• Journal of Korea Technology Innovation Society
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• v.6 no.3
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• pp.373-390
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• 2003

With limited resources, proper allocation of the national R&D budget is very crucial matter for reinforcing the national competence, and the importance of selecting strategic R&D fields have been increasingly emphasized by technology policy-makers and CTOs. This paper deals with technology relevance/cluster analysis, which measures technological dependency and relevancy among technologies, and how it can be used for selecting the strategic R&D fields especially satisfying SMEs(small and medium enterprises)' specific needs. As a result of this study, technology-product tree composed of 7 major technology fields, 22 clusters, 41 groups, 335 core-need technologies and hundreds of related business items are produced that can be used for designing SMEs' R&D/business portfolio as well as R&D investment decision-making of the Ministry of Small and Medium Business Administration.

• Journal of Convergence for Information Technology
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• v.10 no.12
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• pp.126-131
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• 2020

This study analyzed the inflow and outflow patterns of emergency department patients, to identify changes in regional types in cities, counties, and districts in Jeonlado, Korea. Data of areas in Jeonlado for 2014 to 2018 were extracted from the National Emergency Department Information System. The extracted data includes the patients' and emergency medical institution addresses, which were used to calculate the relevance index (RI) and commitment index (CI). The calculated indices were classified into regional types by applying cluster analysis. A non-parametric method, Kruskal-Wallis test, was employed to examine the differences between years for RI and CI by regional types. The results of cluster analysis using the relevance and commitment indices revealed three regional types. Regions in cluster 1 were classified as outflow type, in cluster 2 as inflow type, and in cluster 3 as self-sufficient. RI and CI were calculated for each cluster or regional type. There were no significant differences between years in cluster 2 (inflow type) and cluster 3 (self-sufficient type). In cluster 1 (outflow type), there were no significant differences in CI between the years; however, there were significant differences in RI between 2014 and 2017, and 2014 and 2018. It is difficult to see that the emergency medical environment has improved due to the increased concentration of emergency medical care.

• Nuclear Engineering and Technology
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• v.52 no.5
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• pp.984-994
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• 2020

It is reported that about 20% of accidents at nuclear power plants in Korea and abroad are caused by human error. One of the main factors contributing to human error is fatigue, so it is necessary to prevent human errors that may occur when the task is performed in an improper state by grasping the status of the operator in advance. In this study, we propose a method of evaluating operator's fitness-for-duty (FFD) using various parameters including eye movement data, subjective fatigue ratings, and operator's performance. Parameters for evaluating FFD were selected through a literature survey. We performed experiments that test subjects who felt various levels of fatigue monitor information of indicators and diagnose a system malfunction. In order to find meaningful characteristics in measured data consisting of various parameters, hierarchical clustering analysis, an unsupervised machine-learning technique, is used. The characteristics of each cluster were analyzed; fitness-for-duty of each cluster was evaluated. The appropriateness of the number of clusters obtained through clustering analysis was evaluated using both the Elbow and Silhouette methods. Finally, it was statistically shown that the suggested methodology for evaluating FFD does not generate additional fatigue in subjects. Relevance to industry: The methodology for evaluating an operator's fitness for duty in advance is proposed, and it can prevent human errors that might be caused by inappropriate condition in nuclear industries.

• Journal of the Korean Applied Science and Technology
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• v.37 no.5
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• pp.1418-1429
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• 2020

This study was conducted on men and women in their teens and 50s living in Seoul, Incheon and Gyeonggi-do to find out the relevance between demographic characteristics of lifestyle clusters and the characteristics of beauty salon use. To analyze the data of a total of 522 people collected, statistical processing was performed with analysis of frequency, analysis of factor, analysis of reliability, analysis of cluster, analysis of variance and analysis of cross. As a result, lifestyle group types were classified as fashion and social focus types, family-oriented types, and family-free types. These types were highly related to age, final education, and marital status among demographic characteristics, and were also found to be highly relevant to the characteristics of beauty salon use, such as beauty salon location and frequently used beauty services. The results of this study are thought to be the basic data that can be used for beauty salon marketing.

• International Journal of Advanced Culture Technology
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• v.12 no.1
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• pp.270-275
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• 2024

Introduction. In this study, purpose is to analize the types of golf tourism, inbound or outbound, by using big data and see how movement of industry is being changed and what changes have been made during and after Covid-19 in golf industry. Method Using Textom, a big data analysis tool, "golf tourism" and "Covid-19" were selected as keywords, and search frequency information of Naver and Daum was collected for a year from 1 st January, 2023 to 31st December, 2023, and data preprocessing was conducted based on this. For the suitability of the study and more accurate data, data not related to "golf tourism" was removed through the refining process, and similar keywords were grouped into the same keyword to perform analysis. As a result of the word refining process, top 36 keywords with the highest relevance and search frequency were selected and applied to this study. The top 36 keywords derived through word purification were subjected to TF-IDF analysis, visualization analysis using Ucinet6 and NetDraw programs, network analysis between keywords, and cluster analysis between each keyword through Concor analysis. Results By using big data analysis, it was found out option of oversea golf tourism is affecting on inbound golf travel. "Golf", "Tourism", "Vietnam", "Thailand" showed high frequencies, which proves that oversea golf tour is now the re-coming trends.

