• Journal of KIISE:Computer Systems and Theory
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• v.27 no.3
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• pp.245-254
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• 2000

This paper proposes a new discrete curvature error metric to generate LOD meshes. For mesh simplification, discrete curvatures are defined with geometric attributes, such as angles and areas of triangular polygonal model, and dihedral angles without any smooth approximation. They can represent characteristics of polygonal surface well. The new error metric based on them, discrete curvature error metric, increases the accuracy of simplified model by preserving the geometric information of original model and can be used as a global error metric. Also we suggest that LOD should be generated not by a simplification ratio but by an error metric. Because LOD means the degree of closeness between original and each level's simplified model. Therefore discrete curvature error metric needs relatively more computations than known other error metrics, but it can efficiently generate and control LOD meshes which preserve overall appearance of original shape and are recognizable explicitly with each level.

• Journal of the Korea Society for Simulation
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• v.15 no.3
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• pp.103-114
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• 2006

Now distribution centers include an ASRS (Automated Storage and Retrieving System) and automated transfer systems such as conveyors and AGV (Automated Guided Vehicle). These automated distribution centers have lots of parameters to be considered fur operating performance. The general basic parameters in the distribution centers are specifications of storage equipment, system operating rules, configuration of storage area and unit load features. In this paper, an approach using simulation and metamodeling with response Surface method to optimize the design parameters of an automated distribution center model is presented. The simulation based metamodel will constitute an efficient approximation of the system function, and the approximate function will be used to design rapid optimal parameters of the distribution center model. This paper provides a comprehensive framework for economical material flow system design using the simulation and metamodeling.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.12
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• pp.36-44
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• 2012

We had introduced the backgrounds, history and physical meanings of Jong Nang in "Jeju Jong Nang Channel Code I". In this paper, we introduce practical the root of digital human binary coded Jong Nang communications as the wooden gate in Korea Jeju Island custom. We investigate Jong Nang gatemodels as an approximation of the AWGN model. The objective is to find a deterministic model, which is accessible to capacity analysis. Furthermore, this analysis should provide insights on the capacity of the AWGN model. Motivated by backhaul cooperation in cellular networks where cooperation is among base stations, we term the interference channel with conferencing transmitters. Jong Nang communicationsis normal 3 rafters placed on two vertical stones with three holes to convey the family's whereabouts that is deterministic signal, nowadays it is applied to backhaul in mobile base station and traffic signal.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.194-202
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• 2003

In this paper, we introduce a category of Multi-FNN (Fuzzy-Neural Networks) models, analyze the underlying architectures and propose a comprehensive identification framework. The proposed Multi-FNNs dwell on a concept of fuzzy rule-based FNNs based on HCM clustering and evolutionary fuzzy granulation, and exploit linear inference being treated as a generic inference mechanism. By this nature, this FNN model is geared toward capturing relationships between information granules known as fuzzy sets. The form of the information granules themselves (in particular their distribution and a type of membership function) becomes an important design feature of the FNN model contributing to its structural as well as parametric optimization. The identification environment uses clustering techniques (Hard C - Means, HCM) and exploits genetic optimization as a vehicle of global optimization. The global optimization is augmented by more refined gradient-based learning mechanisms such as standard back-propagation. The HCM algorithm, whose role is to carry out preprocessing of the process data for system modeling, is utilized to determine the structure of Multi-FNNs. The detailed parameters of the Multi-FNN (such as apexes of membership functions, learning rates and momentum coefficients) are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the proposed model, two numeric data sets are experimented with. One is the numerical data coming from a description of a certain nonlinear function and the other is NOx emission process data from a gas turbine power plant.

• The Korean Journal of Applied Statistics
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• v.16 no.2
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• pp.455-467
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• 2003

There has been a long debate on the applicability of the chi-square approximation to statistics based on small sample size. Extending comparison results among Pearson chi-square Χ$^2$, generalized likelihood .ratio G$^2$, and the power divergence Ι(2/3) statistics suggested by Rudas(1986), recently developed disparity statistics (BWHD(1/9), BWCS(1/3), NED(4/3)) we compared and analyzed in this paper. By Monte Carlo studies about the independence model of two dimension contingency tables, the conditional model and one variable independence model of three dimensional tables, simulated 90 and 95 percentage points and approximate 95% confidence intervals for the true percentage points are obtained. It is found that the Χ$^2$, Ι(2/3), BWHD(1/9) test statistics have very similar behavior and there seem to be applcable for small sample sizes than others.

