• (The)Study of the Eastern Classic
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• no.57
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• pp.437-466
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• 2014

This paper, is in the focus of the fact that was popular in the Joseon Dynasty, was clearly private background and fashionable aspects of its creation. In Section 2, as the background of the epidemic of of the Joseon Dynasty, and was derived creative situation of . is the "left Order live music" relationship center rather, was that it was created by the Academy belongs is highlighted "Hanlin year". As a result, collapses the relationship between the order raw Korea early left, in conjunction with the boast to debauchery and evaluated surface Shinyoung of successor Geibun of this academy, be epidemic in the Joseon Dynasty it is the could be. In Chapter 3, and two to the original, it was examined the epidemic surface of of the Joseon Dynasty. The epidemic of surface, in the conventional research and are relatively detailed, but in this paper, while accommodating the existing research results, of consideration "Hanlim feast" has led to Geibun of Korea "immune new Feast" was observed a trend surface is placed around the fact. As a result, , the Joseon is that it has been singing on the occasion official four-kan and Geibun center, first Nara are exempt new feast bonds back to the four Hall again in Geibun, usually it can be seen that it is spread in the Scholar-official and gisaeng's. Lower limit has been singing can be up to around the late 17th century it has been speculated through the time of Gimumanjun.

• Journal of Life Science
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• v.29 no.6
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• pp.712-723
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• 2019

Chemically synthesized compounds are widely used in oral hygiene products. However, excessively long-term use of these chemicals can cause undesirable side effects such as bacterial tolerance, allergy, and tooth discoloration. To solve these issues, significant effort is put into the search for natural antibacterial agents. The aim of this study was to assess the extracts of foreign native plants that inhibit the growth and biofilm formation of Streptococcus mutans. Among the 300 foreign plant extracts used in this study, Chesneya nubigena (D. Don) Ali extract had the highest antimicrobial activity relatively against S. mutans with a clear zone of 9 mm when compared to others. This plant extract also showed anti-biofilm activity and bacteriostatic effect (minimal bactericidal concentration [MBC], 1.5 mg/ml). In addition, the plant extracts of 19 species decreased the ability of S. mutans to form biofilm at least a 6-fold in proportion to the tested concentrations. Of particular note, C. nubigena (D. Don) Ali extract was found to inhibit biofilm formation at the lowest concentration tested effectively. Therefore, our results reveal that C. nubigena (D. Don) Ali extract is a potential candidate for the development of antimicrobial substitutes, which might be effective for caries control as well, as demonstrated by its inhibitory effect on the persistence and pathogenesis of S. mutans.

• The Journal of the Korea Contents Association
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• v.18 no.12
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• pp.140-149
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• 2018

Along with the recent increase in national income, social phenomena such as aging due to a decrease in population and an increase in single households are observed. There are also an increasing number of households raising pets in proportion to aging households and the increase in the number of single households, most of which use animal companions to overcome loneliness and boost domestic vitality. As more and more people consider pets as family members, the size of the domestic pet market is also growing. The growing number of pets in older households and single households is not properly managed by care such as food meals and exercise management for pets. It is necessary to research and develop robots that can monitor animal companions remotely, feed a certain amount of food at regular intervals, and manage their health through exercise. Among pet companions, dog selection is the highest. Therefore, this study identified robot research on driving methods, examples of existing pet care systems, and researched pet care robots using obstacle avoidance algorithms. In order to use the snack pay behavior and obstacle avoidance algorithm of the pet animals by applying IoT and we .oPI technology, it is able to use ultrasonic sensors on the front and has four infrared sensors on the back. However, this study does not reflect the characteristics of other pet animals as a study on pet care robots, and it requires continuous observation and testing.

