• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.2C
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• pp.184-190
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• 2002

This paper compares and analyzes and analyzes the transmission performance of the OpenCable system and the DBD-C system which are adopted as the digital CATV transmission standard in U.S.A. and Europe respectively through computer simulation under the same channel environment. We considered the channel environment including the random noise and the CTB (Composite Tripple Beats) noise as channel impairments in order to compare the two standard fairly. Additionally, we analyzed the transmission performance of the OpenCable system for the various interleaving depths. We implemented each transmission system by software, and we analyzed BER values with respect to the C/N in order to compare their transmission performance. As a result of the computer simulation, to get the BER of ${10}^{-6}$ the OpenCable system requires 1.2 dB kiwer C/N than the DVB-C system in the 64-QAM mode, and the two system require similar C/N in the 256-QAM mode.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1405-1423
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• 2021

Precipitation is one of the main factors that affect water and energy cycles, and its estimation plays a very important role in securing water resources and timely responding to water disasters. Satellite-based quantitative precipitation estimation (QPE) has the advantage of covering large areas at high spatiotemporal resolution. In this study, machine learning-based rainfall intensity models were developed using Himawari-8 Advanced Himawari Imager (AHI) water vapor channel (6.7 ㎛), infrared channel (10.8 ㎛), and weather radar Column Max (CMAX) composite data based on random forest (RF). The target variables were weather radar reflectivity (dBZ) and rainfall intensity (mm/hr) converted by the Z-R relationship. The results showed that the model which learned CMAX reflectivity produced the Critical Success Index (CSI) of 0.34 and the Mean-Absolute-Error (MAE) of 4.82 mm/hr. When compared to the GeoKompsat-2 and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS) rainfall intensity products, the accuracies improved by 21.73% and 10.81% for CSI, and 31.33% and 23.49% for MAE, respectively. The spatial distribution of the estimated rainfall intensity was much more similar to the radar data than the existing products.

• Steel and Composite Structures
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• v.37 no.6
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• pp.711-731
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• 2020

This paper deals with free vibration of FG sandwich annular sector plates on Pasternak elastic foundation with different boundary conditions, based on the three-dimensional theory of elasticity. The plates with simply supported radial edges and arbitrary boundary conditions on their circular edges are considered. The influence of carbon nanotubes (CNTs) waviness, aspect ratio, internal pores and graphene platelets (GPLs) on the vibrational behavior of functionally graded nanocomposite sandwich plates is investigated in this research work. The distributions of CNTs are considered functionally graded (FG) or uniform along the thickness of upper and bottom layers of the sandwich sectorial plates and their mechanical properties are estimated by an extended rule of mixture. In this study, the classical theory concerning the mechanical efficiency of a matrix embedding finite length fibers has been modified by introducing the tube-to-tube random contact, which explicitly accounts for the progressive reduction of the tubes' effective aspect ratio as the filler content increases. The core of structure is porous and the internal pores and graphene platelets (GPLs) are distributed in the matrix of core either uniformly or non-uniformly according to three different patterns. The elastic properties of the nanocomposite are obtained by employing Halpin-Tsai micromechanics model. A semi-analytic approach composed of 2D-Generalized Differential Quadrature Method (2D-GDQM) and series solution is adopted to solve the equations of motion. The fast rate of convergence and accuracy of the method are investigated through the different solved examples. Some new results for the natural frequencies of the plate are prepared, which include the effects of elastic coefficients of foundation, boundary conditions, material and geometrical parameters. The new results can be used as benchmark solutions for future researches.

