• Proceedings of the Korean Vacuum Society Conference
• /
• 2011.02a
• /
• pp.36-36
• /
• 2011

반도체 공정 신호는 주기 신호와 비주기 신호로 구분된다. 특정 패턴을 가지는 주기 신호는 해당 파라미터(parameter)에 대해서 패턴 매칭을 수행하여 관리하는 연구가 진행되고 있다. 반면 비주기 신호 데이터의 경우에는 패턴 매칭 방법을 수행할 수 없다. 또한 반도체 공정에서 얻을 수 있는 두 개 타입의 데이터는 그 파라미터가 방대하기 때문에 현재 실제 공정에 적용되고 있는 방식인 각각 하나의 파라미터에 대해 관리도(control chart)를 구성해 관리하는 것은 많은 비용과 시간의 낭비를 초래한다. 따라서 두 타입 데이터의 여러 개의 파라미터를 동시에 관측할 수 있고 파라미터간의 내재된 상관관계를 고려할 수 있는 장점을 가진 분석 기법에 대한 연구가 필요하다. 주기 신호의 이상탐지를 위한 기존 연구는 신호를 구간으로 나누어 구간별로 SPC 차트적용 시키는 방법, 각 시점 마다 측정되는 값을 하나의 변수로 고려하여 Hotelling's T square, PCA, PLS 등과 같은 다변량 통계 분석을 적용 시키는 방법들이 제시되어 왔다. 이러한 방법들은 다양한 특성을 가지는 주기신호를 분석하고 이상을 탐지 하는데 많은 한계점을 가진다. 이에 본 논문은 다양한 형태를 가지는 신호의 특성을 반영하여 자기구상지도를 기반으로 신호의 분류와 공정의 이상을 탐지하는 기법을 제안한다. 제안하는 기법은 자기구상지도를 이용하여 복잡한(고차원, 시계열) 신호를 2차원 상의 노드로 맵핑시킴으로써 신호의 특질(feature)을 추출하고 새로 표현된 신호의 특질을 기반으로 Logistic regression을 적용시켜 이상을 탐지 한다. 다양한 이상 상황을 가진 반도체 공정 신호를 사용하여 제안한 이상탐지 성능을 평가하였다.

• Korean Journal of Remote Sensing
• /
• v.15 no.2
• /
• pp.91-105
• /
• 1999

Spatial data integration using multiple geo-based data sets has been regarded as one of the primary GIS application issues. As for this issue, several integration schemes have been developed as the perspectives of mathematical geology or geo-mathematics. However, research-based approaches for statistical/quantitative assessments between integrated layer and input layers are not fully considered yet. Related to this niche point, in this study, spatial data integration using multiple geoscientific data sets by known integration algorithms was primarily performed. For spatial integration by using raster-based GIS functionality, geological, geochemical, geophysical data sets, DEM-driven data sets and remotely sensed imagery data sets from the Ogdong area were utilized for geological thematic mapping related by mineral potential mapping. In addition, statistical/quantitative information extraction with respective to relationships among used data sets and/or between each data set and integrated layer was carried out, with the scope of multiple data fusion and schematic statistical assessment methodology. As for the spatial integration scheme, certainty factor (CF) estimation and principal component analysis (PCA) were applied. However, this study was not aimed at direct comparison of both methodologies; whereas, for the statistical/quantitative assessment between integrated layer and input layers, some statistical methodologies based on contingency table were focused. Especially, for the bias reduction, jackknife technique was also applied in PCA-based spatial integration. Through the statistic analyses with respect to the integration information in this case study, new information for relationships of integrated layer and input layers was extracted. In addition, influence effects of input data sets with respect to integrated layer were assessed. This kind of approach provides a decision-making information in the viewpoint of GIS and is also exploratory data analysis in conjunction with GIS and geoscientific application, especially handing spatial integration or data fusion with complex variable data sets.

• The Korean Journal of Applied Statistics
• /
• v.34 no.1
• /
• pp.1-8
• /
• 2021

Abstract concepts in social research must use measurement tools that are assured of validity and reliability. Observation score derived by a measurement tool can be divided into a valid observation score, a biased observation score, and an error. The presence or absence of a biased value is associated with validity, and the presence or absence of an error value is associated with reliability. There are many techniques for seeing whether a measurement tool is valid and reliable. For example, there are construct validity using factor analysis and internal consistency based on the Cronbach alpha. In this study, the calculation of the Cronbach alpha is derived through a sample, so we suggest an estimator of the Cronbach alpha under complex sample design and nonresponse. In a simulation, the proposed method is compared with many other existing estimators of Cronbach alpha under a multivariate normal distribution.

• Journal of Korea Society of Industrial Information Systems
• /
• v.14 no.5
• /
• pp.281-288
• /
• 2009

The purpose of this paper is to provide the statistical models for landslide prediction through quantification and AHP methods. Quantification method is a statistical method of providing quantity to qualitative variables by analyzing the observed data. In this paper, we suggest the quantification process based on the results of cannonical correlation analysis. In contrast with the quantification method which is based on given data the AHP(Analytic Hierarchy Process) technique is a kind of method based on questionaire data which is usually taken from professionals. We analyze both the real data(provided from KIGAM) and questionaire data collected from professionals of various related area. We developed two kinds of evaluation table which provide the scores of land slide possibility and the logistic model providing the probability of occurring landslide. Finally we compare the performance and evaluate the stability of the suggested two models.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2006.05a
• /
• pp.647-651
• /
• 2006

