• Journal of the Mineralogical Society of Korea
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• v.30 no.2
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• pp.45-57
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• 2017

Two single crystals of fully dehydrated $Cd^{2+}$-exchanged zeolites Y were prepared by the exchange of ${\mid}Na_{75}{\mid}[Si_{117}Al_{75}O_{384}]-FAU$ ($Na_{75}-Y$, Si/Al = 1.56) with aqueous $0.05M\;Cd(NO_3)_2$ (pH = 3.65) at 294 K, followed by vacuum dehydration at 723 K (crystal 1) and a second crystal, similarly prepared, was exposed to zeolitically dried benzene for 72 hours at 294 K and evacuated (crystal 2). Their structures were determined crystallographically using synchrotron X-rays and were refined to the final error indices using $F_o$>$4{\sigma}(F_o)$ of $R_1/wR_2=0.040/0.121$ and 0.052/0.168, respectively. In crystal $1({\mid}Cd_{36}H_3{\mid}[Si_{117}Al_{75}O_{384}]-FAU)$, $Cd^{2+}$ ions primarily occupy sites I and II, with additional $Cd^{2+}$ ions at sites I', II', and a second site II. In crystal $2({\mid}Cd_{35}(C_6H_6)_{24}H_5{\mid}[Si_{117}Al_{75}O_{384}]-FAU)$, $Cd^{2+}$ ions occupy five crystallographic sites. The 24 benzene molecules are found at two distinct positions within the supercages. The 17 benzene molecules are found on the 3-fold axes in the supercages where each interacts facially with one of site IIa $Cd^{2+}$ ions. The remaining 7 benzene molecules lie on the planes of the 12-rings where each is stabilized by multiple weak electrostatic and van der Waals interactions with framework oxygens.

• KDI Journal of Economic Policy
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• v.31 no.1
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• pp.169-206
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• 2009

This study stems from a question, "How should we understand the pattern of the Korean economy after the 1990s?" Among various analytic methods applicable, this study chooses a Structural Vector Autoregression (SVAR) with long-run restrictions, identifies diverse impacts that gave rise to the current status of the Korean economy, and differentiates relative contributions of those impacts. To that end, SVAR is applied to four economic models; Blanchard and Quah (1989)'s 2-variable model, its 3-variable extensions, and the two other New Keynesian type linear models modified from Stock and Watson (2002). Especially, the latter two models are devised to reflect the recent transitions in the determination of foreign exchange rate (from a fixed rate regime to a flexible rate one) as well as the monetary policy rule (from aggregate targeting to inflation targeting). When organizing the assumed results in the form of impulse response and forecasting error variance decomposition, two common denominators are found as follows. First, changes in the rate of economic growth are mainly attributable to the impact on productivity, and such trend has grown strong since the 2000s, which indicates that Korea's economic growth since the 2000s has been closely associated with its potential growth rate. Second, the magnitude or consistency of impact responses tends to have subsided since the 2000s. Given Korea's high dependence on trade, it is possible that low interest rates, low inflation, steady growth, and the economic emergence of China as a world player have helped secure capital and demand for export and import, which therefore might reduced the impact of each sector on overall economic status. Despite the fact that a diverse mixture of models and impacts has been used for analysis, always two common findings are observed in the result. Therefore, it can be concluded that the decreased rate of economic growth of Korea since 2000 appears to be on the same track as the decrease in Korea's potential growth rate. The contents of this paper are constructed as follows: The second section observes the recent trend of the economic development of Korea and related Korean articles, which might help in clearly defining the scope and analytic methodology of this study. The third section provides an analysis model to be used in this study, which is Structural VAR as mentioned above. Variables used, estimation equations, and identification conditions of impacts are explained. The fourth section reports estimation results derived by the previously introduced model, and the fifth section concludes.

