• Journal of the Korean Data and Information Science Society
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• 제9권2호
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• pp.289-298
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• 1998

We investigate the distribution of likelihood ratio test(LRT) of null hypothesis a sample is from single gamma with unknown shape and scale against the alternative hypothesis a sample is from a mixture of two gammas, each with unknown scale and unknown (but equal) scale. To obtain stable maximum likelihood estimates(MLE) of a mixture of two gamma distributions, the EM(Dempster, Laird, and Robin(1977))and Modified Newton(Jensen and Johansen(1991)) algorithms were implemented. Based on EM, we made a simple structure likelihood equation for each parameter and could obtain stable solution by Modified Newton Algorithms. Simulation study was conducted to investigate the distribution of LRT for sample size n = 25, 50, 75, 100, 50, 200, 300, 400, 500 with 2500 replications. To determine the small sample distribution of LRT, I considered the model of a gamma distribution with shape parameter equal to 1 + f(n) and scale parameter equal to 2. The simulation results indicate that the null distribution is essentially invariant to the value of the shape parameter. Modeling of the null distribution indicates that it is well approximated by a gamma distribution with shape parameter equal to the quantity $0.927+1.18/\sqrt{n}$ and scale parameter equal to 2.16.

• 한국지반환경공학회 논문집
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• 제2권1호
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• pp.81-89
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• 2001

본 연구에서는 차수벽 재료로 자주 사용되는 흙-벤토나이트 혼합물의 투수계수가 해수로 인하여 어떻게 영향을 받는지 고찰하였으며 혼합물로 축조된 가상의 제방에 해수가 침입할 때 어떻게 영향을 받는지를 고찰하였다. 유한요소법을 이용한 RFP 방법을 사용하였으며 투수계수변화를 이 해석에 도입하였다. 해석을 위하여 정상흐름상태와 비정상 흐름 상태에서 경계면을 구한 결과 투수계수변화를 고려하지 않았던 과거의 방법과 비교할 때 해수쪽으로는 낮게 담수영역에서는 경계면이 높게 나타났다.

• Smart Structures and Systems
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• 제24권6호
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• pp.733-744
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• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

• Computers and Concrete
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• 제14권1호
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• pp.1-18
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• 2014

The addition of steel fibers in concrete mixture is recognized as a non-conventional mass reinforcement scheme that improves the torsional, flexural, and shear behavior of structural members. However, the analysis of fiber reinforced concrete beams under combined torsion, bending, and shear is limited because of the complicated nature of the problem. Therefore, nonlinear 3D finite element analysis was conducted using the "ANSYS CivilFEM" program to investigate the behavior of fiber reinforced concrete L-beams. These beams were tested at different reinforcement schemes and loading conditions. The reinforcement case parameters were set as follows: reinforced with longitudinal reinforcement only and reinforced with steel bars and stirrups. All beams were tested under two different combined loading conditions, namely, torsion-to-shear ratio (T/V) = 545 mm (high eccentricity) and T/V = 145 mm (low eccentricity). Eight intermediate L-beams were constructed and tested in a laboratory under combined torsion, bending, and shear to validate the finite element model. Comparisons with the experimental data reveal that the program can accurately predict the behavior of L-beams under different reinforcement cases and combined loading ratios. The ANSYS model accurately predicted the loads and deformations for various types of reinforcements in L-beams and captured the concrete strains of these beams.

• Structural Engineering and Mechanics
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• 제63권6호
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• pp.809-824
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• 2017

The accurate evaluation of wind characteristics and wind-induced structural responses during a typhoon is of significant importance for bridge design and safety assessment. This paper presents an expectation maximization (EM) algorithm-based angular-linear approach for probabilistic modeling of field-measured wind characteristics. The proposed method has been applied to model the wind speed and direction data during typhoons recorded by the structural health monitoring (SHM) system instrumented on the arch Jiubao Bridge located in Hangzhou, China. In the summer of 2015, three typhoons, i.e., Typhoon Chan-hom, Typhoon Soudelor and Typhoon Goni, made landfall in the east of China and then struck the Jiubao Bridge. By analyzing the wind monitoring data such as the wind speed and direction measured by three anemometers during typhoons, the wind characteristics during typhoons are derived, including the average wind speed and direction, turbulence intensity, gust factor, turbulence integral scale, and power spectral density (PSD). An EM algorithm-based angular-linear modeling approach is proposed for modeling the joint distribution of the wind speed and direction. For the marginal distribution of the wind speed, the finite mixture of two-parameter Weibull distribution is employed, and the finite mixture of von Mises distribution is used to represent the wind direction. The parameters of each distribution model are estimated by use of the EM algorithm, and the optimal model is determined by the values of $R^2$ statistic and the Akaike's information criterion (AIC). The results indicate that the stochastic properties of the wind field around the bridge site during typhoons are effectively characterized by the proposed EM algorithm-based angular-linear modeling approach. The formulated joint distribution of the wind speed and direction can serve as a solid foundation for the purpose of accurately evaluating the typhoon-induced fatigue damage of long-span bridges.

