• Journal of Digital Convergence
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• v.15 no.2
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• pp.527-535
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• 2017

As interest increases in health promotion through sports, demand for public sports facilities is steadily growing. However, there is a lack of research on operation and management compared with the supply plan of public sports facility. In this context, the aim of this study is to address problems of management of public sports centers and suggest strategies for vitalizing the facilities through the big-data. The data are collected from web such as news, blog, and cafe for one year in 2015. From the big-data, We can find that the national sports centers and the open gyms showed similar users' behavior but showed different needs. Both facilities have been used as sports and leisure area and have a high percentage of visitors for other purposes such as walking, picnics, etc. However, while the national sports facilities which were used for more specialized programs, the open sports center were used as leisure space.

• The Journal of the Korea Contents Association
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• v.17 no.9
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• pp.173-183
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• 2017

In this research, analysis based on VR journalism outlet the New York Times' was carried out. The image analysis of was done through the frames of angle, shot (size, length, movement), and limited user-directed interaction (point, sound). The result of this is as follows. Firstly, the direction was done using a basis of normal and low angles. Secondly, it was able to be confirmed that the shooting was done in order by medium, full, and long shot. Thirdly, with regard to the length of the shot, most direction was done through long takes. Fourthly, most images came to consist of fixed shots. Lastly, this is limited user-directed interaction. This may be separated into 2 aspects: sound, and movement of the independent free agent. Through these, interaction was guided through free point of view concerning realistic situations to point of view guidance and users. This research may be referred to as foundational research for the further advancement of in-depth discussion pertaining to VR journalism.

• Hwankyungkyoyuk
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• v.21 no.2
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• pp.51-67
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• 2008

The purpose of this study is to develop and applicate the elementary environmental textbook in order to solve its problem and to improve attitude related to the Asian dust. The results in this research are as follows. First, it was showed that three groups who composed of teachers, parents and students in the elementary school had recognized the serosities and problems caused by the Asian dust form TV, and that such problems was associated with increase of the desertification and the global warming. Especially the student group insist that the cause in Asian dust is due to the natural phenomena or industrialization. Second, as a result in analysis on the Asian dust through both textbooks on the 7th elementary curriculum and subsidiary textbooks, contents concerning Asian dusts was little or noting. In addition, in the subjects of Science, Society and Health for the 5th and 6th grade students in the elementary school, they were explained partially as one of the air pollutants. Third, the elementary environmental textbook on the Asian dust was developed for the 5th and 6th grade students. The textbook is composed of four contents on the material which is harmful of the human health and life in Asian dust, the special news of Asian dust, and the best answer to solve Asian dust as well as the cause and the source of Asian dust. Forth, as a result in classes using the environmental textbook developed by four themes about the Asian dust, its application is meaningful in the level of p value in the view of knowledge, awareness and attitude of the experiment group. They was more improved in 37%, 14%, and 15%, respectively, than the comparative group. In conclusion, the environmental textbook related to Asian dust will play an important role in useful tool to understand the right knowledge, awareness, and attitude which makes an effort on its effective management in the elementary school.

• Journal of Korean Academy of Nursing
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• v.51 no.1
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• pp.68-79
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• 2021

Purpose: This study aimed to analyze the mass and social media contents and structures related to particulate matter before and after the policy enforcement of the comprehensive countermeasures for particulate matter, derive nursing implications, and provide a basis for designing health policies. Methods: After crawling online news articles and posts on social networking sites before and after policy enforcement with particulate matter as keywords, we conducted topic and semantic network analysis using TEXTOM, R, and UCINET 6. Results: In topic analysis, behavior tips was the common main topic in both media before and after the policy enforcement. After the policy enforcement, influence on health disappeared from the main topics due to increased reports about reduction measures and government in mass media, whereas influence on health appeared as the main topic in social media. However semantic network analysis confirmed that social media had much number of nodes and links and lower centrality than mass media, leaving substantial information that was not organically connected and unstructured. Conclusion: Understanding of particulate matter policy and implications influence health, as well as gaps in the needs and use of health information, should be integrated with leadership and supports in the nurses' care of vulnerable patients and public health promotion.

• Journal of English Language & Literature
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• v.57 no.1
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• pp.27-49
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• 2011

This article proposes to stay away from contemporary critical arguments concerning Michael's value system, which is construed mainly from his choice between his patrimonial lands and his son Luke. Presuming that Michael's value system as have been argued so far could never be the poet Wordsworth's own concern at the time of the composition of the poem "Michael," this article proposes to get back to the all too real socio-historical situation of the early nineteenth-century England. Mere consideration of the socio-historical situation, when combined with a close reading of the poetic text (a close reading of both the poetic story and the poetic history from which the story may be said to have been constructed), directs us to the poet working on the simple paradigm of 'the country and the city at war with each other' but the victory having been given to the city already. The guarantee contract for a supposedly prospering nephew's debt and the letter from another prospering relative in London are undoubtedly the key elements that lead us to the war paradigm. Michael's family members, each and all including Michael himself, and all of their village people, have been imbued with the city's commercial values, which renders them all the more easier victims within the war context. Luke's defeat in the city is viewed as being really the consequence, rather than the cause, of Michael's defeat, which became apparent as soon as the news of the latter's financial disaster reached his ear. Michael should therefore be regarded as one of the typical English countryfolk of the time, with whom Wordsworth often, but not always, identifies himself. Insofar as the economic view or attitude is concerned, there certainly is a distance between Michael and Wordsworth, this article argues.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.35-44
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• 2020

