• The Journal of Korean Medicine
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• v.33 no.4
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• pp.60-68
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• 2012

Objectives: By grasping trends in research, technology, and general characteristics of learning support tools, this study was conducted to present a model for research on Korean Medicine (KM) to make use of information technology to support teaching and learning. The purpose is to improve the future clinical competence of medical personnel, which is directly linked to national health. Methods: With papers and patents published up to 2011 as the objects, 438 papers were extracted from "Web of Science" and 313 patents were extracted from the WIPS database (DB). Descriptive analysis and network analysis were conducted on the annual developments, academic journals, and research fields of the papers, patents searched were subjected to quantitative analysis per application year, nation, and technology, and an activity index (AI) was calculated. Results: First, research on medical learning support tools has continued to increase and is active in the fields of computer engineering, education research, and surgery. Second, the largest number of patent applications on medical learning support tools were made in the United States, South Korea, and Japan in this order, and the securement of remediation technology-centered patents, rather than basic/essential patents, seemed possible. Third, when the results of the analysis of research trends were comprehensively analyzed, international research on e-PBL- and medical simulation-centered medical learning support tools was seen to expand continuously to improve the clinical competence of medical personnel, which is directly linked to national health. Conclusions: The KM learning support tool model proposed in the present study is expected to be applicable to computer-based tests at KM schools and to be able to replace certain functions of national KM doctor license examinations once its problem DB, e-PBL, and TKM simulator have been constructed. This learning support tool will undergo a standardization process in the future.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.101-124
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• 2018

Recently, most of the technologies have been developed in various forms through the advancement of single technology or interaction with other technologies. Particularly, these technologies have the characteristic of the convergence caused by the interaction between two or more techniques. In addition, efforts in responding to technological changes by advance are continuously increasing through forecasting promising convergence technologies that will emerge in the near future. According to this phenomenon, many researchers are attempting to perform various analyses about forecasting promising convergence technologies. A convergence technology has characteristics of various technologies according to the principle of generation. Therefore, forecasting promising convergence technologies is much more difficult than forecasting general technologies with high growth potential. Nevertheless, some achievements have been confirmed in an attempt to forecasting promising technologies using big data analysis and social network analysis. Studies of convergence technology through data analysis are actively conducted with the theme of discovering new convergence technologies and analyzing their trends. According that, information about new convergence technologies is being provided more abundantly than in the past. However, existing methods in analyzing convergence technology have some limitations. Firstly, most studies deal with convergence technology analyze data through predefined technology classifications. The technologies appearing recently tend to have characteristics of convergence and thus consist of technologies from various fields. In other words, the new convergence technologies may not belong to the defined classification. Therefore, the existing method does not properly reflect the dynamic change of the convergence phenomenon. Secondly, in order to forecast the promising convergence technologies, most of the existing analysis method use the general purpose indicators in process. This method does not fully utilize the specificity of convergence phenomenon. The new convergence technology is highly dependent on the existing technology, which is the origin of that technology. Based on that, it can grow into the independent field or disappear rapidly, according to the change of the dependent technology. In the existing analysis, the potential growth of convergence technology is judged through the traditional indicators designed from the general purpose. However, these indicators do not reflect the principle of convergence. In other words, these indicators do not reflect the characteristics of convergence technology, which brings the meaning of new technologies emerge through two or more mature technologies and grown technologies affect the creation of another technology. Thirdly, previous studies do not provide objective methods for evaluating the accuracy of models in forecasting promising convergence technologies. In the studies of convergence technology, the subject of forecasting promising technologies was relatively insufficient due to the complexity of the field. Therefore, it is difficult to find a method to evaluate the accuracy of the model that forecasting promising convergence technologies. In order to activate the field of forecasting promising convergence technology, it is important to establish a method for objectively verifying and evaluating the accuracy of the model proposed by each study. To overcome these limitations, we propose a new method for analysis of convergence technologies. First of all, through topic modeling, we derive a new technology classification in terms of text content. It reflects the dynamic change of the actual technology market, not the existing fixed classification standard. In addition, we identify the influence relationships between technologies through the topic correspondence weights of each document, and structuralize them into a network. In addition, we devise a centrality indicator (PGC, potential growth centrality) to forecast the future growth of technology by utilizing the centrality information of each technology. It reflects the convergence characteristics of each technology, according to technology maturity and interdependence between technologies. Along with this, we propose a method to evaluate the accuracy of forecasting model by measuring the growth rate of promising technology. It is based on the variation of potential growth centrality by period. In this paper, we conduct experiments with 13,477 patent documents dealing with technical contents to evaluate the performance and practical applicability of the proposed method. As a result, it is confirmed that the forecast model based on a centrality indicator of the proposed method has a maximum forecast accuracy of about 2.88 times higher than the accuracy of the forecast model based on the currently used network indicators.

