• Korean Journal of Plant Taxonomy
• /
• v.50 no.2
• /
• pp.154-165
• /
• 2020

Coastal regions are experiencing habitat changes due to coastal development and global warming. To estimate the future distribution of coastal plants on the Korean Peninsula due to climate change, the potential distribution of ten species of coastal plants was analyzed using the MaxEnt program. The study covered the eastern, western, and southern coastal areas of the Korean Peninsula. We used the distributional data of coastal plants of the East Asian region and the 19 climate variables of WorldClim 2.0. The future potential distribution was estimated using future climate variables projected from three general circulation models (CCSM4, MIROC-ESM, and MPI-ESM-LR), four representative concentration pathways (2.5, 4.5, 6.0, and 8.5), and two time periods (2050 and 2070). The annual mean temperature influenced the estimation of the potential distribution the most. Under predicted future distribution scenarios, Lathyrus japonicus, Glehnia littoralis, Calystegia soldanella, Vitex rotundifolia, Scutellaria strigillosa, Linaria japonica, and Ixeris repens are expected to show contracted distributions, whereas the distribution of Cnidium japonicum is expected to expand. Two species, Salsola komarovii and Carex kobomugi, are predicted to show similar distributions in the future compared to those in the present. The average potential distribution in the future suggests that the effects of climate change will be greater in the west and the south coastal regions than in the east coastal region. These results will be useful baseline data to establish a conservation strategy for coastal plants.

• Journal of Korean Society of Forest Science
• /
• v.103 no.1
• /
• pp.105-112
• /
• 2014

The main purpose of this study is to measure spatio-temporal variation of forest tree volume based on the RCP(Representative Concentration Pathway) 8.5 scenario, targeting on Pinus densiflora forests which is the main tree species in South Korea. To estimate nationwide scale, $5^{th}$ forest type map and National Forest Inventory data were used. Also, to reflect the impact of change in place and climate on growth of forest trees, growth model reflecting the climate and topography features were applied. The result of the model validation, which compared the result of the model with the forest statistics of different cities and provinces, showed a high suitability. Considering the continuous climate change, volume of Pinus densiflora forest is predicted to increase from $131m^3/ha$ at present to $212.42m^3/ha$ in the year of 2050. If the climate maintains as the present, volume is predicted to increase to $221.92m^3/ha$. With the climate change, it is predicted that most of the region, except for some of the alpine region, will have a decrease in growth rate of Pinus densiflora forest. The growth rate of Pinus densiflora forest will have a greater decline, especially in the coastal area and the southern area. With the result of this study, it will be possible to quantify the effect of climate change on the growth of Pinus densiflora forest according to spatio-temporal is possible. The result of the study can be useful in establishing the forest management practices, considering the adaptation of climate change.

• Journal of Microbiology and Biotechnology
• /
• v.16 no.10
• /
• pp.1561-1569
• /
• 2006

Denaturing gradient gel electrophoresis (DGGE) is one of the most frequently used methods for analysis of soil microbial community structure. Unbiased PCR amplification of target DNA templates is crucial for efficient detection of multiple microbial populations mixed in soil. In this study, DGGE profiles were compared using different pairs of primers targeting different hypervariable regions of thirteen representative soil bacteria and clones. The primer set (1070f-1392r) for the E. coli numbering 1,071-1,391 region could not resolve all the 16S rDNA fragments of the representative bacteria and clones, and moreover, yielded spurious bands in DGGE profiles. For the E. coli numbering 353-514 region, various forward primers were designed to investigate the efficiency of PCR amplification. A degenerate forward primer (F357IW) often yielded multiple bands for a certain single 16S rDNA fragment in DGGE analysis, whereas nondegenerate primers (338f, F338T2, F338I2) differentially amplified each of the fragments in the mixture according to the position and the number of primer-template mismatches. A forward primer (F352T) designed to have one internal mismatch commonly with all the thirteen 16S rDNA fragments efficiently produced and separated all the target DNA bands with similar intensities in the DGGE profiles. This primer set F352T-519r consistently yielded the best DGGE banding profiles when tested with various soil samples. Touchdown PCR intensified the uneven amplification, and lowering the annealing temperature had no significant effect on the DGGE profiles. These results showed that PCR amplification bias could be much improved by properly designing primers for use in fingerprinting soil bacterial communities with the DGGE technique.

