• Journal of the Korean Statistical Society
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• 제27권3호
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• pp.369-383
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• 1998

This paper deals with a robust regression estimator. We propose an efficient one-step GM-estimator, which has a bounded influence function and a high breakdown point. The main idea of this paper is to use the Mallows-type weights which depend on both the predictor variables and the residuals from a high breakdown initial estimator. The proposed weighting scheme severely downweights the bad leverage points and slightly downweights the good leverage points. Under some regularity conditions, we compute the finite-sample breakdown point and prove the asymptotic normality. Some simulation results and a numerical example are also presented.

• Computers and Concrete
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• 제4권1호
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• pp.67-82
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• 2007

The discrete crack-concept is applied to study the 3D propagation of tensile-dominated failure in plain concrete. To this end the Partition of Unity Finite Element Method (PUFEM) is utilized and the strong discontinuity approach is followed. A consistent linearized implementation of the PUFEM is combined with a predictor-corrector algorithm to track the crack path, which leads to a robust numerical description of concrete cracking. The proposed concept is applied to study concrete failure during the PCT3D test and the predicted numerical results are compared to experimental data. The proposed numerical concept provides a clear interface for constitutive models and allows an investigation of their impact on concrete cracking under 3D conditions, which is of significant scientific interests to interpret results from 3D experiments.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권2호
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• pp.65-75
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• 2004

This paper presents a generalized predictive control method based on a fuzzy neural network(FNN) model, which uses the on-line multi-step prediction, fur the intelligent control of chaotic nonlinear systems whose mathematical models are unknown. In our design method, the parameters of both predictor and controller are tuned by a simple gradient descent scheme, and the weight parameters of FNN are determined adaptively during the operation of the system. In order to design a generalized predictive controller effectively, this paper describes computing procedure for each of the two important parameters. Also, we introduce a projection matrix to determine the control input, which deceases the control performance function very rapidly. Finally, in order to evaluate the performance of our controller, the proposed method is applied to the Doffing and Henon systems, which are two representative continuous-time and discrete-time chaotic nonlinear systems, res reactively.

• 한국의류학회지
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• 제34권6호
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• pp.928-936
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• 2010

This study explores the variables that predict the job satisfaction of clothing salespeople. A total of 270 questionnaires obtained from clothing salespeople were submitted for analysis. The results show that job stress was a negative influence whereas customer-orientation and self-efficacy were positive influences on the job satisfaction of clothing salespeople. The relationships were repeatedly investigated by the groups divided by personal and store characteristics. Job stress was not different by job experience, job position, and the price level of the products for sale; however, customer orientation, self-efficacy, and job satisfaction were different by those factors. In the case of more experienced salespeople with high positions, job stress would not be a predictor of job satisfaction. The study findings confirmed variables predicting job satisfaction, as well as revealed some personal and store characteristics that can affect the relationships of the variables.

• 한국방재학회 논문집
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• 제2권4호
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• pp.97-116
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• 2002

In this papers, a finite element formulation is proposed for dynamic analysis of vehicle-bridge interaction problems under realistic loading conditions. Although the formulation presented in this paper is based on the consideration of only a single traversing vehicle, it can be extended to include several different bridge configurations. The traversing vehicle and the vibrating bridge superstructure are considered as an integrated system. Hence, although material and geometric nonlinearities are excluded, this introduces nonlinearity into the problem. Various vehicle models, including those with suspension systems, are considered. Traveling speed of the vehicle can be varied. The finite element discretization of the bridge structure permits the inclusion of arbitrary geometrical configurations, and surface and boundary conditions. To obtain accurate solutions, time integration of the equation of vehicle-bridge motion is carried out by using the Newmark method in connection with a predictor-corrector algorithm.

• Journal of Advanced Marine Engineering and Technology
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• 제20권3호
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• pp.286-286
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• 1996

A neural predictive tracking system for the control of structure-unknown dynamic system is presented. The control system comprises a neural network modelling mechanism for the the forward and inverse dynamics of a plant to be controlled, a feedforward controller, feedback controller, and an error prediction mechanism. The feedforward controller, a neural network model of the inverse dynamics, generates feedforward control signal to the plant. The feedback control signal is produced by the error prediction mechanism. The error predictor adopts the neural network models of the forward and inverse dynamics. Simulation results are presented to demonstrate the applicability of the proposed scheme to predictive tracking control problems.

