• Communications of the Korean Mathematical Society
• /
• v.18 no.3
• /
• pp.541-550
• /
• 2003

We consider the generalized autoregressive model with conditional heteroscedasticity process(GARCH). It is proved that if (equation omitted) β/sub i/ < 1, then there exists a unique invariant initial distribution for the Markov process emdedding the given GARCH process. Geometric ergodicity, functional central limit theorems, and a law of large numbers are also studied.

• Journal of the Korean Statistical Society
• /
• v.31 no.4
• /
• pp.459-468
• /
• 2002

A class of asymmetric beta-ARCH processes is proposed and connections to traditional ARCH models are explained. Geometric ergodicity of the model is discussed. Conditional least squares as well as maximum likelihood estimators of parameters and their limit results are also presented. A test for symmetry of the model is studied with limiting power of test statistic given.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 1990.10a
• /
• pp.40-46
• /
• 1990

This paper introduces a geometric modelling system adopted in a newly developed preprocessor for finite element analysis of three dimensional structures. The formulation is characterized by hierarchical construction of structural model which consists of control points, curves, surfaces and solids. Various surface and solid modeling schemes based on blending functions and boundary representation are systematized for finite element mesh generation. The modeling system is integrated with model synthesis and operations which facilitate modelling of complex structures.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.22 no.1
• /
• pp.45-52
• /
• 2004

It can be classified in various methods to get the geoidal heights. It can be achieved geometric geoidal heights if we do GPS surveying in leveling point. The aims of this paper are calculation of geometric geoidal heights using GPS/leveling data in study area and evaluation of the global and local geoid models in and around Korean peninsula. For this study, study area was selected in the leveling line from Kunsan to Chonju city and GPS surveying was accomplished in the leveling line. And, also spherical harmonic analysis was made on the three global geopotential models, OSU91A, EGM96, EGM96m under same condition. Then the differences were calculated between geometric geoidal heights and geoidal heights of 3 geopotential models, KOGD2002 which was Korean gravimetric geoid model. The results shows that EGM96m is the best model because the differences between geoidal heights of E6M96m and geometric geoidal heights of GPS/Leveling data appear the smallest value among them.

• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.12
• /
• pp.505-518
• /
• 2023

3D point cloud semantic segmentation is a computer vision task that involves dividing the point cloud into different objects and regions by predicting the class label of each point. Existing 3D semantic segmentation models have some limitations in performing sufficient fusion of multi-modal features while ensuring both characteristics of 2D visual features extracted from RGB images and 3D geometric features extracted from point cloud. Therefore, in this paper, we propose MMCA-Net, a novel 3D semantic segmentation model using 2D-3D multi-modal features. The proposed model effectively fuses two heterogeneous 2D visual features and 3D geometric features by using an intermediate fusion strategy and a multi-modal cross attention-based fusion operation. Also, the proposed model extracts context-rich 3D geometric features from input point cloud consisting of irregularly distributed points by adopting PTv2 as 3D geometric encoder. In this paper, we conducted both quantitative and qualitative experiments with the benchmark dataset, ScanNetv2 in order to analyze the performance of the proposed model. In terms of the metric mIoU, the proposed model showed a 9.2% performance improvement over the PTv2 model using only 3D geometric features, and a 12.12% performance improvement over the MVPNet model using 2D-3D multi-modal features. As a result, we proved the effectiveness and usefulness of the proposed model.

• Advances in aircraft and spacecraft science
• /
• v.7 no.4
• /
• pp.353-369
• /
• 2020

This paper presents the flutter analysis and optimum design of axially functionally graded box beam cantilever wing section by considering various geometric and material parameters. The coupled dynamic equations of the continuous model of wing system in terms of material and cross-sectional properties are formulated based on extended Hamilton's principle. By expressing the lift and pitching moment in terms of plunge and pitch displacements, the resultant two continuous equations are simplified using Galerkin's reduced order model. The flutter velocity is predicted from the solution of resultant damped eigenvalue problem. Parametric studies are conducted to know the effects of geometric factors such as taper ratio, thickness, sweep angle as well as material volume fractions and functional grading index on the flutter velocity. A generalized surrogate model is constructed by training the radial basis function network with the parametric data. The optimized material and geometric parameters of the section are predicted by solving the constrained optimal problem using firefly metaheuristics algorithm that employs the developed surrogate model for the function evaluations. The trapezoidal hollow box beam section design with axial functional grading concept is illustrated with combination of aluminium alloy and aluminium with silicon carbide particulates. A good improvement in flutter velocity is noticed by the optimization.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.21 no.3
• /
• pp.255-260
• /
• 2003

In this paper, we generate the 3D geometric sensor model of SPOT-5 HRG stereo images which are processed in Supermode and have 2.5m ground spatial resolution, and calculate the RPC(Rational Polynomial Coefficients) for acquisition of topographic information using the exterior orientation parameters which are determined in the 3D geometric sensor modelling process. It is shown that SPOT-5 images can be modelled with me 3.3m accuracy by the bundle adjustment method used to model the existing SPOT series. Considering the accuracy of RPC's results with rmse 0.03m accuracy, the RPC model can replace the sensor model, if we emphasize the simplification and the cost.

• International Journal of Highway Engineering
• /
• v.15 no.6
• /
• pp.103-111
• /
• 2013

PURPOSES : Previous Speed Profile reflects the patterns of speeds in sections of tangents to curves in the roads. However these patterns are uniform of speeds and Acceleration/Deceleration. In oder to supplement these shortcomings. this study made a new profile which can contain factors of Acceleration/Deceleration through theories of Previous Speed Profiles. METHODS : For sakes, this study developed the speed prediction model of Rural Multi-Lane Highways and calculated Acceleration/Deceleration by appling a Polynomial model based on developed speed prediction model. Polynomial model is based on second by second. Acceleration/Deceleration Profile is developed with the various scenarios of road geometric conditions. RESULTS : The longer an ahead tangent length is, The higher an acceleration rate in curve occurs due to wide sight distance. However when there are big speed gaps between two curves, the longer tangent length alleviate acceleration rate. CONCLUSIONS : Acceleration/Deceleration Profile can overview th patterns of speeds and Accelerations/Decelerations in the various road geometric conditions. Also this result will help road designer have a proper guidance to exam a potential geometric conditions where may occur the acceleration/deceleration states.

• Earthquakes and Structures
• /
• v.25 no.6
• /
• pp.469-479
• /
• 2023

A probabilistic seismic damage analysis is an essential procedure to identify seismically vulnerable structures, prioritize the seismic retrofit, and ultimately minimize the overall seismic risk. To assess the seismic risk of multiple structures within a region, a large number of nonlinear time-history structural analyses must be conducted and studied. As a result, each assessment requires high computing resources. To overcome this limitation, we explore a deep learning-based metamodel to enable the prediction of the mean and the standard deviation of the seismic damage distribution of track-on steel-plate girder railway bridges in Korea considering the geometric variation. For machine learning training, nonlinear dynamic time-history analyses are performed to generate 800 high-fidelity datasets on the seismic response. Through intensive trial and error, the study is concentrated on developing an optimal machine learning architecture with the pre-identified variables of the physical configuration of the bridge. Additionally, the prediction performance of the proposed method is compared with a previous, well-defined, response surface model. Finally, the statistical testing results indicate that the overall performance of the deep-learning model is improved compared to the response surface model, as its errors are reduced by as much as 61%. In conclusion, the model proposed in this study can be effectively deployed for the seismic fragility and risk assessment of a region with a large number of structures.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2008.11a
• /
• pp.33-34
• /
• 2008