• Journal of the Korean Society of Environmental Restoration Technology
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• v.6 no.1
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• pp.65-70
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• 2003

This research aims to classify wetland types and evaluate functions of a wetland created by abandoned rice paddies. The case study area is Ja-un wetland in Daejeon Metropolitan City. In this study, wetland types were classified based on the basic elements of wetlands such as hydrology, vegetation, and soil conditions. This study was carried out based upon field surveys, drawing maps and publication, and modified-RAM which can evaluate the general functions and conservation values of wetlands. The analysis shows that several types of wetlands were identified, such as Palustrine/ Perennial/ Hydrophytes, Palustrine/ Perennial/ Openwater, Palustrine/ Seasonal/Herbal, Palustrine/ Seasonal/ Shrub and Scrub, Palustrine/ Seasonal/ Hydrophye, and Riparian/ Seasonal/ Shrub and Scrub in the Ja-un wetland. The average level of functions of the wetlands is very "HIGH", and it is recommended that prompt conservation measures should be taken.

• Journal of Microbiology
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• v.34 no.2
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• pp.184-191
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• 1996

The cellular fatty acids and quinones of streptomycetes isolated from volcanic soils were analysed. The strains contained fatty acids of 14 to 17 carbon chains, and 12-methyltetradecanoic acid and 14 methylpentadecanoic acid were dominant in most strains. The total profiles consisted of 74% branched fatty acid family, 16.8% linear family and 8.2% unsaturated family. The largest cluster of grey spore meases defined by numerical classification was separated from the remainders in the principal component analysis, but the other clusters were overlapped with one another. In the analysis of respiratory quinones, all of the strains contained either the menaquinone of 9 isoprene units with 6 hydrogenations of 8 hydrogenations as the major species. The distribution of menaquinones among the clusters could provide an important key in the chemotaxonomy of streptomycetes.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.18-20
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• 2003

Land cover change detection is one of the most important trends in which remote sensing data could be used to assist strategists and the planners to decide the best land use policy. Two images of NOAA-AVHRR and SPOT vegetation acquired in November 1992 and 2002 were used to assess the changes of Agricultural lands in Egypt. A supervised classification together with two change images derived from classification result and NDVI were used to evaluate the trend and form of the change. It was found that agricultural areas increased by about 14.3 % during the study period in particular around the River Nile Delta and near the Northern Lakes of Egypt. The new cultivated lands were extracted mainly from the desert and from the salt marches areas. At the same time, parts of the agricultural lands were turned into non-cultivated land because of the urban expansion and soil degradation.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.03a
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• pp.1365-1371
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• 2008

Based on the data obtained from field investigation and soil testing to slope hazards occurrence section and non-occurrence section in gneiss area, a prediction technique was developed by the use of a decision tree model. The slope hazards data of Seoul and Kyonggi Province were 104 sections in gneiss area. The number of data applied in developing prediction model was 61 sections except a vacant value. The statistical analyses using the decision tree model were applied to the entrophy index. As the results of analyses, a slope angle, a degree of saturation and an elevation were selected as the classification standard. The prediction model of decision tree using entrophy index is most likely accurate. The classification standard of the selected prediction model is composed of the slope angle, the degree of saturation and the elevation from the first choice stage. The classification standard values of the slope angle, the degree of saturation and elevation are $17.9^{\circ}$, 52.1% and 320m, respectively.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.378-381
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• 2008

The regulating and relaxing targets in the Land Use Regulation and Total Maximum Daily Loads are influenced by Land cover information. For the providing more accurate land information, this study attempted to generate an impervious surface map using KOMPSAT-2 image which a Korea manufactured high resolution satellite image. The classification progress of this study carried out by tasseled-cap spectral enhancement through each class extraction technique neither existing classification method. KOMPSAT-2 image of this study is enhanced by Soil Brightness Index(SBI), Green vegetation Index(GVI), None-Such wetness Index(NWI). Then ranges of extracted each index in enhanced image are determined. And then, Confidence Interval of classes was determined through the calculating Non-exceedance Probability. Spectral distributions of each class are changed according to changing of Control coefficient(${\alpha}$) at the calculated Non-exceedance Probability. Previously, Land cover classification map was generated based on established ranges of classes, and then, pervious and impervious surface was reclassified. Finally, impervious ratio of reclassified impervious surface map was calculated with blocks in the study area.

