• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.949-955
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• 2020

In this Study, Social Network Analysis of IoT related patents in South Korea, China, and Japan was conducted from the viewpoint of patent informatics. To this end, 2,526 patents filed by PCT until December 2019 were investigated up to the subclass level of the IPC code. As a result, in the case of South Korea, representative IPC codes are in the order of G06Q, H04L, G06F, H04W, and the highest frequency of interconnection is H04L→H04W, H04W→H04L, H04W→H04B. In China, the representative IPC codes are in the order of H04L, H04W, G05B, G06Q. South Korea has strong technological convergence centered on the G06Q, while China has strong convergence centered around H04L and H04W. Moreover, in China, H04L and H04W have more diverse combinations than in South Korea in Section A, B, G, and H. In the future, it is necessary to study the diversity of technology convergence of H04L and H04W in China.

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.1-18
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• 2021

ICT is the main driving force of Korea's economic growth. Korea has the world's best ICT competitiveness, and several policies are being implemented to maintain it. However, for successful policy implementation, it is crucial to understand ICT trends accurately. Therefore, this study analyzes the trends of 18 core technologies in the ICT field. In particular, the degree of scientific development and commercialization by technology are investigated through research paper analysis and patent analysis, respectively. Then, the trends shown by document type are compared based on the two analysis results. As a result, artificial intelligence and virtual reality are at the stage where commercialization is actively taking place after scientific development, and at the same time, since research is being conducted, it is expected to develop continuously. On the other hand, quantum computer and implantable device are in the basic research stage. It is necessary to understand the current research status and determine the direction of future support. The results of the ICT trend analysis conducted in this study can be used as a criterion for determining the future direction of Korean policy.

• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.37-44
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• 2020

AI technology has developed in the form of decision support technology in law, patent, finance and national defense and is applied to disease diagnosis and legal judgment. To search real-time information with Deep Learning, Big data Analysis and Deep Learning Algorithm are required. In this paper, we try to predict the entrance rate to high-ranking universities using a Deep Learning model, RNN(Recurrent Neural Network). First, we analyzed the current status of private academies in administrative districts and the number of students by age in administrative districts, and established a socially accepted hypothesis that students residing in areas with a high educational fever have a high rate of enrollment in high-ranking universities. This is to verify based on the data analyzed using the predicted hypothesis and the government's public data. The predictive model uses data from 2015 to 2017 to learn to predict the top enrollment rate, and the trained model predicts the top enrollment rate in 2018. A prediction experiment was performed using RNN, a Deep Learning model, for the high-ranking enrollment rate in the special education zone. In this paper, we define the correlation between the high-ranking enrollment rate by analyzing the household income and the participation rate of private education about the current status of private institutes in regions with high education fever and the effect on the number of students by age.

• The Journal of Society for e-Business Studies
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• v.22 no.3
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• pp.87-103
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• 2017

In recent years, many small and medium-sized manufacturing companies are making process innovation and product innovation through the public knowledge services. K Research institute provides different types of knowledge services in combination and due to this complexity, it is difficult to analyze the performance of knowledge service programs precisely. In this study, we derived performance items from bottom-up viewpoints, rather than top-down approaches selecting those items as in previous performance analysis. As a result, 74 items were finded from 82 companies in the K Research Institute case book, and the final result was refined to 17 items. After that a case-performance matrix was constructed, and binary data was entered to analyze. As a result, three clusters were identified through K-means clustering as 'enhancement of core competitiveness (product and patent),' 'expansion of domestic and overseas market,' and 'improvement of operational efficiency.'

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.3
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• pp.478-484
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• 2017

This study analyzed statistically the determinants that affect the royalties of knowledge technology transfer, which accounts for a considerable portion of university technology transfer. As knowledge technology transfer certainly includes a move from tacit knowledge from one side to the other side per se, the scope of knowledge technology transfer is unclear and numerical information of technology transfer, such as research fund scale, which is used widely in previous studies, cannot be used in the analysis. Therefore, this study focused on the researcher characteristics and included its explanatory variables in the present study. In addition, it included the technical characteristics of the knowledge transferred and the characteristics of the contracting company. The knowledge maturity calculated from the appointment year and contract date positively affects the technology royalties, but work experience and patent activity of the researcher are not statistically significant. Statistically significant differences in the technology royalty according to the type of technology transfer and the company location were observed, but there was no meaningful change in the technology royalty depending on the technical field and company business scale.

