• Korean Journal of Ecology and Environment
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• v.55 no.1
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• pp.35-48
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• 2022

Fish assemblage of total 325 of Korean peninsula estuaries were surveyed to analyze the characteristics of community structure and diversity by sea areas for three years from 2016 to 2018. The scale (stream width) of Korean estuaries were various (14~3,356 m), and 68.9% of all estuaries showed salinity of less than 2 psu. Total 149 species classified into 52 families of fish were identified, and the dominant and sub-dominant species were Tribolodon hakonensis (relative abundance, RA, 12.5%) and Mugil cephalus (RA, 9.5%), respectively. The estuary of the Korean Peninsula had different physical and chemical habitat environments depending on the sea area, and accordingly, fish community structure also showed statistically significant differences (PERMANOVA, Pseudo-F=26.69, P=0.001). In addition, the NMDS (nonmetric multidimensional scaling) results showed the patterns that indicating fish community difference by sea areas, even though low community similarity within sea area (SIMPER, 21.79~26.39%). The estuaries of east sea areas were distinguished from the others in the aspects of which, the higher importance of migratory fishes and endangered species, and that of brackish species were characterized at south sea estuaries. However, the estuaries of west sea showed higher importance of species that have a relation with freshwater (primary freshwater species, exotic species), which is the result that associating with the lower salinity of west sea estuaries because of the high ratio of closed estuaries(78.2%). The SIMPER analysis, scoring the contribution rates of species to community similarity, also showed results corresponding to the tendency of different fish community structures according to each sea area. So far, In Korea, most studies on fish communities in estuaries have been conducted in a single estuary unit, which made it difficult to understand the characteristics of estuaries at the national level, which are prerequisite for policy establishment. In present study, we are providing fish community structure characteristics of Korean estuaries in a national scale, including diversity index, habitat salinity ranges of major species, distribution of migratory species. We are expecting that our results could be utilized as baseline information for establishing management policies or further study of Korean estuaries.

• Korean Journal of Ecology and Environment
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• v.55 no.1
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• pp.60-75
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• 2022

The Bayesian algorithm model is a model algorithm that calculates probabilities based on input data and is mainly used for complex disasters, water quality management, the ecological structure between living things or living-non-living factors. In this study, we analyzed the main factors affected Korean Estuary Trophic Diatom Index (KETDI) change based on the Bayesian network analysis using the diatom community and physicochemical factors in the domestic estuarine aquatic ecosystem. For Bayesian analysis, estuarine diatom habitat data and estuarine aquatic diatom health (2008~2019) data were used. Data were classified into habitat, physical, chemical, and biological factors. Each data was input to the Bayesian network model (GeNIE model) and performed estuary aquatic network analysis along with the nationwide and each coast. From 2008 to 2019, a total of 625 taxa of diatoms were identified, consisting of 2 orders, 5 suborders, 18 families, 141 genera, 595 species, 29 varieties, and 1 species. Nitzschia inconspicua had the highest cumulative cell density, followed by Nitzschia palea, Pseudostaurosira elliptica and Achnanthidium minutissimum. As a result of analyzing the ecological network of diatom health assessment in the estuary ecosystem using the Bayesian network model, the biological factor was the most sensitive factor influencing the health assessment score was. In contrast, the habitat and physicochemical factors had relatively low sensitivity. The most sensitive taxa of diatoms to the assessment of estuarine aquatic health were Nitzschia inconspicua, N. fonticola, Achnanthes convergens, and Pseudostaurosira elliptica. In addition, the ratio of industrial area and cattle shed near the habitat was sensitively linked to the health assessment. The major taxa sensitive to diatom health evaluation differed according to coast. Bayesian network analysis was useful to identify major variables including diatom taxa affecting aquatic health even in complex ecological structures such as estuary ecosystems. In addition, it is possible to identify the restoration target accurately when restoring the consequently damaged estuary aquatic ecosystem.

