott, D

    1999-06-01

    In vivo studies in both sheep and humans were plagued by a number of problems including movement artifacts, biological noise, low signal-to-noise ratio (SNR), chest-wall reverberation, and limited bandwidth recordings as discussed by [1]. To overcome these problems it was decided to record heart valve sounds under controlled conditions deep in an anechoic water tank, free from reverberation noise, including surface reflections. Experiments were conducted in a deep water tank at the Transdec facility in San Diego, which satisfies these requirements. The Transdec measurements are free of reverberations, but not totally free of acoustic and electrical noise. We used a high quality hydrophone together with a wide-band data acquisition system [2]. We recorded sounds from 100 repetitions of the opening-closing cycles on each of 50 different heart valves, including 21 SLS valves and 29 intact valves. The power spectrum of the opening and closing phases of each cycle were calculated and outlier spectra removed as described by Candy [2]. In this report, we discuss the results of our classification of the heart valve sound measurements. The goal of this classification task was to apply the fundamental classification algorithms developed for the clinical data in 1994 and 1996 to the measurements from the anechoic water tank. From the beginning of this project, LLNL's responsibility has been to process and classify the heart valve opening sounds. For this experiment, however, we processed both the opening sounds and closing sounds for comparison purposes. The results of this experiment show that the classifier did not perform well. We believe this is because of low signal-to-noise ratio and excessive variability in signal power from beat-to-beat for a given valve.

  5. Classification of heart valve sounds from experiments in an anechoic water tank

    SciTech Connect (OSTI)

    Axelrod, M C; Clark, G A; Scott, D

    1999-06-01

    In vivo studies in both sheep and humans were plagued by a number of problems including movement artifacts, biological noise, low signal-to-noise ratio (SNR), chest-wall reverberation, and limited bandwidth recordings as discussed by [1]. To overcome these problems it was decided to record heart valve sounds under controlled conditions deep in an anechoic water tank, free from reverberation noise. The main goal of this experiment was to obtain measurements of ''pure'' heart valve sounds free of the scattering effects of the body. Experiments were conducted at the Transdec facility in San Diego [2]. We used a high quality hydrophone together with a wide-band data acquisition system [2]. We recorded sounds from 100 repetitions of the opening-closing cycles on each of 50 different heart valves, including 21 SLS valves and 29 intact valves. The power spectrum of the opening and closing phases of each cycle were calculated and outlier spectra removed as described by Candy [2]. In this report, we discuss the results of our classification of the heart valve sound measurements. The goal of this classification task was to apply the fundamental classification algorithms developed for the clinical data in 1994 and 1996 to the measurements from the anechoic water tank. From the beginning of this project, LLNL's responsibility has been to process and classify the heart valve sounds. For this experiment, however, we processed both the opening sounds and closing sounds for comparison purposes. The results of this experiment show that the classifier did not perform well because of low signal-to-noise ratio and excessive variability in signal power from beat-to-beat for a given valve.

  6. Gradient Analysis and Classification of Carolina Bay Vegetation: A Framework for Bay Wetlands Conservation and Restoration

    SciTech Connect (OSTI)

    Diane De Steven,Ph.D.; Maureen Tone,PhD.

    1997-10-01

    This report address four project objectives: (1) Gradient model of Carolina bay vegetation on the SRS--The authors use ordination analyses to identify environmental and landscape factors that are correlated with vegetation composition. Significant factors can provide a framework for site-based conservation of existing diversity, and they may also be useful site predictors for potential vegetation in bay restorations. (2) Regional analysis of Carolina bay vegetation diversity--They expand the ordination analyses to assess the degree to which SRS bays encompass the range of vegetation diversity found in the regional landscape of South Carolina's western Upper Coastal Plain. Such comparisons can indicate floristic status relative to regional potentials and identify missing species or community elements that might be re-introduced or restored. (3) Classification of vegetation communities in Upper Coastal Plain bays--They use cluster analysis to identify plant community-types at the regional scale, and explore how this classification may be functional with respect to significant environmental and landscape factors. An environmentally-based classification at the whole-bay level can provide a system of templates for managing bays as individual units and for restoring bays to desired plant communities. (4) Qualitative model for bay vegetation dynamics--They analyze present-day vegetation in relation to historic land uses and disturbances. The distinctive history of SRS bays provides the possibility of assessing pathways of post-disturbance succession. They attempt to develop a coarse-scale model of vegetation shifts in response to changing site factors; such qualitative models can provide a basis for suggesting management interventions that may be needed to maintain desired vegetation in protected or restored bays.

  7. Application of air classification and formulation to manage feedstock cost, quality and availability for bioenergy

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

    Thompson, Vicki S.; Lacey, Jeffrey A.; Hartley, Damon; Jindra, Michael A.; Aston, John E.; Thompson, David N.

    2016-04-22

    Biomass such as agricultural residues, energy crops and yard waste has significant potential to be used as renewable feedstocks for production of fuels, chemicals and energy. However, in a given location, biomass availability, cost and quality can vary markedly. Strategies to manage these traits must be identified and implemented so that consistent low-cost and high-quality feedstocks can be delivered to biorefineries year round. In this study, we examine air classification as a method to mitigate high ash concentrations in corn stover, switchgrass, and grass clippings. Formulation techniques were then used to produce blends that met ash quality and biomass quantitymore » specifications at the lowest possible cost for biopower and biochemical conversion applications. It was found that air classification can separate the biomass into light fractions which contain concentrated amounts of elemental ash components introduced through soil contamination such as sodium, alumina, silica, iron and titania; and heavy fractions that are depleted in these components and have relatively lower total ash content. Light fractions of corn stover and grass clippings were found to be suitable for combustion applications since they had less propensity to slag than the whole biomass material. The remaining heavy fractions of corn stover or grass clippings could then be blended with switchgrass to produce blends that met the 5% total ash specifications suggested for biochemical conversions. However, ternary blends of the three feedstocks were not possible due to the high ash content of grass clippings. Lastly, it was determined that air classification by itself was not suitable to prepare these feedstocks for pyrolysis due to high ash content.« less

  8. Removal of introduced inorganic content from chipped forest residues via air classification

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

    Lacey, Jeffrey A.; Aston, John E.; Westover, Tyler L.; Cherry, Robert S.; Thompson, David N.

    2015-08-04

    Inorganic content in biomass decreases the efficiency of conversion processes, especially thermochemical conversions. The combined concentrations of specific ash forming elements are the primary attributes that cause pine residues to be considered a degraded energy conversion feedstock, as compared to clean pine. Air classification is a potentially effective and economical tool to isolate high inorganic content biomass fractions away from primary feedstock sources to reduce their ash content. In this work, loblolly pine forest residues were air classified into 10 fractions whose ash content and composition were measured. Ash concentrations were highest in the lightest fractions (5.8–8.5 wt%), and inmore » a heavy fraction of the fines (8.9–15.1 wt%). The removal of fractions with high inorganic content resulted in a substantial reduction in the ash content of the remaining biomass in forest thinnings (1.69–1.07 wt%) and logging residues (1.09–0.68 wt%). These high inorganic content fractions from both forest residue types represented less than 7.0 wt% of the total biomass, yet they contained greater than 40% of the ash content by mass. Elemental analysis of the air classified fractions revealed the lightest fractions were comprised of high concentrations of soil elements (silicon, aluminum, iron, sodium, and titanium). However, the elements of biological origin including calcium, potassium, magnesium, sulfur, manganese, and phosphorous were evenly distributed throughout all air classified fractions, making them more difficult to isolate into fractions with high mineral concentrations. Under the conditions reported in this study, an economic analysis revealed air classification could be used for ash removal for as little as $2.23 per ton of product biomass. As a result, this study suggests air classification is a potentially attractive technology for the removal of introduced soil minerals from pine forest residues.« less

  9. Inductive entanglement classification of four qubits under stochastic local operations and classical communication

    SciTech Connect (OSTI)

    Lamata, L.; Leon, J.; Salgado, D.; Solano, E.

    2007-02-15

    Using an inductive approach to classify multipartite entangled states under stochastic local operations and classical communication introduced recently by the authors [Phys. Rev. A 74, 052336 (2006)], we give the complete classification of four-qubit entangled pure states. Apart from the expected degenerate classes, we show that there exist eight inequivalent ways to entangle four qubits. In this respect, permutation symmetry is taken into account and states with a structure differing only by parameters inside a continuous set are considered to belong to the same class.

  10. OPTICAL CLASSIFICATION OF GAMMA-RAY BURSTS IN THE SWIFT ERA

    SciTech Connect (OSTI)

    Van der Horst, A. J.; Kouveliotou, C.; Gehrels, N.; Cannizzo, J. K.; Rol, E.; Wijers, R. A. M. J.; Racusin, J.; Burrows, D. N.

    2009-07-10

    We propose a new method for the classification of optically dark gamma-ray bursts (GRBs), based on the X-ray and optical-to-X-ray spectral indices of GRB afterglows, and utilizing the spectral capabilities of Swift. This method depends less on model assumptions than previous methods, and can be used as a quick diagnostic tool to identify optically sub-luminous bursts. With this method we can also find GRBs that are extremely bright at optical wavelengths. We show that the previously suggested correlation between the optical darkness and the X-ray/gamma-ray brightness is merely an observational selection effect.

  11. Net-Zero Energy Buildings: A Classification System Based on Renewable Energy Supply Options

    SciTech Connect (OSTI)

    Pless, S.; Torcellini, P.

    2010-06-01

    A net-zero energy building (NZEB) is a residential or commercial building with greatly reduced energy needs. In such a building, efficiency gains have been made such that the balance of energy needs can be supplied with renewable energy technologies. Past work has developed a common NZEB definition system, consisting of four well-documented definitions, to improve the understanding of what net-zero energy means. For this paper, we created a classification system for NZEBs based on the renewable sources a building uses.

  12. Department of energy defense programs perspectives on safeguards, security, and classification

    SciTech Connect (OSTI)

    Eyck, E.Q.T. )

    1989-07-01

    This paper discusses why national and international safeguards and the protection of sensitive information are important to the United States and to other nations. It demonstrates that while the opposite consequence appears logical these functions will probably become even more important if the major powers agree on further arms reductions. Some of the steps taken by the U.S. Department of Energy to improve the effectiveness of its safeguards, security, and classification programs are reviewed. The valuable contributions in these areas since 1968 and 1976, respectively by the Technical Support Organization and the International Safeguards Project Offoce at Brookhaven are noted.

  13. High Throughput Ambient Mass Spectrometric Approach to Species Identification and Classification from Chemical Fingerprint Signatures

    SciTech Connect (OSTI)

    Musah, Rabi A.; Espinoza, Edgard O.; Cody, Robert B.; Lesiak, Ashton D.; Christensen, Earl D.; Moore, Hannah E.; Maleknia, Simin; Drijhout, Falko P.