유역의 수자원 및 환경 상태에 관련된 문제를 파악하고 관리에 적극적인 참여를 유도하기 위해서는 객관적인 평가수단이 필요하다. 따라서 본 연구에서는 지속가능한 유역관리를 위하여 수계의 중권역별 유역관리 상태를 평가할 수 있는 평가지수를 개발하고 지수의 적용성을 평가하였다. 본 연구에서는 유역조사 자료와 다변량 통계 기법의 하나인 요인분석을 이용하여 유역관리 평가지수를 개발하였다. 또한, 평가지수를 한강수계에 적용하여 중권역별 유역관리 상태를 상대적으로 비교하여 평가하였다. 유역관리 평가지수는 이수관리 평가 세부지수, 치수관리 평가 세부 지수, 수질 및 생태 관리 평가 세부 지수로 구성하였다. 각 평가지수는 요인분석을 통하여 추출된 3개의 지표로 구성이 되어 있으며, 각 지표들은 $3{\sim}5$개의 변수들로 구성되어 있다. 한강수계의 중권역별 유역관리 상태를 평가한 결과, 댐상류에 위치한 중권역에서 다른 중권역들 보다 유역관리지수가 높게 나타났으며, 수계의 최하류에 위치한 중권역에서 다른 유역들 보다 작은 값을 나타냈다.

• Journal of the Korean Data and Information Science Society
• /
• v.22 no.5
• /
• pp.931-940
• /
• 2011

Classification is to generate a rule of classifying objects into several categories based on the learning sample. Good classification model should classify new objects with low misclassification error. Many types of classification methods have been developed including logistic regression, discriminant analysis and tree. This paper presents a new classification method using penalized partial least squares. Penalized partial least squares can make the model more robust and remedy multicollinearity problem. This paper compares the proposed method with logistic regression and PCA based discriminant analysis by some real and artificial data. It is concluded that the new method has better power as compared with other methods.

• The Korean Journal of Applied Statistics
• /
• v.13 no.2
• /
• pp.297-306
• /
• 2000

Many users have made the most often use of the SAS system in statistical data analysis all over the world. But it is difficult to use the grand procedures and language of the SAS system. Therefore the side of program development has changed in the graphic-oriented and menu-centered way like SASj ASSIST, SASjINSICHT after a version 6.08 turned into the Window environment. A biplots is a multivariate data analysis technique that graphically describes both relationships among the multidimensional observations and relationships among the variables. But there were not the procedure and graphic interface for a biplots algorithm in the SAS system. In this paper, there are two objects. First, we supply users with the convenience of the environment of CLI, which is constructed with SASj AF and SCL, to solve the problem that we have programed a biplots algorithm with the SASjIML one by one. Second, we reflect the current of the Information Age which means the spread of various kinds of system construction to extract useful information from data with the help of the development of hardware and software.

• The Korean Journal of Applied Statistics
• /
• v.30 no.4
• /
• pp.555-566
• /
• 2017

For multivariate datasets with large number of variables, classical dimensional reduction methods such as principal component analysis may not be effective for data visualization. The underlying reason is that the dimensionality of the space of variables is often larger than two or three, while the visualization to the human eye is most effective with two or three dimensions. This paper proposes a working procedure which first partitions the variables into several "latent" clusters, explores individual data subsets, and finally integrates findings. We use R pakacage "ClustOfVar" for partitioning variables around latent dimensions and the principal component biplot method to visualize within-cluster patterns. Additionally, we use the technique for embedding supplementary variables to figure out the relationships between within-cluster variables and outside variables.

• Journal of the Korean Geographical Society
• /
• v.43 no.6
• /
• pp.1002-1015
• /
• 2008

We investigate the potential of digital elevation model and multivariate geostatistical kriging in mapping of temperature and rainfall based on sparse weather station observations. By using elevation data which have reasonable correlation with temperature and rainfall, and are exhaustively sampled in the study area, we try to generate spatial distributions of temperature and rainfall which well reflect topographic effects and have less smoothing effects. To illustrate the applicability of this approach, we carried out a case study of Jeju island using observation data acquired in January, April, August, and October, 2005. From the case study results, accounting for elevation via colocated cokriging could reflect detailed topographic characteristics in the study area with less smoothing effects. Colocated cokriging also showed much improved prediction capability, compared to that of traditional univariate ordinary kriging. According to the increase of the magnitude of correlation between temperature or rainfall and elevation, much improved prediction capability could be obtained. The decrease of relative nugget effects also resulted in the improvement of prediction capability.

• The korean journal of orthodontics
• /
• v.34 no.5 s.106
• /
• pp.371-379
• /
• 2004

The purpose of this study was to investigate the changes and types of statistical methods used in the Korean Journal of Orthodontics (KJO) and the American Journal of Orthodontics and Dentofacial Orthopedics (AJODO) from )999 to 2003. The frequency of use, transitions, assumption check of statistical methods and types of advanced statistical methods were examined from each journal. The study consisted of 247 articles published in the KJO and randomly chosen 50 articles per year which were original articles and used statistical methods T-test, analysis of variance(ANOVA), correlation analysis, nonparametric analysis. regression analysis chi-square test. factor analysis, were the order of statistical methods most frequently used in the KJO, while t-test. ANOVA, nonparametric analysis, correlation analysis, regression analysis, chi-square test. factor analysis. were the order of statistical methods used in the AJODO The changes of statistical methods observed in the KJO were not significant $(X^2=17.4\;p=0.5881)$ but the changes observed in the AJODO was seen to be significant $(x^2=42.4,\;p=0.0397)$ Some of the studies examined had overlooked the assumptions of the statistical methods employed. Data investigation such as outlier should be performed before analysis and alternative statistical approaches are applied for a small sample size. Types of advanced statistical methods were factor analysis and discriminant analysis in the KJO and Intention-To-Treat (ITT) analysis in clinical trials through multi-center, survival analysis and Generalized Estimating Equations (GEE) in the AJODO. Appropriate analysis approaches and interpretations should be applied for the correlated and repeated measurements of the orthodontic data set.