• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.394-394
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• 2023

The world's sustainable growth is being severely hampered by the inefficient use of water resources. Despite the widely acknowledged importance of trade in global and regional water and food security, societal reliance on local production as well as international trade remains inadequately assessed. Therefore, using South Korea as a case study, this study fills in this research gap by applying the virtual water concept, the logarithmic mean divisia index (LMDI) method, and the Tapio decoupling model. The virtual water concept was used to estimate South Korea's net virtual water trade for major grain crops from 1992 to 2017. Then, the LMDI method was utilized to assess the driving factors causing changes in net virtual water trade. Lastly, the Tapio decoupling model was used to investigate the decoupling relationships between economic growth and the driving factors of net virtual water trade. Results showed that South Korea remains a net importer of virtual water flows with respect to grain crops, with an average import of 16,559.24 million m³ over the study period. In addition, the change in net virtual water trade could be attributed to water intensity effect, product structure effect, economic effect, and population effect. However, water intensity and economic effects were the major decisive factors for decrease and increase in net virtual water trade respectively, while the population and product structure effects had minor positive influences on the net virtual water trade. Furthermore, water intensity and economic growth showed a strong decoupling in most periods, while the decoupling state between product structure and economic growth was observed as expansive negative decoupling. Likewise, population size and economic growth showed a weak decoupling in most periods. The results reveal South Korea's status as it concerns the virtual water trade of grain crops, thus providing valuable insights into the sustainability of trade activities for the management of local water resources.

• Smart Structures and Systems
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• 제22권5호
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• pp.587-609
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• 2018

There is greater significance in identifying the incipient damages in structures at the time of their initiation as timely rectification of these minor incipient cracks can save huge maintenance cost. However, the change in the global dynamic characteristics of a structure due to these subtle damages are insignificant enough to detect using the majority of the current damage diagnostic techniques. Keeping this in view, we propose a hybrid damage diagnostic technique for detection of minor incipient damages in the structures. In the proposed automated hybrid algorithm, the raw dynamic signatures obtained from the structure are decomposed to uni-modal signals and the dynamic signature are reconstructed by identifying and combining only the uni-modal signals altered by the minor incipient damage. We use these reconstructed signals for damage diagnostics using ARMAX model. Numerical simulation studies are carried out to investigate and evaluate the proposed hybrid damage diagnostic algorithm and their capability in identifying minor/incipient damage with noisy measurements. Finally, experimental studies on a beam are also presented to compliment the numerical simulations in order to demonstrate the practical application of the proposed algorithm.

• International Journal of Contents
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• 제4권3호
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• pp.1-9
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• 2008

The Korean life insurance industry has undergone profound changes, such as the beginning of the variable insurance in July 2001 and the bancassurance enforcement in August 2003. However, little empirical research has analyzed data that includes the bancassurance of life insurance companies operating in Korea. In response to this lack of research, this paper applies DEA (data envelopment analysis) models to measure and decompose their efficiency. We discovered that life insurance companies operating in Korea are a little different in their composition ratio of inputs and outputs, due to the increased variety of distribution channels and new products. We provided efficiency scores, return to scale, and reference frequencies. We also decomposed CCR, BCC, and SBM efficiency into scale efficiency and MIX efficiency. So, we try to investigate whether the sources of inefficiency were caused by the inefficient operation of DMU, disadvantageous conditions, the difference of the composition ratio in inputs and outputs with reference sets, or any combination of the above. Most companies in the sample display had either constant or decreasing returns to scale. The efficiency rankings were less consistent among models and efficient DMUs. In response to this problem, we used the super-efficiency model to rank them and then compared the rankings of the DMUs among the various models. It was also concluded that the availability of panel data, rather than cross-sectional data, would greatly improve the validity of the efficiency estimates.

