• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4763-4775
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• 2020

This research aims to develop a deep learning-based method that automatically detects and segments the defects on railroad surfaces to reduce the cost of visual inspection of the railroad. We developed our segmentation model by modifying a fully convolutional network model [1], a well-known segmentation model used for machine learning, to detect and segment railroad surface defects. The data used in this research are images of the railroad surface with one or more defect regions. Railroad images were cropped to a suitable size, considering the long height and relatively narrow width of the images. They were also normalized based on the variance and mean of the data images. Using these images, the suggested model was trained to segment the defect regions. The proposed method showed promising results in the segmentation of defects. We consider that the proposed method can facilitate decision-making about railroad maintenance, and potentially be applied for other analyses.

• Proceedings of the KSME Conference
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• 2001.06e
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• pp.136-141
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• 2001

In this study, we analysed tree surface flow by using the experimental and numerical method with a different surfactant concentration. We compared numerical solution with experimental results for one-dimensional model. The result shows that in general the tree surface velocity can well be reproduced by the one-dimensional model for various surfactant concentration.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.8 no.2
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• pp.39-49
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• 2004

Model of the novel Surface motor (SFM) is briefly discussed, and two types of control method including two-order feedback circuit control, indirect acceleration feedback control are analyzed to solve unstable characteristic such as low damp and negative stiffness. The simulation results demonstrate that the system has plain amplitude and wide frequency band arranging from 0 to 8kHz with no resonant peak through indirect acceleration feedback control.

• International Journal of Aeronautical and Space Sciences
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• v.7 no.2
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• pp.104-109
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• 2006

In this study, the equivalent plate model is developed using a finite element method(FEM) based on the first order shear deformation theory(FSDT). The substructure synthesis method is used to consider the control surface. For the verification of the equivalent model, the results of free vibration analysis are compared with the ones of 3D wing structure modeled by using the MSC/NASTRAN.

• Journal of the Korean Geophysical Society
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• v.8 no.3
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• pp.153-157
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• 2005

A discussion of a method has been used with success in terrain modelling to estimate the height at any point on the land surface from irregularly distributed samples. The special requirements of terrain modelling are discussed as well as a detailed description of the algorithm and an example of its application.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.452-456
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• 2004

A study on gust loads alleviation using aircraft control surface was performed. Aeroservoelastic model including control surface controller was formulated and validated by comparing the results of continuous turbulence response analysis with those of MSC/NASTRAN. Optimal control with output feedback was adopted for designing the control surface controller, and the effects of gust loads alleviation was validated by performing the numerical simulation for the controller designed.

• Advances in aircraft and spacecraft science
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• v.9 no.6
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• pp.493-506
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• 2022

The study presents the results of the research of heat and heat exchange processes on the heat-stressed elements of the structure of an advanced TsAGI descent vehicle. The studies were carried out using a mathematical model based on solving discrete analogs of continuum mechanics equations. Conclusions were drawn about the correctness of the model and the dependence of the intensity of heat and mass transfer processes on the most heat-stressed sections of the apparatus surface on its geometry and the catalytic activity of the surface.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.121-132
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• 2018

Weather is an important factor affecting roadway transportation in many aspects such as traffic flow, driver 's driving patterns, and crashes. This study focuses on the relationship between weather and road surface condition and develops a model to estimate the road surface condition using machine learning. A road surface sensor was attached to the probe vehicle to collect road surface condition classified into three categories as 'dry', 'moist' and 'wet'. Road geometry information (curvature, gradient), traffic information (link speed), weather information (rainfall, humidity, temperature, wind speed) are utilized as variables to estimate the road surface condition. A variety of machine learning algorithms examined for predicting the road surface condition, and a two - stage classification model based on 'Random forest' which has the highest accuracy was constructed. 14 days of data were used to train the model and 2 days of data were used to test the accuracy of the model. As a result, a road surface state prediction model with 81.74% accuracy was constructed. The result of this study shows the possibility of estimating the road surface condition using the existing weather and traffic information without installing new equipment or sensors.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.775-776
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• 2012

• Journal of Korean Society of Steel Construction
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• v.17 no.2 s.75
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• pp.139-149
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• 2005

Post-local buckling behavior is a very important consideration in plastic and seismic design of steel structures. It describes the structural behavior up to the final collapse state. In order to assess the actual reliability of structures under severe repeated loading, such as strong earthquakes, it is necessary to evaluate the progressive cyclic deterioration of stiffness as well as the strength and energy dissipation capacity of the structures after local buckling happens. In this study, a simple analytical model developed for predicting post-local buckling behavior for cyclic and non-proportional loading histories, has been proposed. This analytical model uses the stress resultant model based on the two surface model. Analytical moment-curvature relationship using this model compare well with the experimental results in constant amplitude cycling, and linearized energy deterioration which is very important in seismic design can be predicted from the proposed model.