• Journal of the Korean Association of Geographic Information Studies
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• v.1 no.1
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• pp.99-108
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• 1998

In order to represent the surface characteristics in local meteorological model, soil type, vegetation index, surface roughness length, surface albedo and leaf area index should be prescribed on the surface process parameterization. In this study, the $1^{\circ}/1^{\circ}leaf$ area index, surface roughness length, and snow free surface albedo and fine mesh NDVI with seasonal variation derived from the satellite observation were applied to the land surface process parameterization. From comparison between with and without satellite data in the interactions between biosphere and atmosphere, land and atmosphere, the sensitivity of the simulated heat, energy and water vapor fluxes, ground temperature, wind, canopy water content, specific humidity, and precipitation fields were investigated.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.318-326
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• 2022

Marketing stress testing is a new method of identifying the company's strengths and weaknesses in a turbulent environment. Technically, this is a complex procedure, so it involves artificial intelligence and machine learning. The main problem is currently the development of methodological approaches to the development of the company's digital model, which will provide a framework for machine learning. The aim of the study was to identify and develop an author's approach to the parameterization of the company's business processes for machine learning-based marketing stress testing. This aim provided the company's activities to be considered as a set of elements (business processes, products) and factors that affect them (marketing environment). The article proposes an author's approach to the parameterization of the company's business processes for machine learning-based marketing stress testing. The proposed approach includes four main elements that are subject to parameterization: elements of the company's internal environment, factors of the marketing environment, the company' core competency and factors impacting the company. Matrices for evaluating the results of the work of expert groups to determine the degree of influence of the marketing environment factors were developed. It is proposed to distinguish between mega-level, macro-level, meso-level and micro-level factors depending on the degree of impact on the company. The methodological limitation of the study is that it involves the modelling method as the only one possible at this stage of the study. The implementation limitation is that the proposed approach can only be used if the company plans to use machine learning for marketing stress testing.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09b
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• pp.95-98
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• 2003

In this paper, an adaptive fuzzy neural control of unknown nonlinear systems based on the rapid learning algorithm is proposed for optimal parameterization. We combine the advantages of fuzzy control and neural network techniques to develop an adaptive fuzzy control system for updating nonlinear parameters of controller. The Fuzzy Neural Network(FNN), which is constructed by an equivalent four-layer connectionist network, is able to learn to control a process by updating the membership functions. The free parameters of the AFN controller are adjusted on-line according to the control law and adaptive law for the purpose of controlling the plant track a given trajectory and it's initial values are off-line preprocessing, In order to improve the convergence of the learning process, we propose a rapid learning algorithm which combines the error back-propagation algorithm with Aitken's $\delta$$\^$2/ algorithm. The heart of this approach ls to reduce the computational burden during the FNN learning process and to improve convergence speed. The simulation results for nonlinear plant demonstrate the control effectiveness of the proposed system for optimal parameterization.

• Journal of Korea Multimedia Society
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• v.6 no.7
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• pp.1302-1311
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• 2003

This paper presents a more stable method of multiresolution editing for a triangular mesh. The basic idea of our paper is to embed an editing area of a mesh onto a 2D region and to produce 3D surfaces which interpolate the editing-information. In this paper, we adopt the extended convex combination approach based on the shape-preserving parameterization for the embedding, which guarantees no self-intersection on the 2D embedded mesh. That is, the result of the embedding is stable. Moreover, we adopt the multi-level B-spline approach to generate the surface containing all of 3D editing-information, which can make us control the editing area in several levels. Hence, this method supports interactive editing and thus can produce intuitive editing results.

• Journal of Biomedical Engineering Research
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• v.30 no.4
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• pp.294-305
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• 2009

The bio signals essentially have different characteristics in each person. And the main purpose of automatic diagnosis algorithm based on bio signals focuses on discriminating differences of abnormal state from personal differences. In this paper, we propose automatic ECG diagnosis algorithm which discriminates normal heart beats from premature ventricular contraction using optimization of wavelet parameterization to solve that problem. The proposed algorithm optimizes wavelet parameter to let energy of signal be concentrated on specific scale band. We can reduce the personal differences and consequently highlight the differences coming from arrhythmia via this process. The proposed algorithm using ELM as a classifier show high discrimination performance between normal beat and PVC. From the experimental results on MIT-BIH arrhythmia database the performances of the proposed algorithm are 98.1% in accuracy, 93.0% in sensitivity, 96.4% in positive predictivity, and 0.8% in false positive rate. This results are similar or higher then results of existing researches in spite of small human intervention.