• 한국빅데이터학회지
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• 제2권1호
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• pp.41-48
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• 2017

최근 추천 시스템의 인기와 함께 추천 시스템의 알고리즘의 성능에 대한 평가가 중요해 졌다. 본 연구는 영화 데이터에서 다양한 알고리즘 중 어떤 알고리즘의 효과적인지 판단하기 위하여 모델링과 RMSE를 통한 모델 검증을 하였다. 본 연구의 데이터는 무비렌즈의 평가 데이터 10만건을 활용하여 피어슨 상관계수를 활용한 사용자 기반 협업 필터링, 코사인 상관계수를 활용한 아이템 기반 협업 필터링 그리고 특이 값분해를 활용한 아이템 기반 협업 필터링 모델을 만들었다. 세가지 추천 모델로 평점을 예측한 결과 사용자 기반 협업 필터링보다 아이템 기반 협업 필터링의 정확도가 월등히 높은 것을 확인했고, 행렬 분해를 사용했을 때 더 정확한 추천을 할 수 있었다.

• 산경연구논집
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• 제11권3호
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• pp.7-17
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• 2020

Purpose: This study reexamines the test on the pricing of accruals quality. Theory suggests that information risk is a priced risk factor. Using accruals quality as the proxy for information risk, researchers have tested the pricing of information risk. The results are inconsistent potentially because of the information shock in the realized returns that are used as the proxy for expected returns. Based on this argument, this study revisits this issue excluding information-shock-free measure of expected returns. Research design, data and methodology: This study estimates expected returns using the vector autoregression model. This method extracts information shocks more thoroughly than the methods in prior studies; therefore, the concern regarding information shock is minimized. As risk premiums are larger in recession periods than in expansion periods, recession and expansion subsamples were used to confirm the robustness of the main findings. For the pricing test, this study uses two-stage cross-sectional regression. Results: Empirical results find evidence that accruals quality is a priced risk factor. Furthermore, this study finds that the pricing of accruals quality is observed only in recession periods. Conclusions: This study supports the argument that accruals quality, as well as the pricing of information risk, is a priced risk factor.

• The Journal of Asian Finance, Economics and Business
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• 제7권2호
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• pp.43-52
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• 2020

The paper investigates the mechanism through which corporate credit ratings affect dividend payments by decomposing the mean difference of dividends into a part that is explained by the determinants of dividends and a residual part that is contributed by the pure credit group effect, in the framework of the traditional dividend model of Fama and French (2001). Historically, better credit rated firms have shown consistently higher propensity to pay dividends especially during the economic crisis period. According to the counter-factual decomposition technique of Jann (2008), better rated firms are more responsive to the firm characteristics that have positive impact on dividends and poor rated firms are more responsive to the negative dividend predictors. As a result, good (bad) credit ratings make corporate managers become more bold (timid) in their dividend payments and they tend to pay more (less) dividends than what their firm characteristics prescribe. The degree of information asymmetry increases for the poor group firms during crisis periods and they attempt to reserve more cash in preparation for future investments. The decomposition results suggest that the credit group effect can potentially exceed the effect of firm characteristics because firms of different credit ratings can respond to the very same firm characteristics in a different manner.

• 산경연구논집
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• 제8권1호
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• pp.5-13
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• 2017

Purpose - In this article, a dynamic model like a VAR is an appropriate choice for estimating the possible interrelationship between ownership structure and firm performance as a dynamic process. Research design, data, and methodology - Data of this work are collected from Chinese stock exchange including 350 Chinese-listed firms during the period of 1999-2012. We hypothesize that this interrelationship dynamically exists between ownership structure and firm performance. To examine the correlation, a panel Vector Auto-regression (PVAR) approach generated by GMM method is utilized to test the possible dynamic relation embedded in corporate governance. Another two dynamic analysis solutions such as orthogonalized impulse-response function and variance decomposition are also used simultaneously. Results - Findings of this study indicate the evidence that dynamically endogenous relationship exists between ownership structure and firm performance. Further, there is a dynamical correlation between investment and performance. Impulse response and variance decomposition illustrate that impact of a shock to variables themselves is the main source for their variability. Conclusions - The conclusion in this study is that there is a bidirectional and inter-temporal effect between proportion of ownership and corporate performance for a long run in accordance with impulse response function. Overall, our results suggest that corporate governance in China is more market oriented.

