• Title/Summary/Keyword: CUSUM monitoring

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Monitoring social networks based on transformation into categorical data

  • Lee, Joo Weon;Lee, Jaeheon
    • Communications for Statistical Applications and Methods
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    • v.29 no.4
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    • pp.487-498
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    • 2022
  • Social network analysis (SNA) techniques have recently been developed to monitor and detect abnormal behaviors in social networks. As a useful tool for process monitoring, control charts are also useful for network monitoring. In this paper, the degree and closeness centrality measures, in which each has global and local perspectives, respectively, are applied to an exponentially weighted moving average (EWMA) chart and a multinomial cumulative sum (CUSUM) chart for monitoring undirected weighted networks. In general, EWMA charts monitor only one variable in a single chart, whereas multinomial CUSUM charts can monitor a categorical variable, in which several variables are transformed through classification rules, in a single chart. To monitor both degree centrality and closeness centrality simultaneously, we categorize them based on the average of each measure and then apply to the multinomial CUSUM chart. In this case, the global and local attributes of the network can be monitored simultaneously with a single chart. We also evaluate the performance of the proposed procedure through a simulation study.

Multivariate CUSUM control charts for monitoring the covariance matrix

  • Choi, Hwa Young;Cho, Gyo-Young
    • Journal of the Korean Data and Information Science Society
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    • v.27 no.2
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    • pp.539-548
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    • 2016
  • This paper is a study on the multivariate CUSUM control charts using three different control statistics for monitoring covariance matrix. We get control limits and ARLs of the proposed multivariate CUSUM control charts using three different control statistics by using computer simulations. The performances of these proposed multivariate CUSUM control charts have been investigated by comparing ARLs. The purpose of control charts is to detect assignable causes of variation so that these causes can be found and eliminated from process, variability will be reduced and the process will be improved. We show that the charts based on three different control statistics are very effective in detecting shifts, especially shifts in covariances when the variables are highly correlated. When variables are highly correlated, our overall recommendation is to use the multivariate CUSUM control charts using trace for detecting changes in covariance matrix.

Monitoring mean change via penalized estimation (벌점화 추정기법을 이용한 평균에 대한 모니터링)

  • Na, Okyoung;Kwon, Sunghoon
    • The Korean Journal of Applied Statistics
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    • v.29 no.7
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    • pp.1429-1444
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    • 2016
  • We suggest a monitoring procedure to detect changes in the mean of the stochastic process. The monitoring procedure is based on penalized least squares estimates. Unlike the fluctuation (FL) monitoring, we use the numbers of nonzero estimates not the fluctuations of sequential parameter estimates. We investigate the behavior of the proposed monitoring procedure by means of a simulation study and compare its performance with CUSUM monitoring.

Cumulative Sum Control Charts for Simultaneously Monitoring Means and Variances of Multiple Quality Variables

  • Chang, Duk-Joon;Heo, Sunyeong
    • Journal of the Chosun Natural Science
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    • v.5 no.4
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    • pp.246-252
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    • 2012
  • Multivariate cumulative sum (CUSUM) control charts for simultaneously monitoring both means and variances under multivariate normal process are investigated. Performances of multivariate CUSUM schemes are evaluated for matched fixed sampling interval (FSI) and variable sampling interval (VSI) features in terms of average time to signal (ATS), average number of samples to signal (ANSS). Multivariate Shewhart charts are also considered to compare the properties of multivariate CUSUM charts. Numerical results show that presented CUSUM charts are more efficient than the corresponding Shewhart chart for small or moderate shifts and VSI feature with two sampling intervals is more efficient than FSI feature. When small changes in the production process have occurred, CUSUM chart with small reference values will be recommended in terms of the time to signal.

Properties of VSI CUSUM Chart for Monitoring Dispersion Matrix

  • Chang, Duk-Joon;Shin, Jae-Kyoung
    • Journal of the Korean Data and Information Science Society
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    • v.15 no.4
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    • pp.1003-1010
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    • 2004
  • Properties of the variable sampling interval(VSI) CUSUM chart for monitoring dispersion matrix of related quality characteristics are investigated. Performances of the proposed charts are evaluated for matched fixed sampling interval(FSI) and VSI charts in terms of average time to signal(ATS) and average number of samples to signal (ANSS). Average number of swiches(ANSW) of the proposed VSI Shewhart and CUSUM charts are also investigated.