    2015-07-09

    A high throughput method for species identification and classification through chemometric processing of direct analysis in real time (DART) mass spectrometry-derived fingerprint signatures has been developed. The method entails introduction of samples to the open air space between the DART ion source and the mass spectrometer inlet, with the entire observed mass spectral fingerprint subjected to unsupervised hierarchical clustering processing. Moreover, a range of both polar and non-polar chemotypes are instantaneously detected. The result is identification and species level classification based on the entire DART-MS spectrum. In this paper, we illustrate how the method can be used to: (1) distinguish between endangered woods regulated by the Convention for the International Trade of Endangered Flora and Fauna (CITES) treaty; (2) assess the origin and by extension the properties of biodiesel feedstocks; (3) determine insect species from analysis of puparial casings; (4) distinguish between psychoactive plants products; and (5) differentiate between Eucalyptus species. An advantage of the hierarchical clustering approach to processing of the DART-MS derived fingerprint is that it shows both similarities and differences between species based on their chemotypes. Furthermore, full knowledge of the identities of the constituents contained within the small molecule profile of analyzed samples is not required.

  14. An expert computer program for classifying stars on the MK spectral classification system

    SciTech Connect (OSTI)

    Gray, R. O.; Corbally, C. J.

    2014-04-01

    This paper describes an expert computer program (MKCLASS) designed to classify stellar spectra on the MK Spectral Classification system in a way similar to humans—by direct comparison with the MK classification standards. Like an expert human classifier, the program first comes up with a rough spectral type, and then refines that spectral type by direct comparison with MK standards drawn from a standards library. A number of spectral peculiarities, including barium stars, Ap and Am stars, λ Bootis stars, carbon-rich giants, etc., can be detected and classified by the program. The program also evaluates the quality of the delivered spectral type. The program currently is capable of classifying spectra in the violet-green region in either the rectified or flux-calibrated format, although the accuracy of the flux calibration is not important. We report on tests of MKCLASS on spectra classified by human classifiers; those tests suggest that over the entire HR diagram, MKCLASS will classify in the temperature dimension with a precision of 0.6 spectral subclass, and in the luminosity dimension with a precision of about one half of a luminosity class. These results compare well with human classifiers.

  15. High Throughput Ambient Mass Spectrometric Approach to Species Identification and Classification from Chemical Fingerprint Signatures

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

    Musah, Rabi A.; Espinoza, Edgard O.; Cody, Robert B.; Lesiak, Ashton D.; Christensen, Earl D.; Moore, Hannah E.; Maleknia, Simin; Drijhout, Falko P.

    2015-07-09

    A high throughput method for species identification and classification through chemometric processing of direct analysis in real time (DART) mass spectrometry-derived fingerprint signatures has been developed. The method entails introduction of samples to the open air space between the DART ion source and the mass spectrometer inlet, with the entire observed mass spectral fingerprint subjected to unsupervised hierarchical clustering processing. Moreover, a range of both polar and non-polar chemotypes are instantaneously detected. The result is identification and species level classification based on the entire DART-MS spectrum. In this paper, we illustrate how the method can be used to: (1) distinguishmore » between endangered woods regulated by the Convention for the International Trade of Endangered Flora and Fauna (CITES) treaty; (2) assess the origin and by extension the properties of biodiesel feedstocks; (3) determine insect species from analysis of puparial casings; (4) distinguish between psychoactive plants products; and (5) differentiate between Eucalyptus species. An advantage of the hierarchical clustering approach to processing of the DART-MS derived fingerprint is that it shows both similarities and differences between species based on their chemotypes. Furthermore, full knowledge of the identities of the constituents contained within the small molecule profile of analyzed samples is not required.« less

  16. Hazard Classification of the Remote Handled Low-Level Waste Disposal Facility

    SciTech Connect (OSTI)

    Boyd D. Christensen

    2012-05-01

    The Battelle Energy Alliance (BEA) at the Idaho National Laboratory (INL) is constructing a new facility to replace remote-handled low-level radioactive waste disposal capability for INL and Naval Reactors Facility operations. Current disposal capability at the Radioactive Waste Management Complex (RWMC) will continue until the facility is full or closed for remediation (estimated at approximately fiscal year 2015). Development of a new onsite disposal facility is the highest ranked alternative and will provide RH-LLW disposal capability and will ensure continuity of operations that generate RH-LLW for the foreseeable future. As a part of establishing a safety basis for facility operations, the facility will be categorized according to DOE-STD-1027-92. This classification is important in determining the scope of analyses performed in the safety basis and will also dictate operational requirements of the completed facility. This paper discusses the issues affecting hazard classification in this nuclear facility and impacts of the final hazard categorization.

  17. Adaptive Classification of Landscape Process and Function: An Integration of Geoinformatics and Self-Organizing Maps

    SciTech Connect (OSTI)

    Coleman, Andre M.

    2009-07-17

    The advanced geospatial information extraction and analysis capabilities of a Geographic Information System (GISs) and Artificial Neural Networks (ANNs), particularly Self-Organizing Maps (SOMs), provide a topology-preserving means for reducing and understanding complex data relationships in the landscape. The Adaptive Landscape Classification Procedure (ALCP) is presented as an adaptive and evolutionary capability where varying types of data can be assimilated to address different management needs such as hydrologic response, erosion potential, habitat structure, instrumentation placement, and various forecast or what-if scenarios. This paper defines how the evaluation and analysis of spatial and/or temporal patterns in the landscape can provide insight into complex ecological, hydrological, climatic, and other natural and anthropogenic-influenced processes. Establishing relationships among high-dimensional datasets through neurocomputing based pattern recognition methods can help 1) resolve large volumes of data into a structured and meaningful form; 2) provide an approach for inferring landscape processes in areas that have limited data available but exhibit similar landscape characteristics; and 3) discover the value of individual variables or groups of variables that contribute to specific processes in the landscape. Classification of hydrologic patterns in the landscape is demonstrated.

  18. Threshold selection for classification of MR brain images by clustering method

    SciTech Connect (OSTI)

    Moldovanu, Simona; Obreja, Cristian; Moraru, Luminita

    2015-12-07

    Given a grey-intensity image, our method detects the optimal threshold for a suitable binarization of MR brain images. In MR brain image processing, the grey levels of pixels belonging to the object are not substantially different from the grey levels belonging to the background. Threshold optimization is an effective tool to separate objects from the background and further, in classification applications. This paper gives a detailed investigation on the selection of thresholds. Our method does not use the well-known method for binarization. Instead, we perform a simple threshold optimization which, in turn, will allow the best classification of the analyzed images into healthy and multiple sclerosis disease. The dissimilarity (or the distance between classes) has been established using the clustering method based on dendrograms. We tested our method using two classes of images: the first consists of 20 T2-weighted and 20 proton density PD-weighted scans from two healthy subjects and from two patients with multiple sclerosis. For each image and for each threshold, the number of the white pixels (or the area of white objects in binary image) has been determined. These pixel numbers represent the objects in clustering operation. The following optimum threshold values are obtained, T = 80 for PD images and T = 30 for T2w images. Each mentioned threshold separate clearly the clusters that belonging of the studied groups, healthy patient and multiple sclerosis disease.

  19. The Constitution, waste facility performance standards, and radioactive waste classification: Is equal protection possible?

    SciTech Connect (OSTI)

    Eye, R.V.

    1993-03-01

    The process for disposal of so-called low-level radioactive waste is deadlocked at present. Supporters of the proposed near-surface facilities assert that their designs will meet minimum legal and regulatory standards currently in effect. Among opponents there is an overarching concern that the proposed waste management facilities will not isolate radiation from the biosphere for an adequate length of time. This clash between legal acceptability and a perceived need to protect the environment and public health by requiring more than the law demand sis one of the underlying reasons why the process is deadlocked. Perhaps the most exhaustive public hearing yet conducted on low-level radioactive waste management has recently concluded in Illinois. The Illinois Low-Level Radioactive Waste Disposal Facility Sitting Commission conducted 71 days of fact-finding hearings on the safety and suitability of a site near Martinsville, Illinois, to serve as a location for disposition of low-level radioactive waste. Ultimately, the siting commission rejected the proposed facility site for several reasons. However, almost all the reasons were related, to the prospect that, as currently conceived, the concrete barrier/shallow-land burial method will not isolate radioactive waste from the biosphere. This paper reviews the relevant legal framework of the radioactive waste classification system and will argue that it is inadequate for long-lived radionuclides. Next, the paper will present a case for altering the classification system based on high-level waste regulatory considerations.

  20. GIS Framework for Large River Geomorphic Classification to Aid in the Evaluation of Flow-Ecology Relationships

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

    2296 Prepared for the U.S. Department of Energy under Contract DE-AC05-76RL01830 GIS Framework for Large River Geomorphic Classification to Aid in the Evaluation of Flow-Ecology Relationships CR Vernon EV Arntzen MC Richmond RA McManamay 1 TP Hanrahan CL Rakowski February 2013 PNNL-22296 GIS Framework for Large River Geomorphic Classification to Aid in the Evaluation of Flow-Ecology Relationships CR Vernon EV Arntzen MC Richmond RA McManamay 1 TP Hanrahan CL Rakwoski February 2013 Prepared for

  1. Bayesian Models for Life Prediction and Fault-Mode Classification in Solid State Lamps

    SciTech Connect (OSTI)

    Lall, Pradeep; Wei, Junchao; Sakalaukus, Peter

    2015-04-19

    A new method has been developed for assessment of the onset of degradation in solid state luminaires to classifY failure mechanisms by using metrics beyond lumen degradation that are currently used for identification of failure. Luminous Flux output, Correlated Color Temperature Data on Philips LED Lamps has been gathered under 85°C/85%RH till lamp failure. The acquired data has been used in conjunction with Bayesian Probabilistic Models to identifY luminaires with onset of degradation much prior to failure through identification of decision boundaries between lamps with accrued damage and lamps beyond the failure threshold in the feature space. In addition luminaires with different failure modes have been classified separately from healthy pristine luminaires. It is expected that, the new test technique will allow the development of failure distributions without testing till L 70 life for the manifestation of failure.

  2. Inductive classification of multipartite entanglement under stochastic local operations and classical communication

    SciTech Connect (OSTI)

    Lamata, L.; Leon, J.; Solano, E.

    2006-11-15

    We propose an inductive procedure to classify N-partite entanglement under stochastic local operations and classical communication provided such a classification is known for N-1 qubits. The method is based upon the analysis of the coefficient matrix of the state in an arbitrary product basis. We illustrate this approach in detail with the well-known bipartite and tripartite systems, obtaining as a by-product a systematic criterion to establish the entanglement class of a given pure state without resourcing to any entanglement measure. The general case is proved by induction, allowing us to find an upper bound for the number of N-partite entanglement classes in terms of the number of entanglement classes for N-1 qubits.

  3. ISOLATING CONTENT AND METADATA FROM WEBLOGS USING CLASSIFICATION AND RULE-BASED APPROACHES

    SciTech Connect (OSTI)

    Marshall, Eric J.; Bell, Eric B.

    2011-09-04

    The emergence and increasing prevalence of social media, such as internet forums, weblogs (blogs), wikis, etc., has created a new opportunity to measure public opinion, attitude, and social structures. A major challenge in leveraging this information is isolating the content and metadata in weblogs, as there is no standard, universally supported, machine-readable format for presenting this information. We present two algorithms for isolating this information. The first uses web block classification, where each node in the Document Object Model (DOM) for a page is classified according to one of several pre-defined attributes from a common blog schema. The second uses a set of heuristics to select web blocks. These algorithms perform at a level suitable for initial use, validating this approach for isolating content and metadata from blogs. The resultant data serves as a starting point for analytical work on the content and substance of collections of weblog pages.

