• Economic and Environmental Geology
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• v.28 no.4
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• pp.395-404
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• 1995

The geologic structure of the Cheju volcanic island has been investigated by analyzing the gravity and magnetic data. Bouguer gravity map shows apparent circular low anomalies at the central volacanic edifice, and the maximum difference of the anomaly values on the island appears to be 30 mgal. The subsurface structure of the island is modeled by three-dimensional depth inversion of gravity data by assuming the model consists of a stacked grid of rectangular prisms of volcanic rocks bounded below by basement rocks. The gravity modeling reveals that the interface between upper volvanic rocks and underlying basement warps downward under Mt. Halla with the maximum depth of 5 km. Magnetic data involve aeromagnetic and surface magnetic survey data. Both magnetic anomaly maps show characteristic features which resemble the typical pattern of total magnetic anomalies caused by a magnetic body magnetized in the direction of the geomagnetic field in the middle latitude region, though details of two maps are somewhat different. The reduced-to-pole magnetic anomaly maps reveal that main magnetic sources in the island are rift zones and the Halla volcanic edifice. The apparent magnetic boundaries inferred by the method of Cordell and Grauch (1985) are relatively well matched with known geologic boundaries such as that of Pyosunri basalt and Sihungri basalt which form the latest erupted masses. Inversion of aeromagnetic data was conducted with two variables: depth and susceptibility. The inversion results show high susceptibility bodies in rift zones along the long axis of the island, and at the central volcano. Depths to the basement are 1.5~3 km under the major axis, 1~1.5 km under the lava plateau and culminates at about 5 km under Mt. Halla. The prominent anomalies showing N-S trending appear in the eastern part of both gravity and magnetic maps. It is speculated that this trend may be associated with an undefined fault developed across the rift zones.

• Ocean and Polar Research
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• v.29 no.2
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• pp.87-99
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• 2007

This study was conducted to understand the source and behavior of organic matter using the fluorescent technique (excitation-emission matrix) as a part of environmental monitoring program in the Korea manganese nodule mining site in the Northeastern Pacific Ocean. Water samples were collected at $0^{\circ},\;6^{\circ}N$, and $10.5^{\circ}N$ along $131.5^{\circ}W$ in August 2005. The concentration of total organic carbon (TOC) ranged from 58.01 to $171.93\;{\mu}M-C$. The vertical distribution of TOC was characterized as higher in the surface layer and decreased with depth. At $6^{\circ}N$, depth-integrated (from surface to 200 m depth) TOC was $337.1\;gC/m^2$, which was 1.4 times higher value than other stations. The exponential decay curve fit of vertical profile of TOC indicated that 59% of organic carbon produced by primary production in the surface layer could be decomposed by bacteria in the water column. Dissolved organic matter is generally classified into two distinctive groups based on their fluorescence characteristics using three-dimensional excitation/emission (Ex/Em) fluorescence mapping technique. One is known as biomacromolecule (BM; protein-like substance; showing max. at Ex 280/Em 330), mainly originated from biological metabolism. The other is geomacromolecule (GM; humic-like substance; showing max. at Ex 330/Em 430), mainly originated from microbial degradation processes. The concentration of BM and GM was from 0.42 to 7.29 TU (tryptophan unit) and from 0.06 to 1.81 QSU (quinine sulfate unit), respectively. The vertical distribution of BM was similar to that of TOC as high in the surface and decreased with depth. However, the vertical distribution of GM showed the reverse pattern of that of BM. From these results, it appeared that BM occupied a major part of TOC and was rapidly consumed by bacteria in the surface layer. GM was mainly transformed from BM by microbial processes and was a dominant component of TOC in the deep-sea layer.

• Journal of the Mineralogical Society of Korea
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• v.13 no.3
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• pp.164-170
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• 2000

Neutron single crystal and powder diffraction techniques have been applied to the structure analysis of yttria-stabilized zirconium, Z $r_{0.73}$ $Y_{0.27}$ $O_{1.87}$., prepared by the skull-melting method. The crystal structure has been determined to be cubic symmetry, space group Fm/equation omitted/ with a=5.155(2)$\AA$, V=136.99(5)$\AA$, Z=4, and R(F)=5.65%, $\omega$R(I)=10.57% for 70 integrated intensities of Bragg Peaks observed from single crystal of Z $r_{0.73}$ $Y_{0.27}$ $O_{1.87}$. The stabilizer atoms randomly occupy the zirconium sites and there are displacements of oxygen atoms with amplitudes of $\Delta$/a~0.033 and 0.11 along <110> and <100> directions from the ideal positions of the fluorite structure, respectively. There are no significant differences in crystallographic data between the single crystal and powder studies. Diffraction pattern after Rietveld refinement, using neutron powder data, has shown the evidence of a tetragonal impurity phase, or a slight tetragonal distortion.

