• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.38-49
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• 2021

This study analyzed the trend of the term 'false and exaggerated advertisement' in 5,141 newspaper articles from 1990 to 2019 using text mining methodology. First of all, we identified the most frequent keywords of false and exaggerated advertisements through frequency analysis for all newspaper articles, and understood the context between the extracted keywords. Next, to examine how false and exaggerated advertisements have changed, the frequency analysis was performed by separating articles by 10 years, and the tendency of the keyword that became an issue was identified by comparing the number of academic papers on the subject of the highest keywords of each year. Finally, we identified trends in false and exaggerated advertisements based on the detailed keywords in the topic using the topic modeling. In our results, it was confirmed that the topic that became an issue at a specific time was extracted as the frequent keywords, and the keyword trends by period changed in connection with social and environmental factors. This study is meaningful in helping consumers spend wisely by cultivating background knowledge about unfair advertising. Furthermore, it is expected that the core keyword extraction will provide the true purpose of advertising and deliver its implications to companies and related employees who commit misconduct.

• Journal of Life Science
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• v.31 no.6
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• pp.587-594
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• 2021

By 2050, 70% more food will be needed to fulfill the demands of a growing population. Among the solutions, cultured meat or clean meat is presented as a sustainable alternative for consumers. Scientists have begun to leverage knowledge and tools accumulated in the fields of stem cell and tissue engineering in efforts aimed at the development of cell-based meat. Cultured meat has to recreate the complex structure of livestock muscles with a few cells. Cells start to divide after they are cultured in a culture medium, which provides nutrients, hormones, and growth factors. An initial problem with this type of culture is the serum used, as in vitro meat aims to be slaughter free. Thus, it is contradictory to use a medium made from the blood of dead calves. The serum is expensive and affects to a large extent the production cost of the meat. A positive aspect related to the safety of cultured meat is that it is not produced from animals raised in confined spaces and slaughtered in inhumane conditions. Thus, the risk of an outbreak is eliminated, and there is no need for vaccinations and animal welfare issues. The production of cultured meat is presented as environmentally friendly, as it is supposed to produce less greenhouse gas, consume less water, and use less land in comparison to conventional meat production.

• The Journal of the Convergence on Culture Technology
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• v.8 no.2
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• pp.117-129
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• 2022

The ability to creatively pursue new knowledge and perspectives across various disciplines has established itself as a basic literacy for living in the 21st-century convergence era. With the development of various creative education programs, assessment tools that can objectively and systematically evaluate learners' academic achievement are also required. Therefore, this paper proposed the self assessment, peer assessment, creativity assessment, and reflection tool based on the hyper blended practical model as assessment tools for creative convergence education programs for elementary and secondary school students. The developed assessment tools attempted to develop more completed evaluation methods by modifying two items and deleting four items through validity tests. In addition, the evaluation tool was applied to 596 elementary and secondary school students nationwide, and the application results were analyzed through one-way ANOVA and Wordcloud system. As a result of the analysis, it was found that the self assessment and the reflection tool need to develop questions according to the grade group. In addition, we proposed to use these assessment tools in blended classes or various educational activities in the changing classroom environment. We hope that this paper provides implications for developing evaluation systems and tools for creative convergence education.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.4
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• pp.341-353
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• 2022

Pipelines are buried in urban area, and the position (depth and orientation) of buried pipeline should be clearly identified before ground excavation. Although various geophysical methods can be used to detect the buried pipeline, it is not easy to identify the exact information of pipeline due to heterogeneous ground condition. Among various non-destructive geo-exploration methods, ground penetration radar (GPR) can explore the ground subsurface rapidly with relatively low cost compared to other exploration methods. However, the exploration data obtained from GPR requires considerable experiences because interpretation is not intuitive. Recently, researches on automated detection technology for GPR data using deep learning have been conducted. However, the lack of GPR data which is essential for training makes it difficult to build up the reliable detection model. To overcome this problem, we conducted a preliminary study to improve the performance of the detection model using finite difference time domain (FDTD)-based numerical analysis. Firstly, numerical analysis was performed with homogeneous soil media having single permittivity. In case of heterogeneous ground, numerical analysis was performed considering the ground heterogeneity using fractal technique. Secondly, deep learning was carried out using convolutional neural network. Detection Model-A is trained with data set obtained from homogeneous ground. And, detection Model-B is trained with data set obtained from homogeneous ground and heterogeneous ground. As a result, it is found that the detection Model-B which is trained including heterogeneous ground shows better performance than detection Model-A. It indicates the ground heterogeneity should be considered to increase the performance of automated detection model for GPR exploration.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.333-349
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• 2021

As information is recognized as an important asset of an organization, organizations are increasing their resource input for knowledge management. In particular, the enterprise content management system(ECMS) is a solution for organization-oriented content management, and it has high utility by helping to achieve business performance through systematic utilization of content and improve the level of internal information security. The purpose of this study is to suggest a plan to improve the intention to use organizational employee's ECMS and to suggest the effect of the relationships between information system quality characteristics and work environment characteristics on intention to use. In this study, a research hypothesis was presented based on previous studies, a questionnaire was conducted on workers of organizations that adopted an ECMS, and the hypothesis was verified by applying structural equation modeling. As a result of the analysis, information and service quality of the ECMS and task interdependence increased the intention to use, but task conflict decreased the intention to use. In addition, task interdependence and task conflict moderated the positive relationship between the quality factors of the ECMS and the intention to use it. This study has implications in terms of suggesting the direction of the organization's behavior through factors that increase the use of ECMS.

