• Proceedings of the Korea Concrete Institute Conference
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• 1997.04a
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• pp.392-397
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• 1997

In this paper, design and analysis of anchorage zone in prestressed concrete structure using nonlinear strut and tie model is presented. Nonlinear strut and tie model is an analysis and design model which constructs strut and tie model based on nonlinear analysis considering the nonlinear behavior of concrete. Based on the nonlinear strut and tie model, the analysis and design are performed for the anchorage zone having singular concentric tendons, singular eccentric tendons and multiple tendons, respectively. For verification of the model, comparisons are made with experimental results as well as results by linear strut and tie models. from the comparisons, it is shown that the design of the anchorage zone by the nonlinear model is still economical without loosing the degree of safety and the prediction of the ultimate load by the nonlinear model gives better accuracy than by the linear one.

• Journal of Mechanical Science and Technology
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• v.15 no.11
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• pp.1507-1516
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• 2001

An analytical method is presented for evaluation of the steady state periodic behavior of nonlinear structural systems. This method is based on the substructure synthesis formulation and a harmonic balance procedure, which is applied to the analysis of nonlinear responses. A complex nonlinear system is divided into substructures, of which equations are approximately transformed to modal coordinates including nonlinear term under the reasonable procedure. Then, the equations are synthesized into the overall system and the nonlinear solution for the system is obtained. Based on the harmonic balance method, the proposed procedure reduces the size of large degrees-of-freedom problem in the solving nonlinear equations. Feasibility and advantages of the proposed method are illustrated using the study of the nonlinear rotating machine system as a large mechanical structure system. Results obtained are reported to be an efficient approach with respect to nonlinear response prediction when compared with other conventional methods.

• Structural Engineering and Mechanics
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• v.86 no.4
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• pp.547-571
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• 2023

To deeply probe the actual earthquake level and fragility of typical reinforced concrete (RC) structures under multiple intensity grades, considering diachronic measurement building stock samples and actual observations of representative catastrophic earth shocks in China from 1990 to 2010, RC structures were divided into traditional RC structures (TRCs) and bottom reinforced concrete frame seismic wall masonry (BFM) structures, and the empirical damage characteristics and mechanisms were analysed. A great deal of statistics and induction were developed on the historical experience investigation data of 59 typical catastrophic earthquakes in 9 provinces of China. The database and fragility matrix prediction model were established with TRCs of 4,122.5284×104 m² and 5,844 buildings and BFMs of 5,872 buildings as empirical seismic damage samples. By employing the methods of structural damage probability and statistics, nonlinear prediction of seismic vulnerability, and numerical and applied functional analysis, the comparison matrix of actual fragility probability prediction of TRC and BFM in multiple intensity regions under the latest version of China's macrointensity standard was established. A novel nonlinear regression prediction model of seismic vulnerability was proposed, and prediction models considering the seismic damage ratio and transcendental probability parameters were constructed. The time-varying vulnerability comparative model of the sample database was developed according to the different periods of multiple earthquakes. The new calculation method of the average fragility prediction index (AFPI) matrix parameter model has been proposed to predict the seismic fragility of an areal RC structure.

• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.87-96
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• 2000

Nonlinear time series prediction is derived and compared between statistic of modeling and neural network method. In particular mean squared errors of predication are obtained in generalized random coefficient model and generalized autoregressive conditional heteroscedastic model and compared with them by neural network forecasting.

• 한국전산유체공학회:학술대회논문집
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• 2007.10a
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• pp.134-140
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• 2007

Nonlinear parabolized stability equations for compressible flow in general curvilinear coordinate system are derived to deal with a broad range of transition prediction problems on complex geometry. A highly accurate finite difference PSE code has been developed using an implicit marching procedure. Blasius flow is tested. The results of the present computation show good agreement with DNS data. Nonlinear interaction can make the T-S fundamental wave more unstable and the onset of its amplitude decay is shifted downstream relative to linear case. For nonlinear calculations, rather small difference in initial amplitude can produce large change during nonlinear region. Compressible secondary instability at Mach number 1.6 is also simulated and showed that 1.1% initial amplitude for primary mode is enough to trigger the secondary growth.

