• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.6 no.2
• /
• pp.257-263
• /
• 2002

A graph embedding problem has been studied for applications of resource allocation and mapping the underlying data structure of a parallel algorithm into the interconnection architecture of massively parallel processing systems. In this paper, we consider the embedding problem of the pyramid into the regular 2-dimensional mesh interconnection network topology. We propose a new embedding function which can embed the pyramid of height N into 2$^{N}$ x2$^{N}$ 2-dimensional mesh with dilation max{2$^{N1}$-2. [3.2$^{N4}$＋1)/2, 2$^{N3}$＋2. [3.2$^{N4}$＋1)/2]}. This means an improvement in the dilation measure from 2$^{N}$ $^1$in the previous result into about (5/8) . 2$^{N1}$ under the same condition.condition.

• KIISE Transactions on Computing Practices
• /
• v.22 no.2
• /
• pp.107-112
• /
• 2016

The Sejong Electronic(machine-readable) Dictionary, developed by the 21st century Sejong Plan, contains systematically organized information on Korean words. It helps to solve problems encountered in the electronic formatting of the still-commonly-used hard-copy dictionary. The Sejong Electronic Dictionary, however has a limitation relate to sentence structure and selection-restricted nouns. This paper discuses the limitations of word-sense disambiguation(WSD) that uses subcategorization information suggested by the Sejong Electronic Dictionary and generalized selection-restricted nouns from the Korean Lexico-semantic network. An alternative method that utilized semi-supervised learning, the chi-square test and some other means to make WSD decisions is presented herein.

• The Transactions of the Korea Information Processing Society
• /
• v.7 no.5
• /
• pp.1464-1473
• /
• 2000

In this paper, we analyze the cycle property of the (n,k)-star graph that has an attention as an alternative interconnection network topology in recent years. Based on the graph-theoretic properties in (n,k)-star graphs, we show the pancyclic property of the graph and also present the corresponding algorithm. Based on the recursive structure of the graph, we present such top-down approach that the resulting cycle can be constructed by applying series of "dimension expansion" operations to a kind of cycles consisting of sub-graphs. This processing naturally leads to such property that the resulting cycles tend to be integrated compactly within some minimal subset of sub-graphs, and also means its applicability of another classes of the disjoint-style cycle problems. This result means not only the graph-theoretic contribution of analyzing the pancyclic property in the underlying graph model but also the parallel processing applications such a as message routing or resource allocation and scheduling in the multi-computer system with the corresponding interconnection network.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2018.10a
• /
• pp.213-216
• /
• 2018

As the advantages of smart devices based on artificial intelligence and voice recognition become more prominent, Virtual Assistant is gaining popularity. Virtual Assistant provides a user experience through smart speakers and is valued as the most user friendly IoT device by consumers. The purpose of this study is to investigate whether there are differences in people's perception of the key virtual assistant brand voice recognition. We collected tweets that included six keyword form three companies that provide Virtual Assistant services. The authors conducted semantic network analysis for the collected datasets and analyzed the feelings of people through sentiment analysis. The result shows that many people have a different perception and mainly about the functions and services provided by the Virtual Assistant and the expectation and usability of the services. Also, people responded positively to most keywords.

• Journal of Korean Academy of Nursing
• /
• v.47 no.6
• /
• pp.806-816
• /
• 2017

Purpose: As comprehensive nursing care service has gradually expanded, it has become necessary to explore the various opinions about it. The purpose of this study is to explore the large amount of text data regarding comprehensive nursing care service extracted from online news and social media by applying a semantic network analysis. Methods: The web pages of the Korean Nurses Association (KNA) News, major daily newspapers, and Twitter were crawled by searching the keyword 'comprehensive nursing care service' using Python. A morphological analysis was performed using KoNLPy. Nodes on a 'comprehensive nursing care service' cluster were selected, and frequency, edge weight, and degree centrality were calculated and visualized with Gephi for the semantic network. Results: A total of 536 news pages and 464 tweets were analyzed. In the KNA News and major daily newspapers, 'nursing workforce' and 'nursing service' were highly rated in frequency, edge weight, and degree centrality. On Twitter, the most frequent nodes were 'National Health Insurance Service' and 'comprehensive nursing care service hospital.' The nodes with the highest edge weight were 'national health insurance,' 'wards without caregiver presence,' and 'caregiving costs.' 'National Health Insurance Service' was highest in degree centrality. Conclusion: This study provides an example of how to use atypical big data for a nursing issue through semantic network analysis to explore diverse perspectives surrounding the nursing community through various media sources. Applying semantic network analysis to online big data to gather information regarding various nursing issues would help to explore opinions for formulating and implementing nursing policies.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.11 no.5
• /
• pp.550-555
• /
• 2018

