• Structural Engineering and Mechanics
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• 제48권1호
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• pp.77-91
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• 2013

The paper presents the formulation of 3-nodal line semi-analytical Mindlin-Reissner finite strip in the buckling analysis of thin-walled members, which are subjected to arbitrary loads. The finite strip is simply supported in two opposite edges. The general loading and in-plane rotation techniques are used to develop this finite strip. The linear stiffness matrix and the geometric stiffness matrix of the finite strip are given in explicit forms. To validate the proposed model and study its performance, numerical examples of some thin-walled sections have been performed and the results obtained have been compared with finite element models and the published ones.

• Advances in concrete construction
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• 제9권4호
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• pp.397-406
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• 2020

The present article deals with post-buckling of geometrically imperfect concrete plates reinforced by graphene oxide powder (GOP) based on general higher order plate model. GOP distributions are considered as uniform and linear models. Utilizing a shear deformable plate model having five field components, it is feasible to verify transverse shear impacts with no inclusion of correction factor. The nonlinear governing equations have been solved via an analytical trend for deriving post-buckling load-deflection relations of the GOP-reinforced plate. Derived findings demonstrate the significance of GOP distributions, geometric imperfectness, foundation factors, material compositions and geometrical factors on post-buckling properties of reinforced concrete plates.

• 아시아태평양코퍼스연구
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• 제4권2호
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• pp.57-74
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• 2023

Having a firm grasp of technical terms is essential for learners of Japanese for Specific Purposes (JSP). This research aims to analyze Japanese nursing care vocabulary based on objective corpus-based frequency and subjectively rated word familiarity. For this purpose, we constructed a text corpus centered on the National Examination for Certified Care Workers to extract nursing care keywords. The Log-Likelihood Ratio (LLR) was used as the statistical criterion for keyword identification, giving a list of 300 keywords as target words for a further word recognition survey. The survey involved 115 participants of whom 51 were certified care workers (CW group) and 64 were individuals from the general public (GP group). These participants rated the familiarity of the target keywords through crowdsourcing. Given the limited sample size, Bayesian linear mixed models were utilized to determine word familiarity rates. Our study conducted a comparative analysis of word familiarity between the CW group and the GP group, revealing key terms that are crucial for professionals but potentially unfamiliar to the general public. By focusing on these terms, instructors can bridge the knowledge gap more efficiently.

• 한국멀티미디어학회논문지
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• 제20권8호
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• pp.1406-1420
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• 2017

Artificial neural networks (ANNs) play an important role in the fields of function approximation, prediction, and classification. ANN performance is critically dependent on the input parameters, including the number of neurons in each layer, and the optimal values of weights and biases assigned to each neuron. In this study, we apply the particle swarm optimization method, a popular optimization algorithm for determining the optimal values of weights and biases for every neuron in different layers of the ANN. Several regression models, including general linear regression, Fourier regression, smoothing spline, and polynomial regression, are conducted to evaluate the proposed method's prediction power compared to multiple linear regression (MLR) methods. In addition, residual analysis is conducted to evaluate the optimized ANN accuracy for both training and test datasets. The experimental results demonstrate that the proposed method can effectively determine optimal values for neuron weights and biases, and high accuracy results are obtained for prediction applications. Evaluations of the proposed method reveal that it can be used for prediction and estimation purposes, with a high accuracy ratio, and the designed model provides a reliable technique for optimization. The simulation results show that the optimized ANN exhibits superior performance to MLR for prediction purposes.

• 한국경영과학회지
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• 제14권2호
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• pp.105-114
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• 1989

The actual working time of jobs, in general, is different to the standard time of jobs. In this paper, in order to analyze the actual working time of each job in production, we use the total production amount and the encessary total working time. The method which analyzes the actual working time is as follows. In this paper, we propose the interval regression analysis for obtaining an interval linear regression model with constraint conditions with respect to interval parameters. The merits of this method are the following.1) it is easy to obtain an interval linear model by solving a LP problem to which the formulation of proposed regression analysis is reduced, 2) it is easy to add constraint conditions about interval parameters, which are a sort of expert knowledge. As an application, within a case which has given certain data, the actual working time of jobs and the number of workers in a future plan are estimated through the real data obtianed from the operation of processing line in a heavy industry company. It results from the proposed method that the actual working time and the number of workers can be estimated as intervals by the interval regression model.