• Journal of Electrical Engineering and Technology
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• 제13권2호
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• pp.781-789
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• 2018

This paper proposes a new approach utilizing empirical mode decomposition (EMD) reconstruction to process vibration signals of a transformer under DC bias caused by high voltage direction transmission (HVDC), which is the potential cause of additional vibration and noise from transformer. Firstly, the Calculation Method is presented and a 3D model of transformer is simulated to analyze transformer deformation characteristic and the result indicate the main vibration is produced along axial direction of three core limbs. Vibration test system has been built and test points on the core and shell of transformer have been measured. Then, the signal reconstruction method for transformer vibration based on EMD is proposed. Through the EMD decomposition, the corrupted noise can be selectively reconstructed by the certain frequency IMFs and better vibration signals of transformer have been obtained. After EMD reconstruction, the vibrations are compared between transformer in normal work and with DC bias. When DC bias occurs, odd harmonics, vibration of core and shell, behave as a nonlinear increase and the even harmonics keep unchanged with DC current. Experiment results are provided to collaborate our theoretical analysis and to illustrate the effectiveness of the proposed EMD method.

• The Journal of Asian Finance, Economics and Business
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• 제7권11호
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• pp.83-93
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• 2020

This empirical research aims to modeling and improving the forecasting accuracy of the volatility pattern by employing the Saudi Arabia stock market (Tadawul)by studying daily closed price index data from October 2011 to December 2019 with a number of observations being 2048. In order to achieve significant results, this study employs many mathematical functions which are non-linear spectral model Maximum overlapping Discrete Wavelet Transform (MODWT) based on the best localized function (Bl14), autoregressive integrated moving average (ARIMA) model and generalized autoregressive conditional heteroskedasticity (GARCH) models. Therefore, the major findings of this study show that all the previous events during the mentioned period of time will be explained and a new forecasting model will be suggested by combining the best MODWT function (Bl14 function) and the fitted GARCH model. Therefore, the results show that the ability of MODWT in decomposition the stock market data, highlighting the significant events which have the most highly volatile data and improving the forecasting accuracy will be showed based on some mathematical criteria such as Mean Absolute Percentage Error (MAPE), Mean Absolute Scaled Error (MASE), Root Means Squared Error (RMSE), Akaike information criterion. These results will be implemented using MATLAB software and R- software.

• 한국습지학회지
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• 제21권spc호
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• pp.61-68
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• 2019

최근 장기적인 기후 변동성을 고려하기 위하여 대기-해양 순환 패턴을 수치화한 기상인자가 수문 변수 예측에 널리 사용되고 있다. 또한 정확하고 안정적인 예측을 위해 인공신경망 기반의 예측 모형이 꾸준히 발전하고 있다. 기상인자를 활용하여 기후 변동성을 고려한 수문량 예측은 수자원 및 환경 보존의 장기적인 관리에 효율적으로 활용될 수 있으므로 수문 변수에 유의한 인자의 파악과 이를 활용한 예측 모형의 적용은 꾸준한 도전이 될 것이다. 본 연구에서는 우리나라 한강 유역 댐 유입량에 통계적으로 유의한 상관성이 있는 대표 기상인자를 선정하고, 이를 인공신경망 앙상블 모형에 적용하여 댐 유입량 예측을 수행하였다. 이를 위해 앙상블 경험적 모드분해법을 활용하여 댐 유입량과 기상인자간의 통계적 상관성을 확인하였으며, 기존 단일 인공신경망 모형의 한계를 보완한 인공신경망 앙상블 모형을 구축하였다. 예측 수행 결과, 5개 댐 상관계수 평균이 훈련 기간에서 0.88, 검증 기간에서 0.68의 예측력을 보이는 것을 확인하였으며, 본 연구에서의 절차를 토대로 우리나라의 다양한 수문 변수와 기후 변동성간의 관계를 활용한 다양한 적용 사례가 나오길 기대한다.