• 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.

• The Korean Journal of Applied Statistics
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• v.10 no.2
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• pp.385-401
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• 1997

Cumulative sum (CUSUM) control charts are widely used in industry for the statistical process control. The statistical design procedure in CUSUM charts tells how to choose the decision interval value. The decision interval is primarily determied by the desired in - control ARL - that is, by the acceptable frequency of false out-of-control signals. In this paper we propose a new approximation method for calculating the ARL and determining the decision interval. The performance of the proposed method is examined by evaluating the accuracy of estimated ARLs and decision intervals in normal and exponential cases.

• Journal of the Korean Data and Information Science Society
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• v.23 no.3
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• pp.617-626
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• 2012

Multivariate Shewhart control charts are considered for the simultaneous monitoring the variance-covariance matrix when the joint distribution of process variables is multivariate normal. The performances of the multivariate Shewhart control charts based on control statistic proposed by Hotelling (1947) are evaluated in term of average run length (ARL) for 2 or 4 correlated variables, 2 or 4 samples at each sampling point. The performance is investigated in three cases, that is, the variances, covariances, and variances and covariances are changed respectively.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.18-18
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• 1996

This paper reviews some of the most prominent and promising areas of chemical process control both in relations to batch and continuous processes. These areas include the modeling, optimization, control and monitoring of chemical processes and entire plants. Most of these areas explicitly utilize a model of the process. For this purpose the types of models used are examined in some detail. These types of models are categorized in knowledge-driven and datadriven classes. In the areas of modeling and optimization, attention is paid to batch reactors using the Tendency Modeling approach. These Tendency models consist of data- and knowledge-driven components and are often called Gray or Hybrid models. In the case of continuous processes, emphasis is placed in the closed-loop identification of a state space model and their use in Model Predictive Control nonlinear processes, such as the Fluidized Catalytic Cracking process. The effective monitoring of multivariate process is examined through the use of statistical charts obtained by the use of Principal Component Analysis (PMC). Static and dynamic charts account for the cross and auto-correlation of the substantial number of variables measured on-line. Centralized and de-centralized chart also aim in isolating the source of process disturbances so that they can be eliminated. Even though significant progress has been made during the last decade, the challenges for the next ten years are substantial. Present progress is strongly influenced by the economical benefits industry is deriving from the use of these advanced techniques. Future progress will be further catalyzed from the harmonious collaboration of University and Industrial researchers.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.66
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• pp.19-26
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• 2001

I propose the control chart pattern to provide a more comprehensive scheme for detecting process X and R shifts using individual observations in start-up process. It is important to automate the identification of special disturbances to facilitate real-time manufacturing. This papers formulates X-R charts for interpretation by artificial neural networks. In this papers, which uses the backpropagation algorithm, two samples are fed into the trained neural network to provide outputs ranging from 0 to 1. Simulation results sow that the performance of the proposed control chart using the neural network(NNCC) is quite promising. Using these NN charts, guidelines are given for detecting and classifying process X and R shifts.

• Journal of Korean Society for Quality Management
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• v.36 no.4
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• pp.87-92
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• 2008

It is common to use CUSUM charts for detecting small level shifts in processes control, in which reference value(k) and decision interval(h) are the design parameters to be determined. To control process with autocorrelation, CUSUM charts could be applied to residuals obtained from fitting ARIMA models. However, constant level shifts in processes lead to varying mean shifts in residual processes and thus standard CUSUM charts may need to be modified. In this paper, we study the performance of CUSUM charts with various design parameters applied to autocorrelated processes, especially focussing on ARMA(1,1) models, and propose how they can be determined to get better performance in terms of the average run length.

• Journal of Korean Society for Quality Management
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• v.22 no.1
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• pp.66-81
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• 1994

Several sample statistics to simultaneously monitor both means and variances for multivariate quality characteristics under multivariate normal process are proposed. Performances of multivariate Shewhart schemes and cumulative sum(CUSUM) schemes are evaluated for matched fixed sampling interval(FSI) and variable sampling interval(VSI) feature. Numerical results show that multivariate CUSUM charts are more efficient than Shewhart charts for small or moderate shifts and VSI feature is more efficient than FSI feature.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.4
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• pp.203-212
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• 2018

Quality requirements of manufactured products or parts are given in the form of specification limits on the quality characteristics of individual units. If a product is to meet the customer's fitness for use criteria, it should be produced by a process which is stable or repeatable. In other words, it must be capable of operating with little variability around the target value or nominal value of the product's quality characteristic. In order to maintain and improve product quality, we need to apply statistical process control techniques such as histogram, check sheet, Pareto chart, cause and effect diagram, or control charts. Among those techniques, the most important one is control charting. The cumulative sum (CUSUM) control charts have been used in statistical process control (SPC) in industries for monitoring process shifts and supporting online measurement. The objective of this research is to apply Taguchi's quality loss function concept to cost based CUSUM control chart design. In this study, a modified quality loss function was developed to reflect quality loss situation where general quadratic loss curve is not appropriate. This research also provided a methodology for the design of CUSUM charts using Taguchi quality loss function concept based on the minimum cost per hour criterion. The new model differs from previous models in that the model assumes that quality loss is incurred even in the incontrol period. This model was compared with other cost based CUSUM models by Wu and Goel, According to numerical sensitivity analysis, the proposed model results in longer average run length in in-control period compared to the other two models.

• International Journal of Quality Innovation
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• v.5 no.1
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• pp.52-67
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• 2004

Successful implementation of statistical process control techniques requires for operational definitions and precise measurements. Nevertheless, very often analysts can dispose of process data available only by linguistic terms, that would be a waste to neglect just because of their intrinsic vagueness. Thus a hybrid approach, which integrates fuzzy set theory and common statistical tools, sounds useful in order to improve effectiveness of statistical process control in such a case. In this work, a fuzzy approach is adopted to manage linguistic information, and the use of a Chi-squared control chart is proposed to monitor process performance.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.44
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• pp.69-79
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• 1997

We investigate both of central and dispersion tendencies of the observed test statistics in control charts in order to judge whether a production process is abnormal or not. In order to do it, first, we study about detection of changes of the population mean as a central tendency The $\bar{x}$ and x control charts are used for detecting the change of the population mean $\mu$. We shows the probability detecting the change of population mean using the $\bar{x}$ and x control charts. Secondly, we study about detection of changes of the population standard deviation as a dispersion tendency in the s control chart. In our studies, for the given several parameters the detection probabilities of changes of central and dispersion tendencies are calculated, the necessary sample size values n are suggested for detecting the changes, and their informations are given as various tables.