• Journal of Korea Technology Innovation Society
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• v.14 no.spc
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• pp.1187-1208
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• 2011

This research aims to propose a systematic approach to identify emerging technology-product portfolio for small and medium enterprises (SMEs). Firstly, operational definition of emerging technology for SMEs is presented. Secondly, research framework is suggested and case study to show usefulness of the newly proposed framwork is analyzed. In detail, reference patent set which represent company's capabilities and business area are constructed. The research constructs patent data set for bibliometric analysis using reference patent set and citing patents to 2nd level. Clustering (expert judgement) and keyword based bibliometric approach are used. Then, cluster activity index (AI) and relevance index (RI) comparing with reference patent set are estimated. With emerging technology-product portfolio using AI and RI, a firm can identify emerging technology-product area and monitoring area.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.179-184
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• 2024

Franchise are now a red ocean in Food industry and they need to find other options to appeal for their product, the uprising content, food tech. The franchises are working on R&D to help franchisees with the operations. Through this paper, we analyze the franchise interest on food tech and to help find the necessity of development for franchisees who are in needs with hand, not of human, but of technology. Using Textom, a big data analysis tool, "franchise" and "food tech" were selected as keywords, and search frequency information of Naver and Daum was collected for a year from 01 January, 2023 to 31 December, 2023, and data preprocessing was conducted based on this. For the suitability of the study and more accurate data, data not related to "food tech" was removed through the refining process, and similar keywords were grouped into the same keyword to perform analysis. As a result of the word refining process, a total of 10,049 words were derived, and among them, the top 50 keywords with the highest relevance and search frequency were selected and applied to this study. The top 50 keywords derived through word purification were subjected to TF-IDF analysis, visualization analysis using Ucinet6 and NetDraw programs, network analysis between keywords, and cluster analysis between each keyword through Concor analysis. By using big data analysis, it was found out that franchise do have interest on food tech. "technology", "franchise", "robots" showed many interests and keyword "R&D" showed that franchise are keen on developing food tech to seize competitiveness in Franchise Industry.

• Journal of Veterinary Science
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• v.25 no.3
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• pp.35.1-35.13
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• 2024

Importance: Experimental autoimmune encephalomyelitis (EAE) is an animal model of multiple sclerosis characterized by inflammation within the central nervous system. However, inflammation in non-neuronal tissues, including the lungs, has not been fully evaluated. Objective: This study evaluated the inflammatory response in lungs of EAE mice by immunohistochemistry and histochemistry. Methods: Eight adult C57BL/6 mice were injected with myelin oligodendrocyte glycoprotein_35-55 to induce the EAE. Lungs and spinal cords were sampled from the experimental mice at the time of sacrifice and used for the western blotting, histochemistry, and immunohistochemistry. Results: Histopathological examination revealed inflammatory lesions in the lungs of EAE mice, characterized by infiltration of myeloperoxidase (MPO)- and galectin-3-positive cells, as determined by immunohistochemistry. Increased numbers of collagen fibers in the lungs of EAE mice were confirmed by histopathological analysis. Western blotting revealed significantly elevated level of osteopontin (OPN), cluster of differentiation 44 (CD44), MPO and galectin-3 in the lungs of EAE mice compared with normal controls (p < 0.05). Immunohistochemical analysis revealed both OPN and CD44 in ionized calcium-binding adapter molecule 1-positive macrophages within the lungs of EAE mice. Conclusions and Relevance: Taken together, these findings suggest that the increased OPN level in lungs of EAE mice led to inflammation; concurrent increases in proinflammatory factors (OPN and galectin-3) caused pulmonary impairment.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.127-134
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• 2024

The article highlights the problems of the digitalization of the educational process, which affect the pedagogical cluster and are of a psychological nature. The authors investigate the transformational changes in education in general and the individual beliefs of each subject of the educational process, caused by both the change in the format of learning (distance, mixed), and the use of new technologies (digital, communication). The purpose of the article is to identify the strategic trend of the educational process, which is a synergistic combination of pedagogical methodology and psychological practice and avoiding dialectical opposition of these components of the educational space. At the same time, it should be noted that the introduction of digital technologies in the educational process allows for short-term difficulties, which is a usual phenomenon for innovations in the educational sphere. Consequently, there is a need to differentiate the fundamental problems and temporary shortcomings that are inherent in the new format of learning (pedagogical features). Based on the awareness of this classification, it is necessary to develop psychological techniques that will prevent a negative reaction to the new models of learning and contribute to a painless moral and spiritual adaptation to the realities of the present (psychological characteristics). The methods used in the study are divided into two main groups: general-scientific, which investigates the pedagogical component (synergetic, analysis, structural and typological methods), and general-scientific, which are characterized by psychological direction (dialectics, observation, and comparative analysis). With the help of methods disclosed psychological and pedagogical features of the process of digitalization of education in a mixed learning environment. The result of the study is to develop and carry out methodological constants that will contribute to the synergy for the new pedagogical components (digital technology) and the psychological disposition to their proper use (awareness of the effectiveness of new technologies). So, the digitalization of education has demonstrated its relevance and effectiveness in the pedagogical dimension in the organization of blended and distance learning under the constraints of the COVID-19 pandemic. The task of the psychological cluster is to substantiate the positive aspects of the digitalization of the educational process.

• 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.