  4. Language Classification using N-grams Accelerated by FPGA-based Bloom Filters

    SciTech Connect (OSTI)

    Jacob, A; Gokhale, M

    2007-09-13

    N-Gram (n-character sequences in text documents) counting is a well-established technique used in classifying the language of text in a document. In this paper, n-gram processing is accelerated through the use of reconfigurable hardware on the XtremeData XD1000 system. Our design employs parallelism at multiple levels, with parallel Bloom Filters accessing on-chip RAM, parallel language classifiers, and parallel document processing. In contrast to another hardware implementation (HAIL algorithm) that uses off-chip SRAM for lookup, our highly scalable implementation uses only on-chip memory blocks. Our implementation of end-to-end language classification runs at 85x comparable software and 1.45x the competing hardware design.

  5. Video compression of coronary angiograms based on discrete wavelet transform with block classification

    SciTech Connect (OSTI)

    Ho, B.K.T.; Tsai, M.J.; Wei, J.; Ma, M.; Saipetch, P.

    1996-12-01

    A new method of video compression for angiographic images has been developed to achieve high compression ratio ({approximately}20:1) while eliminating block artifacts which leads to loss of diagnostic accuracy. This method adopts motion picture experts group`s (MPEG`s) motion compensated prediction to take advantage of frame to frame correlation. However, in contrast to MPEG, the error images arising from mismatches in the motion estimation are encoded by discrete wavelet transform (DWT) rather than block discrete cosine transform (DCT). Furthermore, the authors developed a classification scheme which label each block in an image as intra, error, or background type and encode it accordingly. This hybrid coding can significantly improve the compression efficiency in certain cases. This method can be generalized for any dynamic image sequences applications sensitive to block artifacts.

  6. Brownian motors in the low-energy approximation: Classification and properties

    SciTech Connect (OSTI)

    Rozenbaum, V. M.

    2010-04-15

    We classify Brownian motors based on the expansion of their velocity in terms of the reciprocal friction coefficient. The two main classes of motors (with dichotomic fluctuations in homogeneous force and periodic potential energy) are characterized by different analytical dependences of their mean velocity on the spatial and temporal asymmetry coefficients and by different adiabatic limits. The competition between the spatial and temporal asymmetries gives rise to stopping points. The transition through these points can be achieved by varying the asymmetry coefficients, temperature, and other motor parameters, which can be used, for example, for nanoparticle segregation. The proposed classification separates out a new type of motors based on synchronous fluctuations in symmetric potential and applied homogeneous force. As an example of this type of motors, we consider a near-surface motor whose two-dimensional motion (parallel and perpendicular to the substrate plane) results from fluctuations in external force inclined to the surface.

  7. Pelvic Arterial Anatomy Relevant to Prostatic Artery Embolisation and Proposal for Angiographic Classification

    SciTech Connect (OSTI)

    Assis, André Moreira de Moreira, Airton Mota Paula Rodrigues, Vanessa Cristina de; Harward, Sardis Honoria; Antunes, Alberto Azoubel Srougi, Miguel; Carnevale, Francisco Cesar

    2015-08-15

    PurposeTo describe and categorize the angiographic findings regarding prostatic vascularization, propose an anatomic classification, and discuss its implications for the PAE procedure.MethodsAngiographic findings from 143 PAE procedures were reviewed retrospectively, and the origin of the inferior vesical artery (IVA) was classified into five subtypes as follows: type I: IVA originating from the anterior division of the internal iliac artery (IIA), from a common trunk with the superior vesical artery (SVA); type II: IVA originating from the anterior division of the IIA, inferior to the SVA origin; type III: IVA originating from the obturator artery; type IV: IVA originating from the internal pudendal artery; and type V: less common origins of the IVA. Incidences were calculated by percentage.ResultsTwo hundred eighty-six pelvic sides (n = 286) were analyzed, and 267 (93.3 %) were classified into I–IV types. Among them, the most common origin was type IV (n = 89, 31.1 %), followed by type I (n = 82, 28.7 %), type III (n = 54, 18.9 %), and type II (n = 42, 14.7 %). Type V anatomy was seen in 16 cases (5.6 %). Double vascularization, defined as two independent prostatic branches in one pelvic side, was seen in 23 cases (8.0 %).ConclusionsDespite the large number of possible anatomical variations of male pelvis, four main patterns corresponded to almost 95 % of the cases. Evaluation of anatomy in a systematic fashion, following a standard classification, will make PAE a faster, safer, and more effective procedure.

  8. Mechanism-based classification of PAH mixtures to predict carcinogenic potential

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

    Tilton, Susan C.; Siddens, Lisbeth K.; Krueger, Sharon K.; Larkin, Andrew J.; Löhr, Christiane V.; Williams, David E.; Baird, William M.; Waters, Katrina M.

    2015-04-22

    We have previously shown that relative potency factors and DNA adduct measurements are inadequate for predicting carcinogenicity of certain polycyclic aromatic hydrocarbons (PAHs) and PAH mixtures, particularly those that function through alternate pathways or exhibit greater promotional activity compared to benzo[a]pyrene (BaP). Therefore, we developed a pathway based approach for classification of tumor outcome after dermal exposure to PAH/mixtures. FVB/N mice were exposed to dibenzo[def,p]chrysene (DBC), BaP or environmental PAH mixtures (Mix 1-3) following a two-stage initiation/promotion skin tumor protocol. Resulting tumor incidence could be categorized by carcinogenic potency as DBC>>BaP=Mix2=Mix3>Mix1=Control, based on statistical significance. Gene expression profiles measured inmore » skin of mice collected 12 h post-initiation were compared to tumor outcome for identification of short-term bioactivity profiles. A Bayesian integration model was utilized to identify biological pathways predictive of PAH carcinogenic potential during initiation. Integration of probability matrices from four enriched pathways (p<0.05) for DNA damage, apoptosis, response to chemical stimulus and interferon gamma signaling resulted in the highest classification accuracy with leave-one-out cross validation. This pathway-driven approach was successfully utilized to distinguish early regulatory events during initiation prognostic for tumor outcome and provides proof-of-concept for using short-term initiation studies to classify carcinogenic potential of environmental PAH mixtures. As a result, these data further provide a ‘source-to outcome’ model that could be used to predict PAH interactions during tumorigenesis and provide an example of how mode-of-action based risk assessment could be employed for environmental PAH mixtures.« less

  9. Mechanism-based classification of PAH mixtures to predict carcinogenic potential

    SciTech Connect (OSTI)

    Tilton, Susan C.; Siddens, Lisbeth K.; Krueger, Sharon K.; Larkin, Andrew J.; Löhr, Christiane V.; Williams, David E.; Baird, William M.; Waters, Katrina M.

    2015-04-22

    We have previously shown that relative potency factors and DNA adduct measurements are inadequate for predicting carcinogenicity of certain polycyclic aromatic hydrocarbons (PAHs) and PAH mixtures, particularly those that function through alternate pathways or exhibit greater promotional activity compared to benzo[a]pyrene (BaP). Therefore, we developed a pathway based approach for classification of tumor outcome after dermal exposure to PAH/mixtures. FVB/N mice were exposed to dibenzo[def,p]chrysene (DBC), BaP or environmental PAH mixtures (Mix 1-3) following a two-stage initiation/promotion skin tumor protocol. Resulting tumor incidence could be categorized by carcinogenic potency as DBC>>BaP=Mix2=Mix3>Mix1=Control, based on statistical significance. Gene expression profiles measured in skin of mice collected 12 h post-initiation were compared to tumor outcome for identification of short-term bioactivity profiles. A Bayesian integration model was utilized to identify biological pathways predictive of PAH carcinogenic potential during initiation. Integration of probability matrices from four enriched pathways (p<0.05) for DNA damage, apoptosis, response to chemical stimulus and interferon gamma signaling resulted in the highest classification accuracy with leave-one-out cross validation. This pathway-driven approach was successfully utilized to distinguish early regulatory events during initiation prognostic for tumor outcome and provides proof-of-concept for using short-term initiation studies to classify carcinogenic potential of environmental PAH mixtures. As a result, these data further provide a ‘source-to outcome’ model that could be used to predict PAH interactions during tumorigenesis and provide an example of how mode-of-action based risk assessment could be employed for environmental PAH mixtures.

  10. Solid waste bin detection and classification using Dynamic Time Warping and MLP classifier

    SciTech Connect (OSTI)

    Islam, Md. Shafiqul; Hannan, M.A.; Basri, Hassan; Hussain, Aini; Arebey, Maher

    2014-02-15

    Highlights: • Solid waste bin level detection using Dynamic Time Warping (DTW). • Gabor wavelet filter is used to extract the solid waste image features. • Multi-Layer Perceptron classifier network is used for bin image classification. • The classification performance evaluated by ROC curve analysis. - Abstract: The increasing requirement for Solid Waste Management (SWM) has become a significant challenge for municipal authorities. A number of integrated systems and methods have introduced to overcome this challenge. Many researchers have aimed to develop an ideal SWM system, including approaches involving software-based routing, Geographic Information Systems (GIS), Radio-frequency Identification (RFID), or sensor intelligent bins. Image processing solutions for the Solid Waste (SW) collection have also been developed; however, during capturing the bin image, it is challenging to position the camera for getting a bin area centralized image. As yet, there is no ideal system which can correctly estimate the amount of SW. This paper briefly discusses an efficient image processing solution to overcome these problems. Dynamic Time Warping (DTW) was used for detecting and cropping the bin area and Gabor wavelet (GW) was introduced for feature extraction of the waste bin image. Image features were used to train the classifier. A Multi-Layer Perceptron (MLP) classifier was used to classify the waste bin level and estimate the amount of waste inside the bin. The area under the Receiver Operating Characteristic (ROC) curves was used to statistically evaluate classifier performance. The results of this developed system are comparable to previous image processing based system. The system demonstration using DTW with GW for feature extraction and an MLP classifier led to promising results with respect to the accuracy of waste level estimation (98.50%). The application can be used to optimize the routing of waste collection based on the estimated bin level.

  11. Validation and Simplification of the Radiation Therapy Oncology Group Recursive Partitioning Analysis Classification for Glioblastoma

    SciTech Connect (OSTI)

    Li Jing; Wang Meihua; Won, Minhee; Shaw, Edward G.; Coughlin, Christopher; Curran, Walter J.; Mehta, Minesh P.

