• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.58
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• pp.113-118
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• 2000

Estimation of the process deviation is an important problem in statistical process control, especially in the control chart, process capability analysis or measurement system analysis. In this paper we suggest the use of the Gini's mean difference for the estimation of the c, the measure of the process deviation through a lots of simulations in various types of distributions. The Gini's mean difference uses the differences of all possible pairs of data. This point will improve the efficiency of estimation. In various classes of distributions, the Gini's mean difference shows good performance, in sense of bias of estimates or mean squared errors.

• Journal of Korean Society for Quality Management
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• v.23 no.3
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• pp.33-44
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• 1995

This paper presents an automated visual inspection system for the electronic parts manufacturing process. In this system, a statistical process control (SPC) method is integrated into the automated inspection method on a real time base. It shows how the collected data can be analyzed with the SPC to provide process information. Also presented are studies of subpixel image processing technology to improve the accuracy of parts measurements, and the cumulative-sum (CUSUM) control chart for fraction defectives. An application of the developed system to connector manufacturing process as a part of computer integrated manufacturing (CIM) is presented.

• Journal of agricultural medicine and community health
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• v.29 no.1
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• pp.65-75
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• 2004

Objectives: Immunizations have been one of the most effective measures preventing from infectious diseases. It is quite important national infectious disease prevention policy to keep the immunizations rate high and monitor the immunizations rate continuously. To do this, Korean CDC introduced the National Immunization Registry Program(NIRP) which has been implementing since 2000 at the Public Health Centers(PHC). The National Immunization Registry Program will be near completed after sharing, connecting and transfering vaccination data between public and private sector. The aims of this study was to evaluate the immunization module of non-chart system in private clinic with health information system of public health center(made by POSDATA Co., LTD) and immunization registry program(made by BIT Computer Co., LTD). Methods: The analysis and survey were done by specialists in medical, health field, and health information fields from 2001. November to 2002. January. We made the analysis and recommendation about the immunization module of non-chart system in private clinic. Results and Conclusions: To make improvement on immunization module, the system will be revised on various function like receipt and registration, preliminary medical examination, reference and inquiry, registration of vaccine, print-out various sheet, function of transfer vaccination data, issue function of vaccination certification, function of reminder and recall, function of statistical calculation, and management of vaccine stock. There are needs of an accurate assessment of current immunization module on each private non-chart system. And further studies will be necessary to make it an accurate system under changing health policy related national immunization program. We hope that the result of this study may contribute to establish the National Immunization Registry Program.

• Journal of Pharmacopuncture
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• v.8 no.2
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• pp.11-16
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• 2005

Objective : The aim of the study was to investigate the therapeutic effect of BVA in the treatment of patients with acute ankle sprain. Design : A prospective randomized double-blind study of BVA was conducted. Setting : The study was done in the Kyungwon University Seoul Hospital from August 1st, 2004 to June 15th, 2005. Patients : 30 patients diagnosed with acute ankle sprain, especially 2nd degree on the Ankle grade pain chart(AGPC) participated in the study, who were divided into two groups (A and B) randomly by a coordinator flipping a coin. Group A and B were relevant to control and BVA group respectively, of which a coordinator never informed any other participant involved. Eventually 13 of 17 in group A and 11 of 13 in Group B finished all the process of the clinical trial. Intervention : In both group A and B, The Procedure of acupuncture treatment was made similar by appearance that four acupoints such as 坵墟(GB40), 中封(LR4), 商丘(SP5), 解谿(ST41) of the injured side were selected and Normal saline aqua-acupuncture(control, as a placebo) or BVA was done and then acupuncture at 坵墟(GB40), 中封(LR4), 商丘(SP5), 解谿(ST41), 足三里(ST36), 陽陵泉(G34) of the affected side was given again. the needles were retained for 20 minutes under the infrared rays. The treatment was given daily for a week. Outcome Measures : Ankle-Hindfoot Scale (AHS) and Visual Analogue Scale(VAS) were followed by three treatments. Statistical Analysis : Analysis regarding variations in AHS and VAS is carried out by applying Mann-Whitney test and Wilcoxon signed rank test sign test with level of significance at 5%. Results : At the end of the treatment, there was significant statistical differences between the two groups in VAS and AHS as well, while at the 3rd day only a VAS showed statistical significance. In each group, both VAS and AHS showed statistical significance along with duration of treatment. Conclusions : BVA was thought to be effective alternatives for relieving symptoms of acute ankle sprain, although further study was needed on the large scale.

• 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.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.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.999-1008
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• 2010

The objective of this paper is to develop variable sampling interval multivariate control charts that can offer significant performance improvements compared to standard fixed sampling rate multivariate control charts. Most research on multivariate control charts has concentrated on the problem of monitoring the process mean, but here we consider the problem of simultaneously monitoring both the mean and variability of the process.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.1
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• pp.31-40
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• 2022

Six Sigma is a philosophy and systematic methodology for quality improvement. It encourages continuous quality improvement efforts to achieve the ideal goal of 6σ. Sigma(σ) is a statistic representing the standard deviation of the normal distribution, and 6σ level means a level where the tolerance of the specification is six times the standard deviation of the process distribution. In terms of the defective rate, the 6σ level achieves the 0.002 defectives per one million units. However, in the field, the 6σ level is used in the sense of achieving 3.4 defects per one million opportunities, which shows a large gap from the 6σ level in the statistical viewpoint. This is because field practitioners accept a 1.5σ shift of the mean of process when calculating the defective rate under sigma level. It said that the acceptance of 1.5σ shift of the mean is from experience, but there is no research or theoretical explanation to support it logically. Although it is a non-scientific explanation based on experience, considering that there has been no objection to the 1.5σ shift for a long time and it is rather accepted, it is judged that there is a reasonable basis for the 1.5σ shift. Therefore, this study tries to find a reasonable explanation through detective power of control chart via the run-rules to the 1.5σ shift empirically recognized by practitioners.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.191-207
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• 2024

Many studies have been conducted to quickly detect out-of-control situations in autocorrelated processes. The most traditionally used method is a residual control chart, which uses residuals calculated from a fitted time series model. However, many procedures for monitoring autocorrelated processes using statistical learning methods have recently been proposed. In this paper, we propose a monitoring procedure using the latent vector of LSTM Autoencoder, a deep learning-based unsupervised learning method. We compare the performance of this procedure with the LSTM Autoencoder procedure based on the reconstruction error, the RNN classification procedure, and the residual charting procedure through simulation studies. Simulation results show that the performance of the proposed procedure and the RNN classification procedure are similar, but the proposed procedure has the advantage of being useful in processes where sufficient out-of-control data cannot be obtained, because it does not require out-of-control data for training.

• Journal of the Korean Data and Information Science Society
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• v.22 no.4
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• pp.679-690
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• 2011

Two widely used approaches for improving the quality of the output of a process are statistical process control (SPC) and automatic process control (APC). In recent hybrid processes that combine aspects of the process and parts industries, process variations due to both the inherent wandering and special causes occur commonly, and thus simultaneous application of APC and SPC schemes is needed to effectively keep such processes close to target. The simultaneous implementation of APC and SPC schemes is called integrated process control (IPC). In the IPC procedure, the output variables are monitored during the process where adjustments are repeatedly done by its controller. For monitoring the APC-controlled process, control charts can be generally applied to the output variable. However, as an alternative, some authors suggested that monitoring the input variable may improve the chance of detection. In this paper, we evaluate the performance of several monitoring statistics, such as the output variable, the input variable, and the difference variable, for efficiently monitoring the APC-controlled process when we assume IMA(1,1) noise model with a minimum mean squared error adjustment policy.