• Journal of Digital Convergence
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• v.20 no.2
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• pp.203-215
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• 2022

The study is to investigate research trends and knowledge structures in the Smart Farm field. To achieve the research purpose, keywords and the relationship among keywords were analyzed targeting 104 Korean academic journals related to the Smart Farm in KCI(Korea Citation Index), and topics were analyzed using the LDA Topic Modeling technique. As a result of the analysis, the main keywords in the Korean Smart Farm-related research field were 'environment', 'system', 'use', 'technology', 'cultivation', etc. The results of Degree, Betweenness, and Eigenvector Centrality were presented. There were 7 topics, such as 'Introduction analysis of Smart Farm', 'Eco-friendly Smart Farm and economic efficiency of Smart Farm', 'Smart Farm platform design', 'Smart Farm production optimization', 'Smart Farm ecosystem', 'Smart Farm system implementation', and 'Government policy for Smart Farm' in the results of Topic Modeling. This study will be expected to serve as basic data for policy development necessary to advance Korean Smart Farm research in the future by examining research trends related to Korean Smart Farm.

• Ecology and Resilient Infrastructure
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• v.9 no.4
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• pp.237-246
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• 2022

Owing to their high bioproductivity and unique physical environment, estuarine wetlands are gaining importance in national biodiversity management and habitat conservation. With regard to conservation and management of estuarine wetlands, this study analyzed the ecological characteristics of Gahwacheon Estuarine Wetland, an open estuary with various habitat types. Data from vegetation and biotic surveys have shown that 12 plant communities of five physiognomic vegetation types, including lentic herbaceous vegetation, halophytic herbaceous vegetation, and chasmophytic herbaceous vegetation. Due to the discharge of Namgang Dam and the effect of the tide, vegetation are distributed along the narrow waterside area. In terms of biodiversity, a total of 715 species, including 12 endangered wildlife species, were identified. Species diversity was relatively high in sections I and III where various riverbed structures and microhabitats were distributed. Due to the effect of the brackish water area following the inflow of seawater, endangered wildlife of various functional groups were also found to be distributed, indicating the high conservation value of that area. The collection of ecological information of the Gahwacheon Estuarine Wetland can be used as a framework for establishing the basis for conservation and management of the estuarine ecosystem and support policy establishment.

• Korean Journal of Culture and Social Issue
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• v.28 no.3
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• pp.513-527
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• 2022

This study was conducted to examining the socio-cultural impact of the COVID-19 pandemic that swept the world around 2020, and the transformation of norms and social problems due to COVID-19. For this, the characteristics of changes in the socio-cultural norms of the 14th century European Black Death, a representative example of the pandemic, were derived, and based on this, the COVID-19 pandemic was analyzed. The Black Death served as an opportunity to change social norms based on the existing religious authority and the power of the feudal system to the Enlightenment. The population declination and labor shortage also promoted commercialization and mechanization. Printing, which spread during this period, led to the popularization of knowledge, which raised the level of thinking and led to epochal scientific development. This became the foundation of the Industrial Revolution. Like the recent Black Death, COVID-19 has triggered changes in social norms. The technological environment of metaverse, a mixture of virtual and reality, has changed the norm of a consistent identity into free and open identities exerting various potentials through alternate characters. In addition, meme, which are about people being friendly to those with the same worldview as him on the metaverse, weakened the sense of isolation in non-face-to-face situations. Artificial intelligence (AI), which developed during the COVID-19 pandemic, has entered the stage of being used for creative activities beyond the function of assisting humans. Discussions were held on what new social problems would be created by the social norms changed due to the COVID-19 pandemic.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.449-456
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• 2021

An intrusion detection system is a technology that detects abnormal behaviors that violate security, and detects abnormal operations and prevents system attacks. Existing intrusion detection systems have been designed using statistical analysis or anomaly detection techniques for traffic patterns, but modern systems generate a variety of traffic different from existing systems due to rapidly growing technologies, so the existing methods have limitations. In order to overcome this limitation, study on intrusion detection methods applying various machine learning techniques is being actively conducted. In this study, a comparative study was conducted on data preprocessing techniques that can improve the accuracy of anomaly detection using NGIDS-DS (Next Generation IDS Database) generated by simulation equipment for traffic in various network environments. Padding and sliding window were used as data preprocessing, and an oversampling technique with Adversarial Auto-Encoder (AAE) was applied to solve the problem of imbalance between the normal data rate and the abnormal data rate. In addition, the performance improvement of detection accuracy was confirmed by using Skip-gram among the Word2Vec techniques that can extract feature vectors of preprocessed sequence data. PCA-SVM and GRU were used as models for comparative experiments, and the experimental results showed better performance when sliding window, skip-gram, AAE, and GRU were applied.

• Journal of Science Education
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• v.46 no.3
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• pp.255-265
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• 2022

The purpose of this study is to analyze and compare brain activation that appears in the self-regulation process of biology major and non-major college students in life science learning. The self-regulation task implemented a life science learning situation with the concept of biological classification. The brain activation of college students was measured and analyzed by fNIRS. In the assimilation process, bilateral FP and left DLPFC show significant activation, and the two groups show a difference in the left OFC activation related to motivation and reward. In the conflict process, the left DLPFC shows significantly lower activation in common, and the two groups show a difference in activation between BA 46, which is related to recent memory, and BA 47, which is related to long-term memory. In the accommodation process, a significantly high activation was found in right DLPFC in common, and the two groups show a difference in activation between right DLPFC and right FP. These areas are in the right frontal lobe area and are related to the understanding of life science knowledge. As a result of this study, it can be seen that the brain activation patterns of biology major and non-major college students are different in the self-regulation process. In addition, we will propose additional neurological studies on self-regulation and present systems and learning strategies that can be constructed in school settings.