• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.19-43
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• 2016

This study examines the causes of the Asian exchange rate crisis and compares it to the European Monetary System crisis. In 1997, emerging countries in Asia experienced financial crises. Previously in 1992, currencies in the European Monetary System had undergone the same experience. This was followed by Mexico in 1994. The objective of this paper lies in the generation of useful insights from these crises. This research presents a comparison of South Korea, United Kingdom and Mexico, and then compares three different models for prediction. Previous studies of economic crisis focused largely on the manual construction of causal models using linear techniques. However, the weakness of such models stems from the prevalence of nonlinear factors in reality. This paper uses a structural equation model to analyze the causes, followed by a neural network model to circumvent the linear model's weaknesses. The models are examined in the context of predicting exchange rates In this paper, data were quarterly ones, and Consumer Price Index, Gross Domestic Product, Interest Rate, Stock Index, Current Account, Foreign Reserves were independent variables for the prediction. However, time periods of each country's data are different. Lisrel is an emerging method and as such requires a fresh approach to financial crisis prediction model design, along with the flexibility to accommodate unexpected change. This paper indicates the neural network model has the greater prediction performance in Korea, Mexico, and United Kingdom. However, in Korea, the multiple regression shows the better performance. In Mexico, the multiple regression is almost indifferent to the Lisrel. Although Lisrel doesn't show the significant performance, the refined model is expected to show the better result. The structural model in this paper should contain the psychological factor and other invisible areas in the future work. The reason of the low hit ratio is that the alternative model in this paper uses only the financial market data. Thus, we cannot consider the other important part. Korea's hit ratio is lower than that of United Kingdom. So, there must be the other construct that affects the financial market. So does Mexico. However, the United Kingdom's financial market is more influenced and explained by the financial factors than Korea and Mexico.

• Korean journal of applied entomology
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• v.50 no.4
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• pp.373-378
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• 2011

The striped fruit fly, Bactrocera scutellata, damages pumpkin and other cucurbitaceous plants. The developmental period of each stage was measured at seven constant temperatures (15, 18, 21, 24, 27, 30, and $33{\pm}1.0^{\circ}C$). The developmental time of eggs ranged from 4.2 days at $15^{\circ}C$ to 0.9 days at $33^{\circ}C$. The developmental period of larvae was 4.2 days at $15^{\circ}C$, and slowed in temperatures above $27^{\circ}C$. The developmental period of pupa was 21.5 days at $15^{\circ}C$ and 7.6 days at $33^{\circ}C$. The mortality of eggs was 17.1% at $15^{\circ}C$ and 22.9% at $33^{\circ}C$, Larval mortalities (1st, 2nd, 3rd) were 24.1, 27.3 and 18.2%, respectively, at $15^{\circ}C$, Pupal mortalities were 18.2% at $15^{\circ}C$ and 23.1% at $33^{\circ}C$. The relationship between developmental rate and temperature fit both a linear model and a nonlinear model. The lower threshold temperatures of eggs, larvae, and pupae were 12.5, 10.7, and $6.3^{\circ}C$, respectively, and threshold temperature of the total immature period was $8.5^{\circ}C$. The thermal constants required to complete the egg, larval, and pupal stages were 33.2, 118.3, and 181.2 DD, respectively. The distribution of each development stages was described by a 3-parameter Weibull function.

• Korean journal of applied entomology
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• v.51 no.4
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• pp.431-438
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• 2012

The developmental time of the nymphs of Myzus persicae was studied in the laboratory (six constant temperatures from 15 to $30^{\circ}C$ with 50~60% RH, and a photoperiod of 14L:10D) and in a green-pepper plastic house. Mortality of M. persicae in laboratory was high in the first(6.7~13.3%) and second instar nymphs(6.7%) at low temperatures and high in the third (17.8%) and fourth instar nymphs(17.8%) at high temperatures. Mortality was 66.7% at $33^{\circ}C$ in laboratory and $26.7^{\circ}C$ in plastic house. The total developmental time was the longest at $14.6^{\circ}C$ (14.4 days) and shortest at $26.7^{\circ}C$ (6.0 days) in plastic house. The lower threshold temperature of the total nymphal stage was $3.0^{\circ}C$ in laboratory. The thermal constant required for nymphal stage was 111.1DD. The relationship between developmental rate and temperature was fitted nonlinear model by Logan-6 which has the lowest value on Akaike information criterion (AIC) and Bayesian information criterion (BIC). The distribution of completion of each developmental stage was well described by the 3-parameter Weibull function ($r^2=0.95{\sim}0.97$). This model accurately described the predicted and observed occurrences. Thus the model is considered to be good for use in predicting the optimal spray time for Myzus persicae.

