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6 July 2015 Use of the self-organizing feature map to diagnose abnormal engineering change
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Proceedings Volume 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015); 963119 (2015) https://doi.org/10.1117/12.2197118
Event: Seventh International Conference on Digital Image Processing (ICDIP15), 2015, Los Angeles, United States
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.
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Ruei-Shan Lu, Zhi-Ting Wu, Kuo-Wei Peng, and Tai-Yi Yu "Use of the self-organizing feature map to diagnose abnormal engineering change", Proc. SPIE 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015), 963119 (6 July 2015); https://doi.org/10.1117/12.2197118
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