• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.1334-1336
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• 2003

On the basis of comprehensive analysis and summarization of the image fusion algorithms provided by PCI Geomatica software, deficiencies in image fusion processing functions of this software are put forwarded in this paper. This limitation could be improved by further developing PCI Geomatica on the user’ side. Five effective algorithms could be added into PCI Geomatica. In this paper, the detailed description of how to customize and further develop PCI Geomatica by using Microsoft Visual C++ 6.0, PCI SDK Kit and GDB technique is also given. Through this way, the remote sensing imagery fusion functions of PCI Geomatica software can be extended.

• 한국전자통신학회논문지
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• 제2권2호
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• pp.118-122
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• 2007

초기 보안시스템 대부분은 PCI 방식으로서 미숙한 사용자들의 PC 사용에 부적합하다. 특히 사용되어지는 보안 프로그램들은 대부분 크랙에 대하여 검증되지 않았으며 해커나 바이러스 등의 공격에 대하여 노출되어지고 있다. 그러므로 본 논문은 사용자들이 쉽게 사용할 수 있고 범용 컴퓨터에서 사용될 수 있는 USB를 사용하는 Twofish 암호알고리즘을 설계하였다. 사용자들은 USB를 사용하여 보안 시스템을 쉽게 사용하게 된다. 또한 다양한 가변키 길이를 가지는 Twofish 암호알고리즘은 다양한 보안시스템에 적용이 가능하게 된다. 이러한 Twofish 암호알고리즘은 암호화와 복호화에 대한 성능을 향상시킬 수 있으며 하드웨어의 크기를 감소시키는 효과를 가지게 된다.

• 한국정보통신설비학회:학술대회논문집
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• 한국정보통신설비학회 2008년도 정보통신설비 학술대회
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• pp.25-29
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• 2008

The CORBA that supports FPGA has not been used generally and it is difficult to implement and to develop the CORBA for FPGA. In this paper we propose the way to design FPGA to support a CORBA component. For FPGA to support the CORBA component, embedded processor provided by FPGA and PCI based CORBA is utilized. The PCI based CORBA is for improving data transfer throughput. This paper will be organized as follows. In Chapter I, existing research trend and background are presented for why we propose design of FPGA that support the CORBA component. In Chapter II, FPGA design for supporting CORBA components is proposed and described in detail. In Chapter III, simple experiment is tested to confirm the proposed FPGA design. Finally session 4 is conclusion of this paper.

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 2008년도 추계학술대회
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• pp.599-610
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• 2008

It is an important and urgent issue to improve process capability in quality control. Process capability refers to the uniformity of the process. The variability in the process is a measure of the uniformity of output. A simple, quantitative way to express process capability, the degree of variability from target in specification is defined by process capability index(PCI). Almost process capability indices are defined under normal distribution. However, these indices can not be applied to the process of non-normal distribution including reliability. We investigate current research on the process of non-normal distribution, and advanced method and technology for developing more reliable and efficient PCI. Finally we suggest the perspective for future study.

• 보건교육건강증진학회지
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• 제26권2호
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• pp.25-33
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• 2009

Objectives: Smoking is related to periodontal disease and periodontal therapy. So the aim of this study was to investigate the effects of professional tooth cleaning and plaque control instruction (PT & PCI) for smoking behavior. Methods: A total of 151 adults were investigated using the O'Leary Plaque Index (PI), $L\ddot{o}e$ & Silness gingival index (GI) and the number of sextants possessing periodontal pocket (SPP). And adults were given a through dental scaling and Watanabe method for dental plaque control. Follow up examination were conducted after 3 months and compared the pre and post- status. The collected data were analyzed with t-test, paired t-test and one-way analysis of variance. Results: Regardless of smoking behavior, improving effects were identified after PT & PCI on PI, GI and SPP in the whole population. However, the effects of GI improvement were significant in the smoking group alone; those of PI improvement were most significant in the non-smoking group; and those of SPP improvement were more significant in non-smoking and pre-smoking groups than in the smoking group. The shorter period of smoking and the smaller amount of smoking, the greater effects of PT & PCI by smoking-related characteristics. Conclusion: Smoking cessation instruction should necessarily be included in oral health education in that smoking is an important factor to consider in prevention of periodontal diseases and periodontal therapies.

• 응용통계연구
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• 제29권2호
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• pp.369-379
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• 2016

시그마 수준(sigma level)이란 미국 모토롤라사에 의해 소개된 프로세스 능력 지수로서 1970년대 이후 널리 활용되고 있는 다양한 지수들 중의 하나이다. 이는 다른 지수들과 비교할 때 모 프로세스의 확률 분포에 기초한다는 장점을 갖지만 양산 단계를 가정한 것으로 R&D 분야의 시제품 그리고/또는 초도 양산품 단계에 직접 적용하는 것은 적절하지 못할 수 있다. 이에 본 논문은 시그마 수준을 계산할 때 가정하는 치우침에 대한 통계적 고찰을 통하여 양산단계에서 6 시그마 품질 수준을 달성하기 위한 개발 단계의 시제품 그리고/또는 초도 양산품의 목표 시그마 수준 설정 방법을 소개한다. 그리고 이를 기초로 개발과 양산 단계에서 경제성을 달성할 수 있는 설계 공차의 강건 한계 도출 방법을 제시해 보고자 한다.