• 재활복지
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• v.17 no.4
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• pp.127-149
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• 2013

This study analyzed the hazard of unemployment and the influencing factors on the rate. Data came from the Panel Survey of Employment for the Disabled(PSED), 2010-2012, which is a longitudinal survey for 5,092 disabled people in Korea. For the main purpose of this study, the life-table method was used for describing the patterns of unemployment duration by disabled, and the cox proportional hazard model was used to identify significant factors on the unemployment duration. The results were as follows. First, according to the life table analysis, the unemployment rate to remain until the longest period of unemployment(25month) is 90.5%, and the rate of entry into the labor market was only 9.5%. Overall, the unemployment maintenance rate was high, the unemployment escape rate decreased after 12month. Second, looking at the results from the cox proportional hazards model, the unemployment escape possibility were increased for those who are male, are non-public benefit recipient with disability, have mild disability, and have less discrimination experiences. With these results, disability discrimination act which can reduce the disability discrimination in employment site should be strengthened. Also, the scheme of Nation Basic Protection Program should be modified to attract the employment of recipients with disability. Finally, policy targets having employment escape difficulty, such as women with disability, people with severe disabilities should be departmentalized. And employment service is provided in accord with the individual needs and characteristics.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2895-2905
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• 2018

We present a mixed multiplicative quantitative randomized response model which added a unrelated quantitative attribute and forced answer to the multiplicative model suggested by Bar-Lev et al. (2004). We also try to set up theoretical grounds for estimating sensitive quantitative attribute according to circumstances whether or not the information for unrelated quantitative attribute is known. We also extend it into the stratified mixed multiplicative quantitative randomized response model for stratified population along with two allocation methods, proportional and optimum allocation. We can see that the various quantitative randomized response models such as Eichhorn-Hayre's model (1983), Bar-Lev et al.'s model (2004), Gjestvang-Singh's model (2007) and Lee's model (2016a), are one of the special occasions of the suggested model. Finally, We compare the efficiency of our suggested model with Bar-Lev et al.'s (2004) and see that the bigger the value of $C_z$, the more the efficiency of the suggested model is obtained.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.92-111
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• 2021

As fine dust negatively affects disease, industry and economy, the people are sensitive to fine dust. Therefore, if the occurrence of fine dust can be predicted, countermeasures can be prepared in advance, which can be helpful for life and economy. Fine dust is affected by the weather and the degree of concentration of fine dust emission sources. The industrial sector has the largest amount of fine dust emissions, and in industrial complexes, factories emit a lot of fine dust as fine dust emission sources. This study targets regions with old industrial complexes in local cities. The purpose of this study is to explore the factors that cause fine dust and develop a predictive model that can predict the occurrence of fine dust. weather data and fine dust data were used, and variables that influence the generation of fine dust were extracted through multiple regression analysis. Based on the results of multiple regression analysis, a model with high predictive power was extracted by learning with a machine learning regression learner model. The performance of the model was confirmed using test data. As a result, the models with high predictive power were linear regression model, Gaussian process regression model, and support vector machine. The proportion of training data and predictive power were not proportional. In addition, the average value of the difference between the predicted value and the measured value was not large, but when the measured value was high, the predictive power was decreased. The results of this study can be developed as a more systematic and precise fine dust prediction service by combining meteorological data and urban big data through local government data hubs. Lastly, it will be an opportunity to promote the development of smart industrial complexes.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.4
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• pp.148-159
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• 2021

This study estimates the region of freshwater influence (ROFI) by Han River discharge in the Yeomha channel, Gyeonggi Bay. A 3-D numerical model, which is validated for reproducibility of variation in current velocity and salinity, is applied in Gyeonggi Bay. Distance of freshwater influence (DOFI) is defined as the distance from the entrance of Yeomha channel to the point where surface salinity is 28 psu. Model scenarios were constructed by dividing the Han River discharge into 10 categories (200~10,000 m³/s). The relation equation between freshwater discharge and DOFI was calculated based on performing a non-linear regression analysis. ROFI in Yeomha channel expands from the southern sea area of Ganghwa-do to the northern sea area of Yeongheung-do as the intensity of Han River discharge increases. The discharge and DOFI are a proportional relationship, and the increase rate of DOFI gradually decreases as discharge increases. Based on the relation equation calculated in this study, DOFI in the Yeomha channel can be estimated through the monthly mean Han River discharge. Accordingly, it will be possible to respond and predict problems related to damage to water quality and ecology due to rapid freshwater runoff.