• Korean Journal of Optics and Photonics
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• v.16 no.3
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• pp.182-190
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• 2005

The fiber Raman amplifier(FRA) is a distinctly advantageous technology. Due to its wider, flexible gain bandwidth, and intrinsically lower noise characteristics, FRA has become an indispensable technology of today. Various FRA modeling methods, with different levels of convergence speed and accuracy, have been proposed in order to gain valuable insights for the FRA dynamics and optimum design before real implementation. Still, all these approaches share the common platform of coupled ordinary differential equations(ODE) for the Raman equation set that must be solved along the long length of fiber propagation axis. The ODE platform has classically set the bar for achievable convergence speed, resulting exhaustive calculation efforts. In this work, we propose an alternative, highly efficient framework for FRA analysis. In treating the Raman gain as the perturbation factor in an adiabatic process, we achieved implementation of the algorithm by deriving a recursive relation for the integrals of power inside fiber with the effective length and by constructing a matrix formalism for the solution of the given FRA problem. Finally, by adiabatically turning on the Raman process in the fiber as increasing the order of iterations, the FRA solution can be obtained along the iteration axis for the whole length of fiber rather than along the fiber propagation axis, enabling faster convergence speed, at the equivalent accuracy achievable with the methods based on coupled ODEs. Performance comparison in all co-, counter-, bi-directionally pumped multi-channel FRA shows more than 102 times faster with the convergence speed of the Average power method at the same level of accuracy(relative deviation < 0.03dB).

• The Journal of Korean Academy of Prosthodontics
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• v.48 no.1
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• pp.48-54
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• 2010

Purpose: The objective of this study was to evaluate and compare the accuracy of impression body taking by the closed and the open tray impression technique with 3 types of impression tray. Individual tray, metal stock tray and polycarbonate tray were used. Materials and methods: Nine closed tray impressions were taken by individual tray, metal stock tray and polycarbonate stock tray, respectively with polyether impression material. 9 open tray impressions were also acquired by same manner. Precision analysis on the master models was performed by attaching the reference frameworks with alternate single screws and measuring the vertical fit discrepancy of respective analogues in working cast with a stereo microscope. Data were analyzed by 1 way ANOVA and independent t-test. Results: The average fit accuracy of impression bodies was calculated. With the closed tray impression technique, there were significant statistical differences in vertical fit discrepancy according to the types of tray. The individual tray group showed the lowest value and the polycarbonate stock tray group represented the highest. With the open tray impression technique, there was no significant difference in vertical fit discrepancy. Significant statistical difference in vertical fit discrepancy was found between the open and the closed impression technique with the polycarbonate stock tray. Conclusion: From the results above, more precise impressions could be acquired by the rigid individual tray compared with the polycarbonate stock tray. It was hard to get consistent accuracy impressions by the closed tray impression technique with polycarbonate stock trays.

• Journal of the Korean earth science society
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• v.42 no.6
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• pp.632-645
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• 2021

Satellite sea surface temperature (SST) composites provide important data for numerical forecasting models and for research on global warming and climate change. In this study, six types of representative SST composite database were collected from 2007 to 2018 and the characteristics of spatial structures of SSTs were analyzed in seas around the Korean Peninsula. The SST composite data were compared with time series of in-situ measurements from ocean meteorological buoys of the Korea Meteorological Administration by analyzing the maximum value of the errors and its occurrence time at each buoy station. High differences between the SST data and in-situ measurements were detected in the western coastal stations, in particular Deokjeokdo and Chilbaldo, with a dominant annual or semi-annual cycle. In Pohang buoy, a high SST difference was observed in the summer of 2013, when cold water appeared in the surface layer due to strong upwelling. As a result of spectrum analysis of the time series SST data, daily satellite SSTs showed similar spectral energy from in-situ measurements at periods longer than one month approximately. On the other hand, the difference of spectral energy between the satellite SSTs and in-situ temperature tended to magnify as the temporal frequency increased. This suggests a possibility that satellite SST composite data may not adequately express the temporal variability of SST in the near-coastal area. The fronts from satellite SST images revealed the differences among the SST databases in terms of spatial structure and magnitude of the oceanic fronts. The spatial scale expressed by the SST composite field was investigated through spatial spectral analysis. As a result, the high-resolution SST composite images expressed the spatial structures of mesoscale ocean phenomena better than other low-resolution SST images. Therefore, in order to express the actual mesoscale ocean phenomenon in more detail, it is necessary to develop more advanced techniques for producing the SST composites.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• Korean Journal of Crystallography
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• v.8 no.1
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• pp.6-14
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• 1997

