• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.3
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• pp.18-25
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• 2005

Existing methods have performed the reliability analysis using nonlinear optimization techniques. This is mainly due to the fact that they directly apply Multidisciplinary Design Optimization(MDO) frameworks to the reliability analysis formulation. Accordingly, the reliability analysis and the Multidisciplinary Analysis(MDA) are tightly coupled in a single optimizer, which hampers utilizing the recursive and function-approximation based reliability analysis methods such as the Advanced First Order Reliability Method(AFORM). In order to utilize the efficient reliability analysis method under multidisciplinary analysis systems, we propose a new strategy named Sequential Approach on Reliability Analysis under Multidisciplinary analysis systems(SARAM). In this approach, the reliability analysis and the MDA are decomposed and arranged in a sequential manner, making a recursive loop. The efficiency of the SARAM method was verified using three illustrative examples taken from the literatures. Compared with existing methods, it showed the least number of subsystem analyses over other methods while maintaining accuracy.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.489-498
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• 2023

Advancements in hardware performance and computing technology have facilitated the progress of climate prediction models to address climate change. The Korea Meteorological Administration employs the GloSea6 model with supercomputer technology for operational use. Various universities and research institutions utilize the Low-GloSea6 model, a low-resolution coupled model, on small to medium-scale servers for weather research. This paper presents an analysis using Intel VTune Profiler on Low-GloSea6 to facilitate smooth weather research on small to medium-scale servers. The tri_sor_dp_dp function of the atmospheric model, taking 1125.987 seconds of CPU time, is identified as a hotspot. Nonlinear regression models, a machine learning technique, are applied and compared to existing functions conducting numerical operations. The K-Nearest Neighbors regression model exhibits superior performance with MAE of 1.3637e-08 and SMAPE of 123.2707%. Additionally, the Light Gradient Boosting Machine regression model demonstrates the best performance with an RMSE of 2.8453e-08. Therefore, it is confirmed that applying a nonlinear regression model to the tri_sor_dp_dp function during the execution of Low-GloSea6 could be a viable alternative.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.26 no.4
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• pp.283-292
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• 2013

The objective of this research is to evaluate of global and local damage for steel-concrete composite structures under highway bridge exposed to fire loading. To enhance the accuracy and efficiency of the numerical analysis, the proposed transient nonlinear thermal structure interaction(TSI) parallel fire analysis method is implemented in ANSYS. To validate the TSI parallel fire analysis method, a comparison is made with the standard fire test results. The proposed TSI parallel fire analysis method is applied to fire damage analysis and performance evaluation for Buchen highway bridge. The result of analysis, temperature of low flange and web are exceed the critical temperature. The deflection and deformation state show good agreement with the fire accident of buchen highway bridge.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.542-549
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• 2023

Since the growing interest in surrogate modeling, there has been continuous research aimed at simulating nonlinear chemical processes using data-driven machine learning. However, the opaque nature of machine learning models, which limits their interpretability, poses a challenge for their practical application in industry. Therefore, this study aims to analyze chemical processes using Explainable Artificial Intelligence (XAI), a concept that improves interpretability while ensuring model accuracy. While conventional sensitivity analysis of chemical processes has been limited to calculating and ranking the sensitivity indices of variables, we propose a methodology that utilizes XAI to not only perform global and local sensitivity analysis, but also examine the interactions among variables to gain physical insights from the data. For the ammonia synthesis process, which is the target process of the case study, we set the temperature of the preheater leading to the first reactor and the split ratio of the cold shot to the three reactors as process variables. By integrating Matlab and Aspen Plus, we obtained data on ammonia production and the maximum temperatures of the three reactors while systematically varying the process variables. We then trained tree-based models and performed sensitivity analysis using the SHAP technique, one of the XAI methods, on the most accurate model. The global sensitivity analysis showed that the preheater temperature had the greatest effect, and the local sensitivity analysis provided insights for defining the ranges of process variables to improve productivity and prevent overheating. By constructing alternative models for chemical processes and using XAI for sensitivity analysis, this work contributes to providing both quantitative and qualitative feedback for process optimization.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.29 no.5
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• pp.405-411
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• 2016

This paper presented the theory related to a two dimensional linear cross-sectional analysis, recovery relationship and a one-dimensional nonlinear beam analysis for composite beam with initial twist and high aspect ratio. Using VABS including related theory, preceding research data of the composite wing structure has been modeled and compared. Cross-sectional analysis was performed and 1-D beam was modeled at cutting point including all the details of real geometry and material. The 3-D strain distribution and margin of safety at recovery point was calculated based on the global behavior of the 1-D beam analysis and visualize numerical results.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.19 no.2
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• pp.128-135
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• 2007

