• Journal of Korean society of Dental Hygiene
• /
• v.21 no.4
• /
• pp.467-479
• /
• 2021

Objectives: The purpose of this study was to investigate the awareness of the performance and necessity of oral health promotion activities and seek ways to revitalize the professional role of dental hygienists in oral healthcare for the efficient oral care of the elderly. Methods: Eighty-five dental hygienists in charge of oral health promotion projects at public health centers and 38 dental hygienists in network dental clinical trials were investigated for their learning experience, performance experience, feasibility, and necessity of dental hygienists for general and oral health service items for the elderly. The collected data were analyzed using frequency analysis, chi-square test, and Mann-Whitney U test. Results: The degree of performance possibility according to the learning experience and performance experience of the dental hygienist for the whole body and oral health promotion activity items for the elderly showed that the degree of performance possibility was higher among those with experience compared to the non-experienced person, and it was statistically significantly higher (p<0.05). Conclusions: The dental hygienist's professional oral health service is a necessary system to improve practical knowledge and skills and to provide a wide range of professional oral health services for the elderly.

• The Journal of the Korea Contents Association
• /
• v.21 no.5
• /
• pp.561-580
• /
• 2021

This study analyses the historical and cultural streets at Pinggang Road in the city of Suzhou, by understanding the development and conservation of the area, and uses the following ways to investigate its development, re-organization, and current state. This paper comprehensively compares, collates and investigates 4 different historical and cultural areas in Insadong and Samcheong-dong in South Korea, and South Luogu Lane in China. From initial research and analysis, this paper gathers the cultural, economic, and societal perspectives as non-physical measures, and spatial structure, road structure, and building maintenance as physical factor framework. It is significant in that it can provide an evaluation model for the preservation and regeneration of historical and cultural streets by presenting the viewpoint of complex development of non-physical and physical elements in Pyeonggang-ro. In addition, it is necessary to conduct optimization and specific research on insufficient areas, such as maintenance and development of programs and signature systems for visitors, and continuous development of historical and cultural network platforms by combining on-site surveys. Basic data should be provided for reference on the street.

• Journal of the Korean Society for information Management
• /
• v.38 no.2
• /
• pp.65-86
• /
• 2021

In the foreign language education, learners are an important part of education, however in the Korean language education, the study of learners was insufficient compared to the contents of education, teaching methods and textbooks. Therefore, it is meaningful to analyze how learner research, especially learning strategy research, has been conducted and derive areas that need research for better education. In this study, co-word analysis was conducted on the titles of academic journals and dissertations in order to analyze the learning strategy research in Korean language education. I found it is about "reading" that the most studies related to Korean language learners' learning strategies were conducted and those studies' subjects mostly were 'Chinese international students' and 'marriage-immigrants'. In addition, the results of the subgroup analysis on the research topic show four major subgroups: a group related to 'reading for academic purposes', a group related to 'request, rejection, conversation, etc.', a group related to 'writing', and a group related to 'vocabulary, listening'. This shows that the researchers' major interests in studying Korean learner's strategies are "reading" and "speaking" and their studies have been concentrated in the specific areas. Therefore, it is necessary for researchers to study various functions and subjects in Korean language learner's learning strategies.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.24 no.6
• /
• pp.1-13
• /
• 2021

This study was performed to investigate the ecosystem service patterns in relation to climate change acceleration utilizing big data analysis. This study aimed to use big data analysis as one of the network of views to identify convergent thinking in two fields: climate change and ecosystem service. The keywords were analysed to ascertain if there were any differences in the perceiving problems, policy direction, climate change implications, and regional differences. In addition, we examined the research keywords of each continent, the centre of ecosystem service research, and the topics to be referred to in domestic research. The results of the analysis are as follows: First, the keyword centrality of climate change is similar to the detailed indicators of The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) regulations, content, and non-material ecosystem services. Second, the cross-analysis of terms in two journals showed a difference in value-oriented point; the Ecosystem Service Journal identified green infrastructure as having economic value, whereas the Climate Change Journal perceives water, forest, carbon, and biodiversity as management topics. The Climate Change Journal, but not the former, focuses on future predictions. Third, the analysis of the research topics according to continents showed that water and soil are closely related to the economy, and thus, play an important role in policy formulation. This disparity is due to differences in each continent's environmental characteristics, as well as economic and policy issues. This fact can be used to refer to the direction of research on ecosystem services in Korea. Consistent with the recent trend of expanding research regarding the impacts of climate change, it is necessary to study strategies to scientifically predict and respond to the negative effects of climate change.

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.3
• /
• pp.141-148
• /
• 2022

Recently, reinforcement learning combined with deep neural network technology has achieved remarkable success in various fields such as board games such as Go and chess, computer games such as Atari and StartCraft, and robot object manipulation tasks. However, such deep reinforcement learning describes states, actions, and policies in vector representation. Therefore, the existing deep reinforcement learning has some limitations in generality and interpretability of the learned policy, and it is difficult to effectively incorporate domain knowledge into policy learning. On the other hand, dNL-RRL, a new relational reinforcement learning framework proposed to solve these problems, uses a kind of vector representation for sensor input data and lower-level motion control as in the existing deep reinforcement learning. However, for states, actions, and learned policies, It uses a relational representation with logic predicates and rules. In this paper, we present dNL-RRL-based policy learning for transportation mobile robots in a manufacturing environment. In particular, this study proposes a effective method to utilize the prior domain knowledge of human experts to improve the efficiency of relational reinforcement learning. Through various experiments, we demonstrate the performance improvement of the relational reinforcement learning by using domain knowledge as proposed in this paper.

