• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.4
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• pp.382-391
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• 2020

In this study, we used artificial intelligence algorithms for the prediction of dissolved oxygen in Jindong Bay. To determine missing values in the observational data, we used the Bidirectional Recurrent Imputation for Time Series (BRITS) deep learning algorithm, Auto-Regressive Integrated Moving Average (ARIMA), a widely used time series analysis method, and the Long Short-Term Memory (LSTM) deep learning method were used to predict the dissolved oxygen. We also compared accuracy of ARIMA and LSTM. The missing values were determined with high accuracy by BRITS in the surface layer; however, the accuracy was low in the lower layers. The accuracy of BRITS was unstable due to the experimental conditions in the middle layer. In the middle and bottom layers, the LSTM model showed higher accuracy than the ARIMA model, whereas the ARIMA model showed superior performance in the surface layer.

• Proceedings of the KIPE Conference
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• 2002.07a
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• pp.53-57
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• 2002

The solar energy is purity and infinity. Solar power converter were used to convert the electrical energy from the solar arrays to a stable and reliable power source. So many countries research this solar energy system The photovoltaic system is construct many solar cell array. In this paper, new implementation solar system was showed buck converter that V-I curve produced. This system can be used to study the short-term and long-term performances of solar cell and efficiency. This system is a far more cost effective and reliable replacement for field and outdoor flight testing. Study of buck converter, analysis and control series or parallels connection characteristics of solar cell way.

• The Korean Journal of Financial Management
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• v.24 no.3
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• pp.63-89
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• 2007

This study aims at empirically verifying whether long memory properties exist in returns and volatility of the financial time series and then, empirically observing influential factors of long-memory properties. The presence of long memory properties in the financial time series is examined with the Hurst exponent. The Hurst exponent is measured by DFA(detrended fluctuation analysis). The empirical results are summarized as follows. First, the presence of significant long memory properties is not identified in return time series. But, in volatility time series, as the Hurst exponent has the high value on average, a strong presence of long memory properties is observed. Then, according to the results empirically confirming influential factors of long memory properties, as the Hurst exponent measured with volatility of residual returns filtered by GARCH(1, 1) model reflecting properties of volatility clustering has the level of $H{\approx}0.5$ on average, long memory properties presented in the data before filtering are no longer observed. That is, we positively find out that the observed long memory properties are considerably due to volatility clustering effect.

• Magazine of the Korean Society of Agricultural Engineers
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• v.23 no.4
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• pp.46-52
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• 1981

The aim at this study has the stochastic hydrological analysis for the annual mean discharge and monthly discharge which were observed at Lobith of River Rhine in the Netherlands from 1901 to 1972. After this study was analysed by computer IBM 370 and Hewlett Parkard 9800, the results were as follows; 1.When 72 data was divided into two groups of subsample data as 36 data, they do not have their properties to be non-homogeneous and inconsistent due to F-test and t-test. 2.The credit limits of the serial correlation coefficient was fluctuated $\pm$0. 231 which was shown in Fig. 3. at significant level 99% by Anderson's test. 3.The correlogram at short term was shown to be no short-term persistence as Fig. 3. 4.Since the correlogram at long term has displayed that Hurst's coefficient was 0.6144 between 0.6 and 0.7, it was to be no long-term persistence. 5.The stochastic model with annual discharge of this River Rhine was shown with $\chi$t=2195+483. 8 $\varepsilon$t as $\chi$t=$\mu$+oet and $\varepsilon$t=$_1$ø$\varepsilon$t-$_1$+ζt where t=1,2,3,..., ζt is an independent series with mean zero and variance (1-ø2), $\varepsilon$t is the dependent series, and 4' is the parameter of the model. 6.The serial correlation coefficient of monthly discharge was explained as $\chi$$_1$ = 0.34 . sin(6-$\pi$t+$\pi$) as Fig.4. and the River Rhine has no large fluctuation and smoothly changed during that time.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.25-30
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• 2016

It is critical to forecast the maximum daily and monthly demand for power with as little error as possible for our industry and national economy. In general, long-term forecasting of power demand has been studied from both the consumer's perspective and an econometrics model in the form of a generalized linear model with predictors. Time series techniques are used for short-term forecasting with no predictors as predictors must be predicted prior to forecasting response variables and containing estimation errors during this process is inevitable. In previous researches, seasonal exponential smoothing method, SARMA (Seasonal Auto Regressive Moving Average) with consideration to weekly pattern Neuron-Fuzzy model, SVR (Support Vector Regression) model with predictors explored through machine learning, and K-means clustering technique in the various approaches have been applied to short-term power supply forecasting. In this paper, SARMA and intervention model are fitted to forecast the maximum power load daily, weekly, and monthly by using the empirical data from 2011 through 2013. $ARMA(2,\;1,\;2)(1,\;1,\;1)_7$ and $ARMA(0,\;1,\;1)(1,\;1,\;0)_{12}$ are fitted respectively to the daily and monthly power demand, but the weekly power demand is not fitted by AREA because of unit root series. In our fitted intervention model, the factors of long holidays, summer and winter are significant in the form of indicator function. The SARMA with MAPE (Mean Absolute Percentage Error) of 2.45% and intervention model with MAPE of 2.44% are more efficient than the present seasonal exponential smoothing with MAPE of about 4%. Although the dynamic repression model with the predictors of humidity, temperature, and seasonal dummies was applied to foretaste the daily power demand, it lead to a high MAPE of 3.5% even though it has estimation error of predictors.

