• Journal of the Health Care and Life Science
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• v.10 no.2
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• pp.329-339
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• 2022

The purpose of this study is to examine the experiences of new teachers in the process of preparing and implementing the 2019 revised Nuri curriculum, and to find ways to support novice teachers. For these, Data were collected through telephone interviews with 12 first-time teachers with less than 2 years of experience at private kindergartens and daycare centers in Chungnam and Daejeon, and the collected data was analyzed through text network analysis. The results are as follows. First, teachers were worried that they did not know the details of the curriculum while preparing for the 2019 revised Nuri curriculum. To supplement this, they were preparing to observe infants, share information with fellow teachers, and refer to the Nuri curriculum commentary. Also, teachers thought that they were getting help in terms of indirectly experiencing actual play cases through training. Second, the first-time teachers were providing various support by focusing on children's play while implementing the Nuri curriculum. The teachers emphasized that the good point of implementing the Nuri curriculum is that children's interests and thoughts are taken into consideration, and that children-centered play can be carried out, and that the teachers can support children's play while thinking about it. And teachers mentioned the difficulties of reading children's thoughts in children's play, grasping the topic and proceeding with the play, and the lack of time to play. In addition, starting teachers were referring to internet resources and sharing opinions with fellow teachers, and mentioned that direct experience helped them to implement the play. Lastly, teachers' interest in and observation of play, creative thinking, quickness, and willingness to support play were considered important as the competency required for teachers.

• Journal of Korea Port Economic Association
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• v.40 no.1
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• pp.31-59
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• 2024

In June 2016, the term "Fourth Industrial Revolution" was first used at the World Economic Forum in Davos, Switzerland, and it gained worldwide attention. Consequently, the importance of smart ports has increased as the shipping industry has been incorporating various Fourth Industrial Revolution technologies. Currently, major countries around the world are working to achieve digital transformation in the maritime and port industry by establishing comprehensive smart ports. However, the smartification of domestic ports in South Korea is currently limited to a few areas such as Busan, Incheon, and Gwangyang, focusing on port automation. In this context, this study performed keyword analysis to identify key components of smart ports and conducted Analytic Hierarchy Process (AHP) analysis among relevant stakeholders to determine the priorities for the Introduction of smart ports in South Korea. The analysis revealed that universities prioritized automation, intelligenceization, informatization and environmentalization in that order. Research institutes prioritized informatization, intelligenceization, automation and environmentalization. Government agencies prioritized informatization, automation, intelligenceization and environmentalization, while private sector enterprises prioritized automation, intelligenceization, informatization, and environmentalization.

• Journal of Bio-Environment Control
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• v.33 no.2
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• pp.120-128
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• 2024

Since ChatGPT was released in 2022, the generative artificial intelligence (AI) industry has seen massive growth and is expected to bring significant innovations to cognitive tasks. AI-based image generation, in particular, is leading major changes in the digital world. This study investigates the technical foundations of Midjourney, Stable Diffusion, and Firefly-three notable AI image generation tools-and compares their effectiveness by examining the images they produce. The results show that these AI tools can generate realistic images of tomatoes, strawberries, paprikas, and cucumbers, typical crops grown in greenhouse. Especially, Firefly stood out for its ability to produce very realistic images of greenhouse-grown crops. However, all tools struggled to fully capture the environmental context of greenhouses where these crops grow. The process of refining prompts and using reference images has proven effective in accurately generating images of strawberry fruits and their cultivation systems. In the case of generating cucumber images, the AI tools produced images very close to real ones, with no significant differences found in their evaluation scores. This study demonstrates how AI-based image generation technology can be applied in agriculture, suggesting a bright future for its use in this field.

• 유학연구
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• v.49
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• pp.1-23
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• 2019

The purpose of this paper is to examine the aspects and contents of the Nine Chinese Classics, especially those cited in the writings of women during the Chosun Dynasty. Considering that the foundation of the studies and political ideology in Chosun Dynasty is based on the Confucian scriptures and annotations, it is very important to look at how the Nine Chinese Classics were quoted and mentioned in the writings of Chosun women and to examine what their implications are. The main content of this paper is to take note of Queen Sohye's Naehoon to examine in what context the Nine Chinese Classics are quoted and read. Taking into consideration the historical context in which women's reading and knowledge activities were not welcome and the only books women had access to were women's discipline books, women's discipline books were not only just the text for disciplining women but also a mechanism for women to access Confucian scriptures. Through these discussions, the knowledge of traditional women can be re-examined and women can be established as the subject of recognition. By doing so, an opportunity is provided to expand the contents of Confucianism in Chosun Dynasty, which were centered on knowledge of male Confucian scholars until present. It may also address issues such as Chosun women's knowledge experience and activities of the Nine Chinese Classics, as well as the political desire of women inherent in them.

