English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 49378/84106 (59%)
造訪人次 : 7365078      線上人數 : 93
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋
    請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/68620

    題名: Comparison of regression and neural network models for prediction of inspection profiles for aging aircraft
    作者: Luxhoj, James T.;Williams, Trefor P.;徐煥智;Shyur, Huan-jyh
    貢獻者: 淡江大學資訊管理學系
    日期: 1997-02-01
    上傳時間: 2011-10-23 13:19:50 (UTC+8)
    摘要: Currently under phase 2 development by the Federal Aviation Administration (FAA), the Safety Performance Analysis System (SPAS) contains ‘alert’ indicators of aircraft safety performance that can signal potential problem areas for inspectors. The Service Difficulty Reporting (SDR) system is one component of SPAS and contains data related to the identification of abnormal, potentially unsafe conditions in aircraft and/or aircraft components/equipment.
    SPAS contains performance indicators to assist safety inspectors in diagnosing an airline's safety ‘profile’ compared with others in the same peer class. This paper details the development of SDR prediction models for the DC-9 aircraft by analyzing sample data from the SDR database that have been merged with aircraft utilization data. Both multiple regression and neural networks are used to create prediction models for the overall number of SDRs and for SDR cracking and corrosion cases. These prediction models establish a range for the number of SDRs outside which safety advisory warnings would be issued. It appears that a data ‘grouping’ strategy to create aircraft ‘profiles’ is very effective at enhancing the predictive accuracy of the models. The results from each competing modeling approach are compared and managerial implications to improve the SDR performance indicator in SPAS are provided.
    關聯: IIE transaction 29(2), pp.91-101
    DOI: 10.1080/07408179708966316
    顯示於類別:[資訊管理學系暨研究所] 期刊論文





    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回饋