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Xu F, Shu L, Li Y, Wang B. Joint Diagnosis of High-dimensional Process Mean and Covariance Matrix based on Bayesian Model Selection. Technometrics 2023. [DOI: 10.1080/00401706.2023.2182366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Affiliation(s)
- Feng Xu
- College of Science, Guilin University of Technology, Guilin, China
| | - Lianjie Shu
- Faculty of Business Administration, University of Macau, China
| | - Yanting Li
- Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai, China
| | - Binhui Wang
- School of Management, Jinan University, Guangzhou, China
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2
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Zhong Z, Wang H, Xiang D. Weighted Matrix Decomposition for Small Surface Defect Detection. MICROMACHINES 2022; 14:92. [PMID: 36677153 PMCID: PMC9862925 DOI: 10.3390/mi14010092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 12/17/2022] [Accepted: 12/23/2022] [Indexed: 06/17/2023]
Abstract
Detecting small defects against a complex surface is highly challenging but crucial to ensure product quality in industry sectors. However, in the detection performance of existing methods, there remains a huge gap in the localization and segmentation of small defects with limited sizes and extremely weak feature representation. To address the above issue, this paper presents a weighted matrix decomposition model (WMD) for small defect detection against a complex surface. Firstly, a weighted matrix is constructed based on texture characteristics of RGB channels in the defect image, which aims to improve contrast between defects and the background. Based on the sparse and low-rank characteristics of small defects, the weighted matrix is then decomposed into low-rank and sparse matrices corresponding to the redundant background and defect areas, respectively. Finally, an automatic threshold segmentation method is used to obtain the optimal threshold and accurately segment the defect areas and their edges in the sparse matrix. The experimental results show that the proposed model outperforms state-of-the-art methods under various quantitative evaluation metrics and has broad industrial application prospects.
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Affiliation(s)
- Zhiyan Zhong
- School of Automation, Guangdong Polytechnic Normal University, Guangzhou 510665, China
| | - Hongxin Wang
- Machine Life and Intelligence Research Center, Guangzhou University, Guangzhou 510006, China
- School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK
| | - Dan Xiang
- School of Automation, Guangdong Polytechnic Normal University, Guangzhou 510665, China
- Guangzhou Maritime University, Guangzhou 510725, China
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3
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Zhao X, Hu J, Mei Y, Yan H. Adaptive Partially Observed Sequential Change Detection and Isolation. Technometrics 2022; 64:502-512. [PMID: 37388823 PMCID: PMC10310291 DOI: 10.1080/00401706.2022.2124307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Revised: 08/20/2022] [Accepted: 08/20/2022] [Indexed: 10/14/2022]
Abstract
High-dimensional data has become popular due to the easy accessibility of sensors in modern industrial applications. However, one specific challenge is that it is often not easy to obtain complete measurements due to limited sensing powers and resource constraints. Furthermore, distinct failure patterns may exist in the systems, and it is necessary to identify the true failure pattern. This work focuses on the online adaptive monitoring of high-dimensional data in resource-constrained environments with multiple potential failure modes. To achieve this, we propose to apply the Shiryaev-Roberts procedure on the failure mode level and utilize the multi-arm bandit to balance the exploration and exploitation. We further discuss the theoretical property of the proposed algorithm to show that the proposed method can correctly isolate the failure mode. Finally, extensive simulations and two case studies demonstrate that the change point detection performance and the failure mode isolation accuracy can be greatly improved.
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Affiliation(s)
- Xinyu Zhao
- School of Computing and Augmented Intelligence, Arizona State University
| | - Jiuyun Hu
- School of Computing and Augmented Intelligence, Arizona State University
| | - Yajun Mei
- School of Industrial and Systems Engineering, Georgia Institute of Technology
| | - Hao Yan
- School of Computing and Augmented Intelligence, Arizona State University
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4
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Guo J, Yan H, Zhang C. A Bayesian Partially Observable Online Change Detection Approach with Thompson Sampling. Technometrics 2022. [DOI: 10.1080/00401706.2022.2127914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Affiliation(s)
- Jie Guo
- Tsinghua University, Tsinghua University, Beijing, 100084 China
| | - Hao Yan
- Arizona State University, Tempe, United States
| | - Chen Zhang
- Tsinghua University, Tsinghua University, Beijing, 100084 China
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5
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Zhao Y, Huo X, Mei Y. Hot-spots detection in count data by Poisson assisted smooth sparse tensor decomposition. J Appl Stat 2022; 50:2999-3029. [PMID: 37808612 PMCID: PMC10557627 DOI: 10.1080/02664763.2022.2112557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 08/08/2022] [Indexed: 10/15/2022]
Abstract
Count data occur widely in many bio-surveillance and healthcare applications, e.g. the numbers of new patients of different types of infectious diseases from different cities/counties/states repeatedly over time, say, daily/weekly/monthly. For this type of count data, one important task is the quick detection and localization of hot-spots in terms of unusual infectious rates so that we can respond appropriately. In this paper, we develop a method called Poisson assisted Smooth Sparse Tensor Decomposition (PoSSTenD), which not only detect when hot-spots occur but also localize where hot-spots occur. The main idea of our proposed PoSSTenD method is articulated as follows. First, we represent the observed count data as a three-dimensional tensor including (1) a spatial dimension for location patterns, e.g. different cities/countries/states; (2) a temporal domain for time patterns, e.g. daily/weekly/monthly; (3) a categorical dimension for different types of data sources, e.g. different types of diseases. Second, we fit this tensor into a Poisson regression model, and then we further decompose the infectious rate into two components: smooth global trend and local hot-spots. Third, we detect when hot-spots occur by building a cumulative sum (CUSUM) control chart and localize where hot-spots occur by their LASSO-type sparse estimation. The usefulness of our proposed methodology is validated through numerical simulation studies and a real-world dataset, which records the annual number of 10 different infectious diseases from 1993 to 2018 for 49 mainland states in the United States.