• International Journal of Control, Automation, and Systems
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• 제3권2호
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• pp.183-194
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• 2005

In this study, we introduce an advanced architecture of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) and develop a comprehensive design methodology supporting their construction. A series of numeric experiments is included to illustrate the performance of the networks. The construction of gHFNN exploits fundamental technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNNs results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). In this tandem, a FNN supports the formation of the premise part of the rule-based structure of the gHFNN. The consequence part of the gHFNN is designed using PNNs. We distinguish between two types of the linear fuzzy inference rule-based FNN structures showing how this taxonomy depends upon the type of a fuzzy partition of input variables. As to the consequence part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gHFNN, the models are experimented with a representative numerical example. A comparative analysis demonstrates that the proposed gHFNN come with higher accuracy as well as superb predictive capabilities when comparing with other neurofuzzy models.

• Communications for Statistical Applications and Methods
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• 제19권2호
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• pp.293-301
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• 2012

Quantile regression proposed by Koenker and Bassett (1978) is a statistical technique that estimates conditional quantiles. The advantage of using quantile regression is the robustness in response to large outliers compared to ordinary least squares(OLS) regression. A regression tree approach has been applied to OLS problems to fit flexible models. Loh (2002) proposed the GUIDE algorithm that has a negligible selection bias and relatively low computational cost. Quantile regression can be regarded as an analogue of OLS, therefore it can also be applied to GUIDE regression tree method. Chaudhuri and Loh (2002) proposed a nonparametric quantile regression method that blends key features of piecewise polynomial quantile regression and tree-structured regression based on adaptive recursive partitioning. Lee and Lee (2006) investigated wage determinants in the Korean labor market using the Korean Labor and Income Panel Study(KLIPS). Following Lee and Lee, we fit three kinds of quantile regression tree models to KLIPS data with respect to the quantiles, 0.05, 0.2, 0.5, 0.8, and 0.95. Among the three models, multiple linear piecewise quantile regression model forms the shortest tree structure, while the piecewise constant quantile regression model has a deeper tree structure with more terminal nodes in general. Age, gender, marriage status, and education seem to be the determinants of the wage level throughout the quantiles; in addition, education experience appears as the important determinant of the wage level in the highly paid group.

• 응용통계연구
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• 제29권1호
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• pp.157-169
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• 2016

이 논문에서는 비선형 자기회귀 과정을 따르는 오차항을 포함한 회귀모형에서 계수추정법의 비교를 다룬다. 비교를 위해 통상적 최소제곱추정량, 일반화 최소제곱추정량, 모수적 회귀오차 수정법, 비모수적 회귀오차 추정법을 비교하였다. 본 논문에서는 또한 비선형 자기회귀모형의 성질을 전형적인 몇가지 비선형자기회귀 모형을 예를 들어 설명한다. 비교연구의 결과 네 가지 추정량 중에 모든 상황에서 최선인 추정량은 존재하지 않았으나 비모수 회귀오차 수정 방법이 일반적으로 우수한 성능을 보임을 알 수 있다.

• 제어로봇시스템학회논문지
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• 제11권2호
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• pp.127-136
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• 2005

In this paper, a new architecture and comprehensive design methodology of genetically optimized Rule-based Fuzzy Polynomial Neural Networks(gRFPNN) are introduced and a series of numeric experiments are carried out. The architecture of the resulting gRFPNN results from asynergistic usage of the hybrid system generated by combining rule-based Fuzzy Neural Networks(FNN) with polynomial neural networks (PNN). FNN contributes to the formation of the premise part of the overall rule-based structure of the gRFPNN. The consequence part of the gRFPNN is designed using PNNs. At the premise part of the gRFPNN, FNN exploits fuzzy set based approach designed by using space partitioning in terms of individual variables and comes in two fuzzy inference forms: simplified and linear. As the consequence part of the gRFPNN, the development of the genetically optimized PNN dwells on two general optimization mechanism: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gRFPNN, the models are experimented with the use of several representative numerical examples. A comparative analysis shows that the proposed gRFPNN are models with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• 한국기계가공학회지
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• 제18권3호
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• pp.59-65
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• 2019

In many industries, particle packing is adopted quite frequently. In the particle packing process, the Discrete Element Method (DEM) can analyze the multi-collision of particles efficiently. Two types of contact models are frequently used for the DEM. One is the linear spring model, which has the fastest calculation time, and the other is the Hertz-Mindlin model, which is the most frequently used contact model employing the DEM. Meanwhile, very tiny particles in the micrometer order are used in modern industries. In the micro length order, surface force is important to decreased particle size. To consider the effect of surface force in this study, we performed a simulation with the Hertz-Mindlin model and added the Johnson-Kendall-Roberts (JKR) theory depicting surface force with surface energy. In addition, three contact models were compared with several parameters. As a result, it was found that the JKR model has larger residual stress than the general contact models because of the pull-off force. We also validated that surface force can influence particle behavior if the particles are small.