• The Korean Journal of Quaternary Research
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• v.24 no.1
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• pp.1-10
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• 2010

The Indian summer monsoon behaved an abnormal way in 2002 and as a result there was a large deficiency in precipitation (especially in July) over a large part of the Indian subcontinent. For the study of deficient monsoon of 2002, a recent version of the NCAR regional climate model (RegCM3) has been used to examine the important features of summer monsoon circulations and precipitation during 2002. The main characteristics of wind fields at lower level (850 hPa) and upper level (200 hPa) and precipitation simulated with the RegCM3 over the Indian subcontinent are studied using different cumulus parameterization schemes namely, mass flux schemes, a simplified Kuo-type scheme and Emanuel (EMU) scheme. The monsoon circulation features simulated by RegCM3 are compared with the NCEP/NCAR reanalysis and simulated precipitation is validated against observation from the Global Precipitation Climatology Centre (GPCC). Validation of the wind fields at lower and upper levels show that the use of Arakawa and Schubert (AS) closure in Grell convection scheme, a Kuo type and Emanuel schemes produces results close to the NCEP/NCAR reanalysis. Similarly, precipitation simulated with RegCM3 over different homogeneous zones of India with the AS closure in Grell is more close to the corresponding observed monthly and seasonal values. RegcM3 simulation also captured the spatial distribution of deficient rainfall in 2002.

• Journal of Computational Design and Engineering
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• v.2 no.2
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• pp.88-95
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• 2015

Recently, fiber composite materials have been attracting attention from industry because of their remarkable material characteristics, including light weight and high stiffness. However, the costs of products composed of fiber materials remain high because of the lack of effective manufacturing and designing technologies. To improve the relevant design technology, this paper proposes a novel simulation method for deforming fiber materials. Specifically, given a 3D model with constant thickness and known fiber orientation, the proposed method simulates the deformation of a model made of thick fiber-material. The method separates a 3D sheet model into two surfaces and then flattens these surfaces into two dimensional planes by a parameterization method with involves cross vector fields. The cross vector fields are generated by propagating the given fiber orientations specified at several important points on the 3D model. Integration of the cross vector fields gives parameterization with low-stretch and low-distortion.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.8
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• pp.1377-1383
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• 2008

Parameterization of a 3D triangular mesh is a fundamental problem in various applications of geometric modeling and computer graphics. There are two major paradigms in mesh parameterization: energy functional minimization and the convex combination approach. In general, the convex combination approach is wifely used because of simple concept and one-to-one mapping. However, the approach has some problems such as high distortion near the boundary and time complexity. Moreover, the stability of the linear system may not be preserved according to the geometric information of the mesh. In this paper, we present an extension of the convex combination approach based on the mean value coordinates, which resolves the drawbacks of the convex combination approach. This may be a more practical solution because it is able to generate a stable linear system in a short time.

• Journal of KSNVE
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• v.8 no.5
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• pp.964-973
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• 1998

In this paper we propose a levitation and imbalance compensation controller design methodology of magnetic bearing system. In order to achieve levitation and elimination of unbalance vibartion in some operation speed we use the discrete-time Q-parameterization control. When rotor speed p = 0 there are no rotor unbalance, with frequency equals to the rotational speed. So in order to make levitatiom we choose the Q-parameterization controller free parameter Q such that the controller has poles on the unit circle at z = 1. However, when rotor speed p $\neq$ 0 there exist sinusoidal disturbance forces, with frequency equals to the rotational speed. So in order to achieve asymptotic rejection of these disturbance forces, the Q-parameterization controller free parameter Q is chosen such that the controller has poles on the unit circle at z = $exp^{ipTs}$ for a certain speed of rotation p ( $T_s$ is the sampling period). First, we introduce the experimental setup employed in this research. Second, we give a mathematical model for the magnetic bearing in difference equation form. Third, we explain the proposed discrete-time Q-parameterization controller design methodology. The controller free parameter Q is assumed to be a proper stable transfer function. Fourth, we show that the controller free parameter which satisfies the design objectives can be obtained by simply solving a set of linear equations rather than solving a complicated optimization problem. Finally, several simulation and experimental results are obtained to evaluate the proposed controller. The results obtained show the effectiveness of the proposed controller in eliminating the unbalance vibrations at the design speed of rotation.

• Atmosphere
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• v.32 no.2
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• pp.103-118
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• 2022

This study evaluates the simulated meteorological fields with a particular focus on the low-level wind, which plays an important role in air pollutants dispersion, under the varying synoptic environment. Additionally, the effects of subgrid-scale orography parameterization and improved topography/land-use data on the simulated low-level wind is investigated. The WRF model version 4.1.3 is utilized to simulate two cases that were affected by different synoptic environments. One case from 2 to 6 April 2012 presents the substantial low-level wind speed over the Korean peninsula where the synoptic environment is characterized by the baroclinic instability. The other case from 14 to 18 April 2012 presents the relatively weak low-level wind speed and distinct diurnal cycle of low-level meteorological fields. The control simulations of both cases represent the systematic overestimation of the low-level wind speed. The positive bias for the case under the baroclinic instability is considerably alleviated by applying the subgrid-scale orography parameterization. However, the improvement of wind speed for the other case showing relatively weak low-level wind speed is not significant. Applying the high-resolution topography and land-use data also improves the simulated wind speed by reducing the positive bias. Our analysis shows that the increased roughness length in the high-resolution topography and land-use data is the key contributor that reduces the simulated wind speed. The simulated wind direction is also improved with the high-resolution data for both cases. Overall, our study indicates that wind forecasts can be improved through the application of the subgrid-scale orography parameterization and high-resolution topography/land-use data.