• 한국소음진동공학회논문집
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• 제26권2호
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• pp.141-147
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• 2016

Underwater cavitation is one of the most important issues because it causes not only vibration and erosion of submerged bodies but also significant flow noise problems. In this paper, flow noise due to cavitation flows around the NACA66 MOD hydrofoil is numerically investigated. The cavitation flow simulation is conducted using the Reynolds-Averaged Navier-Stokes equations based on finite difference methods. To capture the cavitation phenomena accurately and effectively, the homogeneous mixture model with the Merkle's cavitation model is applied. The predicted results are compared with available experimental data in terms of pressure coefficients and volume fraction, which confirms the validity of numerical results. Based on flow field analysis results, hydro-acoustic noise field due to the cavitation flow is predicted using the Ffowcs-Williams and Hawkings equation derived from the Lighthill's acoustic analogy. The typical lift dipole propagation patterns are identified.

• 한국분말재료학회지
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• 제9권4호
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• pp.245-250
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• 2002

In this paper we presented numerical method for the simulation of powder injection molding filling process, which is one of the key processes in powder injection molding. Rheological properties of powder binder mixture such as slip phenomena and yield stress were introduced into the numerical analysis model of powder injection molding filling simulation. Numerical model can be classified into two types. One is 2.5D model which can be introduced to a arbitrary thin geometry and the other is full 3D model which can be applied to a general 3D shape. For 2.5D model we showed the validity of our CAE system with several verification examples. Finally we suggested flow analysis model for 3D powder injection molding filling simulation.

• Communications for Statistical Applications and Methods
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• 제31권4호
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• pp.365-375
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• 2024

Diverse methods to evaluate the prediction model of a time to event have been proposed in the context of right censored data where all subjects are subject to be susceptible. A time-dependent AUC (area under curve) measures the predictive ability of a marker based on case group and control one which are varying over time. When a substantial portion of subjects are event-free, a population consists of a susceptible group and a cured one. An uncertain curability of censored subjects makes it difficult to define both case group and control one. In this paper, our goal is to propose a time-dependent AUC for a cure rate model when a censoring distribution is related with covariates. A class of inverse probability of censoring weighted (IPCW) AUC estimators is proposed to adjust the possible sampling bias. We evaluate the finite sample performance of the suggested methods with diverse simulation schemes and the application to the melanoma dataset is presented to compare with other methods.

• 대한기계학회논문집A
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• 제25권8호
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• pp.1261-1268
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• 2001

Effective material properties of active fiber composites with interdigitated electrodes are derived as a function of the fiber volume fraction. For the purpose of applying the rule of mixture, three unit cell models are introduced; each for the deformation and stress continuities in the out of plane and in-plane directions, and the continuity of the electrical displacement in the longitudinal direction. Derived effective material properties are compared with the results by the finite element method; good agreements are observed between them. As an application, the electromechanical behavior of the angle ply laminates with the active fiber layers bonded on the top and bottom surfaces are investigated; the angle of piezoelectric fiber to maximize the twisting curvature is obtained using the present model.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 1997년도 봄 학술발표회 논문집
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• pp.254-260
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• 1997

Chloride-induced corrosion of reinforcement is one of the main factors which cause the deterioration of concrete structures. Durability and service lives of the concrete sturctures should be predicted in order to minimize the risk of corrosion of reinforcement. The objective of this study is to suggest the basis of analytical methods of predicting the corrosion threhold time of concrete structures. Based on the chemistry and physics of chloride ion transport and corrosion process, chloride intrusion with various exposure conditions, variability of diffusivity and transport of pore water in concrete are taken into consideration in applying finite element formulation to the predicion of corrosion threhold time. The effects of main factors on the prediction of chloride intrusion and corrosion threhold time are examined. In addition, after chloride diffusivities of several mixture proportions with different parameters are measured by chloride diffusion test, the exemplary anayses of corrosion threhold time of those mixture proportions are carried out.