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Real Time CUSUM Control of Plasma Impedance Matching Network (플라즈마 임피이던스 정합망 실시간 CUSUM 제어)

  • Kim, Woo-Suk;Kim, Byung-Whan
    • Proceedings of the KIEE Conference
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    • 2007.07a
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    • pp.1844-1845
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    • 2007
  • A CUSUM control chart was used to monitor semiconductor plasma equipment. The performance of plasma monitoring was evaluated with various combinations of design variables involved in CUCUM control chart. Experimental data collected by using a real-time matching monitoring system include electrical positions of impedance and phase positions, and reflected power. The evaluation revealed that by determining specific design variables plasma states could be more strictly monitored.

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CUSUM Chart to Monitor Dispersion Matrix for Multivariate Normal Process

  • Chang, Duk-Joon;Kwon, Yong-Man;Hong, Yeon-Woong
    • 한국데이터정보과학회:학술대회논문집
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    • 2003.05a
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    • pp.89-95
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    • 2003
  • Cumulative sum(CUSUM) control charts for monitoring dispersion matrix under multivariate normal process are proposed. Performances of the proposed CUSUM charts are measured in terms of average run length(ARL) by simulation. Numerical results show that small reference values of the proposed CUSUM chart is more efficient for small shifts in the production process.

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Multiparameter CUSUM charts with variable sampling intervals

  • Im, Chang-Do;Cho, Gyo-Young
    • Journal of the Korean Data and Information Science Society
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    • v.20 no.3
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    • pp.593-599
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    • 2009
  • We consider the problem of using control charts to monitor more than one parameter with emphasis on simultaneously monitoring the mean and variance. The fixed sampling interval (FSI) control charts are modified to use variable sampling interval (VSI) control charts depending on what is being observed from the data. In general, approaches of monitoring the mean and variance simultaneously is to use separate charts for each parameter and a combined chart. In this paper, we use three basic strategies which are separate Shewhart charts for each parameter, a combined Shewhart chart and a combined CUSUM chart. We showed that a combined VSI CUSUM chart is comparatively more efficient than any other chart if the shifts in both mean and variance are small.

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A Study on Optimum Value of Design Parameter of Multivariate EWMA and CUSUM charts for Monitoring Dispersion Matrix

  • Chang, Duk-Joon
    • Journal of the Chosun Natural Science
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    • v.14 no.3
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    • pp.116-122
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    • 2021
  • Properties and comparison of multivariate CUSUM and EWMA charts for monitoring Σ of multivariate normal N(${\underline{\mu}}$, Σ) process has considered. Comparison of the performances of the considered charts, the numerical values are obtained by simulation with 10,000 iteration in terms of ATS, ANSS and ANSW. We found that EWMA chart with small values of smoothing constant more effectively detects the process changes than with large smoothing constant. And we also found that CUSUM chart with small value of reference value is more effectively detecting the process change than with large reference value. If a process engineer has interest in detecting small amount of shift rather than large shift, he/she can be recommended to use small smoothing constant in EWMA chart and small reference value in CUSUM chart.

Control charts for monitoring correlation coefficients in variance-covariance matrix

  • Chang, Duk-Joon;Heo, Sun-Yeong
    • Journal of the Korean Data and Information Science Society
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    • v.22 no.4
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    • pp.803-809
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    • 2011
  • Properties of multivariate Shewhart and CUSUM charts for monitoring variance-covariance matrix, specially focused on correlation coefficient components, are investigated. The performances of the proposed charts based on control statistic Lawley-Hotelling $V_i$ and likelihood ratio test (LRT) statistic $TV_i$ are evaluated in terms of average run length (ARL). For monitoring correlation coe cient components of dispersion matrix, we found that CUSUM chart based on $TV_i$ gives relatively better performances and is more preferable, and the charts based on $V_i$ perform badly and are not recommended.