• Restorative Dentistry and Endodontics
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• v.28 no.1
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• pp.64-71
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• 2003

The purpose of this study was to compare the initial apical file(IAF) first Ole that fits to the apex in each canal before and after early flaring to analyze if the size of file to fit to the apex would increase after flaring. Eighty anterior teeth with complete apical formation and patent foramens were selected. The samples were randomly divided into 4 groups(GG, OS, GT, PT Group) of 20 teeth each. A file was fit to the apex in each canal and that size recorded. Radicular flaring were completed using different types of instruments. After flaring a file was again fit to the apex in the same manner as before and its size recorded. The results of this study were as follows : 1. The mean diameter of IAF before flaring(file diameters in $mm{\times}10^{-2}$) was $19.81{\pm}8.32$ before and $25.94{\pm}9.21$ after(p<0.05). 2. The increase in diameter of IAF was approximately one file size for all groups. 3. Ranking of increasing diameter of IAF were GG>CT>OS>PT group. There was a statistically significant difference between before and after flaring(p<0.05). 4 Ranking of the time for flaring were GG>GT>OS>PT group. There was a statistically significant difference between GG group and other groups(p<0.05). 5. In the case without change of IAF diameter, they showed decrease in force after flaring when IAF was pulled out from root canal(p<0.05). This study suggested that early radicular flaring increases the file size that is snug at the apex, and awareness of that difference gives the clinician a better sense of canal size. Early flaring of the canal provides better apical size information and with this awareness, a better decision can be made concerning the appropriate final diameter needed for complete apical shaping.

• Journal of Society of Preventive Korean Medicine
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• v.27 no.1
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• pp.69-87
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• 2023

Objectives : This study collected and analyzed information related to preventive formulas from continuously published and revised COVID-19 treatment guidelines in various regions of China. Methods : We collected treatment guidelines published in different regions of China and categorized formulas for prevention and medical observation period listed in them according to the editions. The categorized preventive formulas were compared by type and target group. Results : Herbal medicines used for prevention included formulas derived from Korean medical classics, such as Okbyeongpungsan(玉屏風散) and Eunkyosan(銀翹散). The newly composed formulas, totaling over 100, were created by adding, subtracting, and combining formulas such as Sang-gukeum(桑菊飮), Eunkyosan(銀翹散), Sasammaekmundongtang(沙參麥門冬湯), Okbyeongpungsan(玉屏風散), Gwakhyangjeong-gisan(藿香正氣散), and Soshihotang(小柴胡湯). Patent medicines including Huoxiangzhengqi capsule(藿香正氣膠囊), Lianhuaqingwen capsule(連花淸瘟膠囊), Shufengjiedu capsule(疏風解毒膠囊), and Jinhuaqinggan granule(金花淸感顆粒) were frequently used, mainly targeting close contacts. These medicines were used differently depending on the specific population group, such as the general population, the elderly, children, pregnant women, and patients with underlying diseases, and were also applied differently according to the individual's constitution. Conclusion : We were able to identify various background factors contained in the guidelines for the use of preventive formulas presented by TCM group, and understand the social conditions that enabled the group to provide such guidelines. Through this, thorough preparation should be made so that the Korean Medicine can actively respond to another future pandemic.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.6
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• pp.47-59
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• 2017

Korean Economy has been developed by the Korean Government's Support for the Large-sized Firms. This Government Policy causes the Polarization between Large-sized and Micro-sized Firms Aggravated. Micro-sized Firms are distributed over the Whole Industry Area, and can also cause the Economic Crisis If They are crashed down. Therefore Government Policy for the Micro-sized Manufacturing Industry is very Important Issue. This Paper Focused on the Analysis of Current Status for the Shoes Micro Manufacturing Industry. For the Effective Analysis, This Paper uses the Statistical Data Open to the Public and also conducts the Survey for the Micro-sized Firms in Busan. Statistical Program is used for Analyzing the Collected Data and the Major Findings are as Follows. First, Shoes Industry is led by the Micro and Small & Medium sized Firms rather than the Large-sized. And the Micro-sized Firms are getting the High Rate among the Whole Shoes Industry. Busan is heavily populated Area as the Origin of Shoes Industry. Second, even though Most of the Owner of the Micro-sized Firms have the High Technology Skill Level, Worker's Aging Phenomenon gets Worse and causes the Technology Handing down to the Next Generation Difficult. Third, Because the Factory Facility of the Micro-sized Firms is Dirt and Unstable, the Modernized Manufacturing Infrastructure such as the Apartment Factory Facility is Necessary. Forth, as the Micro-sized Firms which have the Intangible Asset such as Patent is Few, the Government Policy for Encouraging the Patent Application is strongly Needed. Fifth, Entrepreneurship and Collaboration Mind between Micro-sized Firms are Lacked, so Establishment of the Cooperative Union is required. Finally, the Effort for the Systemic Planning for the Management is lacked, and the Introduction of the Management Innovation is strongly needed. The Limitation and Future Research Direction is also discussed.