    2011-11-01

    Purpose: Previous recursive partitioning analysis (RPA) of patients with malignant glioma (glioblastoma multiforme [GBM] and anaplastic astrocytoma [AA]) produced six prognostic groups (I-VI) classified by six factors. We sought here to determine whether the classification for GBM could be improved by using an updated Radiation Therapy Oncology Group (RTOG) GBM database excluding AA and by considering additional baseline variables. Methods and Materials: The new analysis considered 42 baseline variables and 1,672 GBM patients from the expanded RTOG glioma database. Patients receiving radiation only were excluded such that all patients received radiation+carmustine. 'Radiation dose received' was replaced with 'radiation dose assigned.' The new RPA models were compared with the original model by applying them to a test dataset comprising 488 patients from six other RTOG trials. Fitness of the original and new models was evaluated using explained variation. Results: The original RPA model explained more variations in survival in the test dataset than did the new models (20% vs. 15%) and was therefore chosen for further analysis. It was reduced by combining Classes V and VI to produce three prognostic classes (Classes III, IV, and V+VI), as Classes V and VI had indistinguishable survival in the test dataset. The simplified model did not further improve performance (explained variation 18% vs. 20%) but is easier to apply because it involves only four variables: age, performance status, extent of resection, and neurologic function. Applying this simplified model to the updated GBM database resulted in three distinct classes with median survival times of 17.1, 11.2, and 7.5 months for Classes III, IV, and V+VI, respectively. Conclusions: The final model, the simplified original RPA model combining Classes V and VI, resulted in three distinct prognostic groups defined by age, performance status, extent of resection, and neurologic function. This classification will be used

  12. Mechanism-based classification of PAH mixtures to predict carcinogenic potential

    SciTech Connect (OSTI)

    Tilton, Susan C.; Siddens, Lisbeth K.; Krueger, Sharon K.; Larkin, Andrew J.; Lhr, Christiane V.; Williams, David E.; Baird, William M.; Waters, Katrina M.

    2015-04-22

    We have previously shown that relative potency factors and DNA adduct measurements are inadequate for predicting carcinogenicity of certain polycyclic aromatic hydrocarbons (PAHs) and PAH mixtures, particularly those that function through alternate pathways or exhibit greater promotional activity compared to benzo[a]pyrene (BaP). Therefore, we developed a pathway based approach for classification of tumor outcome after dermal exposure to PAH/mixtures. FVB/N mice were exposed to dibenzo[def,p]chrysene (DBC), BaP or environmental PAH mixtures (Mix 1-3) following a two-stage initiation/promotion skin tumor protocol. Resulting tumor incidence could be categorized by carcinogenic potency as DBC>>BaP=Mix2=Mix3>Mix1=Control, based on statistical significance. Gene expression profiles measured in skin of mice collected 12 h post-initiation were compared to tumor outcome for identification of short-term bioactivity profiles. A Bayesian integration model was utilized to identify biological pathways predictive of PAH carcinogenic potential during initiation. Integration of probability matrices from four enriched pathways (p<0.05) for DNA damage, apoptosis, response to chemical stimulus and interferon gamma signaling resulted in the highest classification accuracy with leave-one-out cross validation. This pathway-driven approach was successfully utilized to distinguish early regulatory events during initiation prognostic for tumor outcome and provides proof-of-concept for using short-term initiation studies to classify carcinogenic potential of environmental PAH mixtures. As a result, these data further provide a source-to outcome model that could be used to predict PAH interactions during tumorigenesis and provide an example of how mode-of-action based risk assessment could be employed for environmental PAH mixtures.

  13. EXPLORING THE VARIABLE SKY WITH LINEAR. III. CLASSIFICATION OF PERIODIC LIGHT CURVES

    SciTech Connect (OSTI)

    Palaversa, Lovro; Eyer, Laurent; Rimoldini, Lorenzo; Ivezi?, eljko; Loebman, Sarah; Hunt-Walker, Nicholas; VanderPlas, Jacob; Westman, David; Becker, Andrew C.; Rudjak, Domagoj; Sudar, Davor; Boi?, Hrvoje; Galin, Mario; Kroflin, Andrea; Mesari?, Martina; Munk, Petra; Vrbanec, Dijana; Sesar, Branimir; Stuart, J. Scott; Srdo?, Gregor; and others

    2013-10-01

    We describe the construction of a highly reliable sample of ?7000 optically faint periodic variable stars with light curves obtained by the asteroid survey LINEAR across 10,000 deg{sup 2} of the northern sky. The majority of these variables have not been cataloged yet. The sample flux limit is several magnitudes fainter than most other wide-angle surveys; the photometric errors range from ?0.03 mag at r = 15 to ?0.20 mag at r = 18. Light curves include on average 250 data points, collected over about a decade. Using Sloan Digital Sky Survey (SDSS) based photometric recalibration of the LINEAR data for about 25 million objects, we selected ?200,000 most probable candidate variables with r < 17 and visually confirmed and classified ?7000 periodic variables using phased light curves. The reliability and uniformity of visual classification across eight human classifiers was calibrated and tested using a catalog of variable stars from the SDSS Stripe 82 region and verified using an unsupervised machine learning approach. The resulting sample of periodic LINEAR variables is dominated by 3900 RR Lyrae stars and 2700 eclipsing binary stars of all subtypes and includes small fractions of relatively rare populations such as asymptotic giant branch stars and SX Phoenicis stars. We discuss the distribution of these mostly uncataloged variables in various diagrams constructed with optical-to-infrared SDSS, Two Micron All Sky Survey, and Wide-field Infrared Survey Explorer photometry, and with LINEAR light-curve features. We find that the combination of light-curve features and colors enables classification schemes much more powerful than when colors or light curves are each used separately. An interesting side result is a robust and precise quantitative description of a strong correlation between the light-curve period and color/spectral type for close and contact eclipsing binary stars (? Lyrae and W UMa): as the color-based spectral type varies from K4 to F5, the median

  14. SPECTRAL CLASSIFICATION OF THE BRIGHTEST OBJECTS IN THE GALACTIC STAR-FORMING REGION W40

    SciTech Connect (OSTI)

    Shuping, R. Y.; Vacca, William D.; Kassis, Marc; Yu, Ka Chun

    2012-10-01

    We present high signal-to-noise, moderate resolution (R Almost-Equal-To 2000) near-infrared spectra, as well as 10 {mu}m imaging, for the brightest members of the central stellar cluster in the W40 H II region, obtained using the SpeX and MIRSI instruments at NASA's Infrared Telescope Facility. Using these observations combined with archival Spitzer Space Telescope data, we have determined the spectral classifications, extinction, distances, and spectral energy distributions (SEDs) for the brightest members of the cluster. Of the eight objects observed, we identify four main-sequence (MS) OB stars (one late-O, three early-B), two Herbig Ae/Be stars, and two low-mass young stellar objects (Class II). Strong He I absorption at 1.083 {mu}m in the MS star spectra strongly suggests that at least some of these sources are in fact close binaries. Two out of the four MS stars also show significant infrared excesses typical of circumstellar disks. Extinctions and distances were determined for each MS star by fitting model stellar atmospheres to the SEDs. We estimate a distance to the cluster of between 455 and 535 pc, which agrees well with earlier (but far less precise) distance estimates. We conclude that the late-O star we identify is the dominant source of Lyman continuum luminosity needed to power the W40 H II region and is the likely source of the stellar wind that has blown a large ( Almost-Equal-To 4 pc) pinched-waist bubble observed in wide-field mid-IR images. We also suggest that 3.6 cm radio emission observed from some of the sources in the cluster is likely not due to emission from ultracompact H II regions, as suggested in other work, due to size constraints based on our derived distance to the cluster. Finally, we also present a discussion of the curious source IRS 3A, which has a very strong mid-IR excess (despite its B3 MS classification) and appears to be embedded in a dusty envelope roughly 2700 AU in size.

  15. The evaluation of an analytical protocol for the determination of substances in waste for hazard classification

    SciTech Connect (OSTI)

    Hennebert, Pierre; Papin, Arnaud; Padox, Jean-Marie; Hasebrouck, Benoît

    2013-07-15

    Highlights: • Knowledge of wastes in substances will be necessary to assess HP1–HP15 hazard properties. • A new analytical protocol is proposed for this and tested by two service laboratories on 32 samples. • Sixty-three percentage of the samples have a satisfactory analytical balance between 90% and 110%. • Eighty-four percentage of the samples were classified identically (Seveso Directive) for their hazardousness by the two laboratories. • The method, in progress, is being normalized in France and is be proposed to CEN. - Abstract: The classification of waste as hazardous could soon be assessed in Europe using largely the hazard properties of its constituents, according to the the Classification, Labelling and Packaging (CLP) regulation. Comprehensive knowledge of the component constituents of a given waste will therefore be necessary. An analytical protocol for determining waste composition is proposed, which includes using inductively coupled plasma (ICP) screening methods to identify major elements and gas chromatography/mass spectrometry (GC–MS) screening techniques to measure organic compounds. The method includes a gross or indicator measure of ‘pools’ of higher molecular weight organic substances that are taken to be less bioactive and less hazardous, and of unresolved ‘mass’ during the chromatography of volatile and semi-volatile compounds. The concentration of some elements and specific compounds that are linked to specific hazard properties and are subject to specific regulation (examples include: heavy metals, chromium(VI), cyanides, organo-halogens, and PCBs) are determined by classical quantitative analysis. To check the consistency of the analysis, the sum of the concentrations (including unresolved ‘pools’) should give a mass balance between 90% and 110%. Thirty-two laboratory samples comprising different industrial wastes (liquids and solids) were tested by two routine service laboratories, to give circa 7000 parameter

  16. THE GALEX TIME DOMAIN SURVEY. I. SELECTION AND CLASSIFICATION OF OVER A THOUSAND ULTRAVIOLET VARIABLE SOURCES

    SciTech Connect (OSTI)

    Gezari, S.; Martin, D. C.; Forster, K.; Neill, J. D.; Morrissey, P.; Wyder, T. K.; Huber, M.; Burgett, W. S.; Chambers, K. C.; Kaiser, N.; Magnier, E. A.; Tonry, J. L.; Heckman, T.; Bianchi, L.; Neff, S. G.; Seibert, M.; Schiminovich, D.; Price, P. A.

    2013-03-20

    We present the selection and classification of over a thousand ultraviolet (UV) variable sources discovered in {approx}40 deg{sup 2} of GALEX Time Domain Survey (TDS) NUV images observed with a cadence of 2 days and a baseline of observations of {approx}3 years. The GALEX TDS fields were designed to be in spatial and temporal coordination with the Pan-STARRS1 Medium Deep Survey, which provides deep optical imaging and simultaneous optical transient detections via image differencing. We characterize the GALEX photometric errors empirically as a function of mean magnitude, and select sources that vary at the 5{sigma} level in at least one epoch. We measure the statistical properties of the UV variability, including the structure function on timescales of days and years. We report classifications for the GALEX TDS sample using a combination of optical host colors and morphology, UV light curve characteristics, and matches to archival X-ray, and spectroscopy catalogs. We classify 62% of the sources as active galaxies (358 quasars and 305 active galactic nuclei), and 10% as variable stars (including 37 RR Lyrae, 53 M dwarf flare stars, and 2 cataclysmic variables). We detect a large-amplitude tail in the UV variability distribution for M-dwarf flare stars and RR Lyrae, reaching up to |{Delta}m| = 4.6 mag and 2.9 mag, respectively. The mean amplitude of the structure function for quasars on year timescales is five times larger than observed at optical wavelengths. The remaining unclassified sources include UV-bright extragalactic transients, two of which have been spectroscopically confirmed to be a young core-collapse supernova and a flare from the tidal disruption of a star by dormant supermassive black hole. We calculate a surface density for variable sources in the UV with NUV < 23 mag and |{Delta}m| > 0.2 mag of {approx}8.0, 7.7, and 1.8 deg{sup -2} for quasars, active galactic nuclei, and RR Lyrae stars, respectively. We also calculate a surface density rate in the

  17. THE CHOICE OF OPTIMAL STRUCTURE OF ARTIFICIAL NEURAL NETWORK CLASSIFIER INTENDED FOR CLASSIFICATION OF WELDING FLAWS

    SciTech Connect (OSTI)

    Sikora, R.; Chady, T.; Baniukiewicz, P.; Caryk, M.; Piekarczyk, B.

