• Proceedings of the Korean Society Of Semiconductor Equipment Technology
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• 2005.09a
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• pp.178-182
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• 2005

품질 관리의 목표는 최종제품의 품질 보증에 있다. 이러한 목표를 달성하기 위해서는 품질 특성이 명확해야 하며, 동시에 품질 특성치에 영향을 주는 공정의 여러 변동 요인을 분명히 해야 한다. 실험계획법(Design of Experiments)은 특성에 영향을 미치는 여러 인자를 선정하며, 또한 이들의 관계를 알아보기 위한 실험을 실시하여 제품의 최적 제조조건을 경제적으로 찾아내는 기법이다. 본 연구에서는 실험계획법을 사용하여 유량을 최적화하는 요인을 선정, 얻어진 데이터를 통계적 방법으로 분석하여 최적의 조건을 나타내었다.

• Korean Journal of Construction Engineering and Management
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• v.10 no.2
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• pp.75-83
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• 2009

PPC stands for the percentage of weekly assignments completed. It makes it possible to improve the performance of production planning and control systems. Recently, the cases of PPC application have been increasing because PPC is easy to apply to construction site. However, to evaluate the average of PPC or analyze PPC as time passes has some problem ; if PPC is the same, the average it is evaluated equally although there are variabilities in production system. Therefore, In order to evaluate the character of PPC in process of production this paper suggests the method of the evaluating PPC by using coefficient of variability besides PPC measurement.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.811-823
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• 2018

Multivariate control chart based on Hotelling's $T^2$ statistic is a powerful tool in statistical process control for identifying an out-of-control process. It is used to monitor multiple process characteristics simultaneously. Detection of the out-of-control signal with the $T^2$ chart indicates mean vector shifts. However, these multivariate signals make it difficult to interpret the cause of the out-of-control signal. In this paper, we review methods of signal interpretation based on the Mason, Young, and Tracy (MYT) decomposition of the $T^2$ statistic. We also provide an example on how to implement it using R software and demonstrate simulation studies for comparing the performance of these methods.

• Proceedings of the KAIS Fall Conference
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• 2007.11a
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• pp.414-416
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• 2007

6 시그마 기법은 Define 단계부터 현상에 대한 수치화를 강조하고 있어, Data의 중요성을 어떤 다른 개선활동 보다도 강조하고 있다. 그러나 현장에서 개선활동 수행시에 가장 큰 문제점은 결과지표인 Y에 대한 측정을 통한 수치화는 가능하지만 -현실적으로도 관리를 하고 있고- 제어인자인 Xs인자에 대한 수치화는 상당한 어려움을 겪고 있다. 그 이유는 가장 큰 경우가 조건변경에 의한 실험을 통해 Data를 수집하려면 상당한 불량의 발생을 감수해야 하고 그로 인한 피해를 중소기업 입장에서 감수하고 실험을 감행하는것이 쉽지 않을 것이다. 따라서 실제 현장 개선에서는 불량을 최소한 줄이기 위해서 제어인자인 Xs인자의 변동을 최소화 하다 보니 X인자의 변화에 따른 Y인자의 변동을 알 수 없어 실제로는 유의한 영향을 줌에도 불구하고 통계적인 결론에만 집착하다 보면 잘못된 판정으로 인해 실제 개선이 되지 않는 경우가 허다하다. 이 논문에서는 6 시그마 활동시 문제가 되는 통계적 기법 적용시 현실과 Data 분석의 결과가 일치하지 않을 때 현실적 판단방법을 적용하여 실질적 개선을 하는 방법과 Xs인자의 작은 변화를 감지할 수 있는 통계적 기법의 적용을 통하여 실제 개선을 할 수 있는 사례를 제시하고자 한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.5
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• pp.962-969
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• 2006

To address the importance of the process fault detection for productivity, support vector machines (SVMs) is employed to assist the decision to determine process faults in real-time. The reactive ion etching (RIE) tool data acquired from a production line consist of 59 variables, and each of them consists of 10 data points per second. Principal component analysis (PCA) is first performed to accommodate for real-time data processing by reducing the dimensionality or the data. SVMs for eleven steps or etching m are established with data acquired from baseline runs, and they are further verified with the data from controlled (acceptable) and perturbed (unacceptable) runs. Then, each SVM is further utilized for the fault detection purpose utilizing control limits which is well understood in statistical process control chart. Utilizing SVMs, fault detection of reactive ion etching process is demonstrated with zero false alarm rate of the controlled runs on a run to run basis.

• Korean Journal of Construction Engineering and Management
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• v.7 no.1 s.29
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• pp.73-79
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• 2006

Even though graphical simulation is very useful for construction planning, the application of graphical simulation has a limitation in dealing with objects without fixed form like earthmoving process. In this case, the mathematical/statistical simulation about the productivity of the whole processes based on the numerical data of working time, waiting time and working capacity of using equipment becomes effective. The mathematical/statistical simulation is not fully utilized in the field of construction due to the difficulties of creating process models and securing trust the numerically expressed results of simulation. In this research, the output of discrete-event simulation programs which are the most common mathematical/statistical simulation tool for construction processes were analyzed for the purpose of earthmoving process visualization. The purpose of this research is to develop a graphical simulation system that can help the construction planner select most suitable equipment and construction methods through the visualize the numerical simulation results of the working time, the queuing time as well as the amount resources etc.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.478-481
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• 2002

In these days, Cost and Scheduling was managed effectively because of introduction of EVMS to construction project. However the EVMS is appropriate methods to advanced country, so it is difficult to apply into domestic construction project. in this paper weighted value n, m was used of compositive index(CI) to forecast Estimate At Completion (EAC) using statistical analysis in credible interval the objective of this paper is to verify compositive index(CI) and to forecast Estimate At Completion (EAC).

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.63
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• pp.45-54
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• 2001

The process capability indices have been widely used in manufacturing industries to provide numerical measures of process potential and performance. This study is concerned with process controls and adjustments by incapability index $C_{pp}$ and its sub-indices. A monitoring for $\^{C}_{pp}$ would provide a convenient way to monitor changes on process capability after statistical control is established, since $C_{pp}$ simultaneously measures process variability and centering. Further, we can separate charting of process location and variability by sub-indices of $C_{pp}$, ($C_{ia}$, $C_{ip}$), without returning to $\={x}$-R chart, even though an out-of-control signals on $\^{C}_{pp}$ control chart is found.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.255-256
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• 2020

생산 현장에서 발생되는 다양한 형태의 데이터는 스마트한 제조관리를 가능하게 하는 원동력으로 이를 효율적으로 저장하고 처리, 분석하는 일련의 과정이 4차 산업혁명 기반의 제조혁신에 능동적으로 대응하기 위한 핵심요소로서, 이와 관련한 다양한 연구들이 활발히 이루어지고 있다. 특히, 제조데이터 분석이라는 영역은 단순하게 기존의 데이터를 통계적인 접근 수단으로만 보는 것이 아니라 다양한 산업별 업종 도메인의 특성에 기반하여 빅데이터 분석과 기계학습 등의 인공지능 모델로 발전하고 있다. 본 논문에서는 다양한 산업별 제조현장을 이해하는 도메인 경험 및 특성을 고려하여 데이터를 효과적으로 저장, 처리, 분석할 수 있는 클라우드 형태의 빅데이터 시스템을 제안한다.

• 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.