• 한국공간정보시스템학회:학술대회논문집
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• 2002.03a
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• pp.45-52
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• 2002

Artificial neural networks (ANN) have been successfully used for classifying remotely sensed imagery. However, ANN still is not the preferable choice for classification over the conventional classification methodology such as the maximum likelihood classifier commonly used in the industry production environment. This can be attributed to the ANN characteristic built-in stochastic process that creates difficulties in dealing with unequally represented training classes, and its training performance speed. In this paper we examined some practical aspects of training classes when using a back propagation neural network model for remotely sensed imagery. During the classification process of remotely sensed imagery, representative training patterns for each class are collected by polygons or by using a region-growing methodology over the imagery. The number of collected training patterns for each class may vary from several pixels to thousands. This unequally populated training data may cause the significant problems some neural network empirical models such as back-propagation have experienced. We investigate the effects of training over- or under- represented training patterns in classes and propose the pattern repopulation algorithm, and an adaptive alpha adjustment (AAA) algorithm to handle unequally represented classes. We also show the performance improvement when input patterns are presented in random fashion during the back-propagation training.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2014.11a
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• pp.214-215
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• 2014

Recently, Russia decided to export an annual 7.5 million tons of natural gas to Korea over 30 years from 2015, as also deal with China, planed to build a pipeline connecting Siberia to Shandong Peninsula about 4000km. Risk management is required depending on the project in extreme cold weather, because it is concerned about the behavior of the seasonal changes in soil temperature and the strain of pipe according to the long-distance pipeline construction. The plan of data management shall be prepared in parallel for a sophisticated risk management, because a data is massive scale and it is generated/accumulated in real time. Therefore, this research is aimed to classify a data items in engineering stage of pipeline by previous studies for managing a generated data depending on the detail works in extreme cold weather. We expect to be provided the foundation of an efficient classification system of a generated data from the pipeline project life cycle.

• Journal of Radiation Industry
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• v.17 no.1
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• pp.11-17
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• 2023

Waste generated during the dismantling of nuclear power plants is not only diverse in types such as metal, concrete, soil, but also in a large amount, requiring systematic and efficient management. It is very important to quickly and accurately measure radioactive contamination of wastes generated simultaneously at the decommissioning site, classify them by level, and make decisions so that they can be disposed of in accordance with related laws and regulations. In this paper, for the technical and economic aspects of recycling of radioactive metal waste generated during the dismantling of nuclear power plants, we propose a management system that can measure the radioactive contamination by shape of metal waste at the decommissioning site and automatically classify it according to the presence or absence of contamination. Accordingly, a system for collecting information on metal samples such as weight measurement and shape acquisition of metal waste, measurement of radioactive contamination and identification of nuclides, and an automatic classification system according to radioactivity measurement results were described.

• Korean Journal of Environment and Ecology
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• v.4 no.1
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• pp.33-43
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• 1990

A survey of Popju Temple district. was conducted using 70 sample plots of 500$m^2$ size. The classification by TWINSPAN and DCA ordination were applied to the study area in order to classify them into several groups based on woody plants and environmental variables. By both techniques. the plant com-munity were divided into six groups by the altitude and soil moisture. The successional trends of tree species seem to be from Pinus densiflora, Sorbus alnifolia through Quercus serrata to Carpinus laxiflora and from P. densiflora, Fraxinus sieboldiana through Q. mongolica in the canopy layer, and from Lespedeza cyrtobotrya, Rhus trichocarpa, Zanthoxylum schnifolium through Rhododendron mucronulatum, Corylus sieboldiana, Lindera obtusiloba, Magnclia sieboldii to Euonymus sieboldianus in the understory and shrub layer. The species diversity of the plant community in the burnt plot was decreased by the forest fire but the importance values of Quercus species were increased in above plot.

• Korean Journal of Environmental Biology
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• v.17 no.3
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• pp.321-330
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• 1999

Nematodes were isolated using silkwom trap through the investigation of 100 soil samples from various biotopes in Korea. The 30 nematode strains from soil and dead insects by the pathogenicity aganinst silkworms (Bombyx mori mori) and insect pests of Calliphora vomitoria, Pseufazetia separata, Palomena angulosa, and Melolontha incana. Mortailty of the silkworm larvae and pupae were as high as 100% by nematode infection, those of insect of pests were varied from 20 to 100%. The 30 strains of entemopathogenic nematodes were classified into five groups of Rhabditidae, Diplogatroidae, Heterorhabitidae, Steinernematidae, and Tylenchida by morphological criteria. The genetic relationships among the 30 nematode strains were analyzed by various RAPD bands with twenty primers. The 30 nematode strains were classified into six major subgroups on the basis of the genetic similarity coefficient of 0.853. The grouping by RAPD was agree with those of morphological taxa in discrimination of the higher group, however, was not completely agree in the subgroup. The family Steinernematidae belong to Rhabditida was clarified as closer to the Tylenchida, rather than the other Rhabditida of Heterorhabitidae, Rhabditidae, and Diplogatroidae in genetic distance valule. From the result of the morphological classification and RAPD of the genomic DNA showed that genetic relationship analysis furnish infurmation on phylogenetic classification and relationships of entomopathogenic nematodes. The application of genetic similarity will overcome the limitation of taxonomy and classification of morphologically simple nematode. Several primers were confirmed those utility of identification for individual nematode strains, the methods of molecular genetics secured the simplicity, rapidity and accuracy on the selection of entomopathogenic nematodes.