• DocumentCode
    584454
  • Title

    Design on Algorithm of Automatic Test Papers Generation for Examination System of Electric Energy Measurement

  • Author

    Yuan-bin, Chen ; Jie, Dai

  • Author_Institution
    Control Sci. & Eng., Huazhong Univ. of Sci. & Technol., Wuhan, China
  • fYear
    2012
  • fDate
    11-13 Aug. 2012
  • Firstpage
    1397
  • Lastpage
    1400
  • Abstract
    With various kinds of intelligent metering equipment coming into service, there has been an urgent need for a set of examination system in the electric power industry to check the staff´s level of measuring electric energy. In this paper, we design and implement a random test paper generation algorithm for this examination system, and analyze the experiment´s data, based on the practical requirement of a training system for the electric energy measurement in a certain electric company. At the same time, in order to get a set of test papers to satisfy the given conditions, this paper discusses how to use optimized genetic algorithm to generate test papers from the question bank. This paper introduce fishnet algorithm to generate test papers automatically for getting a better, more fair and more objective test paper.
  • Keywords
    computer aided instruction; electrical engineering education; electricity supply industry; energy measurement; genetic algorithms; metering; power consumption; automatic test paper generation algorithm; electric company; electric energy measurement; electric power industry; examination system; fishnet algorithm; intelligent metering equipment; optimized genetic algorithm; random test paper generation algorithm; staff level; Algorithm design and analysis; Biological cells; Computers; Energy measurement; Genetic algorithms; Genetics; Heuristic algorithms; Automatic test paper generation; Electric Energy Measurement; Genetic Algorithm Fishnet Algorithm; Random Algorithm;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science & Service System (CSSS), 2012 International Conference on
  • Conference_Location
    Nanjing
  • Print_ISBN
    978-1-4673-0721-5
  • Type

    conf

  • DOI
    10.1109/CSSS.2012.352
  • Filename
    6394590