• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.2
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• pp.69-78
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• 2003

This paper presents a voltage analysis method of distribution systems interconnected with DG(Distributed Generation). Nowadays, small scale DG becomes to be introduced into power distribution systems. But in that case, it is difficult to properly maintain the terminal voltage of low voltage customers by using only ULTC(Under Load Tap Changer). This paper presents a voltage analysis method of distribution systems with DC for proper voltage regulation of power distribution systems with ULTC. In order to develop the voltage analysis method, distribution system modeling method and advanced loadflow method are proposed. Proposed method has been applied to a 22.9 kV practical power distribution systems.

• Journal of Forest and Environmental Science
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• v.28 no.3
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• pp.170-178
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• 2012

Dipterocarps or Dipterocarpaceae is a commercially important timber producing and dominant keystone tree family in the rain forests of Borneo. Borneo's landscape is changing at an unprecedented rate in recent years which affects this important biodiversity. This paper attempts to model the natural occurrence (distribution including those areas with natural forests before being converted to other land uses as opposed to current distribution) of dipterocarp species in Sarawak which is important for forest biodiversity conservation and management. Local modeling method of Inverse Distance Weighting was compared with commonly used statistical method (Binary Logistic Regression) to build the best natural distribution models for three genera (12 species) of dipterocarps. Database of species occurrence data and pseudoabsence data were constructed and divided into two halves for model building and validation. For logistic regression modeling, climatic, topographical and edaphic parameters were used. Proxy variables were used to represent the parameters which were highly (p>0.75) correlated to avoid over-fitting. The results show that Inverse Distance Weighting produced the best and consistent prediction with an average accuracy of over 80%. This study demonstrates that local interpolation method can be used for the modeling of natural distribution of dipterocarp species. The Inverse Distance Weighted was proven a better method and the possible reasons are discussed.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2004.08a
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• pp.297-306
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• 2004

This study investigated Austenite Grain Size (AGS) distribution in Low-Speed Round-Oval Rolling. Rolling experiments were done along with the AGS numerical modeling to characterize the final AGS distribution and its kinetics behavior. For bar rolling experiment, we utilized the pilot rolling mill, operating at 34 fixed rpm, at POSCO Technical Research Laboratories. To investigate the microstructural observation, the rigid-viscoplastic finite element analysis was combined with Hodgson's AGS evolution model. To consider the transient thermal history in the integrative AGS modeling, additivity rule was introduced. The integrated analysis revealed that static or meta-dynamic recrystallization is responsible for the AGS difference in the inner or outer region of rolled bar. Comparative study showed that the current AGS modeling approach can be used to model the overall AGS distribution in bar rolling processes. For more accurate AGS prediction, the AGS modeling method should be verified under the various rolling conditions such as different rolling speeds and different deformations.

• Communications for Statistical Applications and Methods
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• v.25 no.1
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• pp.15-27
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• 2018

Parametric method of flood frequency analysis involves fitting of a probability distribution to observed flood data. When record length at a given site is relatively shorter and hard to apply the asymptotic theory, an alternative distribution to the generalized extreme value (GEV) distribution is often used. In this study, we consider the beta-P distribution (BPD) as an alternative to the GEV and other well-known distributions for modeling extreme events of small or moderate samples as well as highly skewed or heavy tailed data. The L-moments ratio diagram shows that special cases of the BPD include the generalized logistic, three-parameter log-normal, and GEV distributions. To estimate the parameters in the distribution, the method of moments, L-moments, and maximum likelihood estimation methods are considered. A Monte-Carlo study is then conducted to compare these three estimation methods. Our result suggests that the L-moments estimator works better than the other estimators for this model of small or moderate samples. Two applications to the annual maximum stream flow of Colorado and the rainfall data from cloud seeding experiments in Southern Florida are reported to show the usefulness of the BPD for modeling hydrologic events. In these examples, BPD turns out to work better than $beta-{\kappa}$, Gumbel, and GEV distributions.

• Journal of Ecology and Environment
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• v.43 no.2
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• pp.191-201
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• 2019

Macroecologists and biogeographers continue to predict the distribution of species across space based on the relationship between biotic processes and environmental variables. This approach uses data related to, for example, species abundance or presence/absence, climate, geomorphology, and soils. Researchers have acknowledged in their statistical analyses the importance of accounting for the effects of spatial autocorrelation (SAC), which indicates a degree of dependence between pairs of nearby observations. It has been agreed that residual spatial autocorrelation (rSAC) can have a substantial impact on modeling processes and inferences. However, more attention should be paid to the sources of rSAC and the degree to which rSAC becomes problematic. Here, we review previous studies to identify diverse factors that potentially induce the presence of rSAC in macroecological and biogeographical models. Furthermore, an emphasis is put on the quantification of rSAC by seeking to unveil the magnitude to which the presence of SAC in model residuals becomes detrimental to the modeling process. It turned out that five categories of factors can drive the presence of SAC in model residuals: ecological data and processes, scale and distance, missing variables, sampling design, and assumptions and methodological approaches. Additionally, we noted that more explicit and elaborated discussion of rSAC should be presented in species distribution modeling. Future investigations involving the quantification of rSAC are recommended in order to understand when rSAC can have an adverse effect on the modeling process.