• Korean small business review
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• v.42 no.1
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• pp.43-56
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• 2020

The technology conflict between the U.S. and China is deepening recently. The U.S.-China battle began as a national security issue but is comprehending as a U.S.'s check for China's rapid technological advancement. China is rapidly growing in several indexes including R&D expenditure, patent application, and publications, and is challenging the U.S. in 5G and Artificial Intelligence. In 2018, Huawei became the largest 5G network/equipment provider and second largest smart phone manufacturer in the world. Now, Huawei is outperforming at AI chipset manufacturing, Bigdata analysis and cloud, positioning to become a critical player in the 4th industrial revolution. The purpose of this research is to analyze the effect of recent Huawei issues to Korean SMEs focusing on the relation between Huawei and Korean companies; the cooperation status from the Global Value Chain (GVC) perpsective, and Korean government's policies related to Huawei's information security issues will be the three main frames for the analysis. Then, this research proposes policy implications such as increasing Korea's competitiveness in manufacturing and information security.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.2
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• pp.19-34
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• 2021

The government budget for promoting startup have been skyrocketed as catching up with increasing demands for high-tech startup by disruptive innovation resulted from rapid technology change. However, major trend of startup have still fallen on self-employed type of startup due to the lack of expertise and fund in spite of desperate government policy efforts. In reality, the access to high-tech startup has been very limited and too high huddle to would-be entrepreneur. This paper implement empirical analysis on the effects of entrepreneur competency and satisfaction level to government support, considering these as the KSF for the growth and success of high-tech startup, to the performance of the company. In particular, it focus on defining unique characteristics of high-tech startup through differential proving by the backgrounds of entrepreneur such as major, R&D experience, patent possession, CTO possession. This research carry out survey to 217 entrepreneurs in high-tech company in Daejon and Daegue at R&D Special Innopolis Zone. Research results are as follow. First, entrepreneurial achievement competencies, conceptualization competencies, network competencies and market recognition competencies positively affect the financial and non-financial performance and organizational and technical competencies, while organizational and technological competencies only positively impact on non-financial performance. Second, the satisfaction level of government support showed a positive moderating effect on entrepreneurial achievement competencies and financial performance, while no significant effect in other competencies. Third, positive differential effect by the technological background of entrepreneur such as Major, R&D experience, patent possession, CTO possession) have been confirmed. This paper deliver several significant implications and contributions, First, it propose classified and systematized entrepreneur competency through the domestic and foreign literature reviews. Second, it proves the need for the wider spread of team based startup culture rather then sole startup. Third, it also proves the important role of technological background of entrepreneur among the characteristics of high-tech startup.

• IMMUNE NETWORK
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• v.4 no.1
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• pp.23-30
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• 2004

Background: A human orthologue of mouse S100A6-binding protein (CacyBP), Siah-1-interacting protein (SIP) had been shown to be a component of novel ubiquitinylation pathway regulating $\beta$-catenin degradation. The role of the protein seems to be important in cell proliferation and cancer evolution but the expression pattern of SIP in actively dividing cancer tissues has not been known. For the elucidation of the role of SIP protein in carcinogenesis, it is essential to produce monoclonal antibodies specific to the protein. Methods: cDNA sequence coding for ORF region of human SIP gene was amplified and cloned into an expression vector to produce His-tag fusion protein. Recombinant SIP protein and monoclonal antibody to the protein were produced. The N-terminal specificity of anti-SIP monoclonal antibody was conformed by immunoblot analysis and enzyme linked immunosorbent assay (ELISA). To study the relation between SIP and colon carcinogenesis, the presence of SIP protein in colon carcinoma tissues was visualized by immunostaining using the monoclonal antibody produced in this study. Results: His-tag-SIP (NSIP) recombinant protein was produced and purified. A monoclonal antibody (Korea patent pending; #2003-45296) to the protein was produced and employed to analyze the expression pattern of SIP in colon carcinoma tissues. Conclusion: The data suggested that anti-SIP monoclonal antibody produced here was valuable for the diagnosis of colon carcinoma and elucidation of the mechanism of colon carcinogenesis.

• IMMUNE NETWORK
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• v.2 no.3
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• pp.175-181
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• 2002

Background: S100A6 is a calcium-binding protein overexpressed in several tumor cell lines including melanoma with high metastatic activity and involved in various cellular processes such as cell division and differentiation. To detect S100A6 protein in patient' samples (ex, blood or tissue), it is essential to produce a monoclonal antibody specific to the protein. Methods: First, cDNA coding for ORF region of human S100A6 gene was amplified and cloned into the expression vector for GST fusion protein. We have produced recombinant S100A6 protein and subsequently, monoclonal antibodies to the protein. The specificity of anti-S100A6 monoclonal antibody was confirmed using recombinant S100A recombinant proteins of other S100A family (GST-S100A1, GST-S100A2 and GST-S100A4) and the cell lysates of several human cell lines. Also, to identify the specific recognition site of the monoclonal antibody, we have performed the immunoblot analysis with serially deleted S100A6 recombinant proteins. Results: GST-S100A6 recombinant protein was induced and purified. And then S100A6 protein excluding GST protein was obtained and monoclonal antibody to the protein was produced. Monoclonal antibody (K02C12-1; patent number, 330311) has no cross-reaction to several other S100 family proteins. It appears that anti-S100A6 monoclonal antibody reacts with the region containing the amino acid sequence from 46 to 61 of S100A6 protein. Conclusion: These data suggest that anti-S100A6 monoclonal antibody produced can be very useful in development of diagnostic system for S100A6 protein.

