• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1277-1290
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• 2020

Water resources which is formed of surface and groundwater, are considered as one of the pivotal natural resources worldwide. Since last century, the rapid population growth as well as accelerated industrialization and explosive urbanization lead to boost demand for groundwater for domestic, industrial and agricultural use. In fact, better management of groundwater can play crucial role in sustainable development; therefore, determining accurate location of groundwater based groundwater potential mapping is indispensable. In recent years, integration of machine learning techniques, Geographical Information System (GIS) and Remote Sensing (RS) are popular and effective methods employed for groundwater potential mapping. For determining the status of the integrated approach, a systematic review of 94 directly relevant papers were carried out over the six previous years (2015-2020). According to the literature review, the number of studies published annually increased rapidly over time. The total study area spanned 15 countries, and 85.1% of studies focused on Iran, India, China, South Korea, and Iraq. 20 variables were found to be frequently involved in groundwater potential investigations, of which 9 factors are almost always present namely slope, lithology (geology), land use/land cover (LU/LC), drainage/river density, altitude (elevation), topographic wetness index (TWI), distance from river, rainfall, and aspect. The data integration was carried random forest, support vector machine and boost regression tree among the machine learning techniques. Our study shows that for optimal results, groundwater mapping must be used as a tool to complement field work, rather than a low-cost substitute. Consequently, more study should be conducted to enhance the generalization and precision of groundwater potential map.

• Research in Plant Disease
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• v.29 no.1
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• pp.52-59
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• 2023

Cucurbit chlorotic yellows virus (CCYV) is a plant virus that causes damage to cucurbit crops such as watermelon and cucumber, and is transmitted by an insect vector known as the whitefly. Since CCYV was first detected on cucumber in Chungbuk in 2018, it has been reported in other areas including Gyeongsang in Korea. In 2020, we performed field surveys of yellowing diseases in the greenhouses growing melon and watermelon in Chungbuk (Jincheon and Eumseong). Reverse transcription-polymerase chain reaction analysis of 79 collected samples including melon, watermelon, and weeds resulted in detection of CCYV in 4 samples: Three samples were singly infected with CCYV and one samples was mixed infected with CCYV, Cucurbit aphid borne yellows virus, and Watermelon mosaic virus. The complete genome sequences of the four collected CCYV melon isolates (ES 1-ES 4) were determined and genetically compared with those of previously reported CCYV isolates retrieved from GenBank. Phylogenetic analyses of RNA 1 and 2 sequences revealed that four ES isolates were clustered in one group and closely related to the CCYV isolates from China. The analysis also revealed very low genetic diversity among the CCYV ES isolates. In general, CCYV isolates showed little genetic diversity, regardless of host or geographic origins. CCYV has the potential to pose a serious threat to melon, watermelon, and cucumber production in Korea. Further studies are needed to examine the pathogenicity and transmissibility of CCYV in weeds and other cucurbits including watermelon.

• Journal of Bio-Environment Control
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• v.32 no.3
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• pp.190-196
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• 2023

This study was carried out to collect gas emitted from agricultural heaters using kerosene and to identify the emission concentration of carbon dioxide according to the age of agricultural heating machine. As a result of the linear regression analysis, the carbon dioxide emissions according to the year of agricultural heating machine are R² = 0.84, which follows y = 26.99x+721.98. Distributed analysis was classified into three groups according to the age of agricultural heating machine. As a result of the distributed analysis, it was 2.196×10^-13, which was smaller than the 0.05 probability set for the analysis, which means that there is a difference in at least one group. As a result, the age of the agriculture machine was divided into three groups and the difference between groups was tested. A statistical analysis result was derived that there was a difference in the emission concentration of carbon dioxide according to the age of agricultural heating machine. It is thought that it can be used to investigate greenhouse gas emissions by investigating the amount of carbon dioxide generated by agricultural heaters in the agricultural field of Korea.

• Journal of Korean Society of Archives and Records Management
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• v.23 no.4
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• pp.73-89
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• 2023

This study aims to analyze the frequency of keywords used in Korean abstracts, which are unstructured text data in the domestic record management research field, using text mining techniques to identify domestic record management research trends through distance analysis between keywords. To this end, 1,157 keywords of 77,578 journals were visualized by extracting 1,157 articles from 7 journal types (28 types) searched by major category (complex study) and middle category (literature informatics) from the institutional statistics (registered site, candidate site) of the Korean Citation Index (KCI). Analysis of t-Distributed Stochastic Neighbor Embedding (t-SNE) and Scattertext using Word2vec was performed. As a result of the analysis, first, it was confirmed that keywords such as "record management" (889 times), "analysis" (888 times), "archive" (742 times), "record" (562 times), and "utilization" (449 times) were treated as significant topics by researchers. Second, Word2vec analysis generated vector representations between keywords, and similarity distances were investigated and visualized using t-SNE and Scattertext. In the visualization results, the research area for record management was divided into two groups, with keywords such as "archiving," "national record management," "standardization," "official documents," and "record management systems" occurring frequently in the first group (past). On the other hand, keywords such as "community," "data," "record information service," "online," and "digital archives" in the second group (current) were garnering substantial focus.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1009-1014
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• 2023

Comprehensive observations of the Euler method and the Lagrangian method were performed in order to obtain high-resolution observation data in space and time for the complex environment of new city. The two radiosondes, which measure meteorological parameters using Lagrangian methods, produced air pressure, wind speed and wind direction. They were generally consistent with each other even if the observation points or times were different. The temperature measured by the sensor exposed to the air during the day was relatively high as the altitude increased due to the influence of solar radiation. The temporal difference in wind direction and speed was found in the comparison of Euler's wind profiler data with radiosonde data. When the wind field is horizontally in homogeneous, this result implies the need to consider the advection component to compare the data of the two observation methods. In this study, a method of using observation data at different times for each altitude section depending on the observation period of the Euler method is proposed to effectively compare the data of the two observation methods.

