• Computational Structural Engineering
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• v.10 no.2
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• pp.225-232
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• 1997

Material properties of a new composite composed of components with known material properties are usually investigated through experiments. Elastic modulus and Poisson's ratio are measured at various volume fractions of mixed components and utilized as the base information on an analytical model for predicting the mechanical behaviors of a structure constructed by the composite. Elastic material properties of a composite at various volume fractions are numerically estimated by minimizing the error between the static displacements computed from a model for the composite and those computed from a model of homogeneous and isotropic material. A finite element model for a composite is proposed to distribute different types of material components easily into the model depending on the volume fraction. Then, the material properties of a composite filled with solid mircospheres are predicted numerically through a sample study and the estimated results are compared with experimental results and some theoretical equations.

• The Journal of the Acoustical Society of Korea
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• v.37 no.2
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• pp.83-91
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• 2018

The noise sources of a tip-jet driven rotor can be separated by rotor blade noise and jet noise. The rotor blade noise consists of thickness noise, loading noise, nonlinear quadrupole noise, and jet noise is divided into nozzle momentum noise and jet radiation noise. The flow analysis for the prediction of rotor blade noise is performed by CFD (Computational Fluid Dynamics) analysis, and the noise source of the rotor blade noise is identified by simultaneously applying the permeable and impermeable surface based FW-H (Ffowcs Williams-Hawkings) acoustic analogy. The nozzle momentum noise is obtained by permeable surface FW-H, and jet radiation noise is predicted by using empirical method for the fixed-wing jet. Both of jet noises use nozzle exit condition for noise analysis. The accuracy of the technique is verified based on the noise measurements of the tip-jet driven rotor, and the unique noise characteristics of the tip-jet driven rotor is confirmed by spectrum analysis.

• International Journal of Naval Architecture and Ocean Engineering
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• v.7 no.4
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• pp.750-769
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• 2015

The geometry of engineering systems affects their performances. For this reason, the shape of engineering systems needs to be optimized in the initial design stage. However, engineering system design problems consist of multi-objective optimization and the performance analysis using commercial code or numerical analysis is generally time-consuming. To solve these problems, many engineers perform the optimization using the approximation model (response surface). The Response Surface Method (RSM) is generally used to predict the system performance in engineering research field, but RSM presents some prediction errors for highly nonlinear systems. The major objective of this research is to establish an optimal design method for multi-objective problems and confirm its applicability. The proposed process is composed of three parts: definition of geometry, generation of response surface, and optimization process. To reduce the time for performance analysis and minimize the prediction errors, the approximation model is generated using the Backpropagation Artificial Neural Network (BPANN) which is considered as Neuro-Response Surface Method (NRSM). The optimization is done for the generated response surface by non-dominated sorting genetic algorithm-II (NSGA-II). Through case studies of marine system and ship structure (substructure of floating offshore wind turbine considering hydrodynamics performances and bulk carrier bottom stiffened panels considering structure performance), we have confirmed the applicability of the proposed method for multi-objective side constraint optimization problems.

• Journal of the Korean Geotechnical Society
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• v.18 no.5
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• pp.123-132
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• 2002

The purpose of this study is to predict the shear behavior of the weathered mudstone soil using dynamic neural network which mimics the biological system of human brain. SNN and RNN, which are kinds of the dynamic neural network realizing continuously a pattern recognition as time goes by, are used to predict a nonlinear behavior of soil. After analysis, parameters which have an effect on learning and predicting of neural network, the teaming rate, momentum constant and the optimum neural network model are decided to be 0.5, 0.7, 8$\times$18$\times$2 in SU model and 0.3, 0.9, 8$\times$24$\times$2 in R model. The results of appling both networks showed that both networks predicted the shear behavior of soil in normally consolidated state well, but RNN model which is effective fir input data of irregular patterns predicted more efficiently than SNN model in case of the prediction in overconsolidated state.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.3
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• pp.145-156
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• 2000

In this paper, we propose a new methodology which includes the optimal design procedure of Polynomial Neural Networks(PNN) structure for model identification of complex and nonlinear system. The proposed PNN algorithm is based on GMDA(Group Method of Data handling) method and its structure is similar to Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and can be generated. The each node of PNN structure uses several types of high-order polynomial such as linear, quadratic and cubic, and is connected as various kinds of multi-variable inputs. In other words, the PNN uses high-order polynomial as extended type besides quadratic polynomial used in GMDH, and the number of input of its node in each layer depends on that of variables used in the polynomial. The design procedure to obtain an optimal model structure utilizing PNN algorithm is shown in each stage. The study is illustrated with the aid of pH neutralization process data besides representative time series data for gas furnace process used widely for performance comparison, and shows that the proposed PNN algorithm can produce the model with higher accuracy than previous other works. And performance index related to approximation and prediction capabilities of model is evaluated and also discussed.

