• Journal of the Korea Academia-Industrial cooperation Society
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• v.3 no.1
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• pp.20-26
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• 2002

To make practical application of mixing test results to concrete mix design, experimental tests of concrete were done and the relationship between cement-water ratio and compressive strength of concrete was obtained. A computer program which can be used for data base of air content, slump and compressive strength test results was developed. The program draws $\bar{X}$-R or X-Rs control charts and has data sheets for arrangement of material test results. The computer program also helps calculation of concrete mix proportions for mixing tests and contains dictionary of concrete technical terms.

• Journal of Korean Society for Quality Management
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• v.40 no.4
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• pp.467-480
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• 2012

Purpose: This paper introduces new 2-of-3 main and supplementary runs rules to increase the performance of the classical $\bar{X}$ control chart for detecting small process shifts. Methods: The proposed runs rules are compared with other competitive runs rules by numerical experiments. Nonlinear optimization problem to minimize the out-of-control ARL at a specified shift of process mean for determining action and warning limits at a time is formulated and a procedure to find two limits is illustrated with a numerical example. Results: The proposed 2-of-3 main and supplementary runs rules demonstrate an improved performance over other runs rules in detecting a sudden shift of process mean by simultaneous changes of mean and standard deviation. Conclusion: To increase the performance in the detection of small to moderate shifts, the proposed runs rules will be used with $\bar{X}$ control charts.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.45
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• pp.365-374
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• 1998

Control Chart is a powerful tool to detect that process is in control or out of control. CIM can have real effect when CIM involve automated quality control. A neural network approach is used for unnatural pattern detecting of control chart. The previous moving window method uses all unnatural pattern that is detected as moving time window. Therefore, It trains a large number of unnatural pattern and takes training time long. In this paper, the proposed method tests a small number of training unnatural pattern which modifies test data without repeating time. We shows that the proposed method has differences In training time and identification rate on the previous moving windows method. As results, we reduced training time and obtain the same identification rate.

• Journal of the korean Society of Automotive Engineers
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• v.4 no.1
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• pp.1-4
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• 1982

우리가 흔히 쓰는 관리도에도 X over bar-R관리도, P(또는 Pn)관리도, C(또는 U)관리도가관리 도의 대종을 이루고 있다. 그러나 기계공업 특히 자동차공업분야에서는 기계가공 부품을 일일이 재기가 어렵거나 시간이 걸리므로 고-노고우 게이지(go-no go gauge)를 써서 선별하는 경우가 많다. 또 금형베품의 조립시에는 측정개소가 많기 때문에 어느 한 부분만을 제어서 관리할 수 없으므로 여러곳을 제어서 관리해야 하는데 이때 금형전체를 어떻게 맞출것인가, 혹은 제품의 공차관리를 어떻게 해나갈 것인가 하는 문제가 생긴다. 다음에 이러한 경우에 유용하게 쓰일 수 있는 관리도의 예를 소개하고자 한다.

• Journal of the Korea Safety Management & Science
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• v.12 no.4
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• pp.255-263
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• 2010

The research developes short-run standardized control charts(SSCC) and short-run acceptance control charts(SACC) under the various demand patterns. The demand patterns considered in this paper are three types such as high-variety and repetitive low-volume pattern, extremely-high-variety and nonrepetitive low-volume pattern, and high-variety and extremely-low-volume pattern. The short-run standardized control charts developed by extending the long-run ${\bar{x}}$-R, ${\bar{x}}$-s and I-MR charts have strengths for practioners to understand and use easily. Moreover, the short-range acceptance control charts developed in the study can be efficiently used through combining the functions of the inspection and control chart. The weighting schemes such as Shewhart, moving average (MA) and exponentially weighted moving average (EWMA) can be considered by the reliability of data sets. The two types according to the use of control chart are presented in the short-range standardized charts and acceptance control charts. Finally, process capability index(PCI) and process performance index(PPI) classified by the demand patterns are presented.

• The Korean Journal of Nuclear Medicine Technology
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• v.16 no.1
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• pp.115-118
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• 2012

