• 한국환경과학회지
• /
• 제22권8호
• /
• pp.965-977
• /
• 2013

Reliable long-term streamflow forecasting is invaluable for water resource planning and management which allocates water supply according to the demand of water users. It is necessary to get probabilistic forecasts to establish risk-based reservoir operation policies. Probabilistic forecasts may be useful for the users who assess and manage risks according to decision-making responding forecasting results. Probabilistic forecasting of seasonal inflow to Andong dam is performed and assessed using selected predictors from sea surface temperature and 500 hPa geopotential height data. Categorical probability forecast by Piechota's method and logistic regression analysis, and probability forecast by conditional probability density function are used to forecast seasonal inflow. Kernel density function is used in categorical probability forecast by Piechota's method and probability forecast by conditional probability density function. The results of categorical probability forecasts are assessed by Brier skill score. The assessment reveals that the categorical probability forecasts are better than the reference forecasts. The results of forecasts using conditional probability density function are assessed by qualitative approach and transformed categorical probability forecasts. The assessment of the forecasts which are transformed to categorical probability forecasts shows that the results of the forecasts by conditional probability density function are much better than those of the forecasts by Piechota's method and logistic regression analysis except for winter season data.

• Structural Engineering and Mechanics
• /
• 제45권2호
• /
• pp.201-209
• /
• 2013

A new dynamic reliability analysis of structure under repeated random loads is proposed in this paper. The proposed method is developed based on the idea that the probability density of several times random loads can be derived from the probability density of single-time random load. The reliability prediction models of structure based on time responses under several times random loads with and without strength degradation are obtained by using the stress-strength interference theory and probability density evolution method. The resulting differential equations in the prediction models can be solved by using the forward finite difference method. Then, the probability density functions of strength redundancy of the structures can be obtained. Finally, the structural dynamic reliability can be calculated using integral method. The efficiency of the proposed method is demonstrated numerically through a speed reducer. The results have shown that the proposed method is practicable, feasible and gives reasonably accurate prediction.

• 한국해안해양공학회지
• /
• 제4권4호
• /
• pp.250-260
• /
• 1992

본 논문에서는 비교적 장기간의 조위 관측자료가 축적되어 있는 인천, 제주, 여수, 부산, 묵호에 대하여 극치확률법과 결합확률법을 이용하여 극치해면을 산정하였다. 극치확률법의 분포는 Gumbel, Weibull 및 일반화 극치(GEV)분포에 대하여 최소자승법, 모멘트법, 확률가중적률(PWM)법으로 parameter를 추정하여 극치해면을 산출하였다. 그 결과 Gumbel 분포와 최소자승법이 각각 다른 분포와 다른 parameter 추정법에 비해 크게 추정되는 경향이 있다. 그리고 결합확률법이 극치확률법 보다 대략 5-l0cm 정도 크게 나타났다. 이는 극치확률법이 tide와 surge가 동시에 발생한 조위를 사용한 반면 결합확률법은 동시 발생하지 않은 극치 tide와 극치 surge도 포함하여 극치해면을 산출하기 때문에 값이 조금 크게 추정된다.

• 로봇학회논문지
• /
• 제4권1호
• /
• pp.56-61
• /
• 2009

A probability-based data generation is a typical context-generation method that is a not only simple and strong data generation method but also easy to update generation conditions. However, the probability-based context-generation method has been found its natural-born ambiguousness and confliction problems in generated context data. In order to compensate for the disadvantages of the probabilistic random data generation method, a situation propagation network is proposed in this paper. The situation propagating network is designed to update parameters of probability functions are included in probability-based data generation model. The proposed probability-based context-generation model generates two kinds of contexts: one is related to independent contexts, and the other is related to conditional contexts. The results of the proposed model are compared with the results of the probabilitybased model with respect to performance, reduction of ambiguity, and confliction.

