• Proceedings of the KSME Conference
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• 2001.06a
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• pp.674-679
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• 2001

For a large deformation nonlinear dynamic analysis of two-dimensional frictional contact, the linear complementarity formulation combined with a linearization is used. The solution procedure is based on the total Lagrangian formulation with a predictor and corrector scheme. For contact searching, a hierarchical scheme with a circular territory is used. A second-order approximation of displacements is used to detect impact time and position. The formulation is illustrated by means of numerical examples.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.2
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• pp.144-150
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• 2002

In this paper a design of neuro-fuzzy-based predictive controller for nonlinear systems with time-delay is proposed. The proposed control system contains two neuro-fuzzy systems called ANFIS(Adaptive Neuro-Fuzzy Inference System). One is run as a series-parallel mode and the other is run as a parallel mode. An ANFIS running in series-parallel mode emulates the response of the nonlinear system with time-delay. Another ANFIS running in parallel mode generates the predicted output of the nonlinear system to compensate for the time-delays. Therefore, the proposed control system can be thought of as an extension of Smith-predictor scheme to the nonlinear systems with time-delay. A detailed design Procedure is presented and finally computer simulations are executed for the effectiveness of the proposed control scheme.

• Journal of KSNVE
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• v.5 no.2
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• pp.207-213
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• 1995

A new predictor-corrector explicit time integration algorithm is presented for solving structural dynamics problems. The basis of the algorithm is the implicit generalized-.alpha. method recently developed by the authors. Like its implicit parent, the explicit generalized-$\alpha$ method is a one- parameter family of algorithms in which the parameter defines the high-frequency numerical dissipation. The algorithm can be utilized effectively for linear and nonlinear structural dynamics calculations is which numerical dissipation is needed to reduce spurious oscillations inherent in non-dissipative time integration methods used to solve wave propagation problems.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.134-134
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• 2022

Streamflow forecasting plays a crucial role in water resource control, especially in highly urbanized areas that are very vulnerable to flooding during heavy rainfall event. In addition to providing the accurate prediction, the evaluation of effects and importance of the input predictors can contribute to water manager. Recently, machine learning techniques have applied their advantages for modeling complex and nonlinear hydrological processes. However, the techniques have not considered properly the importance and uncertainty of the predictor variables. To address these concerns, we applied the GA-BART, that integrates a genetic algorithm (GA) with the Bayesian additive regression tree (BART) model for hourly streamflow forecasting and analyzing input predictors. The Jungrang urban basin was selected as a case study and a database was established based on 39 heavy rainfall events during 2003 and 2020 from the rain gauges and monitoring stations. For the goal of this study, we used a combination of inputs that included the areal rainfall of the subbasins at current time step and previous time steps and water level and streamflow of the stations at time step for multistep-ahead streamflow predictions. An analysis of multiple datasets including different input predictors was performed to define the optimal set for streamflow forecasting. In addition, the GA-BART model could reasonably determine the relative importance of the input variables. The assessment might help water resource managers improve the accuracy of forecasts and early flood warnings in the basin.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.477-482
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• 2015

Linear regressions and evolutionary nonlinear regression based compensation techniques for the short-range prediction of wind speed are investigated. Development of an efficient MOS(Model Output Statistics) is necessary to correct systematic errors of the model, but a linear regression based MOS is hard to manage an irregular nature of weather prediction. In order to solve the problem, a nonlinear and symbolic regression method using GP(Genetic Programming) is suggested for a development of MOS for wind speed prediction. The proposed method is compared to various linear regression methods for prediction of wind speed. Also, statistical analysis of distribution for UM elements for each method is executed. experiments are performed for KLAPS(Korea Local Analysis and Prediction System) re-analysis data from 2007 to 2013 year for Jeju Island and Busan area in South Korea.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.16 no.1
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• pp.33-42
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• 2003

This paper deals with the FEM analysis of nonlinear sloshing of incompressible, invicid and irrotational flow in two dimensional rectangular tank. We use laplace equation based on potential theory as governing equation. For large amplitude sloshing motion, kinematic and dynamic free surface conditions derived from Bernoulli equation are applied. This problem is solved by FEM using 9-node elements. For the time integration and accurate velocity calculation, we introduce predictor-corrector time marching scheme and least square method. Also, numerical stability in tracking of free surface is obtained by direct calculation of free surface location to time variation. Numerical results of sloshing induced by harmonic excitations, while comparing with those of linear theory and references, prove the accuracy and stability. After verification of our program, we analyze sloshing response characteristics to the fluid height and the excitation amplitude.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.04a
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• pp.1-8
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• 2004

For the linearized differential algebraic equation of the nonlinear constrained system, exact initial values of the acceleration are needed to solve itself. It may be very troublesome to perform the inverse operation for obtaining the incremental quantities since the mass matrix contains the zero element in the diagonal. This fact makes the mass matrix impossible to be positive definite. To overcome this singularity phenomenon the mass matrix needs to be modified to allow the feasible application of predictor and corrector in the iterative computation. In this paper the proposed numerical algorithm based on the modified mass matrix combines the conventional implicit algorithm, Newton-Raphson method and Newmark method. The numerical example presents reliabilities for the proposed algorithm via comparisons of the 4th order Runge-kutta method. The proposed algorithm seems to be satisfactory even though the acceleration, Lagrange multiplier, and energy show unstable behaviour. Correspondingly, it provides one important clue to another algorithm for the enhancement of the numerical results.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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• pp.31-36
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• 2008

This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the enviromuent. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.

• ETRI Journal
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• v.33 no.1
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• pp.27-31
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• 2011

This paper addresses estimating the frequency of a cisoid in the presence of white Gaussian noise, which has numerous applications in communications, radar, sonar, and instrumentation and measurement. Due to the nonlinear nature of the frequency estimation problem, there is threshold effect, that is, large error estimates or outliers will occur at sufficiently low signal-to-noise ratio (SNR) conditions. Utilizing the ideas of averaging to increase SNR and weighted linear prediction, an optimal frequency estimator with smaller threshold SNR is developed. Computer simulations are included to compare its mean square error performance with that of the maximum likelihood (ML) estimator, improved weighted phase averager, generalized weighted linear predictor, and single weighted sample correlator as well as Cramer-Rao lower bound. In particular, with smaller computational requirement, the proposed estimator can achieve the same threshold and estimation performance of the ML method.

• Journal of computational fluids engineering
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• v.16 no.1
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• pp.73-82
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• 2011

In this paper, the vortex-induced vibration of circular cylinders is studied using the immersed boundary method on the Cartesian mesh. The Reynolds numbers considered is from 100 to 200. Using the configuration of tendemly arranged multiple circular cylinders, the vortex shedding behind of the cylinders and their flow-induced motion are investigated. The staggered MAC grid arrangement, which is the typical grid system for the incompressible flow on the Cartesian meshes, is utilized. Pressure correction method is applied for solving the divergence-free incompressible velocity field. The body motion is described by immersed boundary technique that has advantages for moving object on the fixed computational domain. It is also discussed for the computational noise in hydrodynamic forces when body motion is represented by the immersed boundary method. The Predictor/Corrector method is used for simulating the nonlinear response of the elastically mounted cylinder excited by vortex-shedding.