• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.381-386
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• 2005

본 논문에서는 LCD 공정의 효율적인 관리를 위한 방법으로서 품질마이닝 시스템의 설계/개발 그리고 운영방법론을 논하고자 한다. 주요내용으로는 주요공정의 탐색, 설비유의차분석, 공정최적화 및 recipe 최적화, 수율 및 주요특성의 추정/예측 등을 들 수 있다. 이를 위하여 다양한 데이터마이닝 도구와 통계적 모형의 적절한 활용 방법을 논하고자 한다. 또한, 실제현장 중심의 개발사례를 통하여 품질마이닝 시스템의 유용성을 기술하였다.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.456-459
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• 2002

It is lately issued studies on EVMS(Earned Value Management System) throughout construction industry, which is management system integrating cost and schedule effectively. So identifying importance and circumstance of introducing EVMS, CPI(Cost Performance Index) and SPI(Schedule Performance Index), which are critical components on schedule introducing EVMS, calculate and it intends to analyze the trend and problem of cost and time throughout project management, applying various statistical data analysis method.

• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.639-652
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• 2015

In today's manufacturing environment, the process data can be easily measured and transferred to a computer for analysis in a real-time mode. As a result, it is possible to monitor several correlated quality variables simultaneously. Various multivariate statistical process control (MSPC) procedures have been presented to detect an out-of-control event. Although the classical MSPC procedures give the out-of-control signal, it is difficult to determine which variable has caused the signal. In order to solve this problem, data mining and machine learning techniques can be considered. In this paper, we applied the technique of decision tree learning to the MSPC, and we did simulation for MSPC procedures to monitor the bivariate normal process means. The results of simulation show that the overall performance of the MSPC procedure using decision tree learning technique is similar for several values of correlation coefficient, and the accurate classification rates for out-of-control are different depending on the values of correlation coefficient and the shift magnitude. The introduced procedure has the advantage that it provides the information about assignable causes, which can be required by practitioners.

• IE interfaces
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• v.6 no.1
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• pp.19-30
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• 1993

Various types of control charts are available, but their full advantages are not being utilized due to the lack of experts who can select, design, implement, and interpret. An expert system is developed which would perform these functions as if each process had a resident expert.

• Proceedings of the Safety Management and Science Conference
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• 2005.11a
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• pp.493-504
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• 2005

Statistical Process Control and Process Adjustment theory is gaining recognition in the process industries where the process frequently experiences a shift mean. This paper aims to study, the theory difference between Statistical Process Control and Process Adjustment in simple terms and presents a case study that demonstrates successful integration of Statistical Process Control and Process Adjustment theory for a product in drifting industry.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.700-703
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• 2000

국내 대부분의 중소기업에서 품질관리 활동을 실시하고 있지만 품질관리 활동으로부터 제공되는 품질데이터를 체계적으로 수집하여 활용하는 중소기업은 많지 않은 것이 현실이다. 본 연구는 중소기업의 품질정보를 체계적으로 관리하고 활용하기 위한 품질정보시스템 개발사례를 제시하고자 한다. 먼저 수입, 공정, 완제품 검사 단계별로 품질정보를 모듈화하여 품질정보의 흐름을 파악하고, 두 번째로 통계적 공정관리 모듈을 추가하여 하나의 거시적 품질정보시스템으로 완성하였으며, 마지막으로 현재 개발된 거시적 품질정보시스템의 완성도를 높이기 위해 지식 기반에 근거하여 품질문제를 해결하기 위한 미시적 품질 정보시스템을 결합시킨 종합적 품질정보시스템 구축하기 위한 의견을 제시하였다.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.73-82
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• 2020

Most process date such as semiconductor and petrochemical processes, autocorrelation often exists between observed data, but when the existing SPC(Statistical process control) is applied to these processes, it is not possible to effectively detect the average change of the process. In this paper, when the average change of a certain size occurs in the process data following a specific time series model, the average of the residuals changes according to the passage of time, and the change pattern of the average is introduced around the ARMA(1,1) process. Based on this result, the reference value required in the design process of the CUSUM (Cumulative sum) control chart is appropriately considered by considering the type of the time series model of the process data of the CUSUM control chart that can detect small mean changes in the process and the width of the process mean change of interest. It was confirmed through simulation that it should be selected and used.

• Proceedings of the Korea Society for Simulation Conference
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• 1999.04a
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• pp.116-120
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• 1999

현재 S회사의 생산공정은 전 과정에 걸쳐 통합된 정보처리 체계가 없고, 생산계획 및 통제 과정에서의 복잡한 계산 및 중요한 의사결정이 담당자의 경험적 지식에 의존하고 있다. 이러한 상황의 한계로, 현재의 생산공정계획 및 생산실행계획은 시간적 제약이 크고, 유연적인 수정이 어려울 뿐 아니라, 통계적 분석의 자료체계가 부실하다. 그러나, S회사는 보다 개선된 공정관리를 위해 새로운 생산계획전략의 필요성을 느끼고 있다. 본 논문에서는 이러한 문제를 해결하기 위한 하나의 접근방법으로써 DEVS 형식론을 바탕으로 공정을 효과적으로 모델링 및 시뮬레이션화 하였다. 생산계획의 핵심부분인 전문담당자의 경험적 지식을 체계적 규칙으로 정리하여 모델에 반영하였고 이를 통해 습득된 시뮬레이션 결과를 분석하여 생산계획 전략의 신뢰할만한 평가기준을 마련하였다.

• Journal of the Korea Society for Simulation
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• v.20 no.4
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• pp.115-125
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• 2011

Manufacturing companies usually manage the process to achieve high quality using various types of control chart in statistical process control. When an assignable cause occurs in a process, the data in the control chart changes with different patterns by the specific causes. It is important in process control to classify the CCP (Control Chart Pattern) recognition for fast decision making. In former research, gathered data from process used to apply as raw data, leads to degrade the performance of recognizer and to decrease the learning speed. Therefore, feature based recognizer, employing feature extraction method, has been studied to enhance the classification accuracy and to reduce the dimension of data. We propose the method to extract features that take the distances between CCP data and reference vector generated from BDK (Bi-Directional Kohonen Network). We utilize those features as the input vectors in ANN (Artificial Neural Network) and compare with raw data applied ANN to evaluate the performance.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.1025-1034
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• 2015

Traditional Shewhart control charts assume that the observations are independent over time. Current progress in measurement and data collection technology lead to the presence of autocorrelated process data that may affect poor performance in statistical process control. One of the most popular charts for autocorrelated data is to model a correlative structure with an appropriate time series model and apply control chart to the sequence of residuals. Model parameters are estimated by an in-control Phase I reference sample since they are usually unknown in practice. This paper deals with the effects of parameter estimation on Phase II control limits to monitor autocorrelated data.