• Journal of dental hygiene science
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• v.15 no.2
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• pp.172-181
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• 2015

This study intends to improve the radiation safety management and the recognition for handling radiation using structured questionnaires to dental hygienists working at Jeollabuk-do from September 1 to October 31 in 2014. As a result, 63% of respondents have not received education for radiation safety management. Moreover, the practical degree for radiation safety management was $2.58{\pm}1.11$, while the degree of knowledge was $3.74{\pm}0.83$ of total 5.0. The results of insecurity for radiation danger were high as $3.88{\pm}0.92$, and insecurity for fetus during pregnancy shows the highest value as $4.43{\pm}0.71$. From the results of statistical significance level, the knowledge degree of radiation safety management is affected by total numbers of radiograpy for a day (p<0.05), and the practical degree of radiation safety management is affected by age group, academic background, monthly income, continuous service year, practice area, present position, and status of radiography in present (p<0.05). In addition, the knowledge degree of radiation safety management have a negative correlation (r=-0.232) with the practical degree, but have a positive correlation (r=0.262) with the insecurity for radiation danger. The high knowledge degree of radiation safety management (${\beta}=0.252$, p<0.001) and the short radiography work period (${\beta}=-0.341$, p<0.05) were the influential factors to the insecurity for radiation danger. Consequently, countermeasures are necessary to encourage dental hygienists to put their radiation safety management knowledge into the practice and to reduce the insecurity degree for radiation danger. Furthermore, it is important to prevent psychological and physical risks by radiation exposure through the improvement of radiation safety management level and recognition for handling radiation to improve medical environment.

• Journal of dental hygiene science
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• v.12 no.6
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• pp.558-565
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• 2012

The purpose of this study was to work related musculoskeletal disorders are a major. Occupational disease of the dental care profession is no exception. The survey was self-reported questionars of 300 dental hygienists that 268 dental hygienists reply to self-reported survey. This study results are as follows: Subjects of research analyzing the degree of physical musculoskeletal disorders pain, shoulder 90.3%, neck 89.2%, leg 83.6%, 81.7% back, hand/wrist/fingers 75.7%, arm/elbow, according to 52.8%. Therefore the work province of the research object people the musculoskeletal disorders appeared different. Generally characteristic was taller dental hygienists lower back pain and were out of less weight, study subjects had neck and arm pain. 29~33 year-old age the shoulder, over the age of 34 the arm/elbow to be high (p<0.05). Working environment to become a career, the more hand/wrist/fingers and the pain increased (p<0.05). The neck, shoulders (p<0.05), arm (p<0.01), waist high in the 3~4 years experience. And leg/foot was in the 1~2 years experience. This increase in working hours had increased pain in the neck but the hand/wrist/finger pain in the small hours of experience in the high pain(p<0.01). Conclusion of the musculoskeletal disorders of the dental hygienists often than the average for this risk is recognized. When it occurs early in treatment can be simple, but time is left to revert to normal when you do not already. Therefore, maintaining proper posture and dental hygienists, pain or fatigue appeared to accumulate immediately treated continued efforts are needed.

• Korean Journal of Child Education & Care
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• v.19 no.2
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• pp.177-196
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• 2019

Objective: This study aims to analyze research trends related to software education for young children, focusing on studies published in Korea from 2016 to 2019 March. Methods: A total of 26 research publications on software education for young children, searched from Korea Citation Index and Research Information Sharing Service were identified for the analysis. The trend in these publications was classified and examined respectively by publication dates, types of publications, and the fields of study. To investigate a means of research, the analysis included key topics, types of research methods, and characteristics of the study variables. Results: The results of the analysis show that the number of publications on the topic of software education for young children has increased over the three years, of which most were published as a scholarly journal article. Among the 26 research studies analyzed, 16 (61.5%) are related to the field of early childhood education or child studies. Key topics and target subjects of the most research include the curriculum development of software education for young children or the effectiveness of software education on 4- and 5-year-old children. Most of the analyzed studies are experimental research designs or in the form of literature reviews. The most frequently studied research variable is young children's cognitive characteristics. For the studies that employ educational programs, the use of a physical computing environment is prevalent, and the most frequently used robot as a programming tool is "Albert". The duration of the program implementation varies, ranging from 5 weeks to 48 weeks. In the analyzed research studies, computational thinking is conceptualized as a problem-solving skill that can be improved by software education, and assessed by individual instruments measuring sub-factors of computational thinking. Conclusion/Implications: The present study reveals that, although the number of research publications in software education for young children has increased, the overall sufficiency of the accumulated research data and a variety of research methods are still lacking. An increased interest in software education for young children and more research activities in this area are needed to develop and implement developmentally appropriate software education programs in early childhood settings.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.82-98
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• 2019

