• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.1
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• pp.35-41
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• 2010

In developing modern aircraft, the reconfiguration control that can improve the safety and the survivability against the unexpected failure by partitioning control surfaces into several parts has been actively studied. This paper deals with the reconfiguration control using model predictive control method considering the saturation of control surfaces under the control surface failure. Linearized aircraft model at trim condition is used as the internal model of model predictive control. We propose the controller that performs optimization using LMI (linear matrix inequalities) based semi-definite programming in case that control surface saturation occurs, otherwise, uses analytic solution of the model predictive control. The performance of the proposed control method is evaluated by nonlinear simulation under the flight scenario of control surface failure.

• Bulletin of the Korean Chemical Society
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• v.31 no.8
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• pp.2163-2169
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• 2010

G-protein coupled receptors (GPCRs) are involved in a wide variety of physiological processes and are known to be targets for nearly 50% of drugs. The various functions of GPCRs are affected by their cognate ligands which are mainly classified as agonists and antagonists. The purpose of this study is to develop a Bayesian classification model, that can predict a compound as either human GPCR agonist or antagonist. Total 6627 compounds experimentally determined as either GPCR agonists or antagonists covering all the classes of GPCRs were gathered to comprise the dataset. This model distinguishes GPCR agonists from GPCR antagonists by using chemical fingerprint, FCFP_6. The model revealed distinctive structural characteristics between agonistic and antagonistic compounds: in general, 1) GPCR agonists were flexible and had aliphatic amines, and 2) GPCR antagonists had planar groups and aromatic amines. This model showed very good discriminative ability in general, with pretty good discriminant statistics for the training set (accuracy: 90.1%) and a good predictive ability for the test set (accuracy: 89.2%). Also, receiver operating characteristic (ROC) plot showed the area under the curve (AUC) to be 0.957, and Matthew's Correlation Coefficient (MCC) value was 0.803. The quality of our model suggests that it could aid to classify the compounds as either GPCR agonists or antagonists, especially in the early stages of the drug discovery process.

• Journal of Korean Society for Quality Management
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• v.48 no.1
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• pp.171-186
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• 2020

Purpose: The purpose of this study is conducting of predictive models that considered multicollinearity of independent variables in order to carry out more efficient and reliable predictions about differential pressure in seawater reverse osmosis. Methods: The main variables of each RO system are extracted through factor analysis. Common variables are derived through comparison of RO system # 1 and RO system # 2. In order to carry out the prediction modeling about the differential pressure, which is the target variable, we constructed the prediction model reflecting the regression analysis, the artificial neural network, and the support vector machine in R package, and figured out the superiority of the model by comparing RMSE. Results: The number of factors extracted from factor analysis of RO system #1 and RO system #2 is same. And the value of variability(% Var) increased as step proceeds according to the analysis procedure. As a result of deriving the average RMSE of the models, the overall prediction of the SVM was superior to the other models. Conclusion: This study is meaningful in that it has been conducting a demonstration study of considering the multicollinearity of independent variables. Before establishing a predictive model for a target variable, it would be more accurate predictive model if the relevant variables are derived and reflected.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.204-207
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• 2009

This paper presents how to implement Markerless Augmented Reality and how to create and apply reference data sets. There are three parts related with implementation: setting camera, creation of reference data set, and tracking. To create effective reference data sets, we need a 3D model such as CAD model. It is also required to create reference data sets from various viewpoints. We extract the feature points from the model image and then extract 3D positions corresponding to the feature points using ray tracking. These 2D/3D correspondence point sets constitute a reference data set of the model. Reference data sets are constructed for various viewpoints of the model. Fast tracking can be done using a reference data set the most frequently matched with feature points of the present frame and model data near the reference data set.

• Journal of the Society of Disaster Information
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• v.15 no.2
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• pp.186-197
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• 2019

Purpose: This study intends to develop a nonlinear cyclic plasticity damage model in the framework of finite element formulation, which is capable of taking large deformation effects into account, in order to accurately predict the hysteretic behavior of stainless steel structures. Method: The new cyclic constitutive equations that utilize the combined isotropic-kinematic hardening rule for plastic deformation is incorporated into the damage mechanic model in conjunction with the large strain formulation. The damage growth law is based on the experimental observations that the evolution of microvoids yields nonlinear damage accumulation with plastic deformation. The damage model parameters and the procedure for their identification are presented. Results and Conclusion: The proposed nonlinear damage model has been verified by simulating uniaxial strain-controlled monotonic and cyclic loading tests, and successfully applied to a thin-walled stainless steel pipe subjected to constant and alternating strain-controlled cyclic loadings.

