• Journal of Korean Institute of Industrial Engineers
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• v.23 no.1
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• pp.39-54
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• 1997

This paper presents four process models for machining processes : 1) an economical mathematical model of machining process, 2) a prediction model for surface roughness, 3) a decision model for fuzzy cutting conditions, and 4) a judgment model of machinability with automatic selection of cutting conditions. Each model was developed the economic machining, and these models were applied to theories widely studied in industrial engineering which are nonlinear programming, computer simulation, fuzzy theory, and neural networks. The results of this paper emphasize the human oriented domain of a nonlinear programming problem. From a viewpoint of the decision maker, fuzzy nonlinear programming modeling seems to be apparently more flexible, more acceptable, and more reliable for uncertain, ill-defined, and vague problem situations.

• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.2
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• pp.149-154
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• 2016

The use of nonlinear ultrasonics wave has been accepted as a promising tool for monitoring material states related to microstructural changes, as it has improved sensitivity compared to conventional non-destructive testing approaches. In this paper, third harmonic generation of shear horizontal guided waves propagating in an isotropic plate is investigated using the perturbation method and modal analysis approach. An experimental procedure is proposed to detect the third harmonics of shear horizontal guided waves by electromagnetic transducers. The strongly nonlinear response of shear horizontal guided waves is measured. The accumulative growth of relative acoustic nonlinear response with an increase of propagation distance is detected in this investigation. The experimental results agree with the theoretical prediction, and thus providing another indication of the feasibility of using higher harmonic generation of electromagnetic shear horizontal guided waves for material characterization.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1999.10a
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• pp.19-26
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• 1999

This paper describes an analytical study on nonlinear hysteretic behavior of hybrid steel beam with reinforced concrete ends. Two types of analytical model, Polygonal Model[PM] and Hybrid Model[HM], were used to represent the nonlinear hysteretic behavior PM used three parameters, HM used an additional parameter to consider the initial stiffness reduction. The parameters calibrated comparing the hysteretic performance obtained from experiments. The purpose of this study is to develop an analytical model which can take into account the initial stiffness reduction of the hybrid members and to represent exactly the hysteretic performance for the hybrid structures with RC and steel. The analytical study showed PM tends to overestimate initial stiffness and strength. However, HM which is capable to consider the initial stiffness reduction gave good prediction on initial stiffness, post-yielding performance, strength, pinching response and so on.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1132-1133
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• 2007

A circuit-domain model of PWM inverter provides accurate simulation results in consideration of detail switching characteristics. Although, a huge amount of computation time is demanded for the simulation results of several ten seconds, which is the required time to analyze system behaviors or control performances of Electric Power Steering(EPS) on real drive condition. This paper describes the nonlinear inverter model for fast dynamic simulation of EPS without the PWM concept through analyzing the effect of nonlinear switching characteristics like dead time, forward voltage drop and conduction resistance. Some inverter models including proposed model are compared from two standpoints which are computation time and accuracy. The comparison results show the usefulness of the developed model in order to develop the control algorithm through the fast prediction of system behaviors.

• Proceedings of the Korea Water Resources Association Conference
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• 2001.05a
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• pp.45-51
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• 2001

The daily inflow is apparently one of nonlinear and complicated phenomena. The nonlinear and complexity make it difficult to model the prediction of daily flow, but attractive to try the neural networks approach which contains inherently nonlinear schemes. The study focuses on developing the forecasting models of daily inflows to a large dam site using neural networks. In order to reduce the error caused by high or low outliers, the back propagation algorithm which is one of neural network structures is modified by combining a regression algorithm. The study indicates that continuous forecasting of a reservoir inflow in real time is possible through the use of modified neural network models. The positive effect of the modification using tole regression scheme in BP algorithm is showed in the low and high ends of inflows.

• Food Science and Biotechnology
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• v.17 no.6
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• pp.1352-1356
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• 2008

Bootstrap method, a computer-intensive statistical technique to estimate the distribution of a statistic was applied to deal with uncertainty and variability of the experimental data in stochastic prediction modeling of microbial growth on a chill-stored food. Three different bootstrapping methods for the curve-fitting to the microbial count data were compared in determining the parameters of Baranyi and Roberts growth model: nonlinear regression to static version function with resampling residuals onto all the experimental microbial count data; static version regression onto mean counts at sampling times; dynamic version fitting of differential equations onto the bootstrapped mean counts. All the methods outputted almost same mean values of the parameters with difference in their distribution. Parameter search according to the dynamic form of differential equations resulted in the largest distribution of the model parameters but produced the confidence interval of the predicted microbial count close to those of nonlinear regression of static equation.

• Computers and Concrete
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• v.16 no.4
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• pp.569-585
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• 2015

Various computational tools are available for modeling highly nonlinear structural engineering problems that lack a precise analytical theory or understanding of the phenomena involved. This paper adopts a fairly simple nonparametric adaptive regression algorithm known as multivariate adaptive regression splines (MARS) to model the nonlinear interactions between variables. The MARS method makes no specific assumptions about the underlying functional relationship between the input variables and the response. Details of MARS methodology and its associated procedures are introduced first, followed by a number of examples including three practical structural engineering problems. These examples indicate that accuracy of the MARS prediction approach. Additionally, MARS is able to assess the relative importance of the designed variables. As MARS explicitly defines the intervals for the input variables, the model enables engineers to have an insight and understanding of where significant changes in the data may occur. An example is also presented to demonstrate how the MARS developed model can be used to carry out structural reliability analysis.

• International Journal of Naval Architecture and Ocean Engineering
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• v.3 no.3
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• pp.159-173
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• 2011

The two different numerical approaches for solving the nonlinear ship wave problem are discussed in the present paper. One is based on a panel method, which neglects the viscous effects. The other is based on a finite volume method, which take into account the viscous effects by solving RANS equations. Focus is laid upon on the advantages and disadvantages of two methods. The developed methods are applied to calculating the flow around Series 60 hull to validate the performance of the present nonlinear methods. Although the two methods employ quite different numerical approaches, the calculated wave patterns from both methods show good agreements with the experiments. However the potential method simu-lates the global wave pattern accurately, while the viscous method shows better performance for the local wave prediction near a ship.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.1
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• pp.597-605
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• 2019

This paper proposes a novel method for efficient prediction of joint distributions of heights and periods of nonlinear ocean waves. The proposed novel method utilizes a transformed linear simulation which is based on a Hermite transformation model where the transformation is chosen to be a monotonic cubic polynomial, calibrated such that the first four moments of the transformed model match the moments of the true process. This proposed novel method is utilized to predict the joint distributions of wave heights and periods of a sea state with the surface elevation data measured at the Gulfaks C platform in the North Sea, and the novel method's accuracy and efficiency are favorably validated by using comparisons with the results from an empirical joint distribution model, from a linear simulation model and from a second-order nonlinear simulation model.

• Journal of the Earthquake Engineering Society of Korea
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• v.5 no.6
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• pp.65-76
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• 2001

Determination of ductility demand and prediction of nonlinear seismic responses of a structure under the earthquake ground motions have become a very important subject for evaluation of seismic performance in the performance based seismic design. In this study, the system ductility demand and nonlinear seismic responses of the steel moment framed structures by the nonlinear time history analysis are estimated and compared with those obtained from the capacity spectrum method suggested in ATC-40 and proposed method that is an improvement on the capacity spectrum method using the equivalent responses derived directly from a multi degree of freedom system. the adequacy and validity of the proposed method is verified by comparing the results evaluated by the method proposed in this study and the results obtained from method suggested in ATC-40 to the nonlinear seismic responses of the example structures from the nonlinear time history analysis.