• Korean small business review
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• v.42 no.4
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• pp.309-326
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• 2020

There often is information asymmetry between start-ups and the investors, which is because start-up companies in the early stages do not have track records. Meanwhile, since the government grants programs go through a fair and the intense competition process, the government grants can provide a more objective information for start-ups in the early stages and perform a signal function that guarantees a company's capabilities and potential. This study confirms the quantitative relationship between government grants and investment attraction by using the hurdler model. We found that, although there is the proportionate relationship between the scale of government grants and that of external funds, more than a certain amount of government grants is required for technology-based start-ups to exceed the stage of attracting their first external funds. Our findings suggest that it is necessary to consider the hurdles structure in the study of signaling theory perspective, as the mechanisms for determining whether or not to attract external funds are different from determining the level of external funds. In addition, differentiated policy support is needed to help early-stage technology start-ups go beyond the threshold of investment attraction-the creation of a 'threshold effect'.

• Journal of Internet Computing and Services
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• v.23 no.6
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• pp.1-13
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• 2022

As the information and communication environment develops, the environment of military facilities is also development remarkably. In proportion to this, cyber threats are also increasing, and in particular, APT attacks, which are difficult to prevent with existing signature-based cyber defense systems, are frequently targeting military and national infrastructure. It is important to identify attack groups for appropriate response, but it is very difficult to identify them due to the nature of cyber attacks conducted in secret using methods such as anti-forensics. In the past, after an attack was detected, a security expert had to perform high-level analysis for a long time based on the large amount of evidence collected to get a clue about the attack group. To solve this problem, in this paper, we proposed an automation technique that can classify an attack group within a short time after detection. In case of APT attacks, compared to general cyber attacks, the number of attacks is small, there is not much known data, and it is designed to bypass signature-based cyber defense techniques. As an attack model, we used MITRE ATT&CK® which modeled many parts of cyber attacks. We design an impact score considering the versatility of the attack techniques and proposed a group similarity score based on this. Experimental results show that the proposed method classified the attack group with a 72.62% probability based on Top-5 accuracy.

• Journal of the Korean Society of Radiology
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• v.16 no.1
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• pp.25-34
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• 2022

Brain computed tomography (CT) is useful for brain lesion diagnosis, such as brain hemorrhage, due to non-invasive methodology, 3-dimensional image provision, low radiation dose. However, there has been numerous misdiagnosis owing to a lack of radiologist and heavy workload. Recently, object detection technologies based on artificial intelligence have been developed in order to overcome the limitations of traditional diagnosis. In this study, the applicability of a deep learning-based YOLOv5s model was evaluated for brain hemorrhage detection using brain CT images. Also, the effect of hyperparameters in the trained YOLOv5s model was analyzed. The YOLOv5s model consisted of backbone, neck and output modules. The trained model was able to detect a region of brain hemorrhage and provide the information of the region. The YOLOv5s model was trained with various activation functions, optimizer functions, loss functions and epochs, and the performance of the trained model was evaluated in terms of brain hemorrhage detection accuracy and training time. The results showed that the trained YOLOv5s model is able to provide a bounding box for a region of brain hemorrhage and the accuracy of the corresponding box. The performance of the YOLOv5s model was improved by using the mish activation function, the stochastic gradient descent (SGD) optimizer function and the completed intersection over union (CIoU) loss function. Also, the accuracy and training time of the YOLOv5s model increased with the number of epochs. Therefore, the YOLOv5s model is suitable for brain hemorrhage detection using brain CT images, and the performance of the model can be maximized by using appropriate hyperparameters.