• Journal of Korean Society of Forest Science
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• v.108 no.4
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• pp.540-551
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• 2019

This study was conducted to provide basic data for classifying patterns of land creep in 37 areas in South Korea using geological and soil property analyses. Geological time, as it relates to land creep areas in South Korea, had been most impactful for the Gyeongsang Supergroup and its sedimentary bedrock during the Cretaceous period. In this area, perfect ridge cliffs in land creeping areas included 20 plots (approximately 54.0%), while tension cracking areas with ambiguous ridge cliff characteristics included 17 plots (approximately 46.0%). Hesitant slide slope types included 20 plots (approximately 54.0%) within theslide slope of an incident pattern (slide slope figure) in land creeping areas. Colluvial debris types among land creep patterns were the most frequent and included 25 plots (approximately 68.0%). The direct causes of land creep were cutting of foothills, quarrying, land-clearing in mountains, mining exploration, and the creation of burial grounds, all of which added to geological impacts. Among land creeping areas, 27 plots (approximately 73.0%) were the result of man-made activities, and 10 plots (approximately 27.0%) were derived via natural causes such as earthquakes, heavy rainfall, and caving.

• Geophysics and Geophysical Exploration
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• v.5 no.4
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• pp.257-261
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• 2002

We have carried out integrated research including field survey and numerical modeling to appraise the applicability of SP method to the leakage problems of irrigation facilities. The leakage pattern of the dike studied here can be classified into the three categories: leakage through the abutment, leakage by piping through dike, and leakage due to the composite effects of landslide and distortion of the dike structure. for the numerical modeling to interpret quantitatively SP survey results acquired at dike, we have modified the computer code proposed by Sill (1983) to apply to the leakage problems. The numerical studies match the characteristic patterns of SP anomalies according to the leakage types and appear to be very useful to interpret the leakage zone and path. The SP monitoring results were also well coincided with tidal variations observed at every embankment so we found the SP method is quite effective not only to detect the leakage zone but also to determine the leakage trend. The numerical modeling results also reproduced the SP anomalies due to seawater leakage in the embankment.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.4
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• pp.129-134
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• 2018

Recently, the artificial neural network (ANN) model is a promising technique in the prediction, numerical control, robot control and pattern recognition. We predicted the outside temperature of greenhouse using ANN and utilized the model in greenhouse control. The performance of ANN model was evaluated and compared with multiple regression model(MRM) and support vector machine (SVM) model. The 10-fold cross validation was used as the evaluation method. In order to improve the prediction performance, the data reduction was performed by correlation analysis and new factor were extracted from measured data to improve the reliability of training data. The backpropagation algorithm was used for constructing ANN, multiple regression model was constructed by M5 method. And SVM model was constructed by epsilon-SVM method. As the result showed that the RMSE (Root Mean Squared Error) value of ANN, MRM and SVM were 0.9256, 1.8503 and 7.5521 respectively. In addition, by applying the prediction model to greenhouse heating load calculation, it can increase the income by reducing the energy cost in the greenhouse. The heating load of the experimented greenhouse was 3326.4kcal/h and the fuel consumption was estimated to be 453.8L as the total heating time is $10000^{\circ}C/h$. Therefore, data mining technology of ANN can be applied to various agricultural fields such as precise greenhouse control, cultivation techniques, and harvest prediction, thereby contributing to the development of smart agriculture.

