Support vector machines and model selection for control chart pattern recognition
學年 113
學期 2
出版（發表）日期 2025-02-11
作品名稱 Support vector machines and model selection for control chart pattern recognition
作品名稱（其他語言）
著者 Chih-Jen Su; I-Fei Chen; Tzong-Ru Tsai; Tzu-Hsuan Wang; Yuhlong Lio
單位
出版者
著錄名稱、卷期、頁數 Mathematics 13(4), p.592
摘要 Resource-intensiveness often occurs in modern industrial settings; meanwhile, common issues and irregular patterns in production can lead to defects and variations in work-piece dimensions, negatively impacting products and increasing costs. Utilizing traditional process control charts to monitor the process and identify potential anomalies is expensive when intensive resources are needed. To conquer these downsides, algorithms for control chart pattern recognition (CCPR) leverage machine learning models to detect non-normality or normality and ensure product quality is established, and novel approaches that integrate the support vector machine (SVM), random forest (RF), and K-nearest neighbors (KNN) methods with the model selection criterion, named SVM-, RF-, and KNN-CCPR, respectively, are proposed. The three CCPR approaches can save sample resources in the initial process monitoring, improve the weak learner’s ability to recognize non-normal data, and include normality as a special case. Simulation results and case studies show that the proposed SVM-CCPR method outperforms the other two competitors with the highest recognition rate and yields favorable performance for quality control.
關鍵字 K-nearest neighbors; Monte Carlo simulation; pattern recognition; random forest; support vector machines
語言 en
ISSN 2227-7390
期刊性質 國外
收錄於 SCI Scopus
產學合作
通訊作者 Tzong-Ru Tsai
審稿制度 否
國別 CHE
公開徵稿
出版型式 ,電子版
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機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/127676 )

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