• CHINESE LITERATURE
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• v.100
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• pp.85 -100
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• 2019

In the 21st century, there was a boom of research in the field of Taiwanese historical narrative on Netherland's colonial rule of Taiwan in the 17th century. Such trend expanded to the literature field, and creations were made using the forgotten historical material. Representative works are 《婆 娑之島》 and 《福爾摩沙三族記》, written by Ping Lu and Chen Yao-chang. By analyzing these texts, this study studies what kind of a message the author presents through the literary form, and what his view is on the present and the future. In 《婆娑之島》, Ping Lu presents the fate of Taiwan being in the midst of imperialism for 400 years, and points out the betrayal of imperialism on Taiwan. Ping Lu argues that in order for Taiwan to evade from such situation, the future of Taiwan should be discussed in the global context, outside of the limited perspective of "China/Taiwan". Chen Yao-chang established the Tiwanese identity based on the multiple history narrative, by referencing to the spacial identity instead of the innate kinship. Such establishment of identity gave a breakthrough to the identity politics in Taiwan. Amidst the rapidly changing world politics and economy, it is essential for Taiwan to reestablish the cultural identity for itself for its development. What is more important is that the multiple history narrative maintains openness of history and communicates with the grand narrative to prevent history from being baised.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.69-94
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• 2017

Recently, increase of demand for big data analysis has been driving the vigorous development of related technologies and tools. In addition, development of IT and increased penetration rate of smart devices are producing a large amount of data. According to this phenomenon, data analysis technology is rapidly becoming popular. Also, attempts to acquire insights through data analysis have been continuously increasing. It means that the big data analysis will be more important in various industries for the foreseeable future. Big data analysis is generally performed by a small number of experts and delivered to each demander of analysis. However, increase of interest about big data analysis arouses activation of computer programming education and development of many programs for data analysis. Accordingly, the entry barriers of big data analysis are gradually lowering and data analysis technology being spread out. As the result, big data analysis is expected to be performed by demanders of analysis themselves. Along with this, interest about various unstructured data is continually increasing. Especially, a lot of attention is focused on using text data. Emergence of new platforms and techniques using the web bring about mass production of text data and active attempt to analyze text data. Furthermore, result of text analysis has been utilized in various fields. Text mining is a concept that embraces various theories and techniques for text analysis. Many text mining techniques are utilized in this field for various research purposes, topic modeling is one of the most widely used and studied. Topic modeling is a technique that extracts the major issues from a lot of documents, identifies the documents that correspond to each issue and provides identified documents as a cluster. It is evaluated as a very useful technique in that reflect the semantic elements of the document. Traditional topic modeling is based on the distribution of key terms across the entire document. Thus, it is essential to analyze the entire document at once to identify topic of each document. This condition causes a long time in analysis process when topic modeling is applied to a lot of documents. In addition, it has a scalability problem that is an exponential increase in the processing time with the increase of analysis objects. This problem is particularly noticeable when the documents are distributed across multiple systems or regions. To overcome these problems, divide and conquer approach can be applied to topic modeling. It means dividing a large number of documents into sub-units and deriving topics through repetition of topic modeling to each unit. This method can be used for topic modeling on a large number of documents with limited system resources, and can improve processing speed of topic modeling. It also can significantly reduce analysis time and cost through ability to analyze documents in each location or place without combining analysis object documents. However, despite many advantages, this method has two major problems. First, the relationship between local topics derived from each unit and global topics derived from entire document is unclear. It means that in each document, local topics can be identified, but global topics cannot be identified. Second, a method for measuring the accuracy of the proposed methodology should be established. That is to say, assuming that global topic is ideal answer, the difference in a local topic on a global topic needs to be measured. By those difficulties, the study in this method is not performed sufficiently, compare with other studies dealing with topic modeling. In this paper, we propose a topic modeling approach to solve the above two problems. First of all, we divide the entire document cluster(Global set) into sub-clusters(Local set), and generate the reduced entire document cluster(RGS, Reduced global set) that consist of delegated documents extracted from each local set. We try to solve the first problem by mapping RGS topics and local topics. Along with this, we verify the accuracy of the proposed methodology by detecting documents, whether to be discerned as the same topic at result of global and local set. Using 24,000 news articles, we conduct experiments to evaluate practical applicability of the proposed methodology. In addition, through additional experiment, we confirmed that the proposed methodology can provide similar results to the entire topic modeling. We also proposed a reasonable method for comparing the result of both methods.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.1-27
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• 2017

