• Journal of the Korean Geotechnical Society
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• v.21 no.2
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• pp.145-150
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• 2005

It is useful to select an appropriate mathematical model to predict landslide. Through observation and analysis of real-time measured time series, a reasonable mathematic model is chosen to do prediction of landslide. Two theoretical models, such as polynomial function and growth model, are suggested for the description and analysis of measured defermation from an active landslides. These models are applied herein to describe the main characteristics of defermation process for two types of landslide, namely polynomial and growth models. The TRS (tensiof rotation and settlement) sensors are applied to adopt two models, and the data analysis of two field (Neurpjae and Buksil) resulted in good coincidence between measured data and models.

• Journal of Fisheries and Marine Sciences Education
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• v.11 no.2
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• pp.184-202
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• 1999

The roll damping characteristics of the three models of a small fishing(3 ton class fishing vessel), such as the bare hull, hull with bilge keels, and hull with bilge keels and a central wing are investigated by the free roll and head waves tests in calm water in a towing tank with the various forward speeds, initial angles and OG's. The experimental results are compared with the numerical results of mathematical modelings by the energy method for these three models and the energy dissipation patterns are also compared. And the wave length variations are also included. The experimental results are compared with the numerical results of mathematical modelings by the energy method for these three models and the energy dissipation patterns are also compared. The roll damping moment of the models is increased for zero speed cases, but as the speed increases, the effect of the waves on the roll damping of the models with the additional devices is negligible due to the much increased damping caused by the lift increase.

• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.282-285
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• 2001

In general, the PSS/E program based on RMS mathematical models is used for analyzing the steady state and transient stability phenomena of full-scale large power system. Whereas, the EMTDC program unlike PSS/E, studies the specific reduced small-scale power systems as a basis of instantaneous value mathematical models and used to analyze the Electro-Magnetic transient characteristics. The PSS/E provides various control models such as exciter, governor, PSS models and TGOV5 model but there are few control models in EMTDC. In this paper, we developed EMTDC model for TGOV5 of a steam turbine and boiler which represents governor action, main, reheat and low-pressure effects, including boiler effects. The EMTDC model is developed by examining PSS/E control block and using User Define Model(UDM) in addition to default.lib provided by EMTDC. We verify the correctness of developed TGOV5 model with PSS/E and EMTDC simulation results using Governor Step(GSTEP) method.

• Nuclear Engineering and Technology
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• v.49 no.1
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• pp.134-140
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• 2017

A well-performed core power control to track load changes is crucial in pressurized water reactor (PWR) nuclear power stations. It is challenging to keep the core power stable at the desired value within acceptable error bands for the safety demands of the PWR due to the sensitivity of nuclear reactors. In this paper, a state-space model predictive control (MPC) method was applied to the control of the core power. The model for core power control was based on mathematical models of the reactor core, the MPC model, and quadratic programming (QP). The mathematical models of the reactor core were based on neutron dynamic models, thermal hydraulic models, and reactivity models. The MPC model was presented in state-space model form, and QP was introduced for optimization solution under system constraints. Simulations of the proposed state-space MPC control system in PWR were designed for control performance analysis, and the simulation results manifest the effectiveness and the good performance of the proposed control method for core power control.

• Journal of the Ergonomics Society of Korea
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• v.19 no.1
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• pp.11-22
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• 2000

In this research report, we are presenting several optimization models for cognitive systems by using stimulus-response matrix (S-R Matrix). Stimulus-response matrices are widely used for tabulating results from various experiments and cognition systems design in which the recognition and confusability of stimuli. This paper is relevant to analyze the optimization/mathematical programming models. The weakness and restrictions of the existing models are resolved by generalization considering average confusion of each subset of stimuli. Also, clustering strategies are used in the extended model to obtain centers of cluster in terms of minimal confusion as well as the character of each cluster.

• Journal of the Korea Institute of Building Construction
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• v.8 no.6
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• pp.91-98
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• 2008

It is time-consuming and difficulty to solve the time-cost trade-off problems, as there are trade-offs between time and cost to complete the activities in construction projects and this problems do not have unique solutions. Typically, heuristic methods, mathematical models and GA models has been used to solve this problems. As heuristic methods and mathematical models are have weakness in solving the time-cost trade-off problems, GA based model has been studied widely in recent. This paper suggests the time-cost trade-off optimization algorithm using particle swarm optimization. The traditional particle swarm optimization model is modified to generate optimal tradeoffs among construction time and cost efficiently. An application example is analyzed to illustrate the use of the suggested algorithm and demonstrate its capabilities in generating optimal tradeoffs among construction time and cost. Future applications of the model are suggested in the conclusion.

• Communications of the Korean Mathematical Society
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• v.17 no.4
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• pp.647-673
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• 2002

There are many models to study topological $R^2$-planes. Unlike topological $R^2$-planes, it is difficult to find models to study topological R$^3$)-spaces. If an 4-dimensional affine plane intersects with R$^3$, we are able to get a geometrical structure on R$^3$ which is similar to R$^3$-space, and called $R^2$-divisible R$^3$-space. Such spatial geometric models is useful to study topological R$^3$-spaces. Hence, we introduce some classes of topological $R^2$-divisible R$^3$-spaces which are induced from 4-dimensional anne planes.

• Environmental Engineering Research
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• v.14 no.3
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• pp.143-149
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• 2009

In this paper, review of stormwater quality and quantity in the urban environment is presented. The review is presented in three parts. This second part reviews the mathematical techiques used in the stromwater quality modelling and has been undertaken by examining a number of models that are in current use. The important features of models are discussed.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.2043-2047
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• 2003

The distributions of blood pressure, blood flow, and blow volume in the left common artery (LCCA) were determined using the lumping parameter method. In order to develop a mathematical model for microcirculation in LCCA, the present study adopted preexisted set of measured morphological data on anatomy, mechanical properties of the coronary vessels, viscosity of blood, the basic laws of physics, and the appropriate boundary condition. Pressures and volumes of blood and flow resistance were expressed in terms of electrical voltages, current, and resistances, respectively, in the electrical analog model. The results of two mathematical models, symmetrical and asymmetrical models, were compared with other investigator's data. The present results were in good agreement with previous studies. It was found that the mean pressure profiles were similar in both models.

• The Pure and Applied Mathematics
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• v.27 no.2
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• pp.71-81
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• 2020

Multicollinearity is a common problem in linear regression models when two or more regressors are highly correlated, which yields some serious problems for the ordinary least square estimates of the parameters as well as model validation and interpretation. In this paper, first the problem of multicollinearity and its subsequent effects on the linear regression along with some important measures for detecting multicollinearity is reviewed, then the role of eigenvalues and eigenvectors in detecting multicollinearity are bolded. At the end a real data set is evaluated for which the fitted linear regression models is investigated for multicollinearity diagnostics.