• Journal of Power System Engineering
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• v.2 no.1
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• pp.31-38
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• 1998

A diffuser, an important equipment to change kinetic energy into pressure energy, has been studied for a long time. Though experimental and theoretical researches have been done, the understanding of energy transfer and detailed mechanism of energy dissipation is unclear. As far as numerical prediction of diffuser flows are concerned, various numerical studies have also been done. On the contrary, many turbulence models have constraint to the applicability of diffuser-like complex flows, because of anisotropy of turbulence near the wall and of local nonequilibrium induced by an adverse pressure gradient. The existing $k-{\epsilon}$ turbulence models have some problems in the case of being applied to complex turbulent flows. The purpose of this paper is to test the applicability of the nonlinear $k-{\epsilon}$ model concerning diffuser-like flows with expansion and streamline curvature. The results show that the nonlinear $k-{\epsilon}$ turbulence model predicted well the coefficient of pressure, velocity profiles and turbulent kinetic energy distributions, however the shear stress prediction was failed.

• Animal Bioscience
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• v.37 no.4
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• pp.622-630
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• 2024

Objective: Pig breeders cannot obtain phenotypic information at the time of selection for sow lifetime productivity (SLP). They would benefit from obtaining genetic information of candidate sows. Genomic data interpreted using deep learning (DL) techniques could contribute to the genetic improvement of SLP to maximize farm profitability because DL models capture nonlinear genetic effects such as dominance and epistasis more efficiently than conventional genomic prediction methods based on linear models. This study aimed to investigate the usefulness of DL for the genomic prediction of two SLP-related traits; lifetime number of litters (LNL) and lifetime pig production (LPP). Methods: Two bivariate DL models, convolutional neural network (CNN) and local convolutional neural network (LCNN), were compared with conventional bivariate linear models (i.e., genomic best linear unbiased prediction, Bayesian ridge regression, Bayes A, and Bayes B). Phenotype and pedigree data were collected from 40,011 sows that had husbandry records. Among these, 3,652 pigs were genotyped using the PorcineSNP60K BeadChip. Results: The best predictive correlation for LNL was obtained with CNN (0.28), followed by LCNN (0.26) and conventional linear models (approximately 0.21). For LPP, the best predictive correlation was also obtained with CNN (0.29), followed by LCNN (0.27) and conventional linear models (approximately 0.25). A similar trend was observed with the mean squared error of prediction for the SLP traits. Conclusion: This study provides an example of a CNN that can outperform against the linear model-based genomic prediction approaches when the nonlinear interaction components are important because LNL and LPP exhibited strong epistatic interaction components. Additionally, our results suggest that applying bivariate DL models could also contribute to the prediction accuracy by utilizing the genetic correlation between LNL and LPP.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.740-745
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• 1988

New design methods for constructing nonlinear adaptive control system are considered. The proposed adaptive controllers are applicable to the case where the degree of the controlled process is unknown. It is shown that the degree of the controller is determined independently of the degree of the process. Several types of nonlinear functions are introduced to deal with uncertainties of the degree of the process. Finally, some simulation results show the effectiveness and simplicity of the proposed methods.

• Journal of Ocean Engineering and Technology
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• v.13 no.2 s.32
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• pp.116-128
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• 1999

A deep water wave prediction model applicable to the East Sea is presnted. This model incorporates rediative transter of energy specrum, atmospheric input form the wind, nonlinear interaction, and energy dissipation by white capping. The propagation scheme by Gadd shows satisfactory results and the characteristics of the nonlinear interaction is simulated well by discrete interaction approximatiion. The application of the model to the sea around the Korean Peninsula shows reasonable agreement with the observation.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.238-241
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• 2003

This paper presents a nonlinear least squares decision feedback equalizer Bilinear systems are attractive because of the ability to approximate a large class of nonlinear systems efficiently. The nonlinearity of channel is modeled using a bilinear system. The algorithms are derived by using the QR decomposition for minimization covariance matrix of prediction error by applying Givens rotation to the bilinear model. Result of computer simulation experiments that compare the performance of the bilinear DFE to two other DFE's in eliminating the intersymbol interference caused by a nonlinear channel are presented In the paper.