• Nuclear Engineering and Technology
• /
• v.55 no.3
• /
• pp.968-980
• /
• 2023

The dynamic structural responses are sensitive to the time-frequency content of seismic waves, and seismic input motions in time-history analysis are usually required to be compatible with design response spectra according to nuclear codes. In order to generate spectra-compatible input motions while maintaining the intrinsic non-stationarity of seismic waves, an improved time-domain approach is proposed in this paper. To maintain the nonstationary characteristics of the given seismic waves, a new time-frequency envelope function is constructed using the Hilbert amplitude spectrum. Based on the intrinsic mode functions (IMFs) obtained from given seismic waves through variational mode decomposition, a new corrective time history is constructed to locally modify the given seismic waves. The proposed corrective time history and time-frequency envelope function are unique for each earthquake records as they are extracted from the given seismic waves. In addition, a dimension reduction iterative technique is presented herein to simultaneously superimpose corrective time histories of all the damping ratios at a specific frequency in the time domain according to optimal weights, which are found by the genetic algorithm (GA). Examples are presented to show the capability of the proposed approach in generating spectra-compatible time histories, especially in maintaining the nonstationary characteristics of seismic records. And numerical results reveal that the modified time histories generated by the proposed method can obtain similar dynamic behaviors of AP1000 nuclear power plant with the natural seismic records. Thus, the proposed method can be efficiently used in the design practices.

• Environmental and Resource Economics Review
• /
• v.18 no.1
• /
• pp.53-73
• /
• 2009

This study examines the existence of ${\beta}$-convergence of carbon dioxide emissions in 24 countries over the period 1971~2002. For that purpose, The model of economic growth developed by Barro and Sala-i-Martin (1995) is extended and conducted Dynamic panel analysis and unit root testing by employing the panel stationarity test of Levin et al. (2002) and 1m et al. (2003). A dynamic panel estimation is well known method including capacity to control for both the endogeneity problem and the unobserved country-specific effects problem. Dynamic panel estimation method has been widely used in similar empirical studies. therefore, we also used the dynamic panel estimation method in our estimation. The result show that evidence of ${\beta}$-convergence exists among both the Obligatory GHG reduction countries (Annex) and the Non-obligatory GHG reduction countries (Non-Annex). but China discharge amount of $CO_2$ gas more than any other country. This fact can cause some bias in overall test. and so we reexamined test of convergence for Non-annex countries excluding china. As expected, in the Non-annex countries excluding china, I couldn't find any evidence of convergence.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.44 no.5
• /
• pp.1-10
• /
• 2007

Most of natural images have a special property, what is called self-similarity, which is the basis of fractal image coding. Even though an image has local stationarity in several homogeneous regions, it is generally non-stationarysignal, especially in edge region. This is the main reason that poor results are induced in linear techniques. In order to overcome the difficulty we propose a non-linear technique using self-similarity in the image. In our work, an image is classified into stationary and non-stationary region with respect to sample variance. In case of stationary region, do-noising is performed as simply averaging of its neighborhoods. However, if the region is non-stationary region, stationalization is conducted as make a set of center pixels by similarity matching with respect to bMSE(block Mean Square Error). And then do-nosing is performed by Gaussian weighted averaging of center pixels of similar blocks, because the set of center pixels of similar blocks can be regarded as nearly stationary. The true image value is estimated by weighted average of the elements of the set. The experimental results show that our method has better performance and smaller variance than other methods as estimator.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.14 no.3
• /
• pp.223-235
• /
• 2011

Although ordinary least squares (OLS) regression analysis can be used to examine the spatial relationships between environmental equity and urban quality of life, this global method may mask the local variations in the relationships between them. These geographical variations can not be captured without using local methods. In this context, this paper explores the spatially varying relationships between environmental equity and urban quality of life across the Atlanta metropolitan area by geographically weighted regression (GWR), a local method. Environmental equity and urban quality of life were quantified with an integrated approach of GIS and remote sensing. Results show that generally, there is a negatively significant relationship between them over the Atlanta metropolitan area. The results also suggest that the relationships between environmental equity and urban quality of life vary significantly over space and the GWR (local) model is a significant improvement on the OLS (global) model for the Atlanta metropolitan area.

• Journal of Korean Society of Forest Science
• /
• v.89 no.1
• /
• pp.1-8
• /
• 2000

This paper investigates the empirical validity of market integration for the five softwood lumber markets in Canada : Atlantic, Quebec, Ontario, Prairie, and British Columbia (BC). The Augmented Dickey-Fuller (ADF) tests of monthly price series for the period 1987 : 10-1998 : 11 reveal strong evidence for the presence of a unit root in each series. Accordingly, the Johansen cointegration technique is used to test for the law of one price in the five regional markets. Results show that the law holds in the pair, three, four, and five markets, supporting the hypothesis of market integration.

• International Area Studies Review
• /
• v.20 no.1
• /
• pp.147-165
• /
• 2016

We empirically examine the validity of second generation endogenous growth theory suing 21 OECD countries' panel data(1981~2011). Due to non-stationarity in all variables, we test the cointegrated relationships strongly supporting the semi-endogenous growth model. In the estimation of total factor productivity growth function, the growth of domestic and foreign R&D investment levels statistically significantly affect total factor productivity growth. R&D intensity, however, has significant impacts on the total factor productivity growth only in a few models, and international technology gap also has positive impacts on GDP growth. Thus the semi-endogenous growth model is relatively supported while fully endogenous growth model is weakly and occasionally supported in OECD countries. The policy implication of supporting the semi-endogenous growth model is that the sustaining growth requires increasing R&D expenditures.

