• Smart Structures and Systems
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• 제31권1호
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• pp.29-43
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• 2023

This study investigates the possibility of damage detection of a real bridge by means of a modal parameter-based finite element (FE) model update. Field moving vehicle experiments were conducted on an actual steel plate girder bridge. In the damage experiment, cracks were applied to the bridge to simulate damage states. A fast Bayesian FFT method was employed to identify and quantify uncertainties of the modal parameters then these modal parameters were used in the Bayesian model update. Material properties and boundary conditions are taken as uncertainties and updated in the model update process. Observations showed that although some differences existed in the results obtained from different model classes, the discrepancy between modal parameters of the FE model and those experimentally obtained was reduced after the model update process, and the updated parameters in the numerical model were indeed affected by the damage. The importance of boundary conditions in the model updating process is also observed. The capability of the MCMC model update method for application to the actual bridge structure is assessed, and the limitation of FE model update in damage detection of bridges using only modal parameters is observed.

• Computers and Concrete
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• 제13권5호
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• pp.659-677
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• 2014

A Bayesian response surface updating procedure is applied in order to update the parameters of the covariance function of a random field for concrete properties based on a limited number of available measurements. Formulas as well as a numerical algorithm are presented in order to update the parameters of response surfaces using Markov Chain Monte Carlo simulations. The parameters of the covariance function are often based on some kind of expert judgment due the lack of sufficient measurement data. However, a Bayesian updating technique enables to estimate the parameters of the covariance function more rigorously and with less ambiguity. Prior information can be incorporated in the form of vague or informative priors. The proposed estimation procedure is evaluated through numerical simulations and compared to the commonly used least square method.

• 한국컴퓨터정보학회논문지
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• 제14권11호
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• pp.159-167
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• 2009

경제가 성장함에 따라 자동차는 현대인의 생활에 많은 영향을 끼치고 있다. 차량항법장치는 운전자에게 목적지까지의 경로를 안내해 주기 때문에 많은 편의를 제공하고 있다. 그러나 개인의 차량 소유가 대중화됨에 따라 교통혼잡이 발생하지만 차량항법장치는 환경을 고려하지 않는 일방적인 경로를 계획한다. 기존의 차량항법장치는 시간대와 상관없이 출발지와 목적지만 같으면 항상 동일한 경로와 소요시간을 제공하는 한계를 가지고 있다. 본 논문에서는 누적된 과거의 주행정보를 퍼지이론과 베이지안 갱신에 적용하여 교통상황을 예측하고 경로 계획에 반영하는 방법을 제안한다. 퍼지 이론을 통해 과거 주행정보의 출발 시간대와 속도율로 분류하고 베이지안 갱신을 사용하여 각 시간대에서 벌어질 교통상황을 확률로 계산한다. 전자지도에서 출발지와 목적지를 포함한 타원관심영역을 설정하고 Dijkstra와 $A^*$ 알고리즘을 융합하여 교통상황을 고려한 최적의 경로를 계획한다. 제안한 알고리즘의 성능과 정확성은 계획된 경로를 실제 주행함으로써 예측된 소요시간과 실제 주행시간을 비교하여 검증하였다.

• Smart Structures and Systems
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• 제21권5호
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• pp.601-609
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• 2018

The estimated probabilistic model of wind data based on the conventional approach may have high discrepancy compared with the true distribution because of the uncertainty caused by the instrument error and limited monitoring data. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method has been developed in the companion paper and is conducted to formulate the joint probability density function (PDF) of wind speed and direction using the wind monitoring data of the investigated bridge. The established bivariate model of wind speed and direction only represents the features of available wind monitoring data. To characterize the stochastic properties of the wind parameters with the subsequent wind monitoring data, in this study, Bayesian inference approach considering the uncertainty is proposed to update the wind parameters in the bivariate probabilistic model. The slice sampling algorithm of Markov chain Monte Carlo (MCMC) method is applied to establish the multi-dimensional and complex posterior distribution which is analytically intractable. The numerical simulation examples for univariate and bivariate models are carried out to verify the effectiveness of the proposed method. In addition, the proposed Bayesian inference approach is used to update and optimize the parameters in the bivariate model using the wind monitoring data from the investigated bridge. The results indicate that the proposed Bayesian inference approach is feasible and can be employed to predict the bivariate distribution of wind speed and direction with limited monitoring data.

• Journal of the Korean Data and Information Science Society
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• 제18권3호
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• pp.689-696
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• 2007

This paper considers a Bayesian approach to determine an optimal PM policy with random maintenance quality. Thus, we assume that the quality of a PM action is a random variable following a probability distribution. When the failure time is Weibull distribution with uncertain parameters, a Bayesian approach is established to formally express and update the uncertain parameters for determining an optimal PM policy. Finally, the numerical examples are presented for illustrative purpose.

