• Journal of agriculture & life science
• /
• 제46권4호
• /
• pp.41-55
• /
• 2012

On the basis of vegetation data collected by point-centered quarter method in analysis in Baekdudaegan of Gangwon province in the area of Hyangrobong, Odaesan, Seokbyeongsan, Dutasan, Deokhangsan and Hambaeksan, the study was carried out to classify forest communities and to evaluate the successional trends. The classification method of cluster analysis was used to make various disordered forests into several common groups for 1,004 sample points all together. By clustering the forests in the six study areas were classified into 28 forest communities, which were subjected to aggregate 8 representative forest communities on the count of species composition and species diversity. They were Mesophytic mixed forest community, others deciduous forest community, Quercus mongolica (dominant) community, Q. mongolica (pure) community, Pinus densiflora - Q. mongolica community, P. densiflora community, Betula ermanii community and Q. mongolica - Pinus koraiensis community. The ecological outlook from the result indicated that P. densiflora community and P. densiflora - Q. mongolica community, which were located in Seokbyeongsan, Dutasan and Deokhangsan around 1,000m above the sea level showed lower species diversity index. On the contrary Mesophytic mixed forest community, others deciduous forest community which was located in Hyangrobong, Odaesan and Hambaeksan mostly in protected area and national park around 1,500m above the sea level displayed higher species diversity index. As the composition ratio of Q. mongolica within a certain community was decreased, the species diversity was generally increased, assumed that abundance of Q. mongolica might be negatively associated with species diversity in the national deciduous forest.

• Korean Journal of Breeding Science
• /
• 제43권5호
• /
• pp.422-429
• /
• 2011

This study was conducted to investigate the genetic diversity and to develop a technique for cultivar identification using SSR markers in grapevine. Thirty Korean bred and introduced grapevine cultivars were evaluated by 28 SSR markers. A total of 143 alleles were produced ranging from 2 to 8 alleles with an average of 5.1 alleles per locus. Polymorphic information contents (PIC) were ranged from 0.666 (VVIp02) to 0.975 (VVIn33 and VVIn62) with an average of 0.882. UPGMA (unweighted pair-group method arithmetic average) clustering analysis based on genetic distances using 143 alleles classified 30 grapevine cultivars into 7 clusters by similarity index of 0.685. Similarity values among the tested grapevine cultivars ranged from 0.575 to 1.00, and the average similarity value was 0.661. The similarity index was the highest (1.00) between 'Jinok' and 'Campbell Early', and the lowest (0.575) between 'Alden' and 'Narsha'. The genetic relationships among the 30 studied grapevine cultivars were basically consistent with the known pedigree. The three SSR markers sets (VVIn61, VVIt60, and VVIu20) selected from 28 primers were differentiated all grapevine cultivars except for 'Jinok' and 'Campbell Early'. Five cultivars ('Narsha, 'Alden', 'Dutchess', 'Pione', and 'Muscat Hamburg') were identified by VVIn61 at the first step. Then 21 cultivars including 'Hongsodam' by VVIt60 at the second step and 2 cultivars ('Heukbosuck' and 'Suok') by VVIu20 at the third step were identified. These markers could be used as a reliable tool for the identification of Korean grapevine cultivars.

• Journal of Cadastre & Land InformatiX
• /
• 제48권2호
• /
• pp.197-211
• /
• 2018

We conducted this research to examine how well regional housing prices are suited to use as an alternative to conventional census-based regional deprivation indices in health and medical geography studies. To examine the relative performance of mean regional housing prices compared to conventional census-based regional deprivation indices, we compared several multilevel logistic regression models, where the first level was individuals and the second was health districts in the Seoul Metropolitan Area (SMA) in Korea, for the sake of adjusting the regional clustering tendency of unknown factors. In these models, we predicted two dichotomous variables that represented individuals' after-lunch tooth brushing behavior and use of dental floss by individual characteristics and regional indices. Then, we compared the relative predictive performance of the models using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The results from the estimations showed that mean regional housing prices and census-based deprivation indices were correlated with the two types of dental health behavior in a statistical sense. The results also revealed that the model with mean regional housing prices showed smaller AIC and BIC compared with other models with conventional census-based deprivation indices. These results imply that it is possible for housing prices summarized using aerial units to be used as an alternative to conventional census-based deprivation indices when the census variables employed cannot properly reflect the characteristics of the aerial units.