Purpose: The Levey-Jennings chart controlled measurement values that deviated from the tolerance value (mean ${\pm}2SD$ or ${\pm}3SD$). On the other hand, the upgraded Westgard Multi-Rules are actively recommended as a more efficient, specialized form of hospital certification in relation to Internal Quality Control. To apply Westgard Multi-Rules in quality control, credible quality control substance and target value are required. However, as physical examinations commonly use quality control substances provided within the test kit, there are many difficulties presented in the calculation of target value in relation to frequent changes in concentration value and insufficient credibility of quality control substance. This study attempts to improve the professionalism and credibility of quality control by applying Westgard Multi-Rules and calculating credible target value by using a commercialized quality control substance. Materials and Methods : This study used Immunoassay Plus Control Level 1, 2, 3 of Company B as the quality control substance of Total T3, which is the thyroid test implemented at the relevant hospital. Target value was established as the mean value of 295 cases collected for 1 month, excluding values that deviated from ${\pm}2SD$. The hospital quality control calculation program was used to enter target value. 12s, 22s, 13s, 2 of 32s, R4s, 41s, $10\bar{x}$, 7T of Westgard Multi-Rules were applied in the Total T3 experiment, which was conducted 194 times for 20 days in August. Based on the applied rules, this study classified data into random error and systemic error for analysis. Results: Quality control substances 1, 2, and 3 were each established as 84.2 ng/$dl$, 156.7 ng/$dl$, 242.4 ng/$dl$ for target values of Total T3, with the standard deviation established as 11.22 ng/$dl$, 14.52 ng/$dl$, 14.52 ng/$dl$ respectively. According to error type analysis achieved after applying Westgard Multi-Rules based on established target values, the following results were obtained for Random error, 12s was analyzed 48 times, 13s was analyzed 13 times, R4s was analyzed 6 times, for Systemic error, 22s was analyzed 10 times, 41s was analyzed 11 times, 2 of 32s was analyzed 17 times, $10\bar{x}$ was analyzed 10 times, and 7T was not applied. For uncontrollable Random error types, the entire experimental process was rechecked and greater emphasis was placed on re-testing. For controllable Systemic error types, this study searched the cause of error, recorded the relevant cause in the action form and reported the information to the Internal Quality Control committee if necessary. Conclusions : This study applied Westgard Multi-Rules by using commercialized substance as quality control substance and establishing target values. In result, precise analysis of Random error and Systemic error was achieved through the analysis of 12s, 22s, 13s, 2 of 32s, R4s, 41s, $10\bar{x}$, 7T rules. Furthermore, ideal quality control was achieved through analysis conducted on all data presented within the range of ${\pm}3SD$. In this regard, it can be said that the quality control method formed based on the systematic application of Westgard Multi-Rules is more effective than the Levey-Jennings chart and can maximize error detection.

• The Korean Journal of Applied Statistics
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• v.30 no.3
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• pp.487-501
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• 2017

A machine vision system (MVS) is a computer system that utilizes one or more image-capturing devices to provide image data for analysis and interpretation. Recently there have been a number of industrial- and medical-device applications where control charts have been proposed for use with image data. The use of image-based control charting is somewhat different from traditional control charting applications, and these differences can be attributed to several factors, such as the type of data monitored and how the control charts are applied. In this paper, we investigate the adjustment effect of image size and region of interest (ROI) size, when we use control charts to monitor grayscale image data in industry.

• Journal of the Korean Society of Marine Environment & Safety
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• v.21 no.2
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• pp.179-187
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• 2015

The purpose of this study is to identify the aspect that the traffic distribution function changes, according to the direction of the datum line and the horizontal and vertical positions of the datum point applied when it is calculated. Targeting routes at the entrance of Mokpo Harbor, this study tested using AIS survey data of January 2013 the effects of the three variables-direction of the datum line(${\theta}$), horizontal position($\mathfrak{L}_H$) and vertical position($\mathfrak{L}_V$) on mean ($\bar{x}$) and standard deviation (${\delta}$). The test result showed that $\bar{x}$ and ${\delta}$ were changed according to the change of ${\theta}$, because the extracted sample data were changed according to ${\theta}$; and the changes of $\bar{x}$ and ${\delta}$ according to ${\theta}$ were drawn as the relation of the sine function' sum. In addition, it was found that setting up ${\theta}$ that the change value of ${\delta}$ becomes the least as the direction of the datum line was valid, to determine the optimum passage distribution function on complex waters with multiple branches of route. The result of this study is expected to be used as basic data to understand maritime traffic flow based on more quantified data of normal distribution and make decisions related to maritime traffic safety management.

• Journal of Industrial Technology
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• v.28 no.A
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• pp.149-153
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• 2008

$C_p$ and other process capability indices are used extensively in industry, However, They are inadequate and widely misused. In a practical application, process average ${\mu}$ is almost always drifted by various assignable causes in process. And control charts will not detect these shifts in process average. In this study, incorporating these undetected shifts, a new capability analysis method is introduced.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.2
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• pp.245-252
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• 2011

VoIP transmits voices over IP-based networks and it is the abbreviation of Voice over Internet Protocol. Recently, VoIP provides various services in addition to voices. Since VoIP services' provision is extending, VoIP service quality management is becoming an important issue. Therefore, in this paper, we study VoIP service quality management. We examine VoIP technology, service types, and network architecture. Then, we investigate key quality indicators(KQIs)/key performance indicators(KPIs) in terms of customers, not network service providers. Toward this, we also study the concept of general service quality management as well as the concept of telecommunication related service quality management. Moreover, we apply $\bar{x}$ and R charts to show how to use statistical quality control techniques in real telecommunication companies with one KQI.