It is known that the economic sentiment index and macroeconomic indicators are closely related because economic agent's judgment and forecast of the business conditions affect economic fluctuations. For this reason, consumer sentiment or confidence provides steady fodder for business and is treated as an important piece of economic information. In Korea, private consumption accounts and consumer sentiment index highly relevant for both, which is a very important economic indicator for evaluating and forecasting the domestic economic situation. However, despite offering relevant insights into private consumption and GDP, the traditional approach to measuring the consumer confidence based on the survey has several limits. One possible weakness is that it takes considerable time to research, collect, and aggregate the data. If certain urgent issues arise, timely information will not be announced until the end of each month. In addition, the survey only contains information derived from questionnaire items, which means it can be difficult to catch up to the direct effects of newly arising issues. The survey also faces potential declines in response rates and erroneous responses. Therefore, it is necessary to find a way to complement it. For this purpose, we construct and assess an index designed to measure consumer economic sentiment index using sentiment analysis. Unlike the survey-based measures, our index relies on textual analysis to extract sentiment from economic and financial news articles. In particular, text data such as news articles and SNS are timely and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. There exist two main approaches to the automatic extraction of sentiment from a text, we apply the lexicon-based approach, using sentiment lexicon dictionaries of words annotated with the semantic orientations. In creating the sentiment lexicon dictionaries, we enter the semantic orientation of individual words manually, though we do not attempt a full linguistic analysis (one that involves analysis of word senses or argument structure); this is the limitation of our research and further work in that direction remains possible. In this study, we generate a time series index of economic sentiment in the news. The construction of the index consists of three broad steps: (1) Collecting a large corpus of economic news articles on the web, (2) Applying lexicon-based methods for sentiment analysis of each article to score the article in terms of sentiment orientation (positive, negative and neutral), and (3) Constructing an economic sentiment index of consumers by aggregating monthly time series for each sentiment word. In line with existing scholarly assessments of the relationship between the consumer confidence index and macroeconomic indicators, any new index should be assessed for its usefulness. We examine the new index's usefulness by comparing other economic indicators to the CSI. To check the usefulness of the newly index based on sentiment analysis, trend and cross - correlation analysis are carried out to analyze the relations and lagged structure. Finally, we analyze the forecasting power using the one step ahead of out of sample prediction. As a result, the news sentiment index correlates strongly with related contemporaneous key indicators in almost all experiments. We also find that news sentiment shocks predict future economic activity in most cases. In almost all experiments, the news sentiment index strongly correlates with related contemporaneous key indicators. Furthermore, in most cases, news sentiment shocks predict future economic activity; in head-to-head comparisons, the news sentiment measures outperform survey-based sentiment index as CSI. Policy makers want to understand consumer or public opinions about existing or proposed policies. Such opinions enable relevant government decision-makers to respond quickly to monitor various web media, SNS, or news articles. Textual data, such as news articles and social networks (Twitter, Facebook and blogs) are generated at high-speeds and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. Although research using unstructured data in economic analysis is in its early stages, but the utilization of data is expected to greatly increase once its usefulness is confirmed.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.91-108
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• 2020