• Steel and Composite Structures
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• v.44 no.1
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• pp.49-63
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• 2022

This study aims to develop ensemble machine learning (ML) models for estimating the peak floor acceleration and maximum top drift of steel moment frames. For this purpose, random forest, adaptive boosting, gradient boosting regression tree (GBRT), and extreme gradient boosting (XGBoost) models were considered. A total of 621 steel moment frames were analyzed under 240 ground motions using OpenSees software to generate the dataset for ML models. From the results, the GBRT and XGBoost models exhibited the highest performance for predicting peak floor acceleration and maximum top drift, respectively. The significance of each input variable on the prediction was examined using the best-performing models and Shapley additive explanations approach (SHAP). It turned out that the peak ground acceleration had the most significant impact on the peak floor acceleration prediction. Meanwhile, the spectral accelerations at 1 and 2 s had the most considerable influence on the maximum top drift prediction. Finally, a graphical user interface module was created that places a pioneering step for the application of ML to estimate the seismic demands of building structures in practical design.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.24 no.1
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• pp.1-11
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• 2018

In this study, pro-oxidant($d2w^{(R)}$) and rPP/ZnO nanocomposite flexible films for food packaging were prepared, and their mechanical and antimicrobial properties were investigated. As a result, the carbonyl index and hydroxyl index increased with exposured time to heat and UV rays. Surface analysis showed that the addition of zinc oxide improved the dispersibility and compatibility of the polymer, so that the surface of the composite film was smooth and the zinc oxide particles were smaller than the compared film. And it kept the physical properties by heat and UV ray blocking effect, and it worked to reduce decomposition. In the antimicrobial activity test, the microbial reduction rate was 3 logs or more at the use concentration of zinc oxide. The tensile strength was increased and the elongation was decreased. Oxidative degradability of multi-layered film in UV exposured for 72 hours, the molecular weight of the film decreased by 75.6%, 1,294 g/mol Mn and 5,920 g/mol Mw. In the safety analysis of food packaging materials, we obtained that are in standard of polypropylene, a food contact material of domestic law.

• Steel and Composite Structures
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• v.41 no.6
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• pp.831-850
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• 2021

Surface subsidence caused by mining subsidence has an impact on neighboring structures and utilities. In other words, subsurface voids created by mining or tunneling activities induce soil movement, exposing buildings to physical and/or functional destruction. Soil-structure is evaluated employing probability distribution laws to account for their uncertainty and complexity to estimate structural vulnerability. In this study, to investigate the displacement field and surface settlement profile caused by mining subsidence, on the basis of a Winklersoil model, analytical equations for the moment-rotation response ofsoil during mining induced ground movements are developed. To define the full static moment-rotation response, an equation for the uplift-yield state is constructed and integrated with equations for the uplift- and yield-only conditions. The constructed model's findings reveal that the inverse of the factor of safety (x) has a considerable influence on the moment-rotation curve. The maximal moment-rotation response of the footing is defined by X = 0:6. Despite the use of Winkler model, the computed moment-rotation response results derived from the literature were analyzed through the ELM-SVM hybrid of Extreme Learning Machine (ELM) and Support Vector Machine (SVM). Also, Monte Carlo simulations are used to apply continuous random parameters to assess the transmission of ground motions to structures. Following the findings of RMSE and R2, the results show that the choice of probabilistic laws of input parameters has a substantial impact on the outcome of analysis performed.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.3_4
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• pp.224-238
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• 2004

Multi-modality image registration is a widely used image processing technique to obtain composite information from two different kinds of image sources. This study proposes an image registration method based on moment information and surface distance, which improves the previous surface-based registration method. The proposed method ensures stable registration results with low registration error without being subject to the initial position and direction of the object. In the preprocessing step, the surface points of the object are extracted, and then moment information is computed based on the surface points. Moment information is matched prior to fine registration based on the surface distance, in order to ensure stable registration results even when the initial positions and directions of the objects are very different. Moreover, surface comer sampling algorithm has been used in extracting representative surface points of the image to overcome the limits of the existed random sampling or systematic sampling methods. The proposed method has been applied to brain MRI(Magnetic Resonance Imaging) and PET(Positron Emission Tomography), and its accuracy and stability were verified through registration error ratio and visual inspection of the 2D/3D registration result images.