• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.4
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• pp.867-874
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• 1994

The complex nature of low flow transport and transformation of nonconservative pollutants in natural streams has been investigated using a numerical solution of a proposed mathematical model that is based on a pair of mass balance equations describing the advection, dispersion, decay and mass exchange mechanisms in streams and in storage zones. In the present study, a mathematical model (named "Storage-Transformation Model") has been developed to predict adequately the non-Fickian nature of mixing and transformation mechanisms for decaying substances in natural streams under low flow conditions. Comparisons of the computed concentration-time curves with the measured data show that the Storage-Transformation Model yields better agreements in general shape, peak concentration and time to peak than the conventional 1-D dispersion model. The proposed model shows significant improvement over the 1-D dispersion model in predicting natural transport and transformation processes in streams through pools and riffles.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1812-1818
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• 2005

Even though SMA actuators have high power to volume ratio, there exist disadvantages such as hysteresis and saturation. So the model identification for SMA actuators is very difficult. For the qualitative model identification, we described the behavior of SMA actuators using a so-called diagonal model, which can readily expect the turning point of an incomplete phase transformation. For the quantitative model identification, we developed the general dynamics of SMA actuators using the modified Liang's model. Using this dynamics we can describe the hysteresis and the saturation very well. It is also very important to notice that the modified Liang's model maintains a continuous martensite fraction at the change of the phase transformation but the original model cannot.

• Transactions of Materials Processing
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• v.19 no.8
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• pp.468-473
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• 2010

Shape memory alloys (SMAs) have the ability to recover their original shape upon thermo-mechanical loading even after large inelastic deformation. The unique feature is known as pseudoelasticity and shape memory effect caused by the crystalline structural transformation between two solid-state phases called austenite and martensite. To support the engineering application, a number of constitutive models, which can be formally classified into either micromechanics-based or phenomenological model, have been developed. Most of the constitutive models include a kinetic law governing the crystallographic transformation. The present work presents a one-dimensional, phenomenological constitutive model for SMAs in the context of the unified viscoplasticity theory. The proposed model does not incorporate the complex mechanisms of phase transformation. Instead, the effects induced by the transformation are depicted through the growth law for the back stress that is an internal state variable of the model.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.2
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• pp.103-129
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• 1998

Corporate level decision making with multiple decision makers in a consistent way is essential in Decision Support System. However, since the decision makers have different interests and knowledge, the models used by them are also different in their level of abstraction. This makes decision makers waste a lot of efforts for an integrated decision making. The purpose of this paper is to propose an integration mechanism so that collaborative decision making models may be used synthetically in multi-abstraction level. Models are classified as multimedia model, mathematical model, qualitative model, causal ＆ directional model, causal model, directional model and relationship model according to the level of abstraction. The proposed integration mechanism consists of model interpretation phase. model transformation phase, and model integration phase. Specifically, the model transformation Phase is divided into (1) model tightening mode which gather information to make a model transformed into upper level model, and (2) model relaxing mode which makes lower level model. In the model integration phase, models of same level are to be integrated schematically. An illustrative M＆A-decision example is given to show the possibility of the methodology.

• The Korean Journal of Applied Statistics
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• v.37 no.1
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• pp.39-48
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• 2024

In this paper, we introduce an extended GARCH model designed to address asymmetric leverage effects. The variance in the standard GARCH model is composed of past conditional variances and past squared residuals. However, it is not possible to model asymmetric leverage effects with squared residuals alone, so in this paper, we propose a new extended GARCH model to explain the leverage effects using the Yeo-Johnson transformation which adjusts transformation parameter to make asymmetric data more normal or symmetric. We utilize the reverse properties of Yeo-Johnson transformation to model asymmetric volatility. We investigate the characteristics of the proposed model and parameter estimation. We also explore how to derive forecasts and forecast intervals in the proposed model. We compare it with standard GARCH and other extended GARCH models that model asymmetric leverage effects through empirical data analysis.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.3
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• pp.48-56
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• 2004

This research focuses on the development of a fast datum transformation model to be used in GIS that utilizes real-time data transformation. Instance, when a GIS data constructed according to a datum is conformed to another datum, instead of transforming the axes of the original data, the data is transformed right before the results are reflected on the monitor. In this research, the prospects of calculating transformation parameters for every grid cells on the area based on two-dimensional conformal transformation model in order to decrease real-time datum transformation time while maintaining a high accuracy has been investigated. Research results showed that for a fixed area, the accuracies of the two-dimensional conformal transformation and the three-dimensional datum transformation, which requires more computing time, were almost equal and fast transformation speed, high accuracy real-time datum transformation is made feasible by implementing the grid-divided two-dimensional conformal transformation model.