Korea's famous soccer players are steadily performing well in international leagues, which led to higher interests of Korean fans in the international leagues. Reflecting the growing social phenomenon of rising interests on international leagues by Korean fans, the study examined the overall consumer perception in the consumption of uniform by domestic soccer fans and compared the changes in perception following the transfers of the players. Among others, the paper examined the consumer perception and purchase factors of soccer fans shown in social media, focusing on periods before and after the recruitment of Heung-Min Son to English Premier League's Tottenham Football Club. To this end, the EPL uniform is the collection keyword the paper utilized and collected consumer postings from domestic website and social media via Python 3.7, and analyzed them using Ucinet 6, NodeXL 1.0.1, and SPSS 25.0 programs. The results of this study can be summarized as follows. First, the uniform of the club that consistently topped the league, has been gaining attention as a popular uniform, and the players' performance, and the players' position have been identified as key factors in the purchase and search of professional football uniforms. In the case of the club, the actual ranking and whether the league won are shown to be important factors in the purchase and search of professional soccer uniforms. The club's emblem and the sponsor logo that will be attached to the uniform are also factors of interest to consumers. In addition, in the decision making process of purchase of a uniform by professional soccer fan, uniform's form, marking, authenticity, and sponsors are found to be more important than price, design, size, and logo. The official online store has emerged as a major purchasing channel, followed by gifts for friends or requests from acquaintances when someone travels to the United Kingdom. Second, a classification of key control categories through the convergence of iteration correlation analysis and Clauset-Newman-Moore clustering algorithm shows differences in the classification of individual groups, but groups that include the EPL's club and player keywords are identified as the key topics in relation to professional football uniforms. Third, between 2002 and 2006, the central theme for professional football uniforms was World Cup and English Premier League, but from 2012 to 2015, the focus has shifted to more interest of domestic and international players in the English Premier League. The subject has changed to the uniform itself from this time on. In this context, the paper can confirm that the major issues regarding the uniforms of professional soccer players have changed since Ji-Sung Park's transfer to Manchester United, and Sung-Yong Ki, Chung-Yong Lee, and Heung-Min Son's good performances in these leagues. The paper also identified that the uniforms of the clubs to which the players have transferred to are of interest. Fourth, both male and female consumers are showing increasing interest in Son's league, the English Premier League, which Tottenham FC belongs to. In particular, the increasing interest in Son has shown a tendency to increase interest in football uniforms for female consumers. This study presents a variety of researches on sports consumption and has value as a consumer study by identifying unique consumption patterns. It is meaningful in that the accuracy of the interpretation has been enhanced by using a cluster analysis via convergence of iteration correlation analysis and Clauset-Newman-Moore clustering algorithm to identify the main topics. Based on the results of this study, the clubs will be able to maximize its profits and maintain good relationships with fans by identifying key drivers of consumer awareness and purchasing for professional soccer fans and establishing an effective marketing strategy.

• Journal of the Korean Applied Science and Technology
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• v.37 no.3
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• pp.564-570
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• 2020

The purpose of this study is to examine the preceding studies and suggest alternatives to establish the role and value of martial arts sports as a leisure activity. To achieve the purpose of this study, the keywords and themes of leisure-related journals were extracted and the articles and current status of martial arts-related journals in Korea were derived using SPSS descriptive statistics method. The subjects of the analysis were 'mudo' and 'leisure' of leisure-related journals between 2005 and 2017, and an interpretative textual analysis was conducted to analyze the contents of individual studies. According to the results of the survey on the actual condition of participation in the 2016 National Sports for all, Taekwondo was ranked 5th among the top 5 sports with 6.1% of the sports for all, and Taekwondo and Kendo were ranked 1st and 2nd respectively in the sports for all students and the clubs that they want to join in the future. Second, the study on martial arts in the journals related to leisure was found most in 2006 and 2010, but only one study was not conducted after 2014, which confirmed that the absolute number of studies was very insufficient. Third, the research themes of the journals related to leisure were serious leisure, female college students, physical self-concept, social development, leisure recreation class, job satisfaction, life satisfaction, training, leisure constraints, etc., and the study of martial arts related to leisure was found to require quantitative and qualitative multilateral approaches. Fourth, in the current status of dance related studies by year in domestic journals, two of 23 studies in 2007 were conducted on leisure topics, and the average number of studies related to domestic martial arts and leisure related papers was 5.65%, which is very low. In conclusion, as a result of analyzing the trend of research on leisure as a martial arts sport, it is necessary to suggest the direction of future research that can reconsider the role and value of martial arts sport as a leisure activity that can improve the quality of life and happiness in future society through quantitative and qualitative improvement.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.1-19
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• 2018

Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company's news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a contribution that we testified firms classified as Global Industrial Classification Standard have heterogeneity and suggested it is necessary to define the relevance through machine learning and statistical analysis methodology rather than simply defining it in the Global Industrial Classification Standard. It has also contribution that we proved the efficiency of the prediction model reflecting heterogeneity.