• The KIPS Transactions:PartD
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• v.19D no.1
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• pp.81-94
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• 2012

FORM(Feature-Oriented Reuse Method) is one of representative product line engineering methods. The essence of FORM is the FORM architecture models, which can be reused in the development of multiple products of a software product line. The FORM architecture models, however, have the following problems when applied in practice. First, they are not standardized models like UML(Unified Modeling Language) and therefore they can be constructed only through a specific modeling tool. Second, they do not represent architectural variability explicitly. Instead their variability is only managed through a mapping from a feature model. To address these two problems, we developed at first a method for representing the FORM architecture models using UML, which enables the FORM architecture models to be constructed through various available UML modeling tools. Also, we developed an effective method for representing as well as managing the variability of the FORM architecture models through a mapping from a feature model.

• School Mathematics
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• v.14 no.4
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• pp.495-516
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• 2012

This study distinguished thinking related to statistical variability into six components - the noticing of variability, the explanation of variability, the control of variability, the modeling of variability, the understanding of samples, and the understanding of sampling distribution and investigated the relationships among the thinking components. This study found that this distinction of thinking components related to statistical variability is reasonable. The results showed that each correlation coefficient of the modeling of variability, the understanding of samples, and the understanding of sampling distribution with regard to the noticing of variability, the explanation of variability, and the control of variability is similar. Based on this results, new variable, the understanding of sampling, has been drawn. The results also showed that while the noticing of variability and the control of variability influence the understanding of sampling, the explanation of variability does not influence it.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.67-74
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• 2008

The regional-scale transient groundwater-river interaction model is developed to gain a better understanding of the regional-scale relationships and interactions between groundwater and river system and quantify the residual river flow after groundwater abstraction from the aquifers with climate variability in the Waimea Plains, New Zealand. The effect of groundwater abstraction and climate variability on river flows is evaluated by calculating river flows at the downstream area for three different drought years (a 1 in 10 drought year, 1 in 20 drought year, and 1 in 24 drought year) and an average year with metered water abstraction data. The effect of future water demand (50 year projection) on river flows is also evaluated. A significant increase in the occurrence of zero flow, or very low flow of 100 L/sec at the downstream area is predicted due to large groundwater abstraction increase with climate variability. Modeling results shows the necessity of establishing dynamic cutback scenarios of water usage to users over the period of drought conditions considering different climate variability from current allocation limit to reduce the occurrence of low flow conditions at the downstream area.

• Computational Structural Engineering : An International Journal
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• v.1 no.2
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• pp.139-150
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• 2001

An initial approach for the identification of physical causes underlying the spatial coherency of seismic ground motions it presented. The approach relies on the observation that amplitude and phase variability of seismic data recorded over extended areas around the amplitude and phase of a common, coherent component are correlated. It suffices then to examine the physical causes for the amplitude variability in the seismic motions, in order to recognize the causes for the phase variability and, consequently, the spatial coherency. In this study, the effect of randomness in the shear wave velocity at a site on the amplitude variability of the surface motions mi investigated by means of simulations. The amplitude variability of the simulated motions around the amplitude of the common component is contained within envelope functions, the shape of which suggests, on a preliminary basis, the trend of the decay of coherency with frequency.

• Journal of the Korean Chemical Society
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• v.67 no.1
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• pp.42-53
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• 2023

The purpose of this study is to obtain basic data for constructing a modeling practice program integrated with meta-modeling knowledge by analyzing the cognition level for each meta-modeling knowledge components through modeling practice in the context of the chemistry discipline content. A chemistry teacher conducted inquiry-based modeling practice including anomalous phenomena for 16 students in the second year of a science gifted school, and in order to analyze the cognition level for each of the three meta-modeling knowledge components such as model variability, model multiplicity, and modeling process, the inquiry notes recorded by the students and observation note recorded by the researcher were used for analysis. The recognition level was classified from 0 to 3 levels. As a result of the analysis, it was found that the cognition level of the modeling process was the highest and the cognition level of the multiplicity of the model was the lowest. The cause of the low recognitive level of model variability is closely related to students' perception of conceptual models as objective facts. The cause of the low cognitive level of model multiplicity has to do with the belief that there can only be one correct model for a given phenomenon. Students elaborated conceptual models using symbolic models such as chemical symbols, but lacked recognition of the importance of data interpretation affecting the entire modeling process. It is necessary to introduce preliminary activities that can explicitly guide the nature of the model, and guide the importance of data interpretation through specific examples. Training to consider and verify the acceptability of the proposed model from a different point of view than mine should be done through a modeling practice program.