• Journal of Advanced Marine Engineering and Technology
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• 제20권3호
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• pp.154-161
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• 1996

A neural predictive tracking system for the control of structure-unknown dynamic system is presented. The control system comprises a neural network modelling mechanism for the the forward and inverse dynamics of a plant to be controlled, a feedforward controller, feedback controller, and an error prediction mechanism. The feedforward controller, a neural network model of the inverse dynamics, generates feedforward control signal to the plant. The feedback control signal is produced by the error prediction mechanism. The error predictor adopts the neural network models of the forward and inverse dynamics. Simulation results are presented to demonstrate the applicability of the proposed scheme to predictive tracking control problems.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.179-187
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• 2022

The project performances can be measured in terms of meeting the project schedule, budget, and conformance to functional and technical specifications. Numerous studies have been conducted to examine the causes and effects of change orders for both vertical and horizontal construction, respectively. However, these studies mainly focus on a single project type, so this paper examines the impact of change order for cost growth and schedule overruns using four different building types to close the gap in the change order research area. A total of 211 building projects are collected from four building types: healthcare, residential, office, and education. Statistical analyses using ANOVA tests and linear regression models are used to examine the created metric $CO/day on the cost and schedule impacts. The results found that mean $CO/day values were not statistically different among building types, and that the sum of change orders is a statistically significant predictor of $CO/day. The results will help project stakeholders mitigate the negative change orders effects can be a challenge for project managers and researchers alike.

• 응용통계연구
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• 제33권4호
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• pp.483-497
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• 2020

본 연구에서는 심층 신경망모형을 사용하여 KOSPI 100의 개별 종목인 기아차 및 신세계의 주가를 예측하였다. 예측변수로는 흔히 사용되었던 기술적 변수들과 함께 온라인 뉴스로부터 도출된 감성변수를 사용하였다. 특히 소셜 네트워크 분석을 활용하여 분류된 산업군에 특화된 감성사전을 구축한 후, 감성분석을 통하여 산업군에 속하는 각 기업들의 감성점수의 평균을 산업군 감성변수로 생성하였다. 여러 예측변수들의 조합으로 이루어진 모형들 중에서 기술적 변수와 산업군의 온라인 뉴스에 기초한 감성변수를 함께 사용하였을 때 우수한 예측력과 수익률을 보여주었다.

• Geomechanics and Engineering
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• 제12권3호
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• pp.441-464
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• 2017

The determination of the mixture parameters of stabilization has become a great concern in geotechnical applications. This paper presents an effort about the application of artificial intelligence (AI) techniques including radial basis neural network (RBNN), multi-layer perceptrons (MLP), generalized regression neural network (GRNN) and adaptive neuro-fuzzy inference system (ANFIS) in order to predict the unconfined compressive strength (UCS) of silty soil stabilized with bottom ash (BA), jute fiber (JF) and steel fiber (SF) under different freeze-thaw cycles (FTC). The dosages of the stabilizers and number of freeze-thaw cycles were employed as input (predictor) variables and the UCS values as output variable. For understanding the dominant parameter of the predictor variables on the UCS of stabilized soil, a sensitivity analysis has also been performed. The performance measures of root mean square error (RMSE), mean absolute error (MAE) and determination coefficient ($R^2$) were used for the evaluations of the prediction accuracy and applicability of the employed models. The results indicate that the predictions due to all AI techniques employed are significantly correlated with the measured UCS ($p{\leq}0.05$). They also perform better predictions than nonlinear regression (NLR) in terms of the performance measures. It is found from the model performances that RBNN approach within AI techniques yields the highest satisfactory results (RMSE = 55.4 kPa, MAE = 45.1 kPa, and $R^2=0.988$). The sensitivity analysis demonstrates that the JF inclusion within the input predictors is the most effective parameter on the UCS responses, followed by FTC.