• Korean Journal of Environmental Biology
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• v.25 no.2
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• pp.168-177
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• 2007

By using DEM and digital actual vegetation map with MGE GIS software program, topographic features (altitude, slope, latitude, etc.) quantitatively were analysed and their data integrated as the index of climatic conditions (WI, CI, air temperature, etc.) in South Korea. Warmth Index (WI) decreases $5.27^{\circ}C{\cdot}month$ with latitudinal $1^{\circ} degree, and $3.41^{\circ}C{\cdot}month$ with attitudinal 100 m increase. The relationship between CI and WI values is expressed as a linear regression, $WI=116.01+0.96{\times}CI,\;R^2=0.996$. The distributional peaks of different plant communities along Warmth Index gradient showed the sequence of Abies nephrolepis, Taxus cuspidata, Abies koreana, Quercus mongolica, Carpinus laxiflora, Q. dentata, C. tschonoskii, Q. serrate, Pinus densiflora, Q. aliena, Q. variabilis, Q. acutissima, P. thunbergii, Q. acute, Castanopsis cuspidata var. sieboldii, Camellia japonica, Machilus thunbergii community from lower to higher values. The Quercus mongolica forest occurred frequently on E-NW and SE slope aspect within WI $70{\sim}80^{\circ}C{\cdot}month$ optimal range at mesic sites, NW and SE slope than xeric sites S and SW slope. The Q. serrata forest showed the most distributional frequency in NW and W slope aspect within WI $90{\sim}100^{\circ}C{\cdot}month$ range, Q. variabilis and Q. acutissima forest showed the high frequency of distribution in SE slope in WI $95{\sim}100^{\circ}C{\cdot}month$ range. By the slope gradient analysis, five groups were found: 1. Abies nephrolepis, Machilus thunbergii, 2. Taxus cuspidata, Abies koreana, Quercus mongolica, Q. dentata, Q. serrata, Q. variabilis, Castanopsis cuspidata var. sieboldii 3. Pinus densiflora, Q. aliena, Q. acutissima, P. thunbergii, Q. acuta 4. Carpinus laxiflora, Camellia japonica 5. C. tschonoskii from steep slope to gentle slope sequence.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.125-141
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• 2012

These days, the malicious attacks and hacks on the networked systems are dramatically increasing, and the patterns of them are changing rapidly. Consequently, it becomes more important to appropriately handle these malicious attacks and hacks, and there exist sufficient interests and demand in effective network security systems just like intrusion detection systems. Intrusion detection systems are the network security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. Conventional intrusion detection systems have generally been designed using the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. However, they cannot handle new or unknown patterns of the network attacks, although they perform very well under the normal situation. As a result, recent studies on intrusion detection systems use artificial intelligence techniques, which can proactively respond to the unknown threats. For a long time, researchers have adopted and tested various kinds of artificial intelligence techniques such as artificial neural networks, decision trees, and support vector machines to detect intrusions on the network. However, most of them have just applied these techniques singularly, even though combining the techniques may lead to better detection. With this reason, we propose a new integrated model for intrusion detection. Our model is designed to combine prediction results of four different binary classification models-logistic regression (LOGIT), decision trees (DT), artificial neural networks (ANN), and support vector machines (SVM), which may be complementary to each other. As a tool for finding optimal combining weights, genetic algorithms (GA) are used. Our proposed model is designed to be built in two steps. At the first step, the optimal integration model whose prediction error (i.e. erroneous classification rate) is the least is generated. After that, in the second step, it explores the optimal classification threshold for determining intrusions, which minimizes the total misclassification cost. To calculate the total misclassification cost of intrusion detection system, we need to understand its asymmetric error cost scheme. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, total misclassification cost is more affected by FNE rather than FPE. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 10,000 samples from them by using random sampling method. Also, we compared the results from our model with the results from single techniques to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell R4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on GA outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that the proposed model outperformed all the other comparative models in the total misclassification cost perspective. Consequently, it is expected that our study may contribute to build cost-effective intelligent intrusion detection systems.

