• 청정기술
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• 제7권2호
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• pp.119-126
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• 2001

인공신경 회로망과 통계적 방법을 이용하여 오존 형성의 예측에 관한 연구를 수행하였다. 파라미터 평가방법으로는 실시간 파라미터를 평가하기 위하여 ELS 및 RML 방법이 사용되었으며 오존 형성의 모델로는 ARMAX 모델을 사용하였다. 또한 3층 구조를 갖는 인공신경 회로망 방법을 이용하여 오존 형성의 예측 시험을 수행하였으며 본 연구에 사용된 통계적 방법의 성능을 평가하기 위하여 오존 형성의 예측결과를 실제 자료와 비교 분석을 하였다. 실제 자료와의 비교를 통하여 파라미터 평가 방법 및 인공신경 회로망 방법에 근거한 예측방법이 제한된 예측 구간 내에서 만족할 만한 성능을 보임을 확인할 수 있었다.

• 품질경영학회지
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• 제35권4호
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• pp.61-66
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• 2007

The performance of a building material degrades as time goes by and the failure of the material is often defined as the point at which the performance of the material reaches a pre-specified degraded level. Based on a stochastic deterioration model, a performance based service life prediction method for building materials and components is developed. As a stochastic degradation model, a gamma process is considered and lifetime distribution and service life of a material are predicted using the degradation model. A numerical example is provided to illustrate the use of the proposed service life prediction method.

• 한국지진공학회논문집
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• 제24권1호
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• pp.9-17
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• 2020

The stochastic point-source model has been widely used in generating artificial ground motions, which can be used to develop a ground motion prediction equation and to evaluate the seismic risk of structures. This model mainly consists of three different functions representing source, path, and site effects. The path effect is used to emulate decay in ground motion in accordance with distance from the source. In the stochastic point-source model, the path attenuation effect is taken into account by using the geometrical attenuation effect and the inelastic attenuation effect. The aim of this study is to develop accurate equations of ground motion attenuation in the Korean peninsula. In this study, attenuation was estimated and validated by using a stochastic point source model and observed ground motion recordings for the Korean peninsula.

• Journal of Electrical Engineering and Technology
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• 제12권3호
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• pp.1053-1063
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• 2017

Power prediction is critical to improve power efficiency in Smart Grids. Markov chain provides a useful tool for power prediction. With careful investigation of practical power datasets, we find an interesting phenomenon that the stochastic property of practical power datasets does not follow the Markov features. This mismatch affects the prediction accuracy if directly using Markov prediction methods. In this paper, we innovatively propose a spatial transform based data processing to alleviate this inconsistency. Furthermore, we propose an enhanced power prediction method, named by Spatial Mapping Markov-Difference (SMMD), to guarantee the prediction accuracy. In particular, SMMD adopts a second prediction adjustment based on the differential data to reduce the stochastic error. Experimental results validate that the proposed SMMD achieves an improvement in terms of the prediction accuracy with respect to state-of-the-art solutions.

• Journal of Electrical Engineering and Technology
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• 제3권1호
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• pp.14-19
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• 2008

The paper presents an intelligent time series model to predict uncertain electricity market price in the deregulated industry environment. Since the price of electricity in a deregulated market is very volatile, it is difficult to estimate an accurate market price using historically observed data. The parameter of an intelligent time series model is obtained based on the simultaneous perturbation stochastic approximation (SPSA). The SPSA is flexible to use in high dimensional systems. Since prediction models have their modeling error, an error compensator is developed as compensation. The SPSA based intelligent model is applied to predict the electricity market price in the Pennsylvania-New Jersey-Maryland (PJM) electricity market.

• 센서학회지
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• 제28권2호
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• pp.88-93
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• 2019

This paper is concerned with distributed fusion moving average prediction for continuous-time linear stochastic systems with multiple sensors. A distributed fusion with the weighted sum structure is applied to the optimal local moving average predictors. The distributed fusion prediction algorithm represents the optimal linear fusion by weighting matrices under the minimum mean square criterion. The derivation of equations for error cross-covariances between the local predictors is the key of this paper. Example demonstrates effectiveness of the distributed fusion moving average predictor.

• 한국지진공학회논문집
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• 제5권4호
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• pp.17-26
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• 2001

한반도 남부에서 발생 가능한 강지진동의 최대 지반운동과 주파수에 따른 특성을 추계학적 모사법을 이용하여 간접적으로 추정하였다. 또한 추계학적 모사법에 적용할 진원과 지진파 감쇠에 관한 입력자료를 계산하였다. 응력강하($\Delta$$\sigma$)는 한반도 남부와 미국 동부 및 중국의 연구결과를 종합하여 100-bar로 추정하였다. 감쇠상수는 x는 1996년 9월부터 1997년 12월까지 발생한 지진 중 비교적 기록상태가 양호한 57개의 관측자료를 이용하여 계산하였으며 진원거리(R)에 대하여 0.00112+0.000224 R로 추정되었다. 이와 같은 응력강하($\Delta$$\sigma$)와 감쇠상수 x등의 입력자료를 추계학적 모사법에 적용한 결과를 바탕으로 진원거리에 따른 강진동 감쇠공식을 유도하였다.한 결과를 바탕으로 진원거리에 따른 강진동 감쇠공식을 유도하였다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.437-437
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• 2022

The prediction of dam inflow rate is crucial for the management of the largest multi-purpose dam in South Korea, the Soyang Dam. The main issue associated with the management of water resources is the stochastic nature of the reservoir inflow leading to an increase in uncertainty associated with the inflow prediction. The Autoregressive (AR) model is commonly used to provide the simulation and forecast of hydrometeorological data. However, because its estimation is based solely on the time-series data, it has the disadvantage of being unable to account for external variables such as climate information. This study proposes the use of the Autoregressive Exogenous Stochastic Volatility (ARXSV) model within a Bayesian modeling framework for increased predictability of the monthly dam inflow by addressing the exogenous and stochastic factors. This study analyzes 45 years of hydrological input data of the Soyang Dam from the year 1974 to 2019. The result of this study will be beneficial to strengthen the potential use of data-driven models for accurate inflow predictions and better reservoir management.

• 한국대기환경학회지
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• 제7권3호
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• pp.156-168
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• 1991

The purpose of this paper is to develop a stochastic model for daily sulphur dioxide $(SO_2)$ concentrations prediction in urban area (Seoul). For this, the influence of the meteorological parameters on the $SO_2$ concentrations is investigated by a statistical analysis of the 24-hr averaged $SO_2$ levels of Seoul area during 1989 $\sim$ 1990. The annual fluctuations of the regression trend, periodicity and dependence of the daily concentration are also analyzed. Based on these, a nonlinear regression transfer function model for the prediction of daily $SO_2$ concentrations is derived. A statistical procedure for using the model to predict the concentration level is also proposed.

• Structural Engineering and Mechanics
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• 제55권3호
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• pp.525-534
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• 2015

A new structural dynamic fuzzy reliability analysis under stochastic loads which are applied several times is proposed in this paper. The fuzzy reliability prediction models based on time responses with and without strength degeneration are established using the stress-strength interference theory. The random loads are applied several times and fuzzy structural strength is analyzed. The efficiency of the proposed method is demonstrated numerically through an example. The results have shown that the proposed method is practicable, feasible and gives a reasonably accurate prediction. The analysis shows that the probabilistic reliability is a special case of fuzzy reliability and fuzzy reliability of structural strength without degeneration is also a special case of fuzzy reliability with structural strength degeneration.