In response to anthropogenic global warming due to a buildup greenhouse gas, the effect of the air temperature on water temperature has been noticed. Therefore, effects have been made to build an air/water temperature relationship at three study regions using the data collected by the Ministry of the Maritime Affairs and Fisheries (MOMAF). The air/water relationship varies with time-scale and weekly time-scale was chosen for the study. The data were fitted to the S-shaped non-linear relationship, and the parameters for the S-curve were derived using a single-criteria multi-parameter optimization scheme. Separate regression curves were fitted to consider seasonal hysteresis at the Masan site. The study results support the S-shaped non-linear relationship is the best fit for the air/water relationship at the Korean coastal zone. This study will contribute to determine the future policy regarding water quality and ecosystem for the decision-driving organization.

• Korean Journal of Remote Sensing
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• v.26 no.5
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• pp.537-547
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• 2010

The importance of vegetation in studies of global climate and biogeochemical cycles is well recognized. Especially. the FPAR (fraction of photosynthetically active radiation) is one of the important parameters in ecosystem productivity and carbon budget models. Therefore, accurate estimates of vegetation parameters are increasingly important in environmental impact assessment studies. In this study, optical FPAR using the Terra MODIS (MODerate resolution Imaging Spectroradiometer), SPOT VEGETATION and ECOCLIMAP data reproduced on the Korean peninsula. We applied the empirical method which is usually estimated as a linear or nonlinear function of vegetation indices. As results, we estimated the accurate expression which is 0.9039 of $R^2$ in cropland and 0.7901 of $R^2$ in forest. Finally, this study could be demonstrated to calibrate that produced FPAR while the overall pattern and random noise through the comparative analysis of FPAR on the reference data. Optimal use of input parameter on the Korean peninsula should be helping the accuracy of output as well as the improved quality of research.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.26 no.3
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• pp.173-182
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• 2013

The building design projects which are being proceeded nowadays pursue a complex and various shape of structures, escaping from the traditional and regular shape of buildings. In this new trend of the architecture, there rises a demand of the research in the structural engineering for the effective realization of such complex-shaped buildings which disassembles the orthogonality of frames. As a distinguished characteristics of the buildings in a complex-shape, there frequently are inclined columns included in the structural frame. The inclined column causes extra axial force and bending moment at the beam-column connection so it is necessary to assess those effects on the structural behavior of the frame and the connection by experiment or analysis. However, with comparing to the studies on the normal beam-column connections, the inclined column connections have not been studied sufficiently. Therefore, this study evaluated the beam-column connections having an inclined column using nonlinear and finite element analysis method. In this paper, steel moment frames having inclined columns were analyzed by the nonlinear pushover analysis to check the global behavior and beam-column connection models were analyzed by the finite element analysis to check the buckling behavior and the fracture potentials.

• Journal of Astronomy and Space Sciences
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• v.36 no.4
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• pp.249-264
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• 2019

In this study, the precise orbit determination (POD) software is developed for optical observation. To improve the performance of the estimation algorithm, a nonlinear batch filter, based on the unscented transform (UT) that overcomes the disadvantages of the least-squares (LS) batch filter, is utilized. The LS and UT batch filter algorithms are verified through numerical simulation analysis using artificial optical measurements. We use the real optical observation data of a low Earth orbit (LEO) satellite, Cryosat-2, observed from optical wide-field patrol network (OWL-Net), to verify the performance of the POD software developed. The effects of light travel time, annual aberration, and diurnal aberration are considered as error models to correct OWL-Net data. As a result of POD, measurement residual and estimated state vector of the LS batch filter converge to the local minimum when the initial orbit error is large or the initial covariance matrix is smaller than the initial error level. However, UT batch filter converges to the global minimum, irrespective of the initial orbit error and the initial covariance matrix.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.12
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• pp.1238-1245
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• 2014

This paper proposes an RBFN-based adaptive tracking controller for an electrically driven mobile robot with parametric uncertainties and external disturbances. A mobile robot model considered in this paper includes all models of the robot body and actuators with uncertain kinematic and dynamic parameters, and uncertain frictions and external disturbances. The proposed controller consists of an RBFN(Radial Basis Function Network) and a robust adaptive controller. The presented RBFN is used to approximate unknown nonlinear robot dynamic functions. The proposed controller is adjusted by the adaptation laws obtained through the Lyapunov stability analysis. The proposed control scheme does not a priori need the accurate knowledge of all parameters in the robot kinematics, robot dynamics and actuator dynamics. Also, nominal parameter values are not required in the controller. The global stability of the closed-loop robot control system is guaranteed using the Lyapunov stability theory. Simulation results show the validity and robustness of the proposed control scheme.