• Communications for Statistical Applications and Methods
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• 제16권1호
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• pp.1-11
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• 2009

마이크로데이터를 이용자에게 제공하면 레코드 단위의 데이터가 노출되고 응답자의 정보 노출위험이 불가피하다. 통계적 노출조절기법은 통계데이터 제공시 노출위험을 줄이면서 데이터 유용성을 높이기 위한 통계적 기법이다. 본 논문에서는 노출과 노출위험, 그리고 통계적 노출조절기법을 고찰하였고 데이터 유용성과 연관하여 노출조절기법 선택 전략을 살펴보았으며, '위험-유용성 경계 지도' 방법의 예를 알아보았다. 마지막으로 마이크로데이터를 이용자에게 제공할 때 단계별로 검토할 사항을 알아보았다.

• International Journal of Quality Innovation
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• 제5권1호
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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.

• 품질경영학회지
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• 제28권2호
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• pp.70-88
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• 2000

Short runs where it is neither possible nor practical to obtain sufficient subgroups to estimate accurately the control limit are common in modem business environments. In this study, the standardized control chart, Hillier's exact method, Q chart, EWMA(Exponentially Weighted Moving Average) chart for Q statistics and EWMA chart for mean and absolute deviation among many SPC(Statistical Process Control) techniques for short runs have been reviewed and advantages and disadvantages of these techniques are discussed. The simulation experiments to compare performances of these variable charts for process mean and variations are conducted for combination of subgroup size, scale and timing of shifts of process mean an/or standard deviation. Based upon simulation results, some guidelines for practitioners to choose short run SPC techniques are recommended.

• Journal of the Korean Statistical Society
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• 제30권3호
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• pp.367-385
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• 2001

In this paper, we characterize the smoothest density with prescribed moments. Hong and Kim(1995) proved the existence and uniqueness of such as density. we introduce the general optimal control problem and prove some theorems on the characterization of the minimizer using the optimal control problem techniques.

• 대한산업공학회지
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• 제41권6호
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• pp.521-529
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• 2015

Statistical process control techniques have been greatly implemented in industries for improving product quality and saving production costs. As a primary tool among these techniques, control charts are widely used to detect the occurrence of assignable causes. In most works on the control charts it considered the problem of monitoring the mean and variance, and the quality characteristic of interest is normally distributed. In some situations monitoring of the minimum and maximum values is more important and the quality characteristic of interest is the Weibull distribution rather than a normal distribution. In this paper, we consider the statistical design of minimum and maximum control charts when the distribution of the quality characteristic of interest is Weibull. The proposed minimum and maximum control charts are applied to the wind data. The results of the application show that the proposed method is more effective than traditional methods.

• 한국품질경영학회:학술대회논문집
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• 한국품질경영학회 2006년도 추계 학술대회
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• pp.205-210
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• 2006

Nowaday manufacture technology and manufacture environment are changing rapidly. By development of computer and enlargement of technique, most of manufacture field are computerized. It is measured automatically do much quality characteristics thereby and great many data happen in a day. corporations is important if have gotten fast information that are useful from wide data to go first in international competition according to these change. Statistical process control(SPC) techniques are used as a problem solution tool at manufacturing process until present. However, this statistical methods is not applied more extensively because have much restrictions in realistic problem. In this paper, wish to develop more realistic and scientific new statistical design techniques doing to integrate data mining(DM) and statistical methods by the alternative to cope these problem. First step selects significant factor using DM techniques from datas of manufacturing process including much factors and second step wish to find optimum of process after get the estimated response function through response surf ace methodology(RSM) that is statistical techniques.

• Communications for Statistical Applications and Methods
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• 제7권3호
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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.

• 품질경영학회지
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• 제34권4호
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• pp.33-39
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• 2006

These days manufacture technology and manufacture environment are changing rapidly. By development of computer and enlargement of technique, most of manufacture field are computerized. In order to win international competition, it is important for companies how fast get the useful information from vast data. Statistical process control(SPC) techniques have been used as a problem solution tool at manufacturing process until present. However, these statistical methods are not applied more extensively because it has much restrictions in realistic problems. These statistical techniques have lots of problems when much data and factors are analyzed. In this paper, we proposed more practical and efficient a new statistical design technique which integrated data mining (DM) and statistical methods as alternative of problems. First step is selecting significant factor using DM feature selection algorithm from data of manufacturing process including many factors. Second step is finding optimum of process after estimating response function through response surface methodology(RSM) that is a statistical techniques

• 경영과학
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• 제10권2호
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• pp.111-120
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• 1993

The most important factors for a product to survive in the market are cost and quality. In recent years, quality proceeds to cost. There are many techniques of use to improve the quality of a product. One of the techniques is applying statistical methods (especially Taguchi method) to real operational conditions for a continuous manufacturing process in P company. There are 91 factors to control in the process. So, we predetermined 7 main effect factors and 6 interactive effect factors by statistical methods and advices of engineers. With these 13 factors, we determined the optimal level of operations for the process.

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 1999년도 추계학술대회
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• pp.217-236
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• 1999

Engineering process control (EPC) is one of the techniques very widely used in process. EPC is based on control theory which aims at keeping the process on target. Statistical process control (SPC), also known as statistical process monitoring. The main purpose of SPC is to look for assignable causes (variability) in the process data. The combined SPC/EPC scheme is gaining recognition in the process industries where the process frequently experiences a drifting mean. This paper aims to study the difference between SPC and EPC in simple terms and presents a case study that demonstrates successful integration of SPC and EPC for a product in drifting industry. Statistical process control (SPC) monitoring of the special causes of a process, along with engineering feedback control such as proportional-integral-derivative (PID) control, is a major tool for on-line quality improvement.