• Journal of the Korean Society of Propulsion Engineers
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• v.12 no.6
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• pp.1-6
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• 2008

This work proposes a fuzzy trend monitoring method for the fault detection of a gas turbine engine through analyzing measured performance data trend. The proposed trend monitoring technique can diagnose the engine status by monitoring major engine measured parameters such as fuel flow rate, exhaust gas temperature, rotor rotational speed and vibration, and then analyzing their time deppendent changes. In order to perform this, firstly the measured engine performance data variation is formulated using Linear Regression, and then faults are isolated and identified using fuzzy logic.

• Journal of the Korea institute for structural maintenance and inspection
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• v.14 no.4
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• pp.86-93
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• 2010

The wear of railway track affects loss of rough ride, noise or vibration of train and traveling safety. Moreover as the track is worn away, this promotes destruction of structural mechanism of rail track which can bring about increasing of rail track maintenance cost drastically. For this reason, it is very important and interested research subject to design railway track structure and to analyse train movement mechanism based on systematic analysis of the reasons causing rail wear possible in real field. In this research, for the efficient maintenance, Life Cycle Performance of rail track and maintenance characteristics are computed considering some track components such as track type, contracting type, sleeper type and roadbed type. Time - Wear probabilistic distribution relationship as well as multiple regression analysis based on time, curvature and wear data are computed to predict the service life remainder of railway track and to be adapted to safety assessment.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.2
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• pp.12-26
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• 2014

This Study deals with effect of low visibility condition at nighttime on traffic accident. Roads for experiment of this study are Gyeongbu expressway, Seohaean expressway, Yeongdong expressway and Seoul beltway in Gyeonggi province. For this study, I subdivided basic straight section of them into 58 short section. And I analyzed effect of low visibility condition by darkness at nighttime on traffic accident by using 410 traffic accidents between January 1, 2009 and June 30, 2012 on those sections. The Quasi-experimental and negative binomial regression were applied to analyze effect of low visibility condition at nighttime on traffic accident. In this study, I only analyzed visibility difference of daytime and nighttime on traffic accident except other effective variables on traffic accidents. As a result, I have found that it is for low visibility condition at nighttime to have effect on traffic accidents at such specific conditions as Los A speed is maintained in basic straight section of expressway in fine weather. And I tried to do various analysis on types and causes of traffic accidents using the result of analysis.

• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.47-59
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• 2020

In this paper, we present the longitudinal Markov binary regression model with t-link function when its transition order is known or unknown. It is assumed that logit or probit models are considered in binary regression models. Here, t-link function can be used for more flexibility instead of the probit model since the t distribution approaches to normal distribution as the degree of freedom goes to infinity. A Markov regression model is considered because of the longitudinal data of each individual data set. We propose Bayesian method to determine the transition order of Markov regression model. In particular, we use the deviance information criterion (DIC) (Spiegelhalter et al., 2002) of possible models in order to determine the transition order of the Markov binary regression model if the transition order is known; however, we compute and compare their posterior probabilities if unknown. In order to overcome the complicated Bayesian computation, our proposed model is reconstructed by the ideas of Albert and Chib (1993), Kuo and Mallick (1998), and Erkanli et al. (2001). Our proposed method is applied to the simulated data and real data examined by Sommer et al. (1984). Markov chain Monte Carlo methods to determine the optimal model are used assuming that the transition order of the Markov regression model are known or unknown. Gelman and Rubin's method (1992) is also employed to check the convergence of the Metropolis Hastings algorithm.

• The Korean Journal of Applied Statistics
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• v.20 no.1
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• pp.195-204
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• 2007

In order to represent multiple regression data, an alternative graphical method, called as SSR Plot, is proposed by using geometrical description methods. This plot uses the relation that the sum of sqaures for regression (SSR) of two explanatory variables is known as the sum of the SSR of one variable and the increase in the SSR due to the addition of other variable to the model that already contains a variable. This half circle shaped SSR plot contains vectors corresponding explanatory variables. We might conclude that some explanatory variables corresponding to vectors which locate near the horisontal axis do affect the response variable. Also, for the regression model with two explanatory variables, a magnitude of the angle between two vectors can be identified for suppression.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.26 no.4
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• pp.207-216
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• 2014

Total organic carbon (TOC) is an important indicator used as an direct biological index in the research field of the marine carbon cycle. It is possible to produce the sufficient TOC estimation data by using the Chemical Oxygen Demand(COD) data because the available TOC data is relatively poor than the COD data. The outlier detection and treatment (removal) should be carried out reasonably and objectively because the equation for a COD-TOC conversion is directly affected the TOC estimation. In this study, it aims to suggest the optimal regression model using the available salinity, COD, and TOC data observed in the Korean coastal zone. The optimal regression model is selected by the comparison and analysis on the changes of data numbers before and after removal, variation coefficients and root mean square (RMS) error of the diverse detection methods of the outlier and influential observations. According to research result, it is shown that a diagnostic case combining SIQR (Semi - Inter-Quartile Range) boxplot and Cook's distance method is most suitable for the outlier detection. The optimal regression function is estimated as the TOC(mg/L) = $0.44{\cdot}COD(mg/L)+1.53$, then determination coefficient is showed a value of 0.47 and RMS error is 0.85 mg/L. The RMS error and the variation coefficients of the leverage values are greatly reduced to the 31% and 80% of the value before the outlier removal condition. The method suggested in this study can provide more appropriate regression curve because the excessive impacts of the outlier frequently included in the COD and TOC monitoring data is removed.

