• Steel and Composite Structures
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• v.42 no.5
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• pp.657-669
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• 2022

Structural materials can experience large plastic deformation under extreme cyclic loading that is caused by events like earthquakes. To evaluate the seismic safety of a structure, accurate numerical material models should be used. For a steel structure, the cyclic strain hardening behavior of structural steel should be correctly modeled. In this study, a combined hardening model, consisting of one isotropic hardening model and three nonlinear kinematic hardening models, was used. To determine the values of the combined hardening model parameters efficiently and accurately, the improved opposition-based particle swarm optimization (iOPSO) model was adopted. Low-cycle fatigue tests were conducted for three steel grades commonly used in Korea and their modeling parameters were determined using iOPSO, which was first developed in Korea. To avoid expensive and complex low cycle fatigue (LCF) tests for determining the combined hardening model parameter values for structural steel, empirical equations were proposed for each of the combined hardening model parameters based on the LCF test data of 21 steel grades collected from this study. In these equations, only the properties obtained from the monotonic tensile tests are required as input variables.

• Environmental Engineering Research
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• v.21 no.1
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• pp.74-83
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• 2016

The purpose of this study is to identify the driven factors affecting the changes in energy-related $CO_2$ emissions in Hebei Province of China from 1995 to 2013. This study confirmed that energy-related $CO_2$ emissions are correlated with the population, urbanization level, economic development degree, industry structure, foreign trade degree, technology level and energy proportion through an improved STIRPAT model. A reasonable and more reliable outcome of STIRPAT model can be obtained with the introducing of the Ridge Regression, which shows that population is the most important factor for $CO_2$ emissions in Hebei with the coefficient 2.4528. Rely on these discussions about affect abilities of each driven factors, we conclude several proposals to arrive targets for reductions in Hebei's energy-related $CO_2$ emissions. The method improved and relative policy advance improved pointing at empirical results also can be applied by other province to make study about driven factors of the growth of carbon emissions.

• Journal of Wetlands Research
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• v.23 no.4
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• pp.287-295
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• 2021

As facilities such as dam reservoir wetlands and agricultural irrigation reservoir wetlands are built, sedimentation occurs over time through erosion, sedimentation transport, and sediment deposition. Sedimentation issues are very important for the maintenance of reservoir wetlands because long-term sedimentation of sediments affects flood and drought control functions. However, research on resignation has been estimated mainly by empirical formulas due to the lack of available data. The purpose of this study was to calculate and compare the sediment deposition rate by developing a multiple regression model along with actual data and empirical formulas. In addition, it was attempted to identify potential causes of collapse by applying it to 64 reservoir wetlands that suffered flood damage due to the long rainy season in 2020 due to reservoir wetland sedimentation and aging. For the target reservoir, 10 locations including the GaGog reservoir located in Miryang city, Gyeongsangnam province in South Korea, where there is actual survey information, were selected. A multiple regression model was developed in consideration of physical and climatic characteristics, and a total of four empirical formulas and sediment deposition rate were calculated. Using this, the error of the sediment deposition rate was compared. As a result of calculating the sediment deposition rate using the multiple regression model, the error was the lowest from 0.21(m³km²/yr) to 2.13(m³km²/yr). Therefore, based on the sediment deposition rate estimated by the multi-regression model, the change in the available capacity of reservoir wetlands was analyzed, and the effective storage capacity was found to have decreased from 0.21(%) to 16.56(%). In addition, the sediment deposition rate of the reservoir where the overflow damage occurred was relatively higher than that of the reservoir where the piping damage occurred. In other words, accumulating sediment deposition rate at the bottom of the reservoir would result in a lack of acceptable effective water capacity and reduced reservoir flood and drought control capabilities, resulting in reservoir collapse damage.

• International Journal of Control, Automation, and Systems
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• v.6 no.5
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• pp.639-650
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• 2008

Electricity price forecasting has become an integral part of power system operation and control. In this paper, a wavelet transform (WT) based neural network (NN) model to forecast price profile in a deregulated electricity market has been presented. The historical price data has been decomposed into wavelet domain constitutive sub series using WT and then combined with the other time domain variables to form the set of input variables for the proposed forecasting model. The behavior of the wavelet domain constitutive series has been studied based on statistical analysis. It has been observed that forecasting accuracy can be improved by the use of WT in a forecasting model. Multi-scale analysis from one to seven levels of decomposition has been performed and the empirical evidence suggests that accuracy improvement is highest at third level of decomposition. Forecasting performance of the proposed model has been compared with (i) a heuristic technique, (ii) a simulation model used by Ontario's Independent Electricity System Operator (IESO), (iii) a Multiple Linear Regression (MLR) model, (iv) NN model, (v) Auto Regressive Integrated Moving Average (ARIMA) model, (vi) Dynamic Regression (DR) model, and (vii) Transfer Function (TF) model. Forecasting results show that the performance of the proposed WT based NN model is satisfactory and it can be used by the participants to respond properly as it predicts price before closing of window for submission of initial bids.

• Structural Engineering and Mechanics
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• v.68 no.2
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• pp.237-245
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• 2018

This study proposes an empirical formulation to predict the maximum deformation of offshore blast wall structure that is subjected to impact loading caused by hydrocarbon explosion. The blast wall model is assumed to be supported by a simply-supported boundary condition and corrugated panel is modelled. In total, 1,620 cases of LS-DYNA simulations were conducted to predict the maximum deformation of blast wall, and they were then used as input data for the development of the empirical formulation by regression analysis. Stainless steel was employed as materials and the strain rate effect was also taken into account. For the development of empirical formulation, a wide range of parametric studies were conducted by considering the main design parameters for corrugated panel, such as geometric properties (corrugation angle, breadth, height and thickness) and load profiles (peak pressure and time). In the case of the blast profile, idealised triangular shape is assumed. It is expected that the obtained empirical formulation will be useful for structural designers to predict maximum deformation of blast wall installed in offshore topside structures in the early design stage.