• Journal of the Korean Society of International Agriculture
• /
• 제30권4호
• /
• pp.357-369
• /
• 2018

The Korean common bean (Phaseolus vulgaris L.) has been receiving increased attention as a functional food. The objective of this study was to reveal the phytochemicals genetic variation and antioxidant activity of 209 Korean common bean landraces. Antioxidant activity was evaluated with the DPPH (2,2-diphenyl-1-picryl-hydrazyl-hydrate), ABTS (2,2'-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid), ferric reducing antioxidant power (FRAP), and superoxide dismutase (SOD) assay. Antioxidant activities among common bean accessions showed wide variation. Four flavonoids (kaempferol, myricetin, quercetin, and naringenin) of the 209 Korean common bean landraces were measured using HPLC. Among them, kaempferol had the highest phytochemicals compared to the other three flavonoids. Using the relative antioxidant capacity index (RACI), it was found out that the IT104587 had the highest antioxidant activity. Meanwhile, in clustering analysis, the Korean common bean landraces were classified into three clusters. Among them, cluster II contained 64 landraces with higher antioxidant activities and phytochemicals than the other clusters, except DPPH. The results could provide information on the valuable Korean common bean landraces for the development of new common bean varieties.

• Journal of Korea Water Resources Association
• /
• 제53권2호
• /
• pp.107-119
• /
• 2020

This study analyzed past drought characteristics based on the observed rainfall data and performed a long-term outlook for future extreme droughts using Representative Concentration Pathways 8.5 (RCP 8.5) climate change scenarios. Standardized Precipitation Index (SPI) used duration of 1, 3, 6, 9 and 12 months, a meteorological drought index, was applied for quantitative drought analysis. A single long-term time series was constructed by combining daily rainfall observation data and RCP scenario. The constructed data was used as SPI input factors for each different duration. For the analysis of meteorological drought observed relatively long-term since 1954 in Korea, 12 rainfall stations were selected and applied 10 general circulation models (GCM) at the same point. In order to analyze drought characteristics according to climate change, trend analysis and clustering were performed. For non-stationary frequency analysis using sampling technique, we adopted the technique DEMC that combines Bayesian-based differential evolution ("DE") and Markov chain Monte Carlo ("MCMC"). A non-stationary drought frequency analysis was used to derive Severity-Duration-Frequency (SDF) curves for the 12 locations. A quantitative outlook for future droughts was carried out by deriving SDF curves with long-term hydrologic data assuming non-stationarity, and by quantitatively identifying potential drought risks. As a result of performing cluster analysis to identify the spatial characteristics, it was analyzed that there is a high risk of drought in the future in Jeonju, Gwangju, Yeosun, Mokpo, and Chupyeongryeong except Jeju corresponding to Zone 1-2, 2, and 3-2. They could be efficiently utilized in future drought management policies.

• Journal of Intelligence and Information Systems
• /
• 제16권2호
• /
• pp.19-32
• /
• 2010

