• The KIPS Transactions:PartA
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• v.18A no.5
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• pp.215-224
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• 2011

In this paper, a mechanism connecting all weighted migration routes with minimum cost with EGOSST is proposed. Weighted migration routes may be converted to weighted input edges considered as not only traces but also traffics or trip frequencies of moving object on communication lines, roads or railroads. Proposed mechanism can be used in more wide and practical area than mechanisms considering only moving object traces. In our experiments, edge number, maximum weight for input edges, and detail level for grid are used as input parameters. The mechanism made connection cost decrease average 1.07% and 0.43% comparing with the method using weight minimum spanning tree and weight steiner minimum tree respectively. When grid detail level is 0.1 and 0.001, while each execution time for a connecting solution increases average 97.02% and 2843.87% comparing with the method using weight minimum spanning tree, connecting cost decreases 0.86% and 1.13% respectively. This shows that by adjusting grid detail level, proposed mechanism might be well applied to the applications where designer must grant priority to reducing connecting cost or shortening execution time as well as that it can provide good solutions of connecting migration routes with weights.

• Journal of Korean Society of Forest Science
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• v.101 no.2
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• pp.213-219
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• 2012

The $5^{th}$ national forest inventory (NFI5) has been reorganized to annual inventory system for providing multi-resources forest statistics at a point in time. The objective of this study is to evaluate statistical estimators for estimating forest growing stock in Chungcheongbuk-Do from annual inventory data. When comparing two estimators; simple random sampling (SRS) and double sampling for post-stratification (DSS), for estimating mean forest growing stock ($m^3/ha$) at each surveyed year, the estimate for DSS in which a population of interest is stratified into three sub-population (forest cover types) was more precise than that for SRS. To combine annual inventory field data, three estimators (Temporally Indifferent Method; TIM, Moving Average; MA, and Weighted Moving Average; WMA) were compared. Even though the estimated mean for TIM and WMA is identical, WMA-DSS is preferred to provide more smaller variance of estimated mean and to adjust for catastrophic events at a surveyed year (so-called "lag bias") by annual inventory data.

• Management Science and Financial Engineering
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• v.6 no.1
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• pp.1-19
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• 2000

This papeer proposes a general approach of the multivariate expontially weighted moving average(MEWMA) chart, in which the smoothing matrix has full elements instead of only diagonal elements. The average run length (ARL) properties of this scheme are examined for a diverse set of quality control environments and the information to design the chhart is provied. Performance of the scheme is measured by estmating ARL and compared to those of two group cumulative sum (CUSUM) chats. The comparison resullts show that the MEWMA chart can improve its ARL performance in detecting a small shifts out-of-control in the start-up stage, the general MEWMA chart of a full smoothing matrix appears to offer an exceptional protection aginst departures from control in the process mean.

• The Korean Journal of Applied Statistics
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• v.25 no.3
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• pp.465-470
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• 2012

EWMA(exponentially weighted moving average) charts and CUSUM(cumulative sum) charts are very effective to detect small shifts in the process mean. These charts have some control-chart parameters that allow the charts and be tuned and be more sensitive to certain shifts. The EWMA chart requires users to specify the value of a smoothing parameter, which can also be designed for the size of the mean shift. However, the size of the mean shift that occurs in applications is usually unknown and EWMA charts can perform poorly when the actual size of the mean shift is significantly different from the assumed size. In this paper, we propose the design procedure to find the optimal smoothing parameter of the EWMA chart when the size of the mean shift is unknown.

• Journal of Korean Society for Quality Management
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• v.26 no.1
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• pp.108-125
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• 1998

The EWMA(Exponentially Weighted Moving Average) has recently received a great deal of attention in the quality control literature as a process monitoring tool on the shop floor of manufacturing industires, since it is easy to plot, to interpret, and its control limits are easy to obtain. Most a, pp.ications of the EWMA for process monitoring have concentrated on the problem of detecting shifts of a process mean and a process standard deviation with ARL(Average Run Length) properties. But there may be the necessity of controlling linearity on product quality such as the correlation coefficient to the process operator. Control managers may want to protect the increase of a process correlation coefficient value, such as 0, between two variables of interest. However, there are few studies concerned on this part. Therefore, we propose EWMA models for a process correlation coefficient using two transformed statistics, T-statistic and (Fisher's) Z-statistic. We also present some results of simulation by SAS/IML and compare two models.

