• Journal of the Korean Society of International Agriculture
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• v.31 no.2
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• pp.178-182
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• 2019

'Muha' is a new strawberry (Fragaria x ananassa Duch.) cultivar, which was released by the Highland Agriculture Research Institute in 2015. The 'Muha' cultivar originated from a cross between 'Maehyang' and 'Selva' that showed excellent ever-bearing characteristics, including continuous flowering habit and high soluble-solid content under long-day and high temperature conditions in 2010. This cultivar was initially named 'Saebong No. 7' after examining its characteristics and productivity in summer culture from 2011 to 2014. After regional adaptability tests in 2015, 'Muha' was selected from Saebong No. 7 as an elite cultivar. The general characteristics of 'Muha' include semispreading type, elliptical leaf, and strength vigor in growth. The fruits are conical in shape, red in color. 'Muha' plants have 18.7 flower clusters, 8.7 more than 'Flamenco' plants. The fruit hardness of 'Muha' and Flamenco' was about the same. The number of fruits of 'Muha' was 37.2, which was 20 higher than that of 'Flamenco'. The marketable yield was 23,981 kg·ha^-1, 159% higher than 'Flamenco'. 'Muha' is suitable for north and highland area of the Southeast Asia as a high hardness and yield cultivar, because it showed continuous flowering habit under long-day and high temperature conditions.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.245-257
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• 2023

The representative crop in the Republic of Korea, rice, is cultivated over extensive areas every year, which resulting in reduced resistance to pests and diseases. One of the major rice diseases, rice blast disease, can lead to a significant decrease in yields when it occurs on a large scale, necessitating early detection and effective control of rice blast disease. Drone-based crop monitoring techniques are valuable for detecting abnormal growth, but frequent image capture for potential rice blast disease occurrences can consume significant labor and resources. The purpose of this study is to early detect rice blast disease using remote sensing data, such as drone and satellite images, along with weather data. Satellite images was helpful in identifying rice cultivation fields. Effective detection of paddy fields was achieved by utilizing vegetation and water indices. Subsequently, air temperature, relative humidity, and number of rainy days were used to calculate the risk of rice blast disease occurrence. An increase in the risk of disease occurrence implies a higher likelihood of disease development, and drone measurements perform at this time. Spectral reflectance changes in the red and near-infrared wavelength regions were observed at the locations where rice blast disease occurred. Clusters with low vegetation index values were observed at locations where rice blast disease occurred, and the time series data for drone images allowed for tracking the spread of the disease from these points. Finally, drone images captured before harvesting was used to generate spatial information on the incidence of rice blast disease in each field.

• Journal of the Korean earth science society
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• v.18 no.6
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• pp.515-521
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• 1997

To investigate the nature of the waste materials in the Nanjido Landfill, we have conducted multivariate statistical analysis of geophysical data set comprised of magnetic, gravity, LandSat TM thermal band and surface depression measurement data. Because these data sets show different responses to the depth, we have transformed the observed total field magnetic data and gravity data to the residual reduced-to-pole(RTP) magnetic anomalies and the three dimensional density anomalies, respectively, and utilized the informations about the upper shallow part of the landfills only in the following process. For the statistical analysis at the points of depression measurement, the magnetic, density and LandSat data values at these points are determined by interpolation process. Since the multivarite statistical analysis technique utilizes a clustering algorithm for classification of data set and we have measured the dissimilarity between objects by using Euclidean distance, standardization was applied prior to distance calculation in order to eliminate any scaling effects due to different measurement unit of each data set. The hierarchial grouping technique was used to construct the dendrogram. The optimum number of statistical groups(clusters), which are classified on the basis of geophysical and geotechnical characteristics, appeared to be six on the resulting dendrogram. The result of this study suggests that the dimension and nature of the multicomponent waste landfills can be identified by application of the multivarite statistical analysis technique to integrated geophysical data sets.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.2
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• pp.134-145
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• 2004