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Affiliation(s)
- Yujie Zhao
- Biostatistics and Research Decision Sciences Department, Merck & Co., Inc, North Wales, PA, USA
| | - Xiaoming Huo
- School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Yajun Mei
- School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA
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6
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Liu X, Yeo K, Lu S. Statistical Modeling for Spatio-Temporal Data From Stochastic Convection-Diffusion Processes. J Am Stat Assoc 2022. [DOI: 10.1080/01621459.2020.1863223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Xiao Liu
- Department of Industrial Engineering, University of Arkansas, Fayetteville, AR
| | - Kyongmin Yeo
- IBM T. J. Watson Research Center, Yorktown Heights, NY
| | - Siyuan Lu
- IBM T. J. Watson Research Center, Yorktown Heights, NY
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7
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Palm BG, Bayer FM, Cintra RJ. 2-D Rayleigh autoregressive moving average model for SAR image modeling. Comput Stat Data Anal 2022. [DOI: 10.1016/j.csda.2022.107453] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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8
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Xiang D, Qiu P, Wang D, Li W. Reliable Post-Signal Fault Diagnosis for Correlated High-Dimensional Data Streams. Technometrics 2021. [DOI: 10.1080/00401706.2021.1979100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Dongdong Xiang
- KLATASDS-MOE, School of Statistics, East China Normal University, Shanghai, China
| | - Peihua Qiu
- Department of Biostatistics, University of Florida, Gainesville, USA
| | - Dezhi Wang
- KLATASDS-MOE, School of Statistics, East China Normal University, Shanghai, China
- School of Mathematics and Statistics, Lanzhou University, Lanzhou, China
| | - Wendong Li
- School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China
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9
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Gómez AME, Li D, Paynabar K. An Adaptive Sampling Strategy for Online Monitoring and Diagnosis of High-Dimensional Streaming Data. Technometrics 2021. [DOI: 10.1080/00401706.2021.1967198] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
| | - Dan Li
- School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA
| | - Kamran Paynabar
- School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA
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10
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Fang Z, Li W, Liu X, Pu X, Xiang D. Online monitoring of high-dimensional binary data streams with application to extreme weather surveillance. J Appl Stat 2021; 49:4122-4136. [DOI: 10.1080/02664763.2021.1971633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Zhiwen Fang
- KLATASDS-MOE, School of Statistics, East China Normal University, Shanghai, People's Republic of China
| | - Wendong Li
- School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, People's Republic of China
| | - Xin Liu
- School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, People's Republic of China
| | - Xiaolong Pu
- KLATASDS-MOE, School of Statistics, East China Normal University, Shanghai, People's Republic of China
| | - Dongdong Xiang
- KLATASDS-MOE, School of Statistics, East China Normal University, Shanghai, People's Republic of China
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11
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Shen B, Wang R, Law ACC, Kamath R, Choo H, Kong Z(J. Super Resolution for Multi-Sources Image Stream Data Using Smooth and Sparse Tensor Completion and Its Applications in Data Acquisition of Additive Manufacturing. Technometrics 2021. [DOI: 10.1080/00401706.2021.1905074] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Bo Shen
- Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA
| | - Rongxuan Wang
- Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA
| | - Andrew Chung Chee Law
- Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA
| | - Rakesh Kamath
- Department of Materials Science and Engineering, The University of Tennessee, Knoxville, Knoxville, TN
| | - Hahn Choo
- Department of Materials Science and Engineering, The University of Tennessee, Knoxville, Knoxville, TN
| | - Zhenyu (James) Kong
- Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA
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12
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Zhao Y, Yan H, Holte S, Mei Y. Rapid detection of hot-spots via tensor decomposition with applications to crime rate data. J Appl Stat 2021; 49:1636-1662. [PMID: 35707553 PMCID: PMC9042044 DOI: 10.1080/02664763.2021.1874892] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 01/04/2021] [Indexed: 10/22/2022]
Abstract
In many real-world applications of monitoring multivariate spatio-temporal data that are non-stationary over time, one is often interested in detecting hot-spots with spatial sparsity and temporal consistency, instead of detecting system-wise changes as in traditional statistical process control (SPC) literature. In this paper, we propose an efficient method to detect hot-spots through tensor decomposition, and our method has three steps. First, we fit the observed data into a Smooth Sparse Decomposition Tensor (SSD-Tensor) model that serves as a dimension reduction and de-noising technique: it is an additive model decomposing the original data into: smooth but non-stationary global mean, sparse local anomalies, and random noises. Next, we estimate model parameters by the penalized framework that includes Least Absolute Shrinkage and Selection Operator (LASSO) and fused LASSO penalty. An efficient recursive optimization algorithm is developed based on Fast Iterative Shrinkage Thresholding Algorithm (FISTA). Finally, we apply a Cumulative Sum (CUSUM) Control Chart to monitor model residuals after removing global means, which helps to detect when and where hot-spots occur. To demonstrate the usefulness of our proposed SSD-Tensor method, we compare it with several other methods including scan statistics, LASSO-based, PCA-based, T2-based control chart in extensive numerical simulation studies and a real crime rate dataset.