Volatility plays a central role in both academic and practical applications, especially in pricing financial derivative products and trading volatility strategies. This study presents a novel mechanism based on generalized autoregressive conditional heteroskedasticity (GARCH) models that is able to enhance the performance of intelligent volatility trading systems by predicting Korean stock market volatility more accurately. In particular, we embedded the concept of the volatility asymmetry documented widely in the literature into our model. The newly developed Korean stock market volatility index of KOSPI 200, VKOSPI, is used as a volatility proxy. It is the price of a linear portfolio of the KOSPI 200 index options and measures the effect of the expectations of dealers and option traders on stock market volatility for 30 calendar days. The KOSPI 200 index options market started in 1997 and has become the most actively traded market in the world. Its trading volume is more than 10 million contracts a day and records the highest of all the stock index option markets. Therefore, analyzing the VKOSPI has great importance in understanding volatility inherent in option prices and can afford some trading ideas for futures and option dealers. Use of the VKOSPI as volatility proxy avoids statistical estimation problems associated with other measures of volatility since the VKOSPI is model-free expected volatility of market participants calculated directly from the transacted option prices. This study estimates the symmetric and asymmetric GARCH models for the KOSPI 200 index from January 2003 to December 2006 by the maximum likelihood procedure. Asymmetric GARCH models include GJR-GARCH model of Glosten, Jagannathan and Runke, exponential GARCH model of Nelson and power autoregressive conditional heteroskedasticity (ARCH) of Ding, Granger and Engle. Symmetric GARCH model indicates basic GARCH (1, 1). Tomorrow's forecasted value and change direction of stock market volatility are obtained by recursive GARCH specifications from January 2007 to December 2009 and are compared with the VKOSPI. Empirical results indicate that negative unanticipated returns increase volatility more than positive return shocks of equal magnitude decrease volatility, indicating the existence of volatility asymmetry in the Korean stock market. The point value and change direction of tomorrow VKOSPI are estimated and forecasted by GARCH models. Volatility trading system is developed using the forecasted change direction of the VKOSPI, that is, if tomorrow VKOSPI is expected to rise, a long straddle or strangle position is established. A short straddle or strangle position is taken if VKOSPI is expected to fall tomorrow. Total profit is calculated as the cumulative sum of the VKOSPI percentage change. If forecasted direction is correct, the absolute value of the VKOSPI percentage changes is added to trading profit. It is subtracted from the trading profit if forecasted direction is not correct. For the in-sample period, the power ARCH model best fits in a statistical metric, Mean Squared Prediction Error (MSPE), and the exponential GARCH model shows the highest Mean Correct Prediction (MCP). The power ARCH model best fits also for the out-of-sample period and provides the highest probability for the VKOSPI change direction tomorrow. Generally, the power ARCH model shows the best fit for the VKOSPI. All the GARCH models provide trading profits for volatility trading system and the exponential GARCH model shows the best performance, annual profit of 197.56%, during the in-sample period. The GARCH models present trading profits during the out-of-sample period except for the exponential GARCH model. During the out-of-sample period, the power ARCH model shows the largest annual trading profit of 38%. The volatility clustering and asymmetry found in this research are the reflection of volatility non-linearity. This further suggests that combining the asymmetric GARCH models and artificial neural networks can significantly enhance the performance of the suggested volatility trading system, since artificial neural networks have been shown to effectively model nonlinear relationships.

• Korean Journal of Environment and Ecology
• /
• 제22권5호
• /
• pp.521-529
• /
• 2008

This research conducted a survey on waterbirds visiting this area four times by season from February to October in 2007 to look into the habitat use of waterbirds, to do a value and grade testing of the tidal flat by dividing the foreshore on Muan Bay located in Jeollanam-do into four areas (Dongam, Guro, Bokryong and Wangsan tidal flats). The survey results revealed that there existed a total of 15,755 individuals of 54 species including 2 species of grebes, 7 species of herons, 7 species of dabbling ducks, 6 species of diving ducks, 20 species of waders, 3 species of gulls and 9 other species and this survey also observed 9,291 individuals of the wading birds as a dominant group on Muan Bay. In these classified groups, the gulls and waders were observed to mostly use Dongam tidal flat as their habitat, while the group using Guro tidal flat as their habitat was mostly grebes, dabbling and diving ducks. As a result of UPGMA clustering analysis in consideration of the species and number of individuals, there appear the close similarities between Dongam and Bokryong tidal flats and so do Guro and Wangsan tidal flats. Taking a look at the grading of tidal flats by setting up ecological indexes, such as diversity index, abundance index, and dominance index, etc. legally reserved species and maximum number of individuals as a standard, the rank for the value and importance degree of Bokryong tidal flat appeared higher than that of the other three tidal flats. Like this, the gradation of tidal flats according to waterbirds are judged to able to suggest objective data on the issue of proper judgment and designation of valuable tidal flat areas and its subsequent effective preservation and management.

