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Proceedings Paper

Use of the self-organizing feature map to diagnose abnormal engineering change
Author(s): Ruei-Shan Lu; Zhi-Ting Wu; Kuo-Wei Peng; Tai-Yi Yu
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Paper Abstract

This study established identification manners with self-organizing feature map (SOM) to achieve the goal of monitoring Engineering Change (EC) based on historical data of a company that specializes in computers and peripherals. The product life cycle of this company is 3–6 months. The historical data were divided into three parts, each covering four months. The first part, comprising 2,343 records from January to April (the training period), comprise the Control Group. The second and third parts comprise Experimental Groups (EG) 1 and 2, respectively. For EG 1 and 2, the successful rate of recognizing information on abnormal ECs was approximately 96% and 95%, respectively. This paper shows the importance and screening procedures of abnormal engineering change for a particular company specializing in computers and peripherals.

Paper Details

Date Published: 6 July 2015
PDF: 5 pages
Proc. SPIE 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015), 963119 (6 July 2015); doi: 10.1117/12.2197118
Show Author Affiliations
Ruei-Shan Lu, Takming Univ. of Science and Technology (Taiwan)
Zhi-Ting Wu, Takming Univ. of Science and Technology (Taiwan)
Kuo-Wei Peng, Ming Chuan Univ. (Taiwan)
Tai-Yi Yu, Ming Chuan Univ. (Taiwan)

Published in SPIE Proceedings Vol. 9631:
Seventh International Conference on Digital Image Processing (ICDIP 2015)
Charles M. Falco; Xudong Jiang, Editor(s)

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