• Environmental and Resource Economics Review
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• v.27 no.3
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• pp.495-520
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• 2018

Because of structural changes in the international oil market and the economy, it is widely recognized that the impact of oil shocks on the economy has weaken since the mid-1980s. This study tries to examine the validity of the recent perception about the relationship between oils shocks and the economy, estimating the time-varying effects of oil shocks on the Korean economy. The results show that the dynamic effects of oil shocks normalized to a one standard deviation has been relatively constant, in contrast to the recent perception and a number of existing studies. In addition, because the shape of impulse response functions at each point in time spanning from 1984:II to 2017:IV has not been changed significantly, it seems that the propagation mechanism of oil shocks also has not been substantially altered. These findings indicate that even though structural changes of the economy, such as the reduction in the share of oil consumption and the spread of high-efficiency energy technologies, have been rapidly progressed, it is not still enough to offset the negative effects of oil shocks. Rather, it seems that the recent perception about the shrinking effects of oil shocks is mainly due to the assumptions that do not reflect changes in the size of oil shocks. In particular, this problem appears more pronounced in the case of the typical a one standard deviation increase oil shock under homoskedasticity assumption, which is widely adopted in the most VAR analyses. Therefore, in estimating the effects of oil shocks on the economy, it is important to specify the correct model and normalization method, to reflect changes in the size of oil shocks.

• Journal of Korea Water Resources Association
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• v.43 no.8
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• pp.721-731
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• 2010

The rainfall-runoff relationship is very difficult to predict because it is complicate factor affected by many temporal and spatial parameters of the basin. In recent, models which is based on artificial intelligent such as neural network, genetic algorithm fuzzy etc., are frequently used to predict discharge while stochastic or deterministic or empirical models are used in the past. However, the discharge data which are generally used for prediction as training and validation set are often estimated from rating curve which has potential error in its estimation that makes a problem in reliability. Therefore, in this study, water stage is predicted from antecedent rainfall and water stage data for short term using three models of neural network which trained by error back propagation algorithm and optimized by genetic algorithm and training error back propagation after it is optimized by genetic algorithm respectively. As the result, the model optimized by Genetic Algorithm gives the best forecasting ability which is not much decreased as the forecasting time increase. Moreover, the models using stage data only as the input data give better results than the models using precipitation data with stage data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1A
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• pp.45-52
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• 2009

In conventional structural design, deterministic optimization which satisfies codified constraints is performed to ensure safety and maximize economical efficiency. However, uncertainties are inevitable due to the stochastic nature of structural materials and applied loads. Thus, deterministic optimization without considering these uncertainties could lead to unreliable design. Recently, there has been much research in reliability-based design optimization (RBDO) taking into consideration both the reliability and optimization. RBDO involves the evaluation of probabilistic constraint that can be estimated using the RIA (Reliability Index Approach) and the PMA(Performance Measure Approach). It is generally known that PMA is more stable and efficient than RIA. Despite the significant advancement in PMA, RBDO still requires large computation time for large-scale applications. In this paper, A new reliability-based design optimization (RBDO) method is presented to achieve the more stable and efficient algorithm. The idea of the new method is to integrate a response surface method (RSM) with PMA. For the approximation of a limit state equation, the moving least squares (MLS) method is used. Through a mathematical example and ten-bar truss problem, the proposed method shows better convergence and efficiency than other approaches.

• Korean Journal of Food Science and Technology
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• v.50 no.1
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• pp.28-36
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• 2018

Studies on suspected endocrine disrupting pesticide (EDP) residues in agricultural products were carried out in 2016 in Busan, Korea. Twelve different EDPs, ranging in concentration between 0.003-2.049 mg/kg, were detected in 19.5% of 462 samples. About 0.2% of agricultural product samples exceeded the maximum residue limits (MRLs). Risk indices of all of the EDPs were less than 10% of the acceptable daily intake (ADI). The outcomes indicated that the risk groups at highest risk of exposure to diazinon (found in Korean cabbages) and carbendazim (found in apples) were females aged 40 to 49 and young males less than 10 years old, respectively. Based on the stochastic assessment at $95^{th}$ percentile (P95), risk index in these risk groups accounted for 8.38 and 2.98% of ADIs. The results showed that the occurrence of EDP residues in agricultural products could not be considered a public health problem.