• Journal of Korean Society of Forest Science
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• v.90 no.1
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• pp.43-54
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• 2001

Genetic differentiation of populations is resulted from the environmental and the genetic effects, and the interactions between them. Whereas, the major factors influencing to the genetic differentiation within populations are the gene flow induced by seed or pollen dispersial, the microsite heterogeneity, and the density-dependent distribution of individuals. For the purpose of studying spatial genetic structure and the distribution pattern of Korean pines(Pinus koraiensis), we set up one $100{\times}100m$ plot at a Korean pine stand in Quercus mongolica community on Mt. Jumbong in Korea. To estimate the coefficient of spatial autocorrelation as Moran's index and an analogue, simple block distance, isozyme markers were analyzed in 325 Korean pines. For 11 polymorphic loci observed in 9 enzyme systems, the average percentage of polymorphic loci, the observed and expected heterozygocity were 72.2% 0.200, and 0.251, respectively. It was revealed the excess of homozygotes was observed in the plot, which suggests that here may be more number of consanguineous trees than expected. On the basis of isozyme genotypes observed in this study, 325 trees were classified into 147 groups in which the maximum number of trees for one group was 34. From the distance class of 24-32m, the genetic heterogeneity began to increase. The variation of simple block distance against the growth performance by tree height and diameter also showed the same trend at 24~32m class. According to high fixation index(F=0.204), the spatial genetic structure within a stand, the analysis of the growth performance, and the distribution patterns of identical genotypes, we inferred that the genetic structure of a Korean pine stand in Mt. Jumbong has been maintained rather density-dependent mechanism than the gene flow, such as the pollen dispersial or the heavy input of seeds following the forest gaps. The genetic patchy size was determined between 24~32m, which suggests that the selection of individuals for the ex situ conservation of Korean pine in Mt. Jumbong may be desirable to be made with the spatial distance over 37 meters between trees.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.1
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• pp.70-82
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• 2021

The purpose of this study is to reveal the tendency of planting design through the analysis of the planting of designer's gardens in the Suncheon Bay National Garden and to derive implications for future garden planting designs. The results of the study are as follows: First, the results of the study show that the practice of tree-based planting is still valid. Large growing trees such as 'Pinus densiflrora', 'Celtis sinensis', 'Zelkova serrata', 'Machilus thunbergii', 'Pinus strobus' overwhelmed the size of the designer's garden(150 to 390㎡). Second, the selection of trees tended to be made considering the designer's intention and the decorative effects rather than by considering the physiological and ecological conditions of the site. Third, among the herbaceous, the rate of the planting of perennials was high. Fourth, the flowering period of planted herbaceous was the most common in summer, followed by spring, fall, and winter. Fifth, the frequency color of the planted herbaceous was the most common in summer, followed by spring, fall, and winter. Fifth, in terms of flower color frequency, the most common was the yellow-series, followed by red-series, blue-series Sixth, average height herbaceous plants(20~60cm) were planted the most(47.4%). Seventh, structural plants that determined the garden's framework depended on trees, and the focal plants mainly utilized were evergreen trees, and the midrange plants were the planted herbaceous plants. The implications derived from the above findings are as follows: First, to ensure the garden's quality and sustainability, the selection of trees should be carefully considered, not considering only the artist's intention but also taking into account the physical and ecological conditions. Second, herbaceous plants can be used in various ways― the garden's focal plants, midrange plants, and ground covers, so more active herbaceous planting needs to be considered. Third, in consideration of the winter landscape, herbaceous planting using characteristics, such as fruits and stems, as well as flower colors should be considered. Fourth, blue and black color herbaceous plants have a noticeable effect even in a small amount, so it is necessary to plant them actively. Fifth, for the design of herbaceous planting, where the individual property of plants can be expressed, the design method should be considered.

• Ecology and Resilient Infrastructure
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• v.8 no.1
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• pp.32-43
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• 2021

Multichannel automated chamber systems (MCACs) were developed for the continuous monitoring of soil CO2 efflux in forest ecosystems. The MCACs mainly consisted of four modules: eight soil chambers with lids that automatically open and close, an infrared CO2 analyzer equipped with eight multichannel gas samplers, an electronic controller with time-relay circuits, and a programmable logic datalogger. To examine the stability and reliability of the developed MCACs in the field during all seasons with a high temporal resolution, as well as the effects of temperature and soil water content on soil CO2 efflux rates, we continuously measured the soil CO2 efflux rates and micrometeorological factors at the Nam-san experimental site in a Quercus mongolica forest floor using the MCACs from January to December 2010. The diurnal and seasonal variations in soil CO2 efflux rates markedly followed the patterns of changes in temperature factors. During the entire experimental period, the soil CO2 efflux rates were strongly correlated with the temperature at a soil depth of 5 cm (r2 = 0.92) but were weakly correlated with the soil water content (r2 = 0.27). The annual sensitivity of soil CO2 efflux to temperature (Q10) in this forest ranged from 2.23 to 3.0, which was in agreement with other studies on temperate deciduous forests. The annual mean soil CO2 efflux measured by the MCACs was approximately 11.1 g CO2 m-2 day-1. These results indicate that the MCACs can be used for the continuous long-term measurements of soil CO2 efflux in the field and for simultaneously determining the impacts of micrometeorological factors.

