• Journal of the Korean Data and Information Science Society
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• 제10권1호
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• pp.147-154
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• 1999

Using a noninformative prior and an inverted gamma prior, the Bayesian predictive density and the prediction intervals for a future observation or the p - th order statistic of n' future observations from the censord Pareto model have been obtained. In additions, numerical examples are given in order to illustrate the proposed predictive procedure.

• 대한토목학회논문집
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• 제28권3D호
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• pp.287-293
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• 2008

국내의 경우 현재 연결로 접속부의 서비스수준 분석은 2005년에 제정된 우리나라의 도로용량편람(KHCM)에서 제시하고 있는 영향권 내 밀도를 효과척도로 하여 분석하고 있다. 국내의 경우 현재 연결로 접속부의 서비스수준 분석은 2005년에 개정된 KHCM에서 제시하고 있는 영향권 내 밀도를 효과척도로 하여 분석하고 있다. KHCM의 영향권 내 밀도추정모형은 고속도로만을 대상으로 구축된 것으로 도시고속도로와는 특성의 차이가 상당하여 KHCM에서 제시한 모형을 도시고속도로에 적용하는데 검증이 필요한 것으로 판단된다. 따라서 이 연구의 목적은 고속도로와는 다른 특성을 가진 도시고속도로상의 유입연결로 합류영향권내 실시간 교통특성 자료를 바탕으로 연결로 접속부의 서비스수준 평가를 위한 효과척도인 영향권 내 밀도를 보다 정확하게 추정하는 분석모델을 개발하고 2005년 의 KHCM모형과 더불어 2000년에 개정된 미국의 도로용량편람(USHCM)에서 제시하고 있는 모형을 도시고속도로(번영로)에 적용하여 그 타당성을 검토하는데 있다.

• Journal of the Korean Data and Information Science Society
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• 제24권4호
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• pp.857-865
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• 2013

집중 호우로 인한 피해가 증가하면서 다양한 기법들을 이용하여 강우량 예측에 대한 관심이 높아졌다. 최근에는 극단분포를 활용하여 강우량을 예측하려는 시도가 늘고 있다. 본 연구에서는 일반화 극단 분포를 활용하여 실제 서울시의 1973년부터 2010년까지 7월달의 사후예측분포를 생성하고, 수치적인 계산을 위해서 MCMC (Markov chain Monte Carlo)알고리즘을 활용하였다. 이 연구를 통해서 사후예측분포의 점추정값들을 비교하였고 2011년 7월달의 자료와 비교해 봤을 때 집중 호우의 확률이 증가한 것을 알 수 있었다.

• Communications for Statistical Applications and Methods
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• 제8권3호
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• pp.797-803
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• 2001

This paper obtained Bayes prediction density for the spatial linear model with non-informative prior. It showed the results that predictive inferences is completely unaffected by departures from the normality assumption in the direction of the elliptical family and the structure of prediction density is unchanged by more than one additional future observations.

• 대한토목학회논문집
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• 제26권1D호
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• pp.59-66
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• 2006

이 연구는 도시고속도로의 진입연결로 접속부내 선형의 밀도예측모형 구축에 관한 연구로서 실시간 교통특성분석과 선형의 밀도예측모형 구축 및 검증을 통해 밀도예측모형 구축에 있어서 결정계수($R^2$)값이 대체적으로 0.7이상으로 나타나 선형회귀모형구축에 상당히 높은 설명력을 보이는 것으로 나타났고, 선형모형검증에 있어서 상관계수(r)값도 대체적으로 0.8 이상으로 상당히 높은 상관성을 보이는 것으로 나타났다. 따라서 향후 선형의 밀도예측모형을 이용하여 도시고속도로의 진입연결로 접속부내 차량의 밀도추정 및 지체분석에 상당히 유효할 것으로 판단된다.

• Membrane and Water Treatment
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• 제7권2호
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• pp.127-141
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• 2016

Electrodialysis (ED) is known to be a useful membrane process for desalination, concentration, separation, and purification in many fields. In this process, it is desirable to work at high current density in order to achieve fast desalination with the lowest possible effective membrane area. In practice, however, operating currents are restricted by the occurrence of concentration polarization phenomena. Many studies showed the occurrence of a limiting current density (LCD). The limiting current density in the electrodialysis process is an important parameter which determines the electrical resistance and the current utilization. Therefore, its reliable determination is required for designing an efficient electrodialysis plant. The purpose of this study is the development of a predictive model of the limiting current density in an electrodialysis process using response surface methodology (RSM). A two-factor central composite design (CCD) of RSM was used to analyze the effect of operation conditions (the initial salt concentration (C) and the linear flow velocity of solution to be treated (u)) on the limiting current density and to establish a regression model. All experiments were carried out on synthetic brackish water solutions using a laboratory scale electrodialysis cell. The limiting current density for each experiment was determined using the Cowan-Brown method. A suitable regression model for predicting LCD within the ranges of variables used was developed based on experimental results. The proposed mathematical quadratic model was simple. Its quality was evaluated by regression analysis and by the Analysis Of Variance, popularly known as the ANOVA.

