Journal of Quality Technology最新文献

{"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":"48 1","pages":""},"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}

{"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":"9 2","pages":"0"},"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}

{"title":"A change-point–based control chart for detecting sparse mean changes in high-dimensional heteroscedastic data","authors":"Zezhong Wang, Inez Maria Zwetsloot","doi":"10.1080/00224065.2023.2250884","DOIUrl":"https://doi.org/10.1080/00224065.2023.2250884","url":null,"abstract":"AbstractBecause of the “curse of dimensionality,” high-dimensional processes present challenges to traditional multivariate statistical process monitoring (SPM) techniques. In addition, the unknown underlying distribution of and complicated dependency among variables such as heteroscedasticity increase the uncertainty of estimated parameters and decrease the effectiveness of control charts. In addition, the requirement of sufficient reference samples limits the application of traditional charts in high-dimension, low-sample-size scenarios (small n, large p). More difficulties appear when detecting and diagnosing abnormal behaviors caused by a small set of variables (i.e., sparse changes). In this article, we propose two change-point–based control charts to detect sparse shifts in the mean vector of high-dimensional heteroscedastic processes. Our proposed methods can start monitoring when the number of observations is a lot smaller than the dimensionality. The simulation results show that the proposed methods are robust to nonnormality and heteroscedasticity. Two real data examples are used to illustrate the effectiveness of the proposed control charts in high-dimensional applications. The R codes are provided online.Keywords: change-pointheteroscedasticityhigh-dimensional control chartsparse changesstatistical process monitoring (SPM) Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsZezhong WangZezhong Wang received her doctoral degree from the Department of Systems Engineering, City University of Hong Kong. She is a postdoc researcher at the Hong Kong Industrial Artificial Intelligence and Robotics Centre (FLAIR). Her research interests include statistical process monitoring, data science, and artificial intelligence applications. Her email address is zzwang@hkflair.org.Inez Maria ZwetslootInez M. Zwetsloot is an associate professor in the Department of Business Analytics at the University of Amsterdam, The Netherlands. Previously she was assistant professor at the Department of Systems Engineering, City University of Hong Kong. She is the recipient of the Young Statistician Award (ENBIS, 2021) and the Feigenbaum Medal (ASQ, 2021). Her research focuses on using statistics and analytics for solving business challenges using data. This includes work on statistical process monitoring, network analytics, quality engineering, and data science. Her email address is i.m.zwetsloot@uva.nl.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":"4 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136381710","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}

{"title":"Best practices for multi- and mixed-level supersaturated designs","authors":"Rakhi Singh","doi":"10.1080/00224065.2023.2259022","DOIUrl":"https://doi.org/10.1080/00224065.2023.2259022","url":null,"abstract":"AbstractSupersaturated designs offer cost-effective efficacy in discerning significant factors among a vast array of potential factors, thereby rendering them valuable. The current literature studies several design selection criteria and analysis methods for such designs. For two-level designs, the screening performance of optimal designs constructed under different optimality criteria remains similar, especially when the effect directions are not known in advance. The Gauss-Dantzig Selector (GDS) is the preferred analysis method for two-level designs. For the multi- and mixed-level supersaturated designs, despite the existence of multiple design optimality criteria and design construction methods, the literature lacks guidance for both the design selection and the choice of analysis method. Through extensive simulation studies, we show that the multi- and mixed-level designs constructed using different optimality criteria have equivalent screening performance for the unknown effect directions. For known effect directions, generalized minimum aberration-optimal designs have slightly better screening performance. On the analysis front, however, the story differs from two-level designs. While LASSO and GDS show superior performance among the analysis methods compared, they depend on the parameterization or the coding of factors. Since no single choice of parameterization is best across sparsity levels, scenarios, and designs, we propose using group LASSO, which is invariant to parameterizations. Finally, we characterize the settings in terms of the number of runs, factors, and the effect sparsity, which are too complex to get meaningful results from group LASSO.Keywords: group LASSOmain effectsmixed-levelscreening experimentsthree-level supersaturated design Disclosure statementNo potential conflict of interest was reported by the authors.Data availability statementData availability is not applicable to this article as no new data were created or analyzed in this study.Additional informationNotes on contributorsRakhi SinghDr. Rakhi Singh is an Assistant Professor in the Department of Mathematics and Statistics at Binghamton University in New York, USA. She did her PhD in Mathematics (with specialization in Statistics) under a joint PhD program between Indian Institute of Technology Bombay and Monash University, Australia. She also did a postdoc for a couple of years at TU Dortmund and UNC Greensboro. Her primary research areas are design and analysis of experiments and subdata selection for high-dimensional data.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135857330","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}

