• Journal of the Korean Statistical Society
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• v.27 no.3
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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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• v.4 no.1
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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.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.2
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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.

• Journal of the Korean Society of Clothing and Textiles
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• v.34 no.6
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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.

• Journal of the Korean Society of Hazard Mitigation
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• v.2 no.4 s.7
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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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• v.20 no.3
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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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• v.20 no.3
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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.

• International conference on construction engineering and project management
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• 2022.06a
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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.

• The Korean Journal of Applied Statistics
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• v.33 no.4
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• pp.483-497
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• 2020

We used a deep neural network model for the prediction of the stock prices of Kia Motors and Shinsegae as listed in the KOSPI 100. We used an emotional variable derived from online news in addition to the various technical indicators most often used. The emotional variable used as a predictor variable was generated from the average of the emotional scores for companies in the industrial group after building an emotional dictionary specific to each industrial group classified in a social network analysis. The study was conducted with various combinations of predictors and confirmed that good predictive and profitable power could be expected when jointly using technical indicators and an emotional variable based on online news by industrial groups.

• Geomechanics and Engineering
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• v.12 no.3
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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.