• Journal of Internet Computing and Services
• /
• v.10 no.6
• /
• pp.99-111
• /
• 2009

The result of Image segmentation, an indispensable process in image processing, significantly affects the analysis of an image. Despite the significance of image segmentation, it produces some problems when the variation of pixel values is large, or the boundary between background and an object is not clear. Also, these problems occur frequently when many objects in an image are placed very close by. In this paper, when the shape of objects in an image is circular, we proposed an algorithm which segment an each object in an image using the geometric characteristic of circular shape. The proposed algorithm is composed of 4 steps. First is the boundary edge extraction of whole object. Second step is to find the candidate points for further segmentation using the boundary edge in the first step. Calculating the representative circles using the candidate points is the third step. Final step is to draw the line connecting the overlapped points produced by the several erosions and dilations of the representative circles. To verify the efficiency of the proposed algorithm, the algorithm is compared with the three well-known cell segmentation algorithms. Comparison is conducted by the number of segmented region and the correctness of the inner segment line. As the result, the proposed algorithm is better than the well-known algorithms in both the number of segmented region and the correctness of the inner segment line by 16.7% and 21.8%, respectively.

• Journal of Wetlands Research
• /
• v.24 no.4
• /
• pp.288-300
• /
• 2022

As industrialization and urbanization progress extensively, climate change is intensifying due to greenhouse gas emissions. In Korea, the average temperature increased, and the annual precipitation also increased due to climate change. In addition, the meaning of the solar term, which expresses seasons according to the movement of the sun, is also being overshadowed. Therefore, this study investigated the seasonal changes and solar-term changes of average temperature and precipitation observed in the past as well as simulated for future RCP climate change scenarios for five major regions (Capital Region, Gyeongsang, Chungcheong, Jeolla, and Gangwon). For the seasonal length, the length of summer became longer, the length of winter became shorter nationwide, and the precipitation in summer generally increased compared to the past. In the Chungcheong area, under the RCP 8.5 scenario, the length of summer increased by 46%, precipitation increased by 16.2%, and the length of winter decreased by 31.8% compared to the past. For the solar term, the temperature rose in all seasons. In the Chungcheong area, under the RCP 8.5 scenario, the temperature of major heat increased by 15.5%, and the temperature of major cold increased by 75.7% compared to the past. The overall results showed that the hydrological characteristics of the season and solar term were identified by region, which can be used as basic data to prepare policies to respond to climate change.

• Composites Research
• /
• v.36 no.1
• /
• pp.53-58
• /
• 2023

The PIC design method is assigning different stacking sequences for each shell element through the preliminary FE analysis. In previous study, machine learning was applied to the PIC design method in order to assign the region efficiently, and the training data is labeled by dividing each region into tension, compression, and shear through the preliminary FE analysis results value. However, since buckling is not considered, when buckling occurs, it can't be divided into appropriate loading type. In the present study, it was proposed PIC-NTL (PIC design using novel technique for analyzing load type) which is method for applying a novel technique for analyzing load type considering buckling to the conventional PIC design. The stress triaxiality for each ply were analyzed for buckling analysis, and the representative loading type was designated through the determined loading type within decision area divided into two regions of the same size in the thickness direction of the elements. The input value of the training data and label consisted in coordination of element and representative loading type of each decision area, respectively. A machine learning model was trained through the training data, and the hyperparameters that affect the performance of the machine learning model were tuned to optimal values through Bayesian algorithm. Among the tuned machine learning models, the SVM model showed the highest performance. Most effective stacking sequence were mapped into PIC tube based on trained SVM model. FE analysis results show the design method proposed in this study has superior external loading resistance and energy absorption compared to previous study.

• 한국해양학회지
• /
• v.4 no.2
• /
• pp.49-67
• /
• 1969

A quantitative phytoplankton study in Korean waters was commenced in 1964 as a part of the primary production studies of Koreans seas, and it was continued with the cruises for Cooperative Studies of the Kuroshio(C.S.K) in 1965-1968. Phytoplankton samples were taken by dipping about 500ml of sea water from the surface, and then fixed by ading neutralized formlin. This report deals with the results obtained during 1965-1966. I examined a total of 298 samples of surface phytoplankton collected in the wate neighboring Korea in the above-mentioned period, and detected 147 species of diatoms and 22 species of dinoflagellates. Among them 123 species of diatoms and 18 species of dinoflagellates occured in the Japan Sea region, 133 species of diatoms and 11 species of dinoflagellates occured in the Korea Strait region, and 49 species of diatom and 8 species of dinoflagellates occured in the Yellow Sea region. And thd phytoplankton standing crops are dept in a fair abundance in the Japan Sea area all the year round, and are poor in the Yellow Sea area. The seas surrounding Korea are divided into seven regions by the planktological characteristics; northern and southern parts of the Japan Sea, eastern, western and southern parts of the Korea Strait, southern and northern parts of the Yellow Sea. The representative of the phytoplankton community in each sea region is generalized as follows; northern part of the Japan Sea is dominant with Chaetoceros group, southern part of the Japan Sea is dominant with Chaetoceros group and Skeletonema costaum, eastern part of the Korea Strait is dominant with Chaetoceros group and Pleurosigma sp., southern part of the Korea Strait is dominant with Chaetoceros group and Rizosolenia group, western part of the Korea Strait is most poor in phytoplankton, southern part of the Yellow Sea is dominant with Pleurosigma sp. and Coscinodiscus group, and northern part of the Yellow Sea is dominant with Pleurosigma sp. and Eucampia zoodiacus. Chaetoceros curvisetus, Leptocylindrus danicus, Pleurosigma normanii, Thalassionema nitzschioides, Thalassiothrix flauenfeldii appeared all the year round in the neighboring sea of Korea. There were 24 species (18 species of diatoms and 6 species of dinoflagellates) of the pecuriar phytoplankton in the Japan Sea, 27 species (25 species of diatoms and 2 species of dinoflagellates) of that in the Korea, and 7 species (5 species of diatoms and 2 species of dinoflagellates) of that in the Yellow Sea, respectively.

