Recognition performance of imputed control chart patterns using exponentially weighted moving average

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2018-09-11 DOI:10.1504/EJIE.2018.10015686

R. Haghighati, A. Hassan

{"title":"Recognition performance of imputed control chart patterns using exponentially weighted moving average","authors":"R. Haghighati, A. Hassan","doi":"10.1504/EJIE.2018.10015686","DOIUrl":null,"url":null,"abstract":"Performance of control chart pattern recogniser (CCPR) is dependent on the quality of data. Furthermore, when data is partially missing, false alarms and misclassification rate are high. This paper studied CCPR with incomplete data and investigated effectiveness of the exponential smoothing in restoring the patterns aiming to increase the recognition accuracy. The results demonstrated that average overall recognition accuracy degrades from 99.57 (without missingness) to 76.33 in severe missingness. Classification errors in the incomplete random and trend patterns increased up to 38 and 44 times, respectively. Exponential smoothing with a constant of 0.9 is found to be an effective imputation technique. In 50% missingness, recognition accuracy of imputed dataset improved by 99.2% and 19.4% in stable and unstable patterns respectively. Type I error in trend and type II error in random and cyclic patterns were reduced significantly with EWMA imputation. Sensitivity tests proved pattern recognition using proposed imputation technique resulted in superior robustness performance. [Received 28 April 2016; Revised 4 November 2017; Accepted 26 March 2018]","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":null,"pages":null},"PeriodicalIF":16.4000,"publicationDate":"2018-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1504/EJIE.2018.10015686","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 3

Abstract

Performance of control chart pattern recogniser (CCPR) is dependent on the quality of data. Furthermore, when data is partially missing, false alarms and misclassification rate are high. This paper studied CCPR with incomplete data and investigated effectiveness of the exponential smoothing in restoring the patterns aiming to increase the recognition accuracy. The results demonstrated that average overall recognition accuracy degrades from 99.57 (without missingness) to 76.33 in severe missingness. Classification errors in the incomplete random and trend patterns increased up to 38 and 44 times, respectively. Exponential smoothing with a constant of 0.9 is found to be an effective imputation technique. In 50% missingness, recognition accuracy of imputed dataset improved by 99.2% and 19.4% in stable and unstable patterns respectively. Type I error in trend and type II error in random and cyclic patterns were reduced significantly with EWMA imputation. Sensitivity tests proved pattern recognition using proposed imputation technique resulted in superior robustness performance. [Received 28 April 2016; Revised 4 November 2017; Accepted 26 March 2018]

查看原文本刊更多论文

基于指数加权移动平均的输入控制图模式识别性能

控制图模式识别器(CCPR)的性能取决于数据的质量。此外，当数据部分缺失时，误报率和误分类率很高。为了提高识别精度，本文研究了不完全数据下的CCPR，并研究了指数平滑恢复模式的有效性。结果表明，平均整体识别准确率从99.57(无缺失)下降到76.33(严重缺失)。不完全随机模式和趋势模式的分类误差分别增加了38倍和44倍。常数为0.9的指数平滑是一种有效的插值方法。当缺失率为50%时，在稳定模式和不稳定模式下，输入数据集的识别准确率分别提高了99.2%和19.4%。趋势型误差和随机型和循环型误差均显著降低。灵敏度测试表明，采用该方法的模式识别具有较好的鲁棒性。[2016年4月28日收到;2017年11月4日修订;接受2018年3月26日]

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.