• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.40-53
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• 2021

Research vessels(R/Vs) used for ocean research move to the planned research area and perform ocean observations suitable for the research purpose. The five research vessels of the Korea Institute of Ocean Science & Technology(KIOST) are equipped with global positioning system(GPS), water depth, weather, sea surface layer temperature and salinity measurement equipment that can be observed at all times during cruise. An information platform is required to systematically manage and utilize the data produced through such continuous observation equipment. Therefore, the data flow was defined through a series of business analysis ranging from the research vessel operation plan to observation during the operation of the research vessel, data collection, data processing, data storage, display and service. After creating a functional design for each stage of the business process, KIOST Underway Meteorological & Oceanographic Information System(KUMOS), a Web-Geographic information system (Web-GIS) based information platform, was built. Since the data produced during the cruise of the R/Vs have characteristics of temporal and spatial variability, a quality management system was developed that considered these variabilities. For the systematic management and service of data, the KUMOS integrated Database(DB) was established, and functions such as R/V tracking, data display, search and provision were implemented. The dataset provided by KUMOS consists of cruise report, raw data, Quality Control(QC) flagged data, filtered data, cruise track line data, and data report for each cruise of the R/V. The business processing procedure and system of KUMOS for each function developed through this study are expected to serve as a benchmark for domestic ocean-related institutions and universities that have research vessels capable of continuous observations during cruise.

• Clean Technology
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• v.27 no.1
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• pp.9-16
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• 2021

Since the active matrix organic light-emitting diode (AMOLED) encapsulation process is very vulnerable to moisture and oxygen, high-purity nitrogen with minimal moisture and oxygen must be used. In this study, a copper-based catalyst used to remove oxygen from nitrogen in the AMOLED encapsulation process was optimized. Two-component and three-component catalysts composed of CuO, Al2O3, or ZnO were prepared through a co-precipitation method. The prepared catalysts were characterized by using BET, XRD, TPR, and XRF analysis. In order to verify the oxygen removal performance of the catalyst, several catalytic reactions were conducted in a fixed bed reactor, and the corresponding oxygen contents were measured through an oxygen analyzer. In addition, reusability of the catalysts was proven through repetitive regeneration. The properties and oxygen removal capacity of the catalysts prepared with CuO and Al2O3 ratios of 6 : 4, 7 : 3, and 8 : 2 were compared. The number of active sites of the catalyst with a ratio of CuO and Al2O3 of 8 : 2 was the highest among the 2-component catalysts. Moreover, the reducibility of the catalyst with a ratio of CuO and Al2O3 of 8 : 2 was the best as it had the highest CuO dispersion. As a result, the oxygen removal ability of the catalyst with a ratio of CuO and Al2O3 of 8 : 2 was the best among the 2-component catalysts. The best oxygen removal capacity was obtained when 2wt% of ZnO was added to the sub-optimized catalyst (i.e., CuO : Al2O3 = 8 : 2) probably due to its outstanding reducibility. Furthermore, the optimized catalyst kept its performance during a couple of regeneration tests.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.66 no.1
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• pp.18-28
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• 2021

The growth period and productivity of cropping system of winter wheat-rice, winter wheat-bean and winter wheat-grain corn for 4 years from 2015 to 2018 were compared at the experimental field of National Institute of Crop Science in Miryang city. The harvest period of winter wheat was in mid-June, and summer crops were sown (transplanted) in late June. In transplanting of rice in late June, there was no difficulty in securing the heading of panicle and the yield of rice, but there was a lot of trouble in sowing wheat in proper time because the harvest time of rice was delayed to early November due to late maturity of rice, particularly in the mid-late maturing cultivar. There was no problem in soybean planting after winter wheat because the proper period of soybean planting is late-June. In addition, there was no problem in winter wheat sowng after soybean because the maturity period of soybean was mid-October. Selection of grain maize in double cropping with winter wheat in terms of growing periods, was desirable because grain maize had the fastest maturity among summer crops. In double cropping of winter wheat-summer crops, wheats combined with soybean and grain maize showed stable yields during three years, but there was a risk of yield declines in the wheat combined with rice in heavy rainfall year. It was possible to secure high yields in three summer crops as yields of rice, soybean, and corn were 600, 350, and 800 kg/10a, respectively. Summer crops with medium maturity was recommended because of no significant difference in yield between medium maturity and medium-late maturity cultivar. Soil physical properties were improved in soils cultivated with soybean and grain maize. Therefore, It was thought that double cropping systems of winter wheat with soybean and grain maize were superior to that of winter wheat with rice in terms of connecting period between winter wheat - summer crops and improvement of soil physical properties, and total income, particularly in soybean.

