• The Journal of the Acoustical Society of Korea
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• v.18 no.7
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• pp.17-22
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• 1999

In this paper we propose a new mixed method of LDA and tri-tone model to predict Korean prosodic break indices(PBI) for a given utterance. PBI can be used as an important cue of syntactic discontinuity in continuous speech recognition(CSR). The model consists of three steps. At the first step, PBI was predicted with the information of syllable and pause duration through the linear discriminant analysis (LDA) method. At the second step, syllable tone information was used to estimate PBI. In this step we used vector quantization (VQ) for coding the syllable tones and PBI is estimated by tri-tone model. In the last step, two PBI predictors were integrated by a weight factor. The proposed method was tested on 200 literal style spoken sentences. The experimental results showed 72% accuracy.

• Proceedings of the Korea Concrete Institute Conference
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• 2003.11a
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• pp.604-607
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• 2003

Recently, hybrid concrete structures such as a concrete-filled steel tubular(CFT), a steel reinforced concrete(SRC) and a composite material are popular in structure applications. They also have merit of high strength, high ductility, and large energy absorption capacity. But the analysis of hybrid concrete structures is very difficult owing to the complex behavior of concrete under passive confinement. This paper has analyzed CFT, which receives passive confinement using Tri-Surface concrete model for three dimension finite element analysis. By the result of that, the proposed model was properly forecasted a concrete behavior that receives passive restraint as well as non-linear analysis of concrete which receive uniaxial stress and high active confinement of 400Mpa. If the model through the steady study is set up especially on the factor of concrete under passive confinement, the proposed concrete model will be surely useful for analysis of the hybrid concrete structures.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.307-314
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• 2023

As supercomputing and hardware technology advances, climate prediction models are improving. The Korean Meteorological Administration adopted GloSea5 from the UK Met Office and now operates an updated GloSea6 tailored to Korean weather. Universities and research institutions use Low-GloSea6 on smaller servers, improving accessibility and research efficiency. In this paper, profiling Low-GloSea6 on smaller servers identified the tri_sor_dp_dp subroutine in the tri_sor.F90 atmospheric model as a CPU-intensive hotspot. Applying linear regression, a type of machine learning, to this function showed promise. After removing outliers, the linear regression model achieved an RMSE of 2.7665e-08 and an MAE of 1.4958e-08, outperforming Lasso and ElasticNet regression methods. This suggests the potential for machine learning in optimizing identified hotspots during Low-GloSea6 execution.

• Journal of the Korean Society of Safety
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• v.33 no.4
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• pp.46-53
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• 2018

This study examined the maximum resistant moment and nonlinear rotational stiffness of wedge joint between the vertical and horizontal members of system supports. To examine the maximum resistant moment and propose the nonlinear rotation stiffness of wedge joint, 6 specimens were tested and additional 3 specimens, where the horizontal member was welded to the vertical member, were tested to compare the moment capacity of wedge joints. The average maximum moment in the tested wedge joint was 1.183 kNm which represented about 70 % of the maximum moment developed in the welded specimens. And, as simulating nonlinear rotational stiffness of the wedge joint, a tri-linear model was suggested. The rotational stiffness was estimated as 23.095 kNm/rad in first stage, 7.945 kNm/rad in second stage, and 3.073 kNm/rad in third stage. For the failure mode, the specimen with the wedge joint showed the failure of joint between vertical and horizontal members. However, the specimen with welded joint represented the yielding of horizontal members.

• Computers and Concrete
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• v.5 no.3
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• pp.261-277
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• 2008

This paper presents methodologies for remaining life prediction of plain concrete structural components considering tension softening effect. Non-linear fracture mechanics principles (NLFM) have been used for crack growth analysis and remaining life prediction. Various tension softening models such as linear, bi-linear, tri-linear, exponential and power curve have been presented with appropriate expressions. A methodology to account for tension softening effects in the computation of SIF and remaining life prediction of concrete structural components has been presented. The tension softening effects has been represented by using any one of the models mentioned above. Numerical studies have been conducted on three point bending concrete structural component under constant amplitude loading. Remaining life has been predicted for different loading cases and for various tension softening models. The predicted values have been compared with the corresponding experimental observations. It is observed that the predicted life using bi-linear model and power curve model is in close agreement with the experimental values. Parametric studies on remaining life prediction have also been conducted by using modified bilinear model. A suitable value for constant of modified bilinear model is suggested based on parametric studies.

