skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Multi-Platform Decision Support.

Abstract

Abstract not provided.

Authors:
; ; ;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1148210
Report Number(s):
SAND2007-3029C
522931
DOE Contract Number:
AC04-94AL85000
Resource Type:
Conference
Resource Relation:
Conference: Proposed for presentation at the EWRI ASCE Annual Conference held May 16, 2007 in Tampa Bay, FL.
Country of Publication:
United States
Language:
English

Citation Formats

Tidwell, Vincent Carroll, Pierce, Suzanne, Sharp, John, and Eaton, Dave. Multi-Platform Decision Support.. United States: N. p., 2007. Web.
Tidwell, Vincent Carroll, Pierce, Suzanne, Sharp, John, & Eaton, Dave. Multi-Platform Decision Support.. United States.
Tidwell, Vincent Carroll, Pierce, Suzanne, Sharp, John, and Eaton, Dave. Tue . "Multi-Platform Decision Support.". United States. doi:. https://www.osti.gov/servlets/purl/1148210.
@article{osti_1148210,
title = {Multi-Platform Decision Support.},
author = {Tidwell, Vincent Carroll and Pierce, Suzanne and Sharp, John and Eaton, Dave},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue May 01 00:00:00 EDT 2007},
month = {Tue May 01 00:00:00 EDT 2007}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share:
  • Abstract not provided.
  • Utilities have invested in many costly enterprise systems - computerized maintenance management systems, document management systems, enterprise grade portals, to name but a few - and often very specialized systems, like data historians, high end diagnostic systems, and other focused and point solutions. From recent industry reports, we now know that the average nuclear power utilizes on average 1900 systems to perform daily work, of which 250 might facilitate the equipment reliability decision-making process. The time has come to leverage the investment in these systems by providing a common platform for integration and decision-making that will further the collective industrymore » aim of enhancing the reliability of our nuclear generation assets to maintain high plant availability and to deliver on plant life extension goals without requiring additional large scale investment in IT infrastructure. (authors)« less
  • Multimedia models are increasingly being used in exposure assessments to support regulatory and business decisionmaking worldwide. Although such models are based on the same fundamental principles of mass balance, different assumptions that are invoked when constructing the model may lead to different results. To assess the precision of exposure assessments obtained using different multi-media models, a model comparison exercise was performed. Several well documented multi-media models used to support exposure assessments in Europe, Canada and the United States were chosen. They are HAZCHEM, CALTOX, CHEMCAN, SimpleBox. A set of five disparate industrial chemicals were run for each model under amore » fixed set of environmental conditions. The chemicals evaluated include: benzene, benzo(a)pyrene, diethylhexyl phthalate, isopropanol, and methyl tertiary butyl ether. Each chemical was evaluated using three discharge scenarios in which a constant unit loading was released into air, water, or soil. The resulting steady-state concentrations in air, water, sediment, and soil for the various discharge scenarios were compared among these models. Estimated response times, defined as the time required for the concentration in a specified media to decline to one-half its original value after the discharge is terminated, were also compared. The implications of this exercise for exposure assessments will be highlighted.« less
  • While it is widely recognized that data can be a valuable resource for any organization, extracting information contained within the data is often a difficult problem. Attempts to obtain information from data may be limited by legacy data storage formats, lack of expert knowledge about the data, difficulty in viewing the data, or the volume of data needing to be processed. The rapidly developing field of Data Mining or Knowledge Data Discovery is a blending of Artificial Intelligence, Statistics, and Human-Computer Interaction. Sophisticated data navigation tools to obtain the information needed for decision support do not yet exist. Each datamore » mining task requires a custom solution that depends upon the character and quantity of the data. This paper presents a two-stage approach for handling the prediction of personal bankruptcy using credit card account data, combining decision tree and artificial neural network technologies. Topics to be discussed include the pre-processing of data, including data cleansing, the filtering of data for pertinent records, and the reduction of data for attributes contributing to the prediction of bankruptcy, and the two steps in the mining process itself.« less
  • The merging of the artificial intelligence (AI) and decision support systems (DSS) philosophies seems to be a logical eventuality in future years of software system development. The complexity of the managerial decision making environment portends that applications of sophisticated concepts such as those associated with AI are an inevitable outcome if responsive DSS are to be provided. A conceptual framework for this merging is proposed in this paper. This model incorporates four modules, one of which, the AI and learning module, is responsible for the active role which characterizes the use of AI concepts in their projected role. 25 references.