• Korean Journal of Environment and Ecology
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• v.35 no.1
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• pp.68-80
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• 2021

This study aimed to provide basic data such as the structure of the Carpinus turczaninowii community and characteristics of the habitat environment for ex situ conservation by analyzing the plant community structure of Carpinus turczaninowii, an island plant resource. For the community structure analysis, this study established 29 plots, sized 100㎡ each, in Seokmo, Yeongjong, Yeongheung, and Daebu islands. TWINSPAN was used for the classification of communities. The classification identified six communities. Group I was the C. turczaninowii-Quercus serrata community, group II was the C. turczaninowii-Pinus densiflora community, group III was the C. turczaninowii-Quercus mongolica community, group IV was the C. turczaninowii-Sorbus alnifolia community, group V was the C. turczaninowii typical community, and group VI was the C. turczaninowii-Quercus variabilis community. The species diversity was 0.90008~1.12868, the dominance was 0.17536~0.25665, and the similarity index was 17.1429~38.2979%. The result of correlation analysis of 7 environmental factors for 6 communities by RDA ordination showed a positive correlation between the crown density and litter layer and a negative correlation between the bare rock, soil hardness, and altitude on the 1st axis. On the 2nd axis, the bare rock and crown density showed a positive correlation, and the slope showed a negative correlation. In the C. turczaninowii-Quercus serrata community (I), the crown density and the litter layer were the environmental factors affecting the vegetation distribution. In the C. turczaninowii-Pinus densiflora (II) and C. turczaninowii-Quercus mongolica (III) communities, the slope was the factor affecting vegetation distribution. In the C. turczaninowii-Sorbus alnifolia (IV), C. turczaninowii typical (V), and C. turczaninowii-Quercus variabilis (VI) communities, the bare rock, altitude, and soil hardness were the factors affecting vegetation distribution.

• Research in Plant Disease
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• v.27 no.1
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• pp.1-7
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• 2021

In August 2020, necrotic ringspots on leaves were observed on 20 from 143 Plantago asiatica plants in open fields in Eumseong, Chungcheongbuk-do. Eight symptomatic Plantago asiatica plants were subjected to investigation on viral infection by reverse transcription and polymerase chain reaction. Impatiens necrotic spot virus, tomato spotted wilt virus and cucumber mosaic virus were detected from the symptomatic plants. Two impatiens necrotic spot virus (INSV) isolates ('INSV-plantain kr1' and '-plantain kr5') were sequenced and analyzed by comparing L genomic segment, nucleoprotein (N) gene and non-structural protein S (NSs) gene sequences. The nucleotide sequence of 'INSV-plantain kr1' isolate (MW114834) was most closely related to that of a 'Phalaenopsis' isolate (GQ336991) from China in the L genomic segment. 'INSV-plantain kr1' and '-plantain kr5' isolates shared the highest identities with those from 'Pepe' isolate (LC384872) and 'J' isolate (AB109100) in the NSs gene, respectively, and with that from 'YSMi-SH' isolate (FN400773) in the N gene. Phylogenetic analysis based on L genomic segment grouped the INSV-plantain kr1 isolate together with isolates from Korea (LC384870), China (GU112505, GQ336991), and Italy (DQ425094). This is the first report on INSV in P. asiatica from Korea.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1421-1434
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• 2020

The frequency and intensity of heatwaves have been increasing due to climate change. Since urban areas are more severely damaged by heatwaves as they act in combination with the urban heat island phenomenon, every possible preparation for such heat threats is required. Many overseas local governments build heat maps using a variety of spatial information to prepare for and counteract heatwaves, and prepare heatwave measures suitable for each region with different spatial characteristics within a relevant city. Building a heat map is a first and important step to prepare for heatwaves. The cases of heat map construction and thermal environment analysis involve various area distributions from urban units with a large area to local units with a small area. The method of constructing a heat map varies from a method utilizing remote sensing to a method using simulation, but there is no standard for using differentiated spatial information according to spatial scale, so each researcher constructs a heat map and analyzes the thermal environment based on different methods. For the above reason, spatial information standards required for building a heat map according to the analysis scale should be established. To this end, this study examined spatial information, analysis methodology, and final findings related to Korean and oversea analysis studies of heatwaves and urban thermal environments to suggest ways to improve the utilization efficiency of spatial information used to build urban heat maps. As a result of the analysis, it was found that spatial, temporal, and spectral resolutions, as basic resolutions, are necessary to construct a heat map using remote sensing in the use of spatial information. In the use of simulations, it was found that the type of weather data and spatial resolution, which are input condition information for simulation implementation, differ according to the size of analysis target areas. Therefore, when constructing a heat map using remote sensing, spatial, spectral, and temporal resolution should be considered; and in the case of using simulations, the spatial resolution, which is an input condition for simulation implementation, and the conditions of weather information to be inputted, should be considered in advance. As a result of understanding the types of monitoring elements for heatwave analysis, 19 types of elements were identified such as land cover, urban spatial characteristics, buildings, topography, vegetation, and shadows, and it was found that there are differences in the types of the elements by spatial scale. This study is expected to help give direction to relevant studies in terms of the use of spatial information suitable for the size of target areas, and setting monitoring elements, when analyzing heatwaves.