• Journal of Korean Society for Quality Management
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• v.50 no.4
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• pp.701-716
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• 2022

Purpose: This study aims to predict the dry cargo transportation market economy. The subject of this study is the BDI (Baltic Dry Index) time-series, an index representing the dry cargo transport market. Methods: In order to increase the accuracy of the BDI time-series, we have pre-processed the original time-series via time-series decomposition and data augmentation techniques and have used them for ANN learning. The ANN algorithms used are Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) to compare and analyze the case of learning and predicting by applying time-series decomposition and data augmentation techniques. The forecast period aims to make short-term predictions at the time of t+1. The period to be studied is from '22. 01. 07 to '22. 08. 26. Results: Only for the case of the MAPE (Mean Absolute Percentage Error) indicator, all ANN models used in the research has resulted in higher accuracy (1.422% on average) in multivariate prediction. Although it is not a remarkable improvement in prediction accuracy compared to uni-variate prediction results, it can be said that the improvement in ANN prediction performance has been achieved by utilizing time-series decomposition and data augmentation techniques that were significant and targeted throughout this study. Conclusion: Nevertheless, due to the nature of ANN, additional performance improvements can be expected according to the adjustment of the hyper-parameter. Therefore, it is necessary to try various applications of multiple learning algorithms and ANN optimization techniques. Such an approach would help solve problems with a small number of available data, such as the rapidly changing business environment or the current shipping market.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.4
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• pp.33-49
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• 2014

This study empirically examines the impact of firms' environmental (E), social (S), and governancial (G) factors on their short-term and long-term values. To measure firms' non-financial performance, we use ESG performance grades published by KCGS (Korea Corporate Governance Service). We employ stock log return as the proxy of each firm's short-term value, and Tobin's Q ratio as that of its long-term value. From a series of regression analyses, we find each of the ESG factors generally has a negative impact on stock return while it has a positive impact on the Tobin's Q ratio. These results imply that firms' effort for enhancing their non-financial performance may adversely affect their financial performance in a short term; but in the long-term point of view, firms' values increase through their good images engraved by their respective social, environmental and governancial efforts. In addition, we compare the relative strength of impact among E, S, G, the three non-financial factors on the firms' value measured in Tobin's Q ratio, and find that S (social factor) and G (governancial factor) give statistically significant impact on the firms' value respectively. This result tells us it would be advised to strategically embed CSV (creating shared value) pursuing both of profits and social responsibility in the firms' future agenda. While E (environmental factor) is shown to be an insignificant factor for the firms' value, it should be emphasized as a major concern by all the stakeholders in order to form a sound business ecosystem.

• Journal of Korea Port Economic Association
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• v.39 no.4
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• pp.223-242
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• 2023

This study analyzes the spillover effects of returns and volatility between the commodity market and the maritime freight market across various frequency domains (short-term, medium-term, long-term). The key findings of the study can be summarized as follows. First, from the perspective of returns, a high linkage is observed in the short-term between the commodity and maritime freight markets, with the metal commodities market playing a particularly significant role in information transmission effect of return series. Second, in terms of volatility, the total connectedness increases from the short- to the long-term, with substantial long-term risk transmission effects observed especially in the BDI, BDTI, agricultural, and energy commodity markets. Notably, during major global events such as the U.S.-China trade war, COVID-19, and the Russia-Ukraine conflicts, a marked increase in the risk transmission effect in the energy commodities market was identified.

• East Asian mathematical journal
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• v.35 no.3
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• pp.305-318
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• 2019

Over the past decades, the Lee-Carter model [1] has attracted much attention from various demography-related fields in order to project the future mortality rates. In the Lee-Carter model, the speed of mortality improvement is stochastically modeled by the so-called mortality index and is used to forecast the future mortality rates based on the time series analysis. However, the modeling is applied to long time series and thus an important structural change might exist, leading to potentially large long-term forecasting errors. Therefore, in this paper, we are interested in detecting the structural change of the Lee-Carter model and investigating the actuarial implications. For the purpose, we employ the tests proposed by Coelho and Nunes [2] and analyze the mortality data for six countries including Korea since 1970. Also, we calculate life expectancies and whole life insurance premiums by taking into account the structural change found in the Korean male mortality rates. Our empirical result shows that more caution needs to be paid to the Lee-Carter modeling and its actuarial applications.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.666-668
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• 2000

In this paper, a fuzzy prediction method is proposed for time series data having uncertainty and non-stationary characteristics. Conventional methods, which use past data directly in prediction procedure, cannot properly handle non-stationary data whose long-term mean is floating. To cope with this problem, a data preprocessing technique utilizing the differences of original time series data is suggested. The difference sets are established from data. And the optimal difference set is selected for input of fuzzy predictor. The proposed method based the Takigi-Sugeno-Kang(TSK or TS) fuzzy rule. Computer simulations show improved results for various time series.