• Journal of Environmental Science International
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• v.15 no.8
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• pp.701-718
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• 2006

The average ratio of the daily UV-B to total solar (75) irradiance at Busan (35.23$^{\circ}$N, 129.07$^{\circ}$E) in Korea is found as 0.11%. There is also a high exponential relationship between hourly UV-B and total solar irradiance: UV-B=exp (a$\times$(75-b))(R$^2$=0.93). The daily variation of total ozone is compared with the UV-B irradiance at Pohang (36.03$^{\circ}$N, 129.40$^{\circ}$E) in Korea using the Total Ozone Mapping Spectrometer (TOMS) data during the period of May to July in 2005. The total ozone (TO) has been maintained to a decreasing trend since 1979, which leading to a negative correlation with the ground-level UV-B irradiance doting the given period of cloudless day: UV-B=239.23-0.056 TO (R$^2$=0.52). The statistical predictions of daily total ozone are analyzed by using the data of the Brewer spectrophotometer and TOMS in East Asia including the Korean peninsula. The long-term monthly averages of total ozone using the multiplicative seasonal AutoRegressive Integrated Moving Average (ARIMA) model are used to predict the hourly mean UV-B irradiance by interpolating the daily mean total ozone far the predicting period. We also can predict the next day's total ozone by using regression models based on the present day's total ozone by TOMS and the next day's predicted maximum air temperature by the Meteorological Mesoscale Model 5 (MM5). These predicted and observed total ozone amounts are used to input data of the parameterization model (PM) of hourly UV-B irradiance. The PM of UV-B irradiance is based on the main parameters such as cloudiness, solar zenith angle, total ozone, opacity of aerosols, altitude, and surface albedo. The input data for the model requires daily total ozone, hourly amount and type of cloud, visibility and air pressure. To simplify cloud effects in the model, the constant cloud transmittance are used. For example, the correlation coefficient of the PM using these cloud transmissivities is shown high in more than 0.91 for cloudy days in Busan, and the relative mean bias error (RMBE) and the relative root mean square error (RRMSE) are less than 21% and 27%, respectively. In this study, the daily variations of calculated and predicted UV-B irradiance are presented in high correlation coefficients of more than 0.86 at each monitoring site of the Korean peninsula as well as East Asia. The RMBE is within 10% of the mean measured hourly irradiance, and the RRMSE is within 15% for hourly irradiance, respectively. Although errors are present in cloud amounts and total ozone, the results are still acceptable.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.241-241
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• 2012

Semiconductor industry has been taking the advantage of improvements in process technology in order to maintain reduced device geometries and stringent performance specifications. This results in semiconductor manufacturing processes became hundreds in sequence, it is continuously expected to be increased. This may in turn reduce the yield. With a large amount of investment at stake, this motivates tighter process control and fault diagnosis. The continuous improvement in semiconductor industry demands advancements in process control and monitoring to the same degree. Any fault in the process must be detected and classified with a high degree of precision, and it is desired to be diagnosed if possible. The detected abnormality in the system is then classified to locate the source of the variation. The performance of a fault detection system is directly reflected in the yield. Therefore a highly capable fault detection system is always desirable. In this research, time series modeling of the data from an etch equipment has been investigated for the ultimate purpose of fault diagnosis. The tool data consisted of number of different parameters each being recorded at fixed time points. As the data had been collected for a number of runs, it was not synchronized due to variable delays and offsets in data acquisition system and networks. The data was then synchronized using a variant of Dynamic Time Warping (DTW) algorithm. The AutoRegressive Integrated Moving Average (ARIMA) model was then applied on the synchronized data. The ARIMA model combines both the Autoregressive model and the Moving Average model to relate the present value of the time series to its past values. As the new values of parameters are received from the equipment, the model uses them and the previous ones to provide predictions of one step ahead for each parameter. The statistical comparison of these predictions with the actual values, gives us the each parameter's probability of fault, at each time point and (once a run gets finished) for each run. This work will be extended by applying a suitable probability generating function and combining the probabilities of different parameters using Dempster-Shafer Theory (DST). DST provides a way to combine evidence that is available from different sources and gives a joint degree of belief in a hypothesis. This will give us a combined belief of fault in the process with a high precision.