• Korean journal of applied entomology
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• v.51 no.4
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• pp.421-429
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• 2012

The developmental time period of Aphis gossypii was studied in laboratory (six constant temperatures from 15 to $30^{\circ}C$ with 50~60% RH, and a photoperiod of 14L:10D) and in a cucumber plastic house. The mortality of A. gossypii in the laboratory was high in the 2nd (20.0%) and 3rd stage(13.3%) at low temperature but high in the 3rd (26.7%) and 4th stage (33.3%) at high temperatures. Mortality in the plastic house was high in the 1st and 2nd stage but there was no mortality in the 4th stage at low temperature. The total developmental period was longest at $15^{\circ}C$ (12.2 days) in the laboratory and shortest at $28.5^{\circ}C$ (4.09 days) in the plastic house. The lower threshold temperature at the total nymphal stage was $6.8^{\circ}C$ in laboratory. The thermal constant required to reach the total nymphal stage was 111.1DD. The relationship between the developmental rate and temperature fit the nonlinear model of Logan-6 which has the lowest value for the Akaike information criterion(AIC) and Bayesian information criterion(BIC). The distribution of completion of each development stage was well described by the 3-parameter Weibull function ($r^2=0.89{\sim}0.96$). This model accurately described the predicted and observed outcomes. Thus it is considered that the model can be used for predicting the optimal spray time for Aphis gossypii.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.337-357
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• 2021

Because the growth of paddy rice is affected by meteorological factors, the selection of appropriate meteorological variables is essential to build a rice yield prediction model. This paper examines the suitability of multiple meteorological datasets for the rice yield modeling in South Korea, 1996-2019, and a hindcast experiment for rice yield using a machine learning method by considering the nonlinear relationships between meteorological variables and the rice yield. In addition to the ASOS in-situ observations, we used CRU-JRA ver. 2.1 and ERA5 reanalysis. From the multiple meteorological datasets, we extracted the four common variables (air temperature, relative humidity, solar radiation, and precipitation) and analyzed the characteristics of each data and the associations with rice yields. CRU-JRA ver. 2.1 showed an overall agreement with the other datasets. While relative humidity had a rare relationship with rice yields, solar radiation showed a somewhat high correlation with rice yields. Using the air temperature, solar radiation, and precipitation of July, August, and September, we built a random forest model for the hindcast experiments of rice yields. The model with CRU-JRA ver. 2.1 showed the best performance with a correlation coefficient of 0.772. The solar radiation in the prediction model had the most significant importance among the variables, which is in accordance with the generic agricultural knowledge. This paper has an implication for selecting from multiple meteorological datasets for rice yield modeling.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.30 no.6
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• pp.306-318
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• 2018

In order to evaluate the seismic capacity of massive vertical type breakwaters which have intensively been deployed along the coast of South Korea over the last two decades, we carry out the preliminary numerical simulation against the PoHang, GyeongJu, Hachinohe 1, Hachinohe 2, Ofunato, and artificial seismic waves based on the measured time series of ground acceleration. Numerical result shows that significant sliding can be resulted in once non-negligible portion of seismic energy is shifted toward the longer period during its propagation process toward the ground surface in a form of shear wave. It is well known that during these propagation process, shear waves due to the seismic activity would be amplified, and non-negligible portion of seismic energy be shifted toward the longer period. Among these, the shift of seismic energy toward the longer period is induced by the viscosity and internal friction intrinsic in the soil. On the other hand, the amplification of shear waves can be attributed to the fact that the shear modulus is getting smaller toward the ground surface following the descending effective stress toward the ground surface. And the weakened intensity of soil as the number of attacking shear waves are accumulated can also contribute these phenomenon (Das, 1993). In this rationale, we constitute the numerical model using the model by Hardin and Drnevich (1972) for the weakened shear modulus as shear waves go on, and shear wave equation, in the numerical integration of which $Newmark-{\beta}$ method and Modified Newton-Raphson method are evoked to take nonlinear stress-strain relationship into account. It is shown that the numerical model proposed in this study could duplicate the well known features of seismic shear waves such as that a great deal of probability mass is shifted toward the larger amplitude and longer period when shear waves propagate toward the ground surface.