This study aims to identify and compare contents and keywords used in articles related to blockchain applications to various industries. The text mining and Semantic Network Analysis, as methods of keyword network analysis, were used to analyze articles including terms of 'finance' 'energy' and 'logistics', which media and government frequently mentioned as areas that can apply blockchain technologies. For this study, data were collected from 43,093 articles from January, 2017 through July, 2018. Data crawling was carried out by using Python BeautifulSoup and data cleaning was performed in order to eliminate mutual redundancies of the three terms. After that, text mining and semantic network analysis were performed using Textom and UCInet for network analysis between keywords. The results showed that all the three terms were similar in terms of 'technology', but there were differences in the contents of 'government policy' or 'industry' issues. In addition, there were differences in frequencies and centralities of these terms.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.6
• /
• pp.375-384
• /
• 2021

This study explored the change in keywords and topics in newspaper articles related to schizophrenia after the Gangnam murder case. The study examined newspaper articles related to schizophrenia for five years before and after the Gangnam murder case. A semantic network analysis was conducted using the NetMiner 4.4.1 program. 610 articles between 2013 and 2018 were retrieved from 8 national newsletters. The most frequent core keyword was 'treatment' before the murder case, but 'incidents' after the case. Four topics were identified: 'becoming chronic if missing the time of treatment due to prejudice', 'being curable with early treatment', 'living an ordinary life with medication', 'being indicted as a murderer while impaired by a mental disorder' before the murder case. After the case, four topics were identified: 'committing murder for delusions, not misogyny', 'medication non-adherence leads to more impulsive behavior', 'claiming leniency for criminals due to the mental impairment', 'killing the police who were mobilized to stop stabbing rampage'. These findings suggest that newspaper articles should provide accurate information about schizophrenia to reduce prejudice and stigma toward patients with schizophrenia and other forms of mental illness.

• Journal of Korea Entertainment Industry Association
• /
• v.14 no.5
• /
• pp.1-14
• /
• 2020

This study conducted a topic modeling and semantic network analysis of 'korean wave' and its meaning in Korean society from 2000 to 2019 by applying an agenda setting theory. For this purpose, a total of 197,992 newspaper articles which reported 'korean wave' issues were analyzed by applying topic modeling and semantic network analysis. As a result, first, the word 'korean wave' mainly appeared in korean-related regions in the korean press. culture and economy. second, a total of 9 topics related to korean wave issues appeared. This was followed by 'broadcast', 'export', 'domestic and foreign affairs', 'education', 'beauty and fashion', 'music and performance', 'tourism', 'media(platform)', and 'region'. Lastly, korean wave was mainly discussed at the cultural and economic ares. In addition, it was clustered into five characteristics: 'cultural hallyu', 'business hallyu', 'education', 'environment', and 'geography'.

• Journal of the Korea Convergence Society
• /
• v.13 no.2
• /
• pp.1-12
• /
• 2022

The purpose of this study is to identify the social perception characteristics of young children with disabilities over the past decade. For this purpose, Textom, an Internet-based big data analysis system was used to collect data related to young children with disabilities posted on social media. 50 keywords were selected in the order of high frequency through the data cleaning process. For semantic network analysis, centrality analysis and CONCOR analysis were performed with UCINET6, and the analyzed data were visualized using NetDraw. As a result, the keywords such as 'education, needs, parents, and inclusion' ranked high in frequency, degree, and eigenvector centrality. In addition, the keywords of 'parent, teacher, problem, program, and counseling' ranked high in betweenness centrality. In CONCOR analysis, four clusters were formed centered on the keywords of 'disabilities, young child, diagnosis, and programs'. Based on these research results, the topics on social perception of young children with disabilities were investigated, and implications for each topic were discussed.

• Journal of Digital Convergence
• /
• v.19 no.11
• /
• pp.1-10
• /
• 2021

This paper aimed to look at the awareness of foreign workers in social platforms by using text mining, one of the big data techniques and draw suggestions for foreign workers. To achieve this purpose, data collection was conducted with search keyword 'Foreign Worker' from Jan. 1, to Dec. 31, 2020, and frequency analysis, TF-IDF analysis, and degree centrality analysis and 100 parent keywords were drawn for comparison. Furthermore, Ucinet6.0 and Netdraw were used to analyze semantic networks, and through CONCOR analysis, data were clustered into the following eight groups: foreigner policy issue, regional community issue, business owner's perspective issue, employment issue, working environment issue, legal issue, immigration issue, and human rights issue. Based on such analyzed results, it identified national awareness of foreign workers and main issues and provided the basic data on policy proposals for foreign workers and related researches.