• Journal of the Earthquake Engineering Society of Korea
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• v.1 no.2
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• pp.1-15
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• 1997

This paper presents substructural identification methods for the assessment of local damages in complex and large structural systems. For this purpose, an auto-regressive and moving average with stochastic input (ARMAX) model is derived for a substructure to process the measurement data impaired by noises. Using the substructural methods, the number of unknown parameters for each identification can be significantly reduced, hence the convergence and accuracy of estimation can be improved. Secondly, the damage index is defined as the ratio of the current stiffness to the baseline value at each element for the damage assessment. The indirect estimation method was performed using the estimated results from the identification of the system matrices from the substructural identification. To demonstrate the proposed techniques, several simulation and experimental example analyses are carried out for structural models of a 2-span truss structure, a 3-span continuous beam model and 3-story building model. The results indicate that the present substructural identification method and damage estimation methods are effective and efficient for local damage estimation of complex structures.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.572-578
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• 2020

The stock market is affected by unexpected factors, such as politics, society, and natural disasters, as well as by corporate performance and economic conditions. In recent days, artificial intelligence has become popular, and many researchers have tried to conduct experiments with that. Our study proposes an experiment using not only stock-related data but also other various economic data. We acquired a year's worth of data on stock prices, the percentage of foreigners, interest rates, and exchange rates, and combined them in various ways. Thus, our input data became diversified, and we put the combined input data into a nonlinear autoregressive network with exogenous inputs (NARX) model. With the input data in the NARX model, we analyze and compare them to the original data. As a result, the model exhibits a root mean square error (RMSE) of 0.08 as being the most accurate when we set 10 neurons and two delays with a combination of stock prices and exchange rates from the U.S., China, Europe, and Japan. This study is meaningful in that the exchange rate has the greatest influence on stock prices, lowering the error from RMSE 0.589 when only closing data are used.

• Journal of Digital Convergence
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• v.18 no.1
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• pp.343-355
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• 2020

The purpose of this study was to analyze the relationship between game addiction and social stigma of adolescents outside school. After examining the previous research focusing on the Public stigma & Self-stigma theory, We intended to examine how game addiction and social stigma affect each other over time, and the time causal relationship between the both. Using youth outside school panel 3rd, 4th, 5th data, This study analyzed the relationship between game addiction and social stigma of adolescents with school interruption longitudinally. The research method was analyzed by autoregressive cross-lagged model using two variables such as game addiction and social stigma using Amos25 program. The results showed that game addiction did not significantly affect social stigma at the next time, but social stigma had a significant effect on game addiction at the next time. Game addiction and social stigma have a strong auto-regressive effect, and the degree remains constant over time. Accordingly, this study suggested social co-prosperity, support from the local community, multidisciplinary viewpoints and cooperation between the public and private sectors.

• The Korean Journal of Financial Management
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• v.27 no.1
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• pp.1-30
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• 2010

This paper tested the lead-lag relationship as well as the symmetric and asymmetric volatility spillover effects between international currency futures markets and cash markets. We use five kinds of currency spot and futures markets such as British pound, Australian and Canadian dollar, Brasilian Real and won/dollar spot and futures markets. daily closing prices covering from September 15, 2003 to July 30, 2009. For this purpose we employed dynamic time series models such as the Granger causality based on VAR and time-varying MA(1)-GJR-GARCH(1, 1)-M. The main empirical results are as follows; First, according to Granger causality test, we find that the bilateral lead-lag relationship between the five countries' currency spot and futures market. The price discover effect from currency futures markets to spot market is relatively stronger than that from currency spot to futures markets. Second, based on the time varying GARCH model, we find that there is a bilateral conditional mean spillover effects between the five currency spot and futures markets. Third, we also find that there is a bilateral asymmetric volatility spillover effects between British pound, Canadian dollar, Brasilian Real and won/dollar spot and futures market. However there is a unilateral asymmetric volatility spillover effect from Australian dollar futures to cash market, not vice versa. From these empirical results we infer that most of currency futures markets have a much better price discovery function than currency cash market and are inefficient to the information.