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Affiliation(s)
- Yujie Zhao
- School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Hao Yan
- School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Sarah Holte
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Yajun Mei
- School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA
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13
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Ren H, Zou C, Chen N, Li R. Large-Scale Datastreams Surveillance via Pattern-Oriented-Sampling. J Am Stat Assoc 2020. [DOI: 10.1080/01621459.2020.1819295] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Haojie Ren
- School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China
- Department of Statistics, The Pennsylvania State University at University Park, State College, PA
| | - Changliang Zou
- School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin, China
| | - Nan Chen
- Department of Industrial Systems Engineering and Management, National University of Singapore, Singapore
| | - Runze Li
- Department of Statistics, The Pennsylvania State University at University Park, State College, PA
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14
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Klanderman MC, Newhart KB, Cath TY, Hering AS. Fault isolation for a complex decentralized waste water treatment facility. J R Stat Soc Ser C Appl Stat 2020. [DOI: 10.1111/rssc.12429] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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15
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Affiliation(s)
- Peihua Qiu
- Department of Biostatistics, University of Florida, Gainesville, FL
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16
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Wavelet estimation of the dimensionality of curve time series. ANN I STAT MATH 2019. [DOI: 10.1007/s10463-019-00724-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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17
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Li W, Xiang D, Tsung F, Pu X. A Diagnostic Procedure for High-Dimensional Data Streams via Missed Discovery Rate Control. Technometrics 2019. [DOI: 10.1080/00401706.2019.1575284] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Wendong Li
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, China
| | - Dongdong Xiang
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, China
| | - Fugee Tsung
- Department of Industrial Engineering and Decision Analytics, Hong Kong University of Science and Technology, Kowloon, Hong Kong
| | - Xiaolong Pu
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, China
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18
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Jhuang AT, Fuentes M, Jones JL, Esteves G, Fancher CM, Furman M, Reich BJ. Spatial Signal Detection Using Continuous Shrinkage Priors. Technometrics 2019; 61:494-506. [PMID: 31723308 PMCID: PMC6853616 DOI: 10.1080/00401706.2018.1546622] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Revised: 11/01/2018] [Accepted: 11/06/2018] [Indexed: 10/27/2022]
Abstract
Motivated by the problem of detecting changes in two-dimensional X-ray diffraction data, we propose a Bayesian spatial model for sparse signal detection in image data. Our model places considerable mass near zero and has heavy tails to reflect the prior belief that the image signal is zero for most pixels and large for an important subset. We show that the spatial prior places mass on nearby locations simultaneously being zero, and also allows for nearby locations to simultaneously be large signals. The form of the prior also facilitates efficient computing for large images. We conduct a simulation study to evaluate the properties of the proposed prior and show that it outperforms other spatial models. We apply our method in the analysis of X-ray diffraction data from a two-dimensional area detector to detect changes in the pattern when the material is exposed to an electric field.
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Affiliation(s)
- An-Ting Jhuang
- Department of Statistics, North Carolina State University, Raleigh, NC 27695
| | - Montserrat Fuentes
- College of Humanities and Sciences, Virginia Commonwealth University, Richmond, VA 23284
| | - Jacob L Jones
- Department of Materials Science and Engineering, North Carolina State University, Raleigh, NC 27695
| | - Giovanni Esteves
- Department of Materials Science and Engineering, North Carolina State University, Raleigh, NC 27695
| | - Chris M Fancher
- Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831
| | - Marschall Furman
- Department of Statistics, North Carolina State University, Raleigh, NC 27695
| | - Brian J Reich
- Department of Statistics, North Carolina State University, Raleigh, NC 27695
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19
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Affiliation(s)
- Xiaolei Fang
- Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC
| | - Kamran Paynabar
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA
| | - Nagi Gebraeel
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA
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