• Structural Engineering and Mechanics
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• v.34 no.3
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• pp.301-317
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• 2010

This paper studies the reliability of an analytical tool for predicting the lateral load-deformation response of RC columns while subjected to lateral cyclic displacements and axial load. The analytical tool in this study is based on a fiber element model implemented into the program DRAIN-2DX (fiber element). The response of RC column under cyclic displacement is defined by the behavior of concrete, and reinforcing steel under general reversed-cyclic loading. A tri-linear stress-strain relationship for the cyclic behavior of steel is proposed and the improvement in the analytical results is studied. This study only considers the behavior of columns with flexural dominant mode of failure. It is concluded that with the implementation of appropriate constitutive material models, the described analytical tools can predict the response of the columns with reasonable accuracy when compared to experimental data.

• Steel and Composite Structures
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• v.1 no.2
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• pp.245-268
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• 2001

The analysis of steel-concrete composite joints presents some particular aspects that increase their complexity when compared to bare steel joints. In particular, the influence of slab reinforcement and column concrete encasement clearly change the moment-rotation response of the joint. Starting from an energy approach developed in the context of steel joints, an extension to composite joints is presented in this paper that is able to provide closed-form analytical solutions. In addition, the possibility of tri-linear or non-linear component behaviour is also incorporated in the model, enabling adequate treatment of the influence of cracked concrete in tension and the softening response of the column web in compression. This methodology is validated through comparison with experimental tests carried out at the University of Coimbra.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1999.10a
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• pp.387-394
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• 1999

Artificial Neural Network (ANN) is a computational model inspired by the structure and operations of the brain. It is massively parallel system consisting of a large number of highly interconnected and simple processing units. The purpose of this paper is to verify the applicability of ANN to predict experimental results through the use of measured experimental data. Although there have been accumulated data based on hysteretic characteristics of structural element with cyclic loading tests, it is difficult to directly apply them for the analysis of elastic and plastic response. Thus, simple models with mathematical formula such as Bi-Linear Model, Ramberg-Osgood Model, Degrading Tri Model, Takeda Model, Slip type Model, and etc, have been used. To verify the practicality and capability of this study, ANN is adapted to several models with mathematical formula using numerical data To show the efficiency of ANN in nonlinear analysis, it is important to determine the adequate input and output variables of hysteretic models and to minimize an error in ANN process. The application example is Beam-Column joint test using the ANN in modeling of the linear and nonlinear hysteretic behavior of structure.

• Steel and Composite Structures
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• v.17 no.4
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• pp.371-386
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• 2014

This paper presents a tri-linear restoring force model based on the test results of 12 circular RC columns strengthened by CFRP strips under low cyclic loading. The pre-stress of CFRP strips and axial load ratio of specimens are considered as the affect parameters of the proposed model. All essential characteristics of the hysteretic behavior of the proposed model, including the hysteretic rules, main performance points, strength degradation, stiffness degradation and confinement effects are explicitly analyzed. The calculated results from the proposed model are in good agreement with the experimental results, which shows that the recommended model can be reliably used for seismic behavior predictions of circular RC columns strengthened by pre-stressed CFRP strips.

• Journal of Korean Society of Transportation
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• v.18 no.4
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• pp.95-105
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• 2000

The Purpose of this study is to present a new Trip Generation Model using Backpropagation Neural Networks. For this purpose, it is compared the performance between existing linear/nonlinear Regression models and a new TriP Generation model using Neural Networks. The study was performed according to the below. First, it is analyzed the limits of conventional Regression models, next Proved the superiority of Neural Networks model in theoretical and empirical aspects, and lastly Presented a new approach of Trip Generation methodology. The results show that Backpropagation Neural Networks model is predominant in estimation and Prediction comparable to Regression analysis. Such results mean the possibility of Neural Networks\` application in Trip Generation modeling. Specially under the circumstances of the chancing transportation situations and unstable transportation on vironments, its application in transportation fields will be extended.