• 한국전산구조공학회논문집
• /
• 제27권6호
• /
• pp.605-613
• /
• 2014

신뢰성 해석을 수행할 때 정보부족으로 인해 발생하는 인식론적 불확실성(epistemic uncertainty)은 고유의 변동성에 의해 존재하는 내재적 불확실성(aleatory uncertainty)보다 더 중요하게 다뤄야 한다. 그러나 그동안 개발된 확률이론은 주로 내재적 불확실성을 모델링하는데 이용된 반면, 인식론적 불확실성의 모델링에 대해서는 아직 확실한 접근법이 없었다. 최근 이를 위해 probability theory를 포함한 여러 접근법들이 제시되고 있지만 이들은 서로 다른 통계적 이론들을 바탕으로 도출되었기 때문에, 각 방법들의 결과들을 이해하는데 어려움이 있었다. 본 연구에서는 고장 확률을 계산하는 문제를 가지고 이러한 방법들이 인식론적 불확실성을 어떻게 다루는지를 비교, 분석하였다. 이를 위해 probability method, combined distribution method, interval analysis method 그리고 evidence theory를 대상으로 신뢰도 분석문제에 대해 각 방법들의 특징들을 비교하였으며, 그 결과는 다음과 같다. 입력변수의 확률분포 형태를 알 수 있다면 probability method가 가장 우수하나, 이를 전혀 모르면 interval method를 사용해야 한다. 그러나 계산비용 면에서는 두 방법이 유사하므로 결국 입력변수의 확률특성 정보가 얼마나 있느냐에 따라 방법을 선택한다. Combined distribution method는 failure probability의 평균만 제공하므로 사용하지 않는 것이 좋다. 다만 이 방법은 계산비용이 매우 적게 드는 장점이 있다. Evidence theory는 probability와 interval 방법의 중간에 해당하며, 구간별 probability assignment를 세분화 할수록 probability결과에 접근한다. 이 방법은 계산비용이 가장 높은 것이 문제이다.

• 한국조사연구학회:학술대회논문집
• /
• 한국조사연구학회 2007년도 추계학술대회 발표논문집
• /
• pp.213-230
• /
• 2007

Although the response model has been frequently applied to nonresponse weighting adjustment or imputation, the estimation under callbacks has been relatively underdeveloped in the response model. The estimation method using the response probability is developed under callbacks. A replication method for the estimation of the variance of the proposed estimation is also developed. Since the true response probability is usually unknown, we study the estimation of the response probability. Finally, we propose an estimator under callbacks using the ratio imputation as well as the response probability. The simulation study illustrates our techniques.

• 한국컴퓨터정보학회논문지
• /
• 제23권6호
• /
• pp.1-7
• /
• 2018

A multi-label classification is to find multiple labels associated with the input pattern. Multi-label classification can be achieved by extending conventional single-label classification. Common extension techniques are known as Binary relevance, Label powerset, and Classifier chains. However, most of the extended multi-label naive bayes classifier has not been able to accurately estimate posterior probabilities because it does not reflect the label dependency. And the remaining extended multi-label naive bayes classifier has a problem that it is unstable to estimate posterior probability according to the label selection order. To estimate posterior probability well, we propose a new posterior probability estimation method that reflects the probability between all labels and labels efficiently. The proposed method reflects the correlation between labels. And we have confirmed through experiments that the extended multi-label naive bayes classifier using the proposed method has higher accuracy then the existing multi-label naive bayes classifiers.

• Communications for Statistical Applications and Methods
• /
• 제29권2호
• /
• pp.263-275
• /
• 2022

Use of appropriate technique for non-response occurring in sample survey improves the accuracy of the estimation. Many studies have been conducted for handling non-ignorable non-response and commonly the response probability is estimated using the propensity score method. Recently, post-stratification method to obtain the response probability proposed by Chung and Shin (2017) reduces the effect of bias and gives a good performance in terms of the MSE. In this study, we propose a new response probability estimation method by combining the propensity score adjustment method using the logistic regression model with post-stratification method used in Chung and Shin (2017). The superiority of the proposed method is confirmed through simulation.

• Computers and Concrete
• /
• 제15권1호
• /
• pp.37-54
• /
• 2015

In general, cracks significantly deteriorate the in-situ performance of concrete members and structures, especially in urban metro tunnels that have been embedded in saturated soft soils. The microcapsule self-healing method is a newly developed healing method for repairing cracked concrete. To investigate the optimal microcapsule parameters that will have the best healing effect in concrete, a 3D analytical probability healing model is proposed; it is based on the microcapsule self-healing method's healing mechanism, and its purpose is to predict the healing efficiency and healing probability of given cracks. The proposed model comprehensively considers the radius and the volume fraction of microcapsules, the expected healing efficiency, the parameters of cracks, the broken ratio and the healing probability. Furthermore, a simplified probability healing model is proposed to facilitate the calculation. Then, a Monte Carlo test is conducted to verify the proposed 3D analytical probability healing model. Finally, the influences of microcapsules' parameters on the healing efficiency and the healing probability of the microcapsule self-healing method are examined in light of the proposed probability model.

• Communications for Statistical Applications and Methods
• /
• 제3권1호
• /
• pp.189-193
• /
• 1996

In 1992. Boullion obtained the method of the calculus of the moments of discrete probability distributions using differential operator, and he published the method of calculus of the moments. The purpose of this paper is to introduce an idea that this method can be applied to calculate the moments of continuous probability distributions.