• Smart Structures and Systems
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• v.21 no.5
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• pp.591-600
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• 2018

The probabilistic characterization of wind field characteristics is a significant task for fatigue reliability assessment of long-span railway bridges in wind-prone regions. In consideration of the effect of wind direction, the stochastic properties of wind field should be represented by a bivariate statistical model of wind speed and direction. This paper presents the construction of the bivariate model of wind speed and direction at the site of a railway arch bridge by use of the long-term structural health monitoring (SHM) data. The wind characteristics are derived by analyzing the real-time wind monitoring data, such as the mean wind speed and direction, turbulence intensity, turbulence integral scale, and power spectral density. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method is proposed to formulate the joint distribution model of wind speed and direction. For the probability density function (PDF) of wind speed, a double-parameter Weibull distribution function is utilized, and a von Mises distribution function is applied to represent the PDF of wind direction. The SQP algorithm with multi-start points is used to estimate the parameters in the bivariate model, namely Weibull-von Mises mixture model. One-year wind monitoring data are selected to validate the effectiveness of the proposed modeling method. The optimal model is jointly evaluated by the Bayesian information criterion (BIC) and coefficient of determination, $R^2$. The obtained results indicate that the proposed SQP algorithm-based finite mixture modeling method can effectively establish the bivariate model of wind speed and direction. The established bivariate model of wind speed and direction will facilitate the wind-induced fatigue reliability assessment of long-span bridges.

• Journal of Magnetics
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• v.16 no.2
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• pp.114-119
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• 2011

This paper presents a magnetization modeling method combined with material sensitivity information to identify the unknown magnetization distribution of a hull and improve the accuracy of the predicted fields. First, based on the magnetization modeling, the hull surface was divided into three-dimensional sheet elements, where the individual remanent magnetization was assumed to be constant. For a fast search of the optimum magnetization distribution on the hull, a material sensitivity formula containing the first-order gradient information of an objective function was combined with the magnetization modeling method. The feature of the proposed method is that it can provide a stable and accurate field solution, even in the vicinity of the hull. Finally, the validity of the method was tested using a scale model ship.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.27 no.3
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• pp.29-43
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• 2024

Legally protected species are one of the crucial considerations in the field of natural ecology when conducting environmental impact assessments (EIAs). The occurrence of legally protected species, especially 'Endangered Wildlife' designated by Ministry of Environment, significantly influences the progression of projects subject to EIA, necessitating clear investigations and presentations of their habitats. In perspective of statistics, a minimum of 30 occurrence coordinates is required for population prediction, but most of endangered wildlife has insufficient coordinates and it posing challenges for distribution prediction through modeling. Consequently, this study aims to propose modeling methodologies applicable when coordinate data are limited, focusing on Rodgersia podophylla, representing characteristics of endangered wildlife and northern plant species. For this methodology, 30 random sampling coordinates were used as input data, assuming little survey data, and modeling was performed using individual models included in BIOMOD2. After that, the modeling results were evaluated by using discrimination capacity and the reality reflection ability. An optimal modeling technique was proposed by ensemble the remaining models except for the MaxEnt model, which was found to be less reliable in the modeling results. Alongside discussions on discrimination capacity metrics(e.g. TSS and AUC) presented in modeling results, this study provides insights and suggestions for improvement, but it has limitations that it is difficult to use universally because it is not a study conducted on various species. By supporting survey site selection in EIA processes, this research is anticipated to contribute to minimizing situations where protected species are overlooked in survey results.

• Journal of the Korea Safety Management & Science
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• v.10 no.2
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• pp.187-193
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• 2008

Cross docking is a warehouse management concept in which items delivered to a distribution facility by inbound trucks are immediately sorted out and reorganized based on customer demands and are routed and loaded into outbound trucks for delivery to customers without actually being held in inventory in the distribution facility. In this research, the design of distribution facility for cross docking systems was studied. The objective of this research is to find the minimum number of receiving docks and shipping docks, respectively, in order to meet the daily demand of the distribution center. Two solution approaches are employed in modeling and solving the problem The first approach is mathematical modeling and the second approach is a simulation. The logic developed in the simulation model is expected to apply to the real world situation.

• Journal of Korea Multimedia Society
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• v.18 no.12
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• pp.1519-1527
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• 2015

TFT-LCD automatic defect inspection system for detecting defects in place of the visual tester does pre-processing, candidate defect pixel detection, and recognition and classification through a blob analysis. An over-detection result of defects acts as an undue burden of blob analysis for recognition and classification. In this paper, we propose defect detection method based on the histogram distribution modeling of TFT-LCD image to minimize over-detection of candidate defective pixels. Primary defect candidate pixels are detected estimating the skewness of the luminance distribution histogram of the background pixels. Based on the detected defect pixels, the defective pixels other than noise pixels are detected using the distribution histogram model of the local area. Experimental results confirm that the proposed method shows an excellent defect detection result on the image containing the various types of defects and the reduction of the degree of over-detection as well.