In this study, we analyze how stock trend determination affects trend prediction accuracy. In stock markets, successful investment requires accurate stock price trend prediction. Therefore, a volume of research has been conducted to improve the trend prediction accuracy. For example, information extracted from SNS (social networking service) and news articles by text mining algorithms is used to enhance the prediction accuracy. Moreover, various machine learning algorithms have been utilized. However, stock trend determination has not been properly analyzed, and conventionally used methods have been employed repeatedly. For this reason, we formulate the trend determination as a moving average-based procedure and analyze its impact on stock trend prediction accuracy. The analysis reveals that trend determination makes prediction accuracy vary as much as 47% and that prediction accuracy is proportional to and inversely proportional to reference window size and target window size, respectively.

• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.375-380
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• 2022

The purpose of this study is to find out consumers' perception and meaning of convenience store convenience food by using big data. For this study, NNAVER and Daum analyzed news, intellectuals, blogs, cafes, intellectuals(tips), and web documents, and used 'convenience store convenience food' as keywords for data search. The data analysis period was selected as 3 years from January 1, 2019 to December 31, 2021. For data collection and analysis, frequency and matrix data were extracted using TEXTOM, and network analysis and visualization analysis were conducted using the NetDraw function of the UCINET 6 program. As a result, convenience store convenience foods were clustered into health, diversity, convenience, and economy according to consumers' selection attributes. It is expected to be the basis for the development of a new convenience menu that pursues convenience and convenience based on consumers' meaning of convenience store convenience foods such as appropriate prices, discount coupons, and events.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.175-182
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• 2022

In our daily life, we come across different types of information, for example in the format of multimedia and text. We all need different types of information for our common routines as watching/reading the news, listening to the radio, and watching different types of videos. However, sometimes we could run into problems when a certain type of information is required. For example, someone is listening to the radio and wants to listen to jazz, and unfortunately, all the radio channels play pop music mixed with advertisements. The listener gets stuck with pop music and gives up searching for jazz. So, the above example can be solved with an automatic audio classification system. Deep Learning (DL) models could make human life easy by using audio classifications, but it is expensive and difficult to deploy such models at edge devices like nano BLE sense raspberry pi, because these models require huge computational power like graphics processing unit (G.P.U), to solve the problem, we proposed DL model. In our proposed work, we had gone for a low complexity model for Audio Event Detection (AED), we extracted Mel-spectrograms of dimension 128×431×1 from audio signals and applied normalization. A total of 3 data augmentation methods were applied as follows: frequency masking, time masking, and mixup. In addition, we designed Convolutional Neural Network (CNN) with spatial dropout, batch normalization, and separable 2D inspired by VGGnet [1]. In addition, we reduced the model size by using model quantization of float16 to the trained model. Experiments were conducted on the updated dataset provided by the Detection and Classification of Acoustic Events and Scenes (DCASE) 2020 challenge. We confirm that our model achieved a val_loss of 0.33 and an accuracy of 90.34% within the 132.50KB model size.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.1
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• pp.41-50
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• 2024

Korea is facing a significant problem with historically low fertility rates, which is becoming a major social issue affecting the economy, labor force, and national security. This study analyzes the factors contributing to the regional gap in fertility rates and derives policy implications. The government and local authorities are implementing a range of policies to address the issue of low fertility. To establish an effective strategy, it is essential to identify the primary factors that contribute to regional disparities. This study identifies these factors and explores policy implications through machine learning and explainable artificial intelligence. The study also examines the influence of media and public opinion on childbirth in Korea by incorporating news and online community sentiment, as well as sentiment fear indices, as independent variables. To establish the relationship between regional fertility rates and factors, the study employs four machine learning models: multiple linear regression, XGBoost, Random Forest, and Support Vector Regression. Support Vector Regression, XGBoost, and Random Forest significantly outperform linear regression, highlighting the importance of machine learning models in explaining non-linear relationships with numerous variables. A factor analysis using SHAP is then conducted. The unemployment rate, Regional Gross Domestic Product per Capita, Women's Participation in Economic Activities, Number of Crimes Committed, Average Age of First Marriage, and Private Education Expenses significantly impact regional fertility rates. However, the degree of impact of the factors affecting fertility may vary by region, suggesting the need for policies tailored to the characteristics of each region, not just an overall ranking of factors.

• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.113-120
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• 2024

With the advancement of information and communication technology, we can easily generate various forms of data in our daily lives. To efficiently manage such a large amount of data, systematic classification into categories is essential. For effective search and navigation, data is organized into a tree-like hierarchical structure known as a category tree, which is commonly seen in news websites and Wikipedia. As a result, various techniques have been proposed to classify large volumes of documents into the terminal nodes of category trees. However, document classification methods using category trees face a problem: as the height of the tree increases, the number of terminal nodes multiplies exponentially, which increases the probability of misclassification and ultimately leads to a reduction in classification accuracy. Therefore, in this paper, we propose a new node expansion-based classification algorithm that satisfies the classification accuracy required by the application, while enabling detailed categorization. The proposed method uses a greedy approach to prioritize the expansion of nodes with high classification accuracy, thereby maximizing the overall classification accuracy of the category tree. Experimental results on real data show that the proposed technique provides improved performance over naive methods.