• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.282-286
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• 2011

토양침식 및 유사유출로 인한 피해를 예방하고 대응방안을 수립하기 위해서는 침식의 발생원인과 규모에 대한 정량적 평가가 필요하다. 이를 위해서는 지속적인 계측에 의한 토양침식량 산정이 가장 바람직하지만 실질적으로 유역규모의 지속적인 모니터링은 불가능하므로 유역의 수문/지형/지질학적 특성을 고려한 수치모형을 사용하여 토양침식량 및 유사유출량을 산정하는 것이 일반적이다. 이러한 수치모형을 이용한 수문모의의 경우 모형의 구조, 모델링에 사용되는 자료, 매개변수 등에 포함된 다양한 불확실성 요인에 의해 계산결과에 상당한 불확실성을 포함하고 있다. 본 연구에서는 매개변수의 불확실성 전이에 따른 수문모의결과의 불확실성의 정량적인 평가를 위해 서로 다른 두가지 수문량(유출량, 유사유출량)을 제공하는 강우-유사-유출 모형을 선택하고, 다중최적화기법인 MOSCEM-UA을 이용하여 매개변수 상호작용에 의한 Pareto 최적해 군 및 균형최적해를 산정하고, 이에 따른 수문예측결과의 불확실성을 평가하였다.

• Journal of Welding and Joining
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• v.30 no.1
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• pp.78-84
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• 2012

Due to the inherent dimensional uncertainty, tolerances accumulate in the final assembly. Tolerance accumulation has serious effect on the performance of ECV assembly. This paper proposes a method of tolerance accumulation analysis using Monte Carlo simulation, which includes welding process in assemble process. This method can predict the final tolerance distributions of the completed assembly with the prescribed statistical tolerance distribution of each part to be assembled. With the inclusion of welding, another dimensional uncertainties due to partial melting is to be accounted as well. Partial melting of projection height was included in the tolerance propagation analysis. Verification of the proposed method was performed by making use of Monte Carlo simulation. Monte Carlo simulation results showed promising results in that we can predict the final tolerance distributions in advance before actual assembly process of precision machinery.

• Earthquakes and Structures
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• v.3 no.1
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• pp.17-35
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• 2012

Simplified equations for fundamental period of vibration of skeletal structures provided by most seismic design provisions suffer from the absence of any associated confidence levels and of any reference to their empirical basis. Therefore, such equations may typically give a sector of designers the false impression of yielding a fairly accurate value of the period of vibration. This paper, although not addressing simplified codes equations, introduces a set of mathematical equations utilizing the theory of error propagation and First-Order Second-Moment (FOSM) techniques to determine bounds on the relative error in theoretically calculated fundamental period of vibration of skeletal structures. In a complementary step, and for verification purposes, Monte Carlo simulation technique has been also applied. The latter, despite involving larger computational effort, is expected to provide more precise estimates than FOSM methods. Studies of parametric uncertainties applied to reinforced concrete frame bents - potentially idealized as SDOF systems - are conducted demonstrating the effect of randomness and uncertainty of various relevant properties, shaping both mass and stiffness, on the variance (i.e. relative error) in the estimated period of vibration. Correlation between mass and stiffness parameters - regarded as random variables - is also thoroughly discussed. According to achieved results, a relative error in the period of vibration in the order of 19% for new designs/constructions and of about 25% for existing structures for assessment purposes - and even climbing up to about 36% in some special applications and/or circumstances - is acknowledged when adopting estimates gathered from the literature for relative errors in the relevant random input variables.

• Geotechnical Engineering
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• v.13 no.2
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• pp.77-90
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• 1997

A resonable and economical method which can predict permeability of rock mass in underground is needed to overcome the uncertainty of groundwater behavior. For this par pose, one prediction method of permeability has been studied. The artificial neural networks model using error back propagation algorithm, . one of the teaching techniques, is utilized for this purpose. In order to verify the applicability of this model, in-situ permeability results are simulated. The simulation results show the potentiality of utilizing the neural networks for effective permeability prediction of rock mass.