• Earthquakes and Structures
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• v.12 no.2
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• pp.165-175
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• 2017

This paper studies soil properties uncertainty and its implementation in the seismic response evaluation of structures. For this, response sensitivity of two 4- and 12-story RC shear walls to the soil properties uncertainty by considering soil structure interaction (SSI) effects is investigated. Beam on Nonlinear Winkler Foundation (BNWF) model is used for shallow foundation modeling and the uncertainty of soil properties is expanded to the foundation stiffness and strength parameters variability. Monte Carlo (MC) simulation technique is employed for probabilistic evaluations. By investigating the probabilistic evaluation results it's observed that as the soil and foundation become stiffer, the soil uncertainty is found to be less important in influencing the response variability. On the other hand, the soil uncertainty becomes more important as the foundation-structure system is expected to experience nonlinear behavior to more sever degree. Since full This paper studies soil properties uncertainty and its implementation in the seismic response evaluation of structures. For this, response sensitivity of two 4- and 12-story RC shear walls to the soil properties uncertainty by considering soil structure interaction (SSI) effects is investigated. Beam on Nonlinear Winkler Foundation (BNWF) model is used for shallow foundation modeling and the uncertainty of soil properties is expanded to the foundation stiffness and strength parameters variability. Monte Carlo (MC) simulation technique is employed for probabilistic evaluations. By investigating the probabilistic evaluation results it's observed that as the soil and foundation become stiffer, the soil uncertainty is found to be less important in influencing the response variability. On the other hand, the soil uncertainty becomes more important as the foundation-structure system is expected to experience nonlinear behavior to more sever degree. Since full probabilistic analysis methods like MC commonly are very time consuming, the feasibility of simple approximate methods' application including First Order Second Moment (FOSM) method and ASCE41 proposed approach for the soil uncertainty considerations is investigated. By comparing the results of the approximate methods with the results obtained from MC, it's observed that the results of both FOSM and ASCE41 methods are in good agreement with the results of MC simulation technique and they show acceptable accuracy in predicting the response variability.

• Ocean Systems Engineering
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• v.7 no.4
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• pp.319-328
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• 2017

Higher prediction efficacy is a very challenging task in any field of engineering. Due to global warming, there is a considerable increase in the global sea level. Through this work, an attempt has been made to find the sea level variability due to climate change impact at Haldia Port, India. Different statistical downscaling techniques are available and through this paper authors are intending to compare and illustrate the performances of three regression models. The models: Wavelet Neural Network (WNN), Minimax Probability Machine Regression (MPMR), Feed-Forward Neural Network (FFNN) are used for projecting the sea level variability due to climate change at Haldia Port, India. Model performance indices like PI, RMSE, NSE, MAPE, RSR etc were evaluated to get a clear picture on the model accuracy. All the indices are pointing towards the outperformance of WNN in projecting the sea level variability. The findings suggest a strong recommendation for ensembled models especially wavelet decomposed neural network to improve projecting efficiency in any time series modeling.

• International journal of steel structures
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• v.18 no.5
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• pp.1607-1616
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• 2018

Quantitatively modeling and propagating all sources of uncertainty stand at the core of seismic fragility assessment of structures. This paper investigates the effects of various sources of uncertainty on seismic responses and seismic fragility estimates of single-layer reticulated domes. Sensitivity analyses are performed to examine the sensitivity of typical seismic responses to uncertainties in structural modeling parameters, and the results suggest that the variability in structural damping, yielding strength, steel ultimate strain, dead load and snow load has significant effects on the seismic responses, and these five parameters should be taken as random variables in the seismic fragility assessment. Based on this, fragility estimates and fragility curves incorporating different levels of uncertainty are obtained on the basis of the results of incremental dynamic analyses on the corresponding set of 40 sample models generated by Latin Hypercube Sampling method. The comparisons of these fragility curves illustrate that, the inclusion of only ground motion uncertainty is inappropriate and inadequate, and the appropriate way is incorporating the variability in the five identified structural modeling parameters as well into the seismic fragility assessment of single-layer reticulated domes.

• Journal of KIISE:Software and Applications
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• v.31 no.8
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• pp.1027-1042
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• 2004

A Software Component is a module that is reused among a lot of projects, systems, and companies rather than a single application. Components can be reused in various systems if they provide not only the common functionalities required in many applications but also the diverse aspects to be customized for being suitable for customers' demands. From the development phase, components should be designed and developed considering the variable aspects they have for convenient customization. Easily customized components can be frequently reused in lots of applications. In the literature, there are some modeling and customizing techniques. But they suggested only conceptual or basic methods based on Object-Oriented. And the practical instructions for reusing component were not provided sufficiently. Moreover, there are few techniques that consider the proper variability types components have. Thus, those techniques are not appropriate for applying to black box component completely developed and released. In this paper, we classify variabilities that components have in functional aspect into two categories. The one is logic variability, and the other is workflow variability. For each classified variability, we propose the three kind of modeling techniques, which are selection, plug in and externalization. Also detailed instructions for practical design and application are provided.

• 한국방재학회:학술대회논문집
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• 2010.02a
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• pp.51.2-51.2
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• 2010

A novel approach of Poisson cluster stochastic rainfall generator was validated in its ability to reproduce important rainfall and watershed response characteristics at 104 locations of the United States. The suggested novel approach - The Hybrid Model(THM), as compared to the traditional ones, has an additional function to account for the year-to-year variability of rainfall statistics. The two-sample Kolmogorov-Smirnov test was used to see how well THM and traditional approach of Poisson cluster rainfall model reproduce the distribution of the following hydrologic variables: monthly maximum rainfall depths with 1, 3, 6, 12, and 24 hour duration, monthly maximum flow peaks at the virtual watersheds with Curve Number of 50, 60, 70, 80 and 90; and monthly runoff depths at the same virtual watersheds. In all of the testing variables, THM significantly outperformed the traditional approach. This result indicates that the year-to-year variability of rainfall statistics contains important information about the characteristics of rainfall processes that were not considered by the conventional approach of Poisson cluster rainfall modeling and that further considering it in rainfall simulation will enhance the performance of the rainfall models.