• 항공우주시스템공학회지
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• 제10권1호
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• pp.8-14
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• 2016

The optimal estimation of a bearing only target tracking problem be achieved through the solution of the Fokker-Planck equation and the Bayesian update. Recently, a nonlinear filtering algorithm using a direct quadrature method of moments in which the associated Fokker-Planck equation can be propagated efficiently and accurately was proposed. Although this approach has demonstrated its promising in the field of nonlinear filtering in several examples, the "degeneracy" phenomenon, similar to that which exists in a typical particle filter, occasionally appears because only the weights are updated in the modified Bayesian rule in this algorithm. Therefore, in this paper to enhance the performance, a more stable measurement update process based upon the update equation in the Extended Kalman filters and a more accurate initialization and re-sampling strategy for weight and abscissas are proposed. Simulations are used to show the effectiveness of the proposed filter and the obtained results are promising.

• 항공우주시스템공학회지
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• 제10권1호
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• pp.15-20
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• 2016

The optimal estimation of a general continuous-discrete system can be achieved through the solution of the Fokker-Planck equation and the Bayesian update. Due the high nonlinearity of the equation of motion of the system and the measurement model, it is necessary to linearize the both equation. To avoid linearization, the filter based on Fokker-Planck equation is designed. with the unscented transformation update mechanism, in which the associated Fokker-Planck equation was solved efficiently and accurately via discrete quadrature and the measurement update was done through the unscented transformation update mechanism. This filter based on the Direct Quadrature Moment of Method(DQMOM) and the unscented transformation update is applied to the bearing only target tracking problem. The proposed filter can still provide more accurate estimation of the state than those of the extended Kalman filter especially when measurements are sparse. Simulation results indicate that the advantages of the proposed filter based on the DQMOM and the unscented transformation update make it a promising alternative to the extended Kalman filter.

• 한국신뢰성학회:학술대회논문집
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• 한국신뢰성학회 2005년도 학술발표대회 논문집
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• pp.131-137
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• 2005

In this paper, we propose a Bayesian approach to determine the adaptive preventive maintenance(PM) policy for a general sequential imperfect PM model proposed by Lin, Zuo and Yam(2000) that PM not only reduces the effective age of the system but also changes the hazard rate function. Assuming that the failure times follow Weibull distribution, we adopt a Bayesian approach to update unknown parameters and determine the Bayesian optimal sequential PM policies. Finally, numerical examples of the optimal adaptive PM policy are presented for illustrative purposes.

• 로봇학회논문지
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• 제9권4호
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• pp.206-215
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• 2014

Acoustic signal is crucial for the autonomous navigation of underwater vehicles. For this purpose, this paper presents a method of acoustic source localization. The proposed method is based on the probabilistic estimation of time delay of acoustic signals received by two hydrophones. Using Bayesian update process, the proposed method can provide reliable estimation of direction angle of the acoustic source. The acquired direction information is used to estimate the location of the acoustic source. By accumulating direction information from various vehicle locations, the acoustic source localization is achieved using extended Kalman filter. The proposed method can provide a reliable estimation of the direction and location of the acoustic source, even under for a noisy acoustic signal. Experimental results demonstrate the performance of the proposed acoustic source localization method in a real sea environment.

• 한국지능시스템학회논문지
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• 제15권3호
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• pp.330-336
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• 2005

이동로봇의 주행을 위한 초음파 센서 만에 의한 기존의 베이지안 지도 작성법은 초음파 센서 빔의 퍼짐 특성 등에 의해 굴곡이 많은 환경의 경우 양질의 지도가 형성되지 못한다. 이러한 문제의 개선을 위해 본 논문에서는 적외선 센서를 설치하여 초음파 센서 빔의 각 영역에서의 장애물에 대한 정보를 획득하고, 이 정보를 이용 퍼지 추론시스템에 의하여 초음파 센서에 의한 정보의 신뢰도를 구하여 베이지안 지도 작성법에 의한 결과에 융합시킴으로써 보다 정확한 환경 지도를 작성하는 방법을 제시하였다. 또한, 퍼지 추론 시스템을 최적화하기 위하여 유전 알고리즘을 사용하였다. 그리고 시뮬레이션 및 실제 실험에 의해 제안된 방법이 굴곡이 많은 환경의 경우 기존의 방법 보다 정확한 지도 작성이 가능함을 검증하였다.