• Journal of the Korean Chemical Society
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• v.47 no.1
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• pp.31-37
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• 2003

The expanded uncertainties calculated by the application of GUM -approximation and Monte-Carlo simulation were compared about the model equation of one-point calibration which is widely used for the measurements and chemical analysis. For the comparisons, we assumed a set of artificial data at the various level of concentration and dispersion of t or normal distribution. Mistakes of more then 50 % was revealed at the values calculated by GUM-approximation in comparison with those of Monte-Carlo simulation because of the excess dispersion from t-distribution and non-linearity by division in the equation. In contrary, the mistake of calculation due to non-linearity of the equation was not observed in the level of detection limits with the equation of one-point calibration, because of the relatively large values of uncertainty in response.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.39-39
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• 2012

Data assimilation techniques have received growing attention due to their capability to improve prediction in various areas. Despite of their potentials, applicable software frameworks to probabilistic approaches and data assimilation are still limited because the most of hydrologic modelling software are based on a deterministic approach. In this study, we developed a hydrological modelling framework for sequential data assimilation, namely MPI-OHyMoS. MPI-OHyMoS allows user to develop his/her own element models and to easily build a total simulation system model for hydrological simulations. Unlike process-based modelling framework, this software framework benefits from its object-oriented feature to flexibly represent hydrological processes without any change of the main library. In this software framework, sequential data assimilation based on the particle filters is available for any hydrologic models considering various sources of uncertainty originated from input forcing, parameters and observations. The particle filters are a Bayesian learning process in which the propagation of all uncertainties is carried out by a suitable selection of randomly generated particles without any assumptions about the nature of the distributions. In MPI-OHyMoS, ensemble simulations are parallelized, which can take advantage of high performance computing (HPC) system. We applied this software framework for several catchments in Japan using a distributed hydrologic model. Uncertainty of model parameters and radar rainfall estimates is assessed simultaneously in sequential data assimilation.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.375-378
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• 1997

This research deals with a problem of reconstructing 3D surface structures from their 2D projections, which is an important research topic in computer vision. In order to provide robust reconstruction algorithm, that is reliable even in the presence of uncertainty in the range images, we first present a detailed model and analysis of several error sources and their effects on measuring three-dimensional surface properties using the space encoded range imaging technique. Our approach has two key elements. The first is the error modeling for the space encoding range sensor and its propagation to the 3D surface reconstruction problem. The second key element in our approach is the algorithm for removing outliers in the range image. Such analyses, to our knowledge, have never attempted before. Experimental results show that our approach is significantly reliable.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.3
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• pp.201-207
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• 2006

A robust Kalman filtering method for uncertain stochastic systems is suggested by adopting a perturbation estimation process which is to reconstruct total uncertainty with respect to the nominal state transition equation. The predictor and corrector of discrete Kalman filter are reformulated with the perturbation estimator. Successively, the state and perturbation estimation error dynamics and the corresponding error covariance propagation equations are derived as well. Finally we have the recursive algorithm of Combined Kalman Filter-Perturbation Estimator (CKF). The proposed combined Kalman filter-perturbation estimator has the property of integrating innovations and the adaptation capability to system uncertainties. A numerical example is shown to demonstrate the effectiveness of the proposed scheme.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.4
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• pp.312-317
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• 1996

This paper proposes a neural network controller with the computed torque method. The neural network is used not to learn the inverse dynamic model but to compensate the uncertainties of robotic manipulators. When training the neural network, we use the signals present in the proposed controller, which is simpler than that proposed by Ishiguro et al., whose teaching signals of the neural network come from the robot model.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.12
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• pp.3124-3134
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• 1993

Diagnosis is composed of two different but interrelated steps ; retrieving the sensory responses f the system and reasoning the state of the system through the given sensor data. This paper explains the probabilistic nature of reasoning involved in the diagnosis when the uncertainties are inevitably included in experts' diagnostic decision making. Uncertainties in decision making are experts' personal experiences, preferences, and system's coherent characteristics. In order to ensure a consistent decision based on the same responses from the system, expert system technology is adopted with the Bayesian reasoning scheme.