• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.220-224
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• 2005

칼만필터 알고리즘을 분포형 유출모형에 적용하였다. 관측 유량과 상태변수인 유역내 저류량을 갱신하고자 Q-S curve를 도입하였고, 갱신된 저류량과 모형에 의해 모의된 저류량의 비율을 유역 내 각 지점의 수위에 적용하므로써 분포화 된 상태변수를 효율적으로 갱신하였다. 갱신된 상태변수와 상태변수 오차의 시간갱신은 몬테 카를로 시뮬레이션을 이용하여 모의하였다.

• Journal of Mechanical Science and Technology
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• v.20 no.12
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• pp.2189-2196
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• 2006

The present work is concerned with the development of a procedure for adaptive computations of shear localization problems. The maximum jump of equivalent strain rates across element boundaries is proposed as a simple error indicator based on interpolation errors, and successfully implemented in the adaptive mesh refinement scheme. The time step is controlled by using a parameter related to the Lipschitz constant, and state variables in target elements for refinements are transferred by $L_2$-projection. Consistent tangent moduli with a proper updating scheme for state variables are used to improve the numerical stability in the formation of shear bands. It is observed that the present adaptive mesh refinement procedure shows an excellent performance in the simulation of shear localization problems.

• Journal of Korean Society on Water Environment
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• v.36 no.5
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• pp.353-363
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• 2020

Hydrologic models can be classified into two types: those for understanding physical processes and those for predicting hydrologic quantities. This study deals with how to use the model to predict today's stream flow based on the system's knowledge of yesterday's state and the model parameters. In this regard, for the model to generate accurate predictions, the uncertainty of the parameters and appropriate estimates of the state variables are required. In this study, a relatively simple hydrologic partitioning model is proposed that can explicitly implement the hydrologic partitioning process, and the posterior distribution of the parameters of the proposed model is estimated using the Markov chain Monte Carlo approach. Further, the application method of the ensemble Kalman filter is proposed for updating the normalized soil moisture, which is the state variable of the model, by linking the information on the posterior distribution of the parameters and by assimilating the observed steam flow data. The stochastically and recursively estimated stream flows using the data assimilation technique revealed better representation of the observed data than the stream flows predicted using the deterministic model. Therefore, the ensemble Kalman filter in conjunction with the Markov chain Monte Carlo approach could be a reliable and effective method for forecasting daily stream flow, and it could also be a suitable method for routinely updating and monitoring the watershed-averaged soil moisture.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1759-1768
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• 2018

The energy density, power density and ohm resistance of battery change significantly as results of battery aging, which lead to decrease in the accuracy of the equivalent model. A parameter identification method of the equivale6nt circuit model with 3 R-C branches based on the test database of battery life cycle is proposed in this paper. This database is built on the basis of experiments such as updating of available capacity, charging and discharging tests at different rates and relaxation characteristics tests. It can realize regular update and calibration of key parameters like SOH, so as to ensure the reliability of parameters identified. Taking SOH, SOC and T as independent variables, lookup table method is adopted to set initial value for the parameter matrix. Meanwhile, in order to ensure the validity of the model, the least square method based on variable forgetting factor is adopted for optimizing to complete the identification of equivalent model parameters. By comparing the simulation data with measured data for charging and discharging experiments of Li-ion battery, the effectiveness of the full life cycle database and the model are verified.

• Structural Engineering and Mechanics
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• v.20 no.6
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• pp.609-630
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• 2005

A reliability analysis method is proposed in this paper through a combination of the advantages of the response surface method (RSM), finite element method (FEM), first order reliability method (FORM) and the importance sampling updating method. The accuracy and efficiency of the method is demonstrated through several numerical examples. Then the method is used to estimate the serviceability reliability of cable-stayed bridges. Effects of geometric nonlinearity, randomness in loading, material, and geometry are considered. The example cable-stayed bridge is the Second Nanjing Bridge with a main span length of 628 m built in China. The results show that the cable sag that is part of the geometric nonlinearities of cable-stayed bridges has a major effect on the reliability of cable-stayed bridge. Finally, the most influential random variables on the reliability of cable-stayed bridges are identified by using a sensitivity analysis.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.10 no.2
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• pp.18-28
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• 2001

Super computers has been utilized to carry out vehicle dynamics in real time. This research propose an implicit integra-tion method for vehicle state variables. Newton chord method is empolyed to solve the equations of motion and con-straints. The equations of motion and constraints are formulated such that the Jacobian matrix for Newton chord method is needed to be computed only once for a dynamic analysis. Numerical experiments showed that the Jacobian matrix generat-ed at the initial time could have been utilized for the Newton chord iterations throughout simulations under various driving conditions. Convergence analysis of Newton chord method with the proposed Jacobian updating method is carried out. The proposed algorithm yielded accurate solutions for a prototype vehicle multibody model in realtime on a 400 MHz PC compatible.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.17 no.4
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• pp.443-449
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• 2004

Methods for mixing variable types of steel fibers have been developed recently to suppress outbreak of crack or to control the width of crack and improve the load resistible capacity at the same time. On the other hand, uncertainty by complex nature of destruction dynamics of steel fiber reinforced concrete(SFRC) is included. In this study, analysis of reliance considering uncertainty of SFRC beam is done. For this, intensity limit state model was proposed. Moreover, characteristic values about almost every kinds of probability variables were collected and presented according to home and foreign references. Process of improving uncertainty from the result of experiments by Bayseian updating method is also proposed on the purpose of offering better statistical characteristic values with more data in the new future. Fatigue fracture probability equation is proposed and needed statistical characteristic values were presented to analyze fatigue reliance

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.761-774
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• 2022

Accurate hydrologic prediction is essential to analyze the effects of drought, flood, and climate change on flow rates, water quality, and ecosystems. Disentangling the uncertainty of the hydrological model is one of the important issues in hydrology and water resources research. Hydrologic data assimilation (DA), a technique that updates the status or parameters of a hydrological model to produce the most likely estimates of the initial conditions of the model, is one of the ways to minimize uncertainty in hydrological simulations and improve predictive accuracy. In this study, the two ensemble-based sequential DA techniques, ensemble Kalman filter, and particle filter are comparatively analyzed for the daily discharge simulation at the Yongdam catchment using airGRdatassim. The results showed that the values of Kling-Gupta efficiency (KGE) were improved from 0.799 in the open loop simulation to 0.826 in the ensemble Kalman filter and to 0.933 in the particle filter. In addition, we analyzed the effects of hyper-parameters related to the data assimilation methods such as precipitation and potential evaporation forcing error parameters and selection of perturbed and updated states. For the case of forcing error conditions, the particle filter was superior to the ensemble in terms of the KGE index. The size of the optimal forcing noise was relatively smaller in the particle filter compared to the ensemble Kalman filter. In addition, with more state variables included in the updating step, performance of data assimilation improved, implicating that adequate selection of updating states can be considered as a hyper-parameter. The simulation experiments in this study implied that DA hyper-parameters needed to be carefully optimized to exploit the potential of DA methods.