• Journal of the Korean Statistical Society
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• 제33권2호
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• pp.219-231
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• 2004

Process adjustment is a complimentary tool to process monitoring in process control. Although original intention of process adjustment is not identifying a special cause, detection and elimination of special causes may lead to significant process improvement. In this paper, we examine the impact of special causes on process adjustment. The bias in the adjusted output process is derived for each type of special causes, and average run length (ARL) of the Shewhart chart applied to the adjusted output is computed for each special cause types. Numerical results are illustrated for the ARL of the Shewhart chart, thereupon seriousness of special causes on process adjustment is evaluated for each type of special causes.

• 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.

• 대한원격탐사학회지
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• 제21권1호
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• pp.3-13
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• 2005

National Research Lab Project 'Optimal Data Fusion of Geophysical and Geodetic Measurements for Geological Hazards Monitoring and Prediction' supported by Korea Ministry of Science and Technology is briefly described. The research focused on the geohazard analysis with geophysical and geodetic instruments such as superconducting gravimeter, seismometer, magnetometer, GPS, and Synthetic Aperture Radar. The aim of the NRL research is to verify the causes of geological hazards through optimal fusion of various observational data in three phases: surface data fusion using geodetic measurements; subsurface data fusion using geophysical measurements; and, finally fusion of both geodetic and geophysical data. The NRL hosted a special session 'Geohazard Monitoring with Space and Geophysical Technology' during the International Symposium on Remote Sensing in 2004 to discuss the current topics, challenges and possible directions in the geohazard research. Here, we briefly describe the special session papers and their relationships to the theme of the special session. The fusion of satellite and ground geophysical and geodetic data gives us new insight on the monitoring and prediction of the geological hazard.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2003년도 봄 학술발표회 논문집
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• pp.163-168
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• 2003

By introducing corrosion monitoring techniques, steel corrosion in concrete may be evaluated at early stage. The monitoring probes in concrete detect the causes (chlorides and $CO_2$) of steel corrosion by being cast into the concrete or diffusing in from the outside. Various systems for corrosion monitoring in concrete are reviewed in this paper. These techniques are classified according to monitoring purposes such as corrosion potential or corrosion rate of steel and causes for corrosion etc.. Today, special interests are converged in development of corrosion sensor as a monitoring method of new concept.

• Journal of the Korean Data and Information Science Society
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• 제21권5호
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• pp.841-852
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• 2010

통합공정관리는 공정의 변동을 줄이기 위하여 공학적 공정관리와 통계적 공정관리를 병행하는 절차이다. 통합공정관리의 기본적인 절차는 잡음과 이상원인이 공존하는 공정에 대하여 매시점마다 수정절차를 통하여 공정편차를 백색잡음으로 전환하며, 수정된 공정을 관리도를 이용하여 이상원인의 발생 여부를 탐지하게 된다. 이때 공정은 이상원인 발생 전에는 백색잡음이 되지만, 이상원인 발생 후에는 이상원인과 수정절차의 효과가 혼합되어 다양한 형태의 시계열 모형으로 변환하게 된다. 이 논문에서는 잡음모형으로 자기회귀이동평균(1,1) 모형을 가정하고 통합공정관리 절차를 수행하는 경우, 지수가중이동평균 관리도를 사용하여 이상원인을 탐지하는 절차에 대한 효율을 살펴보았다. 또한 이상원인의 신호 후 이를 제거하기 힘든 경우 사용할 수 있는 재수정 절차를 제안하였다.

• 품질경영학회지
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• 제32권2호
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• pp.201-211
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• 2004

Process adjustment is a complimentary tool to process monitoring in process control. Process adjustment directs on maintaining a process output close to a target value by manipulating another controllable variable, by which significant process improvement can be achieved. Therefore, this approach can be applied to the 'Improve' stage of Six Sigma strategy. Though the optimal control rule minimizes process variability in general, it may not properly function when special causes occur in underlying process, resulting in off-target bias and increased variability in the adjusted output process, possibly for long periods. In this paper, we consider a responsive feedback control system and the minimum mean square error control rule. The bias in the adjusted output process is investigated in a general framework, especially focussing on stationary underlying process and the special cause of level shift type. Illustrative examples are employed to illustrate the issues discussed.

• 한국품질경영학회:학술대회논문집
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• 한국품질경영학회 2004년도 품질경영모델을 통한 가치 창출
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• pp.425-429
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• 2004

Process adjustment Is a complimentary tool to process monitoring in process control. Process adjustment directs on maintaining a process output close to a target value by manipulating another controllable variable, by which significant process improvement can be achieved. Therefore, this approach can be applied to the 'Improve' stage of Six Sigma strategy. Though the optimal control rule minimizes process variability in general, it may not properly function when special causes occur in underlying process, resulting in off-target bias and increased variability in the adjusted output process, possibly for long periods. In this paper, we consider a responsive feedback control system and the minimum mean square error control rule. The bias in the adjusted output process is investigated in a general framework, especially focussing on stationary underlying process and the special cause of level shift type. Illustrative examples are employed to illustrate the issues discussed.

• Journal of the Korean Statistical Society
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• 제33권4호
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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.

• Communications for Statistical Applications and Methods
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• 제7권2호
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• pp.435-446
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• 2000

Statistical process control(SPC) and engineering process control(EPC) are two strategies for quality improvement that have been developed independently. EPC seeks to minimize variability by adjusting compensatory variables in order to make the process level close to the target, while SPC seeks to reduce variability by monitoring and eliminating causes of variation. One purpose of this paper is to propose the IMA(0,1,1) model as the in-control process model. For the out-of-control process model we consider two cases; one is the case with a step shift in the level, and the other is the case with a change in the nonstationarity. Another purpose is to suggest the use of an integrated process control procedure with adjustment and monitoring, which can consider the proposed process model effectively. An integrated control procedure will improve the process control activity significantly for cases of the proposed model, when compared to the procedure of using either EPC or SPC, since EPC will keep the process close to the target and SPC will eliminate special causes.

• 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.