• Journal of the Korean Statistical Society
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• v.29 no.3
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• pp.319-336
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• 2000

A mixed model with a white noise process and an IMA(0,1,1) process is considered as a process model. It is assumed that the process is a white noise in the absence of a special cause and the process changes to an IMA(0,1,1) due to a special cause. One useful scheme in measuring the process level is to use the variable measurement interval (VMI) between measurement times according to the value of the previous chart statistic. The advantage of the VMI scheme is to measure the process level infrequently when in control to save the measurement cost and to measure frequently when out of control to save the off-target cost. This paper considers the VMI scheme in order to detect changes in the process model from a white noise to an IMA(0,1,1). The VMI scheme is shown to be effective compared to the standard fixed measurement interval (FMI) scheme in both statistical and economic contexts.

• Journal of Korean Society for Quality Management
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• v.41 no.3
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• pp.487-494
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• 2013

Purpose: In this study, we propose the precision guided munitions verification methodology using the statistical analysis method has been proposed. and it can be applied to the precision guided munitions quality assurance work. Methods: This modeling is based on Failure Mode and Effects Analysis, Statistical Process Control, Defense Quality Managerment System, Production Readiness Review, Manufacturing Readiness Assesment and so on. Results: The Process Control Modeling that has the following procedures ; searching the critical to quality, statistical analysis by process, verify process. Moreover, the effectiveness of the methodology is verified by applying to the precision guided munitions. Conclusion: To achieve a analysis methods of statistical process control and verify process for precision guided munitions.

• Journal of the Korean Operations Research and Management Science Society
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• v.36 no.1
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• pp.39-55
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• 2011

Process monitoring has been emphasized for the monitoring of complex system such as chemical processing industries to achieve the efficiency enhancement, quality management, safety improvement. Recently, ICA (Independent Component Analysis) based MSPC (Multivariate Statistical Process Control) was widely used in process monitoring approaches. Moreover, DICA (Dynamic ICA) has been introduced to consider the system dynamics. However, the existing approaches show the limitation that their performances are strongly dependent on the statistical distributions of control variables. To improve the limitation, we propose a novel approach for process monitoring by integrating DICA and LOF (Local Outlier Factor). In this paper, we aim to improve the fault detection rate with the proposed method. LOF detects local outliers by using density of surrounding space so that its performance is regardless of data distribution. Therefore, the proposed method not only can consider the system dynamics but can also assure robust performance regardless of the statistical distributions of control variables. Comparison experiments were conducted on the widely used benchmark dataset, Tennessee Eastman process (TE process), and showed the improved performance than existing approaches.

• Proceedings of the Safety Management and Science Conference
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• 1999.11a
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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.

• International Journal of Quality Innovation
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• v.1 no.1
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• pp.97-105
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• 2000

In this paper, a two-section method for measuring is introduced and the variation sources of measurement process are analysed. Measuring is a special process in general process. Various variation source must be firstly decomposed so that the statistical distribution law of measuring process can be established, and then implement monitoring control of the measuring process. A special method to obtain the measuring variation is discussed, and a monitoring control technique for measuring process is studied based statistical distribution. Towards the end, we briefly introduce software design for the analysis and control of a measurement process.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.26 no.1
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• pp.7-16
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• 2003

At present, company has to produce a product that consumer like with a competitive price, a good quality, and a fitting time to supply. Process control and quality control are very important to supply with a product uniformly and inexpensively. Process control is given much weight in the quality control in manufacturing system. Statistical process controls(SPC) that are used in process generally have major impact on manufacturing, product design activities, and process development potentially. Control charts in statistical process control method can be interpreted the data from quality characteristics in production process and discriminated between chance variation and assignable variation in process. In addition, control chart can be used to monitor the process output and detect when changes in the inputs are required to bring the process back to an in-control state. The models that relate the influential inputs to process outputs help determine the nature and magnitude of the adjustments required. In this paper, the characteristic of product quality is monitored by control chart during the machining process and construction of quality control cycle is considered to divide into two types in this case that different assignable causes lead to shifts having different magnitudes. Then we are intended to find a process shift magnitude which has economical quality cost policy and are considered to quality cost functions to find a process shift magnitude. Those costs are categorized into the well-known categories of prevention, appraisal, and internal failure and external failure. This paper ends with numerical examples that demonstrate the usefulness of the model.

• Communications for Statistical Applications and Methods
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• v.5 no.2
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• pp.373-381
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• 1998

In this paper a new control chart which accounts for correlation between process subgroups will be proposed. We consider the case where the process fluctuations are autocorrelated by a stationary AR(1) time series and where n($\geq1$) items are sampled from the process at each sampling time. A simulation study is presented and shows that for correlated subgroups, the proposed control chart makes a significant improvement over the traditionally employed X-bar chart which ignores subgroup correlations. Finally, we illustrate the proposed chart by comparing the standardized residuals and X-bar chart on a data set of motor shaft diameters.

• Communications for Statistical Applications and Methods
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• v.23 no.6
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• pp.497-515
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• 2016

Modern processes often monitor more than one quality characteristic that are referred to as multivariate statistical process control (MSPC) procedures. The MSPC is the most rapidly developing sector of statistical process control and increases interest in the simultaneous inspection of several related quality characteristics. Most multivariate detection procedures based on a multi-normality assumptions are independent, but there are many processes that assume non-normality and correlation. Many multivariate control charts have a lack of related joint distribution. Copulas are tool to construct multivariate modelling and formalizing the dependence structure between random variables and applied in several fields. From copula literature review, there are a few copula to apply in MSPC that have multivariate control charts, and represent a successful tool to identify an out-of-control process. This paper presents various types of copulas modelling for the multivariate control chart. The performance measures of the control chart are the average run length (ARL) and the average number of observations to signal (ANOS). Furthermore, a Monte Carlo simulation is shown when the observations were from an exponential distribution.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.10
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• pp.103-112
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• 1998

The evolution of semiconductor manufacturing technology has accelerated the reduction of device dimensions and the increase of integrated circuit density. In order to improve yield within a short turn around time and maintain it at high level, a system that can rapidly determine problematic processing steps is needed. The statistical process control detects abnormal process variation of key parameters. Expert systems in SPC can serve as a valuable tool to automate the analysis and interpretation of control charts. A set of IF-THEN rules was used to formalize knowledge base of special causes. This research proposes a strategy to apply expert system to SPC in semiconductor manufacturing. In analysis, the expert system accomplishes the instability detection of process parameter, In diagnosis, an engineer is supported by process analyzer program. An example has been used to demonstrate the expert system and the process analyzer.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.2
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• pp.1-11
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• 2014

Software reliability in the software development process is an important issue. Software process improvement helps in finishing with reliable software product. In this field, SPC (Statistical process control) is a method of process management through application of statistical analysis, which involves and includes the defining, measuring, controlling, and improving of the processes. The proposed process involves evaluation of the parameter of the mean value function and hence the values of the mean value function at various inter failure times to develop relevant time control chart. In this paper, was proposed a control mechanism, based on time between failures observations using Rayleigh and Burr distribution property, which is based on Non Homogeneous Poisson Process (NHPP). In this study, the proposed model is reliable in terms of hazard function, because it is more efficient in this area can be used as an alternative to the existing model. Through this study, software developers are considered by the various intended functions, prior knowledge of the software to identify failure modes to feed to some extent shall be able to help.