• Journal of Korea Society of Industrial Information Systems
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• v.4 no.3
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• pp.71-78
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• 1999

This paper presents an integrated and automated inspection system using image processing technology for the automotive engine assembly process. The system make it possible for the inspected data to be entered directly from the machine vision into the statistical process control system. Such direct entry enables the prompt preparation of corrective actions against process problems. An IVP-150 machine vision board is installed within the PC for image processing, and a template matching technology is implemented to precisely verify quality factors. The developed system showed robustness to the problems of noise, distortion, and orientation.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.6 s.183
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• pp.57-63
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• 2006

Gravitational centers of precise spinning components must coincide with the rotational centers of those to reduce noise and vibration and to extend those lift as well. Therefore quality control should be performed in the manufacturing process, in which the unbalance moments are accurately measured. In this paper 3-point weighing method is adopted to measure the unbalance moment of small-sized precision spinning elements using electronic scales with 0.1 mg resolution. Firstly methods to eliminate the fixture error and to reduce the effects of frictional force that is known as side effect, are proposed. A measuring system is developed and various experiments are performed to verify the proposed approach. The measured and calculated values are analysed in statistical methods, and this provides the errors of the measuring system. The results show that the proposed theory and test procedures gives reliable unbalance moments and gravitational centers.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.3
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• pp.135-141
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• 2009

A variety of statistical methods are applied to model and optimize responses, related to product or system's quality, in terms of control and noise factors at design and manufacturing stages. Most of them assume continuous response variables but, assessing the performance of a product or system often involves categorical observations, such as ratings and scores. Although most previous works to deal with the categorical data provide sorhisticated response models and ensure unbiased outcomes, they require heavy computation to estimate the model parameters, as well as enough replications. In this study, we present some practical approaches for optimal parameter design with ordered categorical response when only a few or no replication is available. Two real-life examples are given to illustrate the presented methods.

• Proceedings of the Korea Concrete Institute Conference
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• 1996.04a
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• pp.376-381
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• 1996

In this study, the quality contral of high strength reacy-mixed concrete with design compressive strength of 420 kgf/$\textrm{cm}^2$ placed at a tail building for a long period is statistically investigated. The amount of cast-in-place high strength concrete is by about $15000\textrm{m}^3$. The required average compressive striength is 500 kgf/$\textrm{cm}^2$ according to KS F 4009 with assumed coefficient of variation of 11%. Since there are many concrete members in this construction, fly ash is used to reduce the heat of hydration of concrete. As the results of this study, the average actual 28-day compressive strength is 498 kgf/$\textrm{cm}^2$ and the coefficient of variation is 6.7%. The placing speed is comparable to normal strength concrete, however, the pump pressure is higher than that of normal strength concrete.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.3
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• pp.129-135
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• 2012

Companies strive for quality improvement and use process data obtained through measurement process to monitor and control the process. Measurement data contain variation due to error of operator and instrument. The total variation is sum of product variation and measurement variation. Gage R&R is for repeatability and reproducibility of measurement system. Gage R&R study is usually conducted to analyze the measurement process. In performing the gage R&R study, several parameters such as the appropriate number of operators (o), sample size of parts (p), and replicate (r) are used. In this paper we propose how to determine the optimal combination of number of operators (o), sample size of parts (p), and replicates (r) considering measurement time and cost by statistical method.

• Journal of Korean Society on Water Environment
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• v.24 no.4
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• pp.430-435
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• 2008

In order to design Non-point source management, the aspect of statistical features of the entire precipitation data should be focused since non-point source discharge is driven by continuous rainfall runoffs. 3-parameter mixed exponential probability density function is used to establish urban stormwater capture curve instead of previous single-parameter exponential PDF. Then, recent 10-year data in Busan are applied to establish the curve. The result shows that 3-parameter mixed PDF gives better resolution.

• The Korean Journal of Applied Statistics
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• v.33 no.4
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• pp.451-466
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• 2020

CCC-r chart is effective for high-quality processes with a very low fraction nonconforming. The values of process parameters should be estimated from the Phase I sample since they are often not known. However, if the Phase I sample size is not sufficiently large, an estimation error may occur when the parameter is estimated and the practitioner may not achieve the desired in-control performance. Therefore, we adjust the control limits of CCC-r charts using the bootstrap algorithm to improve the in-control performance of charts with smaller sample sizes. The simulation results show that the adjustment with the bootstrap algorithm improves the in-control performance of CCC-r charts by controlling the probability that the in-control average number of observations to signal (ANOS) has a value greater than the desired one.

• 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.

• Journal of the Korean Magnetic Resonance Society
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• v.22 no.4
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• pp.149-157
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• 2018

Diabetes is known to be one of common causes for several types of peripheral nerve damage. Diabetic neuropathy (DN) is a significant complication lowering the quality of life that can be frequently found in diabetes patients. In this study, the metabolomic characteristic of DN and Diabetes was investigated with NMR spectroscopy. The sera samples were collected from DN patients, Diabetes patients, and healthy volunteers. Based on the pair-wise comparison, three metabolites were found to be noticeable: glucose, obviously, was upregulated both in DN patients (DNP) and Diabetes. Citrate is also increased in both diseases. However, the dietary nutrient and biosynthesized metabolite from glucose, ascorbate, was elevated only in DNP, compared to healthy control. The multivariate model of OPLS-DA clearly showed the group separation between healthy control-DNP and healthy control-Diabetes. The most significant metabolites that contributed the group separation included glucose, citrate, ascorbate, and lactate. Lactate did not show the statistical significance of change in t-test while it tends to down-regulated both in DNP and Diabetes. We also conducted the ROC curve analysis to make a multivariate model for discrimination of healthy control and diseases with the identified three metabolites. As a result, the discrimination model between healthy control and DNP (or Diabetes) was successful while the model between DNP and Diabetes was not satisfactory for discrimination. In addition, multiple combinations of lactate and citrate in the OPLS-DA model of healthy control and diabetes group (DNP + Diabetes patients) gave good ROC value of 0.952, which imply these two metabolites could be used for diagnosis of Diabetes without glucose information.

• Journal of Digital Convergence
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• v.20 no.1
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• pp.231-238
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• 2022

This study aimed to analyze the Influence of workplace spirituality on job stress and infection control performance of dental hygienists in the COVID-19 pandemic situation Questionnaire was conducted for 149 dental hygienists from February to March 2021. COVID-19 knowledge and practice of infection control, job stress. and workplace spirituality. Survey data were analyzed t-test, ANOVA, Pearson's correlation using statistical programs of PASW Statistics ver. 21.0. Workplace spirituality was investigated to increase infection control performance and reduce job stress in a pandemic situation. The group with high infection control knowledge and performance showed low levels of job instability, organizational system, and stress. As for job stress, those with less than 2 years of experience reported relationship conflict, those with 3 to 5 years of experience showed high levels of job instability, organizational system, inadequate compensation, and workplace culture. In a pandemic situation, workplace spirituality was investigated to increase infection control performance and reduce job stress, so a plan to improve the quality of medical care was required for holistic and systematic organizational operation in preparation for the post-coronavirus.