• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.963-972
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• 2007

When a control chart signals that a special cause is present, process engineers must initiate a search for and an identification of the special cause. Knowing the time of the process change could lead to identify the special cause more quickly, and to take the appropriate actions immediately to improve quality. In this paper, we propose the maximum likelihood estimator (MLE) for the process change point when a control chart is used in monitoring the parameters of a process in which the observations can be modeled as a first-order autoregressive(AR(1)) process plus an additional random error.

• International Journal of Reliability and Applications
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• v.10 no.1
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• pp.1-15
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• 2009

Even though the impact of manufacturing quality to reliability is not considered much as well as that of design area, a major cause of an early failure of the product is known as manufacturing problem. This research applies two different types of neural network algorithms, the Back propagation (BP) algorithm and Learning Vector Quantization (LVQ) algorithm, to identify and classify the nonrandom variation pattern on the control chart based on knowledge-based diagnosis of dimensional variation. The performance and efficiency of both algorithms are evaluated to choose the better pattern recognition system for auto body assembly process. To analyze hundred percent of the data obtained by Optical Coordinate Measurement Machine (OCMM), this research considers an application in which individual observations rather than subsample means are used. A case study for analysis of OCMM data in underbody assembly process is presented to demonstrate the proposed knowledge-based pattern recognition system.

• Communications for Statistical Applications and Methods
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• v.12 no.2
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• pp.265-273
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• 2005

In this paper, we proposed multivariate EWMA control charts for both combine-accumulate and accumulate-combine approaches to monitor dispersion matrix of multiple quality variables. Numerical performance of the proposed charts are evaluated in terms of average run length(ARL). The performances show that small smoothing constants with accumulate-combine approach is preferred for detecting small shifts of the production process.

• Journal of radiological science and technology
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• v.39 no.4
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• pp.527-534
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• 2016

Dual-energy X-ray absorptiometry (DEXA) is the most widely used technical instrument for evaluating bone mineral content (BMC) and density (BMD) in patients of all ages. In 2016, DEXA devices operating is 5617 in Korea. In this study we investigated the quality of management practices survey for DEXA equipment and we analyzed it. We got a survey response rate of 12.6%. Accurate bone densitometry test is used data for estimation a patient's risk of fracture. However, improper bone densitometry will increase the possibility of causing a false positive. Therefore. it is essential to use the proper aids accurate bone densitomenty to be performed, and the quality control of the device to reduce the error factor of the tester through the training to reduce error for the device and the attitude.

• Progress in Medical Physics
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• v.26 no.2
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• pp.59-71
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• 2015

The goal of quality assurance (QA) is to minimize systematic errors in order to maintain the quality of a certain process. Statistical process control (SPC) has been utilized for QA in radiation therapy field since 2005 and is changing QA paradigm. Its purpose is to maintain a process within the given control limits while monitoring of error trends such as variation or dispersion. SPC can be applied to all QA aspects of radiotherapy; however, a medical physicist should have enough knowledge about the application of SPC to QC/QA procedures. In this paper, the author introduce a concept of SPC and review some previously reported studies those used SPC for QA in radiation therapy.

• Proceedings of the Korea Concrete Institute Conference
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• 1998.10b
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• pp.940-945
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• 1998

Recently, serious cracking problems have been reported in this country while the process of actual massive concrete construction. he hydration heat arising from the chemical reaction of cement with water causes temperature differentials in between inside and outside of a structural member, and these temperature differentials induce thermal stresses. In this paper, we described on the practical application and quality control of the mass concrete mixed with fly-ash. This project is investigating adiabatic temperature rise test of concrete, mock-up test in the laboratory, ad B/P before placing the mass concrete in site. As a result, we can be prevent temperature cracking from the cement hydration heat of mass concrete and also can be showed up secure quality control flow chart of mass concrete.

• Journal of Korean Society for Quality Management
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• v.15 no.1
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• pp.47-54
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• 1987

It is common that the process capability fluctuates as time passes, but concentrates to the mean value. To keep up process capability with given limits is vital to stability of process. Various control charts, especially ${\sigma}-chart$, have been used for analyzing process capability, but It sometimes can not give distinct answer. So this paper introduces another analyzing method by ARMA (autoregressive moving average) which is originally developed for forecasting, and demonstrates the analyzing methodology through a case study.

• Proceedings of the Safety Management and Science Conference
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• 2010.04a
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• pp.509-517
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• 2010

The paper proposes the misuse types of statistical quality tools according to the kind of data and the number of population in DMAIC process of six sigma. The result presented in this paper can be extended to the QC story 15 steps of QC circle. The study also provides the improvement methods about control chart, measurement system analysis, statistical difference, and practical equivalence.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.1141-1149
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• 2009

In Bone Mineral Density(BMD) measurements, accuracy and precision must be superior in order to know the small changes in bone mineral density and actual biological changes. Therefore the purpose of this study is to increase the reliability of bone mineral density inspection through appropriate management of image quality from machines and inspectors. For the machine management method, the recommended phantom from each bone mineral density machine manufacturer was used to take 10~25 measurements to determine the standard amount and permitted limit. On each inspection day, measurements were taken everyday or at least three times per week to verify the whether or not change existed in the amount of actual bone mineral density. Also evaluations following Shewhart control chart and CUSUM control chart rules were made for the bone mineral density figures from the phantoms used for measurements. Various forms of management became necessary for machine installation and movement. For the management methods of inspectors, evaluation of the measurement precision was conducted by testing the reproducibility of the exact same figures without any real biological changes occurring during reinspection. There were two measurement methods followed: patients were either measured twice with 30 measurements or three times with 15 measurements. An important point to make regarding measurements is that after the first inspection and any other inspection following, the patient was required to come off the inspection table completely and then get back on for any further measurements. With a 95% confidence level, the precision error produced from the measurement bone mineral figures produced a precision error of 2.77 times the minimum of the biological bone mineral density change (Least significant change: LSC). In order to assure reliability in inspection, there needs to be good oversight of machine management and measurer for machine operation and inspection error. Accuracy error in machines needs to be reduced to under 1% for scientific development in bone mineral density machines.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.261-269
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• 2015

Nowadays the application of change point analysis has been indispensable in a wide range of areas such as quality control, finance, environmetrics, medicine, geographics, and engineering. Identification of times where process changes would help minimize the consequences that might happen afterwards. The main objective of this paper is to compare the change-point detection capabilities of Bayesian estimate and maximum likelihood estimate. We applied Bayesian and maximum likelihood techniques to formulate change points having a step change and multiple number of change points in a Poisson rate. After a signal from c-chart and Poisson cumulative sum control charts have been detected, Monte Carlo simulation has been applied to investigate the performance of Bayesian and maximum likelihood estimation. Change point detection capacities of Bayesian and maximum likelihood estimation techniques have been investigated through simulation. It has been found that the Bayesian estimates outperforms standard control charts well specially when there exists a small to medium size of step change. Moreover, it performs convincingly well in comparison with the maximum like-lihood estimator and remains good choice specially in confidence interval statistical inference.