• Journal of Intelligence and Information Systems
• /
• 제23권2호
• /
• pp.107-122
• /
• 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.

• Journal of the Korean Association of Geographic Information Studies
• /
• 제13권4호
• /
• pp.111-124
• /
• 2010

To monitor the environment of land surface change is considered as an important research field since those parameters are related with land use, climate change, meteorological study, agriculture modulation, surface energy balance, and surface environment system. For the change detection, many different methods have been presented for distributing more detailed information with various tools from ground based measurement to satellite multi-spectral sensor. Recently, using high resolution satellite data is considered the most efficient way to monitor extensive land environmental system especially for higher spatial and temporal resolution. In this study, we use two different spatial resolution satellites; the one is SPOT/VEGETATION with 1 km spatial resolution to detect coarse resolution of the area change and determine objective threshold. The other is Landsat satellite having high resolution to figure out detailed land environmental change. According to their spatial resolution, they show different observation characteristics such as repeat cycle, and the global coverage. By correlating two kinds of satellites, we can detect land surface change from mid resolution to high resolution. The K-mean clustering algorithm is applied to detect changed area with two different temporal images. When using solar spectral band, there are complicate surface reflectance scattering characteristics which make surface change detection difficult. That effect would be leading serious problems when interpreting surface characteristics. For example, in spite of constant their own surface reflectance value, it could be changed according to solar, and sensor relative observation location. To reduce those affects, in this study, long-term Normalized Difference Vegetation Index (NDVI) with solar spectral channels performed for atmospheric and bi-directional correction from SPOT/VEGETATION data are utilized to offer objective threshold value for detecting land surface change, since that NDVI has less sensitivity for solar geometry than solar channel. The surface change detection based on long-term NDVI shows improved results than when only using Landsat.

• Journal of Environmental Impact Assessment
• /
• 제26권2호
• /
• pp.160-170
• /
• 2017

The purpose of this study is to perform clustering of the habitat types and to identify the characteristics of species in the habitat types using mammal data (70,562) of the 3rd National Ecosystem Survey conducted from 2006 to 2012. The 15 habitat types recorded in the field-paper of the 3rd National ecosystem survey were reclassified, which was followed by the statistical analysis of mammal habitat types. In the habitat types cluster analysis, non-hierarchical cluster analysis (k-means cluster analysis), hierarchical cluster analysis, and non-metric multidimensional scaling method were applied to 14 habitat types recorded more than 30 times. A total of 7 Orders, 16 Families, and 39 Species of mammals were identified in the 3rd National Ecosystem Survey collected nationwide. When 11 clusters were classified by habitat types, the simple structure index was the highest (ssi = 0.07). As a result of the similarities and hierarchies between habitat types suggested by the hierarchical clustering analysis, the residential areas were the most different habitat types for mammals; the next following type was a cluster together with rivers and coasts. The results of the non-metric multidimensional scaling analysis demonstrated that both Mus musculus and Rattus norvegicus restrictively appeared in a residential area, which is the most discriminating habitat type. Lutra lutra restrictively appeared in coastal and river areas. In summary, according to our results, the mammalian habitat can be divided into the following four types: (1) the forest type (using forest as the main habitat and migration route); (2) the river type (using water as the main habitat); (3) the residence habitat (living near residential area); and (4) the lowland type (consuming grain or seeds as the main feeding resource).