• Journal of Korean Society of Water and Wastewater
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• v.21 no.1
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• pp.3-11
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• 2007

THM models have been developed in several researchers in order to better understand and manage the presence of THM in water distribution system. Several developed models were demonstrated in this study for estimating THM concentrations in target water distribution system. In order to investigate the performance of developed THM models, lab and field test were investigated. Predicted THM concentrations by all kind of models were showed good correlation with observed values. When the developed models were compared with lab and field test, the Rodriguez model during tested models was most predictive than the other models.

• Fashion & Textile Research Journal
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• v.3 no.4
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• pp.323-329
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• 2001

The purpose of this study was to identify the relationship between clothing involvement and fashion leadership of fashion models and college women. For this study, 113 fashion models and 265 female college students were analyzed. Factor analysis, Correlation, t-test and Regression analysis were used in data analyses. Clothing involvement was factor analyzed resulting five factors such as interest, pleasure, fashionability, risk perception and symbolism. Three clothing involvement factors had highly positive relations with total clothing involvement. Interest, pleasure and fashionability factors were related with each other: Fashionability and interest factors had an effect on fashion opinion-leadership and fashion innovation. Significant differences were found between fashion models and female college students in regard to clothing involvement and fashion leadership. Fashion models and female college students differed significantly in clothing interest and fashionability. Fashion models and female college students differed significantly in fashion opinion-leadership and fashion innovation. Fashion models were more fashion opinion leaders and fashion innovators than female college students.

• Ocean Systems Engineering
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• v.5 no.1
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• pp.41-54
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• 2015

This study presents the development of predictive models for uplift capacity of suction caisson in clay using an artificial intelligence technique, extreme learning machine (ELM). Other artificial intelligence models like artificial neural network (ANN), support vector machine (SVM), relevance vector machine (RVM) models are also developed to compare the ELM model with above models and available numerical models in terms of different statistical criteria. A ranking system is presented to evaluate present models in identifying the 'best' model. Sensitivity analyses are made to identify important inputs contributing to the developed models.

• Journal of the Korea Safety Management & Science
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• v.9 no.5
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• pp.43-48
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• 2007

This paper is to present linkage parameter to integrate statistical models and physical models for accelerated life test. Statistical models represent the relationship of probability distribution and life. Physical models show the relationship of life and stress. Moreover, this study proposes the four steps for construction of integrated models for accelerated life test using linkage parameter. Finally, this paper develops new integrated models such as extreme value distribution-general Eyring, linearly increasing failure rate function-general Eyring, etc., and estimates various reliability measures.

• Journal of Intelligence and Information Systems
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• v.1 no.2
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• pp.1-16
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• 1995

The field of human judgment and decision making provides useful methodologies for examining the human decision making process and substantive results. One of the methodologies is a lens model analysis which can examine valid nonlinearity in the human decision making process. Using the method, valid nonlinearity in human decision behavior can be successfully detected. Two linear(statistical) models of human experts and two nonlinear models of human experts are compared in terms of predictive accuracy (predictive validity). The results indicate that nonlinear models can capture factors(valid nonlinearity) that contribute to the expert's predictive accuracy, but not factors (inconsistency) that detract from their predictive accuracy. Then, it is argued that nonlinear models cab be more accurate than linear models, or as accurate as human experts, especially when human experts employ valid nonlinear strategies in decision making.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.2
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• pp.285-291
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• 2017

This paper proposes the use of advanced generator models in the studies of Korean power systems to accurately represent the dynamic behaviors of synchronous generators and thus to achieve a better match between transient stability simulations and reality. In the paper, GENTPF and GENTPJ models are described which have appeared over the last decade in the WECC system. Those advanced models are compared with conventional synchronous generator models such as GENROU and GENSAL, which have been used in dynamic studies of Korean power systems. The advancements are investigated by recognizing the differences in block diagram, saturation modeling, and network interface equations. Simulation comparisons between conventional and advanced models in Korean power systems are then provided. Clear distinctions identified in the simulation results demonstrate the necessity of the use of advanced generator models in Korean power system.

• Industrial Engineering and Management Systems
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• v.7 no.1
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• pp.9-22
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• 2008

As an extension of a previous work by the authors (Song and Esogbue, 2006), a new algorithm for automated modeling of nonstationary seasonal time series is presented in this paper. Issues relative to the methodology for building automatically seasonal time series models and periodic time series models are addressed. This is achieved by inspecting the trend, estimating the seasonality, determining the orders of the model, and estimating the parameters. As in our previous work, the major instruments used in the model identification process are correlograms of the modeling errors while the least square method is used for parameter estimation. We provide numerical illustrations of the performance of the new algorithms with respect to building both seasonal time series and periodic time series models. Additionally, we consider forecasting and exercise the models on some sample time series problems found in the literature as well as real life problems drawn from the retail industry. In each instance, the models are built automatically avoiding the necessity of any human intervention.

• Journal of the Korean Society of Safety
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• v.26 no.5
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• pp.130-135
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• 2011

This study deals with the accident models of arterial road sections. The objectives is to develop the models by number of lane. In pursuing the above, this study gives particular emphasis to dividing the 474 small link sections, collecting the accident data of 2007, and applying the statistical programs of SPSS17.0 and NLOGIT4.0. The main results are as follows. First, the number of accidents of two-lane roads were analyzed to be 59.9% of totals and to be the most of all. Second, one Poisson and two negative binomial regression models which were all statistically significant were developed. Finally, the common variables of all models were evaluated to be ADT and number of exit/entry which were all positive to the accidents.

• International Journal of Highway Engineering
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• v.15 no.5
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• pp.31-45
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• 2013

PURPOSES : Determination of particle packing model variables that can be used for formulation of new DEM based particle packing model by examining existing particle packing models METHODS : Existing particle packing models are thoroughly examined by analytical reformulation and sensitivity analysis in order to set up DEM based new particle packing model and to determine its variables. All model equations considered in this examination are represented with consistent expressions and are compared to each others to find mathematical and conceptual similarity in expressions. RESULTS : From the examination of existing models, it is observed that the models are very similar in their shapes although the derivation of the models may be different. As well, it is observed that variables used in some existing models are comprehensive enough to estimate particle packing but not applicable to DEM simulation. CONCLUSIONS : A set of variables that can be used in DEM based particle packing model is determined.

• Journal of Korean Society for Quality Management
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• v.39 no.2
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• pp.320-328
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• 2011

This research proposes comprehensive models for analyzing common cause failures (CCF) due to cumulative shocks and to assess system reliability under the CCF. The proposed cumulative shock models are based on the binomial failure rate (BFR) model. Six kinds of models are proposed so as to explain diverse cumulative shock phenomena. The models are composed of the initial failure probability, shape parameter, and the total shock number. Some parameters of the proposed models can not be explicitly estimated, so we adopt the Expectation-maximization (EM) algorithm in order to obtain the maximum likelihood estimator (MLE) for the parameters. By estimating the parameters for the cumulative shock models, the system reliability with CCF can be assessed sequentially according to the number of cumulative shocks. The result can be utilizes in dynamic probabilistic safety assessment (PSA), aging studies, or risk management for nuclear power plants. Replacement or maintenance policies can also be developed based on the proposed model.