• Journal of the Korean Data and Information Science Society
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• v.4
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• pp.31-44
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• 1993

Multivariate exponentially weighted moving average (EWMA) control charts for monitoring the variance-covariance matrix are investigated. A variable sampling interval (VSI) feature is considered in these charts. Multivariate EWMA control charts for monitoring the variance-covariance matrix are compared on the basis of their average time to signal (ATS) performances. The numerical results show that multivariate VSI EWMA control charts are more efficient than corrsponding multivariate fixed sampling interval (FSI) EWMA control charts.

• Journal of the Korean Data and Information Science Society
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• v.13 no.1
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• pp.105-112
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• 2002

In this paper we establish bivariate exponentially weighted moving average (EWMA) control charts for autocorrelated processes using residual vectors. We first derive the residual vectors, their expectation, variance-covariance matrix, then evaluate the control chart based on the average run length (ARL).

• Journal of the Korean Statistical Society
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• v.21 no.2
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• pp.167-177
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• 1992

In this paper, we investigate the limiting distribution of $M_n = max (X_1, X-2, \cdots, X_n)$ in the infinite moving average process ${X_t = \sum c_i Z_{t-i}}$ generated from i.i.d. negative binomial variables $Z_i$'s. While no limit result is possible, nonetheless asymptotic bounds are derived. We also present the tail behavior of $X_t$, i.e., weighted sum of i.i.d. random variables. This continues a study made by Rootzen (1986) for discrete innovation sequences.

• Journal of Korean Society for Quality Management
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• v.26 no.2
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• pp.51-60
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• 1998

Exponetially weighted moving average(EWMA) control chart to simultaneously monitor correlation coefficients of several correlated quality variables under multivariate normal process are proposed. Performances of the proposed control charts are measured in terms of average run length(ARL) by simulation. Numerical results show that smaller values of smoothing constant are more efficient in terms of ARL.

• Communications for Statistical Applications and Methods
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• v.4 no.3
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• pp.715-723
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• 1997

Multivariate control statistics to simultaneously monitor both means and variances for several quality variables under multivariate normal process are proposed. Performances of the proposed multivariate charts are evaluated in terms of average run length(ARL). Multivariate Shewhart chart is also proposed to compare the performances of multivariate exponentially weighted moving average(EWMA) charts. A numerical comparison shows that multivariate EWMA charts are more efficient than multivariate Shewhart chart for small and moderate shifts and multivariate EWMA scheme based on accumulate-combine approach is more efficient than corresponding multivariate EWMA chart based on combine-accumulate approach.

• Journal of Korean Society for Quality Management
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• v.35 no.1
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• pp.24-34
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• 2007

This paper investigates the average run length (ARL) of a selectively moving average (S-MA) control chart. The S-U chart is designed to detect shifts in the process mean. The basic idea of the S-MA chart is to accumulate previous samples selectively in order to increase the sensitivity. The ARL of the S-MA chart was shown to be monotone decreasing with respect to the decision length in a previous research [3]. This paper derives the steady-state ARL in a closed-form and shows that the monotone property is resulted from head-start assumption. The steady-state ARL is shown to be a sum of head-start ARL and an additional term. The statistical design procedure for the S-MA chart is revised according to this result. Sensitivity study shorts that the steady-state ARL performance is still better than the CUSUM chart or the Exponentially Weighted Moving Average (EWMA) chart.

• Journal of Sensor Science and Technology
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• v.28 no.2
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• pp.88-93
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• 2019

This paper is concerned with distributed fusion moving average prediction for continuous-time linear stochastic systems with multiple sensors. A distributed fusion with the weighted sum structure is applied to the optimal local moving average predictors. The distributed fusion prediction algorithm represents the optimal linear fusion by weighting matrices under the minimum mean square criterion. The derivation of equations for error cross-covariances between the local predictors is the key of this paper. Example demonstrates effectiveness of the distributed fusion moving average predictor.

• Journal of radiological science and technology
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• v.32 no.3
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• pp.313-323
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• 2009

Diffusion-weighted imaging (DWI) has been demonstrated to be a practical method for the diagnosis of various brain diseases such as acute infarction, brain tumor, and white matter disease. In this study, we used two techniques to examine the average signal intensity (SI) and apparent diffusion coefficient (ADC) of the brains of patients who ranged in age from 10 to 60 years. Our results indicated that the average SI was the highest in amygdala (as derived from DWI), whereas that in the cerebrospinal fluid was the lowest. The average ADC was the highest in the cerebrospinal fluid, whereas the lowest measurement was derived from the pons. The average SI and ADC were higher in $T_2^*$-DW-EPI than in FLAIR-DW-EPI. The higher the b-value, the smaller the average difference in both imaging techniques; the lower the b-value, the greater the average difference. Also, comparative analysis of the brains of patients who had experienced cerebral infarction showed no distinct lesion in the general MR image over time. However, there was a high SI in apparent weighted images. Analysis of other brain diseases (e.g., bleeding, acute, subacute, chronic infarction) indicated SI variance in accordance with characteristics of the two techniques. The higher the SI, the lower the ADC. Taken together, the value of SI and ADC in accordance with frequently occurring areas and various brain disease varies based on the b-value and imaging technique. Because they provide additional useful information in the diagnosis and treatment of patients with various brain diseases through signal recognition, the proper imaging technique and b-value are important for the detection and interpretation of subacute stroke and other brain diseases.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.643-644
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• 2013

• Proceedings of the Korean Environmental Health Society Conference
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• 2004.12a
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• pp.74-74
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• 2004