• International Area Studies Review
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• v.13 no.3
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• pp.331-359
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• 2009

We forecast the performance of the Korean biotechnology industry by adopting similar development paths taken by the U.S. biotechnology and Korean ICT industries. Our long-term forecasting techniques predict that Korean BT market size will increase from 3.7 billion to 10.8 billion U.S. dollars by year 2030. The pharmaceutical industry, one of major bio-subindustries, is expected to dominate Korean BT market in the long-run. Also, the relative portion of the exports in the Korean BT industry will be larger and thus the export-oriented government policy is required for the long-run growth of the Korean BT industry. Since the Korean ICT industry has already slowed down in the development, Korean BT industry is likely to catch up with ICT industry in the near future.

• Journal of Korea Water Resources Association
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• v.42 no.11
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• pp.953-961
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• 2009

The objective of this study is to develop a stochastic continuous storage function model for enhancement of an event-oriented watershed and channel storage function models which have been used as an official flood forecast model in Korea. For this study, soil moisture accounting component is added to the original storage function model and each hydrologic component, such as surface flow, subsurface flow, groundwater flow and actual evaportranspiration, is simulated as a function of soil water content. And also, ensemble Kalman filtering technique is used for real-time assimilation of measured streamflow from various stream locations in the watershed. Therefore the enhanced model will be able to simulate hydrologic components for long-term period without additional estimation of model parameters and to give more accurate and reliable results than those from the existing deterministic model due to the assimilation of measured streamflow data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.187-194
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• 2019

Traditional demand forecasting methods are difficult to meet the needs of companies due to rapid changes in the market and the diversification of individual consumer needs. In a diversified production environment, the right demand forecast is an important factor for smooth yield management. Many of the existing predictive models commonly used in industry today are limited in function by little. The proposed model is designed to overcome these limitations, taking into account the part where each model performs better individually. In this paper, variables are extracted through Gray Relational analysis suitable for dynamic process analysis, and statistically predicted data is generated that includes characteristics of historical demand data produced through ARIMA forecasts. In combination with the LSTM model, demand forecasts can then be calculated by reflecting the many factors that affect demand forecast through an architecture that is structured to avoid the long-term dependency problems that the neural network model has.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.7
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• pp.307-314
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• 2022

Recently, researches using deep learning-based models are being actively conducted to replace statistical-based time series forecast techniques to predict electric power demand. The result of analyzing the researches shows that the performance of the LSTM-based prediction model is acceptable, but it is not sufficient for long-term regional-wide power demand prediction. In this paper, we propose a WaveNet deep learning model to predict electric power demand 24-hour-ahead with temperature data in order to achieve the prediction accuracy better than MAPE value of 2% which statistical-based time series forecast techniques can present. First of all, we illustrate a delated causal one-dimensional convolutional neural network architecture of WaveNet and the preprocessing mechanism of the input data of electric power demand and temperature. Second, we present the training process and walk forward validation with the modified WaveNet. The performance comparison results show that the prediction model with temperature data achieves MAPE value of 1.33%, which is better than MAPE Value (2.33%) of the same model without temperature data.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.607-612
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• 2021

This study estimated the impact of the shock of housing price increase on the total fertility rate and the contribution of each variable to changes in the TFR. This study is differentiated by estimating the contribution rate of each variable to the fertility rate through the Shapley decomposition and the panel VAR's forecast error variance decomposition, which previous studies have not attempted. The main results of this study are as follows. First, the decline in the TFR in Korea has been strongly influenced by the recent decline in the total fertility rate, and this influence is expected to continue in the future. In the case of housing costs, in the past, housing sales prices had a relatively small contribution to changes in the total fertility rate compared to the jeonse prices, but their influence is expected to increase in the long term in the future. It has been demonstrated that private education expenses other than housing sale price and Jeonse price also acted as a major cause of the decline in the total fertility rate.

• The Journal of Bigdata
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• v.6 no.2
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• pp.85-98
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• 2021

The pandemic of COVID-19 further promoted the imbalance in the volume of imports and exports among countries using containers, which worsened the shortage of empty containers. Since it is important to secure as many empty containers as the appropriate demand for stable and efficient port operation, measures to predict demand for empty containers using various techniques have been studied so far. However, it was based on long-term forecasts on a monthly or annual basis rather than demand forecasts that could be used directly by ports and shipping companies. In this study, a daily and weekly prediction method using an actual artificial neural network is presented. In details, the demand forecasting model has been developed using multi-layer perceptron and multiple linear regression model. In order to overcome the limitation from the lack of data, it was manipulated considering the business process between the loaded container and empty container, which the fully-loaded container is converted to the empty container. From the result of numerical experiment, it has been developed the practically applicable forecasting model, even though it could not show the perfect accuracy.

