کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
295588 | 511560 | 2009 | 11 صفحه PDF | دانلود رایگان |
This paper presents new results of our continuous effort to develop a computer-aided radiographic weld inspection system. The focus of this study is on improving accuracy by feature selection. To this end, we propose two versions of ant colony optimization (ACO)-based algorithms for feature selection and show their effectiveness to improve the accuracy in detecting weld flaws and the accuracy in classifying weld flaw types. The performances of ACO-based methods are compared with that of no feature selection and that of sequential forward floating selection, which is a known good feature selection method. Four different classifiers, including nearest mean, k-nearest neighbor, fuzzy k-nearest neighbor, and center-based nearest neighbor, are employed to carry out the tasks of weld flaw identification and weld flaw type classification.
Journal: NDT & E International - Volume 42, Issue 4, June 2009, Pages 229–239