• Journal of the Korean Society of Safety
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• v.37 no.2
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• pp.1-9
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• 2022

Most factories deal with toxic or flammable chemicals in their industrial processes. These hazardous substances pose a risk of leakage due to accidents, such as fire and explosion. In the event of chemical release, massive casualties and property damage can result; hence, quantitative risk prediction and assessment are necessary. Several methods are available for evaluating chemical dispersion in the atmosphere, and most analyses are considered neutral in dispersion models and under far-field wind condition. The foregoing assumption renders a model valid only after a considerable time has elapsed from the moment chemicals are released or dispersed from a source. Hence, an initial dispersion model is required to assess risk quantitatively and predict the extent of damage because the most dangerous locations are those near a leak source. In this study, the dispersion model for initial consequence analysis was developed with three-dimensional unsteady advective diffusion equation. In this expression, instantaneous leakage is assumed as a puff, and wind velocity is considered as a coordinate transform in the solution. To minimize the buoyant force, ethane is used as leaked fuel, and two different diffusion coefficients are introduced. The calculated concentration field with a molecular diffusion coefficient shows a moving circular iso-line in the horizontal plane. The maximum concentration decreases as time progresses and distance increases. In the case of using a coefficient for turbulent diffusion, the dispersion along the wind velocity direction is enhanced, and an elliptic iso-contour line is found. The result yielded by a widely used commercial program, ALOHA, was compared with the end point of the lower explosion limit. In the future, we plan to build a more accurate and general initial risk assessment model by considering the turbulence diffusion and buoyancy effect on dispersion.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.51-61
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• 2023

Travel demand estimation of E-Scooter is the start point of solving the regional demand-supply imbalance problem and plays pivotal role in a linked transportation system such as Mobility-as-a-Service (a.k.a. MaaS). Most focuses on developing trip generation model of shared E-Scooter but it is no study on selection of an appropriate zone scale when it comes to estimating travel demand of E-Scooter. This paper aimed for selecting an optimal TAZ scale for developing trip distribution model for shared E-Scooter. The TAZ scale candidates were selected in 250m, 500m, 750m, 1,000m square grid. The shared E-Scooter usage historical data were utilized for calculating trip distance and time, and then applying to developing gravity model. Mean Squared Error (MSE) is applied for the verification step to select the best suitable gravity model by TAZ scale. As a result, 250m of TAZ scale is the best for describing practical trip distribution of shared E-Scooter among the candidates.

• International Journal of Highway Engineering
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• v.10 no.2
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• pp.167-181
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• 2008

A pavement structure analysis model plays a very important role which can correlate input variables to performance models. In this research, a standard shell element model was developed by use of the ABAQUS program so that behaviors of concrete pavements be analyzed. The model was verified in terms of its accuracy by way of comparing the results to those gathered from closed-form Solutions, the Everfe program, and the ABAQUS program with a solid model. Many input variables were analyzed in the model, and the results were stored in a database. Based on the SPSS program, stress equations with respect to temperature and curling effects were developed. All models gave over 0.90 of R2 value except the case considering top-down curling (R2=0.86)

• Journal of the Korean Society of Marine Environment & Safety
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• v.22 no.4
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• pp.335-343
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• 2016

The spatio-temporal distribution of jellyfish in Masan Bay was investigated in this study using a numerical model. First, a three-dimensional hydrodynamic model (POM) was constructed,taking into account residual flows, tides, temperature, salinity, and wind effects. A particle tracking model based on residual flow was then used to investigate the jellyfish present in Masan Port, referred to as the Heavy Industry and Gapo New Port in Masan. Jellyfish distribution was concentrated with maximum (2,533 individual) in the North Sea near Machang Bridge. Itcan be concluded that this concentration was due to multi-directional residual flows and topography effects. Residual flow currents are a dominant factor in understanding the aggregation of jellyfish, and this study used a numerical simulation of tide-induced residual currents, wind-driven currents and density currents in distinct cases to thoroughly address the topic. As a result, wind-driven currents (effect of the wind) was found to be superior to other components as an influence on the distribution of jellyfish near Machang Bridge and Modo in Masan Bay.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.11
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• pp.2335-2340
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• 2009

This paper presents how to implement Markerless Augmented Reality and how to create and apply reference data sets. There are three parts related with implementation: setting camera, creation of reference data set, and tracking. To create effective reference data sets, we need a 3D model such as CAD model. It is also required to create reference data sets from various viewpoints. We extract the feature points from the mode1 image and then extract 3D positions corresponding to the feature points using ray tracking. These 2D/3D correspondence point sets constitute a reference data set of the model. Reference data sets are constructed for various viewpoints of the model. Fast tracking can be done using a reference data set the most frequently matched with feature points of the present frame and model data near the reference data set.