    2010-02-22

    Nondestructive testing and evaluation are under continuous development. Currently researches are concentrated on three main topics: advancement of existing methods, introduction of novel methods and development of artificial intelligent systems for automatic defect recognition (ADR). Automatic defect classification algorithm comprises of two main tasks: creating a defect database and preparing a defect classifier. Here, the database was built using defect features that describe all geometrical and texture properties of the defect. Almost twenty carefully selected features calculated for flaws extracted from real radiograms were used. The radiograms were obtained from shipbuilding industry and they were verified by qualified operator. Two weld defect's classifiers based on artificial neural networks were proposed and compared. First model consisted of one neural network model, where each output neuron corresponded to different defect group. The second model contained five neural networks. Each neural network had one neuron on output and was responsible for detection of defects from one group. In order to evaluate the effectiveness of the neural networks classifiers, the mean square errors were calculated for test radiograms and compared.

  18. Event Classification and Identification Based on the Characteristic Ellipsoid of Phasor Measurement

    SciTech Connect (OSTI)

    Ma, Jian; Diao, Ruisheng; Makarov, Yuri V.; Etingov, Pavel V.; Dagle, Jeffery E.

    2011-09-23

    In this paper, a method to classify and identify power system events based on the characteristic ellipsoid of phasor measurement is presented. The decision tree technique is used to perform the event classification and identification. Event types, event locations and clearance times are identified by decision trees based on the indices of the characteristic ellipsoid. A sufficiently large number of transient events were simulated on the New England 10-machine 39-bus system based on different system configurations. Transient simulations taking into account different event types, clearance times and various locations are conducted to simulate phasor measurement. Bus voltage magnitudes and recorded reactive and active power flows are used to build the characteristic ellipsoid. The volume, eccentricity, center and projection of the longest axis in the parameter space coordinates of the characteristic ellipsoids are used to classify and identify events. Results demonstrate that the characteristic ellipsoid and the decision tree are capable to detect the event type, location, and clearance time with very high accuracy.

  19. Twisted injectivity in projected entangled pair states and the classification of quantum phases

    SciTech Connect (OSTI)

    Buerschaper, Oliver

    2014-12-15

    We introduce a class of projected entangled pair states (PEPS) which is based on a group symmetry twisted by a 3-cocycle of the group. This twisted symmetry is expressed as a matrix product operator (MPO) with bond dimension greater than 1 and acts on the virtual boundary of a PEPS tensor. We show that it gives rise to a new standard form for PEPS from which we construct a family of local Hamiltonians which are gapped, frustration-free and include fixed points of the renormalization group flow. Based on this insight, we advance the classification of 2D gapped quantum spin systems by showing how this new standard form for PEPS determines the emergent topological order of these local Hamiltonians. Specifically, we identify their universality class as DIJKGRAAF–WITTEN topological quantum field theory (TQFT). - Highlights: • We introduce a new standard form for projected entangled pair states via a twisted group symmetry which is given by nontrivial matrix product operators. • We construct a large family of gapped, frustration-free Hamiltonians in two dimensions from this new standard form. • We rigorously show how this new standard form for low energy states determines the emergent topological order.

  20. Classification of Superdeformed Bands in the Mass A{approx}60 Region

    SciTech Connect (OSTI)

    Andersson, L.-L.; Rudolph, D.; Fahlander, C.; Johansson, E. K.; Carlsson, B. G.; Ragnarsson, I.; Torres, D. A.

    2008-11-11

    The experimental knowledge of the {sub 29}{sup 61}Cu{sub 32} and {sub 30}{sup 61}Zn{sub 31} nuclei has been largely extended via the joint results from three experiments. The fusion-evaporation reaction used a {sup 36}Ar beam and a {sup 28}Si target foil to produce the two nuclei via the evaporation of either three protons ({sup 61}Cu) or two protons and a neutron ({sup 61}Zn). The experimental set-ups comprised the Ge-array GAMMASPHERE as well as neutron and charged-particle detectors placed around the target position.The resulting level schemes include around ten rotational superdeformed structures in each isotope. Most of them are linked to normally deformed states and in many cases spins and parities of the low-lying states in each structure have been determined.The collective structures are compared with results from configuration dependent Cranked Nilsson-Strutinsky calculations. The different structures are in general well understood from the calculation but the results do also suggest modifications of the standard Nilsson parameters in the mass A{approx}60 region.

  1. Standard for Communicating Waste Characterization and DOT Hazard Classification Requirements for Low Specific Activity Materials and Surface Contaminated Objects

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

    STD-5507-2013 February 2013 DOE STANDARD Standard for Communicating Waste Characterization and DOT Hazard Classification Requirements for Low Specific Activity Materials and Surface Contaminated Objects [This Standard describes acceptable, but not mandatory means for complying with requirements. Standards are not requirements documents and are not to be construed as requirements in any audit or appraisal for compliance with associated rule or directives.] U.S. Department of Energy SAFT

  2. ROBUSTNESS OF DECISION INSIGHTS UNDER ALTERNATIVE ALEATORY/EPISTEMIC UNCERTAINTY CLASSIFICATIONS

    SciTech Connect (OSTI)

    Unwin, Stephen D.; Eslinger, Paul W.; Johnson, Kenneth I.

    2013-09-22

    The Risk-Informed Safety Margin Characterization (RISMC) pathway is a set of activities defined under the U.S. Department of Energy Light Water Reactor Sustainability Program. The overarching objective of RISMC is to support plant life-extension decision-making by providing a state-of-knowledge characterization of safety margins in key systems, structures, and components (SSCs). A key technical challenge is to establish the conceptual and technical feasibility of analyzing safety margin in a risk-informed way, which, unlike conventionally defined deterministic margin analysis, would be founded on probabilistic characterizations of SSC performance. Evaluation of probabilistic safety margins will in general entail the uncertainty characterization both of the prospective challenge to the performance of an SSC ("load") and of its "capacity" to withstand that challenge. The RISMC framework contrasts sharply with the traditional probabilistic risk assessment (PRA) structure in that the underlying models are not inherently aleatory. Rather, they are largely deterministic physical/engineering models with ambiguities about the appropriate uncertainty classification of many model parameters. The current analysis demonstrates that if the distinction between epistemic and aleatory uncertainties is to be preserved in a RISMC-like modeling environment, then it is unlikely that analysis insights supporting decision-making will in general be robust under recategorization of input uncertainties. If it is believed there is a true conceptual distinction between epistemic and aleatory uncertainty (as opposed to the distinction being primarily a legacy of the PRA paradigm) then a consistent and defensible basis must be established by which to categorize input uncertainties.

  3. Eligibility for Renal Denervation: Anatomical Classification and Results in Essential Resistant Hypertension

    SciTech Connect (OSTI)

    Okada, Takuya Pellerin, Olivier; Savard, Sébastien; Curis, Emmanuel; Monge, Matthieu; Frank, Michael; Bobrie, Guillaume; Yamaguchi, Masato; Sugimoto, Koji; Plouin, Pierre-François; Azizi, Michel; Sapoval, Marc

    2015-02-15

    PurposeTo classify the renal artery (RA) anatomy based on specific requirements for endovascular renal artery denervation (RDN) in patients with drug-resistant hypertension (RH).Materials and MethodsThe RA anatomy of 122 consecutive RH patients was evaluated by computed tomography angiography and classified as two types: A (main RA ≥20 mm in length and ≥4.0 mm in diameter) or B (main RA <20 mm in length or main RA <4.0 mm in diameter). The A type included three subtypes: A1 (without accessory RAs), A2 (with accessory RAs <3.0 mm in diameter), and A3 (with accessory RAs ≥3.0 mm in diameter]. A1 and A2 types were eligible for RDN with the Simplicity Flex catheter. Type B included twi subtypes based on the main RA length and diameter. Patients were accordingly classified into three eligibility categories: complete (CE; both RAs were eligible), partial (PE; one eligible RA), and noneligibility (NE; no eligible RA).ResultsBilateral A1 type was the most prevalent and was observed in 48.4 % of the patients followed by the A1/A2 type (18 %). CE, PE, and NE were observed in 69.7, 22.9, and 7.4 % of patients, respectively. The prevalence of accessory RAs was 41 %.ConclusionsOf RH patients, 30.3 % were not eligible for bilateral RDN with the current Simplicity Flex catheter. This classification provides the basis for standardized reporting to allow for pooling of results of larger patient cohorts in the future.

  4. Mammographic quantitative image analysis and biologic image composition for breast lesion characterization and classification

    SciTech Connect (OSTI)

    Drukker, Karen Giger, Maryellen L.; Li, Hui; Duewer, Fred; Malkov, Serghei; Joe, Bonnie; Kerlikowske, Karla; Shepherd, John A.; Flowers, Chris I.; Drukteinis, Jennifer S.

    2014-03-15

    Purpose: To investigate whether biologic image composition of mammographic lesions can improve upon existing mammographic quantitative image analysis (QIA) in estimating the probability of malignancy. Methods: The study population consisted of 45 breast lesions imaged with dual-energy mammography prior to breast biopsy with final diagnosis resulting in 10 invasive ductal carcinomas, 5 ductal carcinomain situ, 11 fibroadenomas, and 19 other benign diagnoses. Analysis was threefold: (1) The raw low-energy mammographic images were analyzed with an established in-house QIA method, QIA alone, (2) the three-compartment breast (3CB) composition measurederived from the dual-energy mammographyof water, lipid, and protein thickness were assessed, 3CB alone, and (3) information from QIA and 3CB was combined, QIA + 3CB. Analysis was initiated from radiologist-indicated lesion centers and was otherwise fully automated. Steps of the QIA and 3CB methods were lesion segmentation, characterization, and subsequent classification for malignancy in leave-one-case-out cross-validation. Performance assessment included box plots, BlandAltman plots, and Receiver Operating Characteristic (ROC) analysis. Results: The area under the ROC curve (AUC) for distinguishing between benign and malignant lesions (invasive and DCIS) was 0.81 (standard error 0.07) for the QIA alone method, 0.72 (0.07) for 3CB alone method, and 0.86 (0.04) for QIA+3CB combined. The difference in AUC was 0.043 between QIA + 3CB and QIA alone but failed to reach statistical significance (95% confidence interval [0.17 to + 0.26]). Conclusions: In this pilot study analyzing the new 3CB imaging modality, knowledge of the composition of breast lesions and their periphery appeared additive in combination with existing mammographic QIA methods for the distinction between different benign and malignant lesion types.

  5. ACTIVE LEARNING TO OVERCOME SAMPLE SELECTION BIAS: APPLICATION TO PHOTOMETRIC VARIABLE STAR CLASSIFICATION

    SciTech Connect (OSTI)

    Richards, Joseph W.; Starr, Dan L.; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; Berian James, J.; Brink, Henrik; Long, James P.; Rice, John

    2012-01-10

    Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because (1) standard assumptions for machine-learned model selection procedures break down and (2) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting, co-training, and active learning (AL). We argue that AL-where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up-is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and Optical Gravitational Lensing Experiment, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a Web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply AL to classify variable stars in the All Sky Automated Survey, finding dramatic improvement in our agreement with the ASAS Catalog of Variable Stars, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations.

