• Korean Journal of Pharmacognosy
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• v.27 no.4
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• pp.342-346
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• 1996

A new species of Asiasarum, a new forma of A. sieboldii and a new forma of A. heterotropoides var. mandschuricum were found at the central part of Korea. The taxa of new plants are as follows: Asiasarum koreanum J. Kim et C. Yook sp. nov.; Asiasarum heterotropoides var. mandschuricum Maximowicz for. glabrata C. Yook, J. Kim et J. Nam f. nov.; Asiasarum sieboldii Miquel for. chungbuensis C. Yook et J. Kim f. nov. (Aristolochiaceae)

• Journal of Wetlands Research
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• v.13 no.1
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• pp.105-116
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• 2011

Changes in climate have largely increased concentrated heavy rainfall, which in turn is causing enormous damages to humans and properties. Therefore, the exact relationship and the spatial variability analysis of hydrometeorological elements and characteristic factors is critical elements to reduce the uncertainty in rainfall -runoff model. In this study, radar rainfall grid resolution and grid resolution depending on the topographic factor in rainfall - runoff models were how to respond. In this study, semi-distribution of rainfall-runoff model using the model ModClark of Inje, Gangwon Naerin watershed was used as Gwangdeok RADAR data. The completed ModClark model was calibrated for use DEM of cell size of 30m, 150m, 250m, 350m was chosen for the application, and runoff simulated by the RADAR rainfall data of 500m, 1km, 2km, 5km, 10km from 14 to 17 on July, 2006. According to the resolution of each grid, in order to compare simulation results, the runoff hydrograph has been made and the runoff has also been simulated. As a result, it was highly runoff simulation if the cell size is DEM 30m~150m, RADAR rainfall 500m~2km for peak flow and runoff volume. In the statistical analysis results, if every DEM cell size are 500m and if RADAR rainfall cell size is 30m, relevance of model was higher. Result of sensitivity assessment, high index DEM give effect to result of distributed model. Recently, rainfall -runoff analysis is used lumped model to distributed model. So, this study is expected to make use of the efficiently decision criteria for configurated models.

• Korean Journal of Plant Resources
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• v.24 no.4
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• pp.370-378
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• 2011

To address infra-specific relationships of Sedum kamtschaticum Fisch., and to provide the fundamental information for developing new horticultural variations, we analyzed the morphology of individuals in four natural populations (Mt. Gwangdeok, Mt. Samyeong, Mt. Yonghwa, Tongyeong) and investigated the vegetations of these area. Based on 50 morphological characters the principle component analysis (PCA) has been performed. Principle component axis 1, 2, and 3 explain 22.9%, 14.2%, and 7.4% of total variations, respectively. Dot plot of OTUs in PC2 by PC1 area showed that the areas of four populations are completely overlapped. The result of PCA and the statistics of each character indicate that all of morphological characters are overlapped in these four populations. The maximum deviations are found in the characters related in the size and shape of the leaf. In the vegetation analyses, eighteen community plots that we investigated were grouped into 10 subcommunities: subcomm. Boehmeria spicata, subcomm. Artemisia stolonifera, subcomm. Artemisia keiskeana, subcomm. Impatiens nolitangere, subcomm. Crepidiastrum chelidoniifolium, subcomm. Urtica thunbergiana, subcomm. Artemisia gmelini, subcomm. Commelina communis, subcomm. Erigeron annuus-Artemisia princeps, and typical subcommunity.

• Journal of Sericultural and Entomological Science
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• v.51 no.1
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• pp.1-8
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• 2013

This study was conducted for investigation of habitat and development of indoor-rearing conditions of peacock butterfly, Inachis io (Linnaeus). Three different region, Mt. Kwangduk-san, Mt. Hae-san and Mt. Taebaek-san in Kangwon-do, was selected for investigation of domestic distribution and ecological environment of peacock butterfly. In result, there are many butterflies in the only two region, Mt. Kwangduk-san and Mt. Hae-san. On 16th April, 4 overwintered butterflies were observed in Mt. Hae-san. At the end of June and early in July, 51 individual next generation's butterflies were observed in Mt. Kwangduk-san and Mt. Hae-san. For development of indoor-rearing conditions, collected each 15 male and female peacock butterflies was reared in a room condition. Female butterflies laid eggs in the egg cluster on the underside of host-plant's leaves, Urtica angustifolia Fisch., in the ovipositioning room. We took 11 egg cluster with average $404.6{\pm}23.6$ eggs. Hatchablity of eggs was 92.3%, and the eggs hatched within $5.1{\pm}0.9$ days from the day of oviposition under high temperature, long day condition($25^{\circ}C$, 14L:10D). The larval period was $18.3{\pm}1.0$ days under high temperature, long day condition($25^{\circ}C$, 14L:10D), showing 81.0% pupal ratio. The head width of each developmental larval stage were $0.37{\pm}0.01mm$(1st instar), $0.66{\pm}0.02mm$(2nd instar), $1.07{\pm}0.05mm$(3rd instar), $1.81{\pm}0.07$(4th instar), $2.76{\pm}0.08$(5th instar). The pupal period was $8.4{\pm}0.6$ days, and the emergence rate was 87.5%.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1159-1172
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• 2020

This study applied deep convolution neural network based on U-Net and SegNet using long period weather radar data to very short-term rainfall prediction. And the results were compared and evaluated with the translation model. For training and validation of deep neural network, Mt. Gwanak and Mt. Gwangdeoksan radar data were collected from 2010 to 2016 and converted to a gray-scale image file in an HDF5 format with a 1km spatial resolution. The deep neural network model was trained to predict precipitation after 10 minutes by using the four consecutive radar image data, and the recursive method of repeating forecasts was applied to carry out lead time 60 minutes with the pretrained deep neural network model. To evaluate the performance of deep neural network prediction model, 24 rain cases in 2017 were forecast for rainfall up to 60 minutes in advance. As a result of evaluating the predicted performance by calculating the mean absolute error (MAE) and critical success index (CSI) at the threshold of 0.1, 1, and 5 mm/hr, the deep neural network model showed better performance in the case of rainfall threshold of 0.1, 1 mm/hr in terms of MAE, and showed better performance than the translation model for lead time 50 minutes in terms of CSI. In particular, although the deep neural network prediction model performed generally better than the translation model for weak rainfall of 5 mm/hr or less, the deep neural network prediction model had limitations in predicting distinct precipitation characteristics of high intensity as a result of the evaluation of threshold of 5 mm/hr. The longer lead time, the spatial smoothness increase with lead time thereby reducing the accuracy of rainfall prediction The translation model turned out to be superior in predicting the exceedance of higher intensity thresholds (> 5 mm/hr) because it preserves distinct precipitation characteristics, but the rainfall position tends to shift incorrectly. This study are expected to be helpful for the improvement of radar rainfall prediction model using deep neural networks in the future. In addition, the massive weather radar data established in this study will be provided through open repositories for future use in subsequent studies.