• The Korean Journal of Pesticide Science
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• v.11 no.3
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• pp.210-215
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• 2007

This study aims to investigate if the properly use of pesticide in Ginseng farmland at Gangwon-do, Korea. Questionnaire included 36 questions such as control method for plant diseases, pests and weeds and pattern of pesticide use was answered by 271 Ginseng farmers lived in 4 cities and 8 counties at Gangwon-do. Ginseng farmers have noxious plant diseases, insects and weeds such as leaf spot, anthracnose, snail, stem-mining fly, horseweed and hairy crabgrass. To control of these, 35% of farmers relied on various type of pesticides and 31.5% of farmers used pesticides mingling with natural enemy, microorganism and organic materials. Farmers selected the pesticide based on the their own experiences or by recommendation of market dealers and neighbors, resulting in the use of inappropriate pesticides in the Ginseng farmland(78.8 % ). They followed standard dosage of the pesticide from Handbook of Pesticide Application(96.3%). They, however, used the same pesticides from 2 to 5 years(44.1 %). This consecutive use of pesticide could be induced resistance. This survey resulted that study for pesticide resistance and systematic educational program for proper use and selection of pesticide to Gingseng farmers should be conducted in farmland at Gangwon-do, Korea.

• Journal of KIISE:Software and Applications
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• v.29 no.1_2
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• pp.80-90
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• 2002

Clustering is a useful technique for grouping data points such that points within a single group/cluster have similar characteristics. Many clustering algorithms have been developed and used in engineering applications including pattern recognition and image processing etc. Recently, it has drawn increasing attention as one of important techniques in data mining. However, clustering algorithms such as K-means and Fuzzy C-means suffer from difficulties. Those are the needs to determine the number of clusters apriori and the clustering results depending on the initial set of clusters which fails to gain desirable results. In this paper, we propose a new clustering algorithm, which solves mentioned problems. In our method we use evolutionary algorithm to solve the local optima problem that clustering converges to an undesirable state starting with an inappropriate set of clusters. We also adopt a new measure that represents how well data are clustered. The measure is determined in terms of both intra-cluster dispersion and inter-cluster separability. Using the measure, in our method the number of clusters is automatically determined as the result of optimization process. And also, we combine heuristic that is problem-specific knowledge with a evolutionary algorithm to speed evolutionary algorithm search. We have experimented our algorithm with several sets of multi-dimensional data and it has been shown that one algorithm outperforms the existing algorithms.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.77-87
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• 2021

With the development of the Internet and personal computers, various and complex attacks begin to emerge. As the attacks become more complex, signature-based detection become difficult. It leads to the research on behavior-based log anomaly detection. Recent work utilizes deep learning to learn the order and it shows good performance. Despite its good performance, it does not provide any explanation for prediction. The lack of explanation can occur difficulty of finding contamination of data or the vulnerability of the model itself. As a result, the users lose their reliability of the model. To address this problem, this work proposes an explainable log anomaly detection system. In this study, log parsing is the first to proceed. Afterward, sequential rules are extracted by Bayesian posterior probability. As a result, the "If condition then results, post-probability" type rule set is extracted. If the sample is matched to the ruleset, it is normal, otherwise, it is an anomaly. We utilize HDFS datasets for the experiment, resulting in F1score 92.7% in test dataset.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.4
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• pp.423-432
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• 2019

Along with an increase in marine activities every year, the frequency of marine accidents is on the rise. Accordingly, various research activities and policies for marine safety are being implemented. Despite these efforts, the number of accidents are increasing every year, bringing their effectiveness into question. Preliminary studies relying on annual statistical reports provide precautionary measures for items that stand out significantly, through the comparison of statistical provision items. Since the 2000s, large-scale marine accidents have repeatedly occurred, and case studies have examined the "accident response." Likewise, annual statistics or accident cases are used as core data in policy formulation for domestic maritime safety. However, they are just a summary of post-accident results. In this study, limitations of current marine research and policy are evaluated through a literature review of case studies and analyses of marine accidents. In addition, the ontology of the marine accident information classification system will be revised to improve the current limited usage of the information through an attribute analysis of boating accident status reports and text mining. These aspects consist of the reporter, the report method, the rescue organization, corrective measures, vulnerability of response, payloads, cause of oil spill, damage pattern, and the result of an accident response. These can be used consistently in the future as classified standard terms to collect and utilize information more efficiently. Moreover, the research proposes a data collection and quality assurance method for the practical use of ontology. A clear understanding of the problems presently faced in marine safety will allow "suf icient quality information" to be leveraged for the purpose of conducting various researches and realizing effective policies.