• The Korean Journal of Applied Statistics
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• v.35 no.2
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• pp.203-215
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• 2022

In this paper, we consider the control charts applicable to monitoring the change of the population mean for sequentially observed individual data. The most representative control charts are Shewhart's individual control chart, the exponential weighted moving average (EWMA) control chart, and their combined control chart. We compare their performance based on a simulation study, and also, through real data analysis, we present how to apply control charts in practical application and investigate the problems of each control chart.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.3
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• pp.146-153
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• 2010

When the production run is short and process parameters change frequently, it is difficult to monitor the process using traditional control charts. In such a case, the coefficient of variation (CV) is very useful for monitoring the process variability. The CV control chart is an effective tool to control the mean and variability of process simultaneously. The CV control chart, however, is not sensitive at small shifts in the magnitude of CV. The CV-EWMA(exponentially weighted moving average) control chart which was developed recently is effective in detecting a small shifts of CV. Since the CV-EWMA control chart scheme can be viewed as a weighted average of all past and current CV values, it is very sensitive to small change of mean and variability of the process. In this paper, we propose an FIR(Fast initial response) CV-EWMA control chart to improve the sensitivity of a CV-EWMA scheme at process start-up or out-of-control process. Moreover, we suggest the values of design parameters and show the results of the performance study of FIR CV-EWMA control chart by the use of average run length(ARL). Also, we compared the performance of FIR CV-EWMA control chart with that of the CV-EWMA control chart and we found that the CV-EWMA control chart gives longer in-control ARL and much shorter out-of-control ARL.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.4
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• pp.114-120
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• 2008

The control chart is widely used statistical process control(SPC) tool that searches for assignable cause of variation and detects any change of process. Generally, ${\bar{X}}-R$ control chart and ${\bar{X}}-S$ are most frequently used. When the production run is short and process parameter changes frequently, it is difficult to monitor the process using traditional control charts. In such a case, the coefficient of variation (CV) is very useful for monitoring the process variability. The CV control chart is an effective tool to control the mean and variability of process simultaneously. The CV control chart, however, is not sensitive at small shift in the magnitude of CV. In this paper, we propose an CV-EWMA (exponentially weighted moving average) control chart which is effective in detecting a small shift of CV. Since the CV-EWMA control chart scheme can be viewed as a weighted average of all past and current CV values, it is very sensitive to small change of mean and variability of the process. We suggest the values of design parameters and show the results of the performance study of CV-EWMA control chart by the use of average run length (ARL). When we compared the performance of CV-EWMA control chart with that of the CV control chart, we found that the CV-EWMA control chart gives longer in-control ARL and much shorter out-of-control ARL.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.2 s.314
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• pp.19-26
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• 2007

In the sigma filter, there is a specialized neighbours distribution scheme in which the sigma value is computed from local statistics. It is designed to modify a standard average filter to preserve edges. However this filter is vulnerable to details-enhancement and conventional sigma approaches have been focused on denoising, not enhancing the characteristic area. This paper proposes an adaptive image enhancement algorithm using local statistics and functional synthesis which are utilized for adaptive realization of the enhancement, so that not only image noise may be smoothed but also details may be enhanced. For the local adaptation, parameters are estimated and weighted at each moving window that satisfy the criteria. The experimental results illuminates the effectiveness of the proposed method.

• Journal of the Korean Statistical Society
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• v.33 no.4
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• pp.381-399
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• 2004

Statistical process control (SPC) and engineering process control (EPC) are based on different strategies for process quality improvement. SPC re-duces 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 need for an integrated process control (IPC) procedure which combines the two strategies. This paper considers a scheme that simultaneously applies SPC and EPC techniques to reduce the variation of a process. The process model under consideration is an IMA(1,1) model with a step shift. The EPC part of the scheme adjusts the process, while the SPC part of the scheme detects the occurrence of a special cause. For adjusting the process repeated adjustment is applied according to the predicted deviation from target. For detecting special causes the exponentially weighted moving average control chart is applied to the observed deviations. It was assumed that the adjustment under the presence of a special cause may increase the process variability or change the system gain. Reasonable choices of parameters for the IPC procedure are considered in the context of the mean squared deviation as well as the average run length.