• Land and Housing Review
• /
• v.13 no.1
• /
• pp.67-84
• /
• 2022

This study aims to investigate the value of greenbelts exploring how they can be linked with green infrastructure networks. This research interprets the results of geographical information system (GIS) analysis differently from a conventional approach. The findings of the research are four-fold based on the analysis of the Daejeon Metropolitan Area. First, the most controversial greenbelts are laid on Yuseong-gu because the relaxation of the greenbelts for new housing development has caused outstanding issues since the early 2000s. Decisions on further relaxation or restoration of the greenbelts, which will provide a new direction for the establishment of green infrastructure networks, should be made through accurate environmental assessments. Second, the connected north-south corridors of large cities will affect the greenbelts not only in Daejeon, but also in the entire Chungcheong Provinces, and surrounding local municipalities, which should be considered for the revision of the greenbelt policy. Third, it is expected to experience growing development pressures towards neighboring municipalities due to the ongoing strict greenbelt policy. Among them, the most likely areas are Sejong City to the north and Nonsan to the south, requiring policy measures. Fourth, the value of green infrastructure should be added to current evaluation criteria rather than a binary approach - relaxing or preserving the greenbelts - to be holistically integrated with a metropolitan plan.

• Journal of Broadcast Engineering
• /
• v.27 no.3
• /
• pp.318-331
• /
• 2022

With the recent development of hardware computing devices and software based frameworks, machine tasks using deep learning networks are expected to be utilized in various industrial fields and personal IoT devices. However, in order to overcome the limitations of high cost device for utilizing the deep learning network and that the user may not receive the results requested when only the machine task results are transmitted from the server, Collaborative Intelligence (CI) proposed the transmission of feature maps as a solution. In this paper, an efficient compression method for feature maps with vast data sizes to support the CI paradigm was analyzed and presented through experiments. This method increases redundancy by applying feature map reordering to improve compression efficiency in traditional video codecs, and proposes a feature map method that improves compression efficiency and maintains the performance of machine tasks by simultaneously utilizing image compression format and video compression format. As a result of the experiment, the proposed method shows 14.29% gain in BD-rate of BPP and mAP compared to the feature compression anchor of MPEG-VCM.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.3
• /
• pp.53-61
• /
• 2022

Recent years have drawn a great attention to generation MZ and Metaverse, due to 4th industrial revolution and the development of digital environment that blurs the boundary between reality and virtual reality. Generation MZ approaches the information very differently from the existing generations and uses distinguished communication methods. In terms of learning, they have different motivations, types, skills and build relationships differently. Meanwhile, Metaverse is drawing a great attention as a teaching method that fits traits of gen MZ. Thus, the current research aimed to investigate how to increase the use of Metaverse in Educational Technology. Specifically, this research examined the antecedents of popularity of Gather Town, a platform of Metaverse. Big data of news articles have been collected and analyzed using the Bigkinds system provided by Korea Press Foundation. The analysis revealed, first, a rapid increasing trend of media exposure of Gather Town since July 2021. This suggests a greater utilization of Gather Town in the field of education after the COVID-19 pandemic. Second, Word Association Analysis and Word Cloud Analysis showed high weights on education related words such as 'remote', 'university', and 'freshman', while words like 'Metaverse', 'Metaverse platform', 'Covid19', and 'Avatar' were also emphasized. Third, Network Analysis extracted 'COVID19', 'Avatar', 'University student', 'career', 'YouTube' as keywords. The findings also suggest potential value of Gather Town as an educational tool under COVID19 pandemic. Therefore, this research will contribute to the application and utilization of Gather Town in the field of education.

• Journal of the Korean earth science society
• /
• v.43 no.1
• /
• pp.165-175
• /
• 2022

Earthquakes have occurred owing to movements on a fault since several billion years ago. Research on the relationship between earthquakes and groundwater began in the 1960s in the United States, but related works, including hydrogeochemistry research, only began in the 2010s in South Korea. In this study, domestic studies on the relationship between earthquakes and groundwater until 2021 were collected from the Web of Science and characterized by subject area (groundwater level, hydrogeochemistry, combination of the two, and others). The results showed that the number of published articles per year was positively correlated with the 2011 Tohoku earthquake, 2016 Gyeongju earthquake, and 2017 Pohang earthquake, with the maximum numbers observed in 2011, 2018, 2019, and 2020. Most studies on the relationship between earthquakes and groundwater level addressed groundwater level fluctuations in the duration of the subject earthquake, with little consideration of the precursors. Groundwater level monitoring data, as well as hydrogeochemical information and microbial communities, may contribute to a more detailed understanding of groundwater flow and chemical reactions in bedrock caused by earthquakes. Therefore, the establishment of a national groundwater monitoring network for seismic monitoring and prediction is required.

• The Journal of Society for e-Business Studies
• /
• v.27 no.1
• /
• pp.15-28
• /
• 2022

Due to recent expansion of online market such as clothing, utilizing customer review has become a major marketing measure. User review has been used as a tool of analyzing sentiment of customers. Sentiment analysis can be largely classified with machine learning-based and lexicon-based method. Machine learning-based method is a learning classification model referring review and labels. As research of sentiment analysis has been developed, multi-modal models learned by images and video data in reviews has been studied. Characteristics of words in reviews are differentiated depending on products' and customers' categories. In this paper, sentiment is analyzed via considering review data and metadata of products and users. Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Self Attention-based Multi-head Attention models and Bidirectional Encoder Representation from Transformer (BERT) are used in this study. Same Multi-Layer Perceptron (MLP) model is used upon every products information. This paper suggests a multi-modal sentiment analysis model that simultaneously considers user reviews and product meta-information.