The importance of spatial information is rapidly rising. In particular, 3D spatial information construction and modeling for Real World Objects, such as smart cities and digital twins, has become an important core technology. The constructed 3D spatial information is used in various fields such as land management, landscape analysis, environment and welfare service. Three-dimensional modeling with image has the hig visibility and reality of objects by generating texturing. However, some texturing might have occlusion area inevitably generated due to physical deposits such as roadside trees, adjacent objects, vehicles, banners, etc. at the time of acquiring image Such occlusion area is a major cause of the deterioration of reality and accuracy of the constructed 3D modeling. Various studies have been conducted to solve the occlusion area. Recently the researches of deep learning algorithm have been conducted for detecting and resolving the occlusion area. For deep learning algorithm, sufficient training data is required, and the collected training data quality directly affects the performance and the result of the deep learning. Therefore, this study analyzed the ability of detecting the occlusion area of the image using various image quality to verify the performance and the result of deep learning according to the quality of the learning data. An image containing an object that causes occlusion is generated for each artificial and quantified image quality and applied to the implemented deep learning algorithm. The study found that the image quality for adjusting brightness was lower at 0.56 detection ratio for brighter images and that the image quality for pixel size and artificial noise control decreased rapidly from images adjusted from the main image to the middle level. In the F-measure performance evaluation method, the change in noise-controlled image resolution was the highest at 0.53 points. The ability to detect occlusion zones by image quality will be used as a valuable criterion for actual application of deep learning in the future. In the acquiring image, it is expected to contribute a lot to the practical application of deep learning by providing a certain level of image acquisition.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.27-42
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• 2020

Traditional companies with offline stores were unable to secure large display space due to the problems of cost. This limitation inevitably allowed limited kinds of products to be displayed on the shelves, which resulted in consumers being deprived of the opportunity to experience various items. Taking advantage of the virtual space called the Internet, online shopping goes beyond the limits of limitations in physical space of offline shopping and is now able to display numerous products on web pages that can satisfy consumers with a variety of needs. Paradoxically, however, this can also cause consumers to experience the difficulty of comparing and evaluating too many alternatives in their purchase decision-making process. As an effort to address this side effect, various kinds of consumer's purchase decision support systems have been studied, such as keyword-based item search service and recommender systems. These systems can reduce search time for items, prevent consumer from leaving while browsing, and contribute to the seller's increased sales. Among those systems, recommender systems based on association rule mining techniques can effectively detect interrelated products from transaction data such as orders. The association between products obtained by statistical analysis provides clues to predicting how interested consumers will be in another product. However, since its algorithm is based on the number of transactions, products not sold enough so far in the early days of launch may not be included in the list of recommendations even though they are highly likely to be sold. Such missing items may not have sufficient opportunities to be exposed to consumers to record sufficient sales, and then fall into a vicious cycle of a vicious cycle of declining sales and omission in the recommendation list. This situation is an inevitable outcome in situations in which recommendations are made based on past transaction histories, rather than on determining potential future sales possibilities. This study started with the idea that reflecting the means by which this potential possibility can be identified indirectly would help to select highly recommended products. In the light of the fact that the attributes of a product affect the consumer's purchasing decisions, this study was conducted to reflect them in the recommender systems. In other words, consumers who visit a product page have shown interest in the attributes of the product and would be also interested in other products with the same attributes. On such assumption, based on these attributes, the recommender system can select recommended products that can show a higher acceptance rate. Given that a category is one of the main attributes of a product, it can be a good indicator of not only direct associations between two items but also potential associations that have yet to be revealed. Based on this idea, the study devised a recommender system that reflects not only associations between products but also categories. Through regression analysis, two kinds of associations were combined to form a model that could predict the hit rate of recommendation. To evaluate the performance of the proposed model, another regression model was also developed based only on associations between products. Comparative experiments were designed to be similar to the environment in which products are actually recommended in online shopping malls. First, the association rules for all possible combinations of antecedent and consequent items were generated from the order data. Then, hit rates for each of the associated rules were predicted from the support and confidence that are calculated by each of the models. The comparative experiments using order data collected from an online shopping mall show that the recommendation accuracy can be improved by further reflecting not only the association between products but also categories in the recommendation of related products. The proposed model showed a 2 to 3 percent improvement in hit rates compared to the existing model. From a practical point of view, it is expected to have a positive effect on improving consumers' purchasing satisfaction and increasing sellers' sales.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.