• Proceedings of the Safety Management and Science Conference
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• 2013.11a
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• pp.199-207
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• 2013

산업의 빠른 발전 속도에 따라 연구 개발도 함께 발전해야 한다. 따라서 현재 제조공정에 대한 품질 특성치의 분석방법으로 공정 모수의 작은 변화도 쉽게 탐지를 할 수 있는 EWMA 관리도와 Shewhart 관리도보다 공정 변화에 민감하게 탐지 가능한 CUSUM 관리도에 관한 연구가 많이 이루어지고 있다. 하지만 식스시그마 공정관리에 맞춘 평균, 불량률, 미세 분산을 동시에 감지할 수 있는 동시 관리 체계 연구는 많이 미흡하다. 본 연구에서는 기존의 CUSUM, EWMA 관리도 기법보다 빠른 이상 감지를 위해서 평균, 불량률, 분산 3가지가 동시에 관리되어질 수 있도록 Zp-s 관리도를 소개한다. Zp-s 관리도는 ARL을 통해 기존 관리도보다 민감함을 확인할 수 있다.

• Journal of the Korean Data and Information Science Society
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• v.22 no.4
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• pp.803-809
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• 2011

Properties of multivariate Shewhart and CUSUM charts for monitoring variance-covariance matrix, specially focused on correlation coefficient components, are investigated. The performances of the proposed charts based on control statistic Lawley-Hotelling $V_i$ and likelihood ratio test (LRT) statistic $TV_i$ are evaluated in terms of average run length (ARL). For monitoring correlation coe cient components of dispersion matrix, we found that CUSUM chart based on $TV_i$ gives relatively better performances and is more preferable, and the charts based on $V_i$ perform badly and are not recommended.

• Structural Engineering and Mechanics
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• v.81 no.5
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• pp.647-664
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• 2022

The powerful data mapping capability of computational deep learning methods has been recently explored in academic works to develop strategies for structural health monitoring through appropriate characterization of dynamic responses. In many cases, these studies concern laboratory prototypes and finite element models to validate the proposed methodologies. Therefore, the present work aims to investigate the capability of a deep learning algorithm called Sparse Autoencoder (SAE) specifically focused on detecting structural alterations in real-case studies. The idea is to characterize the dynamic responses via SAE models and, subsequently, to detect the onset of abnormal behavior through the Shewhart T control chart, calculated with SAE extracted features. The anomaly detection approach is exemplified using data from the Z24 bridge, a classical benchmark, and data from the continuous monitoring of the San Vittore bell-tower, Italy. In both cases, the influence of temperature is also evaluated. The proposed approach achieved good performance, detecting structural changes even under temperature variations.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1487-1498
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• 2016

In monitoring a process, in-control process parameters must be estimated from the Phase I data. When we design the control chart based on the estimated process parameters, the control limits are usually chosen to satisfy a specific in-control average run length (ARL). However, as the run length distribution is skewed when the process is either in-control or out-of-control, the median run length (MRL) can be used as alternative measure instead of the ARL. In this paper, we evaluate the performance of Shewhart $\bar{X}$ chart with estimated parameters in terms of the average of median run length (AMRL) and the standard deviation of MRL (SDMRL) metrics. In simualtion study, the grand sample mean is used as a process mean estimator, and several competing process standard deviation estimators are used to evaluate the in-control performance for various amounts of Phase I data.

• 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.

• 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 Korean Society for Quality Management
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• v.27 no.4
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• pp.143-152
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• 1999

In Shewhart control chart, the average run length(ARL) is calculated using the mean of a conventional geometric distribution(CGD) assuming a sequence of identical and independent Bernoulli trials. In this, the success probability of CGB is the probability that any point exceeds the control limits. When the process is in-control state, there is no problem in the above assumption since the probability that any point exceeds the control limits does not change if the in-control state continues. However, if the out-of-control state begins and continues during the process, the probability of exceeding the control limits may take two forms. First, once the out-of-control state begins with exceeding probability p, it continues with the same exceeding probability p. Second, after the out-of-control state begins, the exceeding probabilities may very according to some pattern. In the first case, ARL is the mean of CGD with success probability p as usual. But in the second case, the assumption of a sequence of identical and independent Bernoulli trials is invalid and we can not use the mean of CGD as ARL. This paper concentrate on that point. By adopting one generalized binomial distribution(GBD) model that allows correlated Bernoulli trials, generalized geometric distribution(GGD) is defined and its mean is derived to find an alternative ARL when the process is in out-of-control state and the exceeding probabilities take the second form mentioned in the above. Small-scale simulation is performed to show how an alternative ARL works.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.999-1008
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• 2010

The objective of this paper is to develop variable sampling interval multivariate control charts that can offer significant performance improvements compared to standard fixed sampling rate multivariate control charts. Most research on multivariate control charts has concentrated on the problem of monitoring the process mean, but here we consider the problem of simultaneously monitoring both the mean and variability of the process.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.140-152
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• 2016

It is very important for the competitiveness and sustainable management of enterprises that the rapid changes in the managerial environments quickly and accurately are responded. For example, the large-scale investment accompanied by bad alternatives in accordance with misunderstanding of the managerial environments yields the huge cost and effort to modify and improve. In firm management, the quality of products and the productivity are influenced by changes of the endogenous factors yielded in manufacturing process and the exogenous factors as market, etc. These changes include not only changes in 4M (man, machine, material, method) but also those in the market, competitors, and technologies in the process of commodification, i.e., first, such disturbances make dispersion of the process big and odd. By Shewhart chart it can be checked that the process monitored is control-in or out. Business administration executes activities for input stabilization by monitoring changes in 4Ms, comparing with the standards, and taking measures for any abnormality. Second, TRM (technology road map) is to prospect product deployment and technological trend by predicting technologies in the competitive environment as the market, and to suggest the future directions of business. So, TRM must be modified and improved according to DR (design review) stages and changes in mass-production like input material change. Therefore, a role of TRM in input stabilization for reducing cost and man-hour is important. This study purposed to suggest that the environment changes are classified into endogenous factors and exogenous factors in production process, and then, quality and productivity should be stabilized efficiently through connection between TRM and input stabilization, and to prove that it is more effective for the display industry to connect TRM with input stabilization rather than to use TRM separately.

• Journal of the Korea Society for Simulation
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• v.18 no.3
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• pp.103-112
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• 2009

In a nuclear power plant (NPP), periodic sensor calibrations are required to assure sensors are operating correctly. However, only a few faulty sensors are found to be calibrated. For the safe operation of an NPP and the reduction of unnecessary calibration, on-line calibration monitoring is needed. In this paper, principal component-based Auto-Associative support vector regression (PCSVR) was proposed for the sensor signal validation of the NPP. It utilizes the attractive merits of principal component analysis (PCA) for extracting predominant feature vectors and AASVR because it easily represents complicated processes that are difficult to model with analytical and mechanistic models. With the use of real plant startup data from the Kori Nuclear Power Plant Unit 3, SVR hyperparameters were optimized by the response surface methodology (RSM). Moreover the statistical techniques are integrated with PCSVR for the failure detection. The residuals between the estimated signals and the measured signals are tested by the Shewhart Control Chart, Exponentially Weighted Moving Average (EWMA), Cumulative Sum (CUSUM) and generalized likelihood ratio test (GLRT) to detect whether the sensors are failed or not. This study shows the GLRT can be a candidate for the detection of sensor drift.