• Progress in Medical Physics
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• v.16 no.1
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• pp.16-23
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• 2005

The objective of this study was to construct multi-element ultrasound applicators for the treatment of gynecologic cancer with high dose rate brachytherapy. For the treatment of uterus, piezo-ceramic crystal transducer (PZT -5A) with outer diameter of 4 mm, wall thickness of 1.3 mm, and length of 24.5 mm was selected. For the treatment of cervix or vagina, it should be possible to insert the applicator into the vagina. Thus, a cylindrical PZT -8 material with outer diameter of 24.5 mm, wall thickness of 1.3 mm, and length of 15.2 mm was selected. The operating frequencies determined by vector impedance measurement were 3.2 MHz for the PZT 5A cylinder (OD=4 mm) and 1.7 MHz for the PZT -8 cylinder (OD: 24.5 mm). The ratios of generated acoustic output power to applied electric power were 33% and 61% for the tandem type crystal and the cylinder type crystal, respectively. The radiated acoustic pressure fields from both transducers were calculated using a Matlab code and measured in water using hydrophone. There was good agreement between measured and calculated acoustic pressure field distribution. For a tandem type transducer, the calculated acoustic pressure field decreased from 0.023 MPa at 10 mm to 0.010 Mpa at 30 mm, the reduction of 57%. For the cylinder type transducer which will be used for the treatment of vagina showed 78% reduction at 15 mm and 66% at 25 mm as compared to values at 5 mm from the surface. Based on the characteristics of the transducers, this study demonstrated the possibility of using the crystals as a heating source. Finally, a 3-element and 4-element prototype applicators were constructed. The 3-element applicator is 75 mm long and 4 mm thick and will be used for the treatment of uterus. The 4-element applicator is 61 mm long and 24.5 mm thick and will be used for the treatment of vagina. Using these applicators, it is possible to generate enough power to increase temperature to therapeutic level.

• Korean Journal of Remote Sensing
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• v.25 no.1
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• pp.31-44
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• 2009

The objective of this study was to measure backscattering coefficients of paddy rice using a L-, C-, and X-band scatterometer system with full polarization and various angles during the rice growth period and to relate backscattering coefficients to rice growth parameters. Radar backscattering measurements of paddy rice field using multifrequency (L, C, and X) and full polarization were conducted at an experimental field located in National Academy of Agricultural Science (NAAS), Suwon, Korea. The scatterometer system consists of dual-polarimetric square horn antennas, HP8720D vector network analyzer ($20\;MHz{\sim}20\;GHz$), RF cables, and a personal computer that controls frequency, polarization and data storage. The backscattering coefficients were calculated by applying radar equation for the measured at incidence angles between $20^{\circ}$ and $60^{\circ}$ with $5^{\circ}$ interval for four polarization (HH, VV, HV, VH), respectively. We measured the temporal variations of backscattering coefficients of the rice crop at L-, C-, X-band during a rice growth period. In three bands, VV-polarized backscattering coefficients were higher than hh-polarized backscattering coefficients during rooting stage (mid-June) and HH-polarized backscattering coefficients were higher than VV-, HV/VH-polarized backscattering coefficients after panicle initiation stage (mid-July). Cross polarized backscattering coefficients in X-band increased towards the heading stage (mid-Aug) and thereafter saturated, again increased near the harvesting season. Backscattering coefficients of range at X-band were lower than that of L-, C-band. HH-, VV-polarized ${\sigma}^{\circ}$ steadily increased toward panicle initiation stage and thereafter decreased, and again increased near the harvesting season. We plotted the relationship between backscattering coefficients with L-, C-, X-band and rice growth parameters. Biomass was correlated with L-band hh-polarization at a large incident angle. LAI (Leaf Area Index) was highly correlated with C-band HH- and cross-polarizations. Grain weight was correlated with backscattering coefficients of X-band VV-polarization at a large incidence angle. X-band was sensitive to grain maturity during the post heading stage.

• Journal of Korean Society of Forest Science
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• v.100 no.3
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• pp.352-358
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• 2011

Field Cage plots ($1m{\times}1m{\times}1m$) were established (7 units) to find the lowest limit time about the tending of red pine forest (Pinus densiflora) which can no longer be used as a habitat by Monochamus alternatus, vector insect of pine wilt disease at the experimental forest of the southern forest research center of the Korea forest research institute in February in 2010. Thinning slashes (length, 1 m; diameter, 5~10 cm) tended at the different times were put in cages, and 4~6 couples of adult M. alternatus were put into each the cage in June. Presence or absence the larval entrance holes and larval were determined in November in 2010. Incase of the combination 24, 18, 12 and 6-month-old thinning slashes from thinning times to the time of adult emergence inside a single cage, larval entrance holes were found in the 6-month-old and 12-month-old thinning slashes but larvae were found only in the 6-month-old thinning slashes (treatment 1). In case of the combination 24, 18, 15 and 12-month-old thinning slashes inside a single cage, larval entrance holes were found in the 15-month-old and 12-month-old thinning slashes but larvae were found only in the 12-month-old (treatment 2). When 24, 18, 15, 12 and 6-month-old thinning slashes with treated dry and humid condition were put separately inside each cage, larval entrance holes were found in the 18, 15, 12, 6-month-old thinning slashes without the relation of the dry and humid conditions. But larvae were found in the 15, 12, 6-month-old thinning slashes in the dry conditions and only in the 6-month-old thinning slashes in the humid conditions. Results indicated the lowest limit time which can no longer be used as a habitat by M. alternatus is before 24 month from the time of adult emergence.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.95-112
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• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.