  6. An Adaptive Landscape Classification Procedure using Geoinformatics and Artificial Neural Networks

    SciTech Connect (OSTI)

    Coleman, Andre M.

    2008-08-01

    The Adaptive Landscape Classification Procedure (ALCP), which links the advanced geospatial analysis capabilities of Geographic Information Systems (GISs) and Artificial Neural Networks (ANNs) and particularly Self-Organizing Maps (SOMs), is proposed as a method for establishing and reducing complex data relationships. Its adaptive and evolutionary capability is evaluated for situations where varying types of data can be combined to address different prediction and/or management needs such as hydrologic response, water quality, aquatic habitat, groundwater recharge, land use, instrumentation placement, and forecast scenarios. The research presented here documents and presents favorable results of a procedure that aims to be a powerful and flexible spatial data classifier that fuses the strengths of geoinformatics and the intelligence of SOMs to provide data patterns and spatial information for environmental managers and researchers. This research shows how evaluation and analysis of spatial and/or temporal patterns in the landscape can provide insight into complex ecological, hydrological, climatic, and other natural and anthropogenic-influenced processes. Certainly, environmental management and research within heterogeneous watersheds provide challenges for consistent evaluation and understanding of system functions. For instance, watersheds over a range of scales are likely to exhibit varying levels of diversity in their characteristics of climate, hydrology, physiography, ecology, and anthropogenic influence. Furthermore, it has become evident that understanding and analyzing these diverse systems can be difficult not only because of varying natural characteristics, but also because of the availability, quality, and variability of spatial and temporal data. Developments in geospatial technologies, however, are providing a wide range of relevant data, and in many cases, at a high temporal and spatial resolution. Such data resources can take the form of high

  7. Impact of Passive Safety on FHR Instrumentation Systems Design and Classification

    SciTech Connect (OSTI)

    Holcomb, David Eugene

    2015-01-01

    Fluoride salt-cooled high-temperature reactors (FHRs) will rely more extensively on passive safety than earlier reactor classes. 10CFR50 Appendix A, General Design Criteria for Nuclear Power Plants, establishes minimum design requirements to provide reasonable assurance of adequate safety. 10CFR50.69, Risk-Informed Categorization and Treatment of Structures, Systems and Components for Nuclear Power Reactors, provides guidance on how the safety significance of systems, structures, and components (SSCs) should be reflected in their regulatory treatment. The Nuclear Energy Institute (NEI) has provided 10 CFR 50.69 SSC Categorization Guideline (NEI-00-04) that factors in probabilistic risk assessment (PRA) model insights, as well as deterministic insights, through an integrated decision-making panel. Employing the PRA to inform deterministic requirements enables an appropriately balanced, technically sound categorization to be established. No FHR currently has an adequate PRA or set of design basis accidents to enable establishing the safety classification of its SSCs. While all SSCs used to comply with the general design criteria (GDCs) will be safety related, the intent is to limit the instrumentation risk significance through effective design and reliance on inherent passive safety characteristics. For example, FHRs have no safety-significant temperature threshold phenomena, thus, enabling the primary and reserve reactivity control systems required by GDC 26 to be passively, thermally triggered at temperatures well below those for which core or primary coolant boundary damage would occur. Moreover, the passive thermal triggering of the primary and reserve shutdown systems may relegate the control rod drive motors to the control system, substantially decreasing the amount of safety-significant wiring needed. Similarly, FHR decay heat removal systems are intended to be running continuously to minimize the amount of safety-significant instrumentation needed to initiate

  8. Impact of Passive Safety on FHR Instrumentation Systems Design and Classification

    SciTech Connect (OSTI)

    Holcomb, David Eugene

    2015-01-01

    Fluoride salt-cooled high-temperature reactors (FHRs) will rely more extensively on passive safety than earlier reactor classes. 10CFR50 Appendix A, General Design Criteria for Nuclear Power Plants, establishes minimum design requirements to provide reasonable assurance of adequate safety. 10CFR50.69, Risk-Informed Categorization and Treatment of Structures, Systems and Components for Nuclear Power Reactors, provides guidance on how the safety significance of systems, structures, and components (SSCs) should be reflected in their regulatory treatment. The Nuclear Energy Institute (NEI) has provided 10 CFR 50.69 SSC Categorization Guideline (NEI-00-04) that factors in probabilistic risk assessment (PRA) model insights, as well as deterministic insights, through an integrated decision-making panel. Employing the PRA to inform deterministic requirements enables an appropriately balanced, technically sound categorization to be established. No FHR currently has an adequate PRA or set of design basis accidents to enable establishing the safety classification of its SSCs. While all SSCs used to comply with the general design criteria (GDCs) will be safety related, the intent is to limit the instrumentation risk significance through effective design and reliance on inherent passive safety characteristics. For example, FHRs have no safety-significant temperature threshold phenomena, thus enabling the primary and reserve reactivity control systems required by GDC 26 to be passively, thermally triggered at temperatures well below those for which core or primary coolant boundary damage would occur. Moreover, the passive thermal triggering of the primary and reserve shutdown systems may relegate the control rod drive motors to the control system, substantially decreasing the amount of safety-significant wiring needed. Similarly, FHR decay heat removal systems are intended to be running continuously to minimize the amount of safety-significant instrumentation needed to initiate

  9. GIS Framework for Large River Geomorphic Classification to Aid in the Evaluation of Flow-Ecology Relationships

    SciTech Connect (OSTI)

    Vernon, Christopher R.; Arntzen, Evan V.; Richmond, Marshall C.; McManamay, R. A.; Hanrahan, Timothy P.; Rakowski, Cynthia L.

    2013-02-01

    Assessing the environmental benefits of proposed flow modification to large rivers provides invaluable insight into future hydropower project operations and relicensing activities. Providing a means to quantitatively define flow-ecology relationships is integral in establishing flow regimes that are mutually beneficial to power production and ecological needs. To compliment this effort an opportunity to create versatile tools that can be applied to broad geographic areas has been presented. In particular, integration with efforts standardized within the ecological limits of hydrologic alteration (ELOHA) is highly advantageous (Poff et al. 2010). This paper presents a geographic information system (GIS) framework for large river classification that houses a base geomorphic classification that is both flexible and accurate, allowing for full integration with other hydrologic models focused on addressing ELOHA efforts. A case study is also provided that integrates publically available National Hydrography Dataset Plus Version 2 (NHDPlusV2) data, Modular Aquatic Simulation System two-dimensional (MASS2) hydraulic data, and field collected data into the framework to produce a suite of flow-ecology related outputs. The case study objective was to establish areas of optimal juvenile salmonid rearing habitat under varying flow regimes throughout an impounded portion of the lower Snake River, USA (Figure 1) as an indicator to determine sites where the potential exists to create additional shallow water habitat. Additionally, an alternative hydrologic classification useable throughout the contiguous United States which can be coupled with the geomorphic aspect of this framework is also presented. This framework provides the user with the ability to integrate hydrologic and ecologic data into the base geomorphic aspect of this framework within a geographic information system (GIS) to output spatiotemporally variable flow-ecology relationship scenarios.

  10. A support vector machine classifier reduces interscanner variation in the HRCT classification of regional disease pattern in diffuse lung disease: Comparison to a Bayesian classifier

    SciTech Connect (OSTI)

    Chang, Yongjun; Lim, Jonghyuck; Kim, Namkug; Seo, Joon Beom; Lynch, David A.

    2013-05-15

    Purpose: To investigate the effect of using different computed tomography (CT) scanners on the accuracy of high-resolution CT (HRCT) images in classifying regional disease patterns in patients with diffuse lung disease, support vector machine (SVM) and Bayesian classifiers were applied to multicenter data. Methods: Two experienced radiologists marked sets of 600 rectangular 20 Multiplication-Sign 20 pixel regions of interest (ROIs) on HRCT images obtained from two scanners (GE and Siemens), including 100 ROIs for each of local patterns of lungs-normal lung and five of regional pulmonary disease patterns (ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation). Each ROI was assessed using 22 quantitative features belonging to one of the following descriptors: histogram, gradient, run-length, gray level co-occurrence matrix, low-attenuation area cluster, and top-hat transform. For automatic classification, a Bayesian classifier and a SVM classifier were compared under three different conditions. First, classification accuracies were estimated using data from each scanner. Next, data from the GE and Siemens scanners were used for training and testing, respectively, and vice versa. Finally, all ROI data were integrated regardless of the scanner type and were then trained and tested together. All experiments were performed based on forward feature selection and fivefold cross-validation with 20 repetitions. Results: For each scanner, better classification accuracies were achieved with the SVM classifier than the Bayesian classifier (92% and 82%, respectively, for the GE scanner; and 92% and 86%, respectively, for the Siemens scanner). The classification accuracies were 82%/72% for training with GE data and testing with Siemens data, and 79%/72% for the reverse. The use of training and test data obtained from the HRCT images of different scanners lowered the classification accuracy compared to the use of HRCT images from the same scanner. For

  11. A method for EIA scoping of wave energy converters-based on classification of the used technology

    SciTech Connect (OSTI)

    Margheritini, Lucia; Hansen, Anne Merrild; Frigaard, Peter

    2012-01-15

    During the first decade of the 21st Century the World faces spread concern for global warming caused by rise of green house gasses produced mainly by combustion of fossil fuels. Under this latest spin all renewable energies run parallel in order to achieve sustainable development. Among them wave energy has an unequivocal potential and technology is ready to enter the market and contribute to the renewable energy sector. Yet, frameworks and regulations for wave energy development are not fully ready, experiencing a setback caused by lack of understanding of the interaction of the technologies and marine environment, lack of coordination from the competent Authorities regulating device deployment and conflicts of maritime areas utilization. The EIA within the consent process is central in the realization of full scale devices and often is the meeting point for technology, politics and public. This paper presents the development of a classification of wave energy converters that is based on the different impact the technologies are expected to have on the environment. This innovative classification can be used in order to simplify the scoping process for developers and authorities.

  12. Prognostic Value of Subclassification Using MRI in the T4 Classification Nasopharyngeal Carcinoma Intensity-Modulated Radiotherapy Treatment

    SciTech Connect (OSTI)

    Chen Lei; Liu Lizhi; Chen Mo; Li Wenfei; Yin Wenjing; Lin Aihua; Sun Ying; Li Li; Ma Jun

    2012-09-01

    Purpose: To subclassify patients with the T4 classification nasopharyngeal carcinoma (NPC), according to the seventh edition of the American Joint Committee on Cancer staging system, using magnetic resonance imaging (MRI), and to evaluate the prognostic value of subclassification after intensity-modulated radiotherapy (IMRT). Methods and Materials: A total of 140 patients who underwent MRI and were subsequently histologically diagnosed with nondisseminated classification T4 NPC received IMRT as their primary treatment and were included in this retrospective study. T4 patients were subclassified into two grades: T4a was defined as a primary nasopharyngeal tumor with involvement of the masticator space only; and T4b was defined as involvement of the intracranial region, cranial nerves, and/or orbit. Results: The 5-year overall survival (OS) rate and distant metastasis-free survival (DMFS) rate for T4a patients (82.5% and 87.0%, respectively), were significantly higher than for T4b patients (62.6% and 66.8%; p = 0.033 and p = 0.036, respectively). The T4a/b subclassification was an independent prognostic factor for OS (hazard ratio = 2.331, p = 0.032) and DMFS (hazard ratio = 2.602, p = 0.034), and had no significant effect on local relapse-free survival. Conclusions: Subclassification of T4 patients, as T4a or T4b, using MRI according to the site of invasion, has prognostic value for the outcomes of IMRT treatment in NPC.