• IMMUNE NETWORK
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• v.3 no.1
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• pp.16-22
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• 2003

Background: The S100A2 gene, also known as S100L or CaN19, encodes a protein comprised of 99-amino acids, is a member of the calcium-binding proteins of EF-hand family. According to a recent study, this gene was over-expressed in several early and malignant carcinomas compared to normal tissues. To elucidate the role of S100A2 protein in the process during carcinogenesis, production of monoclonal antibody specific to the protein is essential. Methods: First, cDNA sequence coding for ORF region of human S100A2 gene was amplified and cloned into an expression vector to produce GST fusion protein. Recombinant S100A2 protein and subsequently, monoclonal antibody to the protein were produced. The specificity of anti-S100A2 monoclonal antibody was confirmed by immunoblot analysis of cross reactivity to other recombinant proteins of S100A family (GST-S100A1, GST-S100A4 and GST-S100A6). To confirm the relation of S100A2 to cervical carcinogenesis, S100A2 protein in early cervical carcinoma tissue was immunostained using the monoclonal antibody. Results: GST-S100A2 recombinant protein was purified by affinity chromatography and then fusion protein was cleaved and S100A2 protein was isolated. The monoclonal antibody (KK0723; Korean patent pending #2001-30294) to the protein was produced and the antibody did not react with other members of EF-hand family proteins such as S100A1, S100A4 and S100A6. Conclusion: These data suggest that anti-S100A2 monoclonal antibody produced in this study can be very useful for the early detection of cervical carcinoma and elucidation of mechanism during the early cervical carcinogenesis.

• 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.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.79-92
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• 2015

Knowledge map is widely used to represent knowledge in many domains. This paper presents a method of integrating the national R&D data and assists of users to navigate the integrated data via using a knowledge map service. The knowledge map service is built by using a lightweight ontology and a topic modeling method. The national R&D data is integrated with the research project as its center, i.e., the other R&D data such as research papers, patents, and reports are connected with the research project as its outputs. The lightweight ontology is used to represent the simple relationships between the integrated data such as project-outputs relationships, document-author relationships, and document-topic relationships. Knowledge map enables us to infer further relationships such as co-author and co-topic relationships. To extract the relationships between the integrated data, a Relational Data-to-Triples transformer is implemented. Also, a topic modeling approach is introduced to extract the document-topic relationships. A triple store is used to manage and process the ontology data while preserving the network characteristics of knowledge map service. Knowledge map can be divided into two types: one is a knowledge map used in the area of knowledge management to store, manage and process the organizations' data as knowledge, the other is a knowledge map for analyzing and representing knowledge extracted from the science & technology documents. This research focuses on the latter one. In this research, a knowledge map service is introduced for integrating the national R&D data obtained from National Digital Science Library (NDSL) and National Science & Technology Information Service (NTIS), which are two major repository and service of national R&D data servicing in Korea. A lightweight ontology is used to design and build a knowledge map. Using the lightweight ontology enables us to represent and process knowledge as a simple network and it fits in with the knowledge navigation and visualization characteristics of the knowledge map. The lightweight ontology is used to represent the entities and their relationships in the knowledge maps, and an ontology repository is created to store and process the ontology. In the ontologies, researchers are implicitly connected by the national R&D data as the author relationships and the performer relationships. A knowledge map for displaying researchers' network is created, and the researchers' network is created by the co-authoring relationships of the national R&D documents and the co-participation relationships of the national R&D projects. To sum up, a knowledge map-service system based on topic modeling and ontology is introduced for processing knowledge about the national R&D data such as research projects, papers, patent, project reports, and Global Trends Briefing (GTB) data. The system has goals 1) to integrate the national R&D data obtained from NDSL and NTIS, 2) to provide a semantic & topic based information search on the integrated data, and 3) to provide a knowledge map services based on the semantic analysis and knowledge processing. The S&T information such as research papers, research reports, patents and GTB are daily updated from NDSL, and the R&D projects information including their participants and output information are updated from the NTIS. The S&T information and the national R&D information are obtained and integrated to the integrated database. Knowledge base is constructed by transforming the relational data into triples referencing R&D ontology. In addition, a topic modeling method is employed to extract the relationships between the S&T documents and topic keyword/s representing the documents. The topic modeling approach enables us to extract the relationships and topic keyword/s based on the semantics, not based on the simple keyword/s. Lastly, we show an experiment on the construction of the integrated knowledge base using the lightweight ontology and topic modeling, and the knowledge map services created based on the knowledge base are also introduced.