The crystal structures of $Sr_{31}K_{30}-X\;(Sr_{31}K_{30}Si_{100}A1_{92}O_{384};\;a=25.169(5) {\AA}$) and $Sr_{8.5}Tl_{75}-X (Sr_{8.5}Tl_{75}Si_{100}A1_{92}O_{384};\;a=25.041(5) {\AA}$) have been determined by single-crystal X-ray diffraction techniques in the cubic space group $\=F{d3}\;at\;21(1)^{\circ}C$. Each crystal was prepared by ion exchange in a flowing stream of aqueous $Sr(ClO_4)_2\;and\;(K\;or\;T1)NO_3$ whose mole ratio was 1 : 5 for five days. Vacuum dehydration was done at $360^{\circ}C$ for 2d. Their structures were refined to the final error indices $R_1=0.072\;and\;R_w=0.057$ with 293 reflections, and $R_1= 0.058\;and\;R_w=0.044$ with 351 reflections, for which $I>2{\sigma}(I)$, respectively. In dehydrated $Sr_{31}K_{30}-X,\;all\;Sr^{2+}$ ions and $K^+$ ions are located at five different crystallographic sites. Six-teen $Sr^{2+}$ ions per unit cell are at the centers of the double six-rings (site I), filling that position. The remaining 15 $Sr^{2+}$ ions and 17 $K^+$ ions fill site II in the supercage. These $Sr^{2+}$ and $K^+$ ions are recessed ca $0.45{\AA}\;and\;1.06{\AA}$ into the supercage, respectively, from the plane of three oxygens to which each is bound. ($Sr-O=2.45(1){\AA}\;and\;K-O=2.64(1){\AA}$) Eight $K^+$ ons occupy site III'($K-O=3.09(7){\AA}\;and\;3.11(10){\AA}$) and the remaining five $K^+$ ions occupy another site III'($K-O=2.88(7){\AA}\;and\;2.76(7){\AA}$). In $Sr_{8.5}Tl_{75}-X,\;Sr^{2+}\;and\;Tl^+$ ions also occupy five different crystallographic sites. About 8.5 $Sr^{2+}$ ions are at site I. Fifteen $Tl^+$ ions are at site I' in the sodalite cavities on threefold axes opposite double six-rings: each is $1.68{\AA}$ from the plane of its three oxygens ($T1-O=2.70(2){\AA}$). Together these fill the double six-rings. Another 32 $Tl^+$ ions fill site II opposite single six-rings in the supercage, each being $1.48{\AA}$ from the plane of three oxygens ($T1-O=2.70(1){\AA}$). About 18 $Tl^+$ ions occupy site III in the supercage ($T1-O=2.86(2){\AA}$), and the remaining 10 are found at site III' in the supercage ($T1-O=2.96(4){\AA}$).

• Journal of KIISE:Databases
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• v.31 no.5
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• pp.468-478
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• 2004

It is essential in various application areas of data mining and bioinformatics to effectively retrieve the occurrences of interesting patterns from sequence databases. For example, let's consider a network event management system that records the types and timestamp values of events occurred in a specific network component(ex. router). The typical query to find out the temporal casual relationships among the network events is as fellows: 'Find all occurrences of CiscoDCDLinkUp that are fellowed by MLMStatusUP that are subsequently followed by TCPConnectionClose, under the constraint that the interval between the first two events is not larger than 20 seconds, and the interval between the first and third events is not larger than 40 secondsTCPConnectionClose. This paper proposes an indexing method that enables to efficiently answer such a query. Unlike the previous methods that rely on inefficient sequential scan methods or data structures not easily supported by DBMSs, the proposed method uses a multi-dimensional spatial index, which is proven to be efficient both in storage and search, to find the answers quickly without false dismissals. Given a sliding window W, the input to a multi-dimensional spatial index is a n-dimensional vector whose i-th element is the interval between the first event of W and the first occurrence of the event type Ei in W. Here, n is the number of event types that can be occurred in the system of interest. The problem of‘dimensionality curse’may happen when n is large. Therefore, we use the dimension selection or event type grouping to avoid this problem. The experimental results reveal that our proposed technique can be a few orders of magnitude faster than the sequential scan and ISO-Depth index methods.hods.