• 산업경영시스템학회지
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• 제36권4호
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• pp.38-44
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• 2013

Smart phones have brought rapid changes in this competitive world. Smart phone application developers are trying their best to consider the customer requirements in the most efficient way while considering all its attributes. However smart phone service quality has been given less consideration comparatively during the last few years. This paper proposes a measurement method for improving service quality of smart phone application. This method combines the service quality performance model (SQPM) and process capability index (PCI). The service quality performance model is used to identify service items that require improvement. Process capability index is used as a measure for prioritization of those improvements. Case study was carried out to search out important communication application service attributes. customer satisfaction level data was collected for users who used the application service. A total of twenty four service attributes were found during this survey. Using the joint approach of SQPM and PCI, five significant service attributes were prioritized for service quality improvement.

• 산업경영시스템학회지
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• 제42권1호
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• pp.151-157
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• 2019

This study is about the process capability index (PCI). In this study, we introduce several indices including the index $C_{PR}$ and present the characteristics of the $C_{PR}$ as well as its validity. The difference between the other indices and the $C_{PR}$ is the way we use to estimate the standard deviation. Calculating the index, most indices use sample standard deviation while the index $C_{PR}$ uses range R. The sample standard deviation is generally a better estimator than the range R. But in the case of the panel process, the $C_{PR}$ has more consistency than the other indices at the point of non-conforming ratio which is an important term in quality control. The reason why the $C_{PR}$ using the range has better consistency is explained by introducing the concept of 'flatness ratio'. At least one million cells are present in one panel, so we can't inspect all of them. In estimating the PCI, it is necessary to consider the inspection cost together with the consistency. Even though we want smaller sample size at the point of inspection cost, the small sample size makes the PCI unreliable. There is 'trade off' between the inspection cost and the accuracy of the PCI. Therefore, we should obtain as large a sample size as possible under the allowed inspection cost. In order for $C_{PR}$ to be used throughout the industry, it is necessary to analyze the characteristics of the $C_{PR}$. Because the $C_{PR}$ is a kind of index including subgroup concept, the analysis should be done at the point of sample size of the subgroup. We present numerical analysis results of $C_{PR}$ by the data from the random number generating method. In this study, we also show the difference between the $C_{PR}$ using the range and the $C_P$ which is a representative index using the sample standard deviation. Regression analysis was used for the numerical analysis of the sample data. In addition, residual analysis and equal variance analysis was also conducted.

• Communications for Statistical Applications and Methods
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• 제29권2호
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• pp.177-191
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• 2022

This paper addresses the use of machine learning methods for causal estimation of treatment effects from observational data. Even though conducting randomized experimental trials is a gold standard to reveal potential causal relationships, observational study is another rich source for investigation of exposure effects, for example, in the research of comparative effectiveness and safety of treatments, where the causal effect can be identified if covariates contain all confounding variables. In this context, statistical regression models for the expected outcome and the probability of treatment are often imposed, which can be combined in a clever way to yield more efficient and robust causal estimators. Recently, targeted maximum likelihood estimation and causal random forest is proposed and extensively studied for the use of data-adaptive regression in estimation of causal inference parameters. Machine learning methods are a natural choice in these settings to improve the quality of the final estimate of the treatment effect. We explore how we can adapt the design and training of several machine learning algorithms for causal inference and study their finite-sample performance through simulation experiments under various scenarios. Application to the percutaneous coronary intervention (PCI) data shows that these adaptations can improve simple linear regression-based methods.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume I
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• pp.236-238
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• 2006

An algorithm for detection of yellow sand aerosols has been developed with infrared bands from Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-functional Transport Satellite-1 Replacement (MTSAT-1R) data. The algorithm is the hybrid algorithm that has used two methods combined together. The first method used the differential absorption in brightness temperature difference between $11{\mu}m$ and $12{\mu}m$ (BTD1). The radiation at 11 ${\mu}m$ is absorbed more than at 12 ${\mu}m$ when yellow sand is loaded in the atmosphere, whereas it will be the other way around when cloud is present. The second method uses the brightness temperature difference between $3.7{\mu}m$ and $11{\mu}m$ (BTD2). The technique would be most sensitive to dust loading during the day when the BTD2 is enhanced by reflection of $3.7{\mu}m$ solar radiation. We have applied the three methods to MTSAT-1R for derivation of the yellow sand dust and in conjunction with the Principle Component Analysis (PCA), a form of eigenvector statistical analysis. As produced Principle Component Image (PCI) through the PCA is the correlation between BTD1 and BTD2, errors of about 10% that have a low correlation are eliminated for aerosol detection. For the region of aerosol detection, aerosol index (AI) is produced to the scale of BTD1 and BTD2 values over land and ocean respectively. AI shows better results for yellow sand detection in comparison with the results from individual method. The comparison between AI and OMI aerosol index (AI) shows remarkable good correlations during daytime and relatively good correlations over the land.