• Analytical Science and Technology
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• v.11 no.1
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• pp.47-53
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• 1998

This study is to improve the diagnosis of diabetes mellitus and the self-monitoring of blood glucose in people with diabetes by providing a non-invasive method of monitoring blood glucose. A near-infrared (NIR) spectrophotometer was used to measure absorption spectra of 80 glucose samples ranges from 1 mg/dL to 200 mg/dL, and shows the standard error of prediction 1.8 mg/dL. Also, to investigate the effect of interference in blood, NaCl and sand were added in glucose and found the standard error of prediction of 2.8 mg/dL and 3.8 mg/dL, respectively. A new and more accurate calibration system for the spectrophotometer was developed from systematic study of light scattering, which cause nonlinear spectrophotometer response.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.1
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• pp.1-11
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• 2016

The information about the deformations of high-aspect-ratio wings is needed for the real-time monitoring of structural responses. Wing deformation in flight can be predicted by using relationship between the curvatures and the strains on the wing skin. It is also necessary to consider geometric nonlinearity when the large deformation of wing is occurred. The strain distribution on fixed-end is complex in the chordwise direction because of the geometric shape of fixed-wings on fuselages. Hence, the wing displacement can be diversely predicted by the location of the strain sensing lines in the chordwise direction. We conducted a study about prediction method of displacements regardless of the chordwise strain sensing locations. To correct spanwise strains, the ratio of spanwise strain to chordwise strain, Poisson's ratio, and the ratio of the plate strain to the beam strain were used. The predicted displacements using the strain correction were consistent with those calculated by the FEA and verified through the bending testing.

• Journal of Korea Water Resources Association
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• v.41 no.10
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• pp.1035-1044
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• 2008

It is critical to study on data charateristics analysis and prediction for the flood disaster prevention and water quality monitoring because discharge and TOC data in a river channel are strongly nonlinear. Therefore, in the present study, prediction models for discharge, TOC, and TOC load data were developed using approximation component in the last level and detail components segregated by wavelet transform. The results show that the developed model overcame the persistence phenomenon which could be seen from previous models and improved the prediciton accuracy comparing with the previous models. It might be expected that the results from the present study can mitigate flood disaster damage and construct active alternatives to various water quality problems in the future.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.23 no.3
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• pp.1-1
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• 1987

The accurate prediction of the ship wave resistance is very important to design ships which operate satisfactorily in a wave environment. Thus, work should continue on development and validation of methods to compute ship wave patterns and wave resistance. Research efforts to improve the prediction of ship waves and wavemaking resistance are categorized in two major areas. First is the development of higher-order theories to take account of the nonlinear effect of the free surface condition and improved analytical treatment of the body boundary condition. Second is the development of direct numerical methods aimed at solving body and free-surface boundary conditions as accurately as possible. A new formulation of the slender body theory for a ship with constant speed is developed by Maruo. It is quite different from the existing slender ship theory by Vossers, Maruo and Tuck. It may be regarded as a substitute for the Neumann-Kelvin approximation. In present work, the method of asymptotic expansion of the Kelvin source is applied to obtain a new wave resistance formulation in fluid of finite depth. It takes a simple form than existing theory.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.147-147
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• 2019

The Numerical Weather Prediction (NWP) models determine the future state of the weather by forcing current weather conditions into the atmospheric models. The NWP models approximate mathematically the physical dynamics by nonlinear differential equations; however these approximations include uncertainties. The errors of the NWP estimations can be related to the initial and boundary conditions and model parameterization. Development in the meteorological forecast models did not solve the issues related to the inevitable biases. In spite of the efforts to incorporate all sources of uncertainty into the forecast, and regardless of the methodologies applied to generate the forecast ensembles, they are still subject to errors and systematic biases. The statistical post-processing increases the accuracy of the forecast data by decreasing the errors. Error prediction of the NWP models which is updating the NWP model outputs or model output statistics is one of the ways to improve the model forecast. The regression methods (including linear, polynomial and scaling regression) are applied to the present study to improve the real time forecast skill. Such post-processing consists of two main steps. Firstly, regression is built between forecast and measurement, available during a certain training period, and secondly, the regression is applied to new forecasts. In this study, the WRF real-time forecast data, in comparison with the observed data, had systematic biases; the errors related to the NWP model forecasts were reflected in the underestimation of the meteorological data forecast by the WRF model. The promising results will indicate that the post-processing techniques applied in this study improved the meteorological forecast data provided by WRF model. A comparison between various bias correction methods will show the strength and weakness of the each methods.