• Journal of Korean Society of Transportation
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• v.22 no.3 s.74
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• pp.127-135
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• 2004

교차로 안전성 진단과 관련된 기존의 연구는 교차로 상에서 발생한 사고 자료에 기초하여 교차로 기하구조 요소, 교통량 및 신호운영방법 등과 관련된 요인을 변수로 사용하여 교통사고건수 예측모형 개발에 관한 연구가 대부분이다. 그러나, 분석하고자 하는 대상 교차로의 사고건수 예측모형을 개발하기 위해 필요한 교통사고 자료의 경우 단 기일에 걸쳐 획득되지 않으며 몇 년간의 사고 자료를 요구할 수도 있다. 이러한 자료를 이용하더라도 사고 발생 기간동안 교차로 사고에 영향을 미치는 요인(교차로 운영방법, 기하구조 등)이 변화될 수도 있다는 문제점을 지닌다. 이와 같은 이유로 교차로 안전성을 진단하는데 있어 기존 교통사고 자료는 언제나 절대적인 자료가 될 수 없다. 이에 대한 보완책으로, 3일에서 5일정도의 조사 자료만으로도 안전성 진단이 가능한 상충자료를 이용하여 교차로 안전성 진단을 할 수 있다. 본 연구는 기존사고 자료를 이용하여 사고 발생에 기인하는 여러 변수들을 교통사고심각도와의 상관관계를 분석하고, 상관관계가 높은 변수를 이용하여 신경망 사고심각도 예측모형을 개발하였으며, 모형 검증을 위해 다중회귀사고심각도 예측모형을 개발하여 비교 평가한 결과 신경망 사고심각도 예측모형의 예측력이 우수한 것으로 나타났다. 현장에서 조사된 상충자료를 신경망 사고심각도 예측모형에 적용하여 상충이 사고로 연결 될 경우 사고심각도를 예측하였으며, 예측된 사고심각도에 가중치를 부여하여 대상 교차로 위험우선순위를 결정한 결과 사고비용에 기초한 위험우선순위 결정법과 같은 순위의 결과를 도출하였다.

• Journal of the Korea Convergence Society
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• v.9 no.10
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• pp.393-406
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• 2018

The depression and anxiety are the most popular mental disorders and not easy to distinguish because of their lots of similarities in the diagnostic criteria, related theories, and clinical symptoms. In this article, we developed the affect scale for distinguishable diagnosis, utilized the relationships between the Positive and Negative affect, and the depression and anxiety. We made up the seed scale of the items which selected by partial correlation, and set the scoring up by multiple regression method. The Multi-dimentional affect scale is reliable and working similarly as the scales used before, but less correlated to each other. We conclude that the affect scale achieved the diagnosis for distinguish between depression and anxiety. Our suggestions for the further study are to redeem the cultural differences, modify by the elaborate methods, and validate by the actual clinical data.

• Environmental and Resource Economics Review
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• v.7 no.1
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• pp.61-84
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• 1997

본 연구는 기존의 특성가격기법(特性價格技法)(hedonic price technique)에 공간(空間)개념을 도입한 계량경제모형을 이용하여 분석하였다. 이 공간시차모형은 기존의 모형과 달리 특성변수의 변화에 따른 직(直) 간접효과(間接效果)를 동시에 포착할 수 있는 장점을 가지고 있다. 또한 공간시차모형의 회귀진단 및 가설검정 결과는 공간시차모형이 적합한 것으로 나타났다. 이 경우 공간시차를 고려하지 않은 OLS 회귀분석 결과의 계수들은 편기추정(biased)된 동시에 효율적(efficiency)이지 못하다는 것이다. 회귀분석 결과는 주택에 자본화된 대기오염에 대한 잠재가격(潛在價格)(marginal implicit price)은 주택평균가격의 약 1.5% 정도인 것으로 추정된다.

• Journal of Teachers' Pension
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• v.3
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• pp.279-305
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• 2018

공적연금제도는 장기적 유지 및 운영을 위해 기금의 재정건전성 및 지속가능성 진단을 목적으로 재정계산제도를 운영하고 있다. 정확한 재정계산은 매우 중요하며 이를 위한 선행작업으로 재정계산에 요구되는 기본 가정들을 보다 합리적으로 추정해야 할 필요가 있다. 본 연구는 로지스틱 회귀분석(logistic regression)을 이용하여 사학연금의 재정계산에 적용되는 다양한 기초율들 중 퇴직률을 산출하는 것에 그 목적이 있다. 사학연금은 현재 퇴직률을 교원 및 직원에 대하여 각 성별로 총 4개 집단을 구분하여 각 집단별 가입연령과 재직기간에 따라 산출하고 있다. 그러나 본 연구에서는 학교급 등 퇴직률 산출에 있어 보다 유의한 집단 구분이 있는지를 확인하고 보정의 어려움을 피할 수 있는 하나의 대안으로서 로지스틱 회귀분석을 이용하여 퇴직률을 산출해 보았다. 또한 우수한 모형을 판별하기 위해 통계적으로 우수한 모형보다는 실무적으로 사학연금 재정추계에 적합한 모형을 찾는 것을 목표로 하여 퇴직률을 추정한 값을 제시하였다.