• Journal of Korean Society of Forest Science
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• v.85 no.3
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• pp.453-461
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• 1996

Three Phyllostachys stands of P. pubescens, P. bambusoides and P. nigra var, henonis in Sunchon were studied to investigate biomass, net production and nutrient distribution. Five $10m{\times}10m$ quadrats were set up and 20 sample culms of 2 years and over were harvested for dimension analysis in each stand. One year old culms and subterranean parts were estimated by the harvested quadrat method. The largest mean DBH, height and basal area were shown in P. pubescens stand, and followed by P. nigra var. henonis stand and P. bambusoides stand. There was little difference in accuracy among three allometric biomass regression models of logWt=A+B1ogD, $logWt=A+BlogD^2H$ and logWt=A+BlogD+ClogH, where Wt, D and H were dry weight, DBH and height, respectively. Analysis of covariance showed that there were significant differences in intercept among the linear allometric biomass regressons of three Phyllostachys species. Biomass included subterranean parts was the largest in P. pubescens stand(103.621t/ha), and followed by P. nigra var. henonis stand(86.447t/ha) and P. bambusoides stand(36.767t/ha). Leaf biomass was 6.3% to 7.8% of total biomass in each stands. The ratio of aboveground biomass and subterranean biomass in each stand was 1.87 to 2.26. Net production included subterranean parts was the greatest in P. pubescens stand(6.115t/ha/yr), and followed by P. nigra var. henonis stand(5.609t/ha/yr) and P, bambusoides stand(3.252t/ha/yr). The highest net assimilation ratio was estimated in P. pubescens stand(2.979), and followed by P. nigra var. henonis stand(2.752) and P. bambusoides stand(2.187). Biomass accumulation ratio of each stand was 2.679 to 5.358. Concentrations of N, P and Mg were the highest in leaves, and followed by subterranean parts, and culms+branches in all three species. Concentration of Ca was the highest in leaves, and followed by culms+branches, and subterranean parts in all three species. The difference in biomass among three species stands was caused by their culm size, leaf biomass, net assimilation ratio, and efficiency of leaves to produce culms.

• Journal of Food Hygiene and Safety
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• v.29 no.3
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• pp.234-240
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• 2014

Fluxapyroxad is classified as carboxamide fungicide that inhibits succinate dehydrogenase in complex II of mitochondrial respiratory chain, which results in inhibition of mycelial growth within the fungus target species. This study was carried out to assure the safety of fluxapyroxad residues in agricultural products by developing an official analytical method. A new, reliable analytical method was developed and validated using High Performance liquid Chromatograph-UV/visible detector (HPLC-UVD) for the determination of fluxapyroxad residues. The fluxapyroxad residues in samples were extracted with acetonitrile, partitioned with dichloromethane, and then purified with silica solid phase extraction (SPE) cartridge. Correlation coefficient($R^2$) of fluxapyroxad standard solution was 0.9999. The method was validated using apple, pear, peanut, pepper, hulled rice, potato, and soybean spiked with fluxapyroxad at 0.05 and 0.5 mg/kg. Average recoveries were 80.6~114.0% with relative standard deviation less than 10%, and limit of detection (LOD) and limit of quantification (LOQ) were 0.01 and 0.05 mg/kg, respectively. All validation parameters were followed with Codex guideline (CAC/GL 40). LC-MS (Liquid Chromatograph-Mass Spectrometer) was also applied to confirm the analytical method. Base on these results, this method was found to be appropriate fluxapyroxad residue determination and can be used as the official method of analysis.