Journal of Quality Technology最新文献

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A blocked staggered-level design for an experiment with two hard-to-change factors 对含有两个难以改变的因素的实验进行分块错层设计
IF 2.5 2区 工程技术
Journal of Quality Technology Pub Date : 2024-02-08 DOI: 10.1080/00224065.2023.2290560
Peter Goos, Katherine Brickey, Ying Chen
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引用次数: 0
A blocked staggered-level design for an experiment with two hard-to-change factors 对含有两个难以改变的因素的实验进行分块错层设计
IF 2.5 2区 工程技术
Journal of Quality Technology Pub Date : 2024-02-08 DOI: 10.1080/00224065.2023.2290560
Peter Goos, Katherine Brickey, Ying Chen
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引用次数: 0
Quality prediction using functional linear regression with in-situ image and functional sensor data 利用现场图像和功能传感器数据的功能线性回归进行质量预测
IF 2.5 2区 工程技术
Journal of Quality Technology Pub Date : 2024-02-05 DOI: 10.1080/00224065.2023.2293869
Yaser Zerehsaz, Wenbo Sun, J. Jin
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引用次数: 0
Quality prediction using functional linear regression with in-situ image and functional sensor data 利用现场图像和功能传感器数据的功能线性回归进行质量预测
IF 2.5 2区 工程技术
Journal of Quality Technology Pub Date : 2024-02-05 DOI: 10.1080/00224065.2023.2293869
Yaser Zerehsaz, Wenbo Sun, J. Jin
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引用次数: 0
Adaptive sampling and monitoring of partially observed images 部分观测图像的自适应采样和监测
IF 2.5 2区 工程技术
Journal of Quality Technology Pub Date : 2024-01-24 DOI: 10.1080/00224065.2023.2282512
Jinwei Yao, Badrinath Balasubramaniam, Beiwen Li, Eric L. Kreiger, Chao Wang
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引用次数: 0
Augmenting system tests with component tests for reliability assurance 用组件测试增强系统测试,确保可靠性
IF 2.5 2区 工程技术
Journal of Quality Technology Pub Date : 2024-01-24 DOI: 10.1080/00224065.2023.2287747
R. Warr, Jace Ritchie, Michael Hamada
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引用次数: 0
The 100th anniversary of the control chart 控制图诞生 100 周年
IF 2.5 2区 工程技术
Journal of Quality Technology Pub Date : 2024-01-01 DOI: 10.1080/00224065.2023.2282926
Douglas C. Montgomery
{"title":"The 100th anniversary of the control chart","authors":"Douglas C. Montgomery","doi":"10.1080/00224065.2023.2282926","DOIUrl":"https://doi.org/10.1080/00224065.2023.2282926","url":null,"abstract":"","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139632607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Next Editor of the Journal of Quality Technology : Dr. Rong Pan 下一任《质量技术期刊》编辑:潘荣博士
IF 2.5 2区 工程技术
Journal of Quality Technology Pub Date : 2024-01-01 DOI: 10.1080/00224065.2023.2290407
Bianca M. Colosimi, L. A. Jones‐Farmer
{"title":"Next Editor of the\u0000 Journal of Quality Technology\u0000 : Dr. Rong Pan","authors":"Bianca M. Colosimi, L. A. Jones‐Farmer","doi":"10.1080/00224065.2023.2290407","DOIUrl":"https://doi.org/10.1080/00224065.2023.2290407","url":null,"abstract":"","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139638351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
SpTe2M: An R package for nonparametric modeling and monitoring of spatiotemporal data SpTe2M:用于时空数据非参数建模和监测的 R 软件包
IF 2.5 2区 工程技术
Journal of Quality Technology Pub Date : 2023-11-30 DOI: 10.1080/00224065.2023.2278795
Kai-zuan Yang, Peihua Qiu
{"title":"SpTe2M: An R package for nonparametric modeling and monitoring of spatiotemporal data","authors":"Kai-zuan Yang, Peihua Qiu","doi":"10.1080/00224065.2023.2278795","DOIUrl":"https://doi.org/10.1080/00224065.2023.2278795","url":null,"abstract":"Spatio-temporal data are common in practice. Such data often have complicated structures that are difficult to describe by parametric statistical models. Thus, it is often challenging to analyze spatio-temporal data effectively since most existing statistical methods and software packages in the literature are based on parametric modeling and cannot handle certain applications properly. This paper introduces the new R package SpTe2M , which is developed for implementing some recent nonparametric methods for modeling and monitoring spatio-temporal data. This package provides analytic tools for modeling spatio-temporal data nonparametrically and for monitoring dynamic spatial processes sequentially over time. It can be used for different applications, including disease surveillance, environmental monitoring, and more. The use of the package is demonstrated using the Florida influenza-like illness data observed during 2012-2014 and the PM2.5 concentration data in China collected during 2014-2016.