• Knowledge Management Research
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• v.22 no.4
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• pp.71-85
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• 2021

The purpose of this study is to find out which artificial intelligence methodology is most suitable for creating a foreign exchange rate prediction model using the indicators of bond market and interest rate market. KTBs and MSBs, which are representative products of the Korea bond market, are sold on a large scale when a risk aversion occurs, and in such cases, the USD/KRW exchange rate often rises. When USD liquidity problems occur in the onshore Korean market, the KRW Cross-Currency Swap price in the interest rate market falls, then it plays as a signal to buy USD/KRW in the foreign exchange market. Considering that the price and movement of products traded in the bond market and interest rate market directly or indirectly affect the foreign exchange market, it may be regarded that there is a close and complementary relationship among the three markets. There have been studies that reveal the relationship and correlation between the bond market, interest rate market, and foreign exchange market, but many exchange rate prediction studies in the past have mainly focused on studies based on macroeconomic indicators such as GDP, current account surplus/deficit, and inflation while active research to predict the exchange rate of the foreign exchange market using artificial intelligence based on the bond market and interest rate market indicators has not been conducted yet. This study uses the bond market and interest rate market indicator, runs artificial neural network suitable for nonlinear data analysis, logistic regression suitable for linear data analysis, and decision tree suitable for nonlinear & linear data analysis, and proves that the artificial neural network is the most suitable methodology for predicting the foreign exchange rates which are nonlinear and times series data. Beyond revealing the simple correlation between the bond market, interest rate market, and foreign exchange market, capturing the trading signals between the three markets to reveal the active correlation and prove the mutual organic movement is not only to provide foreign exchange market traders with a new trading model but also to be expected to contribute to increasing the efficiency and the knowledge management of the entire financial market.

• Journal of Korea Water Resources Association
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• v.56 no.5
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• pp.311-323
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• 2023

In order to reduce disaster damage by localized heavy rains, floods, and urban inundation, it is important to know in advance whether natural disasters occur. Currently, heavy rain watch and heavy rain warning by the criteria of the Korea Meteorological Administration are being issued in Korea. However, since this one criterion is applied to the whole country, we can not clearly recognize heavy rain damage for a specific region in advance. Therefore, in this paper, we tried to reset the current criteria for a special weather report which considers the regional characteristics and to predict the damage caused by rainfall after 1 hour. The study area was selected as Gyeonggi-province, where has more frequent heavy rain damage than other regions. Then, the rainfall inducing disaster or hazard-triggering rainfall was set by utilizing hourly rainfall and heavy rain damage data, considering the local characteristics. The heavy rain damage prediction model was developed by a decision tree model and a random forest model, which are machine learning technique and by rainfall inducing disaster and rainfall data. In addition, long short-term memory and deep neural network models were used for predicting rainfall after 1 hour. The predicted rainfall by a developed prediction model was applied to the trained classification model and we predicted whether the rain damage after 1 hour will be occurred or not and we called this as 1ST-Model. The 1ST-Model can be used for preventing and preparing heavy rain disaster and it is judged to be of great contribution in reducing damage caused by heavy rain.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1779-1790
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• 2023

Satellite-based fog detection algorithms are being developed to detect fog in real-time over a wide area, with a focus on the Korean Peninsula (KorPen). The GEO-KOMPSAT-2A/Advanced Meteorological Imager (GK2A/AMI, GK2A) satellite offers an excellent temporal resolution (10 min) and a spatial resolution (500 m), while GEO-KOMPSAT-2B/Geostationary Ocean Color Imager-II (GK2B/GOCI-II, GK2B) provides an excellent spatial resolution (250 m) but poor temporal resolution (1 h) with only visible channels. To enhance the fog detection level (10 min, 250 m), we developed a fused GK2AB fog detection algorithm (FDA) of GK2A and GK2B. The GK2AB FDA comprises three main steps. First, the Korea Meteorological Satellite Center's GK2A daytime fog detection algorithm is utilized to detect fog, considering various optical and physical characteristics. In the second step, GK2B data is extrapolated to 10-min intervals by matching GK2A pixels based on the closest time and location when GK2B observes the KorPen. For reflectance, GK2B normalized visible (NVIS) is corrected using GK2A NVIS of the same time, considering the difference in wavelength range and observation geometry. GK2B NVIS is extrapolated at 10-min intervals using the 10-min changes in GK2A NVIS. In the final step, the extrapolated GK2B NVIS, solar zenith angle, and outputs of GK2A FDA are utilized as input data for machine learning (decision tree) to develop the GK2AB FDA, which detects fog at a resolution of 250 m and a 10-min interval based on geographical locations. Six and four cases were used for the training and validation of GK2AB FDA, respectively. Quantitative verification of GK2AB FDA utilized ground observation data on visibility, wind speed, and relative humidity. Compared to GK2A FDA, GK2AB FDA exhibited a fourfold increase in spatial resolution, resulting in more detailed discrimination between fog and non-fog pixels. In general, irrespective of the validation method, the probability of detection (POD) and the Hanssen-Kuiper Skill score (KSS) are high or similar, indicating that it better detects previously undetected fog pixels. However, GK2AB FDA, compared to GK2A FDA, tends to over-detect fog with a higher false alarm ratio and bias.