  13. SALTSTONE VAULT CLASSIFICATION SAMPLES MODULAR CAUSTIC SIDE SOLVENT EXTRACTION UNIT/ACTINIDE REMOVAL PROCESS WASTE STREAM APRIL 2011

    SciTech Connect (OSTI)

    Eibling, R.

    2011-09-28

    Savannah River National Laboratory (SRNL) was asked to prepare saltstone from samples of Tank 50H obtained by SRNL on April 5, 2011 (Tank 50H sampling occurred on April 4, 2011) during 2QCY11 to determine the non-hazardous nature of the grout and for additional vault classification analyses. The samples were cured and shipped to Babcock & Wilcox Technical Services Group-Radioisotope and Analytical Chemistry Laboratory (B&W TSG-RACL) to perform the Toxic Characteristic Leaching Procedure (TCLP) and subsequent extract analysis on saltstone samples for the analytes required for the quarterly analysis saltstone sample. In addition to the eight toxic metals - arsenic, barium, cadmium, chromium, mercury, lead, selenium and silver - analytes included the underlying hazardous constituents (UHC) antimony, beryllium, nickel, and thallium which could not be eliminated from analysis by process knowledge. Additional inorganic species determined by B&W TSG-RACL include aluminum, boron, chloride, cobalt, copper, fluoride, iron, lithium, manganese, molybdenum, nitrate/nitrite as Nitrogen, strontium, sulfate, uranium, and zinc and the following radionuclides: gross alpha, gross beta/gamma, 3H, 60Co, 90Sr, 99Tc, 106Ru, 106Rh, 125Sb, 137Cs, 137mBa, 154Eu, 238Pu, 239/240Pu, 241Pu, 241Am, 242Cm, and 243/244Cm. B&W TSG-RACL provided subsamples to GEL Laboratories, LLC for analysis for the VOCs benzene, toluene, and 1-butanol. GEL also determines phenol (total) and the following radionuclides: 147Pm, 226Ra and 228Ra. Preparation of the 2QCY11 saltstone samples for the quarterly analysis and for vault classification purposes and the subsequent TCLP analyses of these samples showed that: (1) The saltstone waste form disposed of in the Saltstone Disposal Facility in 2QCY11 was not characteristically hazardous for toxicity. (2) The concentrations of the eight RCRA metals and UHCs identified as possible in the saltstone waste form were present at levels below the UTS. (3) Most of the

  14. GUIDANCE FOR THE PROPER CHARACTERIZATION AND CLASSIFICATION OF LOW SPECIFIC ACTIVITY MATERIALS AND SURFACE CONTAMINATED OBJECTS FOR DISPOSAL

    SciTech Connect (OSTI)

    PORTSMOUTH JH; BLACKFORD LT

    2012-02-13

    Regulatory concerns over the proper characterization of certain waste streams led CH2M HILL Plateau Remediation Company (CHPRC) to develop written guidance for personnel involved in Decontamination & Decommissioning (D&D) activities, facility management and Waste Management Representatives (WMRs) involved in the designation of wastes for disposal on and off the Hanford Site. It is essential that these waste streams regularly encountered in D&D operations are properly designated, characterized and classified prior to shipment to a Treatment, Storage or Disposal Facility (TSDF). Shipments of waste determined by the classification process as Low Specific Activity (LSA) or Surface Contaminated Objects (SCO) must also be compliant with all applicable U.S. Department of Transportation (DOE) regulations as well as Department of Energy (DOE) orders. The compliant shipment of these waste commodities is critical to the Hanford Central Plateau cleanup mission. Due to previous problems and concerns from DOE assessments, CHPRC internal critiques as well as DOT, a management decision was made to develop written guidance and procedures to assist CHPRC shippers and facility personnel in the proper classification of D&D waste materials as either LSA or SCO. The guidance provides a uniform methodology for the collection and documentation required to effectively characterize, classify and identify candidate materials for shipping operations. A primary focus is to ensure that waste materials generated from D&D and facility operations are compliant with the DOT regulations when packaged for shipment. At times this can be difficult as the current DOT regulations relative to the shipment of LSA and SCO materials are often not clear to waste generators. Guidance is often sought from NUREG 1608/RAMREG-003 [3]: a guidance document that was jointly developed by the DOT and the Nuclear Regulatory Commission (NRC) and published in 1998. However, NUREG 1608 [3] is now thirteen years old and

  15. Classification System for Identifying Women at Risk for Altered Partial Breast Irradiation Recommendations After Breast Magnetic Resonance Imaging

    SciTech Connect (OSTI)

    Kowalchik, Kristin V.; Vallow, Laura A.; McDonough, Michelle; Thomas, Colleen S.; Heckman, Michael G.; Peterson, Jennifer L.; Adkisson, Cameron D.; Serago, Christopher; McLaughlin, Sarah A.

    2013-09-01

    Purpose: To study the utility of preoperative breast MRI for partial breast irradiation (PBI) patient selection, using multivariable analysis of significant risk factors to create a classification rule. Methods and Materials: Between 2002 and 2009, 712 women with newly diagnosed breast cancer underwent preoperative bilateral breast MRI at Mayo Clinic Florida. Of this cohort, 566 were retrospectively deemed eligible for PBI according to the National Surgical Adjuvant Breast and Bowel Project Protocol B-39 inclusion criteria using physical examination, mammogram, and/or ultrasound. Magnetic resonance images were then reviewed to determine their impact on patient eligibility. The patient and tumor characteristics were evaluated to determine risk factors for altered PBI eligibility after MRI and to create a classification rule. Results: Of the 566 patients initially eligible for PBI, 141 (25%) were found ineligible because of pathologically proven MRI findings. Magnetic resonance imaging detected additional ipsilateral breast cancer in 118 (21%). Of these, 62 (11%) had more extensive disease than originally noted before MRI, and 64 (11%) had multicentric disease. Contralateral breast cancer was detected in 28 (5%). Four characteristics were found to be significantly associated with PBI ineligibility after MRI on multivariable analysis: premenopausal status (P=.021), detection by palpation (P<.001), first-degree relative with a history of breast cancer (P=.033), and lobular histology (P=.002). Risk factors were assigned a score of 0-2. The risk of altered PBI eligibility from MRI based on number of risk factors was 0:18%; 1:22%; 2:42%; 3:65%. Conclusions: Preoperative bilateral breast MRI altered the PBI recommendations for 25% of women. Women who may undergo PBI should be considered for breast MRI, especially those with lobular histology or with 2 or more of the following risk factors: premenopausal, detection by palpation, and first-degree relative with a history of

  16. The need for a characteristics-based approach to radioactive waste classification as informed by advanced nuclear fuel cycles using the fuel-cycle integration and tradeoffs (FIT) model

    SciTech Connect (OSTI)

    Djokic, D. [Department of Nuclear Engineering, University of California, Berkeley, 3115B Etcheverry Hall, Berkeley, CA 94720-1730 (United States); Piet, S.; Pincock, L.; Soelberg, N. [Idaho National Laboratory - INL, 2525 North Fremont Avenue, Idaho Falls, ID 83415 (United States)

    2013-07-01

    This study explores the impact of wastes generated from potential future fuel cycles and the issues presented by classifying these under current classification criteria, and discusses the possibility of a comprehensive and consistent characteristics-based classification framework based on new waste streams created from advanced fuel cycles. A static mass flow model, Fuel-Cycle Integration and Tradeoffs (FIT), was used to calculate the composition of waste streams resulting from different nuclear fuel cycle choices. Because heat generation is generally the most important factor limiting geological repository areal loading, this analysis focuses on the impact of waste form heat load on waste classification practices, although classifying by metrics of radiotoxicity, mass, and volume is also possible. Waste streams generated in different fuel cycles and their possible classification based on the current U.S. framework and international standards are discussed. It is shown that the effects of separating waste streams are neglected under a source-based radioactive waste classification system. (authors)

  17. Navigation and vessel inspection circular No. 12-92. Guidelines for the classification and inspection of oil spill removal organizations (osros). Final report

    SciTech Connect (OSTI)

    1992-12-04

    The purpose of the circular is to facilitate the preparation and review of vessel and facility response plans by providing guidance on the classification of oil spill removal organizations. The guidelines propose a method of estimating the capacity of oil spill removal organizations to contain and remove oil from the water and shorelines.

  18. Waste Classification based on Waste Form Heat Generation in Advanced Nuclear Fuel Cycles Using the Fuel-Cycle Integration and Tradeoffs (FIT) Model

    SciTech Connect (OSTI)

    Denia Djokic; Steven J. Piet; Layne F. Pincock; Nick R. Soelberg

    2013-02-01

    This study explores the impact of wastes generated from potential future fuel cycles and the issues presented by classifying these under current classification criteria, and discusses the possibility of a comprehensive and consistent characteristics-based classification framework based on new waste streams created from advanced fuel cycles. A static mass flow model, Fuel-Cycle Integration and Tradeoffs (FIT), was used to calculate the composition of waste streams resulting from different nuclear fuel cycle choices. This analysis focuses on the impact of waste form heat load on waste classification practices, although classifying by metrics of radiotoxicity, mass, and volume is also possible. The value of separation of heat-generating fission products and actinides in different fuel cycles is discussed. It was shown that the benefits of reducing the short-term fission-product heat load of waste destined for geologic disposal are neglected under the current source-based radioactive waste classification system , and that it is useful to classify waste streams based on how favorable the impact of interim storage is in increasing repository capacity.

  19. Waste Classification based on Waste Form Heat Generation in Advanced Nuclear Fuel Cycles Using the Fuel-Cycle Integration and Tradeoffs (FIT) Model - 13413

    SciTech Connect (OSTI)

    Djokic, Denia [Department of Nuclear Engineering, University of California - Berkeley, 4149 Etcheverry Hall, Berkeley, CA 94720-1730 (United States)] [Department of Nuclear Engineering, University of California - Berkeley, 4149 Etcheverry Hall, Berkeley, CA 94720-1730 (United States); Piet, Steven J.; Pincock, Layne F.; Soelberg, Nick R. [Idaho National Laboratory - INL, 2525 North Fremont Avenue, Idaho Falls, ID 83415 (United States)] [Idaho National Laboratory - INL, 2525 North Fremont Avenue, Idaho Falls, ID 83415 (United States)

    2013-07-01

    This study explores the impact of wastes generated from potential future fuel cycles and the issues presented by classifying these under current classification criteria, and discusses the possibility of a comprehensive and consistent characteristics-based classification framework based on new waste streams created from advanced fuel cycles. A static mass flow model, Fuel-Cycle Integration and Tradeoffs (FIT), was used to calculate the composition of waste streams resulting from different nuclear fuel cycle choices. This analysis focuses on the impact of waste form heat load on waste classification practices, although classifying by metrics of radiotoxicity, mass, and volume is also possible. The value of separation of heat-generating fission products and actinides in different fuel cycles is discussed. It was shown that the benefits of reducing the short-term fission-product heat load of waste destined for geologic disposal are neglected under the current source-based radioactive waste classification system, and that it is useful to classify waste streams based on how favorable the impact of interim storage is in increasing repository capacity. (authors)

  20. Assessment of Exploitable Geothermal Resources Using Magmatic...

    Open Energy Info (EERE)

    parameter in the magmatic transfer method. The method of magmatic heat transfer (Smith and Shaw, 1975; Sanyal et al., 2002) was applied in the main volcanic complexes of the...