• Journal of Korea Technology Innovation Society
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• v.15 no.2
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• pp.443-460
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• 2012

Technology is closely related to industrial development and various studies have been performed to understand the linked structure for knowledge flow between the technology and industry. The research, however, wasn't carried out to flow for Korea National Research and Development projects. In this study, linked structure for technology-industry was discussed by utilizing patent data performed in actual National R&D using NTIS Information of the national research and development, and then it was analyzed how knowledge flows between the technology and industry are flowing. It should be defined that the individual applications expected by researchers at the start of the research and technology-industry applications actually applied from the research performances after research was completed. As a result, it was confirmed in most projects the flow of knowledge was occurring to predicted industries before the start of the R&D. However, the technology was applied to unexpected industry in three industries such as Y09(medical, precision and optical instruments), Y10(electrical and mechanical equipment), Y11(automotive and transportation equipment). The results of this study will be able to contribute to planning for efficient investment strategy of technology-industry by investigating the technology-industry knowledge flow relations of national R&D projects.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.93-111
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• 2013

As Internet and information technology (IT) continues to develop and evolve, the issue of big data has emerged at the foreground of scholarly and industrial attention. Big data is generally defined as data that exceed the range that can be collected, stored, managed and analyzed by existing conventional information systems and it also refers to the new technologies designed to effectively extract values from such data. With the widespread dissemination of IT systems, continual efforts have been made in various fields of industry such as R&D, manufacturing, and finance to collect and analyze immense quantities of data in order to extract meaningful information and to use this information to solve various problems. Since IT has converged with various industries in many aspects, digital data are now being generated at a remarkably accelerating rate while developments in state-of-the-art technology have led to continual enhancements in system performance. The types of big data that are currently receiving the most attention include information available within companies, such as information on consumer characteristics, information on purchase records, logistics information and log information indicating the usage of products and services by consumers, as well as information accumulated outside companies, such as information on the web search traffic of online users, social network information, and patent information. Among these various types of big data, web searches performed by online users constitute one of the most effective and important sources of information for marketing purposes because consumers search for information on the internet in order to make efficient and rational choices. Recently, Google has provided public access to its information on the web search traffic of online users through a service named Google Trends. Research that uses this web search traffic information to analyze the information search behavior of online users is now receiving much attention in academia and in fields of industry. Studies using web search traffic information can be broadly classified into two fields. The first field consists of empirical demonstrations that show how web search information can be used to forecast social phenomena, the purchasing power of consumers, the outcomes of political elections, etc. The other field focuses on using web search traffic information to observe consumer behavior, identifying the attributes of a product that consumers regard as important or tracking changes on consumers' expectations, for example, but relatively less research has been completed in this field. In particular, to the extent of our knowledge, hardly any studies related to brands have yet attempted to use web search traffic information to analyze the factors that influence consumers' purchasing activities. This study aims to demonstrate that consumers' web search traffic information can be used to derive the relations among brands and the relations between an individual brand and product attributes. When consumers input their search words on the web, they may use a single keyword for the search, but they also often input multiple keywords to seek related information (this is referred to as simultaneous searching). A consumer performs a simultaneous search either to simultaneously compare two product brands to obtain information on their similarities and differences, or to acquire more in-depth information about a specific attribute in a specific brand. Web search traffic information shows that the quantity of simultaneous searches using certain keywords increases when the relation is closer in the consumer's mind and it will be possible to derive the relations between each of the keywords by collecting this relational data and subjecting it to network analysis. Accordingly, this study proposes a method of analyzing how brands are positioned by consumers and what relationships exist between product attributes and an individual brand, using simultaneous search traffic information. It also presents case studies demonstrating the actual application of this method, with a focus on tablets, belonging to innovative product groups.