• Korean Journal of Environment and Ecology
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• v.35 no.1
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• pp.48-67
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• 2021

Plant species exhibit current characteristics as a result of interactions with environmental conditions. The plants of Viola sp. have selected chasmogamous flowers with vigorous vegetative propagation or development of cleistogamous flowers as an adaptation strategy. Viola websteri is distributed on the Korean peninsula and the eastern part of Jilin Province, China. The center and edge of the distribution are expected to exhibit different population-dynamics. It is necessary to investigate the cause of its current limited distribution even though V. websteri has a mixed-mating strategy. Firstly, We examined the vegetation environment of habitats and evaluated its characteristics. Growth characteristics were examined through plant phenology. We then evaluated the population structure, characteristics of chasmogamous flowers, and productivity of cleistogamous flowers. Moreover, we compared population sizes between 2014 and 2018. Most habitats were located in deciduous broadleaf mixed forests adjacent to valleys. V. websteri produced chasmogamous flowers with self-incompatibility in April-May and cleistogamous flowers in June-September. The cleistogamous flower production is a strategy ensuring seed production under uncertain environmental fluctuations; these were approximately twice as numerous as chasmogamous flowers. The population structure was distinguished into stable and very unstable regions. There were sites where the population experienced a sharp decline in the 2018 compared to that of 2014. This large decline was found in the edge populations. The habitats had different microsites depending on the natural disturbances of drought and the matrix constituting the habitat, thus supporting various plants. Ensuring the production of seeds through cleistogamous flowers, it was determined that rapid seedling re-establishment and population replenishment were possible when the natural disturbance factor was removed. Environmental factors did not equally affect all populations or individuals. Therefore, it was expected that it would be able to persisted in a long time, despite the rapid decrease in the number of individuals in the population regionally. Local extinction and re-establishment are likely to repeat according to environmental change. We propose the additional population investigation based on this works are required. We also suggest a need to assess the long-term population dynamics and the genetic characteristics of chasmogamous flowers and cleistogamous flowers to establish and implement effective conservation strategies.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1509-1521
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• 2020

Experiments for validation of surface reflectance produced by Korea Multi-Purpose Satellite (KOMPSAT-3A) were conducted using Chinese Baotou (BTCN) data among four sites of the Radical Calibration Network (RadCalNet), a portal that provides spectrophotometric reflectance measurements. The atmosphere reflectance and surface reflectance products were generated using an extension program of an open-source Orfeo ToolBox (OTB), which was redesigned and implemented to extract those reflectance products in batches. Three image data sets of 2016, 2017, and 2018 were taken into account of the two sensor model variability, ver. 1.4 released in 2017 and ver. 1.5 in 2019, such as gain and offset applied to the absolute atmospheric correction. The results of applying these sensor model variables showed that the reflectance products by ver. 1.4 were relatively well-matched with RadCalNet BTCN data, compared to ones by ver. 1.5. On the other hand, the reflectance products obtained from the Landsat-8 by the USGS LaSRC algorithm and Sentinel-2B images using the SNAP Sen2Cor program were used to quantitatively verify the differences in those of KOMPSAT-3A. Based on the RadCalNet BTCN data, the differences between the surface reflectance of KOMPSAT-3A image were shown to be highly consistent with B band as -0.031 to 0.034, G band as -0.001 to 0.055, R band as -0.072 to 0.037, and NIR band as -0.060 to 0.022. The surface reflectance of KOMPSAT-3A also indicated the accuracy level for further applications, compared to those of Landsat-8 and Sentinel-2B images. The results of this study are meaningful in confirming the applicability of Analysis Ready Data (ARD) to the surface reflectance on high-resolution satellites.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1449-1463
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• 2020

As recent global warming and climate changes become more serious, the importance of CO2 absorption by forests is increasing to cope with the greenhouse gas issues. According to the UN Framework Convention on Climate Change, it is required to calculate national CO2 absorptions at the local level in a more scientific and rigorous manner. This paper presents the gridded expansion of forest flux observations and mapping of daily CO2 absorption by the forests in Korea using numerical weather prediction data and satellite images. To consider the sensitive daily changes of plant photosynthesis, we built a machine learning model to retrieve the daily RACA (reference amount of CO2 absorption) by referring to the climax forest in Gwangneung and adopted the NIFoS (National Institute of Forest Science) lookup table for the CO2 absorption by forest type and age to produce the daily AACA (actual amount of CO2 absorption) raster data with the spatial variation of the forests in Korea. In the experiment for the 1,095 days between Jan 1, 2013 and Dec 31, 2015, our RACA retrieval model showed high accuracy with a correlation coefficient of 0.948. To achieve the tier 3 daily statistics for AACA, long-term and detailed forest surveying should be combined with the model in the future.