• Journal of Wetlands Research
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• v.26 no.2
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• pp.147-159
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• 2024

Lack of streamflow observations makes model calibration difficult and limits model performance improvement. Satellite-based remote sensing products offer a new alternative as they can be actively utilized to obtain hydrological data. Recently, several studies have shown that artificial intelligence-based solutions are more appropriate than traditional conceptual and physical models. In this study, a data-driven approach combining various recurrent neural networks and decision tree-based algorithms is proposed, and the utilization of satellite remote sensing information for AI training is investigated. The satellite imagery used in this study is from MODIS and SMAP. The proposed approach is validated using publicly available data from 25 watersheds. Inspired by the traditional regionalization approach, a strategy is adopted to learn one data-driven model by integrating data from all basins, and the potential of the proposed approach is evaluated by using a leave-one-out cross-validation regionalization setting to predict streamflow from different basins with one model. The GRU + Light GBM model was found to be a suitable model combination for target basins and showed good streamflow prediction performance in ungauged basins (The average model efficiency coefficient for predicting daily streamflow in 25 ungauged basins is 0.7187) except for the period when streamflow is very small. The influence of satellite remote sensing information was found to be up to 10%, with the additional application of satellite information having a greater impact on streamflow prediction during low or dry seasons than during wet or normal seasons.

• Journal of Korean Society of Forest Science
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• v.64 no.1
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• pp.11-19
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• 1984

Potted, germinating Pinus rigida ${\times}$ P. taeda seedlings were inoculated with Pisolithus tinctorius (Pt) ectomycorrhizal fungus to test the effectiveness of Pt in relation to organic amendment and changes in soil fertility and soil texture. Pt was cultured as mycelia in vermiculite-peat moss mixture with nutrients and added to sterilized pot soils with or without organic amendment (fully fermented compost) at three soil texture levels (sand, loamy sand, and sandy loam) in a factorial design. Plants were grown in a greenhouse for 4 months and harvested to compare their growth with non-mycorrhizal plants and plants infected by natural fungi. Regardless of sod texture, soil fertility, or organic amendment, seedlings inoculated with Pt were better in dry weight and height than non-mycorrhizal plants or those infected by natural fungi. An exception was observed in the most fertile soil (0.075% N and 1.32% organic matter content in sandy loam with organic amendment), where non-mycorrhizal plants were slightly bigger (8%) and heavier (18%) than Pt-inoculated plants. In over-all average, Pt-inoculated seedlings were 30% taller and 107% heavier than those infected by natural fungi and 31 % taller and 60% heavier than non-mycorrhizal plants. Growth stimulation of seedlings by Pt was more pronounced in less fertile sand soil when organic was not amended. Mycorrhizal frequency of Pt (% of mycorrhizal root tips) was reduced to about half (from 84 to 33% in sandy loam and from 77 to 40% in loamy sand) by organic amendment, while that of natural fungi was not significantly affected. Severe nitrogen deficiency was observed in the needles of non-mycorrhizal plants (1.38% N), while both Pt-inoculated plants (1.68% N) and those infected by natural fungi (1.89% N) did not develop symptom, suggesting an active role of mycorrhizae in absorption of soil nitrogen. Top to root ratio increased with organic amendment to non-mycorrhizal plants, but was not significantly affected by fungal treatment. It was concluded from this study that relative effectiveness of Pt was determined by soil fertility. Organic amendment to less fertile sand soil increased effectiveness of Pt, while the same amendment to more fertile loamy sand and sandy loam decreased effectiveness of Pt. Benefits of Pt mycorrhizae would be expected most either when organic was not added to the soil, or when soil nutrients were not abundant.