With rapid growth in the amount of data transferred on the Internet, traditional storage systems have reached the limits of their capacity and performance. SAN (Storage Area Network), which connects hosts to disk with the Fibre Channel switches, provides one of the powerful solutions to scale the data storage and servers. In this environment, the maintenance of data consistency among hosts is an important issue because multiple hosts share the files on disks attached to the SAN. To preserve data consistency, each host can execute the disk I/O whenever disk read and write operations are requested. However, frequent disk I/O requests cause the deterioration of the overall performance of a SAN cluster. In this paper, we introduce a SANtopia global buffer manager to improve the performance of a SAN cluster reducing the number of disk I/Os. We describe the design and algorithms of the SANtopia global buffer manager, which provides a buffer cache sharing mechanism among the hosts in the SAN cluster. Micro-benchmark results to measure the performance of block I/O operations show that the global buffer manager achieves speed-up by the factor of 1.8-12.8 compared with the existing method using disk I/O operations. Also, File system micro-benchmark results show that SANtopia file system with the global buffer manager improves performance by the factor of 1.06 in case of directories and 1.14 in case of files compared with the file system without a global buffer manager.

• The Korean Journal of Mycology
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• v.46 no.1
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• pp.58-68
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• 2018

Sunn hemp (Crotalaria juncea) is used as a nitrogen-fixing green manure in Korea to improve soil quality, reduce soil erosion, and suppress weeds and nematodes. In 2014, wilting sunn hemp plants were observed in green manure-cultivated fields in Wanju, Korea. Leaves of the infected plants began yellowing, starting with the lower leaves, eventually leading to their death. Moreover, a number of dark perithecia were observed on the wilting stems. Six isolates were obtained from these perithecia by single spore isolation. Based on their morphological characteristics, the isolates were identified as Fusarium udum (teleomorph: Gibberella indica). Macroconidia were slightly curved with almost hooked apical cell, and microconidia were formed on false heads by monophialides. Chlamydospores were produced abundantly in the hyphae, either singly or in clusters. To confirm the identification, multilocus sequence analysis was conducted using translation elongation factor 1 alpha (TEF), calmodulin (CAL), and histone 3 (HIS3). The sequences of TEF, CAL, and HIS3 showed 94.4~96.2%, 99.7%, and 99.6~99.8% similarity to the reference sequences of F. udum in NCBI GenBank, respectively. Pathogenicity was tested on sunn hemp and two soybean cultivars using the inoculation method of soil drenching with spore suspension. The wilting symptoms were observed only in sunn hemp and one cultivar of soybean (cv. Teagwang) after 14~21 days of inoculation. This is the first report of wilt disease in sunn hemp caused by Fusarium udum in Korea.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1235-1243
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• 2023

This study proposes the detection algorithm for the cold water mass (CWM) along the eastern coast of the Korean Peninsula using sea surface temperature (SST) data provided by the Korea Institute of Ocean Science and Technology (KIOST). Considering the occurrence and distribution of the CWM, the eastern coast of the Korean Peninsula is classified into 3 regions("Goseong-Uljin", "Samcheok-Guryongpo", "Pohang-Gijang"), and the K-means clustering is first applied to SST field of each region. Three groups, K-means clusters are used to determine CWM through applying a double threshold filter predetermined using the standard deviation and the difference of average SST for the 3 groups. The estimated sea area is judged by the CWM if the standard deviation in the sea area is 0.6℃ or higher and the average water temperature difference is 2℃ or higher. As a result of the CWM detection in 2022, the number of CWM occurrences in "Pohang-Gijang" was the most frequent on 77 days and performance indicators of the confusion matrix were calculated for quantitative evaluation. The accuracy of the three regions was 0.83 or higher, and the F1 score recorded a maximum of 0.95 in "Pohang-Gijang". The detection algorithm proposed in this study has been applied to the KIOST SST system providing a CWM map by email.

• Journal of Korea Multimedia Society
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• v.12 no.9
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• pp.1297-1308
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• 2009

The life time of a wireless sensor network composed of numerous sensor nodes depend on ones of its sensor nodes. The energy efficiency operation of nodes, therefore, is one of the crucial factors to design the network. Researches based on the hierarchical network topology have been proposed and evolved in terms of energy efficiency. However, in existing researches, application layer data obtained from sensor nodes are not considered properly to compose cluster, including issue that nodes communicate with their cluster heads in TDMA scheduling. In this paper, we suggest an energy-efficient topology control scheme based on application layer data in wireless sensor networks. By using application layer data, sensor nodes form a section which is defined as the area of adjacent nodes that retain similar characteristics of application environments. These sections are further organized into clusters. We suggest an algorithm for selecting a cluster head as well as a way of scheduling to reduce the number of unnecessary transmissions from each node to its cluster head, which based on the degree and the duration of similarity between the node's data and its head's data in each cluster without seriously damaging the integrity of application data. The results show that the suggested scheme can save the energy of nodes and increase the life time of the entire network.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.4
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• pp.124-137
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• 2018