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139206513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Spatial modeling and monitoring considering long-range dependence 考虑远程依赖的空间建模和监测
2区 工程技术
Journal of Quality Technology Pub Date : 2023-11-14 DOI: 10.1080/00224065.2023.2260018
Yunfei Shao, Wujun Si, Yong Chen
{"title":"Spatial modeling and monitoring considering long-range dependence","authors":"Yunfei Shao, Wujun Si, Yong Chen","doi":"10.1080/00224065.2023.2260018","DOIUrl":"https://doi.org/10.1080/00224065.2023.2260018","url":null,"abstract":"AbstractSpatial modeling and monitoring are critical in geometric characterization and quality control of material/product surfaces. With advances in metrology technology, a long-range dependence (LRD) effect has recently been detected in spatial data over different fields. The spatial LRD refers to a type of dependence that decays slowly over the distance with heavy tails and non-summable autocovariances so that the correlation is high among surface measurements across long spatial distances. Physically, the spatial LRD effect can be caused by specific spatial patterns such as certain material textures, surface profiles, or manufacturing defects. In literature, although various Markovian and non-Markovian spatial models have been proposed to study material surfaces, none of them has yet considered the LRD effect, which can lead to inefficient surface characterization and inaccurate surface quality control. To overcome the challenge, in this article, we first propose a novel spatial model that can capture the spatial LRD on material surfaces. Both isotropic and anisotropic scenarios of the model are developed based on the Lévy fractional Brownian random field and the fractional Brownian sheet, respectively. Subsequently, based on the proposed spatial model we develop an LRD-integrated quality control framework to monitor surface quality via generalized likelihood ratio test. Comprehensive simulation studies and a real case study using images of wood surfaces are conducted to validate the proposed approach. Results show that the proposed model that integrates LRD significantly outperforms multiple existing models in anomaly detection, and traditional models mis-detect out-of-control surfaces when the spatial LRD actually presents.Keywords: fractional Brownian sheetimage characterizationLévy fractional Brownian random fieldspatial long-range dependencesurface monitoring AcknowledgmentsThe authors would like to thank the Associate Editor and two anonymous reviewers for their thoughtful and constructive comments that significantly improved the quality of this article.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data of wood images used in the case study is publicly available at: https://www.mvtec.com/company/research/datasets/mvtec-ad.Additional informationFundingThis work was supported in part by the National Science Foundation under Award OIA-1656006, the Kansas NASA EPSCoR Research Infrastructure Development Program under Grant 80NSSC22M0028, and the NASA EPSCoR Program under Grant 80NSSC23M0100 to Wichita State University.Notes on contributorsYunfei ShaoYunfei Shao received a B.S. degree in theoretical and applied mechanics from the University of Science and Technology of China, Hefei, China, in 2016, and a Ph.D. degree in industrial engineering from Wichita State University, Wichita, KS, USA, in 2023. His research interests are in the development of statistical and data mining","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134957309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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