{"title":"Sequential Latin hypercube design for two-layer computer simulators","authors":"Yan Wang, Dianpeng Wang, Xiaowei Yue","doi":"10.1080/00224065.2023.2251178","DOIUrl":"https://doi.org/10.1080/00224065.2023.2251178","url":null,"abstract":"AbstractThe two-layer computer simulators are commonly used to mimic multi-physics phenomena or systems. Usually, the outputs of the first-layer simulator (also called the inner simulator) are partial inputs of the second-layer simulator (also called the outer simulator). How to design experiments by considering the space-filling properties of inner and outer simulators simultaneously is a significant challenge that has received scant attention in the literature. To address this problem, we propose a new sequential optimal Latin hypercube design (LHD) by using the maximin integrating mixed distance criterion. A corresponding sequential algorithm for efficiently generating such designs is also developed. Numerical simulation results show that the new method can effectively improve the space-filling property of the outer computer inputs. The case study about composite structures assembly simulation demonstrates that the proposed method can outperform the benchmark methods.Keywords: composites structures assembly processesGaussian processmaximin criterionoptimal LHDprincipal component scores AcknowledgmentsWe are grateful to the editor and all reviewers for their excellent work. Their constructive criticism and suggestions significantly improved the quality of this manuscript.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe research of Yan Wang was supported by the National Natural Science Foundation of China (Grant no. NSFC 12101024) and the work of Dianpeng Wang was supported by the National Natural Science Foundation of China (Grant no. NSFC 12171033).Notes on contributorsYan WangYan Wang is an assistant professor at the School of Statistics and Data Science, Beijing University of Technology, Beijing, China.Dianpeng WangDianpeng Wang is an assistant professor at the School of Mathematics and Statistics, and the key laboratory of mathematical theory and computation in information security, Beijing Institute of Technology, Beijing, China.Xiaowei YueXiaowei Yue is an associate professor at the Department of Industrial Engineering, and Institute for Quality and Reliability, Tsinghua University, Beijing, China.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135857349","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}

{"title":"Federated generalized scalar-on-tensor regression","authors":"Elif Konyar, Mostafa Reisi Gahrooei","doi":"10.1080/00224065.2023.2246600","DOIUrl":"https://doi.org/10.1080/00224065.2023.2246600","url":null,"abstract":"AbstractComplex systems are generating more and more high-dimensional data for which tensor analysis showed promising results by capturing complex correlation structures of data. Such data is often distributed among various sites creating challenges for developing data-driven models. Specifically, data privacy and security concerns have been exacerbated in recent years and drove the demand to store and analyze data at the edge of networks rather than sharing it with a centralized server. Federated learning frameworks have been introduced as a solution to these concerns. These frameworks allow local clients to learn local models and collaborate with others to develop a more generalizable aggregated model while handling data privacy issues. In this article, we propose a federated generalized scalar-on-tensor regression framework where multiple local tensor models are learned at the edge, and their parameters are shared with and updated by an aggregator. Experiments on synthetic data sets and two real-world data sets from agriculture and manufacturing domains show the superiority of our approach over several benchmarks.Keywords: aggregated modelfederated learningpersonalized modelscalar-on-tensor regression AcknowledgementsWe would like to thank Ioannis Ampatzidis, Lucas Fideles Costa and Vitor Gontijo da Cunha for providing hyperspectral image data collected at the Southwest Florida Research and Education Center. Also, we would like to thank Massimo Pacella for providing access to the vehicle engine sensor data.Data availability statementThe data used in this article are not publicly available. To request access to the data used in Case Study I (Section 6.1) and Case Study II (Section 6.2), one may contact the corresponding authors of (Costa et al. Citation2022) and (Pacella Citation2018), respectively.Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThis work has been partially supported by the National Science Foundation (NSF) award 2212878.Notes on contributorsElif KonyarElif Konyar is a doctoral student in the Department of Industrial and Systems Engineering at University of Florida. Her email address is elif.konyar@ufl.edu.Mostafa Reisi GahrooeiDr. Mostafa Reisi Gahrooei is an Assistant Professor in the Department of Industrial and Systems Engineering at University of Florida. His email address is mreisigahrooei@ufl.edu. He is the corresponding author.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135816361","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}