• Atmosphere
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• v.15 no.2
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• pp.101-118
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• 2005

Atmospheric ozone changes temporally and spatially according to both anthropogenic and natural causes. It is essential to quantify the natural contributions to total ozone variations for the estimation of trend caused by anthropogenic processes. The aims of this study are to understand the intrinsic natural variability of long-term total ozone changes and to estimate more reliable ozone trend caused by anthropogenic ozone-depleting materials. For doing that, long-term time series for Seoul of monthly total ozone which were measured from both ground-based Dobson Spectrophotometer (Beck #124)(1985-2004) and satellite TOMS (1979-1984) are analyzed for selected period, after dividing the whole period (1979~2004) into two periods; the former period (1979~1991) and the latter period (1992~2004). In this study, ozone trends for the time series are calculated using multiple regression models with explanatory natural oscillations for the Arctic Oscillation(AO), North Atlantic Oscillation(NAO), North Pacific Oscillation(NPO), Pacific Decadal Oscillation(PDO), Quasi Biennial Oscillation(QBO), Southern Oscillation(SO), and Solar Cycle(SC) including tropopause pressure(TROPP). Using the developed models, more reliable anthropogenic ozone trend is estimated than previous studies that considered only QBO and SC as natural oscillations (eg; WMO, 1999). The quasi-anthropogenic ozone trend in Seoul is estimated to -0.12 %/decade during the whole period, -2.39 %/decade during the former period, and +0.10 %/decade during the latter period, respectively. Consequently, the net forcing mechanism of the natural oscillations on the ozone variability might be noticeably different in two time intervals with positive forcing for the former period (1979-1991) and negative forcing for the latter period (1992-2004). These results are also found to be consistent with those analyzed from the data observed at ground stations (Sapporo, Tateno) of Japan. In addition, the recent trend analyses for Seoul show positive change-in-trend estimates of +0.75 %/decade since 1997 relative to negative trend of -1.49 %/decade existing prior to 1997, showing -0.74 %/decade for the recent 8-year period since 1997. Also, additional supporting evidence for a slowdown in ozone depletion in the upper stratosphere has been obtained by Newchurch et al.(2003).

• The Journal of Society for e-Business Studies
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• v.24 no.4
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• pp.119-133
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• 2019

Recently, the importance prediction of photovoltaic power (PV) is considered as an essential function for scheduling adjustments, deciding on storage size, and overall planning for stable operation of PV facility systems. In particular, since most of PV power is generated in peak time, PV power prediction in a peak time is required for the PV system operators that enable to maximize revenue and sustainable electricity quantity. Moreover, Prediction of the PV power output in peak time without meteorological information such as solar radiation, cloudiness, the temperature is considered a challenging problem because it has limitations that the PV power was predicted by using predicted uncertain meteorological information in a wide range of areas in previous studies. Therefore, this paper proposes the LSTM (Long-Short Term Memory) based the PV power prediction model only using the meteorological, seasonal, and the before the obtained PV power before peak time. In this paper, the experiment results based on the proposed model using the real-world data shows the superior performance, which showed a positive impact on improving the PV power in a peak time forecast performance targeted in this study.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.201-217
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• 2022

The United States is one of the largest producers of major crops such as wheat, maize, and soybeans, and is a major exporter of these crops. Therefore, it is important to estimate the crop production of the country in advance based on reliable long- term weather forecast information for stable crops supply and demand in Korea. The purpose of this study is to improve the seasonal predictability of the agro-climatic indices over the United States by using regional-scale daily temperature. For long-term numerical weather prediction, a dynamical downscaling is performed using Weather Research and Forecasting (WRF) model, a regional climate model. As the initial and lateral boundary conditions of WRF, the global hourly prediction data obtained from the Pusan National University Coupled General Circulation Model (PNU CGCM) are used. The integration of WRF is performed for 22 years (2000-2021) for period from June to December of each year. The empirical quantile mapping, one of the bias correction methods, is applied to the timeseries of downscaled daily mean, minimum, and maximum temperature to correct the model biases. The uncorrected and corrected datasets are referred WRF_UC and WRF_C, respectively in this study. The daily minimum (maximum) temperature obtained from WRF_UC presents warm (cold) biases over most of the United States, which can be attributed to the underestimated the low (high) temperature range. The results show that WRF_C simulates closer to the observed temperature than WRF_UC, which lead to improve the long- term predictability of the temperature- based agro-climatic indices.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.723-736
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• 2022

In this study, monthly precipitation forecasting models that can predict up to 12 months in advance were constructed for the Geum River basin, and two statistical techniques, multiple linear regression (MLR) and artificial neural network (ANN), were applied to the model construction. As predictor candidates, a total of 47 climate indices were used, including 39 global climate patterns provided by the National Oceanic and Atmospheric Administration (NOAA) and 8 meteorological factors for the basin. Forecast models were constructed by using climate indices with high correlation by analyzing the teleconnection between the monthly precipitation and each climate index for the past 40 years based on the forecast month. In the goodness-of-fit test results for the average value of forecasts of each month for 1991 to 2021, the MLR models showed -3.3 to -0.1% for the percent bias (PBIAS), 0.45 to 0.50 for the Nash-Sutcliffe efficiency (NSE), and 0.69 to 0.70 for the Pearson correlation coefficient (r), whereas, the ANN models showed PBIAS -5.0~+0.5%, NSE 0.35~0.47, and r 0.64~0.70. The mean values predicted by the MLR models were found to be closer to the observation than the ANN models. The probability of including observations within the forecast range for each month was 57.5 to 83.6% (average 72.9%) for the MLR models, and 71.5 to 88.7% (average 81.1%) for the ANN models, indicating that the ANN models showed better results. The tercile probability by month was 25.9 to 41.9% (average 34.6%) for the MLR models, and 30.3 to 39.1% (average 34.7%) for the ANN models. Both models showed long-term predictability of monthly precipitation with an average of 33.3% or more in tercile probability. In conclusion, the difference in predictability between the two models was found to be relatively small. However, when judging from the hit rate for the prediction range or the tercile probability, the monthly deviation for predictability was found to be relatively small for the ANN models.