• Journal of KIISE:Software and Applications
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• v.30 no.12
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• pp.1172-1184
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• 2003

At present, new techniques based on different component reference models for the integration of component and system of different platforms, such as EJB and COM+, are introduced. The operation between the components in the identical component platform is realized by the composition at the source level. In case of the different component platform, however, it is impossible to use combined components in real condition although they are components of similar domain. In this paper we proposed a solution for the composition problem by using component transformation methodology based on model between EJB and COM+ components which are different components. For the composition between EJB and COM+ components, we compared and analyzed each reference model, then proposed the Virtual Component Model which is implementation independent and the Implementation Table for the mutual conversion. Reffering to the Virtual Component Model and the Implementation Table, we can generalize each Implementation model to the Virtual Component Model, make the Virtual Component Model which is implementation independent through the virtual component modeling, transform EJB and COM+ components selectively. Proposing the effective Model Transformation method to the different component platform, we can combine EJB and COM+ components.

• The Journal of Korea Robotics Society
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• v.4 no.3
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• pp.185-191
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• 2009

This paper presents a new shape-based algorithm using affine category shape model for object category recognition and model learning. Affine category shape model is a graph of interconnected nodes whose geometric interactions are modeled using pairwise potentials. In its learning phase, it can efficiently handle large pose variations of objects in training images by estimating 2-D homography transformation between the model and the training images. Since the pairwise potentials are defined on only relative geometric relationship betweenfeatures, the proposed matching algorithm is translation and in-plane rotation invariant and robust to affine transformation. We apply spectral matching algorithm to find feature correspondences, which are then used as initial correspondences for RANSAC algorithm. The 2-D homography transformation and the inlier correspondences which are consistent with this estimate can be efficiently estimated through RANSAC, and new correspondences also can be detected by using the estimated 2-D homography transformation. Experimental results on object category database show that the proposed algorithm is robust to pose variation of objects and provides good recognition performance.

• Journal of the Korean Society for Heat Treatment
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• v.37 no.3
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• pp.103-113
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• 2024

Martensite volume fraction significantly affects the mechanical properties of alloy steels. Martensite start temperature (M_s), transformation temperature for martensite 50 vol.% (M₅₀), and transformation temperature for martensite 90 vol.% (M₉₀) are important transformation temperatures to control the martensite phase fraction. Several researchers proposed empirical equations and machine learning models to predict the Ms temperature. These numerical approaches can easily predict the Ms temperature without additional experiment and cost. However, to control martensite phase fraction more precisely, we need to reduce prediction error of the Ms model and propose prediction models for other martensite transformation temperatures (M₅₀, M₉₀). In the present study, machine learning model was applied to suggest the predictive model for the Ms, M50, M90 temperatures. To explain prediction mechanisms and suggest feature importance on martensite transformation temperature of machine learning models, the explainable artificial intelligence (XAI) is employed. Random forest regression (RFR) showed the best performance for predicting the Ms, M50, M90 temperatures using different machine learning models. The feature importance was proposed and the prediction mechanisms were discussed by XAI.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.6_2
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• pp.613-623
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• 2007

The main purpose of this study is to investigate the coordinate transformation based on two different systems between local geodetic datum(tokyo datum) and international geocentric datum(new Korea geodetic datum). For this purpose, three methods were used to determine seven parameters as follows: Bursa-Wolf model, Molodensky-Badekas model, and Veis model. Also, we adopted multiple regression equation method to convert from Tokyo datum to KTRF. We used 935 control points as a common points and applied gross error analysis for detecting the outlier among those control points. The coordinate transformation was carried out using similarity transformation applied the obtained seven parameters and the precision of transformed coordinate was evaluated about 9,917 third or forth order control points. From these results, it was found that Bursa-Wolf model and Molodensky-Badekas model are more suitable than other for the determination of transformation parameters in Korea. And, transforming accuracy using MRE is lower than other similarity transformation model.