• Water for future
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• v.25 no.1
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• pp.101-110
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• 1992

In this paper for simulating spatially and temporally varied moving storm in a watershed a distributed model was developed. The model is conducted by two major flow simulations which overland flow simulation and channel network flow simulation. Two dimensional continuity equation and momentum equation of kinematic approximation are used in the overland flow simulation. On the other hand, in the channel networks simulation two types of governing equations which are one dimensional continuity and momentum equations between two adjacent sections in a channel, and continuity and energy equations at a channel junction are applied. The finite element formulations were used in the overland flow simulation and the implicit finite difference formulations were used in the channel network simulation. The finite element formulations for the overland flow are analyzed by the Gauss elimination method and the finite difference formulations for the channel network flow are analyzed by the double sweep method having advantages of computational speed and reduced computer storages. Several recurrent coefficient equations for channel network simulation are suggested in the paper.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.34 no.3
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• pp.17-24
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• 2002

An unsteady-state moisture diffusion through cellulosic fibers in paper was characterized from the moisture sorption experiment and the mathematical modeling. The sorption experiment was conducted by exposing thin dry paper specimens to a constant temperature-humidity environment. Oven dried blotting papers and filter papers were used as test samples and the gains of their weights were constantly monitored and recorded as a function of sorption time. For a mathematical approach, the moisture transport was assumed to be an one-dimensional diffusion in thickness direction through the geometrically symmetric structure of paper. The model was asymptotically simplified with a short-term approximation. It gave us a new insight into the moisture uptake phenomena as a function of square root of sorption time. The fiber-phase moisture diffusivities(FPMD) of paper samples were then determined by correlating the experimental data with the unsteady-state diffusion model obtained. Their values were found to be on the order of magnitude of $10^{-6}-10^{-7}cm^2$/min., which were equivalent to the hypothetical effective diffusion coefficients at the limit of zero porosity. The moisture sorption curve predicted from the model fairly agreed with that obtained from the experiment at some limited initial stages of the moisture uptake process. The FPMD value of paper significantly varied depending upon the current moisture content of paper. The mean FPMD was about 0.7-0.8 times as large as the short-term approximated FPMD.

• Nuclear Engineering and Technology
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• v.44 no.4
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• pp.369-378
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• 2012

A load-following operation in APR+ nuclear plants is necessary to reduce the need to adjust the boric acid concentration and to efficiently control the control rods for flexible operation. In particular, a disproportion in the axial flux distribution, which is normally caused by a load-following operation in a reactor core, causes xenon oscillation because the absorption cross-section of xenon is extremely large and its effects in a reactor are delayed by the iodine precursor. A model predictive control (MPC) method was used to design an automatic load-following controller for the integrated thermal power level and axial shape index (ASI) control for APR+ nuclear plants. Some tracking controllers employ the current tracking command only. On the other hand, the MPC can achieve better tracking performance because it considers future commands in addition to the current tracking command. The basic concept of the MPC is to solve an optimization problem for generating finite future control inputs at the current time and to implement as the current control input only the first control input among the solutions of the finite time steps. At the next time step, the procedure to solve the optimization problem is then repeated. The support vector regression (SVR) model that is used widely for function approximation problems is used to predict the future outputs based on previous inputs and outputs. In addition, a genetic algorithm is employed to minimize the objective function of a MPC control algorithm with multiple constraints. The power level and ASI are controlled by regulating the control banks and part-strength control banks together with an automatic adjustment of the boric acid concentration. The 3-dimensional MASTER code, which models APR+ nuclear plants, is interfaced to the proposed controller to confirm the performance of the controlling reactor power level and ASI. Numerical simulations showed that the proposed controller exhibits very fast tracking responses.

• Journal of Korea Water Resources Association
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• v.38 no.12 s.161
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• pp.1021-1028
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• 2005

This study purpose to develop simulation model of optimal design condition of urban storm sewer system considering risk. Urban Storm Sewer Optimal Design Model(USSOD) can compute pipe capacity, pipe slope, crown elevation, excavation depth, risk and return cost in the condition of design discharge. Rational formula is adopted for design discharge and Manning's formula is used for pipe capacity. Discrete differential dynamic programming(DDDP) technique which is a kind of dynamic programming(DP) is used for optimization and first order second moment approximation method and uncertainty analysis is also for developing model. USSOD is applied to hypothetical drainage basin to test and verify, which resulted economical and efficient design in urban drainage sewer system.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.3
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• pp.76-82
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• 2010

In this paper, we develop Linguistic Model (LM) based on information granules as a systematic approach to generating fuzzy if-then rules from a given input-output data. The LM introduced by Pedrycz is performed by fuzzy information granulation obtained from Context-based Fuzzy Clustering(CFC). This clustering estimates clusters by preserving the homogeneity of the clustered patterns associated with the input and output data. Although the effectiveness of LM has been demonstrated in the previous works, it needs to improve in the sense of performance. Therefore, we focus on the automatic generation of linguistic contexts, addition of bias term, and the transformed form of consequent parameter to improve both approximation and generalization capability of the conventional LM. The experimental results revealed that the improved LM yielded a better performance in comparison with LM and the conventional works for automobile MPG(miles per gallon) predication and Boston housing data.