• Asia Pacific Journal of Business Review
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• v.7 no.1
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• pp.1-21
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• 2022

This research aimed to clarify the impacts casted by inwards FDI on New venture creation, industrialization, and the economic growth of Russia. For all of these variables, data was taken about Russia from the site of The World Bank, and the selected duration was from 1995 to 2019. The total duration of the data taken was from 24 years. The time duration was well enough for applying the A.R.D.L. approach to the time series data of the study. This research used the unit root test to know the presence of the unit root for each variable, the lag order selection was made for the data, the bounds cointegration test was also applied, and ARDL Model was used to know about the different effects. With the help of the results derived, it was observed that the impact of private sector investment on new venture creation is significant. In contrast, foreign direct investment and research and development (R&D) effects on new venture creation are insignificant. It was also observed from the results that the impact of R&D on industrialization in Russia is significant, while the effects of FDI and the impact of private sector investment on industrialization in Russia is insignificant. We have fund that the effect of FDI and the impact of private sector investment on the economic growth of Russia is significant. In contrast, the impact of R&D is insignificant to the economic growth of Russia. The study is of great significance as it has raised the importance of R&D for industrialization, FDI, and PSI for economic growth and new venture creation for developing countries.

• Journal of The Korean Astronomical Society
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• v.40 no.4
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• pp.99-106
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• 2007

Statistical analyses were performed to investigate the relative success and accuracy of daily maximum X-ray flux (MXF) predictions, using both multilinear regression and autoregressive time-series prediction methods. As input data for this work, we used 14 solar activity parameters recorded over the prior 2 year period (1989-1990) during the solar maximum of cycle 22. We applied the multilinear regression method to the following three groups: all 14 variables (G1), the 2 so-called 'cause' variables (sunspot complexity and sunspot group area) showing the highest correlations with MXF (G2), and the 2 'effect' variables (previous day MXF and the number of flares stronger than C4 class) showing the highest correlations with MXF (G3). For the advanced three days forecast, we applied the autoregressive timeseries method to the MXF data (GT). We compared the statistical results of these groups for 1991 data, using several statistical measures obtained from a $2{\times}2$ contingency table for forecasted versus observed events. As a result, we found that the statistical results of G1 and G3 are nearly the same each other and the 'effect' variables (G3) are more reliable predictors than the 'cause' variables. It is also found that while the statistical results of GT are a little worse than those of G1 for relatively weak flares, they are comparable to each other for strong flares. In general, all statistical measures show good predictions from all groups, provided that the flares are weaker than about M5 class; stronger flares rapidly become difficult to predict well, which is probably due to statistical inaccuracies arising from their rarity. Our statistical results of all flares except for the X-class flares were confirmed by Yates' $X^2$ statistical significance tests, at the 99% confidence level. Based on our model testing, we recommend a practical strategy for solar X-ray flare predictions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.5
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• pp.1399-1406
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• 2008

This study has been made to research effect of neurofeedback training on the Brain Quotient and baduk strength whose EEC data were obtained both before and after the neurofeedback training from 15th June, to 15th September in 2007. Brain waves were measured on the frontal lobes of subjects (30 baduk players - the experimental group 15 under neuroffedback training and the control group 15 subjects) and analyzed by calculating eight brain quotients characterizing behaviors of EEG rhythms effectively. The results of the analysis show that the experimental group has the higher indexes Attention Quotient(left) p=.041, (right)p=.007, Stress Quotient (left) p=.020, and Stress Quotient (right) p=.019 show statistically significant difference between two groups. The research data show that the experimental group have the higher average than the control group in Baduk strength examination p=.021 after the neuroffedback training. As the brain waves are adjusted by timeseries linear analysis, the brain function quotients can reflect the functional states of the brain.

• Ocean and Polar Research
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• v.42 no.3
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• pp.195-210
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• 2020

The observed time series from the Korea Ocean Research Stations (KORS) in the Yellow and East China Seas (YECS) have various sources of noise, including bio-fouling on the underwater sensors, intermittent depletion of power, cable leakage, and interference between the sensors' signals. Besides these technical issues, intricate waves associated with background tidal currents tend to result in substantial oscillations in oceanic time series. Such technical and environmental issues require a regionally optimized automatic quality control (QC) procedure. Before the achievement of this ultimate goal, we examined the approach of the Ocean Observatories Initiative (OOI)'s standard QC to investigate whether this procedure is pertinent to the KORS. The OOI QC consists of three categorized tests of global/local range of data, temporal variation including spike and gradient, and sensor-related issues associated with its stuck and drift. These OOI QC algorithms have been applied to the water temperature time series from the Ieodo station, one of the KORS. Obvious outliers are flagged successfully by the global/local range checks and the spike check. Both stuck and drift checks barely detected sensor-related errors, owing to frequent sensor cleaning and maintenance. The gradient check, however, fails to flag the remained outliers that tend to stick together closely, as well as often tend to mark probably good data as wrong data, especially data characterized by considerable fluctuations near the thermocline. These results suggest that the gradient check might not be relevant to observations involving considerable natural fluctuations as well as technical issues. Our study highlights the necessity of a new algorithm such as a standard deviation-based outlier check using multiple moving windows to replace the gradient check and an additional algorithm of an inter-consistency check with a related variable to build a standard QC procedure for the KORS.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.24 no.2
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• pp.120-127
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• 2012

Exploratory data analysis was carried out by using the long-term wave climate data in Sokcho coastal zone. The main features found in this study are as follows. The coefficient of variations on the wave height and period are about 0.11 and 0.02, respectively. It also shows that the annual components of the wave height and period are dominant and their amplitudes are 0.24 m and 0.56 seconds, respectively. The amount of intra-annual variation range is about two times greater than that of the inter-annual variation range. The distribution shapes of the wave data are very similar to the log-normal and GEV(generalized extreme value) functions. However, the goodness-of-fit tests based on the KS test show as "rejected" for all suggested density functions. Then, the structure of the timeseries wave height data is roughly estimated as AR(3) model. Based on the wave duration results, it is clearly shown that the continuous and maximum duration is decreased as a power function shape and the total duration is exponentially decreased. Meanwhile, the environment of the Sokcho coastal zone is classified as a wave-dominated environment.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.2
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• pp.123-131
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• 2011

Characteristics of sea surface temperature(SST) difference around Gwang-Yang steel Mill where can affect marine ecosystem in Gwang-Yang bay using 25 collected Landsat-7 ETM+ thermal infrared band data from 2000 to 2010. To analyze accuracy of SST from the Landsat-7 ETM+ thermal infrared image， satellite-induced SST was verfied by compared Yeo-Su tide station and Landsat thermal image. As a result, SST from Landsat-7 ETM+ is $1.22^{\circ}C$ lower than sea temperature from Yeo-Su tide station and correlation coefficient resulted in above 0.991 which means that correlation coefficient between Landsat image temperature and field sea temperature is relatively high. Five regions were selected to analyze sea surface temperature between near Gwang-Yang steel mill and the open sea and analyzed timeseries of sea surface temperature seasonally and regionally. Moreover, the additional analysis has been carried out by comparing the averaged temperatures of Gwang-Yang and Soon-Cheon bays using the dataset over a year.

• 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.