• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2006년도 추계 학술발표회
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• pp.509-514
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• 2006

Considering the risk management, there are many examples and various studies for the corporation risk. However, I have never seen the project risk management that applied a construction site for practical approach. Therefore, I have developed a chart (I-Chart) for the project risk management, and also built a model (I-Chart scenario analysis) that I could use. I applied this model to container yard reclaimed land in harbor construction with approaching of geotechnical engineering.

• 품질경영학회지
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• 제45권2호
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• pp.209-225
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• 2017

Purpose: Since traditional p chart is unable to deal with the variation of attribute data, this paper proposes a new attribute control chart for nonconforming proportions incorporating overdispersion with a beta-binomial model. Methods: Statistical theories for control chart developed under the beta-binomial model and a new approach using this control chart are presented Results: False alarm probabilities of p chart with the beta-binomial model are evaluated and demerits of p chart under overdispersion are discussed from three examples. Hence a concrete procedure for the proposed control chart is provided and illustrated with examples Conclusion: The proposed chart is more useful than traditional p chart, individual chart to treat observed proportions nonconforming as variable data and Laney p' chart.

• 대한토목학회논문집
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• 제30권3B호
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• pp.315-324
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• 2010

본 논문에서는 대규모 유탄성 해석을 통해 초대형 부유식 해상구조물의 설계 차트를 개발했다. 초기 설계단계에 본 챠트를 이용해서 고비용의 유탄성 해석을 사용하지 않고도 초대형 부유식 해상구조물의 유탄성 거동을 예측이 가능하다. 본 논문에서는 두 가지 종류의 설계챠트 I, II을 개발하여 제시하였다. 설계차트 I은 특정 주파수의 파랑에 대한 구조물의 최대응력 응답진폭함수를 얻을 수 있도록 개발되었다. 설계차트 I의 경우 동일한 변장비와 무차원 구조강성계수를 갖는 모든 구조물에 적용이 가능하다. 설계챠트 I과 파랑스펙트럼의 적분을 통해 설계차트 II를 개발하였으며, Beaufort 풍력급에 따른 Bretschneider 스펙트럼을 적용해 해상의 환경요인을 고려하도록 구성되었다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2004년도 추계학술대회 논문집
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• pp.973-976
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• 2004

So far, this report is written by knowledge and standard through several reference drawing and experienced contents with drawing hemming die. The contents about actual design is so huge that I will write about it next time. Timing chart is important to hemming die because of link position and numbers. Timing chart need a very much design time because link number and position have to applied for each case. In this paper developed an automatic timing chart and link position by relation and Pro/Program of Pro/Engineer. The method used relation rule of design parameter for timing chart. In the future, I believe that simple easy to correct and reasonable price hemming die will be producted.

• 한국의류학회지
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• 제16권2호
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• pp.255-262
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• 1992

A computer simulation methiod for costume design has been developed using LUMENA, a generic-purpose 2-dimensional graphic software. In this study the palette, tone chart, fabric chart, styling chart, and costume drawing were constructed on the computer. In costume design simulation, fabric swatches with various colors and patterns were applied to the base garment image taken by using a scanner or a video camera. In this procedure the original 3-dimensional effect was fully retained. Using this simulation method, a number of costume designs could be carried out in short time without actually making the garment. A portfolio including the tone chart, fabric chart, styling chart, costume drawing, and simulation results were made for the purpose of demonstration, using the animation tools of LUMENA.

• 한국품질경영학회:학술대회논문집
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• 한국품질경영학회 1998년도 The 12th Asia Quality Management Symposium* Total Quality Management for Restoring Competitiveness
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• pp.198-207
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• 1998

The p chart approximate to the normal distribution has a difficulty to analyze the process condition precisely when the negative LCL is occurred. Furthermore, the probability of Type I error increases compared with using its original binomial distribution. For a long time the p chart has been used as approximated to the normal distribution because of its easy use. However, it becomes rapid and convenient to calculate the binomial distribution through the development of computer and software, so it is strongly suggested to use the binomial distribution determining control limits to reduce the probability of Type I error. In this study, I suggest that the control limits can be designed in use of binomial distribution and they can be utilized without special software by illustrating the certain work for establishing p-chart with the commercial one(EXCEL).

