English  |  正體中文  |  简体中文  |  Post-Print筆數 : 11 |  Items with full text/Total items : 88645/118187 (75%)
Visitors : 23496535      Online Users : 281
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 商學院 > 企業管理學系 > 期刊論文 >  Item 140.119/100737
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/100737


    Title: Automated Extraction of Welds from Digitized Radiographic Images Based on MLP Neural Networks
    Authors: 唐揆
    Liao, T. W.;Tang, Kwei
    Contributors: 企管系
    Date: 1997
    Issue Date: 2016-08-25 14:12:23 (UTC+8)
    Abstract: It is desired to automate inspection of welding flaws. Automated extraction of welds forms the first step in developing an automated weld inspection system. This article presents a multilayered perceptron (MLP) based procedure for extracting welds from digitized radiographic images. The procedure consists of three major components: feature extraction, MLP-based object classification, and postprocessing. For each object in the line image extracted from the whole image, four features are defined: the peak position (x1), the width (x2), the mean square error between the object and its Gaussian intensity plot (x3), and the peak intensity (x4). Fiftyone training samples were used to train MLP neural networks. The training of MLP classifiers is discussed. Trained MLP neural networks are subsequently used to test unlearned feature patterns and to identify whether the patterns are welds or not. Postprocessing is performed to remove noises (misclassified nonweld objects) and restore the continuity of weld line (discontinuity due to missed weld objects). Test results show that the procedure can successfully extract all welds (100%) from 25 radiographic images.
    Relation: Applied Artificial Intelligence, 11(3), 197-218
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1080/088395197118226
    DOI: 10.1080/088395197118226
    Appears in Collections:[企業管理學系] 期刊論文

    Files in This Item:

    File Description SizeFormat
    197-218.pdf2288KbAdobe PDF321View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback