• Communications for Statistical Applications and Methods
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• v.17 no.4
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• pp.507-513
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• 2010

Let {$X_i,-{\infty}$ < 1 < $\infty$} be a doubly infinite sequence of identically distributed and negatively associated random variables with mean zero and finite variance and {$a_i,\;-{\infty}$ < i < ${\infty}$} be an absolutely summable sequence of real numbers. Define a moving average process as $Y_n={\sum}_{i=-\infty}^{\infty}a_{i+n}X_i$, n $\geq$ 1 and $S_n=Y_1+{\cdots}+Y_n$. In this paper we prove that E|$X_1$|$^rh$($|X_1|^p$) < $\infty$ implies ${\sum}_{n=1}^{\infty}n^{r/p-2-q/p}h(n)E{max_{1{\leq}k{\leq}n}|S_k|-{\epsilon}n^{1/p}}{_+^q}<{\infty}$ and ${\sum}_{n=1}^{\infty}n^{r/p-2}h(n)E{sup_{k{\leq}n}|k^{-1/p}S_k|-{\epsilon}}{_+^q}<{\infty}$ for all ${\epsilon}$ > 0 and all q > 0, where h(x) > 0 (x > 0) is a slowly varying function, 1 ${\leq}$ p < 2 and r > 1 + p/2.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.3
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• pp.52-61
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• 2012

The design method for cumulative sum (CUSUM) control charts, which can be robust to autoregressive moving average (ARMA) modeling errors, has not been frequently proposed so far. This is because the CUSUM statistic involves a maximum function, which is intractable in mathematical derivations, and thus any modification on the statistic can not be favorably made. We propose residual-based robust CUSUM control charts for monitoring autocorrelated processes. In order to incorporate the effects of ARMA modeling errors into the design method, we modify parameters (reference value and decision interval) of CUSUM control charts using the approximate expected variance of residuals generated in model uncertainty, rather than directly modify the form of the CUSUM statistic. The expected variance of residuals is derived using a second-order Taylor approximation and the general form is represented using the order of ARMA models with the sample size for ARMA modeling. Based on the Monte carlo simulation, we demonstrate that the proposed method can be effectively used for statistical process control (SPC) charts, which are robust to ARMA modeling errors.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1992.10a
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• pp.47-51
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• 1992

Automatic detection of tool breakage during NC machining is a key issue not only for improving productivity but to implement the unattended manufacturing system. In this paper, we develop a vibration sensor-based tool breakage detection system for NC milling processes. The system obtains the time-domain vibration signal from the sensor attached on the spindle bracket of our CNC machine and declares tool failures through the on-line monitoring schemes. For on-line detection, our approach is to use the PSC(statistical process control) methods being increasingly used for on-line process control. The main thrust of this paper is to propose and compare the performance of SPC methods including : a) X-bar control scheme, b) S control scheme, c)EWMA (exponentially weighted moving average) scheme, and d) AEWMA (adaptive exponentially weighted moving average) scheme. The performance of the control schemes are compared in terms of the type 1 and 2 error calculated from the experiment data.

• Journal of the Korean Mathematical Society
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• v.45 no.2
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• pp.355-365
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• 2008

Let ${Y_i;-\infty<i<\infty}$ be a doubly infinite sequence of identically distributed and $\phi$-mixing random variables with zero means and finite variances and ${a_i;-\infty<i<\infty}$ an absolutely summable sequence of real numbers. In this paper, we prove the complete moment convergence of ${{\sum}_{k=1}^{n}\;{\sum}_{i=-\infty}^{\infty}\;a_{i+k}Y_i/n^{1/p};n\geq1}$ under some suitable conditions.

• Communications for Statistical Applications and Methods
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• v.7 no.3
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• pp.773-788
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• 2000

Statistical process control (SPC) and engineering process control (EPC) are based on different strategies for processes quality improvement. SPC reduces process variability by detecting and eliminating special causes of process variation. while EPC reduces process variability by adjusting compensatory variables to keep the quality variable close to target. Recently there has been needs for a process control proceduce which combines the tow strategies. This paper considers a combined scheme which simultaneously applies SPC and EPC techniques to reduce the variation of a process. The process model under consideration is an integrated moving average(IMA) process with a step shift. The EPC part of the scheme adjusts the process back to target at every fixed monitoring intervals, which is referred to a repeated adjustment scheme. The SPC part of the scheme uses an exponentially weighted moving average(EWMA) of observed deviation from target to detect special causes. A Markov chain model is developed to relate the scheme's expected cost per unit time to the design parameters of he combined control scheme. The expected cost per unit time is composed of off-target cost, adjustment cost, monitoring cost, and false alarm cost.

• Communications for Statistical Applications and Methods
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• v.17 no.3
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• pp.377-390
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• 2010

In the case of time-series analysis, it is often more convenient to rely on the frequency domain than the time domain. Spectral density is the core of the frequency-domain analysis that describes autocorrelation structures in a time-series process. Possible ways to estimate spectral density are to compute a periodogram or to average the periodogram over some frequencies with (un)equal weights. This can be an attractive tool to measure the similarity between time-series processes. We employ the metrics based on a smoothed periodogram proposed by Park and Kim (2008) for the classification of different classes of time-series processes. We consider several lag windows with unequal weights instead of a modified Daniel's window used in Park and Kim (2008). We evaluate the performance under various simulation scenarios. Simulation results reveal that the metrics used in this study split the time series into the preassigned clusters better than do the raw-periodogram based ones proposed by Caiado et al. 2006. Our metrics are applied to an economic time-series dataset.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.177-189
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• 2024

The run length is defined as the number of samples or subgroups taken before the control chart statistic exceeds the control limits. Because the distribution of run length is typically asymmetric and has a large variability, it may not be appropriate to use ARL (average run length) alone to design control charts and evaluate performance. In this paper, we introduce the concept of percentile (PL)-based design of control charts, and propose the procedure for PL-based design of EWMA (exponentially weighted moving average) charts. For the PL-based design of EWMA, we present a fitted function for the control chart coefficient, given specific percentile parameters. Additionally, we perform simulations to compare the proposed design with the ARL-based design. The simulation results show that the proposed design yields improvements in monitoring in-control processes while maintaining the ability to detect out-of-control performance.

• Journal of Korean Society for Quality Management
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• v.24 no.1
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• pp.73-86
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• 1996

Adaptive EWMA(Exponentially Weighted Moving Average)-x control chart using the Kalman gain recursive average is designed. The designed control chart is effective to on-line process monitoring as continuous flow processes. Performance evaluation between the designed control chart and traditional one is implemented. For this, ARL(Average Run Length) is adopted as a criterion. Results show that the designed adaptive EWMA-x control chart has shorter ARL than EWMA-x control chart when process mean is shifted. This model can be extended to process prevention control. The methodology proposed in this research is turned out to show the high performance than that of the given methodologies.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.45-48
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• 1999

Model predictive control algorithm requires a relevant model of the system to be controlled. Unfortunately, the first principle model describing a polymerization reaction system has a large number of parameters to be estimated. Thus there is a need for the identification and control of a polymerization reactor system by using available input-output data. In this work, the polynomial auto-regressive moving average (ARMA) models are employed as the input-output model and combined into the nonlinear model predictive control algorithm based on the successive linearization method. Simulations are conducted to identify the continuous styrene polymerization reactor system. The input variables are the jacket inlet temperature and the feed flow rate whereas the output variables are the monomer conversion and the weight-average molecular weight. The polynomial ARMA models obtained by the system identification are used to control the monomer conversion and the weight-average molecular weight in a continuous styrene polymerization reactor It is demonstrated that the nonlinear model predictive controller based on the polynomial ARMA model tracks the step changes in the setpoint satisfactorily. In conclusion, the polynomial ARMA model is proven effective in controlling the continuous styrene polymerization reactor.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.187-194
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• 2020

In today's process-oriented industries, such as semiconductor and petrochemical processes, autocorrelation exists between observed data. As a management method for the process where autocorrelation exists, a method of using the observations is to construct a batch so that the batch mean approaches to independence, or to apply the EWMA (Exponentially Weighted Moving Average) statistic of the observed value to the EWMA control chart. In this paper, we propose a method to determine the batch size of UBM (Unweighted Batch Mean), which is commonly used as a management method for observations, and a method to determine the optimal batch size based on ARL (Average Run Length) We propose a method to estimate the standard deviation of the process. We propose an improved control chart for processes in which autocorrelation exists.