{"title":"Adaptive-region sequential design with quantitative and qualitative factors in application to HPC configuration","authors":"Xia Cai, Li Xu, C. Devon Lin, Yili Hong, Xinwei Deng","doi":"10.1080/00224065.2023.2241680","DOIUrl":"https://doi.org/10.1080/00224065.2023.2241680","url":null,"abstract":"AbstractMotivated by the need of finding optimal configuration in the high-performance computing (HPC) system, this work proposes an adaptive-region sequential design (ARSD) for optimization of computer experiments with qualitative and quantitative factors. Experiments with both qualitative and quantitative factors are also encountered in other applications. The proposed ARSD method considers a sequential design criterion under the additive Gaussian process to deal with both qualitative and quantitative factors. Moreover, the adaptiveness of the proposed sequential procedure allows the selection of next design point from the adaptive design region achieving a meaningful balance between exploitation and exploration for optimization. Theoretical justification of the adaptive design region is provided. The performance of the proposed method is evaluated by several numerical examples in simulations. The case study of HPC performance optimization further elaborates the merits of the proposed method.Keywords: Adaptive designdesign of experimentexploitation and explorationGaussian processoptimal configuration AcknowledgementWe are grateful to the editor and the referees for their constructive comments that have helped improve the article significantly.Data availability statementThe data that support the findings of this study are available from the corresponding author upon reasonable request.Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThe work by Cai is supported by the National Natural Science Foundation of China (Grant No. 12001155), and the Natural Science Foundation of Hebei Province of China (Grant No. A2022208001). The work by Lin is supported by the Natural Sciences and Engineering Research Council of Canada.Notes on contributorsXia CaiXia Cai is an Associate Professor in the School of Science at the Hebei University of Science and Technology. Her email address is caixia@hebust.edu.cn.Li XuLi Xu is a PhD candidate in the Department of Statistics at the Virginia Tech. His email address is lix1992@vt.edu.C. Devon LinChunfang Devon Lin is a Professor in the Department of Mathematics and Statistics at the Queen’s University. Her email address is devon.lin@queensu.caYili HongYili Hong is a Professor in the Department of Statistics at the Virginia Tech. His email address is yilihong@vt.edu.Xinwei DengXinwei Deng is a Professor in the Department of Statistics at the Virginia Tech. His email address is xdeng@vt.edu.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136130681","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}

{"title":"Book Review: Computational and Statistical Methods for Chemical Engineering, 1st edition, by Wim P. Krijnen and Ernst C. Wit, Boca Raton, FL: Chapman &amp; Hall/CRC Press, 2023, 308 pp., $89.95 (eBook); ISBN: 97-81-0031-7819-4. <b>Computational and Statistical Methods for Chemical Engineering</b> , 1st edition, by Wim P. Krijnen and Ernst C. Wit. Boca Raton, FL: Chapman &amp; Hall/CRC Press, 2023, 308 pp., $89.95 (eBook); ISBN: 97-81-0031-7819-4.","authors":"Desy Permatasari","doi":"10.1080/00224065.2023.2249555","DOIUrl":"https://doi.org/10.1080/00224065.2023.2249555","url":null,"abstract":"\"Book Review: Computational and Statistical Methods for Chemical Engineering, 1st edition, by Wim P. Krijnen and Ernst C. Wit, Boca Raton, FL: Chapman & Hall/CRC Press, 2023, 308 pp., $89.95 (eBook); ISBN: 97-81-0031-7819-4..\" Journal of Quality Technology, ahead-of-print(ahead-of-print), pp. 1–3 Disclosure statementThe author has reported that there are no potential conflicts of interest.Additional informationFundingThe author thanks the Indonesia Endowment Fund for Education (LPDP) for funding this paper and the authors’ postgraduate studies.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136130393","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}

{"title":"Statistical Analytics for Health Data Science with SAS and R","authors":"Xingyi Yang","doi":"10.1080/00224065.2023.2249563","DOIUrl":"https://doi.org/10.1080/00224065.2023.2249563","url":null,"abstract":"","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135982364","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}

{"title":"Multi-node system modeling and monitoring with extended directed graphical models","authors":"Dengyu Li, Kaibo Wang","doi":"10.1080/00224065.2023.2229458","DOIUrl":"https://doi.org/10.1080/00224065.2023.2229458","url":null,"abstract":"","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":"12 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81342834","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}