  1. Assessing the Rye Patch Geothermal Field, a Classic Basin-and...

    Open Energy Info (EERE)

    the Rye Patch Geothermal Field, a Classic Basin-and-Range Resource Authors S.K Sanyal, J.R McNitt, S. J. Butler, C. W. Klein and and R.E. Elliss Published Journal GRC...

  2. Assessing the Rye Patch geothermal field, a classic Basin-and...

    Open Energy Info (EERE)

    if one or two additional wells are drilled for injection. Authors Sanyal, S.K., McNitt, J.R., Butler, S.J., Klein, C.W., and Ellis and R.K. Conference GRC Annual Meeting;...

  3. Methods for data classification

    DOE Patents [OSTI]

    Garrity, George; Lilburn, Timothy G.

    2011-10-11

    The present invention provides methods for classifying data and uncovering and correcting annotation errors. In particular, the present invention provides a self-organizing, self-correcting algorithm for use in classifying data. Additionally, the present invention provides a method for classifying biological taxa.

  4. Position Management and Classification

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

    2014-10-30

    The Order establishes Departmental requirements and responsibilities for classifying positions using the General Schedule (GS) and the Federal Wage System (FWS) standards.

  5. SU-E-J-125: Classification of CBCT Noises in Terms of Their Contribution to Proton Range Uncertainty

    SciTech Connect (OSTI)

    Brousmiche, S; Orban de Xivry, J; Macq, B; Seco, J

    2014-06-01

    Purpose: This study assesses the potential use of CBCT images in adaptive protontherapy by estimating the contribution of the main sources of noise and calibration errors to the proton range uncertainty. Methods: Measurements intended to highlight each particular source have been achieved by adapting either the testbench configuration, e.g. use of filtration, fan-beam collimation, beam stop arrays, phantoms and detector reset light, or the sequence of correction algorithms including water precorrection. Additional Monte-Carlo simulations have been performed to complement these measurements, especially for the beam hardening and the scatter cases. Simulations of proton beams penetration through the resulting images have then been carried out to quantify the range change due to these effects. The particular case of a brain irradiation is considered mainly because of the multiple effects that the skull bones have on the internal soft tissues. Results: On top of the range error sources is the undercorrection of scatter. Its influence has been analyzed from a comparison of fan-beam and full axial FOV acquisitions. In this case, large range errors of about 12 mm can be reached if the assumption is made that the scatter has only a constant contribution over the projection images. Even the detector lag, which a priori induces a much smaller effect, has been shown to contribute for up to 2 mm to the overall error if its correction only aims at reducing the skin artefact. This last result can partially be understood by the larger interface between tissues and bones inside the skull. Conclusion: This study has set the basis of a more systematical analysis of the effect CBCT noise on range uncertainties based on a combination of measurements, simulations and theoretical results. With our method, even more subtle effects such as the cone-beam artifact or the detector lag can be assessed. SBR and JOR are financed by iMagX, a public-private partnership between the region Wallone

  6. Integrating Epidermal Growth Factor Receptor Assay With Clinical Parameters Improves Risk Classification for Relapse and Survival in Head-and-Neck Squamous Cell Carcinoma

    SciTech Connect (OSTI)

    Chung, Christine H.; Hammond, Elizabeth M.; Trotti, Andy M.; Wang Huijun; Spencer, Sharon; Zhang Huazhong; Cooper, Jay; Jordan, Richard; Rotman, Marvin H.; Ang, K. Kian

    2011-10-01

    Purpose: Epidermal growth factor receptor (EGFR) overexpression has been consistently found to be an independent predictor of local-regional relapse (LRR) after radiotherapy. We assessed the extent by which it can refine risk classification for overall survival (OS) and LRR in patients with head-and-neck squamous cell carcinoma (HNSCC). Methods and Materials: EGFR expression in locally advanced HNSCC was measured by immunohistochemistry in a series of patients randomized to receive accelerated or conventional radiation regimens in a Phase III trial. Subsequently, data of the two series were pooled (N = 533) for conducting a recursive partitioning analysis that incorporated clinical parameters (e.g., performance status, primary site, T and N categories) and four molecular markers (EGFR, p53, Ki-67, and microvessel density). Results: This study confirmed that patients with higher than median levels of tumor EGFR expression had a lower OS (relative risk [RR]: 1.90, p = 0.0010) and a higher LRR (RR: 1.91, p = 0.0163). Of the four markers analyzed, only EGFR was found to contribute to refining classification of patients into three risk classes with distinct OS and LRR outcomes. The addition of EGFR to three clinical parameters could identify patients having up to a fivefold difference in the risk of LRR. Conclusions: Adding pretreatment EGFR expression data to known robust clinical prognostic variables improved the estimation of the probability for OS and LRR after radiotherapy. Its use for stratifying or selecting patients with defined tumor feature and pattern of relapse for enrollment into clinical trials testing specific therapeutic strategy warrants further investigation.

  7. The Genomes OnLine Database (GOLD) v.5: a metadata management system based on a four level (meta)genome project classifications

    SciTech Connect (OSTI)

    Thomas, Alex D.; Stamatis, Dimitri; Bertsch, Jon; Isbandi, Michelle; Jansson, Jakob; Mallajosyula, Jyothi; Pagani, Ioanna; Lobos, Elizabeth A.; Kyrpides, Nikos C.; Reddy, Tatiparthi

    2014-10-29

    The Genomes OnLine Database (GOLD, http://www.genomesonline.org) is a comprehensive online resource to catalogue and monitor genetic studies worldwide. GOLD provides up-to-date status on complete and ongoing sequencing projects along with a broad array of curated metadata. Here we report version 5 (v.5) of the database. The newly designed database schema and web user interface supports several new features including the implementation of a four level (meta)genome project classification system and a simplified intuitive web interface to access reports and launch search tools. The database currently hosts information for about 19,200 studies, 56,000 Biosamples, 56,000 sequencing projects, and 39,400 analysis projects. More than just a catalogue of worldwide genome projects, GOLD is a manually curated, quality controlled metadata warehouse. The problems encountered in integrating disparate and varying quality data into GOLD are briefly highlighted. GOLD fully supports and follows the Genomic Standards Consortium (GSC) Minimum Information standards.

  8. MULTI-WAVELENGTH OBSERVATIONS OF SUPERNOVA 2011ei: TIME-DEPENDENT CLASSIFICATION OF TYPE IIb AND Ib SUPERNOVAE AND IMPLICATIONS FOR THEIR PROGENITORS

    SciTech Connect (OSTI)

    Milisavljevic, Dan; Margutti, Raffaella; Soderberg, Alicia M.; Chomiuk, Laura; Sanders, Nathan E.; Pignata, Giuliano; Bufano, Filomena; Fesen, Robert A.; Parrent, Jerod T.; Parker, Stuart; Mazzali, Paolo; Pian, Elena; Pickering, Timothy; Buckley, David A. H.; Crawford, Steven M.; Gulbis, Amanda A. S.; Hettlage, Christian; Hooper, Eric; Nordsieck, Kenneth H.; O'Donoghue, Darragh; and others

    2013-04-10

    We present X-ray, UV/optical, and radio observations of the stripped-envelope, core-collapse supernova (SN) 2011ei, one of the least luminous SNe IIb or Ib observed to date. Our observations begin with a discovery within {approx}1 day of explosion and span several months afterward. Early optical spectra exhibit broad, Type II-like hydrogen Balmer profiles that subside rapidly and are replaced by Type Ib-like He-rich features on a timescale of one week. High-cadence monitoring of this transition suggests absorption attributable to a high-velocity ({approx}> 12, 000 km s{sup -1}) H-rich shell, which is likely present in many Type Ib events. Radio observations imply a shock velocity of v Almost-Equal-To 0.13 c and a progenitor star average mass-loss rate of M-dot {approx}1.4 Multiplication-Sign 10{sup -5} M{sub sun} yr{sup -1} (assuming wind velocity v{sub w} = 10{sup 3} km s{sup -1}). This is consistent with independent constraints from deep X-ray observations with Swift-XRT and Chandra. Overall, the multi-wavelength properties of SN 2011ei are consistent with the explosion of a lower-mass (3-4 M{sub Sun }), compact (R{sub *} {approx}< 1 Multiplication-Sign 10{sup 11} cm), He-core star. The star retained a thin hydrogen envelope at the time of explosion, and was embedded in an inhomogeneous circumstellar wind suggestive of modest episodic mass loss. We conclude that SN 2011ei's rapid spectral metamorphosis is indicative of time-dependent classifications that bias estimates of the relative explosion rates for Type IIb and Ib objects, and that important information about a progenitor star's evolutionary state and mass loss immediately prior to SN explosion can be inferred from timely multi-wavelength observations.

  9. Nonderivative modified gravity: a classification

    SciTech Connect (OSTI)

    Comelli, D.; Nesti, F.; Pilo, L. E-mail: fabrizio.nesti@irb.hr

    2014-11-01

    We analyze the theories of gravity modified by a generic nonderivative potential built from the metric, under the minimal requirement of unbroken spatial rotations. Using the canonical analysis, we classify the potentials V according to the number of degrees of freedom (DoF) that propagate at the nonperturbative level. We then compare the nonperturbative results with the perturbative DoF propagating around Minkowski and FRW backgrounds. A generic V implies 6 propagating DoF at the non-perturbative level, with a ghost on Minkowski background. There exist potentials which propagate 5 DoF, as already studied in previous works. Here, no V with unbroken rotational invariance admitting 4 DoF is found. Theories with 3 DoF turn out to be strongly coupled on Minkowski background. Finally, potentials with only the 2 DoF of a massive graviton exist. Their effect on cosmology is simply equivalent to a cosmological constant. Potentials with 2 or 5 DoF and explicit time dependence appear to be a further viable possibility.

  10. Rapid classification of biological components

    DOE Patents [OSTI]

    Thompson, Vicki S.; Barrett, Karen B.; Key, Diane E.

    2010-03-23

    A method is disclosed for analyzing a biological sample by antibody profiling for identifying forensic samples or for detecting the presence of an analyte. In an illustrative embodiment of the invention, the analyte is a drug, such as marijuana, cocaine (crystalline tropane alkaloid), methamphetamine, methyltestosterone, or mesterolone. The method involves attaching antigens to a surface of a solid support in a preselected pattern to form an array wherein the locations of the antigens are known; contacting the array with the biological sample such that a portion of antibodies in the sample reacts with and binds to antigens in the array, thereby forming immune complexes; washing away antibodies that do not form immune complexes; and detecting the immune complexes, thereby forming an antibody profile. Forensic samples are identified by comparing a sample from an unknown source with a sample from a known source. Further, an assay, such as a test for illegal drug use, can be coupled to a test for identity such that the results of the assay can be positively correlated to a subject's identity.