• Journal of the Economic Geographical Society of Korea
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• v.15 no.4
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• pp.585-600
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• 2012

The purpose of this study related to the liquidity impact of the housing market variables using vector auto-regressive model(VAR) and empirical analysis is to derive some policy implications. October 2003 until May 2012 using monthly data for liquidity variables mortgage rates, mortgage, financial liquidity, as the composite index and nation, Seoul, Gangnam, Gangbuk, the Apartment sales prices were analyzed. Granger Causality Test Results, mortgage rates and mortgage at a bargain price two regions had a strong causal relationship. Since the impulse response analysis, Geothermal difference there, but housing price housing price itself, the most significant ongoing positive (+) reactions were liquidity-related variables are mortgage loans is large and persistent positive (+), financial liquidity weakly positive (+), mortgage interest rates are negative (-), KOSPI, the negative (-) reacted. Liquidity and housing prices that the rise can be and Gangnam in Gangbuk is greater than the factor that housing investment was confirmed empirically. Government to consider the current economic situation, while maintaining low interest rates and liquidity of the market rather than the real estate industry must ensure that activities can be embedded and local enforcement policies should be differentiated according to the policy will be able to reap significant effect.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.2B
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• pp.163-171
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• 2009

Classical linear models have been generally used to analyze and forecast hydrologic time series. However, there is growing evidence of nonlinear structure in natural phenomena and hydrologic time series associated with their patterns and fluctuations. Therefore, the classical linear techniques for time series analysis and forecasting may not be appropriate for nonlinear processes. In recent, the BDS (Brock-Dechert-Scheinkman) statistic instead of conventional techniques has been used for detecting nonlinearity of time series. The BDS statistic was derived from the statistical properties of the correlation integral which is used to analyze chaotic system and has been effectively used for distinguishing nonlinear structure in dynamic system from random structures. DVS (Deterministic Versus Stochastic) algorithm has been used for detecting chaos and stochastic systems and for forecasting of chaotic system. This study showed the DVS algorithm can be also used for detecting nonlinearity of the time series. In this study, the stochastic and hydrologic time series are analyzed to detect their nonlinearity. The linear and nonlinear stochastic time series generated from ARMA and TAR (Threshold Auto Regressive) models, a daily streamflow at St. Johns river near Cocoa, Florida, USA and Great Salt Lake Volume (GSL) data, Utah, USA are analyzed, daily inflow series of Soyang dam and the results are compared. The results showed the BDS statistic is a powerful tool for distinguishing between linearity and nonlinearity of the time series and DVS plot can be also effectively used for distinguishing the nonlinearity of the time series.

• Korea Trade Review
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• v.42 no.2
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• pp.145-174
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• 2017

Recently, due to the worsening global economic recession, Korea which is a small, export-oriented economy has decreased exports and the domestic economy also continues to stagnate. Therefore, for continued growth of our economy through export growth, we need to analyze the validity of export support system such as export insurance and prepare ways to expand exports. This study is to investigate the effects of Export Insurance on the exports of SMEs as well as LEs. For this purpose, this study conducted Time Series Analysis using data such as export, export insurance acquisition, export price index, exchange rate, and coincident composite index(CCI). First, as a result of the Granger Causality Test, the exports of LEs has found to have a causal relationship with the CCI, and CCI is to have a causal relationship with the short-term export insurance record. Second, the results of VAR analysis show that the export insurance acquisition result and the export price index have a positive effect on the exports of LEs, while the short - term export insurance has a negative effect on the exports of LEs. Third, as a result of variance decomposition, the export of LEs has much more influenced for mid to long term by the short-term export insurance acquisition compared to SMEs. Fourth, short-term export insurance has a positive effect on exports of SMEs. In order to activate short-term export insurance against SMEs, it is necessary to expand support for SMEs by local governments. This study aims to suggest policy implications for establishing effective export insurance policy by analyzing the effects of export insurance on the export of SMEs as well as LEs. It is necessary to carry out a time series analysis on the export results according to the insurance acquisition results by industry to measure the export support effect of export insurance more precisely.