The main purpose of this study is to find the hotspots of crimes that occur frequently in the space and to derive the appropriate CCTV installation location. One of the characteristics of crime is clustered around past occurrence area, and these crimes are strongly correlated. It is also possible to find the cause of the clusters and the variables that affect the crime through the history of the crime. In addition to the traditional OLS model, spatial differential model including spatial autocorrelation and spatial error model were used to select the variables influencing the five major crime rate, the theft rate and the foreign resident rate. The variables affecting the Five major crimes were positive (+) sign for the welfare and the rate of the bar cluster rate, and negative (-) for the street density. The CCTV area occupies 46% of the hotspots based on the overlapping of the areas where the elderly people are crowded, the bar cluster, many multicultural families, and the areas with low density of street lamps. It turned out. Taking into account the current CCTV operation, the total number of new cases to cover the risk point was 89.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.504-510
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• 2009

Recently, personalized-adaptive services became the center of interest in the world. However the services about music are not widely diffused out. That is because the analyzing of music information is more difficult than analyzing of text information. In this paper, we propose a music recommendation system which provides personalized services. The system keeps a user's listening list and analyzes it to select pieces of music similar to the user's preference. For analysis, the system extracts properties from the sound wave of music and the time when the user listens to music. Based on the properties, a piece of music is mapped into a point in the property space and the time is converted into the weight of the point. At this time, if we select and analyze the group which is selected by user frequently, we can understand user's taste. However, it is not easy to predict how many groups are formed. To solve this problem, we apply the K-means clustering algorithm to the weighted points. We modified the K-means algorithm so that the number of clusters is dynamically changed. This manner limits a diameter so that we can apply this algorithm effectively when we know the range of data. By this algorithm we can find the center of each group and recommend the similar music with the group. We also consider the time when music is released. When recommending, the system selects pieces of music which is close to and released contemporarily with the user's preference. We perform experiments with one hundred pieces of music. The result shows that our proposed algorithm is effective.

• Applied Microscopy
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• v.33 no.1
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• pp.79-92
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• 2003

Ultrastructural changes of the pericarp in Citrus reticulata has been investigated during hesperidium abscission. The pericarp was composed of compactly arranged parenchyma cell layers during early stages of fruit development. The outermost exocarp was green and active in photosynthesis. However, cells in the exocarp soon changed into collenchyma cells by developing unevenly thickened walls within a short time frame. As the fruit approached maturation, the chlorophyll gradually disappeared and chloroplasts were transformed into carotenoid-rich chromoplasts. In the mature fruit the exocarp consisted of large, lobed collenchyma cells with primary pit fields and numerous plasmodesmata. The immature mesocarp was a relatively hard and thick layer, located directly under the exocarp. With development, the deeper layers of the exocarp merged into the white, spongy mesocarp. Before separation of the hesperidium from the plant, some unusual features were detected in the plasma membrane of the exocarp cells. The number of small vacuoles and dark, irregular osmiophilic lipid bodies also increased enormously in the exocarp collenchyma after the abscission. They occurred between the plasma membrane and the wall, and invaginated pockets of the plasma membrane containing double-membraned vesicles were also frequently noticed. The lipid bodies in the cytoplasm were often associated with other organelles, especially with plastids and mitochondria. The plastids, which were irregular or amoeboid in shape, contained numerous large lipid droplets, and occasional clusters of phytoferritin, as well as few loosely -oriented peripheral lamellae. Myelin-like configurations of membrane were frequently observed in the vacuoles, as was the association of lipid bodies with the vacuolar membrane. Most vacuoles had an irregular outline, and lipid bodies were often connected to the tonoplast of the vacuoles. The structural changes underlying developmental, particularly to senescence, processes in various hesperidium will be reported in the separate paper.