• Journal of Korea Trade
• /
• v.23 no.5
• /
• pp.87-101
• /
• 2019

Purpose - This paper examines the effectiveness of the foreign exchange risk insurance system in the promotion of SME exports in Korea. The purpose of this study is to analyze the short-term and long-term responses of SME exports to foreign exchange risk insurance support policies. Based on these empirical studies, we would like to present some operational improvements to the operation of the foreign exchange risk insurance system. Design/methodology - In order to analyze the effect of exchange risk insurance on the exports of SMEs, a VAR model consisting of foreign exchange risk insurance underwriting values, export relative price, and domestic demand pressure, including export volume, was established. The study began with tests of the stationarity of time series data. The unit root tests showed that all concerned variables were non-stationary. Accordingly, the results of the cointegration test showed that the tested variables are not cointegrated. Finally, an impulse response function and variance decomposition analysis were conducted to analyze the impulse of foreign exchange risk insurance on exports of SMEs. Findings - As a result of estimating the VAR (1) model, foreign exchange risk insurance was found to be significant at a 1% significance level for SME' export promotion. In the impulse response analysis, SMEs' export response to the impulse of foreign exchange risk insurance showed that exports gradually increased until the third quarter, and then slowed down. However, the impulse did not disappear, and appeared continuously. Originality/value - This study analyzed the effect of foreign exchange insurance on exports of SMEs by applying the VAR model. In particular, this study is the first to analyze the short-term and long-term effects of foreign exchange risk insurance on exports of SMEs. The empirical evidence in the current study have a policy implication for the policy authority to support and promote the foreign exchange risk insurance in the effect of exchange rate volatility on Korea' export SMEs.

• Journal of Korea Port Economic Association
• /
• v.38 no.2
• /
• pp.153-164
• /
• 2022

In this study, we estimate the quantile-regression framework of the shipping industry for the Capesize used ship, which is a typical raw material transportation from January 2000 to December 2021. This research aims two main contributions. First, we analyze the relationship between the Capesize used ship, which is a typical type in the raw material transportation market, and the freight market, for which mixed empirical analysis results are presented. Second, we present an empirical analysis model that considers the structural transformation proposed in the Hyunsok Kim and Myung-hee Chang(2020a) study in quantile-regression. In structural change investigations, the empirical results confirm that the quantile model is able to overcome the problems caused by non-stationarity in time series analysis. Then, the long-run relationship of the co-integration framework divided into long and short-run effects of exogenous variables, and this is extended to a prediction model subdivided by quantile. The results are the basis for extending the analysis based on the shipping theory to artificial intelligence and machine learning approaches.

• Korean Journal of Agricultural Science
• /
• v.50 no.3
• /
• pp.373-389
• /
• 2023

The downstream submergence damages caused during the flood season in 2020, around the Yongdam-dam and five other sites, were analyzed using related dam management data. Hourly- and daily-data were collected from public national websites and to conduct various analyses, such as autocorrelation, partial-correlation, stationary test, trend test, Granger causality, Rescaled analysis, and principal statistical analysis, to find the cause of the catastrophic damages in 2020. The damage surrounding the Yongdam-dam in 2020 was confirmed to be caused by mis-management of the flood season water level. A similar pattern was found downstream of the Namgang- and Hapcheon-dams, however the damage caused via discharges from these dams in same year is uncertain. Conversely, a different pattern from that of the Yongdam-dam was seen in the areas downstream of Sumjingang- and Daecheongdams, in which the management of the flood season water level appeared appropriate and hence, the damages is assumed to have occurred via the increase in the absolute discharge amount from the dams and flood control capacity leakage of the downstream river. Because of the non-stationarity of the management data, we adapted the wavelet transform analysis to observe the behaviors of the dam management data in detail. Based on the results, an increasing trend in the discharge amount was observed from the dams after the year 2000, which may serve as a warning about similar trends in the future. Therefore, additional and continuous research on downstream safety against dam discharges is necessary.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.38 no.4
• /
• pp.579-586
• /
• 2018

Short-term prediction of travel speed has been widely studied using data-driven non-parametric techniques. There is, however, a lack of research on the prediction aimed at urban areas due to their complex dynamics stemming from traffic signals and intersections. The purpose of this study is to develop a hybrid approach combining ensemble empirical mode decomposition (EEMD) and artificial neural network (ANN) for predicting urban travel speed. The EEMD decomposes the time-series data of travel speed into intrinsic mode functions (IMFs) and residue. The decomposed IMFs represent local characteristics of time-scale components and they are predicted using an ANN, respectively. The IMFs can be predicted more accurately than their original travel speed since they mitigate the complexity of the original data such as non-linearity, non-stationarity, and oscillation. The predicted IMFs are summed up to represent the predicted travel speed. To evaluate the proposed method, the travel speed data from the dedicated short range communication (DSRC) in Daegu City are used. Performance evaluations are conducted targeting on the links that are particularly hard to predict. The results show the developed model has the mean absolute error rate of 10.41% in the normal condition and 25.35% in the break down for the 15-min-ahead prediction, respectively, and it outperforms the simple ANN model. The developed model contributes to the provision of the reliable traffic information in urban transportation management systems.