• Korean Journal of Ecology and Environment
• /
• v.44 no.3
• /
• pp.292-302
• /
• 2011

The Lake Sihwa watershed includes industrial, urban, and rural areas simultaneously. In this study, we analyzed the characteristics of dissolved organic matter (DOM) in spatial-temporal runoff patterns at representative sites having different land use in the watershed of Lake Sihwa. The result of synchronous and 3D-EEMs(3-Dimensional Excitation Emission Matrix Spectroscopy) analysis in 4TG (industrial area), fluorescence distribution and variation clearly appeared in the Fulvic-like fluorescence (FLF) and Humic-like fluorescence (HLF) regions along with the Protein-like fluorescence (PLF) region. A characteristic that Peak A (HLF) region fluorescence intensity did not decrease and the HLF region of fluorescence intensity and spatial-temporal changes clearly appeared during rainfall in AS (urban area). The results of fluorescence analysis in MS did not show great changes in PLF and FLF while showing that fluorescence intensity changes over time in the Terrestrial-like fluorescence (THLF) region increased greatly. In conclusion, our results showed significant differences in the runoff characteristics of DOM particularly in industrial, urban and rural area, and these differences should be considered for the efficient controlling of DOM in the watershed.

• Journal of Broadcast Engineering
• /
• v.21 no.3
• /
• pp.425-435
• /
• 2016

In this paper, we propose a classification model by analyzing raw material images recorded using a color CCD camera to automatically classify good and defective agricultural products such as rice, coffee, and green tea, and raw materials. The current classifying agricultural products mainly depends on visual selection by skilled laborers. However, classification ability may drop owing to repeated labor for a long period of time. To resolve the problems of existing human dependant commercial products, we propose a vision based automatic raw material classification combining mean shift clustering and stepwise region merging algorithm. In this paper, the image is divided into N cluster regions by applying the mean-shift clustering algorithm to the foreground map image. Second, the representative regions among the N cluster regions are selected and stepwise region-merging method is applied to integrate similar cluster regions by comparing both color and positional proximity to neighboring regions. The merged raw material objects thereby are expressed in a 2D color distribution of RG, GB, and BR. Third, a threshold is used to detect good and defective products based on color distribution ellipse for merged material objects. From the results of carrying out an experiment with diverse raw material images using the proposed method, less artificial manipulation by the user is required compared to existing clustering and commercial methods, and classification accuracy on raw materials is improved.

• Journal of Korean Society of Environmental Engineers
• /
• v.32 no.12
• /
• pp.1102-1110
• /
• 2010

Tidal mudflat is a reservoir for diverse microbial resources. Microbial diversity in tidal mudflat sediment can be easily influenced by various human activities. It is necessary to take representative samples to monitor microbial diversity in tidal mudflat sediments. In this study, we analyzed the microbial diversity and chemical characteristics of vegetation and non-vegetation tidal mudflat regions in the Kangwha tidal mudflat using geo-statistics and data-mining. According to the geo-statistical analysis, most correlation range values for the vegetation region were smaller than those for the non-vegetation region, which suggested that the shorter number and interval of sampling are required for the vegetation tidal mudflat environment due to its higher degree of chemical and biological complexity and heterogeneity. The data-mining analysis suggested that the organic content and nitrate were the major environmental factors influencing microbial diversity in the vegetation region while pH and sulfate were the major influencing factors in the non-vegetation region. Using the geo-statistical and data-mining integration approach, we proposed a guideline for determining the sampling interval and number to monitor microbial diversity in tidal mudflat.