• Korean Chemical Engineering Research
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• 제46권6호
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• pp.1043-1051
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• 2008

폴리올레핀 공정의 melt index(MI or MFI)는 제품의 품질을 결정짓는 가장 중요한 제어변수이다. MI는 실시간으로 측정하는 것이 어렵기 때문에 MI를 예측하여 상관관계를 나타내고자 하는 많은 방법들이 제안되었다. 본 연구에서는 시스템 인식기법을 바탕으로 MI 예측을 위한 새로운 1차의 동적 예측모델을 고안하였다. 이 모델의 예측성능은 등급변경이 수반되는 고밀도 폴리에틸렌 공장의 실제 운전데이터에 근거한 모사로 검증하였으며 다른 예측방법들과의 비교로부터 본 연구에 의한 예측모델의 우수성을 확인하였다. 구성된 MI 동적 예측모델을 토대로 하는 모델예측제어방법의 적용을 통하여 각 단위공정별 MI를 계산하고 운전데이터와 비교하였다. 제어운전의 모사를 통하여 등급변경이 이루어지는 운전 동안의 전이시간과 불량제품 발생량이 현저한 감소를 보임을 확인하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1998년도 제13차 학술회의논문집
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• pp.468-473
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• 1998

A mathematical model was developed for a continuous re-actor in which free radical polymerization of methyl methacrylate (MMA) occurred. Elementary reactions considered in this study were initiation, propagation, termination, and chain transfers to monomer and solvent. The reactor model took into account the density change of the reactor contents and the gel effect. A control system was designed for a continuous reactor using extended Kalman filter (EKF) based non-linear model predictive controller (NLMPC) to control the conversion and the weight average molecular weight of the polymer product. Control input variables were the jacket inlet temperature and the feed flow rate. For the purpose of validation of the control strategy, on-line digital control experiments were conducted with densitometer and viscometer for the measurement of the polymer properties. Despite the com-plex and nonlinear features of the polymerization reaction system, the EKF based NLMPC performed quite satisfactorily for the property control of the continuous polymerization reactor.

• Smart Structures and Systems
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• 제18권3호
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• pp.449-470
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• 2016

Energy harvesting is an emerging technique that extracts energy from surrounding environments to power low-power devices. For example, it can potentially provide sustainable energy for wireless sensing networks (WSNs) or structural control systems in civil engineering applications. This paper presents a comprehensive study on harvesting energy from earthquake-induced structural vibrations, which is typically of low frequency, to power WSNs. A macroscale pendulum-type electromagnetic harvester (MPEH) is proposed, analyzed and experimentally validated. The presented predictive model describes output power dependence with mass, efficiency and the power spectral density of base acceleration, providing a simple tool to estimate harvested energy. A series of shaking table tests in which a single-storey steel frame model equipped with a MPEH has been carried out under earthquake excitations. Three types of energy harvesting circuits, namely, a resistor circuit, a standard energy harvesting circuit (SEHC) and a voltage-mode controlled buck-boost converter were used for comparative study. In ideal cases, i.e., resistor circuit cases, the maximum electric energy of 8.72 J was harvested with the efficiency of 35.3%. In practical cases, the maximum electric energy of 4.67 J was extracted via the buck-boost converter under the same conditions. The predictive model on output power and harvested energy has been validated by the test data.

• Asian Pacific Journal of Cancer Prevention
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• 제15권2호
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• pp.663-669
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• 2014

Background: With recent progress in health science administration, a huge amount of data has been collected from thousands of subjects. Statistical and computational techniques are very necessary to understand such data and to make valid scientific conclusions. The purpose of this paper was to develop a statistical probability model and to predict future survival times for male breast cancer patients who were diagnosed in the USA during 1973-2009. Materials and Methods: A random sample of 500 male patients was selected from the Surveillance Epidemiology and End Results (SEER) database. The survival times for the male patients were used to derive the statistical probability model. To measure the goodness of fit tests, the model building criterions: Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC) were employed. A novel Bayesian method was used to derive the posterior density function for the parameters and the predictive inference for future survival times from the exponentiated Weibull model, assuming that the observed breast cancer survival data follow such type of model. The Markov chain Monte Carlo method was used to determine the inference for the parameters. Results: The summary results of certain demographic and socio-economic variables are reported. It was found that the exponentiated Weibull model fits the male survival data. Statistical inferences of the posterior parameters are presented. Mean predictive survival times, 95% predictive intervals, predictive skewness and kurtosis were obtained. Conclusions: The findings will hopefully be useful in treatment planning, healthcare resource allocation, and may motivate future research on breast cancer related survival issues.