• 천문학논총
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• 제28권3호
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• pp.37-54
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• 2013

China and Korea have a long history of star charts, dating from the prehistoric period. Historically, Korean astronomy has been deeply influenced by China over the last two thousand years, particularly on constellation system. Therefore, Chinese and Korean traditional star charts have many similarities in terms of shape of constellation, number of star, and so forth. Korean star charts, however, have lots of unique characteristics distinguishing from Chinese ones, such as, size of star and position of constellation. Overall knowledge of the Chinese star chart is required to study the Korean star chart. In this paper, I focus on introducing selected star charts in China and Korea. Although this review is very limited, I hope that this paper is helpful in research in the field of historical astronomy.

• 응용통계연구
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• 제31권4호
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• pp.485-495
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• 2018

CCC-r 관리도는 고품질공정에서 공정불량률을 관리하는 경우 매우 효율적이라고 알려져 있다. 이 관리도를 사용할 때 관리상태의 공정모수는 일반적으로 알려져 있지 않기 때문에 제1국면의 표본을 추출하여 이를 추정해야 한다. 제2국면에서 관리도의 성능은 제1국면에서 추정한 모수와 관리한계에 영향을 받기 때문에, 추정 오차의 영향을 살펴보는 것은 중요하다. 이 논문에서는 일반적으로 많이 사용하는 평균런길이의 평균(average of average run length) 이외에 평균런길이의 표준편차(standard deviation of average run length)를 사용하여 CCC-r 관리도의 관리상태의 성능을 평가하였다. 그 결과 CCC-r 관리도에서 안정적인 관리상태의 성능을 유지하기 위해서는 이전에 권장하던 제1국면의 표본 크기보다 훨씬 더 큰 표본이 필요하다는 사실을 알 수 있었다.

• 산업경영시스템학회지
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• 제39권2호
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• pp.37-45
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• 2016

Control chart is representative tools of statistical process control (SPC). It is a graph that plotting the characteristic values from the process. It has two steps (or Phase). First step is a procedure for finding a process parameters. It is called Phase I. This step is to find the process parameters by using data obtained from in-controlled process. It is a step that the standard value was not determined. Another step is monitoring process by already known process parameters from Phase I. It is called Phase II. These control chart is the process quality characteristic value for management, which is plotted dot whether the existence within the control limit or not. But, this is not given information about the economic loss that occurs when a product characteristic value does not match the target value. In order to meet the customer needs, company not only consider stability of the process variation but also produce the product that is meet the target value. Taguchi's quadratic loss function is include information about economic loss that occurred by the mismatch the target value. However, Taguchi's quadratic loss function is very simple quadratic curve. It is difficult to realistically reflect the increased amount of loss that due to a deviation from the target value. Also, it can be well explained by only on condition that the normal process. Spiring proposed an alternative loss function that called reflected normal loss function (RNLF). In this paper, we design a new control chart for overcome these disadvantage by using the Spiring's RNLF. And we demonstrate effectiveness of new control chart by comparing its average run length (ARL) with ${\bar{x}}-R$ control chart and expected loss control chart (ELCC).

• 품질경영학회지
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• 제19권2호
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• pp.172-180
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• 1991

The null hypothesis being tested by $the{\bar{X}}$ control chart is that the process is in control at a quality level ${\mu}o$. An ${\bar{X}}control$ chart is a tool for detecting process average changes due to assingnable causes. The major weakness of $the{\bar{X}}$ control chart is that it is relatively insensitive to small changes in the population mean. This paper presents one way to remedy this weakness is to allow each plotted value to depend not only on the most recent subgroup average but on some of the other subgroup averages as well. Two approaches for doing this are based on (1) moving averages and (2) exponentially weighted moving averages of forecasting method.