• Structural Engineering and Mechanics
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• v.69 no.2
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• pp.155-162
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• 2019

It is very common to find an empirical formulation in an earthquake design code to calculate fundamental vibration period of a structural system. Fundamental vibration period or frequency is a key parameter to provide adequate information pertinent to dynamic characteristics and performance assessment of a structure. This parameter enables to assess seismic demand of a structure. It is possible to find an empirical formulation related to reinforced concrete structures, masonry towers and slender masonry structures. Calculated natural vibration frequencies suggested by empirical formulation in the literatures has not suits in a high accuracy to the case of rest of the historical masonry bridges due to different construction techniques and wide variety of material properties. For the listed reasons, estimation of fundamental frequency gets harder. This paper aims to present an empirical formulation through Mean Square Error study to find ambient vibration frequency of historical masonry bridges by using a non-linear regression model. For this purpose, a series of data collected from literature especially focused on the finite element models of historical masonry bridges modelled in a full scale to get first global natural frequency, unit weight and elasticity modulus of used dominant material based on homogenization approach, length, height and width of the masonry bridge and main span length were considered to predict natural vibration frequency. An empirical formulation is proposed with 81% accuracy. Also, this study draw attention that this accuracy decreases to 35%, if the modulus of elasticity and unit weight are ignored.

• Journal of the Korean earth science society
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• v.38 no.4
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• pp.283-292
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• 2017

In this study, the empirical models were established to estimate the concentrations of surface-level $PM_{2.5}$ over Seoul, Korea from 1 January 2012 to 31 December 2013. We used six different multiple linear regression models with aerosol optical thickness (AOT), ${\AA}ngstr{\ddot{o}}m$ exponents (AE) data from Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra and Aqua satellites, meteorological data, and planetary boundary layer depth (PBLD) data. The results showed that $M_6$ was the best empirical model and AOT, AE, relative humidity (RH), wind speed, wind direction, PBLD, and air temperature data were used as input data. Statistical analysis showed that the result between the observed $PM_{2.5}$ and the estimated $PM_{2.5}$ concentrations using $M_6$ model were correlations (R=0.62) and root square mean error ($RMSE=10.70{\mu}gm^{-3}$). In addition, our study show that the relation strongly depends on the seasons due to seasonal observation characteristics of AOT, with a relatively better correlation in spring (R=0.66) and autumntime (R=0.75) than summer and wintertime (R was about 0.38 and 0.56). These results were due to cloud contamination of summertime and the influence of snow/ice surface of wintertime, compared with those of other seasons. Therefore, the empirical multiple linear regression model used in this study showed that the AOT data retrieved from the satellite was important a dominant variable and we will need to use additional weather variables to improve the results of $PM_{2.5}$. Also, the result calculated for $PM_{2.5}$ using empirical multi linear regression model will be useful as a method to enable monitoring of atmospheric environment from satellite and ground meteorological data.

• Journal of the Korea Safety Management & Science
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• v.6 no.4
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• pp.123-137
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• 2004

It is an important and hot issue how to improve the competitiveness concerned on product, company and industry. It is necessary to develop the strategy of competitiveness for an efficient operation as well as improving the competitiveness in view of product, system, industry, price, quality and so on. This paper aims at proposing a model to choose dominating factors of competitiveness including a method o( resources allocation which can be applied to all products. And we show its empirical application on tile-industry.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.4
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• pp.627-630
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• 2016

Recently, the size of wind farms is becoming larger, and the integration of high wind generation resources into power gird is becoming more important. Due to intermittency of wind generating resources, it is an essential to predict power outputs. In this paper, we introduce the basic concept of curvilinear regression, which is one of the method of wind power prediction. The empirical data, wind farm power output in Jeju Island, is considered to verify the proposed prediction model.

• Journal of the Economic Geographical Society of Korea
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• v.17 no.1
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• pp.129-146
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• 2014

This paper investigates how industrial diversity affects unemployment and employment instability from the perspective of the regional economy. Through this analysis, we examine how the industry-specific policy to promote some industry strategically in most of areas affects the stability of the regional economy. We measure Herfindahl indexes using the 1993-2010 data of 16 regions in Korea, and use panel regression model for empirical analysis. The main results from this empirical analysis are summarized as follows. First, we confirm that the industrial structure of most regions has been changed to the direction of specialization in 1990s and to the direction of diversification in 2000s through analyzing the changes in the values of Herfindahl indexes during the given period. Second, we find from the estimation results of panel regression model that the higher industrial diversity in most of regions is, the lower the unemployment rate is. However, a statistically significant relationship between industrial diversity and employment instability only partially confirmed. Third, there exist high unemployment rate and employment instability in most metropolitan areas, but it is hard to say that this relationship is highly statistically significant. From the results of the empirical analysis, it is likely that the industry-specific policies such as the regional strategic industry development policies unlike policy goals make the unemployment rate to rise and economic instability to increase. From the viewpoint of employment aspects, the strategies to increase industrial diversity would be desirable rather than those to specialize in the industrial structure.